Reviewerly/exHarmony huggingface dataset:
Utilization of Association Rules for Specialization Recommendations in Informatics and Computer Engineering Education Study Programs, State University of Jakarta

Grade Point Average (GPA) records can be used to provide suggestions for the selection of specializations. To help determine the selection of specializations based on GPA records, an appropriate data analysis technique is needed to provide recommendations for specializations. The Association Rules are applied to assist in the selection of specializations. The data sample used was data from 99 Informatics and Computer Engineering Education Study Program (PTIK) students from the class of 2012-2017 who were randomly selected and each specialization was represented by 33 students. The number of courses used is 55 courses consisting of 24 compulsory courses, 21 specialization courses, and 10 elective courses. The results of the rules obtained in this study with the highest minimum support of 0.8 are 20 rules for Software Engineering specialization, 16 rules for Network Engineering specialization, and 10 rules for Multimedia specialization. Based on this research also, it was found that there were differences in the choice of courses needed for directing the selection of specializations.
AN-HRNS: AN-Coded Hierarchical Residue Number System for Reliable Neural Network Accelerators

Acceleration, power reduction, reliability and low-overhead are four major goals of neural networks. Residue number systems (RNS) intuitionally can improve all of the goals simultaneously. Especially for reliability-critical applications, the generic ability of modularization empowers the redundant RNS (RRNS) fault tolerance. However, the RRNS still suffers four critical issues including dynamic range (DR), sign detection, limited efficient moduli, and redundancy-decoding complexity. In this paper, we apply the piecewise linear units to limit the DR of next layer, stored sign-and-parity bits to make sign detection efficient, the hierarchical structure to make the best of efficient moduli, and the AN-codes for both sub-RNS error cor-rection and MMR checking. An expanded residue-to-binary converter is developed for highly reduction of the whole multi-ple-module redundancy decoder. This is the first paper to incor-porate AN codes to the RRNS applied in highly-reliable neural networks. From our evaluation under approximately equal bit-length L of moduli in a DR of hkL bits, the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$g=(hk+2)(hk+1)/2$</tex> residue-to-binary converters and g(g-1)/2 comparators in the conventional RRNS can be reduced to an expanded residue-to-binary converter with only k decoders. From the BLER anal-yses, more than 126 times of MTBF can be achieved.
Does Gender Influence Presence in Virtual Environments? An Analysis of Open-Source Igroup Presence Questionnaire Data

Technological advances in virtual reality (VR) hardware and software resulted in unparalleled realism and an increased presence. Research has sought to quantify presence in VR through surveys like the igroup Presence Questionnaire (IPQ), which holistically evaluates presence in terms of spatial presence, involvement, and experienced realism. Existing studies indicate that individual traits, e.g., gender, greatly impact the presence formation. Despite previous research indicating a gender difference, these investigations are restricted by small sample sizes, conflicting results, and limited analysis techniques. This current work performs a secondary analysis of publicly available IPQ data to evaluate gender variations in perceived spatial presence, involvement, and experience realism. An exploratory factor analysis (EFA) was performed on a random half of the sample to determine the dimensionality of variables, and a confirmatory factor analysis (CFA) was conducted on the other half to evaluate and refine the measurement model. Finally, we performed a series of measurement invariance testing for evaluating differences in the measurement model by gender and compared the latent means of perceived spatial presence, involvement, and experienced realism between females and males. The results showed that males perceive spatial presence and involvement significantly stronger than females, however, no significant difference in the perceived experience realism was found. Findings in gender differences in spatial presence and involvement are likely to result from increased computer and gaming usage for males. The implication of this work suggests that gender disparities in perceived spatial presence and involvement may result in therapeutic and educational consequences. When using VR for rehabilitation, education, or training, it is crucial to remember that males may have a stronger sense of presence, resulting in varying emotional reactions.
Design Techniques for Exploring Inclusive Automotive Interaction Design in the Age of Vehicular Automation

With the emergence of self-driving vehicles, automotive interaction design is undergoing a seismic shift. Previously, vehicle interactions have been designed around the needs of the driver, who has been assumed to be sighted, while with the rise of automation, emerging automotive technologies will need to be designed around the future operator, who may be visually impaired or have other disabilities. This changing paradigm demands new thinking around the design of automotive user experiences. Within this paper, we survey automotive interaction design techniques by exploring our research. We describe how user enactments, focus groups, participatory design, and quasi-naturalistic studies are promising approaches in the development of emerging self-driving vehicles designed with the new, potentially disabled operator in mind. We then describe 12 best practices for their use. We believe a discussion of our experiences may benefit designers interested in more inclusive automotive interaction design.
Research on preprocessing method based on cylindrical image restoration of steel pipe

Machine vision is more and more widely used in industrial inspection. In order to improve the industrial production of steel pipes, machine vision is used to replace manual inspection of steel pipe surface defects. Because the surface of the steel pipe is cylindrical, there are different degrees of deformation and information loss in the edge parts on both sides of the image when capturing the steel pipe surface image. Aiming at the deformation and distortion problems of the edge of the steel pipe image, a mathematical model is established and calculated. According to the principle of cylindrical projection, this paper deduces the expression of cylindrical orthographic projection, and puts forward a restoration algorithm of cylindrical back projection, which realizes the cylindrical expansion of steel pipe image, and solves the problems of defect shape distortion caused by the deformation of the edges on both sides of the image and the difficulty of defect detection. The results show that this method is effective and lays a foundation for steel pipe detection.
Enhanced style transfer with colorization and super-resolution

Style transfer is a novel and successful technology in the field of computer vision which allow people to create art pieces without training. This research has combined style transfer, colorization, and super-resolution algorithm to create a method to create art pieces from the black-white image as content and sketch art pieces as style with high resolution. This method could significantly lower the demand for art creation and allow people with little artistic skill to create desired artwork. Moreover, this research compares the impact of the different parameters in style transfer and the influence of the colorization in different processing stages resulting that colorizing the content image before style transfer would create a single style image that is more controllable but colorizing the generated image after style transfer would result in a more unpredictable multi-style image which depends on the training dataset of the image and the ratio of the weight in style transfer.
Invoice Recognition Method Based on Separation of Template and Content

It is a necessary task to extract and save structured information from invoices. The existing methods are all to detect and identify the duplication of invoices. Considering that there are a lot of duplicate contents and fixed table structure between invoices of the same type, this method proposes to separate the template and filled contents of invoices by pixel segmentation; The perceptual hash algorithm is used to match the template of the invoice to be tested with the invoice in the template database; After successful matching, use the improved template alignment module to align the new filled content with the template invoice, and then import the new invoice into Excel for saving. Experimental results show that compared with the original method, the text detection time, recognition time and prediction time of this method are reduced by 68%, 91.13% and 89.94% respectively, and the overall prediction time is reduced by 27.26 seconds.
Towards an Auxiliary Perception Conception for Improvement of the Situation Awareness in UAV Operation

With the development of the Human Autonomy Teaming concept in the field of UAV operations and considering the limitations of onboard sensors and perception algorithms, this paper proposes a new engineering conception of auxiliary perception. The principle is to enhance the perception capabilities of UAVs through perceiving, understanding and interpreting the environment from human. A basic structural framework of the UAV auxiliary perception system is developed, and the functions of each subsystem under this structure are analyzed in terms of information flow processing. Moreover. the information exchange system is implemented to a simulation environment through a relational database. Finally, through the experiment, the effectiveness of the auxiliary perception system is verified. Future work and shortcomings of the current approach are also pointed out.
End Face Recognition and Positioning Method in Unstable Definition Image based on Cross Entropy Estimation

The current image recognition algorithm uses the preset operator to extract the edge and uses the Hough Transform to recognize the end face, which is not applicable to the case where the image definition is unstable. This paper presents a method for recognizing and positioning the end faces in unstable definition images based on cross entropy estimation. Gaussian Mixture Model is established for the image intensity and the end face image is segmented based on the model parameters, which are estimated by EM algorithm. A probability distribution is generated to characterize the intensity distribution of the image near the end face. By calculating and minimizing the cross entropy of the image intensity distribution and the designed probability distribution, the position of the end face image feature is estimated. The experimental results show that the proposed method can extract all circular features from 30 test images within 5mm range of axial position error, which shows high robustness and adaptability. The estimated position errors of the centers of end faces are within 1/3 radius, which meets the requirements of automatic assembly process.
Graph Construction Based on Temporal Information and Frame Sampling for Human Action Recognition

With the innovation and progress of data analysis, human action recognition has become a significant research direction with broad applications in many situations. We propose a skeleton temporal graph (STG) based on graph signal processing (GSP), graph spectral domain, and human action recognition. The temporal information between adjacent frames of action data is extracted by uniform sampling and redefining temporal edge weights. We reconstruct the graph Laplacian matrix from the skeleton and temporal information. According to the graph Laplacian matrix, the coefficient matrix used for classification is calculated by spectral graph wavelet transform (SGWT). In addition, we use a recent classification method eXtreme Gradient Boosting (XGBoost), to improve experimental accuracy. Our method outperforms the existing approach when applied to three publicly available datasets.
On No-Reference Error Detection of an Image Stitching System Based on Error-Tolerance

Image stitching technology can stitch images (video frames) from multiple cameras into a single panoramic image, allowing a single image to cover a larger field of view. Image(video) stitching technology has also been widely used in real-time video applications such as online conference, VR or even Advanced Driver Assistance Systems (ADAS). Reliability of this technology is therefore of great importance. In this work, for the first time, we address the issue of error detection of an image stitching system. We show that there are inherent error-tolerability in this system, and the detection focus should be the unacceptable errors that would result in poor stitching results. In this work we also investigate the acceptability evaluation of the stitching results. In particular, a no-reference error detection technique is proposed so that the detection of unacceptable errors can be achieved in an online manner without golden data for comparison. Our experimental results show that the detection (acceptability classification) accuracy of the proposed error detection technique achieves 98.2%. In addition, the incurred performance overhead of our technique is ignorable. There is only 0.4% increase on the stitching execution time.
Thermal Infrared Tracking System with YOLOv4 Algorithm and Visual Servoing Method

This paper presents design and implementation of human object tracking system using an infrared thermal camera and servo-motor-controlled pan-tilt system on a single board computer. The implementation of the system is aimed to develop air to ground EOTS (Electro Optical Targeting System) to track moving object in a low light condition. A thermal camera is used to obtain the visual information from an object in a low light condition. By utilizing visual servoing method, YOLOv4 algorithm is used to give information about object’s position on screen which will be used to control the pan-tilt servo motor to track the object movement. The detection process is performed from approximately 5 meters from the ground. This is to simulate the air to ground environment. This system consists of the acquisition subsystem, data processing subsystem, user interface subsystem, and actuator subsystem. This paper will discuss the process of design and implementation of each subsystem, system integration and the system testing. The experiment result shows that the system is able to track an object with a 14 Frame Per Second (FPS) framerate and human objects could be detected with an 96,61% average precision score.
Dynamic Video Streaming Real-time Headcount Based on Improved YOLO V5 for Open Plazas

The difficulties of dense and dynamic movement of people can cause shelter and deformation, as well as affected by light. This paper proposes an effective measure for the detection tracking and counting of dynamic pedestrian volume in open plazas based on YOLO V5. The small target detection layer is added with the deep learning network YOLO V5, and the Cross Stage Partial (CSP) module is improved. Attention mechanism and bi-directional feature pyramid network are added, too. The experimental results show that the proposed method has high detection speed and low missed detection rate. It can accurately detect and calculate the total pedestrian flow in the open plazas. The method also has accurate identification of pedestrians in different light and weather conditions, as well as ensures real-time detection.
ShadowSync

Mission-critical, real-time, continuous stream processing applications that interact with the real world have stringent latency requirements. For example, e-commerce websites like Amazon improve their marketing strategy by performing real-time advertising based on customers' behavior, and latency long tail can cause significant revenue loss. Recent work [39] showed a positive correlation between latency long tail and variance in the execution time of synchronous invocation chains (critical paths) in microservices benchmarks. This paper shows that asynchronous, very short but intense resource demands (called millibottlenecks) outside of critical paths can also cause significant latency long tail.
EventChain

The number of fake news written by bots or malicious actors on social media is rising. One cause is the ability of users to post anything, at any place, at any time. This offers great flexibility, but it also poses the risk that users share misinformation. One type includes events that allegedly have occurred in a location, without the reporting user necessarily having been present. A user may add her location to posts to appear as an eyewitness and thus more trustworthy; however, basing that trust on commonly used and easily faked GPS locations is not reasonable.
Introduction

Modern applications are being migrated to the cloud like never before. All new applications designed using new-age architectures like microservices, Onion Architecture, and many more are hosted on the cloud. Your applications hosted on the cloud are highly available, fault-tolerant, reliable, and scalable. Cloud computing helps you save infrastructure ownership costs and operational costs to a large extent.
Research on the experimental principle of deep integration of LETS software and criminal procedure under the background of artificial intelligence

Abstract The teaching of law courses in Legal Experimental Teaching System (LETS) enables students to have a clearer understanding of the litigation process, master the format and production of various criminal litigation legal documents and train their communication and collaboration skills in litigation by cooperating to complete the experimental process. However, there are some shortcomings in experimental teaching using the LETS system, such as teachers being unable give real-time guidance and unable to reflect the principle of direct speech in litigation. Given these problems, this paper proposes a framework for the deep integration of LETS software and criminal litigation under the background of artificial intelligence (AI). First, it introduces the current situation and development of criminal litigation under the background of AI. Then, a set of AI parameters is designed based on the background of LETS software and criminal proceedings deep integration framework, and at the same time, the framework is used for intelligent learning criminal proceedings. The experimental results show that LETS software can better match the algorithm of the AI environment to push personalised criminal procedure learning courses. The experiment also shows that this framework can provide new ideas and development for the future study of criminal procedure.
Examining Cryptocurrencies Within the Framework of Sustainability

Cryptocurrencies have been attracting a significant amount of attention in the world since they were first launched in 2009. Pretending to be a decentralized finance solution, it brought out a new era in technology called blockchain. Even though the benefits did not come into action in daily routines for many to be aware of, the market and its variety kept growing. On the other hand, there are also a lot of concerns and unpredictability about the future of this technology. Especially the high energy consumption while generating blocks for mining cryptocurrencies and completing transactions is commonly being criticised. In this study, blockchain technology and the basics of mining and validation procedures such as proof of work (PoW) and proof of stake (PoS) processes will be explained, and the environmental effects of bitcoin mining will be investigated. In the perspective of environmental sustainability of cryptocurrencies, the improvement in usage of renewable energy and its side benefits will be overviewed for a better prediction on the blockchain technology future.
The Impact of Digital Transformation on Personnel Number in the Banking Sector

Digital transformation affects the banking sector as it does every sector and enables significant innovations and changes in the banking and finance market. In this sense, it aims to ensure that banking transactions can be carried out quickly, securely, and at less cost, and to achieve the highest level of satisfaction in customer service. New solutions should continue to be developed to enable customers to benefit from digital banking products in the fastest way possible. There are also negative effects of digitalization. When evaluated within the framework of the banking sector, the increasing number of digital services and the number of customers using these services may lead to a decrease in the number of bank personnel. In the study, quantile regression was applied for the period of 2010.Q1-2020.Q4. This study examines the changes that may occur in the number of personnel as a result of the digitalization process in the banking sector and determines the decreasing effect of digitalization on the number of personnel.
Digital Transformation in Higher Education Institutions

This chapter aims to explore the digital transformation in higher education institutions, especially in regards to the increased use of e-learning in recent years by identifying the parties who are affected by the transformation and the challenges people may face in implementing the use of e-learning. By using the CATWOE analysis technique, it identified that the main parties that are affected by this transformation into e-learning are the students and teachers of higher education institutions and to an extent the institutions themselves. Furthermore, through the construction of the fishbone diagram, it identified the main issues of implementing e-learning: cost, internet connectivity, lack of interaction, suitability of e-learning, and lack of knowledge. As the parties affected and challenges have been identified, this study can be used to improve e-learning processes in the future.
ICT Penetration and Entrepreneurial Activity

Entrepreneurial activity is a critical factor for performance of firms and economies. Therefore, firms and governments try to create an entrepreneurial environment given positive economic and social gains of entrepreneurship. Furthermore, governments stimulate the entrepreneurial activities through financial and legal incentives. This study investigates the mutual interaction between ICT penetration and entrepreneurial activity in 13 high income countries for the 2002-2018 period by a panel causality test which considers the cross-sectional dependence and heterogeneity. The results of panel level causality analysis reveal a bidirectional causal interaction between ICT penetration and entrepreneurial activity. However, the results of country level causality analysis uncover that interaction between ICT penetration and entrepreneurial activity differs among higher income countries depending on country specific characteristics.