Reviewerly/exHarmony huggingface dataset:
The exploitation of intellectual property rights: An overview

The theme of ATRIP’s 39th Annual Congress was: The Exploitation of Intellectual Property Rights: In Search of the Right Balance. The background to ATRIP’s interest in this theme was the realization that even though the legal systems of intellectual property (IP) - the laws on copyright, patents, trademarks and designs, etc. - appear like a carefully tuned instrument: their effects depend largely on how the instrument is being played. If right holders use contracts, algorithms or litigation to push their rights beyond the intended limits, or if they find themselves unable to reap a benefit that corresponds to their contribution to society, the melodies the system plays are out of tune. Similarly, if users are unduly blocked from access and use of protected information or if unfair contracts deprive them of the fruits of their creative labour, this result in disharmony.
Automated Model Selection of the Two-Layer Mixtures of Gaussian Process Functional Regressions for Curve Clustering and Prediction

As a reasonable statistical learning model for curve clustering analysis, the two-layer mixtures of Gaussian process functional regressions (TMGPFR) model has been developed to fit the data of sample curves from a number of independent information sources or stochastic processes. Since the sample curves from a certain stochastic process naturally form a curve cluster, the model selection of TMGPFRs, i.e., the selection of the number of mixtures of Gaussian process functional regressions (MGPFRs) in the upper layer, corresponds to the discovery of the cluster number and structure of the curve data. In fact, this is rather challenging because the conventional model selection criteria, such as BIC and cross-validation, cannot lead to a stable result in practice even with a heavy burden of repetitive computation. In this paper, we improve the original TMGPFR model and propose a Bayesian Ying-Yang (BYY) annealing learning algorithm for the parameter learning of the improved model with automated model selection. The experimental results of both synthetic and realistic datasets demonstrate that our proposed algorithm can make correct model selection automatically during parameter learning of the model.
Inferring Bivariate Polynomials for Homomorphic Encryption Application

Inspired by the advancements in (fully) homomorphic encryption in recent decades and its practical applications, we conducted a preliminary study on the underlying mathematical structure of the corresponding schemes. Hence, this paper focuses on investigating the challenge of deducing bivariate polynomials constructed using homomorphic operations, namely repetitive additions and multiplications. To begin with, we introduce an approach for solving the previously mentioned problem using Lagrange interpolation for the evaluation of univariate polynomials. This method is well-established for determining univariate polynomials that satisfy a specific set of points. Moreover, we propose a second approach based on modular knapsack resolution algorithms. These algorithms are designed to address optimization problems in which a set of objects with specific weights and values is involved. Finally, we provide recommendations on how to run our algorithms in order to obtain better results in terms of precision.
A Simulation and Empirical Study of Differential Test Functioning (DTF)

Detecting and understanding DTF is very important under various DIF conditions. In this study, the performance of DTF, DRF, SIBTEST, and CSIBTEST approaches in detecting DIF effects was investigated using a simulation study and a real dataset. It was observed that different DIF conditions (uniform, non-uniform), the proportion of DIF items in the test, the DIF size, and the DIF effect (balanced, unbalanced) affected the performance of the methods, and especially in the case of the non-uniform DIF condition, the power rates of sDTF, sDRF, and SIBTEST statistics were low. In addition, according to the DTF estimations with the balanced/unbalanced DIF effect condition, in some cases, the effect of DIF on the overall test could be negligible. However, it was clearly emphasized in this study that DTF analyses should accompany DIF studies since DTF analysis may change with different DIF and test conditions.
An Eye Tracking Study on MISRA C Coding Guidelines

C is one of the most widely used programming languages - MISRA C is one of the most known sets of coding guidelines for C. This paper examines the usefulness and comprehensibility of the MISRA C:2012 guidelines in an eye tracking study. There, subjects encounter non-compliant code in four different code review settings: with no additional reference, with an actual MISRA C guideline, with a case-specific interpretation of a MISRA C guideline, and with a compliant version of the code. The data collected was analyzed not only in terms of the four presentation styles, but also by dividing the subjects into experience levels based on their semesters of study or years of work experience. Regarding the difference between actual and interpreted guidelines, we found that for interpreted guidelines the error detection rate is higher whereas the duration and frequency of visits to the guideline itself are mainly lower. This suggest that the actual guidelines are less useful and more difficult to understand. The former is contradicted by the subjects’ opinions: when surveyed, they rated the usefulness of the actual guidelines higher.
Towards Automated Interactive Tutoring - Focussing on Misconceptions and Adaptive Level-Specific Feedback

Programming is an essential cross-disciplinary skill, yet teaching it effectively in large classes can be challenging due to the need for close feedback loops. Identifying and addressing common misconceptions is particularly important during the initial stages of learning to program. While automated interactive tutoring systems have the potential to offer personalized tutoring at scale, current systems tend to emphasize errors and predefined solutions rather than focusing on common misconceptions. In this study, we introduce a novel platform centered on addressing misconceptions in programming education. We describe methods for detecting misconceptions using Abstract Syntax Trees (AST) and providing tailored, level-specific feedback to emulate human-like tutoring. As an empirical basis for this project, we gathered data from various introductory programming courses. Additionally, we advocate for the establishment of a repository of common misconceptions, offering examples derived from both the literature and our own data. Investigating misconceptions can ultimately enhance the teaching strategies of both human educators and AI agents, such as GPT, in guiding learners effectively.
The Expert’s View: Eye Movement Modeling Examples in Software Engineering Education

This study investigates the impact of eye movement modeling examples in Software Engineering education. Software Engineering is a highly visual domain. The daily tasks of a software engineer (e.g., formulating requirements, creating UML diagrams, or conducting a code review) require in many cases the use of certain visual strategies. Although these strategies can be found for experts, it has been observed in different eye tracking studies that students have difficulties in learning and applying them. To familiarize students with these visual strategies and to provide them with a better understanding for the cognitive processes involved, a total of seven eye movement modeling examples was created. The seven eye movement modeling examples cover relevant parts of an introductory Software Engineering lecture; they are focused on typical situations in which visual strategies are applied. The results of a questionnaire-based evaluation shows that students consider the eye movement modeling examples as useful, feel supported in their learning process, and would like to see more use of them in the Software Engineering lecture. Furthermore, the students suggested that eye movement modeling examples should also be used in other lectures.
From Difficulties to Functional Requirements - Deriving Requirements from Literature about Tool-supported Teaching of UML Diagrams in Software Engineering Education

With the increasing complexity of software systems, it is becoming more and more relevant for students in software engineering education to learn how to elicitate requirements from customers or stakeholders and visualize them in a diagram. This continues to present different challenges to novice modellers. To counteract these, educators and researchers have developed support tools. These offer various functionalities to address different challenges. However, it is unclear what functionality needs to be provided to address all of these issues and not just individual ones. Therefore, this paper extracts such functional requirements and summarizes them. It also shows how they can be translated into activities during an exercise lesson.
Evaluating Graph-based Modeling Languages

As humans, we tend to use models to describe reality. Modeling languages provide the formal frameworks for creating such models. Usually, the graphical design of individual model elements is based on subjective decisions; their suitability is determined at most by the prevalence of the modeling language. With other words: there is no objective way to compare different designs of model elements. The present paper addresses this issue: it introduces a systematic approach for evaluating the elements of graph-based modeling languages comprising 14 criteria – derived from standards, usability analyses, or the design theories ‘Physics of Notations’ and ‘Cognitive Dimensions of Notations’. The criteria come with measurement procedures and evaluation schemes based on reasoning, eye tracking, and questioning. The developed approach is demonstrated with a specific use case: three distinct sets of node elements for causal graphs are evaluated in an eye tracking study with 41 subjects.
Tool-Based Software Engineering Education for Software Design Patterns and Software Architecture Patterns - a Systematic Literature Review

This paper aims to gather tools from the scientific literature that can be used to teach software design patterns and software architecture patterns. The underlying procedure is a systematic literture review based on Kitchenham’s methodology. In order to improve categorization and clarify the objectives and focus, this systematic literature review was classified according to Cooper’s taxonomy. The presentation of the results following the underlying research questions of the tools found and listed includes the general description, the presentation of the functionalities, the support of the tools in teaching as well as a classification of the functionalities into the five areas: description, detection, selection, application, and implementation.
Diversity and Teamwork in Student Software Teams

Diversity is becoming an increasingly relevant topic in software engineering. A higher diversity rate in professional software teams has been shown to positively influence communication, innovation, and performance. Understanding whether similar effects exist in software development teams at an educational level would be important to identify potential challenges and opportunities that might affect students at later professional stages. Therefore, in this paper we investigate the impact of diversity on key teamwork skills such as communication, collaboration, and productivity in student software development teams. In particular, we conducted several surveys on project work progress throughout an introductory software engineering course with students and their tutors regarding these teamwork metrics. We investigate how the self-assessments and the project outcomes relate to seven aspects of diversity, and find correlations between teamwork satisfaction and team diversity. We see deteriorations in certain teamwork metrics over the project timeline—independently of the team diversity. Our findings suggest that the impact of diversity on student teams may be more complex than on professional teams: diversity minorities in teams may feel frustrated more easily. Consequently, it is important to ensure that software development courses maintain motivation and decrease possible frustrations regarding teamwork.
Object Tracking in UAV Videos by Multifeature Correlation Filters With Saliency Proposals

The purpose of object tracking is to locate a given target in image sequence, such as people and vehicles.In recent years, with the development of UAV technology, object tracking in UAV videos has engaged many scholars.It has been widely used in traffic control, water quality inspection, wildlife census and other fields.However, low resolution, scale change, occlusion and other challenges have been restricting the development of the tracker.To solve the aforementioned problems, we put forward multifeature correlation filters with saliency proposals.First, we use histogram of oriented gradient (HOG) features, gray (I) features and color names (CN) features to heighten the representation information of target, so that our algorithm can accurately locate small targets.Then, we introduce saliency proposals to reposition the occluded target.Finally, we use dynamic update weights instead of the fixed update weights to mitigate the adverse effects caused by template degradation.Experiments demonstrate that our tracker has achieved satisfactory tracking accuracy and AUC scores have reached 0.462, 0.417, and 0.425 on UAV123@10FPS, UAV20L, and UAVDT datasets respectively.
A pairwise ranking estimation model for surrogate-assisted evolutionary algorithms

Abstract Surrogate-assisted evolutionary algorithms (SAEAs) have attracted considerable attention for reducing the computation time required by an EA on computationally expensive optimization problems. In such algorithms, a surrogate model estimates the solution evaluation with a low computing cost and is used to obtain promising solutions to which the accurate evaluation with an expensive computation cost is then applied. This study proposes a novel pairwise ranking surrogate model called the Extreme Learning-machine-based DirectRanker (ELDR). ELDR integrates two machine learning models: extreme learning machine (ELM) and DirectRanker (DR). ELM is a single-layer neural network capable of fast learning, whereas DR uses pairwise learning to rank using a neural network developed mainly for information retrieval. To investigate the effectiveness of the proposed surrogate model, this study first examined the estimation accuracy of ELDR. Subsequently, ELDR was incorporated into a state-of-the-art SAEA and compared with existing SAEAs on well-known real-valued optimization benchmark problems. The experimental results revealed that ELDR has a high estimation accuracy even on high-dimensional problems with a small amount of training data. In addition, the SAEA using ELDR exhibited a high search performance compared with other existing SAEAs, especially on high-dimensional problems.
An Empirical Study on Cyber Crimes Against Women and Children in India

The aim of the study is to understand the Cyber-crimes against women and Children in India for a period of five years from 2017 to 2021. The study is based on Secondary data collected from the official website of National Crime Records Bureau. Findings of the study shows that the Cyber-crimes in India shows a raising trend year after year. Other Cyber-crimes against women, Cyber Pornography/ Hosting/ Publishing Obscene Sexual Material and Cyber Stalking/ Cyber Bullying of Women are the most occurring cyber-crimes against women in India. Cyber Pornography/ Hosting/ Publishing Obscene Sexual Material and Defamation/ Morphing against women is raising year by year in India.Cyber Pornography/ Hosting or Publishing Obscene Sexual Materials Depicting Children, Other Cyber-crimes against Children and Cyber Stalking/ Bullyingare the most occurring cyber-crimes against children in India. Cyber Pornography/ Hosting or Publishing Obscene Sexual Materials Depicting Children, Cyber Blackmailing/ Threatening/ Harassment andCyber Stalking/ Bullyingagainst children is raising year by year in India.Percentage of Cyber Crimes against women in Total Cyber Crimes and in Total Crimes against women shows an increase year after year. On an average 20.272 percent of the Cyber-crimes in India are against Women and 2.048 percent of the Crimes against women in India are Cyber-crimes.Percentage of Cyber Crimes against Children in Total Cyber Crimes and in Total Crimes against Children shows an increase year after year. On an average 1.346 percent of the Cyber-crimes in India are against Children and 0.446 percent of the Crimes against Children in India are Cyber-crimes
A Helping Hand to the Elderly: Securing Their Freedom through the HAIE Framework

The life expectancy of the elderly has substantially increased compared to earlier times. The primary factors are greater awareness of nutrition, the environment, and personal hygiene. Advances in science and technology have also extended the lifespans of the aging population. Traditional care methods are inadequate to address this situation. To maintain the socioeconomic structure, there is a need for the integration of advanced frameworks. In this context, we propose a smart framework called human activity Internet of Things-enabled environment (HAIE) to provide a non-human assistive environment that helps the elderly live independently. Research into aging in place and assistive environments has focused on modernized environments, largely neglecting the impact of technology on the lives of elderly individuals who stay at home. This work addresses the gap by integrating advanced technologies, such as cloud computing and the Internet of Things (IoT). The inclusion of IoT facilitates a smart and automated environment for the elderly. Cloud integration enables the storage of large volumes of data for further analysis and the identification of patterns for future advancements. It also introduces the concept of accessing data from anywhere, on any device, at any time. To validate this work, two primary parameters were considered: accuracy and latency. Through simulation, the proposed HAIE model has demonstrated an accuracy of 93% out of 100 attempts and a latency of 84.35 ms for the deployed case studies under the HAIE framework.
Contextual Urdu Text Emotion Detection Corpus and Experiments using Deep Learning Approaches

Textual emotion detection aims to discover human emotions from written text. Textual emotion detection is a significant challenge due to the unavailability of facial and voice expressions. Considerable research has been done to identify textual emotions in high-resource languages such as English, French, Chinese, and others. Despite having over 300 million speakers and large volumes of literature available online, Urdu has not been properly investigated for the textual emotion detection task. To address this gap, this study makes two contributions: (1) the creation of a novel dialog-based corpus for Urdu (Contextual Urdu Text Emotion Detection Corpus). CUTEC contains 30,160 training and 5,509 testing labelled dialogues, where each dialogue consists of three Urdu contextual sentences. In addition, all dialogues are labelled using four emotion classes, i.e., Happy, Sad, Angry, and Other. As a second contribution (2) five deep learning models, i.e., RNN, LSTM, Bi- LSTM, GRU, and Bi-GRU have been trained and tested using CUTEC with different parametric settings. The highest results (Accuracy = 87.28 and F1 = 0.87) are attained using a GRU-based architecture.
Dataset for the cumulative unmanned aerial vehicle routing problem

The Cumulative Unmanned Aerial Vehicle Routing Problem applies to area covering operations using UAVs. It is defined on a graph, the nodes of which, ensure the complete coverage of the underlying area of interest. The data generation process takes into account the characteristics of such operations, in particular, the viewing window of the UAVs' sensor, their maximum range, the size of the UAV fleet and the unknown locations of the targets within the area of interest. Instances are created simulating different scenarios, using different values for those UAV characteristics, as well as the different locations where the search targets might be positioned in the area of interest.
Noise-resilient quantum power flow

Quantum power flow (QPF) offers an inspiring direction for overcoming the computation challenge of power flow through quantum computing. However, the practical implementation of existing QPF algorithms in today's noisy-intermediate-scale quantum (NISQ) era remains limited because of their sensitivity to noise. This paper establishes an NISQ-QPF algorithm that enables power flow computation on noisy quantum devices. The main contributions include: (1) a variational quantum circuit (VQC)-based alternating current (AC) power flow formulation, which enables QPF using short-depth quantum circuits; (2) NISQ-compatible QPF solvers based on the variational quantum linear solver (VQLS) and modified fast decoupled power flow; and (3) an error-resilient QPF scheme to relieve the QPF iteration deviations caused by noise; (3) a practical NISQ-QPF framework for implementable and reliable power flow analysis on noisy quantum machines. Extensive simulation tests validate the accuracy and generality of NISQ-QPF for solving practical power flow on IBM's real, noisy quantum computers.
Information Retrieval Based Solutions for Software Engineering Tasks Using C Codebases

Comments are descriptions of laws that a human can understand fluently. It’s easier to identify important law blocks with comments. But, not everyone can write the comments duly. They aren’t streamlined along with the law. Having outdated comments in the law can affect confusion rather than explanation. This paper aims at removing comments which aren’t related to code and not useful using Natural Language Processing( NLP). NLP has come one of the most habituated ways in the analysis of textbooks. In NLP, comments are written from the surrounding code given, and Machine Learning algorithms are applied. A semantic analysis frame for comments using textual and structural features grounded on comment orders and code–comment correlation. A machine learning approach is used to determine whether comments are also consistent and not superfluous based on code and comment correlation.