Manuel Mazzara

Innopolis University · Department of Computer Science and Engineering

In this paper, we examine the use of quantum annealing for the Traveling Salesman Problem (TSP) using the D-Wave Advantage quantum annealer and its "Pegasus" architecture. We introduce a refined Quadratic Unconstrained Binary Optimization (QUBO) formulation that simplifies the problem by eliminating the first node and reallocating its effect, there...

The classification of hyperspectral images (HSI) is a challenging task due to the high spectral dimensionality and limited labeled data typically available for training. In this study, we propose a novel multi-stage active transfer learning (ATL) framework that integrates a Spatial-Spectral Transformer (SST) with an active learning process for effi...

Hyperspectral Imaging (HSI) has proven to be a powerful tool for capturing detailed spectral and spatial information across diverse applications. Despite the advancements in Deep Learning (DL) and Transformer architectures for HSI classification, challenges such as computational efficiency and the need for extensive labeled data persist. This paper...

With the exponential proliferation of digital documents, there arises a pressing need for automated document summarization (ADS). Summarization, a compression technique, condenses a source document into concise sentences that encapsulate its salient information for summary generation. A primary challenge lies in crafting a dependable summary, conti...

This paper presents a novel solution aimed at enhancing news web pages for seamless integration into the Semantic Web. By utilizing advanced pattern mining techniques alongside OpenAI’s GPT-3, we rewrite news articles to improve their readability and accessibility for Google News aggregators. Our approach is characterized by its methodological rigo...

This paper sheds light on the challenge in the Information Technology (IT) and Software Engineering (SE) industries where computer science graduates face a Catch-22 situation, requiring job experience for employment but lacking opportunities to gain it. The literature background and case study examine the demands and dynamics of the IT job market....

Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art (SOTA) models e.g., Attention Graph and Vision Transformer. When training, validation, and test sets overlap or share data, it introduces a bias that inflates performance metrics and prevents accurate assessment of a model’s true ability to generalize to new exam...

Research on measuring attention via EEG during cognitive tasks has been extensive, yet the relationship between attention levels in programming and software bug frequency remains underexplored. Debugging is a central, time-intensive phase in software development that often delays project timelines due to the challenges of detecting and resolving er...

Photonic neural networks (PNNs) represent a promising intersection of optical computing and artificial intelligence, offering potential advancements in speed and energy efficiency. This paper explores the core architecture of PNNs, their underlying principles, and the method-ologies for implementing software-defined neural networks onto photonic pl...

Quantum annealers are entering a new era of technology. The "Advantage 1" quantum annealer, developed by D-Wave Systems Inc. with the "Pegasus" topology, is currently the largest quantum computer in the world. Quantum annealing, a specialized variant of simulated annealing that utilizes quantum properties, offers a wide array of opportunities to ad...

The 3D Swin Transformer (3DST) and Spatial-Spectral Transformer (SST) each excel in capturing distinct aspects of image information: 3DST with hierarchical attention and window-based processing, and SST with self-attention mechanisms for long-range dependencies. However, applying them independently reveals limitations: 3DST struggles with spectral...

The Transformer model encounters challenges with variable-length input sequences, leading to efficiency and scalability concerns. To overcome this, we propose a pyramid-based hierarchical Spatial-Spectral Transformer (PyFormer). This innovative approach organizes input data hierarchically into pyramid segments, each representing distinct abstractio...

Despite extensive research on attention measurement via EEG during cognitive tasks, the specific relationship between attention levels in programming and software bug frequency remains largely unexplored. Debugging, a critical and time-consuming part of software development, often causes delays due to the effort needed to identify and resolve error...

The rapid expansion of the non-fungible token (NFT) market has catalyzed new opportunities for artists, collectors, and investors, yet it has also unveiled critical challenges related to the storage and distribution of associated metadata. This paper examines the current landscape of NFT metadata storage, revealing a significant reliance on central...

In this paper, we examine the use of quantum annealing for the Traveling Salesman Problem (TSP) using the D-Wave Advantage quantum annealer and its "Pegasus" architecture. We introduce a refined Quadratic Unconstrained Binary Optimization (QUBO) formulation that simplifies the problem by eliminating the first node and reallocating its effect, there...

Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing Transformer limitations. However, traditional Mamba models overlook rich spectral information in HSIs and struggle with high dimensionality and sequential data. To address these issues, we propose the SSM with multi-head self-attention an...

Hyperspectral Imaging (HSI) has proven to be a powerful tool for capturing detailed spectral and spatial information across diverse applications. Despite the advancements in Deep Learning (DL) and Transformer architectures for HSI Classification (HSIC), challenges such as computational efficiency and the need for extensive labeled data persist. Thi...

In recent years, Transformers have garnered significant attention for Hyperspectral Image Classification (HSIC) due to their self-attention mechanism, which provides strong classification performance. However, these models face major challenges in computational efficiency, as their complexity increases quadratically with the sequence length. The Ma...

Classical computers face significant challenges when dealing with NP problems, especially given the unresolved question of whether NP equals P. These challenges arise due to the computational complexity and resource limitations inherent in solving such problems efficiently. Quantum computing, on the other hand, shows promise in addressing these cha...

In Transformer-based Hyperspectral Image Classification (HSIC), predefined positional encodings (PEs) are crucial for capturing the order of each input token. However, their typical representation as fixed-dimension learnable vectors makes it challenging to adapt to variable-length input sequences, thereby limiting the broader application of Transf...

Photonic neural networks (PNNs) represent a promising intersection of optical computing and artificial intelligence, offering potential advancements in speed and energy efficiency. This paper explores the core architecture of PNNs, their underlying principles, and the methodologies for implementing software-defined neural networks onto photonic pla...

Collecting personal user data helps software developers to improve the product. However, it must be conducted in an consensual and transparent manner. In this paper, we compared several approaches to collecting personal data from users in the context of an informal e-learning system. More specifically, we asked the learners to donate their personal...

This work focuses on the application of quantum annealing to the mechanics of robot inspectors used for electrical transmission line maintenance. The integration of quantum annealing in robotics enables the resolution of NP-complete problems and the efficient solving of differential equations that describe the dynamics of robot inspectors. The math...

Quantum computing (QC) has become a fascinating and popular topic due to its broad range of applications, particularly in machine learning (ML). The intersection of QC and ML is known as Quantum Machine Learning (QML). QML is a field that investigates how QC can enhance ML, making it one of the most exciting areas of research due to the potential o...

This article delves into the dynamic challenges confronting Higher Education Institutions (HEIs) globally, emphasizing the urgency for innovative solutions. The escalating number of students and the paradigm-shifting impact of the COVID-19 pandemic have forced HEIs to reevaluate pedagogical strategies, integrating blended and online learning models...

This paper conducts an exhaustive exploration of the evolutionary journey of microservices within the domain of software architecture. It meticulously traces the historical trajectory, current status, and potential future pathways of microservices in software design. Additionally, this study introduces a pioneering concept known as Quantum Microser...

Advancements in software development within the era of Industry 4.0 are prompting a reevaluation of traditional risk management methodologies. This survey investigates the integration of machine learning (ML) with established frameworks like those from the Project Management Institute (PMI) and ISO 31000. The study focuses on the potential of ML to...

The hasty advancement of Quantum Computing (QC) technologies has led to offer plethora of promising offering promising opportunities for solving complex optimization problems, especially Quantum Annealing (QA). This paper presents an investigation into the fusion of ML and QA. It also explores and elucidates the capabilities of ML empowered by QA t...

This paper conducts an exhaustive exploration of the evolutionary journey of microservices within the domain of software architecture. It meticulously traces the historical trajectory, current status, and potential future pathways of microservices in software design. Additionally, this study introduces a pioneering concept known as Quantum Microser...

This paper presents a exploration into the integration of Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) within networking in-frastructures, marking a groundbreaking advancement in network security. It meticulously examines the vulnerabilities inherent in classical and post-quantum cryptographic methods, underlining the pressing...

This thesis addresses the decision-making and survival strategies of IT companies in Russia amid socio-political conflicts. It investigates why IT firms choose to stay or leave Russia and the survival tactics they employ. Employing mixed methods, including qualitative interviews and quantitative analysis, two key experiments form the core of this r...

This paper comprehensively investigates collision conditions in three-body problems, incorporating General Relativity (GR) effects. The study analyzes the initial values of the bodies to determine the collision possibility and develops a high-accuracy machine learning model for classifying collision events. The study introduces the concept of GR-ef...

Quantum Computing has emerged as a transformative technology, offering solutions to complex problems across various domains. Leading this innovation is Leap™ by D-Wave Systems, offering Quantum Computing as a Service (QCaaS) with an Integrated Development Environment (IDE) tailored for quantum programming. Recently, the Leap™ IDE transitioned to Gi...

This paper outlines the establishment and vision of the Advanced Engineering School (AES) at Innopolis University (IU), a leading institution in Information Technology (IT) and Robotics in Tatarstan, Russia. The AES aims to address global challenges in IT education by training over 13,000 highly qualified IT engineers by 2030, focusing on technolog...

Online controlled experimentation, and more specifically A/B testing, is an effective method for assessing the impact of software changes. However, when adopting A/B testing, a development team faces various organizational and technical challenges. In this paper, we propose a new notion of reusable controlled experiments (RCE) to simplify and accel...

This article sheds light on the pervasive challenge in the Information Technology (IT) and Software Engineering (SE) industry where computer science graduates face a Catch-22 situation, requiring job experience for employment but lacking opportunities to gain it. The paper offers a nuanced exploration of the elements that define a successful applic...

This paper explores hackathons as an evaluative tool and their contribution to students’ learning. Mixed-data collection methods/qualitative and quantitative data collection were used by conducting a survey among students and semi-structured interviews among hackathon juries and organizers. Results have demonstrated that hackathons can be used as e...

In recent years, the exploration of innovative perspectives within quantum field theories has emerged as a central focus in the domain of mathematical modeling for the physical sciences. The papers submitted for consideration in the 13th International Conference on Mathematical Modeling in Physical Sciences represent a diverse array of pioneering a...

The rapid advancement of quantum computing technologies has led to an intersection with methodologies in machine learning, offering promising avenues for solving complex optimization problems. This paper presents an innovative investigation into the fusion of machine learning and quantum annealing. It explores the enhanced capabilities of machine l...

This paper presents a exploration into the integration of Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) within networking infrastructures, marking a groundbreaking advancement in network security. It meticulously examines the vulnerabilities inherent in classical and post-quantum cryptographic methods, underlining the pressing...

This paper conducts an exhaustive exploration of the evolutionary journey of microservices within the domain of software architecture. It meticulously traces the historical trajectory, current status, and potential future pathways of microservices in software design. Additionally, this study introduces a pioneering concept known as Quantum Microser...

Transformers have proven effective for Hyperspectral Image Classification (HSIC) but often incorporate average pooling that results in information loss. This paper presents WaveFormer, a novel transformer-based approach that leverages wavelet transforms for invertible downsampling. This preserves data integrity while enabling attention learning. Sp...

Hyperspectral Image Classification (HSIC) faces challenges in preserving high-frequency features during down-sampling and hierarchical filtering in the CNN architecture. To overcome this, we propose Sharpened Cosine Similarity (SCS) as an alternative to convolutions within a Neural Network for HSIC. SCS-Net emphasizes parameter efficiency by bypass...

Caching content at base stations has proven effective at reducing transmission delays. This paper investigates the caching problem in a network of highly dynamic cache-enabled Unmanned Aerial Vehicles (UAVs), which serve ground users as aerial base stations. In this scenario, UAVs share their caches to minimize total transmission delays for request...

This thesis addresses the decision-making and survival strategies of IT companies in Russia amid socio-political conflicts. It investigates why IT firms choose to stay or leave Russia and the survival tactics they employ. Employing mixed methods, including qualitative interviews and quantitative analysis, two key experiments form the core of this r...

Smart cities are increasingly challenged by population growth and the environmental emissions of urban transportation systems, necessitating sustainable urban planning to improve public health, environmental quality, and overall urban livability. A notable aspect in this context is the under-utilization of smart healthcare wearable devices or smart...

As the Internet of Things (IoT) landscape rapidly evolves, robust network security measures are imperative. In particular, Intrusion Detection Systems play a very important role in the preservation of an IoT environment from malicious activities. This paper provides a comprehensive performance comparison of various machine learning classifiers, inc...

The AI in games has a large impact on the player's experience, but the large variety of available AI implementation methods makes it difficult to determine which one(s) to use in any particular project, and the differences in their impact on players are mostly unstudied. This paper presents a comparative study to analyse the effects of Behaviour Tr...

Anomalies in switches behavior in railway systems can significantly impact operational efficiency and safety. This paper proposes an approach based on energy consumption data of switches to identify abnormal behaviors. The approach is based on statistical analysis for real-time anomaly detection in switch energy measurements. Our approach is genera...

Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, with a higher prevalence and mortality in developing countries specifically. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. Recent...

This presentation delves deeply into the collision dynamics that arise in scenarios involving three bodies, while also considering the influence of General Relativity. The primary focus is on analyzing the initial conditions of these celestial bodies and assessing the likelihood of collisions. The presentation introduces an advanced machine learnin...

This paper explores hackathons as an evaluative tool and their contribution to students' learning. Mixed-data collection methods/qualitative and quantitative data collection were used by conducting a survey among students and semi-structured interviews among hackathon juries and organizers. Results have demonstrated that hackathons can be used as e...

Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, more specifically with higher prevalence and mortality in developing countries. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. The...

This paper comprehensively investigates collision conditions in three-body problems, incorporating General Relativity (GR) effects. The study analyzes the initial values of the bodies to determine the collision possibility and develops a high-accuracy machine learning model for classifying collision events. The study introduces the concept of GR-ef...

Given that machine learning has recently been increasingly used in all areas of human life, this paper demonstrates the implementation of the automatic number plate recognition (ANPR) model by utilizing machine learning techniques. In our system, TensorFlow is used for license plate detection, while Tesseract is used for optical character recogniti...

This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes. When indi...

With the number of positive cases of Covid-19 infection is increasing, it is essential for everyone to wear a face mask and prevent the spread of Covid. As people are gathering in a large number at different locations, it is quite important for everyone to wear a face mask and prevent the covid spread. With the increase in the crowd gathering, it i...

Fierce winds produced by the Fourth Industrial Revolution, socio-economic upheavals, geopolitical strife and other black swan events can lead to dangerous water conditions threatening the business and operational continuity of organisations. The unprecedented disruptions caused by COVID-19 have significantly impacted all industries worldwide, and t...

The Hook model is used in digital products to engage and retain users through the mechanism of habit formation. This paper explores the use of Hook model techniques in two mobile applications, one being a popular taxi service (Uber taxi) and the other a social network (Instagram). The goal of this paper is to explore the Hook cycle patterns in the...

Many cutting-edge language models have been used in the past to forecast election results. Sentiment analysis aids in opinion mining – a common experiment used to detect public opinions – on a given topic. Twitter has gained popularity and established itself as a crucial instrument for analyzing public opinion on elections and other trending issues...

Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as tools for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Aim: In this work, a Natural Language Processing framework is designed to understand Nigeria 202...

In this lecture we will cover the following topics: • Progress: hard vs soft • Releases at the time of services • It is all about deployment! • …and then DevOps came! • Devops+Process Mining +Anomaly Detection +Machine Learning = ?

In this lecture we will cover the following topics: • From language to formalization of reasoning • The Holy Grail of Automatic Reasoning • Understanding the Turing Machine - the historical background • Understanding the Turing Machine - the mathematical background • Software Engineering as an Alchemical Process

Citation: Lukyanchikova, E.; Askarbekuly, N.; Aslam, H.; Mazzara, M. A Case Study on Applications of the Hook Model in Software Products. Software 2023, 1, 1-18. https://doi.org/ Academic Editor: Francisco José García-Peñalvo Abstract: The Hook model is used in digital products to engage and retain users through the mechanism of habit formation. Th...

Zoom has become a powerful tool of online education in today’s society. Ensuring the quality of online education platforms has been a growing concern for the last several years. However, there is a lack of research in this field especially in relation to the perspectives of students. Hence, there is a need for more empirical research in this area....

One of the most important components of a smart city is smart transport. To design large-scale smart transport systems, simulations are integral to testing the efficacy of various traffic light control algorithms. The traffic light algorithm designers take advantage of the simulation software to build reliable and robust algorithms. In this work, t...

Election outcomes have been predicted in the past with the help of various state-of-the-art language models. Sentiment analysis helps in establishing the opinions of the public about a particular subject, a popular experiment known as opinion mining. Twitter has grown in popularity and proven to be a key tool in mining people’s sentiments concernin...

This study examines social media addiction and prototypes a software application. This application can be integrated with any social media platform to alarm users when they are near the addiction stage. The work is ongoing, and this paper elaborates on the preliminary findings. We surveyed participants in the age range of 18–40 years to investigate...

Web scraping refers to the extraction of data from a specific website. Every website will include web pages and each page has a source code containing HTML tags that show a representation of the data. The problem with any scraping method for a web page is how the page is structured, each page has a different structure. That’s why the process of dat...

Since the introduction of Bitcoin and Ethereum, blockchain introduced new ways to handle payment and dealing with financial transactions. From 2009 till now, several blockchain systems were developed. However, the main issue for each blockchain is to know where to use it and what the best use case fits with it. Transaction per second (TPS) and conf...

In this work we performed simulation modeling of the blood flow of the human aortic valve under pathologies. Such results as: velocity, pressure were visualized. Based on the results, it was found that when the blood flow in the area of the aortic valve, there is a change in velocity and pressure. From what the conclusions about the necessity of th...

Fierce winds produced by the Fourth Industrial Revolution, socio-economic upheavals, geopolitical strife and other black swan events can lead to dangerous water conditions threatening the business and operational continuity of organisations. The unprecedented disruptions caused by COVID-19 have significantly impacted all industries worldwide, and t...

The COVID-19 pandemic has been a game changer for many aspects of the educational environment worldwide. Together with positive aspects of digital acceleration, there have also been obstacles in keeping internationalization processes on track. In this paper, we consider a case study of an educational institution: Innopolis University. We analyze th...