Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison Study of Optimizer on CNN based Finger Number Recognition using sEMG Signals

This work investigates how optimizers have the influence on performance of CNN based Korean Finger Number Recognition using sEMG signals. Optimizer in CNN seeks to make the learning algorithm work fast and accurate. Thus, the CNN learning algorithm is likely to depend on which optimizer to use. The optimizers selected in the study are RMSprop, Adam, Adagrad, Adamax, and Nadam. To this end, the comparison study of optimizers in CNN is carried out by showing how well all of five Korean finger number gestures from one (1) to five (5) are recognized by CNN using sEMG signals. The results of the performed experiment show that the RMSprop optimizer has better recognition rate at same condition in CNN learning algorithm.
FCH-SLAM: A SLAM Method for Dynamic Environments using Semantic Segmentation

Static environments are a prerequisite for most visual simultaneous localization and mapping (SLAM) systems because the dynamic matching points from moving objects in the camera’s field of view interrupt the localization process. The noise of the dynamic objects also contaminates the constructed maps. In this study, we propose a SLAM system designed to reduce the effects on the accuracy caused by dynamic objects to solve this issue. The noise points of dynamic objects are removed by combining depth information and semantic information. We evaluated the proposed method on the TUM RGB-D dataset, and the experimental results show that it performed well in dynamic environments, obtaining a high accuracy in most situations with a relatively high processing speed.
Application of an input-output pairings selection methodology to control multivariable systems based on multi-objective optimization

This paper shows a methodological approach to selecting input-output pairings and tuning controllers for multivariable systems based on multi-objective optimization. The methodology is applied to a nonlinear multivariable system called a boiler-turbine unit (electrical power generation system). Two scenarios and two design concepts (input-output pairings and controllers) were defined, each with six design objectives. It was shown in detail that one design concept is better than the other in each scenario for tracking a reference. It is important to mention that the proposed methodology establishes a unified framework to select suitable loop pairings and tune multivariable controllers defining what in this paper is known as a design concept. The methodology establishes a global comparison framework of each design concept so that a designer according to his/her preferences selects an optimal solution to control the system.
Do Visual Issue Reports Help Developers Fix Bugs?: - A Preliminary Study of Using Videos and Images to Report Issues on GitHub -

Issue reports are a pivotal interface between developers and users for receiving information about bugs in their products. In practice, issue reports often have incorrect information or insufficient information to enable bugs to be reproduced, and this has the effect of delaying the entire bug-fixing process. To facilitate their bug-reproduction work, GitHub has provided a new feature that allows users to share videos (e.g., mp4 files.) Using such videos, reports can be made to developers about the details of bugs by recording the symptoms, reproduction steps, and other important aspects of bug information. While such visual issue reports have the potential to significantly improve the bug-fixing process, no studies have empirically exam-ined this impact. In this paper, we conduct a preliminary study to identify the characteristics of visual issue reports by comparing them with non-visual issue reports. We collect 1,230 videos and 18,760 images from 226,286 issues on 4,173 publicly available repositories. Our preliminary analysis shows that issue reports with images are described in fewer words than non-visual issue reports. In addition, we observe that most dis-cussions in visual issue reports are concerned with either conditions for reproduction (e.g., when) or GUI (e.g., pageviewcontroller.)
Push-Button Synthesis of Watch Companions for Android Apps

Most Android apps lack their counterparts on convenient smart-watch devices, possibly due to non-trivial engineering efforts required in the new app design and code development. Inspired by the observation that widgets on a smartphone can be mirrored to a smartwatch, this paper presents the Jigsaw framework to greatly alleviate such engineering efforts. Particularly, Jigsaw enables a push-button development of smartphone's companion watch apps by leveraging the programming by example paradigm, version space algebra, and constraint solving. Our experiments on 16 popular open-source apps validated the effectiveness of our synthesis algorithm, as well as their practical usefulness in synthesizing usable watch companions.
Understanding How the Relative Abundance of Candida Species Impacts Transcriptional Regulation in Co-culture Biofilms

Candida albicans and Candida glabrata are common fungal species that can change from commensal to pathogen due to their ability to form robust biofilms. Candida species are the leading cause of life-threatening conditions like Candidemia, and the existing treatments for biofilm-related infections are suboptimal. Research shows that the relative abundance of the two Candida species promotes biofilm formation, enhances pathogenicity, and increases antibiotic resistance. Thus, focusing on the importance of co-culture, this paper utilizes RNA sequencing to investigate the gene expression leading to biofilm development in co-culture through a time-series study.
Multimodal Recommendation of Messenger Channels

Collaboration platforms, such as GitHub and Slack, are a vital instrument in the day-to-day routine of software engineering teams. The data stored in these platforms has a significant value for datadriven methods that assist with decision-making and help improve software quality. However, the distribution of this data across different platforms leads to the fact that combining it is a very time-consuming process. Most existing algorithms for socio-technical assistance, such as recommendation systems, are based only on data directly related to the purpose of the algorithms, often originating from a single system. In this work, we explore the capabilities of a multimodal recommendation system in the context of software engineering. Using records of interaction between employees in a software company in messenger channels and repositories, as well as the organizational structure, we build several channel recommendation models for a software engineering collaboration platform, and compare them on historical data. In addition, we implement a channel recommendation bot and assess the quality of recommendations from the best models with a user study. We find that the multimodal recommender yields better recommendations than unimodal baselines, allows to mitigate the overfitting problem, and helps to deal with cold start. Our findings suggest that the multimodal approach is promising for other recommendation problems in software engineering.
Counting Bugs in Behavioural Models using Counterexample Analysis

Designing and developing distributed software has always been a tedious and error-prone task, and the ever increasing software complexity is making matters even worse. Model checking automatically verifies that a model, e.g., a Labelled Transition System (LTS), obtained from higher-level specification languages satisfies a given temporal property. When the model violates the property, the model checker returns a counterexample, but this counterexample does not precisely identify the source of the bug. In this work, we propose some techniques for simplifying the debugging of these models. These techniques first extract from the whole behavioural model the part which does not satisfy the given property. In that model, we then detect specific states (called faulty states) where a choice is possible between executing a correct behaviour or falling into an erroneous part of the model. By using this model, we propose in this paper some techniques to count the number of bugs in the original specification. The core idea of the approach is to change the specification for some specific actions that may cause the property violation, and compare the model before and after modification to detect whether this potential bug is one real bug or not. Beyond introducing in details the solution, this paper also presents tool support and experiments.
ConvGRU-TSNet: A Novel Deep Learning Approach for Multivariate Time Series Prediction

A novel deep learning approach, using Convolutional Gated Recurrent Unit with Temporal Spatial Neural Net-work (ConvGRU-TSNet), is presented in this paper. It is proposed to solve the mixed pattern challenges faced by multivariate time series prediction. Gated Recurrent Unit (GRU) and Convolutional GRU (ConvGRU) are utilized to extract long-term patterns among different periods such that the short-term patterns in one period can be detected. Additionally, the traditional autoregressive (AR) model is employed to tackle the scale insensitive problem. The experiments are carried out using four datasets with complex mixed patterns. It is observed that the ConvGRU-TSNet presents enhanced performance exceeding several baseline methods. Among the four selected datasets, the Relative Squared Error (RSE) can be reduced by 7.604% at most and the Correlation Coefficient (CORR) can be increased by 1.198% at most. It is demonstrated that the proposed ConvGRU-TSNet offers an alternative perspective of solving the mixed-pattern challenges with improved prediction accuracy. The code and four datasets are available at https://github.com/privateos/ConvGRU-TSNet
Web-based Malware Detection for Android OS

Smartphones and tablets have become some of the most consumed electronic devices because they revolutionized many aspects of our lives. Android is one of the most popular mobile operating systems (OS) used by mobile users. These devices store vast amounts of valuable data ranging from per-sonal information (e.g., text messages and contacts) to company information for when companies apply bring your own device (BYOD) policy. Due to its popularity and stored information these devices have been targeted by cybercriminals which introduce a lot of issues to users such as stealing private information, make calls or send SMS messages to premium numbers etc. In this paper we introduce a public web-service tool to analyze Android applications. It runs inspected applications on real Android devices and provide users with a real time analysis report. Moreover, other researchers can utilize our analysis results to learn more about malicious applications behavior to develop a robust technique to detect these type of applications.
Data Analysis of Crime and Rates of Hospitalization due to COVID-19

There has been an increasing concern that African American community has been disproportionately impacted during the coronavirus pandemic. This paper analyzes why the African American community is disproportionately impacted during the coronavirus pandemic and compares the COVID-19 data with hospitalizations, real estate, school closings, and crime data. Human behavior was impacted as a result of lockdown due to COVID pandemic and it lead to a shift in crime dynamics. We analyze shifts in crime types by comparing crimes before and after the COVID pandemic in Baltimore. There was a significant decline in total crimes during the time period immediately following stay at home orders. Findings show that the disproportionality among the African American community is significantly influenced by factors such as living in more crowded housing situations, working in consumer-facing serviced industries, having higher rates of preexisting medical conditions, and lack of insurance or a consistent care source.
Preservation of Manual Changes and Provenance for Data Quality using the Nano Version Control Repo

A new data structure called the Nano Version Control (NanoVC) repo emerges from computer science and the software industry. This data structure efficiently encodes entities at the nano-scale of the modelling spectrum and stores the provenance for that entity. The repo provides an intuitive representation of the history and data-lineage of the entity. Some provenance information can be computed on demand because of the repo structure. A simple algorithm for preservation of manual changes in the light of new data and changing algorithms utilizes the commit history in the repo to give us a sustainable way to merge information while keeping the provenance intact.
Regression Testing of Mobile Apps

Because mobile applications, or apps, are becoming essential in our personal lives and at work, and mobile applications update frequently, it is important that developers perform regression testing to ensure their quality. In this paper we adapt the FSMWeb approach for selective regression testing for mobile applications. We apply rules to classify the original set of tests into obsolete, retestable, and reusable tests based on the types of changes to the model. New tests are added to cover portions the App that have not been tested.
An Altmetric Study of Artificial Intelligence in Medicine

Artificial Intelligence (AI) and its powered technologies have been crucial for medical research and clinical practice [1]. While AI in medicine is a rapidly evolving field, this study provides a new perspective to review its related bodies of literature through a bibliometric analysis of Altmetric data. Altimetric is a system that tracks the attention that research outputs such as scholarly articles receive from different web sources (e.g. social media, mainstream news, blogs). Altmetric Explorer is an online platform that enables users to browse and report on citation-based attention metric data for a given scholarly output (including journal articles and dataset). The purpose of this study is to perform an Altmetric analysis to systematically study research trends on AI in medicine. The study identifies various aspects of research outputs on AI in medicine (mentions, attention scores, a timeline of mentions, a timeline of yearly publication, top journals, top research affiliations, and twitter demographics). These findings would be of interest to both researchers and practitioners in the field. In conclusion, research of AI in medicine has been attracting significant attention in the past 4-5 years and top journals and top research affiliations follow the trend of Bradford’s law and Lotka’s law.
SARS-CoV-2 variant strains and viral phylodynamics of ORF1a-1b genetic aspects in South America

Bioinformatics tools for online sequence analysis of variants have been used worldwide for the phylogenetic approach of SARS-Cov-2 and their variants. The purpose of this work is to contribute to the settlement of the SARS-CoV-2 genetic of the South America pandemic, presenting the ORF1a-1b evaluation. We proposed and reviewed two online bioinformatics pipelines for viral phylodynamic and phylogeographic analysis with an interactive visualization platform. The phylodynamics evaluation of South America shows a strong viral capacity to evoke immunity and an impressive multiplicity of variants in rapid expansion with mutations of potential importance, including ORF 1a-1b. They showed stately vital for infection and lethality, spreading and raising your frequency in South America from 1% to 20-30% in one year of pandemic occurrence.
A Review on Multiple Data Source Based Recommendation Systems

Recommender systems are used by the content or item providers. The content could be video, music, etc. For example, Netflix recommends movies or TV shows, Amazon recommends books or other items, etc. Most content providers use their platform to collect data from their users and then use the collected data to design a recommender system. However, the recommendation results are more useful if a recommender system uses multiple data sources. Both Matrix Factorization (MF) and Deep Neural Network (DNN) models are used to design multiple data source-based recommenders. This paper reviews various approaches that use multiple data sources to design recommender systems using MF and DNN models.
DeepState: Selecting Test Suites to Enhance the Robustness of Recurrent Neural Networks

Deep Neural Networks (DNN) have achieved tremendous success in various software applications. However, accompanied by outstanding effectiveness, DNN-driven software systems could also exhibit incorrect behaviors and result in some critical accidents and losses. The testing and optimization of DNN-driven software systems rely on a large number of labeled data that often require many human efforts, resulting in high test costs and low efficiency. Although plenty of coverage-based criteria have been proposed to assist in the data selection of convolutional neural networks, it is difficult to apply them on Recurrent Neural Network (RNN) models due to the difference between the working nature. In this paper, we propose a test suite selection tool DeepState towards the particular neural network structures of RNN models for reducing the data labeling and computation cost. DeepState selects data based on a stateful perspective of RNN, which identifies the possibly misclassified test by capturing the state changes of neurons in RNN models. We further design a test selection method to enable testers to obtain a test suite with strong fault detection and model improvement capability from a large dataset. To evaluate DeepState, we conduct an extensive empirical study on popular datasets and prevalent RNN models containing image and text processing tasks. The experimental results demonstrate that DeepState outperforms existing coverage-based techniques in selecting tests regarding effectiveness and the inclusiveness of bug cases. Meanwhile, we observe that the selected data can improve the robustness of RNN models effectively.
Tropopause Undulations and the Development of Extratropical Cyclones.

Abstract A case study of a developing cyclone is used to show that the three-dimensional distribution of height change during development can be strongly sensitive to temperature changes which occur in the lower stratosphere in association with an evolving tropopause undulation. Quantitative analysis of the storm with a geopotential height tendency equation indicates that a synergistic process developed between the stratosphere and troposphere, whereby the vertical motion pattern maintained and intensified the upper-level temperature anomalies while the subsequent upper-level temperature advection led to an enhanced vertical circulation. Using the results of this diagnostic study, a conceptual model is constructed. The conceptual model is based on the hydrostatic and wind-field adjustments that occur as tropopause undulations propagate over favored regions of tropospheric warm advection and less stable air.
Design of a new innovative and systematic education program for tech-oriented startups and scaleups leaders

we design a new TVA startup and scaleup program and implement the innovative technology venture education structure that can systematically train tech-based startups and scaleups leaders compared to existing startup business schools. It gives a good chance to acquire knowledge related to technology venture startups and obtain the ability to manage social enterprises and differentiated training program to provide customized education for each stage of corporate growth. In addition, through various technology innovation seminars and hybrid online and offline networking platforms, both startup and scaleup leaders who can promote two growth engines will be trained by sharing implementation strategies through individual business (or startups) presentations and exchanges, analyzing and consulting with experts. In the future, the TVA program keeps track of the 4th industrial revolution driving technology, develops related business models, and prepares an opportunity to challenge niche market.
didiffff: A Viewer for Comparing Changes in both Code and Execution Traces

One of the important purposes of code review is to find potential defects caused by other developers' code changes. When reviewing bug fixes, it is important to check the program behavior is properly changed to remove the bug. On the other hand, it is also impor-tant to check the program behavior that is not related to the bug is not changed. To investigate the program behavior, omniscient debugging which records all the runtime events is proposed. With omniscient debugging techniques, existing tools visualize multiple execution paths and the states of local variables of a method, but they are not focusing on code changes. In this paper, we imple-mented a prototype tool that compares and visualizes the difference between two execution traces caused by code changes. Each vari-able has a maximum of two lists of values, before and after the code changes, so we proposed their categorization based on their difference of length and contents. We also developed a viewer to show both code changes and the difference of execution traces at a glance by extending our previous viewer for omniscient debugging.
Cyberbullying Detection Neural Networks using Sentiment Analysis

The advances in technical evolution have given rise to a serious problem of cyberbullying. Cyberbullying is the use of electronic communication to bully a person, typically by sending messages of an intimidating or threatening nature. Social networking sites in particular Twitter is becoming a platform for this type of bullying. Machine learning (ML) techniques have been widely used to detect cyberbullying through detecting some language patterns that are exploited by bullies to attack their victims. Sentiment Analysis (SA) of text can also contribute useful features in detecting offensive or abusive content. Deep learning specifically the Convolutional Neural Networks (CNN) has been used to improve the performance of feature extraction during the detection of cyberbullying process. In this research, a SA model is proposed for recognizing cyberbullying tweets in Twitter web-based media. Convolutional Neural Network, Support Vector Machines (SVM) and Naïve Bayes (NB) are utilized in this model as supervised ML classifiers. The aftereffects of the analyses led on this model demonstrated empowering results when a higher n-grams language models are applied on such tweets in comparison with comparable past exploration. Moreover, the results showed that CNN classifiers have outperformed NB and SVM classifiers in several measures.