Reviewerly/exHarmony huggingface dataset:
Formal Definition and Automatic Generation of Semantic Metrics: An Empirical Study on Bug Prediction

Bug prediction is helpful for facilitating bug fixes and improving the efficiency in software development and maintenance. In the past decades, researchers have proposed numerous studies on bug prediction by using code metrics. However, most of the existing studies use syntax-based metrics, there exists little work building bug prediction models with semantic metrics from source code. In this paper, we propose a new model, semantic dependency graph (SDG), to represent semantic relationships among source files. Based on the SDG, we formally define a suite of semantic metrics reflecting semantic characteristics of a project’s source files. Moreover, we create a tool to automate the generation of our proposed SDG-based metrics. Through our experimental studies, we have demonstrated that the SDG-based semantic metrics are effective for building bug prediction models, and the SDG-based metrics outperform traditional syntactic metrics on bug prediction. In addition, models using the SDG-based metrics could achieve a better prediction performance than two state-of-the-art models that learn semantic features automatically. Finally, we have also presented that our approach is applicable in practice in terms of execution time and space.
Proxy Re-Encryption Using Vector Decomposition

In the present world, Internet has become inevitable component for communication. But the security of messages sent through Internet highly relies on the computational ability of users. Hence, many situations demand the involvement of a third party. The amount of trust required on this third party is an important concern. Proxy re-encryption allows a third party to alter the ciphertext sent by the sender without decrypting it, and the receiver can decrypt the re-encrypted ciphertext. At the end of the algorithm, the receiver can successfully retrieve the message sent by the sender. We propose a proxy re-encryption scheme which restricts the access of users using role-based access control (RBAC). In our scheme, the proxy re-encryption is performed by the master authority in the network. The security of our scheme is based on the hardness of vector decomposition problem (VDP).
Creating from Anywhere with an Artist-Driven Approach to Workstation Deployment

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Netflix is a prolific producer of visual effects and original animated content. To meet that demand, the studio needs to attract the world’s best artistic talent. Artists like to create groundbreaking entertainment instead of worrying about getting access to the software or source files they need. Production infrastructure engineering (PIE) at Netflix designed remote workstations tailored to artists’ needs that make it easy just to start working, whether in an office in Mumbai or at a house in Vancouver. The technical needs of fast-paced productions can vary vastly and change rapidly. PIE not only needed to make it easy for artists to have a one-click experience, but also for developers to control and customize that experience. This is a technical case study—not just on the how, but on the why. Why were technologies selected, what challenges did they present, and how is this different from an off-the-shelf solution? And where does the industry go from here?</i>
Deploying Kalahok 1.0: Profiling Disaster-Stricken Communities Towards Intervention Initiatives

In this paper, we present the initial deployment of Kalahok 1.0 - a customizable, multilingual, and deliberative web-based participatory toolkit towards informed and data-driven intervention initiatives for disaster-stricken communities in Marawi City, Philippines. A total of 1,168 participants from 9 local communities responded to the survey. Thematic analysis using Biterm Topic Modeling revealed that the needs of the communities can be classified into relief goods, potable water, housing, and employment. The timely feedback to the community partner allowed them to formulate interventions related to the immediate needs, and to plan in addressing the long-term targets.
목포항의 발전 방안에 관한 연구

The aims of this study to analyze the internal and external environment of Mokpo Port from a port marketing perspective and to establish plans for development of Mokpo Port. In order to do this, 16 detailed factors for SWOT Matrix were identified both from previous studies and from brainstorming professionals of the port and these made into a 3 level hierarchy structure. AHP method identified the relative weight of SWOT level as strength(0.152), weakness(0.345), opportunity(0.179) and threat(0.324), also the final ranking of detailed factors by SWOT group was reduction of port volume(0.104), global economic slump(0.095), deterioration port facilities(0.084), promotion of offshore wind plant in southwest area(0.076) and increased competition with neighboring ports. Based on these empirical analysis results, the development plan of Mokpo Port was presented in order of W/T, W/O, S/T, and S/O strategies by priority.
Construction of Multimedia Network English Teaching System Based on Cloud Computing

With the perfect combination of multimedia technology, network technology and cloud computing technology, college English education (CET) is no longer limited to the teacher-centered classroom teaching mode, and the learning environment has undergone radical changes. Cloud computing platform can realize multi-person collaboration, create, edit and share documents and knowledge, which conforms to the characteristics of constructivism and collaborative learning theory, and can create a collaborative environment with strong learning atmosphere. The current college English classroom teaching mode is difficult to meet the requirements of the current reform, and finding new teaching methods has become an important topic at present. This paper analyzes the present situation of CET under the multimedia network environment, and discusses and studies the countermeasures of CET reform under the multimedia network environment based on cloud computing technology. So as to provide theoretical support for the development of CET.
Research on the Design of Music Assistant Teaching System Based on Web Service

research-article Research on the Design of Music Assistant Teaching System Based on Web Service Share on Author: Jing Yao Jiangxi Normal University College, China Jiangxi Normal University College, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2027–2030https://doi.org/10.1145/3482632.3484092Online:22 November 2021Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An Age Invariant Face Recognition Algorithm Based on Decorrelated Feature Decomposition

As people grow old, the intra-class difference caused by face aging is also more and more large, which has a great impact on the effect of face recognition. However, the identity feature obtained by the feature decomposition of the previous AIFR algorithms were always doped with age-dependent feature to a certain extent, which failed to further reduce the intra-class differences caused by age factors. To overcome this problem, a new algorithm is proposed. Firstly, in order to extract the initial facial feature of the face, the full pre-activation residual block is used to construct the backbone of the model, which effectively improves the generalization ability of the model; Secondly, in order to realize feature decomposition, age-dependent feature are extracted through two fully connected layers, and age-independent feature are obtained by subtracted age-dependent feature from initial facial feature. Finally, in order to reduce the intra-class differences caused by age factors, the Euclidean distance between age-dependent and age-independent feature is maximized to further reduce the correlation between them. A large number of evaluation experiments have been conducted on the public age-invariant face recognition (AIFR) datasets (Morph and CACD-VS) to verify the effectiveness of proposed method.
Can Existing Image Quality Metrics Assess Enhanced Images?

Most of image quality metrics are designed for degraded images, and their performance are highly correlated with the human visual system. The rapid development of image enhancement techniques raises a new issue: how to evaluate the performance of image enhancement methods? It is obvious that many benefits can be obtained if existing image quality metrics can be applied to the performance evaluation of image enhancement algorithms. However, quite a few studies have investigated such an issue. In this paper, we conduct psychophysical experiments to investigate the performance of existing image quality metrics on enhanced images. Two databases that specifically designed for enhanced images are selected in the experiments for the collection of subjective evaluation results. The objective evaluation results are obtained from six selected image quality metrics. By calculating the correlation between subjective and objective results we can answer the question: the performance of existing selected image quality metrics is low on enhanced images.
Spread of COVID-19 Deaths in Jakarta: Cluster and Regression Analysis

Jakarta as the center of the capital city of Indonesia has a very high mobility and population density. This has resulted in the spread of COVID-19 cases also have a very high increasing trend. Regional clustering and the detection of variables that affect COVID-19 deaths can be an early warning or the basis for government policies in handling the spread of disease outbreaks. This study aims to classify areas at the subdistrict level in Jakarta based on distribution of COVID-19 cases using the K-Means method. After the regional clusters were formed, Bayesian regression analysis was carried in each cluster and sub-district to identify variables that had an effect on COVID-19 deaths. The number of deaths is assumed to have Normal distribution, and statistical inference in Bayesian regression using the Integrated Nested Laplace Approximation (INLA) approach. This study produced several interesting results including: (1) there are 4 clusters that indicate areas prone to spread with a high case rate, fairly high risk, low risk to very low risk areas. (2) most of Jakarta's sub-districts, which is about 45%, are included in areas with a fairly high risk of spreading. (3) In general, the number of recovered cases is a significant variable on the majority decrease number of COVID-19 deaths in each cluster.
Research on the Construction and Application of Interactive Oral English Mobile Learning Model Based on Internet WeChat Technology

With the increasing popularity of Smart phones, as a social tool and interaction platform WeChat with advantages of convenience, cross-platform, zero cost becomes important means of communication of college student's life and learning. More and more scholars pay attention to the study of the mobile learning model based on WeChat. Through the analysis of the definition of related concepts and theories based on WeChat, this present paper deals with functions and promotion of mobile learning in improving oral English autonomous learning in the application as well as offers some suggestions.
A Preliminary Study on Vowel Recognition via CNN for Disorder People in Malay Language

Stroke is one disease showing an increment trend as people live their lives in a stressful manner. Rehabilitation is one of the procedures to recover the patient to a normal condition. The rehabilitation process and activities require an extended period to retrain back the patient's capability, speak, listen, walk, etc. For this, a dedicated physiotherapy procedure was conducted according to the rehab trainer and expertise. One of the rehabilitations is to help the patient to have back their speaking skill and capability. The rehabilitation activities are generally conducted manually through manual listening and teaching the stroke patient periodically by the rehab trainer. The manual rehabilitation activities physically require the rehab trainer's presence, documentation, and manual data recording. This manual activity could be challenging when we face a lack of trainers and the situation of many patients with less trained in the field. Therefore, an intelligent system could be an alternative for rehabilitation to provide the user-friendly and straightforward technique to learn, repeat, and evaluate. In the paper, as the preliminary study, we proposed a smart vowel recognition for Malay Language using Convolutional Neural Network (CNN). We also proposed a new Malay Language dataset consist of 5 vowels, /a/, /e/, /i/, /o/ and /u/ for the use of future research. The result shows that the vowel recognition using this dataset is comparable and suitable for recognizing the vowel type.
Innovative Research on the Development of Online Education Mode of Internet Thinking Based on the Discrimination of Learning Attention under the Analysis of Head Posture

With the rapid development of Internet technology and the popularity of 5G and broadband, online education in China, especially mobile online education, is in full swing. Based on the development status of online education in China, this paper analyzes the innovative application of learning attention discrimination based on head posture analysis in the development of online education mode of Internet thinking. Learning attention is an important factor of students’ learning efficiency, which directly affects students’ learning effect. In order to effectively monitor students’ learning attention in online teaching, a method of distinguishing students’ learning attention based on head posture recognition is proposed. In the tracking process, as long as the head angle of the current frame is close to the head angle of the key frame in a certain scale model, the visual angle apparent model can reduce the error accumulation in large-scale tracking. A Dynamic Bayesian Network (DBN) model is used to reason students’ Learning Attention Goal (LAG), which combines the relationships among multiple LAGs, multiple students’ positions, multicamera face images, and so on. We measure the head posture through the similarity vector between the face image and multiple face categories without explicitly calculating the specific head posture value. The test results show that the proposed model can effectively detect students’ learning attention and has a good application prospect.
Application and Research of Computer Big Data Based on Structure in Internet Learning

With the advent of the era of big data, many teachers began to adopt online learning teaching methods, and many enterprises and schools also developed a variety of learning systems, examination systems and live broadcast systems, which broke away from the traditional teaching mode and established a brand-new learning system. The application of big data in the education industry is very prominent. Adding Internet thinking in the education process can enable students to learn relevant knowledge online after class, consolidate knowledge and exercise students' self-learning ability. As the core component of the computer application field, online teaching has outstanding advantages. Learners can talk with famous teachers anytime and anywhere, work out their own learning plans, and eliminate knowledge blind spots. This paper mainly discusses the application of computer big data in Internet learning, and analyzes the application process of computer big data in Internet learning based on the structure level, aiming at using Internet technology to achieve efficient learning.
Effect of Probabilistic Similarity Measure on Metric-Based Few-Shot Classification

In developing a few-shot classification model using deep networks, the limited number of samples in each class causes difficulty in utilizing statistical characteristics of the class distributions. In this paper, we propose a method to treat this difficulty by combining a probabilistic similarity based on intra-class statistics with a metric-based few-shot classification model. Noting that the probabilistic similarity estimated from intra-class statistics and the classifier of conventional few-shot classification models have a common assumption on the class distributions, we propose to apply the probabilistic similarity to obtain loss value for episodic learning of embedding network as well as to classify unseen test data. By defining the probabilistic similarity as the probability density of difference vectors between two samples with the same class label, it is possible to obtain a more reliable estimate of the similarity especially for the case of large number of classes. Through experiments on various benchmark data, we confirm that the probabilistic similarity can improve the classification performance, especially when the number of classes is large.
Chinese Remainder Theorem-based Essential Secret Image Sharing

It is well known that the shares generated by most secret image sharing (SIS) schemes are of equal importance. However, in some essential SIS (ESIS) schemes, the generated shares are divided into two groups of different importance. The shares in the more important group are called essential shares, while the shares in the other group are called nonessential shares. In the phase of decryption, the secret image can be decrypted by a sufficient number of essential shares and some non-essential ones. Currently, the existing methods for constructing ESIS schemes have some shortcomings, such as explicit codebook requirements, size expansion, different share sizes, concatenation of sub-shares, and high computational complexity. To eliminate these drawbacks, a Chinese remainder theorem (CRT)-based ESIS scheme is proposed in this paper. The generated shares by the proposed approach have the equal size, and are not formed by concatenating sub-shares. Furthermore, the proposed approach has a low computational complexity since only modular operation is required in the decryption process. Finally, the experimental and comparative results show the validity and superiority of the proposed approach.
Software Complexity Reduction through the Process Automation in Software Development Life Cycle

Numerous software complexity metrics have been introduced to quantify the software complexity in terms of different attributes considered in its written source code. Although the complexity determination is bounded with its source code, it should be expressed beyond its code base level, since the software is implemented as a combination of different phrases inside the Software Developments Life Cycle. The automation of the processes involved in software implementation procedure will mitigate the human effort taken during the phrases, resulting that the overall complexity of the software will also be reduced. The proposed system has the capability to demonstrate the requirement analysis, design, defects tracking, quality analysis and the complexity computation with respective to the different complexity metrics without restraining the software complexity evaluation into several quality attributes within the source code.
Noise Generation GAN Based Identity Privacy Protection for Smart City

The development of Internet of Things (IoT) infrastructure in the city leads to the emergence of the concept of smart city, an integrated solution to provide convenience for various applications in our daily life by understanding and analyzing the collected data from multi-sources. However, the collection of facial images collected from various IoT devices such as surveillance cameras, wearable, and mobile devices increases the risk of an individual’s privacy leak. The facial recognition models augment this risk. These models retrieve facial data collected from IoT devices stored in smart city databases to get personal identity information. With extensive utilization of such IoT devices, which serve as a visual data collector, we compromise the person’s identity. Therefore, to protect the privacy of image data from a database, we propose a Sensitivity Map Noise-Adding model based on generative adversarial networks to provide privacy for facial images against the malicious use of the face recognition models. The proposed models work as a black-box model that does not require any architectural information or the parameters of the target model. Additionally, the model runs at a real-time speed and the average run time for one operation is less than 12 milliseconds. The protection can be deployed for both local images and streaming videos. The data privacy protection is based on our proposed concept of the Sensitivity Maps, which summarizes the effectiveness and efficiency of adding noises on each pixel on the original image to interfere with the target model’s performance. We have built a new dataset of facial images containing 102 celebrities for the proposed model to be trained and evaluated. The experimental results prove the advantage of the proposed method against protecting the identity information in facial images.
Augmented Reality Technology in the System of Modern Education: Philosophical and Pedagogical Aspects

The article considers the augmented reality technology and emphasizes the relevance of its implementation in education in the context of information society formation. A brief overview of different technologies used in AR is given. The advantages and approaches to the use of augmented reality technology as a means of educating students are highlighted and examples of these approaches implementation in the educational process are given. It is concluded that the use of augmented reality technology in education as an additional catalyst in education is unequivocal. Significantly expanding the tools of modern educators, AR technology improves the process of presenting educational material, enhances its learning effect, increases motivation to learn and the overall level of students’ interest in mastering academic disciplines.
Using 3D Graphics and Interfaces in Training Games

Abstract This paper is devoted to the use of computer three-dimensional graphics and interface for developing educational games that contribute to the gamification of the training process. Similar educational games-analogues are considered, which were created by modern scientists and specialists, in order to fill the games with high-quality content for training. There is given a brief description of the software that is used in the development of game logic and building levels and interfaces that is Unity game engine. In addition to theoretical data, the paper describes the process of developing a graphical three-dimensional environment of the game, from the point of view of the psychology of perception, color, shapes and space, and the user interface, from the point of view of psychological techniques, usability and visual design. The analysis of the interaction between the game and the student was the key point discussed in this paper: how the student can learn about the virtual world and interact with it during the game is an important point that affects the creation of the user interface and game levels. After the development of the game was completed, it was implemented, followed by a survey of a group of students who completed testing at the end of the game. The paper final part gives brief description of the results and provides a comparative table of findings compiled according to game implementation and testing of students.