Reviewerly/exHarmony huggingface dataset:
Learn To Align: A Code Alignment Network For Code Clone Detection

Deep learning techniques have achieved promising results in code clone detection in the past decade. However, existing techniques merely focus on how to extract more dis-criminative features from source codes, while some issues, such as structural differences of functional similar codes, are not explicitly addressed. This phenomenon is common when programmers copy a code segment along with adding or removing several statements, or use a more flexible syntax structure to implement the same function. In this paper, we unify the aforementioned problems as the problem of code misalignment, and propose a novel code alignment network to tackle it. We design a bi-directional causal convolutional neural network to extract feature representations of code fragments with rich structural and semantical information. After feature extraction, our method learns to align the two code fragments in a data-driven fashion. We present two independent strategies for code alignment, namely attention-based alignment and sparse reconstruction-based alignment. Both two strategies strive to learn an alignment matrix that represents the correspondences between two code fragments. Our method outperforms state-of-the-art methods in terms of F1 score by 0.5&#x0025; and 3.1 &#x0025; on BigCloneBench and OJClone, respectively<sup>1</sup><sup>1</sup>Our code is available at https://github.com/ArcticHare105/Code-Alignment.
Industrial field work practice information system design

Abstract The Faculty of Engineering, Universitas Negeri Medan requires the development of technology and communication in assisting performance and processes in carrying out their functions. Its development must produce benefits for the Faculty so that academic implementation runs smoothly, as well as assisting organizations in carrying out the required processes to help organizational managers make decisions. Changes in the form of the process used with the development of technology and communication are expected to encourage organizations to have better management.
Scope-aware Re-ranking with Gated Attention in Feed

Modern recommendation systems introduce the re-ranking stage to optimize the entire list directly. This paper focuses on the design of re-ranking framework in feed to optimally model the mutual influence between items and further promote user engagement. On mobile devices, users browse the feed almost in a top-down manner and rarely compare items back and forth. Besides, users often compare item with its adjacency based on their partial observations. Given the distinct user behavior patterns, the modeling of mutual influence between items should be carefully designed. Existing re-ranking models encode the mutual influence between items with sequential encoding methods. However, previous works may be dissatisfactory due to the ignorance of connections between items on different scopes. In this paper, we first discuss Unidirectivity and Locality on the impacts and consequences, then report corresponding solutions in industrial applications. We propose a novel framework based on the empirical evidence from user analysis. To address the above problems, we design a \underlineS cope-aware \underlineR e-ranking with \underlineG ated \underlineA ttention model (SRGA ) to emulate the user behavior patterns from two aspects: 1) we emphasize the influence along the user's common browsing direction; 2) we strength the impacts of pivotal adjacent items within the user visual window. Specifically, we design a global scope attention to encode inter-item patterns unidirectionally from top to bottom. Besides, we devise a local scope attention sliding over the recommendation list to underline interactions among neighboring items. Furthermore, we design a learned gate mechanism to aggregating the information dynamically from local and global scope attention. Extensive offline experiments and online A/B testing demonstrate the benefits of our novel framework. The proposed SRGA model achieves the best performance in offline metrics compared with the state-of-the-art re-ranking methods. Further, empirical results on live traffic validate that our recommender system, equipped with SRGA in the re-ranking stage, improves significantly in user engagement.
What Participation Creates in Experimental Design Practices. The Case of a Mobile Third Place Built in a Retirement Home

This article explores the rise of a new generation of practices combining architecture, design, and art, trying to answer the transition issues faced by society. It develops original operating procedures, including public participation. In doing so, those so-called “specialised” professions expand their sphere of operation and incorporate more immaterial dimensions and resources. The main objective of the article is an attempt to clarify how participation is embodied in specific intervention methods, within those experimental practices. The article will take as a case study a participatory project taking place in a retirement home and aimed at building a mobile third place that brought together various professionals coming from those experimental practices. The study of the participatory project will outline three devices and methods supporting the participation work, as follows: the use of permanence, the use of the prototype and self-construction, and the conception of ephemeral production. The article suggests that based on their analysis, we can understand what architects and designers “manufacture” through the agency of participation. Or more accurately, what participation “manufactures” in those experimental practices. The main result of the article is that the participatory project is more concerned with the motives and aspirations of the design activity, its methods and processes, its context and socialisation than it is with what would be classically considered as the outcome or result (the work, the realisation, the production, the built).
Composition of an automated attendance register of students by security cameras, as part of a smart city

The safety of students represents a serious issue in smart cities.The present research aims to find a solution to how security cameras should be upgraded by expanded intelligence to greatly facilitate the administration in schools and to have a positive influence on safety issues.As security cameras have been developed for surveillance, the mentioned features should be further improved.The cameras installed in hallways and classrooms are possibly capable of face detection and recognition, as well as for headcount.It is recommended that these solutions be expanded by the possibility of registering attendance.If a camera can recognize a student, an attendance register may be composed, so it would be possible to determine who is attending classes regularly and who is not.The data received in this way could periodically be sent to teachers by the system.The composing of attendance registers is a privilege of access control systems; however, this is fairly inconvenient in educational institutions.Hypothetically, the surveillance camera system represents the most adequate choice for a passive, personal identification, as in this case, an active contribution is not necessary.
BIPP: Blockchain-Based Identity Privacy Protection Scheme in Internet of Vehicles for Remote Anonymous Communication

Aiming at the difficult problem of anonymous communication between vehicles and vehicles at long distances in Internet of Vehicles, we propose a identity privacy protection scheme based on blockchain (BIPP). In this scheme, the response value of the PUF function and the continuous timestamp are used as the basis of encryption, which is associated with the pseudonym of the user ID information to prevent adversaries from conducting forgery attacks. The pseudonym mechanism and the confusion mechanism between identity and message protect the privacy of vehicle users’ identities. The information on the blockchain must pass reasonable judgment algorithms and meet the conditions for ending of the event. Blockchain technology can ensure the information received by the vehicles in the target area is credible. The scheme is proved to be secure and feasible through analysis, implementation and evaluation.
Automatic labeling of tracked objects based on an indexing mechanism

Real-time object tracking is still a critical challenge in artificial vision research. In such a mission, it is essential to assign a unique identifier or label to each tracked object, regardless of the area, time of appearance, or detector camera, to distinguish it from other objects and to conserve as much information as possible about the tracked objects with the same label. This conservation is a significant issue, especially in largescale video surveillance systems, due to the linear complexity of the sequential search to find the labels of detected objects in data increasing with time, the number of tracked objects, and the number of active cameras in the network. To overcome this problem, we propose a new automatic multi-object labeling solution for efficient real-time tracking based on an indexing mechanism. This mechanism organizes the massive metadata of objects extracted during tracking into a tree-based indexing structure. The main advantage of this structure in a tracking system is its logarithmic search complexity, which implicitly reduces the search response time, and its quality of research results, which ensure coherent labeling of the tracked objects. This paper discusses the effectiveness of the label search algorithms and the tracking quality compared to other recent tracking systems on real-world datasets. Experimental results showed good performance in reducing search time and improving tracking quality.
Research and Analysis of Obstacle Avoidance Path Planning for Autonomous Vehicles

Obstacle avoidance path planning is a key technology for autonomous vehicles in identifying obstacles and avoiding obstacles, which is of great significance to the development of autonomous driving technology. This article gives an overview of traditional algorithms and intelligent algorithms related to obstacle avoidance path planning technology for autonomous vehicles, analyzes, compares and summarizes the advantages and disadvantages of each algorithm, and introduces their combined application. Comprehensively considering the advantages and disadvantages of using a single algorithm to plan obstacle avoidance paths in practical applications, it is found that a single algorithm shows drawbacks in a dynamic environment, such as poor computing power. Therefore, it is concluded that the use of multiple algorithms can make up for the shortcomings of a single algorithm, which has many advantages and will be focus of automatic obstacle avoidance research in the future.
Improvement of Denoising in Images Using Generic Image Denoising Network (GID Net)

Images are generated in various forms in different sectors. These images are difficult to handle with and thus, cannot be effectively used in various fields. In this paper, two important techniques in the fields of Image Reconstruction and Restoration are carried out. Image Denoising is the process in which an image is reconstructed from the noisy image. In this process, To restore the original image, the undesired noise is removed. In other words, original (unidentified ) signal, i.e., the image is estimated from the available noisy data. There are many traditional methods of Image Processing which have been used for the process of image denoising [1]. But, these methods are not so robust to handle any type of noise signal which results in a deformed reconstructed denoised image. Therefore, in this paper, a method of image denoising using Image Reconstruction technique is proposed. For this purpose, Deep Learning techniques and architectures have been proposed in order to get the reconstructed denoised image in our paper.
Securing Surveillance Data Using Incremental Cryptography

Incremental cryptography is applicable for an environment wherein the data going through cryptographic changes are modified variants of data which have previously gone through similar cryptographic changes. We implemented a secure surveillance image encryption technique using incremental cryptography. Surveillance systems are generally used for real-time abnormal event detection and autonomous video analysis. Video and real-time images captured by surveillance cameras often contain personally identifiable information, which must be protected by privacy, and sometimes contains sensitive information, such as surveillance locations. Here we consider surveillance images which can exploit the verifiable truth that constant video frames generally have only minor differences. A novel incremental symmetric encryption technique is proposed on this paper. Using incremental encryption, it is possible to rapidly refresh the computational result for a newly updated photo. Thus we can reduce the time complexity. Significant hypothetical consideration has been given to major cryptographic primitives such as digital signatures and message authentication code. The meaning of protection and security has been explored in various works. In this investigation, the attention is on incremental encryption to see how it can assist securing surveillance information.
Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks

Automatic classification is one of the hot topics in the field of information retrieval and natural language processing, but it still faces many problems to be solved. The classic automated classification approach has a sluggish classification speed and poor processing accuracy for resources with a large quantity of data. Based on this, an automated classification approach based on the integration of various neural networks for fundamental nursing teaching materials was presented. The automatic classification method of teaching resources was designed by extracting the characteristics of teaching resources, establishing the model of multiple neural network integration, and designing the classification index of basic nursing teaching resources. The experimental findings suggest that this technique has higher chi-square test parameters and better outcomes for the automated classification of large instructional materials than the classic rough set automatic classification method.
A Novel Forward Filter Feature Selection Algorithm Based on Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR) for Machine Learning

In the last few decades, Feature selection is one of the most challenging and open problem to researchers. The rapid progress in computational techniques causes the generation and recording of data in huge size. Though there exists various feature ranking methods, the processing of data is still a challenging task due to its computational complexity. The filter method has many advantages over the wrapper method. The filter methods are classifier independent and have better computational efficiency. Here, a subset of features is selected based on a certain goal function. Most of these goal functions employs the principle of information theory. Most of the algorithms in earlier studies addressed two factors, that is, maximization of relevancy and minimization of redundancy without considering the interaction among the features. This paper developed a new forward filter feature selection algorithm based on mutual information known as Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR). This method considers all the three factors: relevance, redundancy, and feature interaction. This method is experimented on three datasets and compares the performance with existing methods. The results show that MDIMFR outperforms the existing competitive feature selection methods of recent studies: mRMR, JMIM and CMIM. MDIMFR also achieves good stability in average classification accuracy for a certain number of features, say k and above. Hence, these k features can be considered as an optimal feature set.
Semantic Similarity Extraction on Corpora Using Natural Language Processing Techniques and Text Analytics Algorithms

Extraction of Semantic Similarity and relevant information from the corpus is one of the elusive tasks in Text Mining due to the unstructured data, uneven pattern, multiple resolutions, concealed meaning, and other ambiguities. The main focus of semantic similarity analysis lies in meaning concerning the word sense that lies in the arrangements context words and the other words in the sentence with respect to the window size. One of the hurdles to extract the exact semantic similarity from paraphrase statements is the corpus length. The longer corpus has the better chance to match any query statement and it may contain more words, which arises the over penalization problem. To alleviate this problem avoid over penalization by length normalization. The objective of the study is to improve the efficiency in capturing semantic similarity and pertinent information by increased term frequency saturation and increased impact of document normalization with the less penalization method. This study introduced a novel method, Perfect Matching Algorithm (PMA), developed to reduce the over penalization on context corpus with taken into account the length of both Query and Context Documents by the length normalization.
Health data security and privacy: Challenges and solutions for the future

This chapter reviews the common health data security and privacy challenges to provide an overview of the context—the concerns and the types of cybersecurity threats that healthcare institutions face. Also highlighted are the latest scientific work related to two primary solutions for protecting health data used for individual care, namely, access control and cryptography and two additional solutions for protecting data used for secondary purposes, namely, data de-identification and privacy-preserving distributed data mining (PPDDM). We exemplify implementation of these solutions using three real-life use cases in healthcare and pivot our discussion on how healthcare institutions can develop cybersecurity policy and prepare themselves for complying with relevant security and privacy regulations like Europe’s General Data Protection Regulation (GDPR) and the Healthcare Insurance Portability and the Accountability Act (HIPAA) in the United States.
Evaluation of Social Human Sentiment Analysis Using Machine Learning Algorithms

The impact of social networking on human beings is significantly increasing in daily life, academics, sports, business, and amusement. Sentiment analysis becomes easier with the availability of comments or reviews on social media. Sentiment analysis is opinion mining, which analyzes the sentiment of the person based on the comments as input using machine learning algorithms. People are generic to go through the review comments before exploring any new venture. As a result, a consumer's social media review is very valuable for new customers. In this paper, sentiment analysis is carried out based on the restaurant review comments available in the social networks for experimental purposes. The corpora of features are prepared using natural language processing and a bag of words model. The objective of this paper is to present the comparison between learning, training, and classifying the restaurant review data based on the evaluation of the proposed model using six different machine learning algorithms. Inference of these training and testing is presented with a bar graph for straightforward comparative study. This research concludes with a proper selection of the best-fit machine learning algorithm.
Blockchain Developments and Innovations – An Analytical Evaluation of Software Engineering Approaches

Blockchain has received expanding interest from various domains. Institutions, enterprises, governments, and agencies are interested in Blockchain’s potential to augment their software systems. The unique requirements and characteristics of Blockchain platforms raise new challenges involving extensive enhancement to conventional software development processes to meet the needs of these domains. Software engineering approaches supporting Blockchain-oriented developments have been slow to materialize, despite proposals in the literature, and they have yet to be objectively analyzed. A critical appraisal of these innovations is crucial to identify their respective strengths and weaknesses. We present an analytical evaluation of several prominent Blockchain-oriented methods through a comprehensive, criteria-based evaluation framework. The results can be used for comparing, adapting, and developing a new generation of Blockchain-oriented software development processes and innovations.
Real Time Face Recognition Based on Residual Neural Network

This study revisits the global mean pooling layer (GAP) proposed, and sheds light on how it allows Deep Residual Neural Network to have a remarkable localization capacity, work with the same technique, but in three different forms, with the change of the type of network ResNet50, because it also works by the system (GAP) which is used to obtain the final convolutional layer representing the characteristics of the human face. In our experiment, we used &#x201C;yalefaces&#x201D; dataset with the addition of many blurry images to have the efficiency rate of this technique to detect the face. The experimental results from the proposed methods and the face recognition rate for the &#x201C;yalefaces&#x201D; dataset are validated, and we compared with recent techniques. The face recognition rate of the used dataset based on this network is 99&#x0025;, which shows the effectiveness.
Background and Theory

In this chapter the theoretical elements necessary to the development of the proposed approach and methodologies are introduced.
Implementasi Metode Forward Chaining, Certainty Factor dan Dempster Shafer pada Sistem Pakar Diagnosis Penyakit Gigi dan Mulut

&#x0D; &#x0D; &#x0D; &#x0D; Dental and oral disease is one of the diseases that has been felt by most of the people. Insufficient information and the limited level of public awareness of the prevention of dental and oral diseases make the impact quite dangerous if not handled properly. An appropriate information system is needed in overcoming and providing solutions for handling a disease as early as possible. Expert systems can be used as a means of information on the treatment of dental and oral diseases. The manufacture of the expert system in this study initially used the forward chaining method, which is a method that searches based on information that is made into a set of rules so as to get a conclusion. However, after re-analysis, two other methods, namely certainty factor and dempster shafer, were also applied in this study with the aim of overcoming the shortcomings of the forward chaining method, one of which is uncertainty in producing a conclusion or diagnosis of disease. Determining the type of dental and oral disease can be known by looking at the symptoms experienced by the patient. The use of an expert system for diagnosing dental and oral diseases can be used as an initial solution in helping someone to treat the disease. The existence of this expert system can be used as consideration in making decisions to determine the type of dental and oral disease quickly, precisely and accurately.&#x0D; &#x0D; &#x0D; &#x0D;
The Effects of Data Localization on Cybersecurity

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