Reviewerly/exHarmony huggingface dataset:
Semantics of temporal constrained objects

This paper presents the declarative and operational semantics for the paradigm of temporal constrained objects (TCOB). This modeling paradigm is an extension of basic constrained objects where objects specify the structure of a system and constraints specify its behavior. Temporal constrained objects have been shown to provide a clear and high-level declarative specification of the time-dependent behavior of complex dynamic systems. Two key features of this paradigm are series variables and metric temporal operators. The main contribution of this paper lies in showing how we can define the semantics of temporal constrained objects in terms of a new paradigm called temporal constraint logic programs (TCLP), which extend temporal logic programs with constraint-solving. Compared with the several other logical languages incorporating constraints and temporal logic, the TCLP paradigm offers a clear and direct way of translating temporal constrained object programs using three simple temporal predicates: first p, next p, and prev p. The key approach is to map each class in a TCOB program to a predicate in the TCLP paradigm. Thus, the semantics of recursively defined classes is expressed in terms of the semantics of recursively defined predicates, a well-understood topic. Given the close connections between TCLP and CLP, we take advantage of the semantics of CLP for providing the declarative and operational semantics of TCLP and thereby also of temporal constrained objects.
E-Learning for English for Business-Based Podcast: One of Learning Solutions Amid the Pandemic of COVID-19.

Abstract This research used qualitative methods of descriptive where information was required directly and where time and resources were limited. It was designed to investigate whether e-learning podcast-based could be used in learning to listen in English for students of the University of Bina Sarana Informatika Management Study program. This research used samples that are 6 classes as a source of research data with the 2nd-semester students who came from Management study program. The number of 1 average class consisted of 20-25 students. Researchers used the Android-based Anchor Application as a podcast maker as well as Google Classroom as an E-Learning learning medium. To collect data, researchers used observations, interviews, and documentation. Interviews were conducted by giving 25 questions through online based on theoretical approaches related to this study with 90 sample data. Researchers used test instruments with pre-test and post-test as research instruments. This research was focused on the use of ICT in the learning process by way of e-learning Podcasts based. The novelty of this research is this study used a different target and numbers of the target from the previous studies, namely Management Program Students who only get Basic English teaching during lectures. As a result of this research was the use of podcasts more effective than e-learning without using the media podcast, characterized by a grade average result of 76.05 after Post tests were done.
A Visual Content Protection Evaluation Method for CS Coding Images

Recently, the widespread application of image processing technology has caused special visual privacy issues while bringing convenience to our life. In particular, most of the current mainstream intelligent recognition algorithms rely on the detailed content of images, which has significantly increased the risk of personal privacy leakage. Therefore, we propose a multi-layer compressed sensing (CS) coding model, which ensures the security of image content and retains enough information for intelligent recognition at the same time. Besides, drawing on the idea of related feature mapping quality scores in image-quality assessment (IQA), we extract multi-frequency local binary pattern (LBP) and semantic salient features to estimate the content protection degree of CS images. Finally, experiments on three CS databases prove that the proposed method has better performance compared with other IQA methods.
Realistic testing of RTC applications under mobile networks

The increasing usage of Real-Time Communication (RTC) applications for leisure and remote working calls for realistic and reproducible techniques to test them. They are used under very different network conditions: from high-speed broadband networks, to noisy wireless links. As such, it is of paramount importance to assess the impact of the network on users' Quality of Experience (QoE), especially when it comes to the application's mechanisms such as video quality adjustment or transmission of redundant data. In this work, we pose the basis for a system in which a target RTC application is tested in an emulated mobile environment. To this end, we leverage ERRANT, a data-driven emulator which includes 32 distinct profiles modeling mobile network performance in different conditions. As a use case, we opt for Cisco Webex, a popular RTC application. We show how variable network conditions impact the packet loss, and, in turn, trigger video quality adjustments, impairing the users' QoE.
Big Data-Based Smart Grid Data Management Model for Improved Monitoring of Power Consumption

Smart grid is treated as the future of the power grid, practices two-way flows of power and data to create an automated, extensively distributed power conveyance network. This composition explores how a big data environment can be utilised for data management in the smart grid and presented a model called a big data-based smart grid data management (BSGDM). We research the advantages and prospects of both smart grid and cloud computing environment. For security purpose, we designed a security management organisation and also defined applications of BSGDM.
Challenges of Developing AI Applications in the Evolving Digital World and Recommendations to Mitigate Such Challenges

These intelligence in the systems are not organic but programmed. In spite of being extensively used, they suffer from setbacks that are to be addressed to expand their usage and a sense of trust in humans. This chapter focuses on the different hurdles faced during the course of adopting the technology namely data privacy, data scarcity, bias, unexplainable Blackbox nature of AI, etc. Techniques like adversarial forgetting, federated learning approach are providing promising results to address various issues like bias, data privacy are being researched widely to check their competency to mitigate these problems. Hardware advancements and the need for enhancing the skillset in the artificial intelligence domain are also elucidated. Recommendations to resolve each major challenge faced are also addressed in this chapter to give an idea about the areas that need improvement.
Web Encryption Analysis of Internet Banking Websites in Thailand

With Thailand rapidly moving to a full internet banking ecosystem, the demand for online security has never been needed more than it is today. As the security and privacy of internet users depend on HTTPS, a web encryption protocol, for securing communication between users and web servers, HTTPS is essentially the center of the web ecosystem today. Unfortunately, despite the increasing number of HTTPS adoptions, numerous studies have shown that a large number of websites have adopted HTTPS incorrectly, rendering users vulnerable to information leakages e.g., eavesdropping and man-in-the-middle attacks. The correctness of HTTPS deployment is even far greater for internet banking services due to carrying user's sensitive information and being prime targets for criminal activities.In this paper, we present WEAPONS, a novel black-box testing tool for evaluating the completeness and correctness of web encryption deployment including the deployment of HTTPS, and web encryption-related mechanisms i.e., HSTS, secure cookie, HTTPS redirect, HSTS preload. We use WEAPONS to conduct an assessment of 9 popular internet banking websites in Thailand during January - February 2020. We demonstrate that WEAPONS is able to find HTTPS deployment incorrectness. Several of these weaknesses can expose the affected services to man-in-the-middle attacks and sensitive data exposure.
Music Thinking Process: Unfolding the creation of the piece<i>Résonances manifestes</i>

Beyond the social and even the human, sound opens onto the intertwining of movements animating the lived experience. A sonic epistemology considers not only that the acoustic ecologies are enunciative of social relations and power dynamics, but also that they tell about the way reality is lived, experienced, organised: they are expressive , in themselves. This means, for an epistemology of phonosophy, that every perception of a sound implies a conceptual movement, which carries a mental dimension that could become the material for another thinking practice, for a sophia. This article approaches music as thinking in itself: a thought of the sonic. This affirmation will be expanded through the contribution of process philosophy (Whitehead, Deleuze and Guattari, Manning, etc.), which allows a musical event to be considered as an ecology, produced by the encounter of a multiplicity of bodies (human, sonic, technological, etc.). The processes of capture of forces involved and the different techniques required to increase the expressive potentialities of the musical assemblage will be unfolded through the case of Résonances manifestes, a comprovised music piece based on a sound score composed of field recordings from autonomous demonstrations.
Deep Time-Aware Matrix Factorization

The appearance of recommendation system solves the problem of information overload. Traditional recommendation systems generally consider the preferences of users, but ignore external conditions, such as the timeliness and popularity of goods.In this experiment, the time factor is added to form a triple, like User-Item-Time, and the neural network is used for training. Compared with the matrix factorization experiment which integrates time factor, the prediction effect is better when the movie popularity is integrated into the recommendation.
Research on Weapon Equipment Procurement Policy Hot Topics Based on LDA Model

A scientific, systematic and standardized procurement policy of weapon equipment is an important guarantee for the modernization of weapon equipment. This paper proposed the hot topic analysis of weapon equipment procurement policy based on the LDA model, analyzed the keywords of weapon equipment procurement policy text, used the LDA model to analyze the text topic, obtained the topic and topic word probability distribution, and calculates the text topic popularity. On this basis, hot topics such as weapon equipment procurement project management, supervision and inspection, license management, confidentiality management, and fair competition were analyzed. The research conclusions provide reference for finding the hot topics of weapon equipment procurement policy and further improving weapon equipment procurement policy system.
Invited Paper: Perishable Data in Smart Communities

Distributed smart and connected community applications of the present and future will continuously sense perishable data on which they make decisions. We say the data are perishable when they represent information sensed at a past point in time, possibly at a distant part of the distributed application, and for which the ground truth could have since changed. This paper discusses actions the sensing source, the network, and the controller can take to make the best possible decisions given that we know only a delayed version of the truth. Some of these actions to preserve freshness of data are based on known real-time techniques such as Time Sensitive Networking. To these we add Information Theory applied on a message-by-message basis and cross-layer coordination between content and network handling. Both the unexpectedness (surprisal) and timeliness (freshness) properties of perishable information can be encoded using a Shannon information entropy framework. Examples of smart community systems of the future for which these concepts are relevant are given.
Personalized Walking Exercise Support System for Elderly Based on Machine Learning

Over the last decade, the world's population structure has significantly changed where the proportion of elderly people in the population increases and that of young people declines considerably. This change of demographic landscape has caused a severe manpower shortage. It reduces the pool of people in their productive years. Several countries have conducted plans to extend the retirement age to keep elderly workers in the labor market longer. However, maintaining health and physical ability among elderly workers is a primary issue. To address the problem, regular physical exercise is crucial. In this paper, we propose an innovative software system that support the elderly to participate in walking exercise regularly. Our system uses a mobile phone together with a fitness tracker as data collection devices. It applies an ANN algorithm to construct a regression-based model that can predict exercise minutes based on walking statistics. The exercise minutes predicted are then used to generate a personalized exercise plan designed for each elder. The experimental result indicates that our predictive model is practical to be used in real-world settings with reasonable prediction accuracy, about +/-17 weekly exercise minutes observed.
Graph Convolutional Networks: An Analysis of Method and Applications in Different Fields and Systems

With the prosperity of the information era and the maturity of computer technology, artificial intelligence has attracted much more attention. The emergence of Graph Neural Networks (GNNs) as a theory that can process non-Euclidean structure data such as graph data makes more applications possible. Graph Convolutional Neural Networks (GCNs), as a theoretical branch of GNNs, uses new theories to innovate and optimize on the basis of inheriting the ideas of predecessors, allowing the rapid development of this field. In this article, the author mainly introduces the basic theory of converting graph data into Euclidean structure data, which is the most important part of GCNs, distinguishing from Convolutional Neural Networks (CNNs). The successful applications of GCNs in the fields of recommendation systems and traffic prediction are also listed. Through the analysis of theory and applications, the shortcomings and development prospects of GNNs are discussed. Finally, the author points out that GCNs still have room for improvement in terms of data scale, network layers, and dynamics and complex nature of graph data.
Backend Development of Online Marketplace for Male Beauty Care Services

Online marketplace is a medium for connecting sellers and buyers. Besides that, it is currently more focus on selling products rather than selling services. Usually, if there are products and services in the same platform, the transaction systems will be automatically separated. The purpose of this research is to create an information system that can combine services and product which focuses on beauty for men. The platform can facilitate customers and vendor to buy and sell products and services easily. This research is more focus on the back-end system which uses Node JS as REST-API and MySQL as the database system. Furthermore, in the development phase, the scalability of the system is measured using Apache J Meter to find how many capacities that can be handled by the system. As a result, the system is able to accommodate up to 1500 requests. It is quite good result due to NodeJS has advantages in its features, which is non-blocking to process the data faster.
Exploration of the digital display design method of ancient coconut shell hill archaeological site

Abstract With the development of science and computer technology, digital display design breaks through geographical and time and space constraints, not only can realize the restoration and protection of ancient coconut shell mound ruins, at the same time, the rapid spread of ancient coconut shell mound site culture was realized. This paper analyze the current situation of the ancient coconut shell mound site display and discover the core content of the ancient coconut shell mound site display to explore a solution to the digital display design of the ancient coconut shell mound site. The digital display design is conducive to the cultural information dissemination of the ancient coconut shell mound site, and improves the interactive experience and satisfaction of visitors; it aims to play a certain guiding role for the ancient coconut shell mound site to truly and completely explain the value of the site and spread the connotation of the site.
Dirichlet Tessellation’s technique to compress a true color image using a Lossy compression

Abstract In this paper, we show how to use the concept of Dirichlet Tessellations to compress, store and reconstruct an image without affecting on its dimensions and represent it with an acceptable quality, where a true color image has compressed by 60.05% with mean square error (MSE) = 9.6081 which represents the error between the restored image and the original image, and peak signal-to-noise ratio (PSNR) =38.3044 dB which represents the similarity between the restored image and the original image, using MATLAB R2017a. Dirichlet Tessellation has simply defined as dividing the space into geometric shapes by generating finite set of distinct points, each shape contains one of the distinct points and comprising that part of the space nearer to that distinct point than to any of the other points. We have used two algorithms for image compression, First algorithm selects set of distinct points distributed uniformly in an image and store their locations along with pixel values. In the second algorithm random selection of distinct points, which distributed in regions containing more details, using the edges detector algorithm to detect these details. In order to reconstruct the image, Saved distinct points placed at their corresponding locations in a new image that is formed, where two algorithms used, the first algorithm based on the concept of a growing region. It’s Region -Based image segmentation method, by checking the pixels adjacent to the saved distinct points and delimiting whether the pixels should add to the regions of saved distinct points depending on the region’s membership criteria such as pixel intensity. The second algorithm uses one of the Dirichlet Tessellations characteristics, Which divides an image into polygonal regions based on the distinct points that saved, Each pixel in the confined plane of saved distinct points will have the same characteristics of this point, This is done by taking each pixel in an image and calculating the minimum distance between pixel location and saved sites using the distance equation, This process repeated until each pixel assigned its value and specifying all color regions in the image.
An Image Stitching Algorithm Optimization Combined with Two-dimensional Information Entropy of Image

In order to improve the quality of registration and stitching of general scene images, an image stitching optimization algorithm using two-dimensional information entropy to distinguish residual weight is proposed in this paper. First, the scale-invariant feature transform (SIFT) feature detection operator is used to extract the feature points of the reference image and the registered image, and the RANSAC algorithm is used to exclude outliers to obtain a consistent set. Then, two - dimensional maximum entropy segmentation is performed on the image. The image feature points are divided into two types: high-efficiency points and low-efficiency points. The different weights are given to the feature points on the internal point consistency set. The Levenberg-Marquardt algorithm is used to minimize the sum of residuals and calculate the optimized homography matrix. Finally, use the fade-in and fade-out method to fuse the images to eliminate the stitching gap. Experiments show that the algorithm of this article can improve the stitching quality of the stitched image in area with rich details and improve the stability of registration.
Friendly Appearance of Multiple Secret Sharing

This paper presents a new technique for converting the Multiple Secret Sharing (MSS) from noise-like form into more friendly appearance. This technique simply utilizes the Chinese Remainder Theorem (CRT) and eXclusive-OR (XOR) operations on generating a set of shared images and recovering a set of secret image. As reported in the Experimental Section, the proposed method performs well in MSS system with friendly constraint. In addition, the proposed method requires simple operation for MSS task.
Improving Multi-view Stereo with Contextual 2D-3D Skip Connection

Learning-based methods have shown their strong competitiveness in estimating voxel for multi-view stereo. However, due to the modality gap between 2D and 3D space, the quality of the estimated 3D object is limited by the reconstruction of some detailed structures. To tackle this problem, we regard the 3D voxel reconstruction as a semantic segmentation task where skip connections between the 2D encoder and 2D decoder are usually utilized to incorporate significant contextual, aiming to segment more details. Thus, we propose an approach to improve the multi-view 3D voxel reconstruction via contextual 2D-3D skip connection. In our method, a 2D-3D skip connection branch embedded with feature visual hull is designed and plugged into the standard 2D encoder-3D decoder reconstruction architecture, which enables 2D contextual information to be effectively transmitted into the 3D domain. Then, an attention-guided module is designed to adaptively combine the transmitted features with the original 3D decoded features. Finally, a 3D RNN layer is built at the end of network to aggregate individual 3D features from different views. Extensive results have shown that the contextual information from our 2D-3D skip connections can significantly improve the reconstruction performance, especially for the detailed structures recovering.
Text and Sign Recognition for Indoor Localization

In this paper, we propose a modular approach to estimate the position and rotation of any mobile robot more precisely in an indoor environment using text and sign recognition. The modular approach for the text and sign recognition is performed in a twofold method in figure 1. First is the detection of the region with texts and various signs in the image which is done by an object detection system. The second part is the character recognition, where the detected textual region from the image will be passed onto an optical character recognition engine(OCR) engine to be recognized. This modular approach can be modified at any point based on any mobile robot in an indoor environment with texts and signs to help localize its position and rotation.