Reviewerly/exHarmony huggingface dataset:
The Impact of Duplicate Changes on Just-in-Time Defect Prediction

Recently, just-in-time (JIT) defect prediction technique attracted a lot of attention. In JIT defect prediction, all branches and omitting changes outside the main branch should be considered which can significantly affect the performance of JIT defect prediction. However, there are many duplicate changes among all the branches, which are referred to as a pair of changes with identical implementation in different branches. Such changes can influence the calculation of developer experience metrics and are considered as the noisy data for JIT defect prediction. In this article, the impact of duplicate changes on JIT defect prediction is explored. An empirical study on a total of 105 828 changes from eight Apache open-source projects is given. We find that 13% of changes from different branches are duplicate among the studied projects. The duplicate changes have a great influence on the model metrics for JIT defect prediction. For 50% of the changes, removing duplicate changes decreases the experience metrics with an average of 6–55. In addition, the duplicate changes have a significant impact on the evaluation and interpretation of JIT defect prediction models. Removing duplicate changes among the studied projects can significantly improve the performance of JIT defect prediction models ranging from 1 to 125% concerning various performance measures (i.e., area under the curve, Matthews correlation coefficient, and F1). Given the impact of duplicate changes, we suggest that researchers should remove duplicate changes from the original historical changes of software repository when evaluating the performance of JIT defect prediction models in future work.
An efficient image-based indoor positioning approach using ORB and LSH

Indoor localization has recently gained much attention in academia and business. Here, we raise a new approach to deal with this problem, using a variety of indoor images with localization information. Different from previous research, we use Oriented FAST and rotated BRIEF(ORB) instead of SIFT, which saves much time in feature extraction stage. Moreover, we apply Locality Sensitive Hashing(LSH) to do matching work. It reduces time complexity and further improve efficiency. With this novel combination of ORB and LSH, we significantly shortened process time. To test our approach, we use the images in Yorkdale, a shopping mall in Toronto. All the images are labeled with indoor localization information, the coordinate. The result shows that the whole processing time of our approach can be within 0.6 seconds, and all the test images can be matched to the closest picture in the database.
Determining The Effectiveness of Signals Detection During Search

The article analyzes the existing approaches on determining the effectiveness of signal search assessing and identifies their shortcomings. A new approach is proposed. It can be used in the search for periodic radar signals, communication signals, which are characterized by average statistical parameters, and also their variety - signals with pseudo-random frequency tuning. The efficiency of signal detection during the search is evaluated by two parameters: the detection probability during the search and the search time with a specified probability. In the course of research, the dependence of the probability of signal detection on the signal parameters, on the characteristics of the receiving device and on the search time, as well as the dependence of the search time on the signal parameters on the receiving device and on the desired detection probability are established.
Android-Based Educational Learning Game For Children Based On Android

The purpose of this study is as a means of learning English with the game method is one of the innovations that are suitable and much favored by children. Because children still have a tendency to play. So using this game media is a solution to make it easier for children to learn to recognize English. This research uses the development method used in this Jhony Adventure educational game which is the ADDIE method (Analysis, Design, Developer, Implementation, and Evaluation). The aspect value obtained from functionality testing reaches 100% very good, reliability testing reaches 100% very good, compability testing reaches 100% very good category, and playability testing reaches 96% with very good category. So that the feasibility of the educational game Jhony Adventure is categorized as very feasible. Based on the results of the questionnaire obtained, the educational game Jhony Adventure became an effective alternative solution in the learning of PAUD and Elementary School students in grades 1-2. By using the concept of education as a medium in learning subjects that seem difficult to learn, namely English lessons. Game development is very suitable and effective to help students learn English.
Multiple Latent Spaces Learning for Cross-Domain Text Classification

When the training data and test data are drawn from similar but different data distributions, transfer learning (TL) can be exploited to learn a consistent distribution for knowledge transfer. To reduce distribution differences, some recent transfer learning approaches typically build potential feature spaces to exploit the potential information and learn multiple high-level concepts to model a latent potential shared structure. However, only utilizing the potential information in one latent space will neglect some other potential information existing in different latent feature spaces. And this neglected potential information may also help model potential structures shared as bridges. In this paper, we propose Multiple Latent Spaces Learning (MLSL), a novel approach which mines a massive amount of potential information on multiple latent spaces to construct a shared bridge (or multiple bridges) across domains by learning different high-level concepts. Our strategy can dig out the latent information that exists in the latent space ignored by the previous methods to build a knowledge transfer bridge. Compared with the TL method that only learns a latent space, our strategy is more suitable for actual scenarios, and the use of data is also fuller. In addition, an iterative algorithm is developed to solve the optimization problem. Finally, the system test on benchmark data sets shows the superiority of the MLSL method.
An Improved Object Tracking Algorithm Combining LCT Kernel Correlation Filtering and Kalman Prediction

In the process of long-time object tracking based on LCT, aiming at the problems of re detection after tracking failure in the case of scale change, background similar interference and severe occlusion, the paper presents a better algorithm based on the combination of LCT kernel correlation filter and Kalman prediction. The Kalman filter is introduced into the re detection module of LCT. When the object tracking fails, the Kalman filter is used to predict the position of the object in the current frame before the re detection. Compared with the original algorithm, it avoids the whole frame image traversal search, reduces the search range of the object, and lower the interference of similar objects in the background. The experimental results show that the improved LCT algorithm has better accuracy and rapidity, and has better tracking performance in the case of object occlusion.
A Secured Real Time Scheduling Model for Cloud Hypervisor

Virtualization is critical to cloud computing and is possible through hypervisors, which maps the Virtual machines((VMs) to physical resources but poses security concerns as users relinquish physical possession of their computation and data. Good amount of research is initiated for resource provisioning on hypervisors, still many issues need to be addressed for security demanding and real time VMs. First work SRT-CreditScheduler (Secured and Real-time), maximizes the success rate by dynamically prioritizing the urgency and the workload of VMs but ensures highest security for all. Another work, SA-RT-CreditScheduler (Security-aware and Real-time) is a dual objective scheduler, which maximizes the success rate of VMs in best possible security range as specified by the VM owner. Though the algorithms can be used by any hypervisor, for the current work they have been implemented on Xen hypervisor. Their effectiveness is validated by comparing it with Xen's, Credit and SEDF scheduler, for security demanding tasks with stringent deadline constraints.
A Framework for Aspectual Requirements Validation: An Experimental Study

Requirements engineering is a discipline of software engineering that is concerned with the identification and handling of user and system requirements. Aspect-Oriented Requirements Engineering (AORE) extends the existing requirements engineering approaches to cope with the issue of tangling and scattering resulted from crosscutting concerns. Crosscutting concerns are considered as potential aspects and can lead to the phenomena “tyranny of the dominant decomposition”. Requirements-level aspects are responsible for producing scattered and tangled descriptions of requirements in the requirements document. Validation of requirements artefacts is an essential task in software development. This task ensures that requirements are correct and valid in terms of completeness and consistency, hence, reducing the development cost, maintenance and establish an approximately correct estimate of effort and completion time of the project.
Feature axes orthogonalization in semantic face editing

Human image synthesis is the technology that allows a computer program to create realistic photos of non-existing people. Though it is a relatively novel research topic that is mostly used to synthesize human faces, generating moving human figures is also possible using this method.At first, composing the believable and realistic images was a complex process. To achieve decent results, photo-realistic modelling, animating and mapping of the soft dynamics of the human body was required. Nowadays these methods are replaced by approaches based on machine learning and neural networks.Our system is able to create realistic images, consisting of three main components. The first component is a Generative Adversarial Network (GAN) that can generate a random face from a noise vector. Secondly, a convolutional neural network is responsible to recognize facial features on the input photos. Lastly, a regression model computes the correspondence between the input noise vector and output features of the generated face.Using a well-known face dataset, we report results applying the newly proposed model and we also analyze the accuracy and the plausibility of these results.
Learning classifier systems

tutorial Learning classifier systems: from principles to modern systems Share on Authors: Anthony Stein University of Hohenheim, Germany University of Hohenheim, GermanyView Profile , Masaya Nakata Yokohama National University, Japan Yokohama National University, JapanView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 561–589https://doi.org/10.1145/3377929.3389860Online:08 July 2020Publication History 1citation79DownloadsMetricsTotal Citations1Total Downloads79Last 12 Months23Last 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
Factors Influencing Information Security Policy Compliance Behavior

One of the major concerns of organizations in today's networked world is to unravel how employees comply with information security policies (ISPs) since the internal employee has been identified as the weakest link in security policy breaches. A number of studies have examined ISP compliance from the perspective of deterrence; however, there have been mixed results. The study seeks to examine information security compliance from the perspective of the general deterrence theory (GDT) and information security climate (ISC). Data was collected from 329 employees drawn from the five top-performing banks in Ghana and analyzed with PLS-SEM. Results from the study show that security education training and awareness, top-management's commitment for information security, and peer non-compliance behavior affect the information security climate in an organization. Information security climate, punishment severity, and certainty of deterrent were also found to influence employees' intention to comply with ISP. The implications, limitations, and directions for future research are discussed.
Feature Extraction of Video Using Artificial Neural Network

In deep neural networks, which have been gaining attention in recent years, the features of input images are expressed in a middle layer. Using the information on this feature layer, high performance can be demonstrated in the image recognition field. In the present study, we achieve image recognition, without using convolutional neural networks or sparse coding, through an image feature extraction function obtained when identity mapping learning is applied to sandglass-style feed-forward neural networks. In sports form analysis, for example, a state trajectory is mapped in a low-dimensional feature space based on a consecutive series of actions. Here, we discuss ideas related to image analysis by applying the above method.
Autonomic Computing Architecture for SCADA Cyber Security

Autonomic computing paradigm is based on intelligent computing systems that can autonomously take actions under given conditions. These technologies have been successfully applied to many problem domains requiring autonomous operation. One such area of national interest is SCADA systems that monitor critical infrastructures such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks. The SCADA systems have evolved into a complex, highly connected system requiring high availability. On the other hand, cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. This highlights the need for newer measures that can proactively and autonomously react to an impending threat. This article proposes a SCADA system framework to leverage autonomic computing elements in the architecture for coping with the current challenges and threats of cyber security.
Research on Image Verification Code based on Watermarking Technology

With the development of the Internet, security on networked computers is becoming more and more necessary. Identity verification codes provide additional security for web systems when visitors log in. Among various verification code technologies, dynamic picture verification code has become the most widely used. With the development of machine learning and neural network algorithm, automatically identify information from verification code become convenient for machines. Meanwhile, multimedia security has become an important field of research about watermarking, which is a technique that embeds some identity information into audio or image, and then the embedded information can be restored for different purposes. In this work, an imaging technology combining DWT(Discrete Wavelet Transform), SVD(Singular Value Decomposition), and DCT(Discrete Cosine Transform) is applied to the field of image verification code. We embed image caption as a watermark into its corresponding image. When authenticating users, the watermark will be extracted and the mapping relationship between watermark and image will be shuffled based on rules. When the mouse is over one image, the shuffled caption is displayed as clue words. Only when the visitors select all image with correct mapping relationship, it will be validated. This method can effectively distinguishs whether the visitor is a person and prevent computers identifying.
The Application and Thinking of Big Data in the Career Planning Education of College Students

Doing a good job of career planning education for college students is one of the key tasks of colleges and universities, and it is an important guarantee for improving the quality of employment and the success rate of employment, and the employment of college graduates is related to the country’s economic and social development and social stability. This paper comprehensively analyzes the current situation of college students’ career planning, discusses the technical characteristics of big data in college education applications, and studies the impact of big data on college students’ career planning education. At the same time, it puts forward the practical strategy of applying big data technology to the career planning education of college students to improve the professional ability of the career planning guidance team and provide students with a full range of career planning guidance services.
Cloud Computing Data Storage Security Based on Different Encryption Schemes

Cloud Computing (CC) became one of the prominent solutions that organizations do consider to minimize and lean their information technology infrastructure cost by fully utilizing their resources. However, with all the benefits that CC promises, there are many security issues that discourage clients from making the necessary decision to easily embrace the cloud. To encourage the use of CC, clients need to be able to strategically plan their future investments without the uncertainties of security issues that come with hosting their data in the cloud. This chapter will discuss different mitigation techniques and the common proposed security algorithm schemes for data storage encryption based on classical “symmetric and asymmetric” and with an emphasis on fully homomorphic encryption schemes.
Anomaly Traffic Detection with Verifiable Interpretation in Industrial Networks

Anomaly traffic detection in industrial networks plays a key role in protecting critical infrastructure assets and private data. Deep Neural Networks (DNNs) based anomaly traffic detection has received increased attention in recent work. Although their high accuracy, the lack of interpretability of deep learning models has seriously hindered its application in industrial high-risk decision-making fields. To address this issue, this paper presents a framework for DNN based interpretable anomaly traffic detection and interpretation verification based on an adversarial approach. When the DNN detects an anomalous event, in addition to the prediction, the framework provides the user with the confidence of the prediction and the input features that were relevant in making the prediction. In order to verify the validity of the interpretation, we use an adversarial method to find the minimum modifications of real features that make the classification change. This paper implements an experimental evaluation of the presented framework on the benchmark CICDDoS2019 dataset and KDD-NSL dataset for attack detection. The experimental results which are shown using intuitive visualizations prove the validity of the interpretation presented in this paper.
Training Methods

There are many available methods to develop training on cybersecurity issues, and it is not easy for a company to identity the most suitable one. However, the choice of the most appropriate training method requires a preliminary assessment of the training needs of the company. Methods can be classified according to different criteria, such as the place where they are carried out (internally or externally to the organization), tools and techniques used, the role of the instructor, the level of interaction with participants, and so on. Over the last few years, training based on the use of technologies has been widely developed, as have methods to engage employees actively—interactive and practical approaches—like gamification and serious games, simulation, role playing. Considering the limitations of the different training methods, a blended approach can provide a balanced solution. To achieve the goal of changing employees’ behaviour and their attitude towards security, training should be part of a continuous education process, and for what concerns its implementation within organizations, it should be constantly monitored. Moreover, considering the availability of innovative trainings methods exploiting digital environment, a combination between these and conventional approaches could be an excellent strategy towards security-conscious behaviour. Regardless of the approach used, the key element for its positive outcome is the ability of designing training programmes carefully and making them emotionally attractive. In this chapter we discuss different methods which can be used for cybersecurity training—identifying their points of strength and weakness—and implications for measuring their effectiveness.
Incorporating Economic Conditions in Synthetic Microdata for Business Programs

Abstract Many agencies are currently investigating whether releasing synthetic microdata could be a viable dissemination strategy for highly sensitive data, such as business data, for which disclosure avoidance regulations would otherwise prohibit the release of public use microdata. The U.S. Census Bureau has identified the Economic Census as a candidate program and has been developing synthetic data generators. The synthetic data should account for skewed and irregular distributions, satisfy predetermined edit constraints, and preserve selected privacy features. Previous research on these generators was confined to businesses that were in operation for the full year, ignoring the special features of births and deaths in the models. These generators preserve multivariate relationships and yield marginal totals that closely correspond to the published official statistics. However, these synthetic data consequently do not reflect the state of economic expansion or contraction. This missing information is a severe deficiency for the targeted data users comprising economists, policymakers, and methodologists, especially since the global pandemic of 2020. This paper introduces an approach that addresses this deficiency, producing partially synthetic data with high utility and privacy protection. We provide preliminary results using selected industry data from the 2012 Economic Census.
A Robust System for Road Sign Detection and Classification Using LeNet Architecture Based on Convolutional Neural Network

In this chapter, the authors report a system for detection and classification of road signs. This system consists of two parts. The first part detects the road signs in real time. The second part classifies the German traffic signs (GTSRB) dataset and makes the prediction using the road signs detected in the first part to test the effectiveness. The authors used HOG and SVM in the detection part to detect the road signs captured by the camera. Then they used a convolutional neural network based on the LeNet model in which some modifications were added in the classification part. The system obtains an accuracy rate of 96.85% in the detection part and 96.23% in the classification part.