Reviewerly/exHarmony huggingface dataset:
Comparison of software defect prediction models based on machine learning

Abstract Software defect prediction has been widely used in software system development, among which the method based on machine learning has proved to be more effective. Firstly, the basic framework of the prediction model and the metric elements used in the prediction process are introduced in this paper. Secondly, the three main machine learning-based software defect prediction models (LR, SVM, and BPNN) are analyzed, and finally the prediction effects of the three models are compared and analyzed by using the experimental results of MDP.
The Factor Analysis of Online Learning Barriers on Learning Evaluation Course During Covid 19 Pandemic

Abstract In covid 19 pandemic, since march UNP through UNP rector circular letter no 1593/UN35/AK/2020 obligates the practise of online learning using UNP platform. This decision surely becomes one barrier for some parties in the term of practise. Lecturer readiness in preparing the design of right message for online learning is main point. Even though the socialization related to the design of message in online learning has been long implemented, but it is not fully practised, it becomes particular burden for lecturer. This barrier can be seen from the utilization of synchronous which is not utilized yet, as result some of lecturer are still practising e-learning like face to face lecture i.e same time lecture. It emerges barrier for students. Based on the result of analysis done from the shared questionnaire, the dominant barriers perceived by the students is the readiness of material which is minimum which are available on e-learning, such as e-book, e-module, and the right recommended of e-resources. One thing that must be prepared by university in preparing digital material from various courses is motivating the lecturer to produce the sources of digital (Shurygin, V. Y., &amp;Krasnova, L. A. (2016)). Network aspect and technical problems is aspect which is improvement in the institution which is compulsory to be able to do fast improvement in line with 4,0 revolution era which require the high adoption level on e-resources digital literacy.
Path Planning for Unmanned Surface Vehicle based on genetic algorithm and sequential quadratic programming

Path planning and obstacle avoidance of Unmanned Surface Vehicle (USV) is one of the hottest research topics in modem national defense and ocean engineering. Considering the issue of obstacle-free path planning of USV, this paper focuses on a 3-DoF USV and develops an algorithm design. We adopt Gauss pseudo-spectral method to discretize control model and make use of a hybrid algorithm to optimize which combines the advantage of genetic algorithm and sequential quadratic programming algorithm. Simulation results show that this method can quickly explore a high-precision route in an unknown environment which meets the mobility requirement of USV without setting the initial value artificially.
Mitigating Dataset Imbalance via Joint Generation and Classification

Supervised deep learning methods are enjoying enormous success in many practical applications of computer vision and have the potential to revolutionize robotics. However, the marked performance degradation to biases and imbalanced data questions the reliability of these methods. In this work we address these questions from the perspective of dataset imbalance resulting out of severe under-representation of annotated training data for certain classes and its effect on both deep classification and generation methods. We introduce a joint dataset repairment strategy by combining a neural network classifier with Generative Adversarial Networks (GAN) that makes up for the deficit of training examples from the under-representated class by producing additional training examples. We show that the combined training helps to improve the robustness of both the classifier and the GAN against severe class imbalance. We show the effectiveness of our proposed approach on three very different datasets with different degrees of imbalance in them. The code is available at https://github.com/AadSah/ImbalanceCycleGAN .
High Imperceptibility Medical Image Watermarking Scheme Based on Slantlet Transform by using Dynamic Visibility Threshold

The following topics are dealt with: computer aided instruction; virtual reality; Internet; feature extraction; diseases; educational institutions; learning (artificial intelligence); face recognition; smart phones; and teaching.
Spectral Correspondence Framework for Building a 3D Baby Face Model

Early detection of facial dysmorphology - variations of the normal facial geometry - is essential for the timely detection of genetic conditions, which has a significant impact in the reduction of the mortality and morbidity associated with them. A model encoding the normal variability in the healthy population can serve as a reference to quantify the often subtle facial abnormalities that are present in young patients with such conditions. In this paper, we present the first facial model constructed exclusively from newborn data, the Baby Face Model (BabyFM). Our model is built from 3D scans with an innovative pipeline based on least squared conformal maps (LSCM). LSCM are piece-wise linear mappings that project the training faces to a common 2D space minimising the conformal distortion. This process allows improving the correspondences between 3D faces, which is particularly important for the identification of subtle dysmorphology. We evaluate the ability of our BabyFM to recover the babys facial morphology from a set of 2D images by comparing it to state-of-the-art facial models. We also compare it to models built following an analogous pipeline to the one proposed in this paper but using nonrigid iterative closest point (NICP) to establish dense correspondences between the training faces. The results show that our model reconstructs the facial morphology of babies with significantly smaller errors than the state-of-the-art models (p = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> ) and the “NICP models” (p <; 0.01).
On the Challenges of Automated Testing of Web Vulnerabilities

Pentesting is an important process to reduce the risks associated with attacks in Web applications. However, testers may find some difficulties while conducting this process. This paper aims to discuss the main challenges founded while performing pentesting. For this, an exploratory study was conducted with five pentesting tools and seven Web applications. We analyzed and executed these tools and applications, and identified and evaluated some of their characteristics and functionalities. Preliminary results showed that one of the major challenges relates to pentesting tools execution in Web applications is the poor standardization of input parameters in the authentication process.
A Lightweight Blockchain-Based Trust Model for Smart Vehicles in VANETs

Nowadays, vehicular networks can play a pivotal role in providing an efficient and safe traffic transportation by bringing a reliable platform for transmitting messages among participants. However, we are facing an integral challenge in trusting received messages. In this paper, we propose a lightweight blockchain-based decentralized trust model for preserving the privacy in vehicular ad hoc network (VANET). So, smart vehicles use reputation score of senders and the distance between sender and the location of reported event to evaluate the reliability of each received message. Thus, they are able to make an appropriate decision about the correctness of received messages by using Dempster-Shafer theory. Afterwards, nodes generate a trust value for each received message from the source vehicle and Roadside Units (RSUs) aggregate uploaded trust values from vehicles to accomplish trustful reported events. Eventually, RSUs store the verified and reliable messages into the cloud servers and add the hash of the data into blockchain to assure that our data will not be manipulated. An algorithm is proposed to punish or reward smart vehicles based on their historical experience to restrain the dissemination of false messages in the network. The experimental analyses reveal that the proposed trust model can set up a trust model to deal with imprecise data in vehicular networks.
A Modified Whale Optimization Algorithm for Electromagnetic Inverse Problems

Whale optimization algorithm (WOA), inspired from the natural hunting behavior of humpback whales, is a stochastic searching algorithm for electromagnetic inverse problem and other optimization problems. However, the algorithm may converge very slowly and the exploration reliability of it needs to be improved when dealing with multimodal and complex problems. Thus, some improvements are introduced. Initially, a farthest individual first dynamic topology (FIFDT) is put forward to better balance the exploration and exploitation searches. Additionally, an adaptive fully informed learning mechanism (AFILM) is applied to avoid premature convergence and the decrease of convergence speed. Meanwhile, a gradient descent based accelerated search (GDBAS) is adopted to improve the convergence speed of the algorithm. Finally, a chaotic map based population mutation (CMBPM) is added to enhance the exploration capability in order to find the global optimum solution. Experiments have been conducted on well-known multimodal benchmark test functions and an electromagnetic inverse problem. The numerical results show the merit and efficiency of the proposed modified WOA.
Application of "Face Recognition" Technology for Class Room Electronic Attendance Management System

The attendance management system is a system that needed for learning activity in a University. In some University, the attendance management system has been used tapping system which using NFC technology. Actually, that attendance management system is effective for managing each of attendance information. However, until now we can still see many cheats for this attendance system. For example, leave their cards to their classmates and tell them to tap for them. For that, one of the many effective solutions to resolve that problem is to add face recognition technology in the current attendance system. In our experiment, we know that to add face recognition to the current attendance management system surely need a camera and also face dataset for the system. At the beginning, we need at least 9 images with different emotions and face positions to let the system recognizes one's face. To make this system more accurate at recognizing one's face, we would update the face dataset in every face recognizing process.
Track Association Method Based on Target Mutual-Support of Topology

A new target association method based on target Mutual-Support of Topology model is proposed to solve the association problem with dense flying targets. In the method, the Mutual-Support Matrixes are defined, and the proper target relationship can be achieved by optimal method with restrict condition. The validity and robustness of the algorithm are verified by Monte-Carlo simulation, which shows good performance of the algorithm in different environments with dense flying targets.
State evaluation of secondary equipment based on combination weighting and cloud model

In view of the complex fault mechanism of secondary device on intelligent substation, and traditional single index was difficult to mine the potential risk of the equipment, according to the data that the secondary equipment can collect, this paper proposes a secondary equipment status assessment method based on combination weighting method and cloud model. Firstly, the comprehensive state assessment indicator set of secondary device is constructed, and then weight of each index will be obtained by using combination weighting method of AHP with anti-entropy theory. Finally, membership function is replaced by cloud model for achieving evaluation of secondary equipment status. The evaluation outcome indicates that this model works well for condition assessment of intelligent substation secondary equipment.
Privacy in the Era of 5G, IoT, Big Data and Machine Learning

Technological advances, such as IoT devices, cyberphysical systems, smart mobile devices, cloud systems, data analytics, social networks and increased communication capabilities, are making possible to capture, quickly process and analyze huge amounts of data from which to extract information critical for many tasks, such as healthcare and cyber security. Such an intensive use of data raises however major privacy concerns, especially in the mobile application ecosystem. In this paper, we first show different examples of privacy breaches that can arise in such ecosystem. Those examples show that achieving privacy is challenging and there is no unique technique that one can use; rather one must combine different techniques depending also on the intended use of data. We then outline critical challenges towards achieving comprehensive privacy solutions.
AIC-GAN: An Auxiliary Information Classification GAN for Learning Deep Models

Deep learning models have shown excellent performance on several problems, but their training process often requires a great amount of computational resources, which limits its applications. To solve this problem, a common method is to use data augmentation. Specifically, augmentation methods based on GANs have been greatly studied in recent researches. To be specific, we propose a novel generative model named Auxiliary Information Classification Generative Adversarial Networks (AIC-GAN), which introduces latent space information from the data source domain to the data target domain. To verify our proposed AIC-GAN, we performed experiments on four datasets, MNIST, Fashion-MNIST, CIFAR-10 and the SVHN. The classification results on these datasets through the extended generative adversarial network show that, compared with the mainstream classification algorithm and the state-of-the-art GAN models, the proposed method could achieve better classification performance.
Application of Personalized Ranking Models Based on Expert Evaluations for Sorting Goods on E-commerce Web Resources

The article describes the application of a personalized model for ranking search results based on expert evaluations when ranking goods for e-commerce resources. The role of experts is played by system users, with a high rate of agreement on opinions relative to the current user. The measure of consensus is determined according to a model built using the generalized iterative algorithm of the Group Method of Data Handling GIA GMDH. This model is based on an analysis of the users’ social and personal characteristics.
Visually Analyzing and Steering Zero Shot Learning

We propose a visual analytics system to help a user analyze and steer zero-shot learning models. Zero-shot learning has emerged as a viable scenario for categorizing data that consists of no labeled examples, and thus a promising approach to minimize data annotation from humans. However, it is challenging to understand where zero-shot learning fails, the cause of such failures, and how a user can modify the model to prevent such failures. Our visualization system is designed to help users diagnose and understand mispredictions in such models, so that they may gain insight on the behavior of a model when applied to data associated with categories not seen during training. Through usage scenarios, we highlight how our system can help a user improve performance in zero-shot learning.
Dietary Habits for Toddler Growth using Particles Swarm Optimization Algorithms

The age grouping of children used in the health program at the Indonesian Ministry of Health, the term baby (0 to less than one year), toddler (1 to 3 years), and preschool (3 to 5 years). Giving the wrong food to children can cause children to experience malnutrition. The danger of malnutrition in children for a long time can cause stunting. Stunting is a condition of growth failure in children (body and brain growth) due to a lack of nutrition for a long time. Thus, the child is shorter than normal children his age and has a delay in thinking. Malnourished children do not only have an impact on health complaints in the future. But it also makes children unable to grow optimally, thus interfering with their productivity when they grow up. Nutritional needs are an important factor during a child's growth period. In preparing a diet, you must pay attention to a balanced and varied menu, including the type of food in an appropriate amount. To meet the body's nutritional needs for the maintenance of cells in the body and the process of child development. Therefore, an application was created that can provide a combination of menus and dietary patterns for toddler and preschool age. This application uses the Particle Swarm Optimization algorithm in finding the best menu combination according to the child's age and physical condition. The results obtained in this study are dietary suggestions for toddlers and preschool age. The system test results have an accuracy of 90%.
Multi-Metric Induced Robust Graph

Graph-based learning model has a wide range of applications in machine learning and computer vision.The key issue of the graph-based applications is to construct an informative graph to effectively represent data correlations.In practice, real-world data is usually contaminated by complex noise beyond Gaussian noise and sparse noise, which degrades learning performance dramatically.To construct a robust graph that represents real-world data distribution well, we propose a novel graph construction method.The proposed method is designed to be robust to complex noise beyond sparse or Gaussian noise by combining different previously given metrics of measuring data errors, which robustly and correctly generates a set of edges.To demonstrate the superiority of the proposed method, we employed the clustering and classification tasks on the real-world datasets.The experimental results demonstrate that the proposed method is superior to the existing methods in both accuracy and robustness to complex noise.
Research Professors’ Self-Assessment of Competencies

Research professors develop scientific products that impact and benefit society, but their competencies in doing so are rarely evaluated. Therefore, by employing a mixed two-stage sequential design, this study developed a self-assessment model of research professors’ competencies with four domains, seven competencies, and 30 competency elements. Next, we conducted descriptive statistical analysis of those elements. In the first year, 320 respondents rated themselves on four levels: initial, basic, autonomous, and consolidated. In the assessment model’s second year, we compared 30 respondents’ results with those of their initial self-assessment. The main developmental challenge was Originality and Innovation, which remained at the initial level. Both Training of Researchers and Transformation of Society were at the basic level, and Digital Competency was at the autonomous level. Both Teaching Competence and Ethics and Citizenship attained the consolidated level. This information helps establish priorities for accelerating researchers’ training and the quality of their research.
A File Distribution Mechanism based on Netty Communication Framework

Abstract The efficiency of file distribution in large-scale data processing platform is essential besides system security. Traditional CIFS file sharing method cannot meet the requirement due to its lack of security and ability to distribute fragile files. This paper introduces an innovated file distribution mechanism based on Netty framework. It utilizes zero-copy mode provided by Netty, along with multi-thread asynchronous communication. Experiment shows that it outperforms CIFS in real-time applications.
Design and Implementation of the Interactive Virtual Reality Touring System - A Case Study of Shulin Ji'an Temple in Taiwan

Most current tour guiding methods for Taiwanese temples employ graphic webpage frameworks combined with captioned pictures for introduction. This type of tour guiding lacks interactive presence. In addition, the audience may not be able to focus on browsing webpages or learn essential information from the introduction. This study adopted the Delphi method to evaluate the current developed system. This system was aimed at designing VR-based interaction that differs from conventional tour guiding methods to aid users in viewing the display space from their viewpoints. Users can not only control camera view angles but also select the paths and guiding information as if they were walking in the temple. The analysis results revealed that in general, the users perceived Web VR tour guiding as convenient and easy to use. The display and content of the tour guiding system presented clear information to the users, aiding them in gaining further understanding of the introduced item. Finally, the study results can serve as a reference for design research on VR applications in tour guiding.
DeepVI: A Novel Framework for Learning Deep View-Invariant Human Action Representations using a Single RGB Camera

In this paper, we address the problem of cross-view action recognition from a monocular RGB camera. This topic has been considered extremely challenging due to the lack of 3D information in 2D images. Exploiting the advances in 3D pose estimation from a single RGB camera, we propose a new framework termed DeepVI, for cross-view action recognition without the need for pose alignment. Virtual viewpoints are used to augment the variability of training data along with the use of an end-to-end Deep Neural Network (DNN). The proposed network is composed of two modules. The first one, called SmoothNet, implicitly smooths skeleton joint trajectories using revisited temporal convolution in order to reduce the noise in the estimated 3D skeletons. The second module consists of a state-of-the-art approach designed for action recognition based on Spatial Temporal Graph Convolutional Networks (ST-GCN [40]). Experiments have been conducted in cross-view settings on two datasets, namely, NTU RGB-D and Northwestern-UCLA. The obtained results show the effectiveness of the proposed framework.
Auction-based Truthful Distributed Resource Allocation for Smart Grid Systems

As the continued use of fossil fuels exacerbates the effects of climate change, various government protocols are calling for increases in the utilization of renewable energy sources. Recent improvements in the efficiency of photovoltaic (PV) cells and energy storage system (ESS) capacity have prompted many electricity consumers to install such systems and become energy prosumers. Peer-to-peer (P2P) energy trading between consumers of energy deficit and prosumers with surplus energy reserves is desirable to provide incentives to install clean energy resources and achieve balance between supply and demand. This paper proposes the application of the Vickery-Clarke-Groves (VCG) auction-based trustful algorithm on such P2P energy trading. By doing so, improved auction environments for truthful energy bids and optimal energy resource allocation can be established.