Reviewerly/exHarmony huggingface dataset:
Semisupervised Classification with High-Order Graph Learning Attention Neural Network

At present, the graph neural network has achieved good results in the semisupervised classification of graph structure data. However, the classification effect is greatly limited in those data without graph structure, incomplete graph structure, or noise. It has no high prediction accuracy and cannot solve the problem of the missing graph structure. Therefore, in this paper, we propose a high-order graph learning attention neural network (HGLAT) for semisupervised classification. First, a graph learning module based on the improved variational graph autoencoder is proposed, which can learn and optimize graph structures for data sets without topological graph structure and data sets with missing topological structure and perform regular constraints on the generated graph structure to make the optimized graph structure more reasonable. Then, in view of the shortcomings of graph attention neural network (GAT) that cannot make full use of the graph high-order topology structure for node classification and graph structure learning, we propose a graph classification module that extends the attention mechanism to high-order neighbors, in which attention decays according to the increase of neighbor order. HGLAT performs joint optimization on the two modules of graph learning and graph classification and performs semisupervised node classification while optimizing the graph structure, which improves the classification performance. On 5 real data sets, by comparing 8 classification methods, the experiment shows that HGLAT has achieved good classification results on both a data set with graph structure and a data set without graph structure.
Security Assurance in Agile Software Development Methods

Agile software development was introduced in the beginning of the 2000s to increase the visibility and efficiency software projects. Since then it has become as an industry standard. However, fitting sequential security engineering development models into iterative and incremental development practices in agile methods has caused difficulties in defining, implementing, and verifying the security properties of software. In addition, agile methods have also been criticized for decreased quality of documentation, resulting in decreased security assurance necessary for regulative purposes and security measurement. As a consequence, lack of security assurance can complicate security incident management, thus increasing the software's potential lifetime cost. This chapter clarifies the requirements for software security assurance by using an evaluation framework to analyze the compatibility of established agile security development methods: XP, Scrum, and Kanban. The results show that the agile methods are not inherently incompatible with security engineering requirements.
Development of Support Program for Specialized Institutions for the Development of Free-Issuing Textbooks in Unit Schools

The purpose of this study was to refine the process of developing textbooks at the unit school level and to develop support standards programs for professional institutions for developing high-quality free-issuance textbooks. In order to derive the results of this study, five textbook experts were conducted two-time target group interviews (FGIs) and analyzed the results. The results of this study were as follows. The areas of the program to support the development of free issued textbooks at unit schools were divided into management support such as textbook development support, training and seminars, textbook development consulting, textbook development consulting, and textbook modification supplementation. The detailed programmes for each program area were as follows. First, the programs in the textbook development support area were the textbook development idea contest, textbook development support, textbook development pool provision system, and textbook development support design(LAB). Second, programs in the field of training and seminars were curriculum improvement, curriculum development, school-level textbook development specialist training(Basic), school-level textbook development specialist training (deepening), textbook development, copyright, textbook model development, and textbook efficient use. Third, programs in the consulting area were matching consulting such as textbook development consulting, textbook copyright consulting, new textbook use consulting, and textbook development publishing institutions. Fourth, programs in the counseling area include counseling on the design and operation of school curriculum, counseling on online and offline experts by the stage of textbook development, and counseling on dispute response such as copyrights and portrait rights of textbooks. Fifth, programs in management support areas such as revision and supplementation were providing information services for textbooks, on-site monitoring of textbooks, and supporting revision and supplementation of textbooks.
Sustainability of cryptocurrency in blockchain technology for business development in African Countries

The aim of the paper is to analyze the sustainability of cryptocurrency in blockchain technology in African countries for securing financial business transactions. Following the subprime crisis that shook the world economy, a new perception of money has emerged. It is a fully digital currency whose transactions are made through a distributed network. This algorithm-encrypted currency, reputed to be tamper-proof, transparent and inclusive, relies on a distributed network called the Blockchain. By comparison with traditional registers in which operations are paginated and successively recorded, transactions in blockchain technology are aggregated within the chain of blocks. It is decentralized since it is replicated on several geographic sites around the world. It enables peer-to-peer transactions, automated in real-time, reliable, secure, without intermediaries and non-repudiable. To ensure maximum security during financial transactions, blockchain miners use cryptography. This distributed system is, therefore, a major technological innovation capable of securing the financial infrastructure and mitigating failures by reducing operational risks. According to our analysis based on the Merkle tree model and blockchain energy consumption, the sustainability of cryptocurrency is a major issue for developing countries. Especially in Africa, its practicality poses a number of constraints.
Sentiment-Based Neural Network Approach for Predicting the Severity of Bug Reports

During the software maintenance process, bugs encountered by software users need to be solved according to their severity level to improve the quality of the software. Therefore, bug reports with high severity should have the highest priority to be fixed. A numerous number of bug reports are submitted daily through Bug Tracking Systems (BTS) such as Bugzilla. The bug triager examines the incoming bug reports manually and verifies whether the assigned severity level is correct or not. This manual process is time-consuming, requires much effort and is possibly error-prone. However, a limited number of research works have considered the sentiments of the bug reporters in predicting the severity of bug reports. This paper proposes two approaches based on sentiment analysis, the Lexicon-based and Multilayer Perceptron (MLP) neural network approaches. The sentiment analysis process determines and measures the sentiment words and their sentiment scores according to the popular sentiment lexicon called SentiWordNet. The proposed approaches are validated on the Eclipse open-source project, and the sentiment-based models performance is evaluated. According to the experimental results, the sentiment-based MLP outperforms the Lexicon-based approach (baseline approach). The F-measure has been improved significantly from 0.52 for the Lexicon-based approach to 0.81 when applying the MLP approach.
Fast multi-scale target detection in apron based on variable resolution mechanism

In the apron target detection, the target size is very small, and a large number of fine particle characteristics are lost in the compression process, resulting in recognition errors. Fine granularity directly affects the accuracy of target recognition. However, due to the limited energy consumption, computational power of equipment, and the insufficient feature extraction of small targets by universal target detection algorithms, the improvement of speed and accuracy of such algorithms is restricted. This paper presents a real-time detection algorithm which is named DT-YOLOV4 (difficult targets-YOLO V4) for apron target detection. By using the variable resolution mechanism, the algorithm detects the compressed image once, and then identifies the difficult targets twice. Finally, the unimportant channels in the network are trimmed based on the scaling factor of the batch normalization layer. The trimmed and slimmed algorithm has a higher speed. The test results on our airport-image data set show that the map (mean average precision) index of DT-YOLOV4 algorithm is about 21% higher than that of YOLO V4 algorithm, and the detection speed reaches 84 FPS (Frames Per Second), which is about equal to 83 FPS of YOLO V4.
Imitation Learning with Approximated Behavior Cloning Loss

Recent Imitation Learning (IL) techniques focus on adversarial imitation learning algorithms to learn from a fixed set of expert demonstrations. While these approaches are theoretically sound, they suffer from a number of problems such as poor sample efficiency, poor stability, and a host of issues that Generative Adversarial Networks (GANs) suffer from. In this paper we introduce a generalization of Behavior Cloning (BC) that is applicable in any IL setting. Our algorithm first approximates behavior cloning loss using a neural network and then uses that loss network to generate a loss signal which is minimized using standard supervised learning. We call the resulting algorithm family Approximated Behavior Cloning (ABC), introduce variants for each IL setting, and demonstrate an order of magnitude improvement in sample efficiency and increased stability in standard imitation learning environments.
Antinoise Learning and Coalitional Game GAN

Adversarial learning stability has an important influence on the generated image quality and convergence process in generative adversarial networks (GANs). Training dataset (real data) noise and the balance of game players have an impact on adversarial learning stability. In the gradient backpropagation of the discriminator, the noise samples increase the gradient variance. It can increase the uncertainty in the network convergence progress and affect stability. In the two-player zero-sum game, the game ability of the generator and discriminator is unbalanced. Generally, the game ability of the generator is weaker than that of the discriminator, which affects the stability. To improve the stability, an antinoise learning and coalitional game generative adversarial network (ANL-CG GAN) is proposed, which achieves this goal through the following two strategies. (i) In the real data loss function of the discriminator, an effective antinoise learning method is designed, which can improve the gradient variance and network convergence uncertainty. (ii) In the zero-sum game, a generator coalitional game module is designed to enhance its game ability, which can improve the balance between the generator and discriminator via a coalitional game strategy. To verify the performance of this model, the generated results of the designed GAN and other GAN models in CELEBA and CIFAR10 are compared and analyzed. Experimental results show that the novel GAN can improve adversarial learning stability, generate image quality, and reduce the number of training epochs.
Cloud Computing Implementation to Support a Disaster Recovery Plan: A Case Study of Institut Agama Islam Negeri Manado

The need for information systems and information technology is currently increasing, can be seen from the use of IS / IT to carry out important activities of an organization. Information and data are important assets for organizations that must be protected and kept confidential, to support the sustainability of an organization. With the rapid development and the increased use of technology, the opportunities for risk to information and data are also getting bigger. If an information system is not maintained, there will be the risk of disrupting the business processes of an organization. These risks can be in the form of financial losses decreased productivity and even damage to the reputation of an organization so they loss the trust of the public. As one of the universities in Manado that has quite a lot of students, the Institute of Agama Islam Negeri (IAIN) Manado have several information systems to increase the productivity of faculty, assistants and students, one of the systems is Academic Information System (SISKA). But there are several problems right now in IAIN Manado information system such as data loss, downtime system and some more. Therefore, this research tried to reduce the impact by implementing DRP. In this research two strategies were introduced that could help IAIN Manado implement DRP and preventive components on the main site as an initial precaution. All strategies are based on DRaaS in Microsoft Azure to help IAIN Manado ensure the availability of their systems.
Fast Spatial-Temporal Transformer Network

In computer vision, the restoration of missing regions in an image can be tackled with image inpainting techniques. Neural networks that perform inpainting in videos require the extraction of information from neighboring frames to obtain a temporally coherent result. The state-of-the-art methods for video inpainting are mainly based on Transformer Networks, which rely on attention mechanisms to handle temporal input data. However, such networks are highly costly, requiring considerable computational power for training and testing, which hinders its use on modest computing platforms. In this context, our goal is to reduce the computational complexity of state-of-the-art video inpainting methods, improving performance and facilitating its use in low-end GPUs. Therefore, we introduce the Fast Spatio-Temporal Transformer Network (FastSTTN), an extension of the Spatio-Temporal Transformer Network (STTN) in which the adoption of Reversible Layers reduces memory usage up to 7 times and execution time by approximately 2.2 times, while maintaining state-of-the-art video inpainting accuracy.
Content based Lecture Video Retrieval using Textual Queries: to be Smart University

The amount of lecture videos is rapidly growing due to the popularity of massive online open courses in academic institutions. Thus, the efficient method for lecture video retrieval in various languages is needed. In this paper, we propose an approach for automated lecture video indexing and retrieval. First, the lecture video is segmented into keyframes in a manner that the duplication of these frames is minimal. The textual information embedded in each keyframe is then extracted. We consider this issue as a matter of text detection and recognition. The text detection is solved by our segmentation network in which we propose a binarization approach for optimizing text locations in an image. For text recognition, we take advantage of VietOCR to recognize both English and Vietnamese text. Lastly, we integrate a vector-based semantic search in ElasticSearch to enhance the ability of lecture video search. The experimental results show that our approach gives high performance in detecting and recognizing the text content in both English and Vietnamese as well as enhancing the speed and accuracy of lecture video retrieval.
The Use of Augmented and Virtual Reality in the Study of Circuitry and Household Appliances

The paper discusses the possibilities and advantages of using augmented and virtual reality to study household appliances and circuitry. The analysis of examples of the application of this technology in education is carried out. Examples of the application of this technique are given. The methodology of using augmented reality in the organizational and educational process of a technical university is presented. One of the options for implementing the methodology in educational activities in the format of a multilevel game is considered. For example, we take 3D models of a circuit board, a refrigerator, and a microwave oven. Their devices and principles of action in the format of three-dimensional figures and 2D models are considered. It gives the student the opportunity to explore the actual appearance of each component of complex systems.
PSG-GAN: Progressive Person Image Generation with Self-Guided Local Focuses

This paper proposes PSG-GAN, a novel Generative Adversarial Network for pose-guided person image synthesis, which can progressively generate realistic person images of desired poses together with corresponding semantic segmentation masks. Specifically, PSG-GAN consists of a sequence of Region-Focal Transfer Blocks (RFBs) where each contains two generation pathways: the appearance generation pathway and the semantic generation pathway. The former pathway is responsible for generating the target image by explicitly preserving appearance-related features within certain regions, where local region transformations are considered. The latter pathway is used to generate semantic masks which define the areas for the local transformations to attend to. These two learning pathways work together and reinforce each other to simultaneously generate the target image and semantic masks progressively. Qualitative and quantitative experimental results on two benchmark datasets demonstrate PSG-GAN’s superiority over other approaches in generating realistic person images in pose transfer tasks.
Factor analysis of the geochemical associations in the Plavica ore deposit, Republic of North Macedonia

The aim of this study is to investigate the geochemical associations in the Plavica deposit in Republic of North Macedonia. The analyses of drill core samples from the detail exploration works were statistically processed to determine the groups of chemical elements with common spatial distributions. The resulting geochemical groups represent different stages of the ore forming hydrothermal processes. The main ore elements are represented by geochemical association of ([As, Sb, Au, Sn] Cu, Bi, Fe, Ag) which group outlines the ore bodies.
Concealed Objects in a Scene: A Closer Look at the Localization Block of Object Detection Models

Object detection models typically accomplish two tasks, the localization and classification of objects. Object localization algorithms have come a long way in the advancement of computer vision applications. However, object detection models still face challenges in localizing objects that are concealed in a scene. In this paper, we investigate the localization block of different object detectors when detecting these types of objects. Our results show that for localizing objects with high color and texture similarity with its immediate background, region proposal networks perform better than single-stage networks. To relate such blending characteristics of objects with its environments, as one possible cause of misdetection, we examine the characteristics of commonly misdetected objects with high color and texture similarity with its background. Statistical study reveals that commonly misdetected objects have significantly similar color and texture features than those of the detected objects. This implies that color and texture similarity relate to misdetection in object detection. To the best of our knowledge, this work presents the first empirical study on localizing objects with high color and similarity with its background.
A Computer-Aided Diagnoses Program for Leukemia Detection Using Blood Samples

During the last decades, the number of people given a diagnosis of leukemia had increased significantly. This article aims to automate detection from peripheral blood based on artificial vision methods for more speed and accuracy. The proposed method is applied to data sets from different sources to achieve diversity, which was not pursued before. This required implementation of noise removal, enhancing, and segmentation techniques on the images by applying linear scaling filter, global-local contrast enhancement, and marker-controlled watershed and K -means clustering for each step, respectively. One hundred three features of texture, shape, and color were extracted from cell, nucleus, and cytoplasm subimages, followed by a reduction to 50 most relevant features. The support vector machine model was trained seeking a suitable accuracy to be used in practical experiments. Here, we achieved an accuracy of 91.0256% by implementing the steps previously mentioned in a MATLAB executable program. The software detected leukemia in 5 of 6 images from a new data set, which indicates the usefulness of this approach despite the variation in images.
The Design and Implementation of an Improved Lightweight BLASTP on CUDA GPU

In the field of computational biology, sequence alignment is a very important methodology. BLAST is a very common tool for performing sequence alignment in bioinformatics provided by National Center for Biotechnology Information (NCBI) in the USA. The BLAST server receives tens of thousands of queries every day on average. Among the procedures of BLAST, the hit detection process whose core architecture is a lookup table is the most time-consuming. In the latest work, a lightweight BLASTP on CUDA GPU with a hybrid query-index table was proposed for servicing the sequence query length shorter than 512, which effectively improved the query efficiency. According to the reported protein sequence length distribution, about 90% of sequences are equal to or smaller than 1024. In this paper, we propose an improved lightweight BLASTP to speed up the hit detection time for longer query sequences. The largest sequence is enlarged from 512 to 1024. As a result, one more bit is required to encode each sequence position. To meet the requirement, an extended hybrid query-index table (EHQIT) is proposed to accommodate three sequence positions in a four-byte table entry, making only one memory access sufficient to retrieve all the position information as long as the number of hits is equal to or smaller than three. Moreover, if there are more than three hits for a possible word, all the position information will be stored in contiguous table entries, which eliminates branch divergence and reduces memory space for pointers to overflow buffer. A square symmetric scoring matrix, Blosum62, is used to determine the relative score made by matching two characters in a sequence alignment. The experimental results show that for queries shorter than 512 our improved lightweight BLASTP outperforms the original lightweight BLASTP with speedups of 1.2 on average. When the number of hit overflows increases, the speedup can be as high as two. For queries shorter than 1024, our improved lightweight BLASTP can provide speedups ranging from 1.56 to 3.08 over the CUDA-BLAST. In short, the improved lightweight BLASTP can replace the original one because it can support a longer query sequence and provide better performance.