Science topics: Computer Science
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Computer Science - Science topic

Explore the latest questions and answers in Computer Science, and find Computer Science experts.
Questions related to Computer Science
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Hi everyone,
I am currently seeking ideas for a research topic to pursue for my PhD in Computer Science. I am open to suggestions across other exciting and emerging areas within the field.
If you have any recommendations or if you are working on projects that could use collaboration, I would love to hear your thoughts!
Looking forward to connecting with you all and exploring potential research opportunities.
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Thank you Aparna Sathya Murthy for your response and the valuable guidance! I truly appreciate it. I’m excited to explore these areas and will start reading to get a better understanding. Looking forward to discussing potential research ideas soon !
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Looking for research collaboration for edited book and other kind of publication
Domain computer science preferably Image Processing
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Interested, I am interested in collaboration for an editorial book. Kindly contact me at mkhankku@gmail.com for further discussion. Pranshu Saxena
Dr Mudassir Khan
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I’m planning to apply for an MSc in Computer Science with a specialization in Secure and Reliable Systems at ETH Zurich. For this, I aim to develop a robust Master’s thesis proposal. As a foundation, I want to create a Bachelor’s thesis focused on cutting-edge cryptographic technologies, particularly Zero Knowledge Proofs, Multi-Party Computation, and blockchain.
I’m reaching out to fellow researchers for potential research questions and ideas on how to extend a Bachelor’s thesis into a solid Master’s thesis proposal. Your insights and experiences would be invaluable in shaping my research direction!
#Cryptography #ZeroKnowledgeProofs #MultiPartyComputation #Blockchain #SecureSystems #ResearchIdeas #ETHZurich #ThesisProposal
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I am reaching out to express my interest in potential research collaboration opportunities in the field of Computer Science and Engineering. I am willing to cover any Article Processing Charges (APCs) associated with collaborative research work, as I understand the importance of facilitating such partnerships. I would be grateful for the opportunity to discuss potential collaboration ideas further. Please let me know if you would be interested in exploring this possibility or if you have any ongoing projects where my involvement could be beneficial.
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Hello. I am really interested in initiating a research on innovating a new pattern in complex prompts of Generative AI. If it interests you, we can collaborate
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Computer science
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The Olduvai Declarative focuses on the evolution of human culture and technology, often drawing parallels between ancient civilizations and modern practices. Here are some similarities between Roman and Greek culture in the context of software engineering:
1. **Emphasis on Knowledge and Education**
- Both Greek and Roman cultures valued education, philosophy, and knowledge. This emphasis can be likened to the foundational importance of theoretical knowledge in software engineering, where understanding algorithms and data structures is crucial.
2. **Innovation and Problem-Solving**
- Greek culture is known for its innovations in mathematics and logic (e.g., Euclid, Archimedes), while the Romans excelled in engineering and architecture. This spirit of innovation parallels the software engineering field, where problem-solving and creative thinking are essential for developing new technologies and solutions.
3. **Collaboration and Community**
- Both cultures fostered collaboration through forums and public discourse (e.g., the Agora in Greece and the Forum in Rome). Similarly, software engineering thrives on collaboration, with practices like pair programming and open-source projects emphasizing teamwork.
4. **Practical Application of Theory**
- The Greeks laid the groundwork for theoretical knowledge, while the Romans applied that knowledge practically in engineering feats. In software engineering, theoretical concepts (like design patterns) are crucial, but their practical application in coding and systems design is equally important.
5. **Documentation and Knowledge Sharing**
- Ancient Greeks and Romans documented their knowledge extensively (e.g., through texts and scrolls), which is akin to modern software documentation practices. Good documentation is vital in software engineering for knowledge transfer and maintenance.
6. **Ethical Considerations**
- Both cultures engaged in discussions about ethics and the role of technology in society. Similarly, software engineers today must consider the ethical implications of their work, such as privacy concerns and the impact of software on users.
7. **Legacy and Influence**
- The intellectual legacies of Greece and Rome continue to influence modern thought, similar to how foundational programming languages and methodologies (like C, Agile, etc.) shape contemporary software engineering practices.
Conclusion
While separated by centuries, the core values of knowledge, innovation, collaboration, and practical application connect ancient Greek and Roman cultures to the principles of software engineering today. These similarities highlight a continuous thread in the evolution of human thought and technology.
Partial credit ai
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I was asked to reflect upon do's and don'ts for industrial guest lectures in computer science courses. Who has already included such guest lectures in their courses or given such guest lectures (I have organised this at several occasions with success). What worked well for you, what didn't. All input is useful (even negative input).
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I am personally convinced that industrial guest lectures (and related initiatives) are a lightweight yet important instrument to bridge the gap between academia and industry.
I have experienced (and received experiences of) different kinds of such interventions :
(1) More practical industrial lectures which replace typical theory lectures;
(2) Taking students on a company visit in an area related to the course content;
(3) Industry-inspired course projects (e.g., a software development project) where the topic is provided by an industrial partner;
(4) Using anonymised industrial data in course projects (e.g., in a data mining or machine learning course) to make the project more tangible and realistic with real-world constraints;
(5) Invite industrials to participate as attendees in cutting-edge courses to stay on top of recent theoretical developments or refresh their knowledge;
(6) A full industrial seminar course with different lectures by different industrial lecturers every week, with feedback and reflection activities for students;
(7) Bachelor/Master thesis collaborations on topics in partnership with industry;
(8) Industry-Academia post-doc research positions.
As positive experiences I see that:
(a) Such industrial interventions can show to students that topics seen in the course are actually relevant and used in industry;
(b) It can change students' perception and views on potential industrial carreers;
(c) It enhances the balance between more theoretical courses and practical application of those courses;
(d) It can be interesting for the teacher to be more in touch with industrial reality as well as for industrials to stay in touch with academic advancement;
(e) It facilitates academia-industry networking and collaboration.
In my experience with such interventions, overall student engagement and interest in industry involvement have been highly positive (but should not be exaggerated).
There are some possible issues to be aware of though and strategies for success to overcome these issues:
(i) Need to incite students to attend, e.g. by telling them there will be an exam question on the lecture, or asking them to submit a small summary of the intervention; and by clearly communicating possible benefits and assessment relevance.
(ii) It can be difficult to schedule an agenda that fits both the students and the industry presenter. Agendas need to be schedule several months in advance and backup options foreseen in case of last-minute agenda changes.
(iii) There is a need to verify and synchronise in detail what content will be provided by the industry partner to avoid too high-level business presentations or presentations that are off-topic. Content quality should be controlled by reviewing and potentially adjusting slides from industry speakers.
(iv) In the end, what motivates some industrials to participate is potential recruitment of good students. This needn't be a problem though, as long as the intervention is not turned into a job event. The focus should remain on education, not recruitment; the educational intent of interventions should be maintained.
(v) Personal contacts through ex-researchers or industrial collaborators often makes things easier.
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Last year I studied a course on Computer Systems Architecture/Organization. During a lecture, I learned about data hazards and one of the common solutions to them: Reordering the instructions. Modern processors solve this using OOE, but since this is integrated into the processor, it increases chip size, power consumption, and thermal efficiency. So I thought "What if we had an AI-driven processor which does that for the CPU?"
Does anyone know if this has already been successfully researched or implemented? I would greatly appreciate any insightful comments.
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Current research shows promising potential for using AI to improve out-of-order instruction execution in CPUs. However, there are still challenges that require further research and development to achieve effective results.
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Currently working on e-voting using cryptographic Techniques. I Would like to implement RSA and also to know how SHA works technically with RSA.
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I have been seeing and following a lot of work on these topics, it even seems that there are more results on them than on the corresponding classical topics, particularly on general topology.
What could be the cause of such results?
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Dear Colleagues,
If U and E are fixed sets, A is a subset of E, and F is a function
of A to the power set of U , then F should be called a soft set
over U and E , instead of the pair (F, A).
Thus, since set-valued functions can be identified
vith relations, a soft set over U and E is actually a relation
on E to X. That is, a subset of the product set of E and U.
Therefore, several defininitions and theorems on soft sets,
consuming a lot of pages, are superfluous. Moreover, notations
and terminology can be simplified.
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I'm curious to get the community's thoughts on where educational technology should be classified - under the field of computer science or under the field of education?
There are arguments that could be made for both perspectives. Educational technology involves the use of digital tools, software, and computing principles to enhance teaching and learning. This could suggest it falls more under the domain of computer science.
However, educational technology is ultimately focused on improving educational outcomes and the learning experience. The primary goal is not technology for its own sake, but rather using technology as a means to enhance pedagogy and support student learning. This points more towards education as the primary field.
I'm interested to hear how researchers and practitioners in this area view the classification of educational technology. Should it be considered a subfield of computer science, a subfield of education, or perhaps its own distinct interdisciplinary field? What are the implications of how it is classified?
I look forward to an engaging discussion on this topic. Please share your thoughts and perspectives.
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Educational technology is an interdisciplinary field that is concerned with improving instructional methods through incorporation and modernization of pedagogical technology and advancements. For example, online classes, especially synchronous ones, is a popular advancement in educational technology. It allows an educator to teach and a student to learn from the location of their choice, virtualizing the school experience.
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I am applying for a research grant in Computer Science and particularly in AI and decision support. I am looking for a collaborations with a senior faculty from UK or any other as far as the university is one of top 200 university.
Please let me if you are interested.
Thanks
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Dr. Peter Schnupp is a acknowledged expert. We do not get support of a top 200 university due to the different approach we follow. It works without LLM. In order to separate our approach from Generative AI which has got the status of a synonym of AI we are going to establish Digital Intelligence as a new path. Thus, we follow the same idea as machine learning with "Machine Intelligence". We provide a framework with a core in expert systems and symbolic AI. Dr. Schnupp has gone much farther including some theories like Kahnemann, Deutscher and others.
If you are interested in a sustainable approach we would offer you the framework for research purposes for free. Also we can provide a list of more than 25 application concepts - four of them available as proof of concept.
Start with an advantage of 12 years research of an expert and add your own nest step. There is also a roadmap of some functions to be added.
Best regards
Irene Teich
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i have joined as research scholar in computer science. i would like to do my PhD work in NLP domain. Please suggest some of the research topic related to NLP
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Generally, natural language processing is the sub-branch of Artificial Intelligence (AI). Natural language processing is otherwise known as NLP. It is compatible in dealing with multi-linguistic aspects and they convert the text into binary formats in which computers can understand it.  Primarily, the device understands the texts and then translates according to the questions asked. These processes are getting done with the help of several techniques. As this article is concentrated on delivering the natural language processing thesis topics, we are going to reveal each and every aspect that is needed for an effective NLP thesis.
Regards,
Shafagat
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What’s the most common programming paradigm of no-code platforms? Why?
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The most common programming paradigm of no-code platforms is declarative programming.
Why Declarative Programming?
  • Focus on "What" not "How": No-code platforms allow users to describe what they want to achieve (e.g., "create a form," "generate a report") rather than how to do it. This makes it accessible to users who may not have traditional programming skills.
  • Visual Interfaces: These platforms often provide drag-and-drop interfaces, workflows, and rule-based systems, which align well with declarative principles where users specify the desired outcomes rather than writing detailed procedural code.
  • Ease of Use: Declarative paradigms reduce the complexity involved in traditional coding. Users can build applications, workflows, and automations by configuring pre-built components or templates, which is ideal for business users or non-developers.
  • Abstraction: Declarative programming abstracts the underlying logic and implementation details. This allows the platform to handle complex tasks behind the scenes, which is crucial for empowering users without deep technical knowledge.
These factors make declarative programming the ideal paradigm for no-code platforms, supporting their goal of democratizing software development and making it more accessible to a broader audience
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Hello, I am looking out for Scopus Indexed Journals in Computer Science with short review time and short time to publish after acceptance (with / without APC). Please mention the journals that you find suitable as per the stated requirements. Thanks in advance.
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Hello! If you’re looking for Scopus-indexed Computer Science journals with a relatively short review time, consider checking out the detailed here ,
https://researchbrains.com/fast-responding-scopus-indexed-journals/. This resource lists journals with review times of around 2-3 months, including their areas of coverage and publisher details, ensuring a swift yet thorough peer-review process.
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Please suggest me IEEE SCI journal for computer science with fast publication and low impact factor.
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Many SCI journals have valid impact factors and quick publication times, typically around 2 to 3 months. For a detailed list of these fast-publishing SCI journals, including their impact factors, areas of coverage, and publisher information, you can visit the full article on this page:https://researchbrains.com/fast-publishing-sci-journals/ .This resource will help you identify journals suitable for your research based on the specific field and your publication timeline.
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2024 4th International Conference on Electronic Information Engineering and Computer Science (EIECS 2024) will be held on September 27-29, 2024 in Yanji, China.
Conference Website: https://ais.cn/u/7n6Vva
---Call for papers---
The topics of interest for submission include, but are not limited to:
◕ Electronic Information Engineering
· Signal processing
· Wireless network
· Information system
· Next generation mobile communication technology
· Internet of things
......
◕ Computer Science
· Computer system
· Artificial intelligence
· Machine learning and deep learning
· Pattern recognition
· Computer vision and graphics
......
---Publication---
All accepted full papers will be published in the conference proceedings and will be submitted to EI Compendex / Scopus for indexing.
---Important Dates---
Registration Deadline: August 30, 2024
Final Paper Submission Date: August 30, 2024
Conference Dates: September 27-29, 2024
--- Paper Submission---
Please send the full paper(word+pdf) to Submission System:
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I am interrested
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Epigenetics refers to the study of changes in gene expression that do not involve alterations to the underlying DNA sequence. This field has garnered significant attention for its potential to influence aging, combat diseases, and mitigate unwanted side effects of genetic engineering.
Aging is associated with various epigenetic changes, such as DNA methylation and histone modifications, which can lead to altered gene expression and contribute to age-related diseases like cancer and neurodegenerative disorders. By targeting these epigenetic modifications, researchers believe it may be possible to reverse or slow down the aging process. For instance, interventions that modify epigenetic markers could potentially restore youthful gene expression patterns, thereby improving cellular function and longevity.
Epigenetic therapies hold promise for treating a range of diseases. By understanding the specific epigenetic alterations associated with conditions like cancer, researchers can develop targeted therapies that either activate or repress certain genes without changing the genetic code itself. This approach could lead to more effective treatments with fewer side effects compared to traditional genetic engineering methods, which often involve irreversible changes to the genomeOne of the significant concerns with genetic engineering is the potential for unintended consequences, such as off-target effects or the activation of harmful genes. Epigenetic modifications can provide a more flexible approach to gene regulation, allowing for temporary changes that can be reversed if necessary.
This flexibility could help in fine-tuning therapeutic interventions, reducing the risk of adverse effects associated with permanent genetic alterations.
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I'm currently seeking postdoctoral research opportunities in multidisciplinary areas within Computer Science, with an interest in both academic and industry settings. My research interests include advanced cloud-based data management for smart buildings, NLP for low-resource languages like Amharic, AI and machine learning, data science and big data, human-computer interaction, and robotics. I'm open to discussing potential opportunities and collaborations in these fields. Please feel free to contact me if you are aware of any suitable positions.
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Dear Dagim Sileshi Dill,
I would recommend the use of Artificial Intelligence in the Internet of Things as a postdoc research area in computer science with multidisciplinary applications.
For this purpose, I would analyze the use of Digital Twinning for the realization of various Intelligent Services.
See my presentation:
Here, Fig. 11 shows the most important areas of application of Digital Twins.
The article "Intelligent IoT - Replicating human cognition in the Internet of Things" can also help you:
Best regards and much success
Anatol Badach
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Q3- or - Q4 journal (SCOPUS Indexed Journals &WOS) fast publish (computer science) ?
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If you are looking for fast Publication then contact me at +1 (773) 654-4399.
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Can we apply the theoretical computer science for proofs of theorems in Math?
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The pumping lemma is a valuable theoretical tool for understanding the limitations of finite automata and regular languages. It is not used for solving computational problems directly but is important for proving non-regularity and understanding the boundaries of regular languages.
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会议征稿:2024年计算机与信息安全国际会议 (WCCIS 2024)
Call for papers: Organized by Universiti Malaya, 2024 World Conference of Computer and Information Security(WCCIS 2024) will be held on 20-22 September 2024 in Malaysia.
Conference website(English):https://ais.cn/u/JnMNfa
重要信息
大会官网(投稿网址):https://ais.cn/u/JnMNfa
大会时间:2024年9月20-22日
大会地点:吉隆坡,马来西亚 | 中国郑州 双会场
收录类型:EI Compendex、Scopus
主办单位:马来亚大学、郑州大学公共安全研究院
会议详情
由马来亚大学主办,2024年计算机与信息安全国际会议 (WCCIS 2024) 将于2024年9月20-22日召开。会议将设置中国/马来西亚双会场。中国会场将在郑州举行;马来西亚会场将在吉隆坡召开。会议旨在为从事计算机与信息安全的专家学者、工程技术人员、技术研发人员提供一个共享科研成果和前沿技术,了解学术发展趋势,拓宽研究思路,加强学术研究和探讨,促进学术成果产业化合作的平台。
会议征稿主题(包括但不限于)
网络
访问控制
信息系统安全与管理
社交网络安全、隐私和信任
计算机网络与通信
信号和图像处理
信息安全算法与协议
嵌入式系统安全
通信工程
卫星通信
云与边缘计算安全
区块链安全与隐私
身份访问管理
网络物理系统安全
知识产权保护
智能信息处理技术
数据挖掘
数据可视化
图像处理
语音识别
信息技术在企业的应用
信号处理
智能通信与计算
数学与信息网络
数字媒体信息处理
卫星通信
无线通信
系统模型设计与信息管理
生物识别技术
嵌入式系统和软件
物联网信息安全
社交网络
物联网信息分析处理
......
出版信息
本会议投稿经过2-3位组委会专家严格审核之后,最终所录用的论文将由全球知名的科技出版社380220803174252410.png斯普林格旗下的Springer-LNICST(Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering), ISSN: 1867-822X 出版,出版后提交至EI Compendex,Scopus检索。
参会报名
1、投稿作者参会:投稿全文经审稿后文章被录用且完成注册,每篇文章由一名作者代表参会,在会议现场进行口头报告或海报展示。
2、报告者参会(无投稿):在会议上进行口头报告展示,口头报告摘要不提交出版。
3、海报参会(无投稿):在会议上进行论文海报展示,A1尺寸,彩色打印(具体要求咨询会议负责老师)。
4、听众参会(无投稿):出席并参加这次会议, 并可全程旁听会议所有展示与报告。
*参会报名方式:https://ais.cn/u/JnMNfa
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me bridaria mas infiormacion por favor
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Why specifically does Wolfram Matematica theorize reality is discrete in nature?
Maybe because reality is too unpredictable to be continuous. Plus, discrete data suggests either every entity is unique or simply too different for perfect predictions.
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You may find huge raw data for it at www.rawdatalibrary.net
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会议征稿:第四届电子信息工程与计算机科学国际会议(EIECS 2024)
Call for papers: 2024 4th International Conference on Electronic Information Engineering and Computer Science (EIECS 2024) will be held on September 27–29, 2024, in Yanji, China.
重要信息
会议官网(投稿网址):https://ais.cn/u/vum2Mr
会议时间:2024年9月27-29日
会议地点:中国-延吉
收录检索:EI Compendex,Scopus
主办单位:长春理工大学、延边大学
大会简介
2024年第四届电子信息工程与计算机科学国际会议(EIECS 2024)将于2024年9月27日至29日在中国延吉举行。会议由长春理工大学、延边大学主办,长春理工大学电子信息工程学院、长春理工大学计算机学院、长春理工大学人工智能学院、延边大学工学院承办,多所高校共同协办。此次会议将聚焦电子信息工程与计算机科学的国际研究和关键应用领域,围绕智能社会创新发展的主题,开展高水平的学术交流和最新成果展示,搭建国际协同创新平台。诚邀各位作者向EIECS 2024提交您的最新研究论文,并与来自世界各地的其他顶尖科学家、工程师和学者分享最新研究成果和宝贵经验。
同时也欢迎暂无论文但对会议感兴趣的社会各界人士参加会议。
征稿主题(包括但不限于)
Track I:电子信息工程
(通信、网络、信号和图像处理、计算机科学与工程 、大规模集成电路、系统与控制、电子能源系统、光子学与光学、电磁学、计算机结构、嵌入式软件、微机电系统等)
Track II:计算机科学
(系统与网络,人工智能与机器人,计算机隐私与安全,编程语言,数据库,计算机图形学,算法,软件工程,计算机视觉,人机交互等)
Track III:控制科学与工程
(控制理论与控制工程,检测技术与自动装置,系统工程,模式识别与智能系统,导航、制导与控制等)
......
投稿参会链接:https://ais.cn/u/vum2Mr
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Hi Isaac Agyapong ,please see the official website of the conference, which is in English: https://ais.cn/u/BBfQBb
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Why does information theory explain aging, evolution vs creationism, critical rationalism, computer programming and much more?
Perhaps information has a very open definition thus, is very robust.
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All that is cut and paste salesmanship. How about some concrete examples of each?
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Definitely yes
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Since most anti-aging experimenting is humanely on mice, how about a machine to automatically draw equivalence between the human and mouse body?
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Developing a machine to automatically draw equivalence between the human and mouse body for anti-aging experiments would be a significant technological advancement. Such a machine would aim to enhance the accuracy and efficiency of translating findings from mouse models to potential human applications.
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I have to select my MS Computer Science Thesis topic relating to the field of Machine Learning. Can any one guide me topic of my research?
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José Gabriel Pereira Tavares Thanks for your reply. My interest areas are "Computer Vision (medical image processing, computational photography, activity recognition, image generation)". I have studies different research papers and prepared summaries Human disease prediction and detection using Deep Learning and Computer Vision. I also studies research papers relating to Detection of Plant Diseases using Deep Learning and Machine Learning. Can you please recommend me further research areas relating to this? I would be happy if you can add me as a Researc intrenee in your team so that I can work within your research group?
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Fundamentalmente: Muriéndose. Solo envejece lo vivo, aunque vivas muy sanamente. Lo no vivo no envejece, solo cambia.
Envejecer es inevitable a corto o a largo plazo.
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Hello everyone,
I am reaching out to express my interest in potential research collaboration opportunities in the field of Computer Science and Engineering. I am willing to cover any Article Processing Charges (APCs) associated with collaborative research work, as I understand the importance of facilitating such partnerships. I would be grateful for the opportunity to discuss potential collaboration ideas further. Please let me know if you would be interested in exploring this possibility or if you have any ongoing projects where my involvement could be beneficial.
Best regards,
Jawad Khan Ph.D | Assistant Professor
School of Computing, Gachon University, Republic of Korea
https://www.linkedin.com/in/ jawad-khan-56808762/
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I am interested in undertaking scientific cooperation in the problems of new technologies, including Industry 4.0/5.0, such as Big Data Analytics, machine learning, deep learning, artificial intelligence, including generative artificial intelligence. Besides, I am also eager for scientific cooperation in the issues of sustainable development, green circular economy, green transformation of the economy.
Best regards
Dariusz Prokopowicz
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Updated information of my thoughts and activities.
This is meant to be a one-way blog, albeit you can contribute with your recommendations and comments.
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Use (free PDF, also in print) the book QUICKEST CALCULUS with programmed instruction. Integral is immrdiate, just the inverse of differentiation plus a constant.
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My expertise in computer science is exclusively in data mining and medical datasets.
I am interested in working in a research group remotely and without salary (only for conducting research and writing articles).
Is anyone interested in having me in their research group?
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Our research team in Iran is pleased to utilize your expertise in Deep Learning (DL) and Machine Learning (ML) in the near future for Structural Health Monitoring of civil structures, including bridges and buildings. I will keep you informed when we begin a new research paper. If you would like to participate, we will have an introductory virtual meeting to further discuss our research framework and responsibilities.
Best regards,
Hossein
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I built an AI powered tool that gives you a detailed PDF report on your manuscript to improve your chances of publication.
The report includes:
- Strengths and Weaknesses
- Quantitative scoring for each section
- Actionable recommendations with examples
It's $1 per upload to cover costs. The first 20 users can use discount code "FREE" to try it out now.
Would love to hear any feedback and thoughts - https://reviewmypaper.ai/
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That's a great question and definitely a valid concern of protecting IP!
Here's how data is handled:
- The manuscript is sent to our backend and deleted from our database within 72 hours of generating your report.
- In creating the review the only external service used is the OpenAI API, and they've explicitly stated "data sent to the OpenAI API will not be used to train or improve OpenAI models" [reference here](https://platform.openai.com/docs/models/how-we-use-your-data#:~:text=As%20of%20March%201%2C%202023%2C%20data%20sent%20to%20the%20OpenAI%20API%20will%20not%20be%20used%20to%20train%20or%20improve%20OpenAI%20models)
It won't save the data longer than it needs to and will never be used to train a model.
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Computer Science Department
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Both the microprocessing and computer architecture are fundamental subjects in computer science, each serving different purposes. Although these subjects are interconnected, your choice should depend on your learning style and goals. Regardless of the order in which you study these topics, mastering both will greatly enhance your understanding of computing system concepts.
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Recently, I spent a significant amount of time developing a new model, but its accuracy is lower than some existing models. My model's accuracy is above 90%, while some existing models achieve 95-96% accuracy. Is this work still publishable? If so, why? Additionally, how should I handle the recent work and model comparison part?
I would appreciate any insights or guidance on this matter.
Thank you.
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A new deep learning model with an accuracy lower than the existing model can still be publishable if it offers other significant contributions to the field. Factors such as novel architecture, improved computational efficiency, interpretability, robustness to specific types of data, or addressing previously unsolved problems can make the model valuable. The research might also provide insights into the limitations and challenges of current models, offer theoretical advancements, or propose innovative techniques that could be beneficial for future developments. Therefore, while accuracy is an important metric, the overall impact, originality, and potential applications of the research can justify its publication.
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Dear Researchers,
I hope this message finds you well. I am conducting PhD research in Computer Science on fetal heart rate analysis.
I am encountering difficulties in importing and using the CTU-UHB dataset (.dat files) in Python. Could you please provide guidance or a file to help  me properly utilize this dataset? Your assistance would be greatly appreciated. Thank you for your time and support.
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Artificial intelligence (AI) 's pervasive influence in diverse sectors, including AI components in various computer science subjects, is beneficial, but blending those applications is also crucial. Specialities include programming, databases, human-computer interaction, analysis techniques, and algorithm design.
I am interested in exploring the following:
  • Benefits: How can integrating AI into computer science subjects enhance students' interdisciplinary skills and practical application knowledge?
  • Implementation: What are the best practices for integrating AI topics across various computer science courses? Are there any successful case studies or examples from universities that have implemented such changes?
  • Challenges: What challenges might educators face when integrating AI into existing curricula, and how can they be addressed?
I would greatly appreciate any insights, research papers, case studies, or personal experiences related to integrating AI into computer science education. Thank you!
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Welcome any different ideas to discuss~
Benefits:
  • Interdisciplinary Skills: AI integration exposes students to various disciplines.
  • Practical Application: Hands-on experience prepares students for real-world challenges.
  • Enhanced Problem-Solving: AI tasks develop critical and creative thinking.
Implementation:
  • Curriculum Design: Integrate AI topics into existing courses or create new ones.
  • Hands-On Projects: Assign projects to reinforce theoretical concepts practically.
  • Collaborative Learning: Foster teamwork through group AI projects.
Challenges:
  • Resource Constraints: Limited access to specialized resources.
  • Faculty Training: Ensure educators are proficient in AI concepts.
  • Curriculum Adaptation: Balance AI with existing course content.
  • Ethical Considerations: Address AI-related ethical issues in teaching.
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Currently, AI is being applied in many areas of society, in its economic, social, educational, and many other components.
But normally the current applications of computer science are combinations of different specialties: programming, databases, use of interfaces, analysis techniques and algorithm design, etc.
Would it be convenient to include AI elements in each of its subjects in the computer science specialist's learning, to facilitate this cooperation/coordination between AI and the other components of practical applications?
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Integrating Artificial Intelligence (AI) into computer science education is a topic of great importance.
  1. Understanding AI: AI is a branch of computer science focused on creating systems that can perform tasks normally requiring human intelligence. These tasks range from understanding natural language, recognizing patterns, making decisions, and learning from experience1. AI encompasses various subfields, each with unique objectives and specializations.
  2. Types of AI: AI can be categorized into three levels based on its capabilities: Artificial Narrow Intelligence (ANI): This is the most common form of AI we interact with today. ANI is designed to perform a single task, like voice recognition or recommendations on streaming services. Artificial General Intelligence (AGI): AGI can understand, learn, adapt, and implement knowledge across a wide range of tasks at a human level. While large language models and tools such as ChatGPT have shown the ability to generalize across many tasks, as of 2023, this is still a theoretical concept. Artificial Super Intelligence (ASI): ASI refers to a future scenario where AI surpasses human intelligence in nearly all economically valuable work. However, this concept remains largely speculative.
  3. Integration of AI in Computer Science Education: Including AI elements in computer science education can have several benefits: Holistic Understanding: Students gain a holistic understanding of AI’s role in various domains, including programming, databases, and algorithm design. Interdisciplinary Skills: AI bridges the gap between computer science and other fields, such as natural language processing, computer vision, and robotics. Practical Applications: Students learn how to apply AI techniques to real-world problems, enhancing their problem-solving abilities. Industry Relevance: As AI becomes more prevalent, professionals with AI knowledge are in high demand across industries. Ethical Considerations: Teaching AI involves discussing ethical implications, bias, and responsible AI development.
  4. Curriculum Considerations: Here are some ways to incorporate AI into computer science curricula:Foundations: Introduce fundamental AI concepts, including machine learning, neural networks, and data preprocessing. Specialized Courses: Offer specialized courses on natural language processing, computer vision, and reinforcement learning. Projects and Labs: Assign projects where students build AI models or analyze real-world data. Guest Lectures: Invite industry experts to discuss AI applications and trends. Ethics and Bias: Include discussions on ethical AI development and mitigating bias.
  5. Resources for Learning AI: Online platforms like Coursera offer courses that cover essential AI skills, including machine learning, robotics, and data interpretation2. Explore beginner’s guides and resources to understand the basics of AI and automation3.
In summary, integrating AI elements into computer science education can empower students to navigate the evolving landscape of technology and contribute to practical applications across various domains.
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Dear ResearchGate community,
I am looking for someone with endorsement rights on arXiv in the fields of computer science -computer vision and pattern recognition (cs.CV). I would like to submit a preprint paper for visibility and need an endorsement.
If you're able to endorse and willing to help, please visit the following URL:
or visit the link and enter the code:
Endorsement Code: 88LWI9
I have a few peer-reviewed publications in the area of endorsement which can be checked from my profile.
Thank you for your consideration.
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No Problem, Thanks Kristina P.S
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Dear ResearchGate community,
I am looking for someone with endorsement rights on arXiv in the fields of computer science - computer vision and pattern recognition (cs.CV). I would like to submit a preprint paper for visibility and need an endorsement.
If you're able to endorse and willing to help, please visit the following URL:
or visit the link and enter the code:
Endorsement Code: 88LWI9
I have a few peer-reviewed publications in the area of endorsement which can be checked from my profile.
Thank you for your consideration.
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The paper is now available on arxiv: http://arxiv.org/abs/2405.05619. I release the code and dataset publicly: https://paperswithcode.com/paper/rectified-gaussian-kernel-multi-view-k-means
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Dear all,
I would like to publish my papers in a journal. Since it is strongly required to publish the paper in an international journal indexed by Scopus, I face some difficulties due to some fees that must be paid (which is very high) by the author.
My research areas are computer science, artificial intelligence, machine learning, Pattern recognition, natural language processing and Social Media Analytics.
Are there any Scopus-indexed journals without without any article processing charge or other hidden charges for publication and suitable for my research areas?
I would like to thanks for your kind help.
With best regards,
Amit
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There are several Scopus-indexed journals related to computer science, artificial intelligence, machine learning, Pattern recognition, natural language processing, and Social Media Analytics. without APC or submission fees, and their review process time varies. I have provided the links:
INTERNATIONAL JOURNAL OF INTERACTIVE MULTIMEDIA AND ARTIFICIAL INTELLIGENCE
ISSN: 1989-1660
Vietnam Journal of Computer Science
ISSN (print): 2196-8888 | ISSN (online): 2196-8896
Journal of Applied Research and Technology
ISSN 1665-6423
e-ISSN 2448-6736
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I heard on television a famous professor of ethics speaking about the rights of intelligent machines. There are no intelligent machines and there is no artificial intelligence. Machines function in the way people designed and programmed them to function. A discourse bout intelligent machines leads into a totalitarian society, in which people will succumb to what machines say. And machines will say what power-holders ordered to programmers to program them to say and do.
I graduated computer science; I am now retired and I regret the fact that I spent my career teaching something what now leads people into a mental and physical slavery.
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If there is artificial intelligence, then human intelligence if prior to it. The so called artificial intelligent machines, like computers and robots, only do what they were programed to do by their respective human designers/programers. The so called artificial intelligent machines have rules and grammar, but know nothing about meaning and semantics.
In a nutshell, the so called artificial intelligent machines are not able to go beyond the information they get from their designers/programers. They are blind to some extent.
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Hi, I am an educator and an early researcher in the field of Computer Science Education and Education Technology. As part of my journey, I am seeking your valuable recommendations on the most reputable journals in this field.
I am particularly interested in journals that publish high-quality research articles focusing on topics related to computer science education and the application of emerging technologies in Education among other related topics.
Thank you for your kind attention.
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Here are some of the top journals on computer science education and educational technology:
  • Computer Science Education (CSE)pen_spark📷Opens in a new window📷www.researchgate.netComputer Science Education (CSE) JournalFocuses on high-quality papers with a specific focus on teaching and learning within the computing discipline. Aims to be accessible and of interest to researchers and practitioners alike. Covers learners of all ages and across both classroom and out-of-classroom learning contexts. Currently ranked in the first quartile (Q1) according to Scimago Journal & Country Rank.
  • Journal of Educational Computing Research (JECR)📷Opens in a new window📷www.researchgate.netJournal of Educational Computing Research (JECR)Publishes scholarly articles that investigate the design, development, implementation, and evaluation of computing technologies in educational settings. Covers a wide range of topics related to computer-supported learning environments, including:Instructional design Learning theories Assessment Teacher education Technology integration Equity and access
  • Computers & Education (C&E)📷Opens in a new window📷www.researchgate.netComputers & Education (C&E) JournalA leading international journal that publishes scholarly articles on the theory, design, development, implementation, and evaluation of educational technology in all its forms. Aims to bridge the gap between theory, research and practice. Covers topics including:Artificial intelligence in education Learning analytics Game-based learning Virtual reality Educational robotics Mobile learning
  • British Journal of Educational Technology (BJET)📷Opens in a new window📷bera-journals.onlinelibrary.wiley.comBritish Journal of Educational Technology (BJET)An international journal that publishes scholarly articles on the use of technology in education. Covers a wide range of topics related to the design, development, implementation, and evaluation of learning technologies. Aims to promote the effective use of technology to improve teaching and learning.
  • Journal of Computer Assisted Learning (JCAL)📷Opens in a new window📷onlinelibrary.wiley.comJournal of Computer Assisted Learning (JCAL)An international journal that publishes scholarly articles on the use of computers to support learning. Covers a wide range of topics related to the design, development, implementation, and evaluation of computer-assisted learning (CAL) applications. Aims to promote the effective use of CAL to improve teaching and learning.
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Dear Experts and Researchers Greetings!
I am waiting for your response to fill my survey. It is essential part of my PhD research work. Kindly give 15/20 minutes and participate in the survey. I will really thankful if you kindly share it with other colleagues.
The link of the survey is given below:
Best Regards.
MUHAMMAD ILYAS
PhD Scholar
Software Engineering Research Group (SERG-UOM),
Department of Computer Science & IT,
University of Malakand, Pakistan.
Assistant Professor of Computer Science.
Federal Govt: College Batkhela Malakand, Pakistan.
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Good luck!
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I am a research scholar in computer science. Recently, my topic, "Predicting E-Waste Generation using AI and ML," was rejected as it is an interdisciplinary topic and not pure computer science, Getting data sets is also difficult, it seems. I am interested in ESG/Decarbonization/Green IT/LLM. It would be great if I get any suggestions on how to extend this as a computer science topic.
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Research into the prediction of e-waste generation using AI and ML, while interdisciplinary, can be firmly rooted in the field of computer science by focusing on the challenges and solutions specific to this field. here I present a few ways in which this can be achieved:
1. Optimizing ML algorithms to reduce their energy consumption and carbon footprint : thus contributing to the global effort to decarbonize IT infrastructures.
2. Computational modeling of decarbonization : using simulations to assess the environmental impact of information and communication technologies and their e-waste management.
3. Development of ESG data platforms : to facilitate the collection and analysis of environmental, social and governance data, including e-waste.
4. Application of AI to improve e-waste management : through the design of intelligent systems to optimize waste collection, sorting and recycling.
5. Use of blockchain for e-waste traceability : to improve transparency and combat illegal waste management practices.
6. Assessing the environmental impact of IT : by developing AI/ML-based tools to measure and reduce the impact of IT on the environment.
To overcome the difficulty of accessing data, it is advisable to collaborate with various organizations, to use learning approaches adapted to limited data, and to generate synthetic datasets where necessary.
These directions help to reposition research within computing while addressing pressing environmental issues, offering a significant contribution to both green computing and global sustainability efforts.
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AD Scientific Index is a reliable quality measure or a valuable tool for assessing institution and research quality, so it could soon become a significant factor in assessing both institutions and researchers.
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Yes you are absolutely right but some students donot know about the right indexing journals
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Can we consider this topic for Ph.D in computer Science?
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Precisely,Predicting E-Waste Generation using AI & ML is a compelling and interdisciplinary research topic that is well-suited for a Ph.D. in Computer Science. It blends AI and ML innovations with environmental insights, making it both a computer science study and a project with broader, interdisciplinary applications. This focus offers significant potential for advancing recycling efforts, policy-making, and sustainable practices, positioning it as a strong candidate for doctoral research in the field....
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I'm a mathematics master's student currently working on my thesis, which focuses on developing a deep learning model. While I'm excited about my research, I'm concerned about defending it, especially since computer science students are also exploring similar areas. I'm seeking advice and strategies on how to present and defend my work effectively, emphasizing the unique contributions of my mathematical perspective in this interdisciplinary field. Any insights or suggestions would be greatly appreciated!
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I defended my thesis in Applied Mathematics by doing a machine learning model for buildings crack detection. I can say that using CNN, ANN or whatever you want alone will not be enough. In my case, I had to cover the mathematical part behind neural networks. In addition, I included crack detection for pipes as a test case and had to cover image projection on crack part. For short, include as much maths and formulas as you can and you will be fine.
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Hello Everyone,
What would be the most common theories/articles to understand why their are so few women in Computer Science? I have a good grasp on reasons and practical issues, but am trying to get at relevant theories. Any books or articles would be greatly appreciated.
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Hmmm,
I am strongly interested in this issue.
Just in case
you find any kind of theoretical answer on this
(gender&& discrimination) issue...
.... I would be glad, to hear from you.
Just drop me note here on ReasearchGate.net
My Motivation:
I am working on an analysis about
what would be translated to English "Cause and Reason".
Some theoretical (description||model)
of some of the mechanisms that hinders 1/2
of the world’s human resources
would be a nice to have
in order to extend my own research results.. If you like, we can exchange on some relevant scientific opinions and or guesses. l will send you my skype address via PN.
Yours,
- Frank. Haferkorn
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for example, computer science
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Gentleman, the most reasonable thing in research is to focus on efficiency ...
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As a recent graduate currently pursuing a master's degree in Business Analytics and having completed a bachelor's degree in Computer Science last year, my focus is on securing employment in the UK promptly. I aim to achieve this by leveraging my academic background, emphasizing relevant coursework and projects in both fields, actively networking with professionals in the industry, and applying to internships or graduate programs offered by companies in the UK. Additionally, I am continuously refining my resume, customizing job applications, and preparing for interviews to maximize my chances of securing a position as a fresher in the UK job market. What else i need to do to stand out and to get a job after my course completion or mostly in between during my course ?
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Securing a job as a fresher in the UK, especially in the field of Business Analytics with a background in Computer Science, requires a strategic approach and active engagement in the job search process. Here are some tips to help you land a job sooner:
  1. Networking: Networking is crucial in the job search process. Attend industry events, career fairs, and networking sessions to connect with professionals in the field of Business Analytics. Join relevant online forums, LinkedIn groups, and professional associations to expand your network and learn about job opportunities.
  2. Internships and Work Experience: Consider applying for internships or work placements in companies that offer opportunities in Business Analytics. Gain practical experience and build your skills to enhance your employability.
  3. Career Services: Make use of the career services provided by your university. They can help you with CV writing, interview preparation, and connecting you with potential employers.
  4. Job Portals and Recruitment Agencies: Utilize online job portals such as Indeed, Glassdoor, and LinkedIn to search for entry-level positions in Business Analytics. Register with recruitment agencies specializing in the field to access job opportunities and receive guidance on job applications.
  5. Tailor Your CV and Cover Letter: Customize your CV and cover letter to highlight your relevant skills, qualifications, and experiences in Business Analytics and Computer Science. Showcase any projects, coursework, or internships that demonstrate your expertise in data analysis and problem-solving.
  6. Develop Technical Skills: Enhance your technical skills in data analytics tools such as Python, R, SQL, Tableau, or Power BI. Employers value candidates who can analyze data effectively and derive actionable insights.
  7. Stay Updated: Stay informed about industry trends, developments, and job market demands in Business Analytics. Continuous learning and upskilling will make you more competitive in the job market.
  8. Prepare for Interviews: Practice common interview questions related to Business Analytics and be prepared to discuss your skills, experiences, and career goals. Showcase your enthusiasm for the field and your willingness to learn and grow.
  9. Be Persistent and Positive: Job hunting can be challenging, especially for fresh graduates. Stay positive, be persistent in your job search efforts, and keep refining your approach based on feedback and experiences.
By following these tips and actively engaging in the job search process, you can increase your chances of securing a job in Business Analytics as a fresher in the UK sooner. Good luck with your job search!
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The short answer is “no” in foreseeable future.
Regarding some optimism about Generative Pre-trained Transformers (GPTs) and Large Language Models (LLMs), I am sorry to disappoint you. Such technologies are not suitable for automatically generating reliable code. And they are not suitable for this in principal, regardless of their maturity. They are very useful for other tasks: natural language translation, simple refactoring of existing code etc., but not for tasks requiring deep conceptualisation. It’s because GPTsand LLMshave no logical reasoning model.
Current GPTsand LLMs are based on justneurocomputing.To generate programs automatically, AI should be based on neurosymbolic paradigms. But this is a long story.
From my everyday experience, I see how aGPT fails to complete simple coding tasks, generates obscure code and inserts bugs.
However there are some nuances of what we mean by “coders”. If we mean narrow coding, i.e. just coding without other important integrated steps in software development, then such coders have already become obsolete even without involvement of AI. If we mean broad coding, including integrated steps of requirement elicitation, conceptualisation, design, implementation, debugging, testing, evaluation, deployment etc., then the demand for such skills getting higher and higher, especially when AI grows.
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A human can only aspire to fluency in so many different languages before mixing up words due to code switching. Thus, MAYBE those who cannot learn so many languages turn to linguistics and coding to earn money.
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Some ideas and associations:
You state “human can only aspire to fluency in so many different languages before mixing up words due to code switching”. I don’t know if this is true at all. On what research is it based? In my own situation, I am fluent in 5 languages and do not mix up words or get confused to which language a word belongs to.
Fluency in language corresponds to numeracy, that has been demonstrated. Children who are read a lot of stories in pre-school, for example, were no better in general subjects compared to other children, but they were better at maths later on. Both implicate logical thinking. Without logical thinking humans cannot string words into a longer narrative either.
People choosing coding or computer sciences may prefer to work individually and not in groups. That is a different dynamic, social vs solo, than proficiency at languages.
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I have 5 on computer science papers. I want to publish it in Scopus (q2, q3)/SCI . I am open to collaboration. Can anyone do the funding and become coauthor...
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not getting publication fund
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Explore the nuanced relationship between patents and copyrights in the realm of software development, addressing challenges and opportunities for intellectual property protection. Seeking insights from experts in law and computer science on ResearchGate.
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This is a PhD research topic in its own right. There's absolutely no way it can be answered in a short researchgate text.
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This algorithm has been devised by me in 2003. What I think is that this algorithm can be useful to evaluate testing the speed of processor of computer and furthermore, this algorithm can be used in data flow with encryption algorithms. I would love to know ideas about this subject from people who are expert in mathematics and computer science. I upload a pdf below. Thank you.
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The question touches upon an important issue in the education system, particularly in India. The distinction between various master's programs in Computer Science disciplines like Master of Computer Application (MCA), Master of Technology (M.Tech), and Master of Engineering (M.E), and the differences in their recognition and acceptance in academic and professional spheres, is a topic of concern for many. The perception of these degrees and their applicability in different job roles can vary widely, leading to questions about fairness and equality.
  1. Recognition and Equal Standing: All these master in computer science programs, such as MCA, M.Tech, or M.E, are recognized by All India Council for Technical Education (AICTE), indicating that they meet certain standards of education. Despite this, there seems to be a disparity in how they are perceived and accepted in the job market, and especially in academic institutions.
  2. Duration and Content: The duration of these programs, is often the same (for example, B.Tech + M.Tech, B.E + M.E, and BCA + MCA all taking six years). However, the focus of the curriculum and the skills acquired can vary.
  3. NET Qualifications: The eligibility criteria for UGC NET and similar exams also play a role. The fact that M.Tech/M.E/MCA graduates can appear for University Grants Commission (UGC) NET (National Eligibility Test) implies a certain level of academic qualification.
  4. Fairness and Equality: It raises a valid concern about whether the current system is fair to all graduates, regardless of their specific master's program.
I am making one video on this issue. If you think the video needs correction and improvement, and you want to give suggestions, this is the right place.
Possible factors could include historical practices, industry demands, or specific skill sets associated with different degrees. So, the question is, "Why there is discrimination between these programs?"
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Yes Dr. absolutely, your thought is very acceptable in the case of background qualifications and priorities of teaching in comparison with engineering and mastering studies.
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I have completed a master's degree in computer science and have good research experience in the human-computer interface area. I have published 3 IF journals as a first author and 7 IEEE conference articles to date. I am struggling to find a good fully funded PhD position. I am open about the country as well.
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You will. Anywhere. You are on STEM and have a good profile. Just apply. Give preference to European Universities.
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Hello ResearchGate community,
I'm currently seeking recommendations for computer science journals indexed in SCIE that are known for having a relatively low review time. I'm specifically interested in journals outside of Springer and Elsevier. If you have any suggestions or insights based on your experiences with journals that maintain an efficient review process, I would greatly appreciate your input.
Additionally, any information on average review times or personal experiences with the submission process would be valuable.
Thank you in advance for sharing your knowledge!
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As I mentioned in a recent response to a similar query, MDPI is a publishing platform known for its fast publication process. I recommend you to explore their journals, namely 'Mathematics', 'Applied Sciences' and 'AI', but there are others that may be more appropriate according to what you want to publish in computer sciences. Personally, I have had positive experiences publishing and reviewing articles for the first two journals. The review process is remarkably short, as MDPI gives reviewers a maximum of 10 days to evaluate submitted articles. This agility contributes to the speed of the publication process for researchers.
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Dear Participant,
We invite you to be an integral part of a crucial study focusing on "Cybersecurity in Intelligent Transportation Systems (ITS)." Your insights and experiences are invaluable in understanding the challenges and opportunities in this rapidly evolving field. Whether you're an everyday user of ITS or a seasoned IT professional, your voice matters.
Why Your Participation is Essential:
  • Make an Impact: Your feedback will directly contribute to enhancing the security and reliability of ITS.
  • Share Your Experience: Your unique perspective on using or managing ITS can provide critical insights that surveys and studies alone cannot capture.
  • Be Part of the Solution: By sharing your thoughts on cybersecurity challenges and practices, you'll be part of developing more robust and secure transportation systems.
How to Participate:
Your participation is voluntary, but each response adds significant value to our understanding of cybersecurity in ITS. We respect your time and effort in helping us with this research.
Thank you for considering this invitation. Together, we can work towards creating a safer, more secure future in intelligent transportation.
Sincerely,
Dr. Dimitrios Sargiotis
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Dear Dr. Sargiotis!
Your project is very important also from the point of military logistics - cyberspace is already at risk in the context of EU - Russia relations:
1) Simola, J., & Pöyhönen, J. (2022). Emerging Cyber risk Challenges in Maritime Transportation. In R. P. Griffin, U. Tatarand, & B. Yankson (Eds.), ICCWS 2022 : Proceedings of the 17th International Conference on Cyber Warfare and Security (17, pp. 306-314). Academic Conferences International. The proceedings of the ... international conference on cyber warfare and security. https://doi.org/10.34190/iccws.17.1.46 Available at:
2 )Zezhou Wang, Xiang Liu, Cyber security of railway cyber-physical system (CPS) – A risk management methodology, Communications in Transportation Research, Volume 2, 2022, https://doi.org/10.1016/j.commtr.2022.100078, Open access:
Yours sincerely, Bulcsu Szekely
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Désirant faire une thèse en génie civil option structure en lien avec l'informatique ou l'environnement.
(Structure+informatique ou structure+environnement.)
Besoin des propositions des thèmes. Merci d'avance
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You can choose Seismic retrofitting of regular or plan irregular building with short length BRB on the software openseespy.
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For computer science, can some methods in the paper be written without experiments, just theoretical analysis of the results?
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Orlov A.I. Methods of reducing the dimension of statistical data space / A.I. Orlov, E.V. Lutsenko // Polythematic network electronic scientific journal of Kuban State Agrarian University (Scientific Journal of KubGAU) [Electronic resource]. – Krasnodar: KubGAU, 2016. – №05(119). P. 92 – 107. – IDA [article ID]: 1191605005. – Access mode: http://ej.kubagro.ru/2016/05/pdf/05.pdf , 1 u.p.l.
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I finished my Master Thesis and get the degree in Computer Science last year, Alhamdulellah.
My question is: Which is better, publish the thesis on the internet or not?
What are the disadvantages and advantages?
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You will find readers outside your narrow community.
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  1. What is the reputation and impact factor of IAENG journals in the field of computer science?
  2. Can you provide information about the review process followed by IAENG journals for computer science papers?
  3. What is the average duration from submission to the first decision for papers submitted to IAENG computer science journals?
  4. Are IAENG journals recognized by relevant indexing services or databases in the field of computer science?
  5. Can you share any recent success stories or notable publications in the field of computer science from IAENG journals?
  6. Are there any specific focus areas or topics within computer science where IAENG journals excel?
  7. What is the acceptance rate for papers submitted to IAENG computer science journals?
  8. Are IAENG journals open access, and what are the publication fees, if any, associated with publishing in these journals?
  9. Can you provide references to researchers or institutions that have had positive experiences publishing in IAENG computer science journals?
  10. How does IAENG ensure the quality and rigor of its peer-review process for computer science paper
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Thank you very much Peter.
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The method is simple and effective. How to write a computer science paper that will help it be accepted?
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Writing a computer science paper that stands a good chance of being accepted involves several key steps and considerations. Here's a concise guide to help you:
1. Select a Relevant and Interesting Topic:
  • Choose a topic that is relevant to the current trends and challenges in computer science.
  • Ensure it's an area that genuinely interests you, as your passion will reflect in your writing.
2. Extensive Literature Review:
  • Conduct a thorough review of existing literature to understand the state of the art in your chosen area.
  • Identify gaps or areas where your work can make a meaningful contribution.
3. Define Clear Objectives:
  • Clearly state the objectives of your research at the beginning of the paper.
  • Define the problem you're addressing and what you aim to achieve.
4. Develop a Strong Methodology:
  • Describe your research methodology in detail, making it clear and replicable.
  • Justify your approach and explain why it's appropriate for your research.
5. Results and Analysis:
  • Present your results clearly and comprehensively.
  • Use visual aids like graphs and charts to enhance understanding.
  • Analyze the results, discussing their implications and significance.
6. Write a Clear and Engaging Paper:
  • Structure your paper logically with sections like introduction, methodology, results, discussion, and conclusion.
  • Write in clear and concise language, avoiding unnecessary jargon.
  • Keep the reader engaged with a well-organized narrative.
7. Cite Properly:
  • Ensure proper citation of all sources and adhere to a recognized citation style (e.g., APA, IEEE).
  • Give credit to previous work and acknowledge contributions.
8. Proofread and Edit:
  • Carefully proofread your paper for grammar, spelling, and formatting errors.
  • Consider seeking feedback from peers or mentors.
9. Address Reviewer Comments:
  • If your paper receives feedback from reviewers, carefully address their comments and revise accordingly.
10. Ethical Considerations:
  • Ensure ethical research practices, including proper data handling and authorship attribution.
11. Submit to Reputable Journals or Conferences:
  • Target reputable journals or conferences in your field for submission.
  • Follow their guidelines and deadlines meticulously.
12. Persistence:
  • Be prepared for potential rejections and don't be discouraged. Many great papers go through multiple submissions and revisions before acceptance.
Remember that the peer review process is rigorous, so patience and perseverance are key. By following these steps and continuously improving your writing and research skills, you can increase your chances of having your computer science paper accepted.
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Are computer science papers generally not as complex as mathematics papers?
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Just curious:
Are there any complex and profound differences with the other one:
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Are computer science papers generally not as profound as mathematics papers?
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Well, the Impact Factor for both can be quickly checked
vs
... quite in favor of CS ...
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For computer science, sometimes, writing can elevate a paper to a very high level. Right?
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Yes sometimes.
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AI research topic that will requires coding in either Pyhton, Java or VB.Net is what I needed.
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You may search for an nature inspired optimization algorithm or a recent swarm intelligence based optimization algorithm and try improving it. Following steps might be helpful when creating a variant of an existing algorithm:
1. Understand the target algorithm thoroughly. understand the base paper, try reproducing results reported in the original paper.
2. Literature Review: Look at the available variants of the target algorithm as well as others work on "How to improve an optimization algorithm", you may consider variants of PSO or WOA or GWO, these are mature algorithms with a lot of existing variants.
3. Brainstorm modification: it is time to finalize modification for your chosen algorithm.
4. Define a clear Hypothesis: Formulate a clear hypothesis about how your modifications will lead to an improvement in the algorithm's performance. Your hypothesis should be testable and measurable.
5. Implementation: Easiest way is to use matlab, many codes are available online and many tutorials on how to program in matlab. You may use python as well.
6. Test on the common benchmark functions
7. Compare and Analyze results.
8. Iterate and refine: Refine the algorithm based on the testing and comparison.
9. Document the approach.
10. Publish your work in a suitable Journal.
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Thank you for sharing Sunny Sharma
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Interdisciplinary research involves collaboration between researchers from different fields to solve complex problems that cannot be tackled within the confines of a single discipline. It seeks to combine knowledge, methods, and tools from different fields to create new solutions that are not possible within one field alone.
Interdisciplinary research can involve natural sciences, social sciences, and humanities, as well as other areas such as engineering, computer science, and economics.
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I consider myself an interdisciplinary researcher and do enjoy interdisciplinary research. I enjoy mixing research areas from different fields and see the outcome/s. It allows for one to gain knowledge of other fields, whilst also adding your own perspectives to another field. Hence it becomes win-win situation. In addition, some problems cannot be solved from one area/field, but may require different knowledge from multiple research fields to reach a comprehensive solution. However, whilst I do interdisciplinary research, I am also cautious not to overstep boundaries on any specific field that may be totally out of my ambit/expertise.