Reviewerly/exHarmony huggingface dataset:
Recommender Systems for Education: A case of Study Using Formative Assessments

A recommender system is a personalized information filtering technology, used to either predict whether a target user will like a particular item (prediction problem), or to identify a set of N items that will be of interest to a certain user (top-N recommendation problem). Applying recommender systems to the field of education requires taking into account a broad set of variables that may include, among others, level of knowledge, competences, and learning styles on the part of students. Accordingly, this domain may dramatically influence both the prediction and the recommendation. Most approaches by using recommender systems are based on data explicitly collected from users to build profiles such as rankings, opinions and the like. However, in e-learning, this can be considered intrusive. This work presents the deployment and experimental evaluation of collaborative filtering algorithms on three datasets of performance history collected from students in a first-year class of a Chilean university. The aim of this paper is to analyze the impact of recommender system algorithms for educational data mining. Experimental results show that recommender systems can be a promising tool for both predicting student performance and helping students in their learning process by recommending meaningful resources.
Full-Field Visual Vibration Measurement of Rotating Machine Under Complex Conditions via Unsupervised Retinex Model

Vision-based measurement, as the most common non-contact vibration measurement method, has received increasing attention and development in recent years. However, it is still highly dependent on the shooting environment and data quality, and is difficult to apply to complex mechanical structures. This article focuses on the full-field extraction of micro-vibration signals of high-speed rotating machinery under complex conditions. A novel visual vibration measurement scheme is proposed, called the texture-perception motion estimation frame (TPME). First, an unsupervised image decomposition model called the texture-perception network (TPNet) is designed to linearly separate the image reflection layer and illumination layer. The high-frequency texture features of the reflection layer can strengthen the data representation ability. Using it alone for phase-based micro-motion extraction (PME) can not only expand the measurement range and accuracy but also ensure measurement robustness under complex luminance changes. Furthermore, we evaluate the computational cost of this approach, which achieves significant measurement improvements with a very low computational overhead. Experiments on three different rotor platforms demonstrate the effectiveness of TPME.
On developing deep learning models with particle swarm optimization in the presence of poisoning attacks

Deep learning (DL) has demonstrated several successes in a variety of fields, particularly in the era of big data. The process of training a deep learning model entails selecting the ideal learning parameters such as the number of hidden layers and neurons. Particle Swarm Optimization (PSO) is one useful nature-inspired algorithm to set those two influential learning parameters. In this study, two different datasets are used to study and evaluate the particle swarm optimizer with deep learning on datasets of different concentrations of poisoning attack, where some adversarial samples were crafted by attackers to ruin the learning process. The results showed that particle swarm optimization could find settings for deep learning with the existence of poisoned data that maximizes the model accuracy on the unseen testing dataset, and could also offer better recommendations compared to those recommended on all benign samples. This may introduce a concern that optimizers might conceal the existence of data poisoning, which may lead to unreliable learning in the advanced stages of upgrading the model on updated datasets.
Federated Learning via Attentive Margin of Semantic Feature Representations

Federated learning (FL) in Internet of Things (IoT) systems enables distributed model training using a large corpus of decentralized training data dispersed among multiple IoT clients. In this distributed setting, system and statistical heterogeneity, in the form of highly imbalanced, and nonindependent and identically distributed (non-i.i.d.) data stored on multiple devices, are likely to hinder model training. Existing methods aggregate models disregarding the internal representations being learned, which yet play an essential role to solve the pursued task, especially in the case of deep learning modules. To leverage feature representations in an FL framework, we introduce a method, called FedMargin, which computes client deviations using margins over feature representations learned on distributed data, and applies them to drive federated optimization via an attention mechanism. Local and aggregated margins are jointly exploited, taking into account local representation shift and representation discrepancy with the global model. In addition, we propose three methods to analyse statistical properties of feature representations learned in FL, in order to elucidate the relationship between accuracy, margins, and feature discrepancy of FL models. In experimental analyses, FedMargin demonstrates state-of-the-art accuracy and convergence rate across image classification and semantic segmentation benchmarks by enabling maximum margin training of FL models. Moreover, FedMargin reduces the uncertainty of predictions of FL models compared to the baseline. In this work, we also evaluate FL models on dense prediction tasks, such as semantic segmentation, proving the versatility of the proposed approach.
Blockchain-Based P2P Content Delivery With Monetary Incentivization and Fairness Guarantee

Peer-to-peer (P2P) content delivery is up-and-coming to provide benefits comprising cost-saving and scalable peak-demand handling compared with centralized content delivery networks (CDNs), and also complementary to the popular decentralized storage networks such as Filecoin. However, reliable P2P delivery demands proper enforcement of delivery fairness, i.e., the deliverers should be rewarded in line with their in-time delivery. Unfortunately, most existing studies on delivery fairness are on the basis of non-cooperative game-theoretic assumptions that are arguably unrealistic in the ad-hoc P2P setting. We propose an expressive yet still minimalist security requirement for desired fair P2P content delivery, and give two efficient blockchain-enabled and monetary-incentivized solutions <inline-formula><tex-math notation="LaTeX">${\mathsf {FairDownload}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">${\mathsf {FairStream}}$</tex-math></inline-formula> for P2P downloading and P2P streaming scenarios, respectively. Our designs not only ensure delivery fairness where deliverers are paid (nearly) proportional to their in-time delivery, but also guarantee exchange fairness where content consumers and content providers are also fairly treated. The fairness of each party can be assured even when other two parties collude to arbitrarily misbehave. Our protocols provide a general design of fetching content chunk from any specific position so the delivery can be resumed in the presence of unexpected interruption. Further, our systems are efficient in the sense of achieving asymptotically optimal on-chain costs and optimal delivery communication. We implement the prototype and deploy on the Ethereum Ropsten network. Extensive experiments in both LAN and WAN settings are conducted to evaluate the on-chain costs as well as the efficiency of downloading and streaming. Experimental results show the practicality and efficiency of our protocols.
Identification and Mitigation of Toxic Communications Among Open Source Software Developers

Toxic and unhealthy conversations during the developer’s communication may reduce the professional harmony and productivity of Free and Open Source Software (FOSS) projects. For example, toxic code review comments may raise pushback from an author to complete suggested changes. A toxic communication with another person may hamper future communication and collaboration. Research also suggests that toxicity disproportionately impacts newcomers, women, and other participants from marginalized groups. Therefore, toxicity is a barrier to promote diversity, equity, and inclusion. Since the occurrence of toxic communications is not uncommon among FOSS communities and such communications may have serious repercussions, the primary objective of my proposed dissertation is to automatically identify and mitigate toxicity during developers’ textual interactions. On this goal, I aim to: i) build an automated toxicity detector for Software Engineering (SE) domain, ii) identify the notion of toxicity across demographics, and iii) analyze the impacts of toxicity on the outcomes of Open Source Software (OSS) projects.
An Empirical Study on Numerical Bugs in Deep Learning Programs

The task of a deep learning (DL) program is to train a model with high precision and apply it to different scenarios. A DL program often involves massive numerical calculations. Therefore, the robustness and stability of the numerical calculations are dominant in the quality of DL programs. Indeed, numerical bugs are common in DL programs, producing NaN (Not-a-Number) and INF (Infinite). A numerical bug may render the DL models inaccurate, causing the DL applications unusable. In this work, we conduct the first empirical study on numerical bugs in DL programs by analyzing the programs implemented on the top of two popular DL libraries (i.e., TensorFlow and PyTorch). Specifically, We collect a dataset of 400 numerical bugs in DL programs. Then, we classify these numerical bugs into nine categories based on their root causes and summarize two findings. Finally, we provide the implications of our study on detecting numerical bugs in DL programs.
Identifying Solidity Smart Contract API Documentation Errors

Smart contracts are gaining popularity as a means to support transparent, traceable, and self-executing decentralized applications, which enable the exchange of value in a trustless environment. Developers of smart contracts rely on various libraries, such as OpenZeppelin for Solidity contracts, to improve application quality and reduce development costs. The API documentations of these libraries are important sources of information for developers who are unfamiliar with the APIs. Yet, maintaining high-quality documentations is non-trivial, and errors in documentations may place barriers for developers to learn the correct usages of APIs. In this paper, we propose a technique, DocCon, to detect inconsistencies between documentations and the corresponding code for Solidity smart contract libraries. Our fact-based approach allows inconsistencies of different severity levels to be queried, from a database containing precomputed facts about the API code and documentations. DocCon successfully detected high-priority API documentation errors in popular smart contract libraries, including mismatching parameters, missing requirements, outdated descriptions, etc. Our experiment result shows that DocCon achieves good precision and is applicable to different libraries: 29 and 22 out of our reported 40 errors have been confirmed and fixed by library developers so far.
Accelerating OCR-Based Widget Localization for Test Automation of GUI Applications

Optical character recognition (OCR) algorithms often run slow. They may take several seconds to recognize the texts on a GUI screen, which makes OCR-based widget localization in test automation unfriendly for use, especially on GPU-free computers. This paper first concludes a common type of widget text to be located in GUI testing: label text, which are short texts in widgets like buttons, menu items, and window titles. We then investigate the characteristics of texts on a GUI screen and introduce a fast GPU-independent Label Text Screening (LTS) technique to accelerate the OCR process for label text localization. The technique opens the black box of OCR engines and uses a combination of simple methods to avoid excessive text analysis on a screen as much as possible. Experiments show that, on the subject datasets, LTS reduces the average OCR-based label text localization time to a large extent. On 4k resolution GUI screens, it keeps the localization time below 0.5 seconds for over about 60% of cases without GPU support on a normal laptop computer. In contrast, the existing CPU-based approaches built on popular OCR engines Tesseract, PaddleOCR, and EasyOCR usually need over 2 seconds to achieve the same goal on the same platform. Even with GPU acceleration, they can hardly keep the analysis time in 1 second. We believe the proposed approach would be helpful for implementing OCR-based test automation tools.
Taming Multi-Output Recommenders for Software Engineering

Recommender systems are a valuable tool for software engineers. For example, they can provide developers with a ranked list of files likely to contain a bug, or multiple auto-complete suggestions for a given method stub. However, the way these recommender systems interact with developers is often rudimentary—a long list of recommendations only ranked by the model’s confidence. In this vision paper, we lay out our research agenda for re-imagining how recommender systems for software engineering communicate their insights to developers. When issuing recommendations, our aim is to recommend diverse rather than redundant solutions and present them in ways that highlight their differences. We also want to allow for seamless and interactive navigation of suggestions while striving for holistic end-to-end evaluations. By doing so, we believe that recommender systems can play an even more important role in helping developers write better software.
Camera-based Blink Detection using 3D-Landmarks

Working in front of the computer for long periods of time leads to exhaustion, fatigue and high strain on the eyes. Often the natural eye blink activity is disturbed and the eyes do not form enough tears to stay moist. Computer workstations are usually equipped with a webcam, making it convenient to monitor eye blinking and easy to perform blink interventions. In this paper, we present a blink detection method that uses 3D landmarks. After presenting existing publicly available blink databases, we evaluate the accuracy of our blink detection method and discuss the results. In addition, we illustrate the scope of our eye blink detection method in the application of a non-contact stress detection software to support stress reduction in the field of occupational health management.
Video Annotation Tool using Human Pose Estimation for Sports Training

This paper presents and discusses the integration of human pose estimation techniques into an existing web-based multimodal video annotation tool, applying it to the sports context, where basketball is the first case study. The relevance of video analysis extends across many fields of work (e.g., professional sports, education). In sports, systematic, detailed analysis using videos of players and teams is vital to evaluate many aspects of both training and competition. MotionNotes annotation tool now combines human pose and motion information with existing traditional annotation mechanisms (e.g., text and drawings annotations), allowing users to add further details to their annotation work. The paper reports feedback from a pilot study based on a participatory workshop involving people with relevant competitive experience in basketball. Based on this use case feedback, we conclude with an outlook of future iterations for our video annotation tool.
Break, Repair, Learn, Break Less: Investigating User Preferences for Assignment of Divergent Phrasing Learning Burden in Human-Agent Interaction to Minimize Conversational Breakdowns

Conversational agents (CA) occasionally fail to understand the user's intention or respond inappropriately due to natural language complexity. These conversational breakdowns can happen because of low intent and entity prediction confidence scores. A promising repair strategy in such cases is that the CA proposes to users likely alternatives to proceed. If one of these options matches the user's intention, the breakdown is repaired successfully. We propose that successful repairs should be followed by a learning mechanism to minimize future breakdowns. After a successful repair, the CA, user, or both can learn each other's specific phrasing. This prevents similar phrasings from causing reoccurring breakdowns. We compared user preferences for these learning mechanisms in a scenario-based study with manufacturing workers (). Our result showed that users first prefer to share the learning burden with the CA (61.3%), followed by entirely outsourcing the learning burden to the CA (60.7%) as opposed to themselves.
The Potential Application Of NFT in the Publishing Industry; Opportunities and Challenges

In the past few years, the world has witnessed a massive development in the digital world, which has led to the emergence of a new advanced trading technology called Non-Fungible Tokens (NFTs) as digital crypto assets using blockchain technology. This technology has expanded and developed by leaps and bounds in various aspects of the digital world, including virtual reality and cryptocurrency. This rapid development has created a marketplace that allows users to create, buy and sell their own unique NFT digital assets quickly and easily compared to the real world while maintaining ownership rights. The huge trading market of NFTs and their remarkable technical advantages have attracted Attention and concerns of musicians, artists, collectors and gamers to such an extent that it has sparked interest in studying the applicability of NFT technology in various sectors of the real world. This research came to study the applicability of non-fungible tokens (NFTs) and their opportunities and challenges in the publishing industry, taking into consideration all its stakeholders, in order to open doors for this industry, and maybe to other potential non-digital (real) industries to benefit from it.
Breast cancer identification using feature level fusion and hybrid GA-PSO optimized neural network

Breast cancer, being a critical disorder in urban women, needs to be screened as early as possible for an effective treatment. In this study, the mammographic image analysis society (MIAS) database mammogram images are utilized. The images are first passed to a median filter before thresholding. The pectoral muscles are also removed with image background and labels. Contrast limited adaptive histogram equalization (CLAHE), wavelet denoising and guided filtration are applied for a region of interest (RoI) extraction. The resultant image features are extracted using gray level co-occurrence matrix GLCM, improved local binary pattern (iLBP) and discrete wavelet transform (DWT). These features are used to classify the image data using an artificial neural network (ANN), whose weights and bias are optimized with hybrid genetic algorithm and particle swarm optimization (HGAPSO). This technique helped solve the scalability issue in GA and the local optima in PSO, with the hybrid algorithm converging faster with better results. The resultant optimized network gives an accuracy of 99.72% in classifying the images as normal, benign or malignant.
A New Metaheuristic Approach Based Feature Selection for Arabic Text Categorization

With the increase in the number of electronic documents stored on various electronic media and the Web, mainly textual data, the development of tools for analysis and automatic processing of texts, particularly the automatic text categorization, has become essential. Most of the work done in this area has been devoted primarily to Western languages, especially English. Arabic, a morphologically rich and strongly inflected language, has little study. The number of features is a significant challenge in classifying Arabic documents, introducing difficulties at several levels, such as complexity and computation time. This paper proposes a new metaheuristic approach to dimensionality reduction, aiming to find a representation of the initial data in a smaller space. The model is validated using classifiers, namely NB, SVM and KNN and three evaluation measures, including precision, recall, and F -measure. The proposed method achieves a precision value equal to 98%.
ASMR Sound Generation Simulating the Sounds Heard by a Fetus Using Interactive Evolutionary Computation

Autonomous Sensory Meridian Response (ASMR) is a very popular form of entertainment among internet users nowadays. Users can listen to and watch ASMR content easily because they just need a headphone and a smart phone. Listening to ASMR sounds makes users relaxed and happy. Previous studies have demonstrated the relaxing effect of the sounds in the womb on the fetus. We believe that intrauterine sounds have a relaxation effect on adults as well. In this paper, we propose a ASMR sound generation system that simulates the intrauterine sound using Interactive Evolutionary Computation (IEC). IEC is a method to search for media content that matches the user’s preference. We downloaded eighteen ASMR sounds that the human body makes from the internet to simulate the sound in the womb. We presented the ASMR sounds to users by combining five of eighteen sounds using IEC. Two listening experiments were conducted. In experiment 1, the participants evaluated and scored every ASMR sound. In experiment 2, the participants evaluated and scored two ASMR sounds, which were the best individuals in the initial and final generations created in experiment 1. The evaluation values of both experiments have a trend of increasing.
Autonomous Navigation of a Mobile Robot on Robot Operating System Using Graphical User Interface

Many applications of mobile robotics necessitate the safe execution of a collision-free motion to a defined place. Real-time obstacle avoidance strategies enable reactive motion in dynamic and unpredictable situations, whereas planning approaches are best suited for achieving a goal position in known static environments. A ROS-based approach is presented to address the challenge of robot SLAM, which has been used in real-time applications to map the environment, localize the robot within the environment, plan paths, and avoid obstacles. It is demonstrated that all navigation modules can coexist and work together to reach the destination without colliding with static and dynamic obstacles. This paper provides a platform for guiding a mobile robot in a real-world environment autonomously for different waypoints while avoiding static and dynamic obstacles using the Graphical User Interface (GUI). The developed GUI-based navigation simplifies the complex ROS navigation system control to an easy user-friendly interface so that anyone can control the mobile robot platform. Our results were validated at Chosun University in South Korea, through the experimental test in an indoor environment.
Wellness Detection Using Clustered Federated Learning

One of the most prevalent mental illnesses, depression, can present with various symptoms, making a clinical diagnosis and pathological research challenging. Even while technical documents show artificial intelligence can aid in treating and diagnosing depression, conventional centralised machine learning demands the collection of patient data, which restricts the therapeutic application of machine learning algorithms. We use federated learning to evaluate and diagnose depression to solve the concern of patient privacy regarding their medical history. To enable federated learning across institutions or parties, we first provide a cluster-based federated learning framework built on multi-source data that can be used to improve any standard machine learning model. The performance of the federated framework in comparison to other cooperative learning frameworks is examined and explained in the simulation section.