Reviewerly/exHarmony huggingface dataset:
Snake Game with Motion Control Using Computer Vision

This research paper explores the use of artificial intelligence (AI) techniques to create a modified version of the classic snake game. The game is designed to be played using motion gestures captured through a webcam, with the help of the OpenCV library in Python. The game’s objective remains the same: The player controls a snake that moves around the screen and eats food to grow longer while avoiding collisions with walls or the snake’s own body. The study involved implementing the game using Pygame and using OpenCV to capture motion gestures, including tracking the user’s hand movement and mapping it to game controls. User studies showed that the game with motion gesture control is comparable in accuracy to traditional keyboard controls but offers a more enjoyable gaming experience. This study demonstrates the potential of using computer vision and natural user interfaces for interactive and immersive gaming experiences.
New Optimization Method Based on Binary Tumbleweed Algorithm

Tumbleweed algorithm (TA) is a meta-heuristic algorithm that simulates the growth of Tumbleweed. Compared with other advanced meta-heuristic algorithms, TA has strong optimization performance and competitiveness. However, TA is a continuous optimization algorithm, which is not suitable for binary problems. This paper presents a binary Tumbleweed optimization algorithm (BTA). The continuous search space is mapped into the discrete space by V-shaped and S-shaped transfer functions. In addition, this paper uses the CEC2013 benchmark function to evaluate the efficiency of BTA. To prove the effectiveness of the proposed algorithm, the BTA is compared with four advanced binary optimization algorithms. The experimental results reflect that BTA has preferable optimization capabilities on unimodal, multimodal, and composite functions.
Requirements Engineering

Requirements Engineering aims at supporting the understanding of the purpose of a software system to be built, and at keeping the whole design and development process aligned with it. Research in Requirements Engineering (RE) provides methods and techniques to support various activities in the requirements life cycle, from requirements elicitation to requirements verification and validation. Artificial Intelligence (AI) techniques are more and more exploited in such methods, including natural language processing techniques, since many RE artefacts are expressed as natural language text; techniques based on optimisation, machine learning, and deep learning with the objective of improving the efficiency of the analysts and decision-makers performing RE activities. In this chapter, we focus on two specific use cases in RE, namely, requirements elicitation from textual user feedback and requirements prioritisation. We present solutions to the two problems based on AI techniques, specifically machine learning, natural language processing, and Genetic Algorithms. The application of the proposed methods in industrial contexts allowed us to validate their usefulness in terms of increased efficiency of organisations during their decision-making processes. Finally, we discuss the use cases in the broader context of the RE management process, highlighting opportunities and limits of the AI approaches and current trends in the use of AI in RE.
Learning a Detail-Oriented Transformer for Image Denoising

CNN-based methods have achieved significant success in image denoising with powerful and generous local priors. However, these methods are not good at exploring non-local self-similarity which is also an effective prior for image denoising. To overcome this weakness, we propose a detail-oriented transformer with Dense Network for Image Denoising (DDNet), exploiting global and local self-similarity for better reconstruction quality. Specifically, DDNet consists of a global feature extraction branch and a detail-maintained graph attentive branch. The global feature extraction branch utilizes a number of densely connected transformer blocks for contextual information. The detail-maintained graph attentive branch employs a dynamic graph attentive network to distinguish the image structure and noises for retaining the texture details. Numerous experiments show the superiority of DDNet on real image denoising, synthetic image denoising, and image deraining.
Comparison of Noisy Medical Image Lossy Compression by BPG and AGU

Medical images are frequently large in size and can be distorted by noise, leading to long transfer times and potentially negative outcomes for patients. To address this, medical image files must be compressed, but the presence of noise must be considered when evaluating compression efficiency. This study compares the effects of additive white Gaussian noise on lossy compression methods (BPG and AGU coders) for medical images. The PSNR metric (peak signal/noise ratio, measured in dB) is used to evaluate compression quality, with three possible relationships between the metric and compressed-noisy images identified. Depending on the situation, different recommendations for setting the parameter controlling compression are provided, with compression recommended in the vicinity of an optimal operating point (OOP) if one exists. Dependences of PSNR on parameter that controls compression (PCC) are presented with the position and presence of OOP depending on image complexity and noise intensity for both compression methods.
Instruction of Plane Filing of Metals Using a System With the Aid of VR Goggles, AR Glasses and a Tool Dynamometer

Plane finishing work using metal files requires mastery of subtle force sensations. The subtle force sensations cannot be learned only with a video training material using VR or AR. In this paper, we describe a support system for mastering a flat metal filing operation using VR, AR, and a tool dynamometer. In addition to the conventional training, the system has added practice of model work with VR goggles, grasping the instincts and tricks of work with AR smart glasses, and grasping force sensations with a tool dynamometer. The trial of this system confirmed a significant improvement in trainees’skill proficiency (effect size 1.73) compared to conventional training. Furthermore, it was confirmed a reduction in the variability in trainee mastery that depended on the instructor’s skill instruction method.
Clustering quantum Markov chains on trees associated with open quantum random walks

&lt;abstract&gt;&lt;p&gt;In networks, the Markov clustering (MCL) algorithm is one of the most efficient approaches in detecting clustered structures. The MCL algorithm takes as input a stochastic matrix, which depends on the adjacency matrix of the graph network under consideration. Quantum clustering algorithms are proven to be superefficient over the classical ones. Motivated by the idea of a potential clustering algorithm based on quantum Markov chains, we prove a clustering property for quantum Markov chains (QMCs) on Cayley trees associated with open quantum random walks (OQRW).&lt;/p&gt;&lt;/abstract&gt;
中学校技術科教員における教科専門知識の獲得プロセス

In this paper, how junior high school teachers of technology subject that is called “Gijyutsuka” acquire subject specialty knowledge are explained. To get a teacher’s license, it is usually necessary to go on to an accredited educational institution and to acquire the prescribed credits. Among those, the number of the credits related to specialty for the subject is 10 or 20. On teacher’s license for “Gijyutsuka”, the prescribed subjects related to subject specializations consist of six fields: wood working, metal working, machine, electricity, cultivation, and information and computer. Before going on the accredited educational institution, the students have not learned contents related to “Gijyutsuka” outside of junior high school. And there are relatively many “Gijyutsuka” teachers without the teacher’s license for it.
Comparative Analysis on Breast Cancer Prediction Using Machine Learning Techniques

One of the most prevalent cancers in women is breast cancer, and it is thought that a number of clinical, lifestyle, social, and economic factors may be involved. Based on characteristics hidden in data, machine learning has the ability to forecast breast cancer. The most common data mining modeling goals are classification and prediction, and they employ a range of techniques to predict breast cancer. Patients with breast cancer made up 25% of the 5178 independent records in the Wisconsin Diagnostic Breast Cancer (WDBC) database that was used for this analytical investigation. Each record included 24 attributes. In this study, we compare the support vector machine, random forest, decision tree, Naive Bayes, K nearest neighbor, and logistic regression algorithms. A crucial component in modernizing the healthcare system, notably in the treatment of cancer, is the effective integration of ICT into medical practice. The four steps of the research process were data collection, preprocessing, model design, and outcome evaluation. Our goal is to use machine learning algorithms to find and identify breast cancer. The performance of each classifier in terms of confusion matrix, accuracy, precision, F score, specificity, and sensitivity will indicate the most efficient technique.
Website Phishing Detection Using Deep Learning and Genetic Algorithms

Phishing is the easiest way to get sensitive information from people who do not expect to be asked for it. Phishing attempts to steal confidential information like usernames, passwords, and bank account details. The cybersecurity platform is currently looking for dependable and consistent phishing website detection methods. This work proposes the use of Logistic Regression, DT, SVM, Random Forests, KNN, Neural Networks, Naïve Bayes, and Genetic Algorithms to detect phishing website sizes. This is done by extracting and evaluating numerous aspects of both authentic and phishing URLs. The project’s objective is to compare accuracy rates to find the best ML algorithm and identify phishing URLs.
Foundations of Machine Learning for Software Engineering

Data is everywhere. When we build software, we generate not only code artefacts and documentation, but also tonnes of data about the development process and the software itself. If we collect and analyse such data, we can better understand our own profession, make informed decisions and anticipate problematic situations. Many ways of analysing data exist, but in this chapter, we will focus on machine learning (ML). ML is the branch of artificial intelligence that gives programs the ability to solve tasks by learning from experience, without explicitly programming them. This chapter presents the elementary foundations of ML, providing the reader with a gentle but sufficiently technical introduction to its main concepts and methods. Therefore, this chapter is intended to help the reader deepen in the techniques applied in the examples presented in the rest of the book.
Enhancing Transaction Scalability of Blockchain Network Using Sharding Technique

Modern-day blockchain systems are far from being widely adopted due to the limited scalability of blockchain systems. Scalability enhancement of blockchain systems is a widely addressed problem by many researchers and academicians. The serial execution of transactions in blockchain systems limits the transaction throughput and increases the network latency. The process of finding the PoW is further carried out serially. Even in the presence of modern multicore resources, blockchain systems remain unable to conduct transactions concurrently. In order to address the scalability issues of blockchain systems, a static analysis-based data sharding technique has been proposed in order to improve transaction throughput. This method allows for the processing of transactions in parallel, and a framework for this task called parallel execution of transactions existing in a block in a distributed context has also been implemented. The fundamental concept driving the execution of this framework is that trusted nodes in the blockchain environment come together as a community to carry out transactions while also concurrently determining the PoW. A master–slave system is used in the community, with the master analyzing the transactions. To execute in parallel, it divides up non-conflicting transactions into various shards and distributes them across the slaves. The miner experiences a maximum speed up of 1.81 ×, while the validator exhibits a maximum speed up of 1.80x. The suggested technique is applied to a workload by using six community members, and there are 150–550 transactions in each block.
Introduction

Software engineering focuses on the creation of high-quality software products according to basic design principles and best practices. Since its inception in the 1950s, the complexity of software systems, their environment and infrastructure, the associated requirements, and the methods and methodologies used have increased dramatically. This greater complexity of project management brings a significant increase in the associated risk, which is reflected in the search for techniques to mitigate it through automation and optimisation of activities of the software development process, as well as their improvement by learning from previous experiences. In this context, the synergy between software engineering and artificial intelligence has been a revulsive for companies. In this chapter, we introduce the field of AI4SE and explore the problems and application domains where these techniques have proven to work successfully. This volume focuses on these solutions. Therefore, at the end of the chapter, we provide a brief introduction to the organisation of the book and explain very briefly each of the chapters that comprise it.
Firefly Algorithm Based on Division of Labor for Solving Constrained Optimization Problems

The firefly algorithm is prone to premature convergence and poor diversity when dealing with constrained optimization problems, and to address this problem, this paper proposes a firefly algorithm based on division of labor for solving constrained optimization problems (FADL). The firefly population was first divided, and the individuals were sorted in order of fitness value from best to worst. The first half of the better firefly individuals is used for local exploitation, and the second half of the worse firefly individuals are used for global exploration, replacing the previous inferior solution with the better solution resulting from exploitation and exploration, thus effectively improving the diversity of the population; the addition of Lévy flights search mechanism on top of the division of labor effectively increases the population search range and improves the global traversal capability of the algorithm. In the experimental part, the algorithm is evaluated on 28 constrained optimization problems of CEC2017 benchmark, and the results show that it has higher convergence accuracy and faster convergence speed compared to the comparison algorithm FADL, which can effectively solve the constrained optimization problems.
Designing Recommendation System for Hotels Using Cosine Similarity Function

The cosine similarity function is a popular tool in hotel recommendation systems. It computes the similarity of two vectors by calculating the cosine of their angle. The vectors in the context of a hotel recommendation system indicate hotel attributes such as amenities, location, and pricing. The function accepts these vectors as input and returns a score indicating how similar the hotels are. The closer the score is to one, the more similar the hotels are, and the more likely it is that a user who loved one will also enjoy the other. Because the cosine similarity function is efficient and scalable, it is well suited for huge data sets and real-time applications. Modern hotel recommendation algorithms rely heavily on the cosine similarity function. These systems can deliver customised hotel suggestions to users based on their tastes and behaviour by leveraging the capability. The feature aids in the resolution of the cold start problem, which occurs when new users or hotels have no rating history. In such circumstances, the algorithm can utilise the hotel's attributes to propose the ones that are most close to the user's tastes. Moreover, the function enables for the inclusion of other elements, such as user reviews, to increase the accuracy of the suggestions. Overall, the cosine similarity function is a strong tool that allows hotel recommendation systems to provide individualised and relevant suggestions to consumers, resulting in increased user satisfaction and engagement.
Deep Learning-Based Track Prediction and Correction for a Radar Target

Tracking targets using noisy observations is of crucial importance for various civilian and military applications. This involves, among other tasks, ascertaining the state of a target at a next time instant, called as track prediction, and combining the predicted state with a noisy observation to obtain an updated state at the current time instant, called as track correction. We consider track prediction and correction tasks of a radar target tracking application in a highly manoeuvring scenario where the targets exhibit motion in a 3D space under randomly changing constant velocity, acceleration and turn models. The detections from the targets are available at irregular time intervals. We pose the problem of prediction (correction) as a time-series regression problem where several past detections along with their time stamps are used as input, and the next (respectively, current) detection is used as the expected output. We obtain temporal convolutional network (TCN) based models for prediction and correction, which are shown to perform significantly better than the classical pre-fixed interacting multiple model (IMM) algorithm under various different scenarios in terms of the root mean squared error and standard deviation of the error between the true and predicted/corrected values, especially during model transitions and ascend/descend phases with manoeuvres.
Differential Analysis of Triggers and Benign Features for Black-Box DNN Backdoor Detection

This paper proposes a data-efficient detection method for deep neural networks against backdoor attacks under a black-box scenario. The proposed approach is motivated by the intuition that features corresponding to triggers have a higher influence in determining the backdoored network output than any other benign features. To quantitatively measure the effects of triggers and benign features on determining the backdoored network output, we introduce five metrics. To calculate the five-metric values for a given input, we first generate several synthetic samples by injecting the input’s partial contents into clean validation samples. Then, the five metrics are computed by using the output labels of the corresponding synthetic samples. One contribution of this work is the use of a tiny clean validation dataset. Having the computed five metrics, five novelty detectors are trained from the validation dataset. A meta novelty detector fuses the output of the five trained novelty detectors to generate a meta confidence score. During online testing, our method determines if online samples are poisoned or not via assessing their meta confidence scores output by the meta novelty detector. We show the efficacy of our methodology through a broad range of backdoor attacks, including ablation studies and comparison to existing approaches. Our methodology is promising since the proposed five metrics quantify the inherent differences between clean and poisoned samples. Additionally, our detection method can be incrementally improved by appending more metrics that may be proposed to address future advanced attacks.
A Subdivision Method of Flight Phases Based on Divide-and-Conquer Gaussian Mixture Model

Monitoring the flight status of aircrafts is crucial for ensuring safe and reliable flights. A global monitoring model is a commonly used method to adapt to the monitoring of whole flight process. However, the global monitoring model does not accurately capture the features when the distribution of flight data is dynamically transformed with flight phases, which affects the performance of the model. Therefore, it is essential to subdivide the data into different flight phases by analyzing distributions before building the monitoring model. In view of this, this paper proposes the Divide-and-conquer Gaussian Mixture Model (DcGMM) method for a subdivision of flight phases. To improve the division performance of complex flight phases, the proposed method divides the subdivision task into independent subtasks based on both longitudinal and lateral aircraft motion. Then, separately conquer problems of both subtasks based on GMM models, not only to divide general flight phases by clustering, but also to divide transition phases by using probabilities generated from GMM with optimization thresholds. Finally, combine division results of both subtasks and a boundary-based flight phase correction method is designed for decreasing misclassified phases to achieve a high-performance subdivision. In summary, a new high-precision subdivision method for complex flight phases is proposed in this paper. This paper validates the performance of the proposed method experimenting on NASA public datasets from flight recorded data. Compared with state-of-the-art methods, the proposed method demonstrates higher performance by the accuracy and macro <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> 1 score.
Comparison of Different Part-of-Speech Tagging Techniques for Mongolian

In this paper, we presented two POS taggers for Mongolian, namely Neural Networks—Multilayer Perceptron and Hidden Markov Model with Viterbi. The accuracy of the former tagger is 95.6%, whereas the latter is 85.6%. Also, we compared the performance of our taggers with the previous works. The Comparison shows that the Neural Network tagger performs better for Mongolian POS tagging than other approaches. Our dataset consists of about 5000 sentences and includes almost 100,000 words for training and testing.
Theoretical and Deep Learning Based Analysis of Biases in Salsa 128 Bits

Salsa, designed by Daniel J. Bernstein is one of the finalists candidate of eSTREAM projects in 2005. It is a well known stream cipher and it gained its popularity when the most used stream cipher RC4 was subjected to multiple cryptanalytic techniques. The stream cipher Salsa comes in two versions - 128 bits and 256 bits depending upon the size of the seed key. Salsa has went through various key recovery attacks up to its 8-th round where most of the attacks were focused on Salsa 256 bits. There are many more prevalent experimental attacks on Salsa while leaving space for theoretical analysis. Theoretical analysis helps in spotting the feature that subjects the cipher to attack and thus can be used to design a better cipher that is resistive to attacks. Dey et al. [1] have theoretically analyzed the cause of distinguishers in Salsa 256 bits and Chacha 256 bits that occur in experimental attacks. Motivated by this work, in this paper, we have theoretically analyzed differential attack on Salsa 128 bits up to four rounds and mathematically proved the probabilities of the observations. Also, our theoretical analysis results of Salsa 128 bits are on par with the experimental results. As an extension of this work and to find more accurate distinguishers for round 4 and higher rounds, we are working on the idea to find neural distinguisher in Salsa 128-bits based on the model proposed by Gohr in CRYPTO 2019 [2].