Reviewerly/exHarmony huggingface dataset:
Development and utility of a close contact information management system for the COVID-19 pandemic

Abstract Background Since the outbreak started in 2019, COVID-19 pandemic has a significant global impact. Due to the highly infective nature of SARS-CoV-2, the COVID-19 close contacts are at significant risk of contracting COVID-19. China’s experience in successfully controlling COVID-19 emphasized the importance of managing close contacts because this strategy helps to limit potential infection sources, prevent the unconscious spread of COVID-19 and thus control this pandemic. As a result, to understand and consider the management of close contacts may be beneficial to other countries. However, managing close contacts is challenging owing to the huge number of close contacts and a lack of appropriate management tools and literature references. Methods A new system called the COVID-19 Close Contact Information Management System was developed. Here we introduced the design, use, improvement and achievements of this system. Results This system was designed from the standpoint of the Centers for Disease Control and Prevention in charge of managing close contacts. Two main functions and eight modules/themes were ultimately formed after two development stages. The system introduces what information need to be collected in the close contact management. Since the system allows information flow across cities, the geographical distance and administrative regional boundaries are no longer obstacles for managing close contacts, which promotes the management of each close contact. Moreover, when this system is used in conjunction with other data tools, it provides data assistance for understanding the COVID-19 characteristics and formulating targeted COVID-19 control policies. To date, the system has been widely used in Guangdong Province for over 1 year and has recorded tens of thousands of pieces of data. There is sufficient practical experience to suggest that the system is capable of meeting the professional work requirements for close contact management. Conclusions This system provides a new way to manage close contacts and restrict the spread of COVID-19 by combining information technology with disease prevention and control strategies in the realm of public health. We hope that this system will serve as an example and guide for those anticipating similar work in other countries in response to current and future public health incidents.
Solution Discovery over Feature Toggling with Built-in Abstraction in OutSystems

Technology is evolving rapidly, so are the users’ needs and requirements. Software companies need to be competitive and deliver new versions of their product at a fast pace. That is why the demand for continuous integration and continuous delivery is increasing. Feature toggling is one of the techniques used to reduce the development cycle time. By including toggles in their code, developers can isolate code related to the new features under development and prevent it from being executed. Toggling allows the delivery of code with incomplete features without impacting the remaining functionalities.Feature toggling not only helps with the process of continuous integration and continuous delivery but also allows more control over the features. By implementing toggles, developers can control if a feature is visible and to whom. This gives access to a new world of possibilities, from progressive rollouts to business-directed user targeting. However, toggling must be used with caution, as it increases code complexity and technical debt.There is yet no solution at OutSystems for feature toggling that helps developers introduce this technique seamlessly into their workflow, thus toggling is rarely used. The goal of this work is to support feature toggling in the OutSystems platform, given developers easy access to the advantages inherent to this technique, without worrying about the technical debt involved. This work explores possible solutions to be adopted, as well as its strengths and challenges, focusing on reducing effort and maintaining a higher abstraction level, characteristic of low-code languages. Solutions proposed in this discovery work can also be adapted and replicated in other low-code development platforms.
RTVS: A Lightweight Differentiable MPC Framework for Real-Time Visual Servoing

Recent data-driven approaches to visual servoing have shown improved performances over classical methods due to precise feature matching and depth estimation. Some recent servoing approaches use a model predictive control (MPC) framework which generalise well to novel environments and are capable of incorporating dynamic constraints, but are computationally intractable in real-time, making it difficult to deploy in real-world scenarios. On the contrary, single-step methods optimise greedily and achieve high servoing rates, but lack the benefits of the MPC multi-step ahead formulation. In this paper, we make the best of both worlds and propose a lightweight visual servoing MPC framework which generates optimal control near real-time at a frequency of 10.52 Hz. This work utilises the differential cross-entropy sampling method for quick and effective control generation along with a lightweight neural network, significantly improving the servoing frequency. We also propose a flow depth normalisation layer which ameliorates the issue of inferior predictions of two view depth from the flow network. We conduct extensive experimentation on the Habitat simulator and show a notable decrease in servoing time in comparison with other approaches that optimise over a time horizon. We achieve the right balance between time and performance for visual servoing in six degrees of freedom (6DoF), while retaining the advantageous MPC formulation. Our code and dataset are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> .
FA-net: Attention-based Fusion Network For Malware HTTPs Traffic Classification

With the wide application of HTTPs, malware HTTPs traffic classification is usually the first step in anomaly detection system. The existing classification methods mainly use the raw bytes (containing the discriminative features) or the statistical features (containing the global information) as the input, which leads to a low Fl-score. Therefore, this paper presents a novel Attention-based Fusion Network (FA-net), which combines two types of features properly to improve the classification performance. FA-net consists of three sub-networks: RF -net and SF -net extract the representative features of raw bytes and statistical features through the Convolutional Neural Network (CNN) and reconstruction mechanism respectively, and C-net combines two types of features through the attention mechanism and a regulating factor. The experiments indicate that FA-net obtains markedly better results (the average Fl-score of 0.941 and 0.997 respectively on two datasets) than the baselines. We also explore the influence of different regulating factor values on classification performance.
Toward a Smart Organization, Integrating KM Processes, and Organizational Capabilities

It is common knowledge that organizations today face the challenges of growing competition. Therefore, organizations need to develop smarter to plan for several diverse and real-time challenges. The aim of this paper is to test the impact of knowledge management (KM) processes, organizational capabilities (OC), and mediating KM mechanisms and KM technologies toward a smart organization (SO). In this paper, structural equation modeling was applied by using Smart PLS 3.3.3 software to test hypotheses and the accuracy of the suggested model. The results showed a direct impact between all constructs and are a reflection of the SO of the employees (N=225) working in three Jordanian telecommunications companies. Additionally, the outcomes provision the partial mediating role of KM mechanisms and KM technology with OC in the SO. These outcomes can support leaders in generating a consciousness in addition to favorable OC, KM mechanisms, and KM technologies inside the organization, which enables the actual implementation of SO and uses the outcomes to plan their SO strategies.
Walking on the Bright Sight: Evaluating a Photovoltaics Virtual Reality Education Application

Virtual reality (VR) has demonstrated its potential in educational settings, allowing students to explore complex and otherwise inaccessible learning scenarios and material. We present a virtual reality learning application based on a real photovoltaics (PV)-array as an interdisciplinary work-in-progress approach towards an engaging VR-application with long-lasting learning effects. Students can inspect, alter and connect individual PV-modules and engage with the learning material in a game-based way. The application was developed by an interdisciplinary team in a participatory approach. To test, expand and improve the VR-application in future iterations, it was used in a course with students, who evaluated their experience via a pre-/post-online questionnaire and additional semi-structured individual interviews (N=7). The results show positive (self-reported) effects on different aspects covered in the evaluation (e.g. usability, learning motivation) regarding PV-arrays. Students recognized the benefit over traditional learning material and praised the strong resemblance of the VR-environment with the real counterpart.
Aspect-based Sentiment Analysis with Weighted Relational Graph Attention Network

The aim of aspect-based sentiment analysis (ABSA) is to determine the sentiment polarity of a specific aspect in a sentence. Most recent works resort to exploiting syntactic information by utilizing Graph Attention Network (GAT) over dependency trees, and have achieved great progress. However, the models based on traditional GAT do not fully exploit syntactic information such as the diversified types of dependency relations. The variant of GAT called relational graph attention network (R-GAT) takes different types of dependency relations into consideration, but ignores the information hidden in the word-pairs. In this paper, we propose a novel model called weighted relational graph attention network (WRGAT). It can exploit more accurate syntactic information by employing a weighted relational head, in which the contextual information from word-pairs is introduced into the computation of the attention weights of dependency relations. Furthermore, we employ BERT instead of Bi-directional Long Short-term Memory (Bi-LSTM) to generate contextual representations and aspect representations respectively as inputs to the WRGAT, and adopt an index selection method to keep the word-level dependencies consistent with the word-piece unit of BERT. With the proposed BERT-WRGAT architecture, we achieve the state-of-the-art performances on four ABSA datasets.
Optimal Design and Analysis of Intelligent Vehicle Suspension System Based on ADAMS and Artificial Intelligence Algorithms

Abstract A complete suspension model is established, and the suspension system is simulated and optimized. The method of suspension system establishment and simulation is explained in detail, and the influence of suspension parameter changes on vehicle handling and stability is analyzed in detail. The dynamic simulation analysis of wheel parallel runout test was carried out on the system, and the suspension system was optimized by artificial intelligence algorithm. The research results provide a technical basis for the design of automobile suspension.
End-to-End Scene Text Recognition System for Devanagari and Bengali Text

Scene text detection and recognition have been explored extensively in the recent past but very few among those are in Indian languages. In this paper, an end-to-end system for the detection and recognition of Devanagari and Bengali text from scene images is proposed. The work is done in three stages namely detection, matra removal, and character recognition. Firstly, the PP-YOLO network for scene text detection is used. As both the languages under consideration have matra-line, a U-Net based matra-line removal strategy is used next. U-Net almost achieves 100% accuracy for segmentation, which contributes to the overall performance of the proposed scheme. Finally, character recognition is done with the help of a CNN. To train the CNN a dataset is created with Devanagari and Bengali text images. Experimental results show the efficiency of the individual stages as well as the efficiency of the scheme as a whole.
The Correlation between the Development of IoT Robot Technology and the Effect of Economic Development

In order to explore the application of IoT technology in robots and the promotion of IoT robot technology to the economy, by comparing traditional technology and IoT intelligent robot technology, this article combines it with economic development to analyze the promotion of IoT robot to economic development. Based on the ultra-wideband ranging method, this paper designs an ultra-wideband radio frequency positioning system and applies it to the robot’s positioning process. Moreover, this article combines the application of robots in the current social and economic development to construct the system structure, and conducts functional analysis with manufacturing robots and monitoring robots as the main body. After constructing an intelligent robot based on the Internet of Things technology, by comparing the traditional technology and the intelligent robot technology of the Internet of Things, this article combines it with economic development to analyze the promotion of IoT robot to economic development. From the analysis results of this article, it can be seen that the advancement of IoT robot technology can effectively promote economic development.
Instance Segmentation in Very High Resolution Remote Sensing Imagery Based on Hard-to-Segment Instance Learning and Boundary Shape Analysis

Although great success has been achieved in instance segmentation, accurate segmentation of instances remains difficult, especially at object edges. This problem is more prominent for instance segmentation in remote sensing imagery due to the diverse scales, variable illumination, smaller objects, and complex backgrounds. We find that most current instance segmentation networks do not consider the segmentation difficulty of different instances and different regions within the instance. In this paper, we study this problem and propose an ensemble method to segment instances from remote sensing images, considering the enhancement of hard-to-segment instances and instance edges. First, we apply a pixel-level Dice metric that reliably describes the segmentation quality of each instance to achieve online hard instance learning. Instances with low Dice values are studied with emphasis. Second, we generate a penalty map based on the analysis of boundary shapes to not only enhance the edges of objects but also discriminatively strengthen the edges of different shapes. That is, different areas of an object, such as internal areas, flat edges, and sharp edges, are distinguished and discriminatively weighed. Finally, the hard-to-segment instance learning and the shape-penalty map are integrated for precise instance segmentation. To evaluate the effectiveness and generalization ability of the proposed method, we train with the classic instance segmentation network Mask R-CNN and conduct experiments on two different types of remote sensing datasets: the iSAID-Reduce100 and the JKGW_WHU datasets, which have extremely different feature distributions and spatial resolutions. The comprehensive experimental results show that the proposed method improved the segmentation results by 2.78% and 1.77% in mask AP on the iSAID-Reduce100 and JKGW_WHU datasets, respectively. We also test other state-of-the-art (SOTA) methods that focus on inaccurate edges. Experiments demonstrate that our method outperforms these methods.
Fast Drone Detection using SSD and YoloV3

This paper aims to introduce the method of detection of high-speed drones using both Single Shot Detector (SSD) and YOLOv3 (You Only Look Once)v3. After conducting experiments and obtaining footage of the fast-flying drones, the software and algorithms are being put to the test. In a motion detector, there are 3 main fundamentals - unmanned aerial vehicle (UAV) detection, UAV identification and tracking of the UAV, which will be introduced as a preliminary UAV detection system to spark of the use of other more advanced image recognition based detector. The alternative of using SSD and YOLOv3 will be the main discussion to target high-speed drones.
MultiPhiLDA for Detection Irrelevant Software Requirement Specification

Software Requirement Specification (SRS) is the most important artifact in the software development process. Many quality-related SRS in software development is full of ineffective information. Seven common errors are causing this weakness defined as "The Seven Sins of Specifier". Noise is one of the seven sins of the specifier divided into irrelevant needs and non-requirement statements. It will appear When a software developer adds some irrelevant information to the overall software requirement. Some studies detect noise in SRS documents, but the results are less than maximum because they cannot distinguish between actors and actions on the statement of need. The requirement statement contains two important parts, namely actors and actions. This study proposes the modification of LDA under the name MultiPhiLDA. MultiPhiLDA detects irrelevant requirement statements on SRS. To detect irrelevant requirement statements on SRS, MultiPhiLDA will identify SRS to find actors and actions. The expected result of MultiPhiLDA is to assist the analyst system in detecting irrelevant requirement statements on SRS to obtain good quality software
Hybrid camouflage pattern generation using neural style transfer method

Abstract Manipulating existing camouflage patterns is a challenging issue in the process of camouflage pattern design. In this article, we present an effective approach based on the neural style transfer method to generate a hybrid camouflage pattern by manipulating two given camouflage patterns. Using a convolutional network trained on image recognition, content and style are represented by the correlations between feature maps in several layers of the network. In this regard, we utilized different commonly used camouflage patterns as content and style images. Then, by performing the style transfer algorithm on selected camouflage patterns, a new hybrid camouflage pattern was generated. The hybrid pattern inherits the appearance features of the content and the style images. Also, the colors of the hybrid pattern were controlled by carefully selecting the input colors. It was concluded that the proposed method is useful for adding or deleting some features of an existing camouflage pattern. In fact, it is a professional tool for pixelating, depixelating, blurring, and gradual coloring of camouflage patterns. Furthermore, we demonstrate the effectiveness of hybrid camouflage patterns using visual assessment. The results of the subjective assessment show that the proposed method is efficient for generating successful camouflage patterns.
Spontaneous Gaze-Based Application Using 2D Correlation and Velocity Threshold Identification

Demand for touchless technology has grown with the surge of Covid-19 pandemic. Spontaneous gaze-based application is one of several promising technologies for touchless interaction. Despite of this potential, little attention has been paid to the performance of traditional eye movements classification methods on improving accuracy of gaze-based object selection. To handle this research gap, we proposed a novel workflow of spontaneous gaze-based object selection using 2D Correlation as similarity measure and Velocity Threshold Identification (I-VT) as a method for eye movements classification. We compared our method with Pearson Product-Moment Correlation (PPMC) as similarity measure and Dispersion Threshold Identification (I-DT) for eye movements classification. Our experimental results showed that the proposed method yielded object selection accuracy up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$95.62 \%\pm 3.48\%$</tex> . In future, our proposed method can be implemented in the development of touchless interactive technologies that adhere to the World Health Organization guidelines, especially during the Covid-19 pandemic.
Extractive Summarization of Text from Images

The modern age has brought with it an abundance in the inflow of information. The consumption of such large chunks of information leads to a latency in the gathering of relevant information. The condensation of such information becomes necessary as the volume of this inflow keeps expanding. The most efficient way for the retrieval of the most important contents of the data is to summarize the data such that only non-redundant and useful information is contained in it. The manual summarization of textual content is a laborious task and is not very efficient. Therefore, automatic summarization is of utmost importance. Text summarization is the process of identifying the most relevant information and discarding the unnecessary and the irrelevant information. In this paper, textual content is extracted from images using optical character recognition and the extracted text is subjected to various extractive summarization algorithms. The summary is evaluated with respect to the summary generated by an abstractive summarization model using Rouge-N and Rogue-L metric to calculate the Precision, Recall and F-Score.
The Use of ISO/IEC 27001 Family of Standards in Regulatory Requirements in Some Countries

ISO/IEC 27001 is an international standard with contain an information security management systems (ISMS) framework as a guidance for auditor and implementer to comply the ISMS standard. The application of the ISO/IEC 27001 standard can help a country or organization in building and maintaining an information security management system (ISMS). In addition to protecting data and information, ISO/ IEC27001 can also manage information security risks to increase the trust of relevant stakeholders. Nowadays, there is no structured information regarding the use of standards in information security. In Indonesia, the ISO/IEC 27001 family is widely used for implementing information security in the cyber field. Therefore, there is a need for a comparative study of the use of ISO/IEC 27001 in Indonesia and other countries. There is a need for details regarding the implementation of the ISO/IEC 27001 family in various countries for academics or implementers. This study aims to provide information to the government, information security experts, academics, and implementers regarding which countries are implementing the ISO/IEC 27001 family in the implementation of their information security policy requirements also compared to the GCI rank. Our comparative analysis has shown that Sweden uses ISO/IEC 27001 family the most, the second is the United Kingdom and the third is Malaysia. So, the ISO/IEC 27001 family of standards use is affecting the GCI rank which lies in the legal measures.
Evaluation of martial arts demonstration focusing on peak timing using acceleration data

Martial arts, such as Taekwondo, require knowledge of one ’s own skill level for skill improvement. In this study, we propose a method of Taekwondo performance evaluation by attaching a smartphone to a performer’s waist and collecting acceleration data. When a martial performer stops moving, they instantly hold the entire body still. Acceleration data shows a peak at this moment of stillness, known as "focus". If the "focus" timing is identical across all three axes, the performer is considered to possess considerable skill. In this study, we use the timing differences between the three axes to calculate feature values and create decision trees from the acceleration data to evaluate movement based on these features as explanatory variables. We evaluate the effectiveness of the decision trees to confirm their accuracy.