Reviewerly/exHarmony huggingface dataset:
Children at Risk: A Needs Analysis for School Crossing Signs Attention Framework from the Experts’ Perspective

Road signs are crucial in helping road users to assess potential dangers that lie ahead, thus, paying attention to these signs can potentially save their lives and those around them. Motorists are required to pay even more attention to their surroundings to avoid accidents. Following the high rate of children being involved in road accidents, especially in front of the school area, this paper aims to explore experts' opinions on the need to develop a framework concerning motorists’ attention towards school crossing signs. This paper adopts the use of the emphasis principle in visual theory to construct the said framework as it has the capacity to attract attention. Employing the Problem-Centred Expert Interview (P-CEI) approach in the semi-structured interview, a thematic analysis was conducted to investigate the experts' consensus. The findings of the research demonstrate the direction of the framework development as suggested by the experts. This paper also presents 52 elements or codes related to motorist attention classified under 18 themes of six categories where these codes offer opportunity for further research in the area of visual attention.
Detection of Sexually Harassing Tweets in Hindi Using Deep Learning Methods

In the modern era, social networking platforms play a vital role in day-to-day life. It is used by personalities, bureaucrats, and common people alike. It provides a platform for everyone to express their opinions, share their experiences with their family, friends, and the world. The advent of such platforms has completely changed the outlook of the world. Sexual harassment has been a problem for a long time, but with the advancement in technology, some users have taken the harassment to the digital level. It is common these days to find users posting derogatory remarks, uploading sensual and private content of others in order to sexually harass them. This is not all. Some targeted audience might even take some wrong steps. This article presents a way to detect such derogatory and harassing remarks using Twitter as a database. The classification part is done with the help of two deep learning models, and at the end, the performance of both models is evaluated on various parameters.
ASSET MANAGEMENT SYSTEM IN HIGHER EDUCATION INSTITUTION USING TDWI BIG DATA MATURITY MODEL

Asset management is the process of recording, collecting data, reporting and documenting assets at a specific time based on existing provisions, proper asset management can support operational activities especially in the maintenance, repair, and procurement of assets for the organization. This study aims to addition to acquiring additional assets data and calculating devaluation of assets, including tracking their location, collating data regarding their operation and maintenance status, with the aim of improving the overall utilization of the assets The methodology used in this research is TDWI Big Data Maturity, which can accommodate changes during the system design process, by taking samples based on the business process of asset management in one of the private institution. This study produced a web-based asset management information system that can assist in managing and tracking asset data and logistic items.
A Hybrid Tree Point Cloud Compression Method for Complex City Scenes

Point cloud compression has become an important research issue of computer vision. A lossy point cloud compression method with hybrid tree is proposed to cope with the problems of feature loss and high bit-rates of complex 3D surfaces in existing techniques with tree structure. In complex city scenes, due to surface differences between various regions of the LiDAR point cloud, a partitioning module based on Euclidean clustering is added obtain the different partitions of point cloud, and then the number of partitions is reduced by Gaussian curvature and mean curvature. In the hybrid tree compression module, the minimum partition bounding box is employed to select the tree structure and hyper-parameters, and obtain the optimal bit rates allocation scheme. Experimental results reveal that the proposed method can effectively reduce the coding redundancy of point cloud and have better rate distortion performance while ensuring compression quality in complex city scenes.
Cryptocurrency Price Prediction With Multi-task Multi-step Sequence-to-Sequence Modeling

In the scientific and commercial worlds, predicting cryptocurrency prices over time has gotten a lot of interest. Most related studies use variations of Recurrent Neural Networks to forecast the next value of a single coin due to the temporal nature of the challenge. As a result, determining how effectively such a model would perform across various tasks (cryptocurrencies), several future timesteps, and forecasting horizons will be difficult. This paper proposes a multi-task and multi-step sequence-to-sequence model that is trained jointly on 22 cryptocurrencies' time series. Our findings show the value of sequence-to-sequence modeling for future predictions, as well as the significant improvements in accuracy and training time that can be achieved by using a single multi-task model rather than numerous distinct models for each task.
A Systems Approach for Cybersecurity Risk Assessment

This paper presents a methodology for assessing the cybersecurity risk of an information system to support trade studies between competing architectures, risk mitigation, and risk-informed decision making. It draws on key concepts from the fields of risk analysis, reliability engineering and cybersecurity. Standard system reliability studies do not readily translate to cybersecurity since (1) the cyber-failures are not random but caused by an attacker, and (2) the behavior of the attacker is not predictable. In this paper, a new approach is presented to extend key concepts from risk and reliability engineering to include cyber failures. The methods developed in this paper can be readily applied to quantitatively assess system cyber risk and provide reliability metrics. These methods are therefore very practical for real world applications. Cyber vulnerabilities are divided into three classes: known vulnerabilities with known threats; known vulnerabilities with unknown threats; and unknown vulnerabilities, which can occur due to zero-day vulnerabilities, or from a failure to follow procedures or successfully scan for identifiable vulnerabilities. The classic definition of risk is extended to encompass cyber exploits. The failure behavior of systems due to cyber exploits is defined and modeled using a combination of Markov Chains, Weibull analysis and Fault Trees. Knowledge about a system’s network topology is used to assess exploitation paths. Data used for quantifying the likelihood of an exploit is based on system performance metrics, such as cyber-hygiene; history of vulnerabilities, threats and exploits; and finally, industry statistics.
Is a Deep Learning Algorithm Effective for the Classification of Medieval Hebrew Scripts?

In this research, we apply deep-learning techniques to Hebrew paleography to automatically classify and process medieval Hebrew manuscripts. Our work is based on contemporary Hebrew paleography (Malachi Beit-Arie, Colette Sirat, Norman Golb, Ada Yardeni, Benjamin Richler) that recognizes fifteen subtypes of medieval Hebrew script. Automatic recognition of these scripts allows to determine the approximate origin and date of writing for not-dated, fragmentary, and damaged manuscripts. To train the deep neural network, we compile a Visual Media Lab - Hebrew Paleography (VML-HP) dataset that contains 537 high- resolution manuscript page images. The images were hand-picked from the SfarData (http:/sfardata.nli.org.il/) dataset; in some rare cases, we also included pages from other manuscripts’ collections. For testing the model, we define a notion of typical and blind test sets. The typical test set consists of the unseen pages of the manuscripts used in training. The blind test set, on the contrary, consists of pages from unseen manuscripts, thus, providing us with a real-life scenario. To train the model, we used patches extracted from the documents’ pages. To filter irrelevant patches (empty patches or patches that contain decorations), we developed a clean patch generation algorithm that can generate patches containing pure text regions (for the VML-HP dataset, we generated 150K train patches). In all the experiments, we trained the network on the training set and tested it on both test sets, typical and blind. The objective training function was cross-entropy loss and was minimized using the Adam optimizer algorithm. The training was performed until there was no improvement in validation loss with five epochs’ patience. The model with the least validation loss was used for testing.
Automatic Refactoring and Compile Time Optimization of Cpp Projects by Directly Including Header Files

The compilation of a project from source to machine code can take up to hours of computation time which in large part depends on how many lines of code are compiled. In the programming language C++, source code is written in separate header and source files. The code in a header file is compiled in each source file it is included in. In order to minimize the compilation time of the project, code should therefore be included only in source files where it is actually used. We present an optimization model that minimizes the number of lines of code that are included and hence compiled but not used. The restrictions of the model are the dependencies in the code. For example, when a function is called in a source file, the header file where this function is defined in must be included in the source file. The variables of the model are which file includes which other files. We propose the solution method to replace indirect includes by more direct ones. We provide computational results on read-world C++ projects and show that the compilation times can be reduced by up to 9%.
SysRepoAnalysis: A tool to analyze and identify critical areas of source code repositories

SysRepoAnalysis is an open-source, multi-user web tool that allows online analysis of git repositories by extracting historical information from source code files over time, using mining software repository techniques. Such data can be useful for the development team to find critical areas of the code repository that have a high maintenance effort over time. The main metrics used are cyclomatic complexity, accumulation of LOC modifications (based on code-churn), and frequency of occurrence of files in commits over time. In addition, we use the composition of these metrics to find very complex source code files that are frequently modified and have many LOCs changed over time. Besides, to calculate these metrics, the tool allows the generation and display of a treemap of the repository’s directory and file structure and the rendering of a heatmap based on the metric chosen to be analyzed. Such features can be useful in helping the development team to find critical source code files or areas that contain such files. In this way, it can help the development team to make decisions regarding the choice of candidate files for refactorings that can generate a high maintenance effort. Among the main features of SysRepoAnalysis, we can highlight: control of user authentication and authorization, cloning and asynchronous analysis of user repositories (via message broker using producers and consumers), historical analysis and export of modified commits and files, calculation and export of results of the analyzed metrics and treemap generation of the repository’s directory and file structure, as well as the rendering of heatmap based on the metric chosen to be analyzed. The source code tool is published in GitHub repository1 and there is a presentation about the tool in Video2 3
TeamPlus: A Decision Support System for Software Team Formation

The literature presents several search and optimization-based solutions for the software team formation problem. However, the existing solutions do not reflect the real needs of practitioners. In general, they do not systematically address the problem, provide adequate visual resources, or integrate with data from enterprise systems. Therefore, the existing solutions are unaware of the actual dynamics of projects and miss important historical information from team members. This paper introduces TeamPlus, a Decision Support System capable of communicating with the people management environment and applying quantitative measures to data to build profiles, suggest teams based on entry criteria and allow the manager to manipulate the suggestions and adjust the preferences before the best decision is made. We developed TeamPlus through a qualitative study with industry practitioners and validated it, given its practical utility and effectiveness. This paper presents an overview of its main features, a comparison with other existing solutions, and preliminary evaluation results.
”We are dying!” On Death Signals of Software Ecosystems

research-article Share on ”We are dying!” On Death Signals of Software Ecosystems Authors: Felipe Soupinski Universidade Federal de Mato Grosso do Sul, Brazil Universidade Federal de Mato Grosso do Sul, BrazilView Profile , Pedro Arantes Universidade Federal de Mato Grosso do Sul, Brazil Universidade Federal de Mato Grosso do Sul, BrazilView Profile , Igor Steinmacher Universidade Tecnológica Federal do Paraná, Brazil Universidade Tecnológica Federal do Paraná, BrazilView Profile , Igor Wiese Universidade Tecnológica Federal do Paraná, Brazil Universidade Tecnológica Federal do Paraná, BrazilView Profile , Hudson Borges Universidade Federal de Mato Grosso do Sul, Brazil Universidade Federal de Mato Grosso do Sul, BrazilView Profile , Bruno Cafeo Universidade Federal de Mato Grosso do Sul, Brazil Universidade Federal de Mato Grosso do Sul, BrazilView Profile , Awdren Fontão Universidade Federal de Mato Grosso do Sul, Brazil Universidade Federal de Mato Grosso do Sul, BrazilView Profile Authors Info & Claims SBES '22: Proceedings of the XXXVI Brazilian Symposium on Software EngineeringOctober 2022 Pages 363–369https://doi.org/10.1145/3555228.3555264Published:05 October 2022Publication History 0citation46DownloadsMetricsTotal Citations0Total Downloads46Last 12 Months46Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Empathy in the Structure of a Person’s Social and Communicative Competence

One of the urgent problems of our time has become the problem of empathy in the context of socio–psychological problems. Being a socio–psychological mechanism of mutual understanding in the process of communication, empathy determines mobility, pace and structuring of social interaction. In the structure of social competencies, empathic response becomes the basis for solving interpersonal problems, correct interpretation of social signals.
Deep manifold embedding of attributed graphs

Unsupervised attribution graph embedding is challenging because the structure and attribute information should be represented in the latent space. Existing reconstruction-based methods may corrupt the manifold of the attributed graph because they indirectly optimize the latent space with the help of decoders. Therefore, we propose a new graph embedding framework called Deep Manifold Embedding of Attribution Graphs (DMEAG). DMEAG directly imposes constraints on the latent space without decoders, thus better preserving the structural information. Further, we propose a node-to-node geodesic similarity metric to measure data relationships. And we design a novel loss function based on Bergman divergence to minimize the difference between embedding and structure/features. Extensive experiments on graphical and image datasets demonstrate the superiority of DMEAG. Furthermore, the proposed DMEAG outperforms state-of-the-art methods in three downstream tasks: clustering, link prediction, and visualization.
Electronic Co-design (ECO-design) Workshop for Increasing Clinician Participation in the Design of Health Services Interventions: Participatory Design Approach

Participation from clinician stakeholders can improve the design and implementation of health care interventions. Participatory design methods, especially co-design methods, comprise stakeholder-led design activities that are time-consuming. Competing work demands and increasing workloads make clinicians' commitments to typical participatory methods even harder. The COVID-19 pandemic further exacerbated barriers to clinician participation in such interventions.The aim of this study was to explore a web-based participatory design approach to conduct economical, electronic co-design (ECO-design) workshops with primary care clinicians.We adapted traditional in-person co-design workshops to web-based delivery and adapted co-design workshop series to fit within a single 1-hour session. We applied the ECO-design workshop approach to codevelop feedback interventions regarding abnormal test result follow-up in primary care. We conducted ECO-design workshops with primary care clinicians at a medical center in Southern Texas, using videoconferencing software. Each workshop focused on one of three types of feedback interventions: conversation guide, email template, and dashboard prototype. We paired electronic materials and software features to facilitate participant interactions, prototyping, and data collection. The workshop protocol included four main activities: problem identification, solution generation, prototyping, and debriefing. Two facilitators were assigned to each workshop and one researcher resolved technical problems. After the workshops, our research team met to debrief and evaluate workshops.A total of 28 primary care clinicians participated in our ECO-design workshops. We completed 4 parallel workshops, each with 5-10 participants. We conducted traditional analyses and generated a clinician persona (ie, representative description) and user interface prototypes. We also formulated recommendations for future ECO-design workshop recruitment, technology, facilitation, and data collection. Overall, our adapted workshops successfully enabled primary care clinicians to participate without increasing their workload, even during a pandemic.ECO-design workshops are viable, economical alternatives to traditional approaches. This approach fills a need for efficient methods to involve busy clinicians in the design of health care interventions.
Heralded Single Photons for Holography

We introduce holographic concept based on heralded single photons. The developed quantum approach can extract holographic information even in situations when classical approach fails to detect the information.
Multimedia Meets Deep Reinforcement Learning

Multimedia data analysis methods based on artificial intelligence and machine learning have achieved great success in the past decades. However, it remains challenging to generate automated decision-making based on the multimedia data directly. Recent advances in deep reinforcement learning (DRL) have made it a practical framework for solving various sequential decision-making problems. However, it lacks the capability to be effectively used in real-world applications. Multimedia data are useful sources of information to support reliable decision-making. By incorporating DRL with multimedia content analysis, it is promising to develop reliable and effective decision-making systems and frameworks for critical applications, such as disaster management, autonomous driving, healthcare, and so on. Hence, it creates the opportunities for the multimedia community to develop novel techniques to address the existing challenges to further improve the usability and performance of automated decision-making based on multimedia data.
A Cyclical Approach to Synthetic and Natural Speech Mismatch Refinement of Neural Post-filter for Low-cost Text-to-speech System

Neural-based text-to-speech (TTS) systems achieve very high-fidelity speech generation because of the rapid neural network developments. However, the huge labeled corpus and high computation cost requirements limit the possibility of developing a high-fidelity TTS system by small companies or individuals. On the other hand, a neural vocoder, which has been widely adopted for the speech generation in neural-based TTS systems, can be trained with a relatively small unlabeled corpus. Therefore, in this paper, we explore a general framework to develop a neural post-filter (NPF) for low-cost TTS systems using neural vocoders. A cyclical approach is proposed to tackle the acoustic and temporal mismatches (AM and TM) of developing an NPF. Both objective and subjective evaluations have been conducted to demonstrate the AM and TM problems and the effectiveness of the proposed framework.
Detecting Anemia Based on Palm Images using Convolutional Neural Network

Hemoglobin is a protein in the blood that conveys oxygen from the lungs to the body's tissues. Hemoglobin levels under the normal limit cause anemia. Hemoglobin estimation is generally utilizing a needle to take the patient’s blood as a sample and afterward testing it at the chemicals laboratory. This technique has a shortcoming, specifically, it is less proficient because it requires a few hours. Likewise, it needs to hurt the patient's skin with a hypodermic needle. In this study, we will discuss the Convolutional Neural Network (CNN) in classifying hemoglobin levels based on palm images. Hemoglobin levels are partitioned into two classes, to be anemia and non-anemia. The image size utilized is 500×375 pixels with the number of Red, Green, and Blue (RGB) channels. The data utilized in this study were images of the patient's palm. The first important phase in this research was data retrieval, which went on with preprocessing data, then the data is clustered into two clusters using a random state, then at that point, each cluster will be classified using the CNN algorithm. The best results are obtained by the value of accuracy reached 96.43% with a precision score of 93.75% achieved, recall of 100%, and specificity of 92.31% for cluster 1 in random state 1, and the similar random state for cluster 2 is obtained the value of accuracy reached 96.43% with a precision score of 93.33%, recall of 100%, and specificity of 92.86% were achieved this way.