Yutaka Watanobe

• PhD
• Professor (Associate) at University of Aizu

In this study, we introduce Hierarchical Local Interpretable Model-agnostic Explanations (H LIME), an innovative extension of the LIME technique, designed to enhance the interpretability of machine learning models by offering explanations at multiple levels of data hierarchy. This study focuses on predicting student adaptability across various educ...

The transportation industry contributes significantly to climate change through carbon dioxide ( CO 2 ) emissions, intensifying global warming and leading to more frequent and severe weather phenomena such as flooding, drought, heat waves, glacier melting, and rising sea levels. This study proposes a comprehensive approach for predicting CO 2 emiss...

Programming is an essential skill in computer science and across a wide range of engineering disciplines. However, errors, often referred to as ’bugs’ in code, can be challenging to identify and rectify for both students learning to program and experienced professionals. Understanding, identifying, and effectively addressing these errors are critic...

Accurate product sales forecasting is critical for inventory management, pricing strategies, and supply chain optimization in the retail industry. This paper proposes a novel deep learning architecture that integrates Temporal Convolutional Networks (TCNs) with Transformer-based attention mechanisms to capture both short-term and long-term dependen...

In this paper, we propose a novel deep learning model that integrates a Temporal Convolutional Network (TCN) with an Attention mechanism to predict stock prices and assess risk for MasterCard (MA) and Visa (V). The model is designed with a dual output to forecast future stock prices (Open, Close, High, Low) while simultaneously predicting risk metr...

In recent years, there has been a notable surge in the generation of coding data on various platforms, including programming competitions and educational institutions. These platforms serve as repositories for substantial volumes of real-world code, problem descriptions, test cases, and activity logs. Despite this wealth of coding data, its potenti...

Sentiment analysis is a pivotal tool in understanding public opinion, consumer behavior, and social trends, underpinning applications ranging from market research to political analysis. However, existing sentiment analysis models frequently encounter challenges related to linguistic diversity, model generalizability, explainability, and limited ava...

This paper describes a method for integrating multiple dense point clouds using a shared landmark to generate a single real-scale integrated result for photogrammetry. It is difficult to integrate high-density point clouds reconstructed by photogrammetry because the scale differs with each photogrammetry. To solve this problem, this study places a...

This study introduces a Preference-Based Reinforcement Learning (PbRL) approach tailored for autonomous vehicle (AV) applications within a simulated environment. Traditional RL methods often struggle with the complexities of reward function engineering, failing to perform behaviors of human desire. The proposed framework integrates human preference...

Programming is an essential skill in computer science and in a wide range of engineering-related disciplines. However, occurring errors, often referred to as "bugs" in code, can indeed be challenging to identify and rectify, both for students who are learning to program and for experienced professionals. These errors can lead to unexpected behavior...

Enormous amounts of data are generated in the form of feedback or comments from online platforms such as social media, e-commerce, education, and programming. This feedback and comments hold significant value for making important strategic decisions; therefore, effectively analyzing them poses a major challenge. This research addresses the imperati...

Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL), are integral components of smart homes, sports, surveillance, and investigation activities. To recognize daily activities, researchers are focusing on lightweight, cost-effective, wearable sensor-based technologies as traditional vision-based technologies lack elderly privac...

As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of XAI techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset of 1205 instances, we employed several machine learni...

Effectively analyzing the comments to uncover latent intentions holds immense value in making strategic decisions across various domains. However, several challenges hinder the process of sentiment analysis including the lexical diversity exhibited in comments, the presence of long dependencies within the text, encountering unknown symbols and word...

As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of (XAI) techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset, we employed several machine learning algorithms wi...

In the modern era, we find ourselves immersed in an ever-expanding flow of data where data is increasing exponentially. Data is generated from different platforms like Education, Business, E-commerce, and predominantly, social media platforms such as Twitter, YouTube, Facebook, and Instagram. Amidst this proliferation of content, user comments have...

Over the years, data generation from various sources (social media, business, medical, education, programming, images, videos, etc.) has increased exponentially due to technological development, application, and daily usage. Organizing these large amounts of data efficiently is not a trivial task. Therefore, an efficient sorting algorithm can be he...

In ICT education, most of the courses particularly those focused on programming, are designed to enhance computational and practical skills. However, the selection of appropriate programming languages holds great significance for novice programmers embarking on their journey of learning programming. This paper presents a comprehensive analysis util...

An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. Ho...

Reddit is the largest topically structured social network. Existing literature, reporting results of Reddit-related research, considers different phenomena, from social and political studies to recommender systems. The most common techniques used in these works, include natural language processing, e.g., named entity recognition, as well as graph n...

Assessing children for specific language impairment (SLI) or other communication impairments can be challenging for doctors due to the extensive battery of tests and examinations required. Artificial intelligence and computer-aided diagnostics have aided medical professionals in conducting rapid, reliable assessments of children’s neurodevelopmenta...

This study introduces and compares three innovative approaches for recommending programming problems within an Online Judge system (OJ), tackling the challenge of deriving implicit ratings from user interactions without explicit user ratings. Conventional collaborative filtering (CF) methods often struggle with the sparse and implicit feedback typi...

Programmers often struggle to identify and fix bugs in their programs. In recent years, many language models (LMs) have been proposed to fixe rroneous programs and support error recovery. However, the LMs tend to generate solutions that differ from the original input programs. This leads to potential comprehension difficulties for users.In this pap...

Partial periodic pattern (3P) mining is a vital data mining technique that aims to discover all interesting patterns that have exhibited partial periodic behavior in temporal databases. Previous studies have primarily focused on identifying 3Ps only in row temporal databases. One can not ignore the existence of 3Ps in columnar temporal databases as...

Programmers are allowed to solve problems using multiple programming languages, resulting in the accumulation of a huge number of multilingual solution codes. Consequently, identifying codes from this vast archive of multilingual codes is a challenging and non-trivial task. Considering the codes' complexity compared to natural languages, convention...

A less complex and more straightforward program is a crucial factor that enhances its maintainability and makes writing secure and bug-free programs easier. However, due to its heavy workload and the risks of breaking the working programs, programmers are reluctant to do code refactoring, and thus, it also causes the loss of potential learning expe...

Finding and fixing errors is a time-consuming task not only for novice programmers but also for expert programmers. Prior work has identified frequent error patterns among various levels of programmers. However, the differences in the tendencies between novices and experts have yet to be revealed. From the knowledge of the frequent errors in each l...

The music industry is facing challenges in engaging fans and providing transparency, feedback, and rewards. Blockchain technology presents a potential solution by enabling new forms of fan engagement and participation. This paper proposes a blockchain-based music platform that leverages Ethereum’s decentralized platform and smart contract functiona...

As the largest open social medium on the Internet, Reddit is widely studied in the scientific literature. Due to its structured form and division into topical subfora (subreddits), conducted research often concerns connections and interactions between users and/or whole, subreddit-structure-based communities. Overall, the relations between communit...

This paper explores the use of Non-Negative Factorization for adapting collaborative filtering to programming exercises. Traditional collaborative filtering uses user preferences as ratings, but user feedback is expressed differently in programming exercises. The proposed approach captures user satisfaction based on the number of attempts and the f...

Referring to the solution programs written by the other users is helpful for learners in programming education. However, current online judge systems just list all solution programs submitted by users for references, and the programs are sorted based on the submission date and time, execution time, or user rating, ignoring to what extent the progra...

The automated code evaluation system is designed to reliably evaluate user-submitted code. Code is first compiled and then tested on a homogeneous surface using defined input and output test cases. Automated code evaluation systems are gaining popularity due to their wide range of applications and valuable accumulated resources. The success of mach...

The automated code evaluation system (AES) is mainly designed to reliably assess user-submitted code. The code is compiled and then tested in a unified environment with predefined input and output test cases. Due to their extensive range of applications and the accumulation of valuable resources, AESs are becoming increasingly popular. Research on...

Using large language models (LLMs) for source code has recently gained attention. LLMs, such as Transformer-based models like Codex and ChatGPT, have been shown to be highly capable of solving a wide range of programming problems. However, the extent to which LLMs understand problem descriptions and generate programs accordingly or just retrieve so...

Modern programming languages are very complex, diverse, and non-uniform in their structure, code composition, and syntax. Therefore, it is a difficult task for computer science students to retrieve relevant code snippets from large code repositories, according to their programming course requirements. To solve this problem, an AI-based approach is...

In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education and research. One such technology is ChatGPT, a powerful large language model developed by OpenAI. This technology offers exciting opportunities for students and educators, including personalized feedback, increas...

In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education and research. One such technology is ChatGPT, a powerful large language model developed by OpenAI. This technology offers exciting opportunities for students and educators, including personalized feedback, increas...

The maintenance of proper health records is essential to patient health care. Electronic Health Records (EHR) are now replacing traditional Manual Health Records. Audit logs or Audit Trails are a record of events and changes done in a system. Majority of hospitals are required to maintain an audit trail of each and every EHR. Currently, the audit t...

This paper proposes a recommendation model for similar programming problems to support programming education. In the proposed model, problem similarity is determined according to the similarity of source codes, in terms of the term frequency-inverse document frequency and the effort required to solve the given problem, as calculated according to Ha...

Aging social infrastructure needs maintenance and inspection for which robot technology is highly effective. It is also effective for disaster rescue and recovery operations. Tunnel disaster rescue is risky for human workers. Robot technology can perform this work easily and accelerate rescue and recovery operations. This paper introduces a framewo...

Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disregarded the occurrences of these itemsets in column...

Discovering periodic-frequent patterns in temporal databases is a challenging data mining problem with abundant applications. It involves discovering all patterns in a database that satisfy the user-specified minimum support (minSup) and maximum periodicity (maxPer) constraints. MinSup controls the minimum number of transactions in which a pattern...

In this modern era of the internet and information technology, a mentionable amount of data is generated from different sources consistently which refers to big data. This huge amount of data not only draws great attention for further research but also helps to extract different knowledge and infor-mation in various areas. The Information and Commu...

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupe...

The rapid increase in Internet users has led to increased online concerns such as hate speech, abusive texts, and harassment. In Bangladesh, hate text in Bengali is frequently used on various social media platforms to condemn and abuse individuals. However, Research on recognizing hate speech in Bengali texts is lacking. The pervasive negative impa...

Finding periodic-frequent patterns in temporal databases is a prominent data mining problem with bountiful applications. It involves discovering all patterns in a database that satisfy the user-specified minimum support ( min_sup ) and maximum periodicity ( max_per ) constraints. Min_sup controls the least number of transactions in which a patt...

In this paper, we research whether the reconstruction model integrated based on the 2d code is as valid as the model reconstructed from the whole image group. We investigated the difference in accuracy between the whole reconstruction model and integrate the reconstruction model in simulation. At first, we generate images assuming four linear orbit...

Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evalua...

Finding partial periodic patterns in temporal databases is a challenging problem of great importance in many real-world applications. Most previous studies focused on finding these patterns in row temporal databases. To the best of our knowledge, there exists no study that aims to find partial periodic patterns in columnar temporal databases. One c...

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report...

Recommender systems (RSs) are increasingly recognized as intelligent software for predicting users’ opinions on specific items. Various RSs have been developed in different domains, such as e-commerce, e-government, e-resource services, e-business, e-library, e-tourism, and e-learning, to make excellent user recommendations. In e-learning technolog...

In software, an algorithm is a well-organized sequence of actions that provides the optimal way to complete a task. Algorithmic thinking is also essential to break-down a problem and conceptualize solutions in some steps. The proper selection of an algorithm is pivotal to improve computational performance and software productivity as well as to pro...

A popular language model that can solve introductory programming problems, OpenAI’s Codex, has drawn much attention not only in the natural language processing field but also in the software engineering field. It supports programmers by suggesting the next tokens to write, and it can even generate a whole function definition from a document string....

Efficient path planning and minimization of path movement costs for collision-free faster robot movement are very important in the field of robot automation. Several path planning algorithms have been explored to fulfill these requirements. Among them, the A-star (A*) algorithm performs better than others because of its heuristic search guidance. H...

Despite the incredible adoption of cryptocurrencies, blockchain-based cryptocurrencies have likewise raised some concerns. The scalability problem is the major one among them. An off-blockchain payment channel network (PCN) has been introduced to solve this issue. PCN can fundamentally reduce blockchain scalability by constructing a number of payme...

In this paper, we propose a visual interface for manipulating relational databases (RDBs). This interface, unlike structured query language (SQL), describes queries in a procedural language with graphs. This helps inexperienced users to interact with RDBs, and also helps experienced users to express nontrivial queries properly. It also supports SQL...

Recent breakthroughs in computer vision have led to the invention of several intelligent systems in different sectors. In transportation, this advancement led to the possibility of proposing autonomous vehicles. This recent technology relies heavily on wireless sensors and Deep learning. For an autonomous vehicle to navigate safely on highways, the...

Feature selection is employed to reduce feature dimensions and computational complexity by eliminating irrelevant and redundant features. A vast amount of increasing data and its processing generate many feature sets, that are reduced by the feature selection process to improve the performance in all sorts of classification, regression, clustering...

Extracting stable periodic-frequent patterns in very large temporal databases is a key task in big data analytics. Existing studies have mainly concentrated on discovering these patterns only in row temporal databases, and completely ignored the existence of these patterns in columnar databases, which are widely becoming popular for storing big dat...

Periodic-frequent pattern mining involves finding all periodically occurring patterns in a temporal database. Most previous studies found these patterns by storing the temporal occurrence information of an item in a list structure. Unfortunately, this approach makes pattern mining computationally expensive on dense databases due to increased list s...

The rise of big data has resulted in the proliferation of numerous heterogeneous data stores. Even though multiple models are used for integrating these data, combining such huge amounts of data into a single model remains challenging. There is a need in the database management archives to manage such huge volumes of data without any particular str...

The development and operation of Online Judge System (OJS), which is used to evaluate the correctness of programs, is a nontrivial and difficult task due to the various functional and non-functional requirements. However, although many OJSs have been developed and operated, and their usefulness reported, the theory for constructing OJSs has not bee...

A geo-referenced time series database represents the data generated by a set of fixed locations (or items) observing a particular phenomenon over time. Useful information that can facilitate the users to achieve socio-economic development lies hidden in this data. This paper introduces a novel model of fuzzy geo-referenced periodic-frequent pattern...

Simple Summary This study represents a resourceful review article that can deliver resources on neurological diseases and their implemented classification algorithms to reveal the future direction of researchers. Researchers interested in studying neurological diseases and previously implemented techniques in this field can follow this article. Var...

Computer programming has attracted a lot of attention in the development of information and communication technologies in the real world. Meeting the growing demand for highly skilled programmers in the ICT industry is one of the major challenges. In this point, online judge (OJ) systems enhance programming learning and practice opportunities in ad...

There are extremely large heterogeneous databases in the astronomical data domain, which keep increasing in size. The data types vary from images of astronomical objects to unstructured texts, relations, and key-values. Many astronomical data repositories manage such kinds of data. The Zwicky Transient Facility (ZTF) is one such data repository wit...

Over the years, programmers have improved their programming skills and can now write code in many different languages to solve problems. A lot of new code is being generated all over the world regularly. Since a programming problem can be solved in many different languages, it is quite difficult to identify the problem from the written source code....

Inter-robot communication and high computational power are challenging issues for deploying indoor mobile robot applications with sensor data processing. Thus, this paper presents an efficient cloud-based multirobot framework with inter-robot communication and high computational power to deploy autonomous mobile robots for indoor applications. Depl...

Quantum computing is expected to fundamentally change computer systems in the future. Recently, a new research topic of quantum computing is the hybrid quantum–classical approach for machine learning, in which a parameterized quantum circuit, also called quantum neural network (QNN), is optimized by a classical computer. This hybrid approach can ha...

In the case of performing 3D reconstruction from images called photogrammetry, we assume that it can be applied alignment and size adjustment of multiple reconstructions based on the object when the known objects exist in the reconstruction target area. We adopt QR codes as a known object. However, we could not reconstruct the QR code attached to t...

The IoRT (Internet of Robotic Things) system is a “system of systems” consisting of multiple layers such as robots, cloud, and networks. In most cases, separate solutions are used for each elemental system. On the other hand, the ZeroMQ messaging middleware enables us to develop each elemental system in an IoRT system comprehensively with a single...

Since the Internet of Robotic Things (IoRT) is composed of robots with actuators, interferences with the real-world activities are necessary, and safety is essential. In addition, some IoRT services may require bidirectional communication between multiple machines. One of the communication protocols that satisfy these requirements is AMQP, a broker...

An adaptive user interface for smart programming exercise and its platform is presented. The proposed adaptive user interface is oriented to repetitive exercises with many pro-gramming tasks through different learning phases. The learning phases include searching, reading, coding, testing, debugging, and refactoring, and the learner can receive sma...

The COVID-19 pandemic has had a catastrophic effect on education across the world. The biggest compulsory disruption in the history of education has been caused by COVID-19, which is affecting about 1.6 billion students across 190 countries on all continents. This pandemic has forced a shift from traditional face-to-face class-based systems to onli...