Reviewerly/exHarmony huggingface dataset:
What Norwegian Developers Want and Need From Security-Directed Program Analysis Tools

Code enforcing access control policies often has high inherent complexity, making it challenging to test using only classical review and testing techniques. To more thoroughly test such code, it is strategic to also use program analysis tools, which often can find subtle, critical bugs going unnoticed to humans. These powerful tools are however rarely used in software consultancy practice, due to factors such as bad usability or unsatisfactory non-functional characteristics. To encourage wider adoption of such tools, more must be learned about how to design them to the preferences of software consultants. Towards this goal, we conducted a survey of Norwegian software consultants. Among our findings is a positive relation between preference for soundness over completeness in tools and preference for annotation-based over automated tools. 51% of the developers surveyed prefer soundness over completeness when detecting access control vulnerabilities, while only 37.5% view completeness as the more important characteristic. Qualitative responses illuminate concerns regarding usability, soundness, completeness, and performance.
Application of network virtual cloud computing data center based on fuzzy algorithm

Network virtualization technology releases human resources to some extent through network and cloud computing technology, reducing the workload of staff. The application of network virtualization technology in cloud computing data centers is based on this condition to improve work quality and effic iency. The purpose of this paper is to use the fuzzy algorithm to realize network virtualization of cloud computing data center. In this paper, we study the adaptive fuzzy control in depth, and conduct the practical application based on the basic knowledge of adaptive fuzzy control we learned, achieved “learn to use”. Apply the design of adaptive fuzzy control to the load balancing algorithm of the network virtual cloud computing data center, realized the load balancing algorithm of the network virtual cloud computing data center based on adaptive fuzzy control. According to the load balancing algorithm based on adaptive fuzzy control to achieve this algorithm by using Internet knowledge, and designed the load balancing system of the whole network virtual cloud computing data center. Test the whole load balancing system which has been achieved, and obtained the performance variance curve of the system under different algorithms. Then obtained advantages and disadvantages of the algorithm by analyzing the experimental data. The experimental results show that the proposed method can effectively improve the execution performance of communication-intensive applications and ensure the stable execution of the application. At the same time, the algorithm inherits the advantages of the general fuzzy control load balancing algorithm. The stability is strong and the variance curve does not appear pulsed fluctuation. There is also no divergence phenomenon with time increased.
Learning Topic Models with Arbitrary Loss

Topic modeling is an area of text analysis actively developing over the past 20 years. A probabilistic topic model (PTM) finds a set of hidden topics from a collection of text documents. It defines each topic as a probability distribution over words and describes each document as a probability mixture of topic distributions. Learning algorithms for topic models are usually based on Bayesian inference or log-likelihood maximization. In both cases, EM-like algorithms are used. In this paper, we propose to replace the logarithm in the log-likelihood by an arbitrary smooth loss function. We prove that such a modification preserves both the structure of the algorithm and compatibility with any regularizers in terms of additive regularization of topic models (ARTM). Moreover, in the case of a linear loss, the Estep becomes much faster due to the omission of a normalization. We study combinations of the fast and usual E-steps and compare them to regularization using different number of topics in both offline and online versions of EM-algorithm. For an empirical comparison of the algorithms, we estimate perplexity, coherence, and learning time. We use an efficient parallel implementation of the EM-algorithm from the BigARTM open-source library. We show that in most cases the two-stage strategy wins, which uses fast E-steps at the beginning of iterations, then proceeds with usual E-steps.
Teaching Eye Tracking Visual Analytics in Computer and Data Science Bachelor Courses

Making students aware of eye tracking technologies can have a great benefit on the entire application field since they may build the next generation of eye tracking researchers. On the one hand students learn the usefulness and benefits of this technique for different scientific purposes like user evaluation to find design flaws or visual attention strategies, gaze-assisted interaction to enhance and augment traditional interaction techniques, or as a means to improve virtual reality experiences. However, on the other hand, the large amount of recorded data means a challenge for data analytics in order to find rules, patterns, but also anomalies in the data, finally leading to insights and knowledge to understand or predict eye movement patterns which can have synergy effects for both disciplines - eye tracking and visual analytics. In this paper we will describe the challenges of teaching eye tracking combined with visual analytics in a computer and data science bachelor course with 42 students in an active learning scenario following four teaching stages. Some of the student project results are shown to demonstrate learning outcomes with respect to eye tracking data analysis and visual analytics techniques.
Blockchain Models to Improve the Service Security on Board Communications

The present article introduces a definition of Blockchain, describes services implemented by this technology and the field of its application. The authors identify the main technical characteristics and their features associated with data transmission through the network. This article provides the operation principles of Blockchain technology and a block structure. The authors determine the network scheme working with blockchain transactions and the dependence of network characteristics on application parameters. The security aspects of data transmission within Blockchain technology are analyzed as well. The types of network attacks and Blockchain devices are listed in the article. In addition, the authors propose a Blockchain traffic identification model that allows discrediting the existing security mechanisms of this technology and makes services vulnerable to these attacks. Furthermore, the article offers recommendations on hiding the Blockchain traffic profile, which will significantly complicate its identification on the data network.
Effect of an Augmentation on CNNs in Classifying a Cirrhosis Liver on B-Mode Ultrasound Images

We investigate the effect of a region of interest(ROI) image augmentation on a convolution neural networks(CNN) in classifying a cirrhosis liver on B-mode ultrasound images. The experimental results show the effectiveness of the ROI image augmentation by a perspective transformation.
Exemplifying the Effects of Distance Metrics on Clustering Techniques: F-measure, Accuracy and Efficiency

Clustering is a type of unsupervised learning in which similar data or objects are collected into a single group. These objects can be exhibited in an n-dimensional Euclidean space. Hence based on distance metrics, similarities/dissimilarities can be measured. This paper reviews and analyzes two different clustering techniques' performance: K-means and FCM. These algorithms are applied on a dataset which contains information of those patients who had gone under breast cancer surgery. When K-means and FCM are applied using different distance metrics, it's found that precision, efficiency and accuracy of classification is also affected. On analysis, the K-means algorithm using Manhattan is found to be more suitable for clustering whereas FCM gives better result when Euclidean distance is chosen.
Real-time Bangladeshi Currency Detection System for Visually Impaired Person

This paper presents a real-time Bangladeshi currency detection system for visually impaired persons. The proposed system exploits the image processing algorithms to facilitate the visually impaired people to prosperously recognize banknotes. The recent banknotes of Bangladesh have blind embossing or blind dots, which could be effective to recognize the value of the bill by touching. As the embossing fades away in the long-term used notes, detecting right value of the banknote using image processing algorithms could be considered as a challenging task. Particularly in Bangladesh, each banknote seems similar using the direct exertion of simplified image processing algorithms. In this paper, a recognition system was implemented that can detect Bangladeshi banknote in different viewpoints and scales. The detection system is also able to detect currency those are rumpled, decrepit or even worn. The detection system includes image preprocessing, image analysis and image recognition. To enhance the determination of currency recognition, the descriptor of an individual input scene is matched with various training images of the same category. After that, by analyzing their matching result it recognizes the currency with higher confidence. For real-time recognition, we have deployed the system into a mobile application.
Hierarchical Clustering-Based Video Summarization

In order to improve the quality of key frames and generate video summaries that are closer to the user’s viewing habits, a video summarization key frame extraction method based on hierarchical clustering is proposed. First, the color and texture features in the video image are extracted and fused. Secondly, the initial clustering parameters are obtained by hierarchical clustering, and K-means clustering is employed to optimize the initial results and categorize similar frames. Finally, the frame closest to the cluster center is selected as the key frame. The interpretation of the experimental results shows that using the proposed method achieves an improvement in accuracy of the extracted key frames by 0.71 on average. Moreover, the average recall rate is 0.76, and the average F-score is 0.73, which is higher than the other tested methods, and effectively improves the overall effect of the video summary. Also, it generates video key frames that are closer to human summaries.
Neighborhood-Based Collaborative Recommendations: An Introduction

The amount of information on the World Wide Web (or simply Web) is large and diverse. The explosive growth of information on the Web frequently overwhelms Internet users. Recommender systems (RSs) help individuals who are not able to make decisions from the potentially overwhelming number of alternatives available on the Web. Among various recommendation approaches, collaborative filtering-based recommender systems (CFRS) are the most popular (due to their simplicity and efficiency) and are traditional approaches for recommendations. This chapter describes the main components of a generic framework that may be employed for neighborhood-based collaborative recommendations. Firstly, a few notations have been described to formulate the recommendation process mathematically; hereafter, we have explained the two main categories of neighborhood-based approaches: User-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF). Secondly, user-item interactions, in the form of rating data (UI-matrix), are analyzed with respect to the recommendation process, i.e., different types of ratings, various ways for collecting rating data, key properties of rating matrices, etc. Finally, the chapter concludes by discussing a few well-known problems associated with the rating data such as sparsity, long-tail, and cold-start problem.
Mining Signed Networks: Theory and Applications

Signed networks transform the information encoded by conventional graphs by attaching either a positive or a negative sign to every edge. This subtle modification vastly enhances the modelling capabilities of graphs. For instance, in a social network, where edges might represent interactions between users, the sign may determine whether an exchange was friendly or hostile. However, the introduction of edge signs invalidates many established methods and results from the graph-mining toolbox, and thus, problem formulations and algorithmic techniques must be studied anew. In this tutorial we aim to provide an overview of the literature in mining signed networks. We will present the most important theoretical results since their inception to the present day, we will discuss some of the most common applications, and we will reflect on emerging applications and directions for future work.
Ranking Algorithm Based on User Behavior Model

PageRank algorithm is based on network link to calculate the importance of the web pages. It is successful for web search, but there are still many problems, such as web spam, invalid links etc. It can not be a good description of the user's behavior. In this paper, through the analysis of thousands of user's behavior data, a semi-Markov process method is proposed to simulate the user browsing behavior, and its stationary probability distribution can be used as the measure of page im-portance. At the taking the influence of web content and length into consideration and com-bining with traditional hyperlink analysis, the new web pages are reconsidered and the old web ones are ranked appropriately. The results show that degree of satisfaction by this method is increased by about24% compared with PageRank algorithm, which can be better to calculate the importance of web pages.
A WORK OF ART IN THE SPACE OF NETWORK CULTURE: CREATIVITY AS BRICOLAGE

The development of modern informational-communication technologies has led to the occurrence of the new unique sociocultural phenomenon – a network culture, with irony as the dominating rhetoric. In the space of network culture, under digital technologies, the forms, types, and functions of art, and creativity in general, change. The paper states that communication becomes the main function of art, while a work of art more and more becomes an object of communication. The authors propose to broaden the volume of creativity conception, going beyond the classical interpretation towards a broader understanding of this phenomenon, namely, creativity as bricolage. The methodological basis of the bricolage model of creativity, relevant for the new media art, is the concept of “bricolage” developed by Claude Lévi-Strauss.
Who Are the People in Your Neighborhood? Personas Populating Unregulated mHealth Research

A key feature of unregulated mHealth research is the diversity of participants in this space. Applying an approach drawn from user experience design, we describe a set of archetypal unregulated mHealth researcher “personas,” which range from individuals who seek empowerment or have philanthropic objectives to those who are primarily motivated by financial gain or have misanthropic objectives. These descriptions are useful for evaluating policies applicable to mHealth to understand how they will impact various stakeholders.
Wavelet-based method for coherence analysis with suppression of low frequency envelope modulation in non-stationary signals

Techniques for non-stationary signal analysis are important in understanding dynamical behaviour of complex systems. Time-frequency coherence is widely used to analyse time-varying characteristics in non-stationary signals. This paper presents wavelet-based methods, using Airy wavelet, to estimate coherence. We incorporate a novel technique for removal of low frequency components due to envelope modulation in non-stationary signals. The technique is demonstrated on synthetic and real neurophysiological data. Results not only provide a clear description of desired features in non-stationary signals, but also suppress low frequency components due to envelope modulation. Our novel technique shows an effectiveness in extracting features hidden within the signals. It may lead to improved results in coherence analysis of medical, biological, physical and geophysical data containing low frequency envelope modulation besides non-stationarities.
A Pair Estimation Technique of Effort Estimation in Mobile App Development for Agile Process

Effort estimation plays a vital role in the software development process in order to ensure that development tasks are delivered within the planned time. The characteristics of the mobile app environment make it different in terms of development from other traditional software. This paper presents a case study in an IT company, which examined their current estimation techniques, planning poker and expert judgment techniques, and its process in agile, and it provides and has validated a proposed estimation technique in order to enhance the accuracy of the existing technique. Moreover, this study presents the effectiveness of estimation factors/predictors in supporting the development team to manage, estimate and create subtasks for their user stories.
Analysis of abnormal short circuit between turns of generator rotor windings

In December 2018, during the normal operation of the NO.1 generator in a power plant, the online monitoring device of interturn-to-turn short circuit of the rotor #5 coil showed a suspected interturn-to-turn short circuit phenomenon. Check the rotor voltage, current, vibration and other data, are not found abnormal. The monitoring results of the on-line monitoring device for inter-turn short circuit in rotor of NO.1 generator show that there is some abnormal situation in NO.5 coil. After checking the test reports of RSO, ac impedance and power loss of generator rotor, no abnormal data was found. The correlation between the vibration of the rotor and the reactive power and the excitation current was checked. In addition, the rotor vibration of NO.1 generator does not increase during operation. It is suggested that the relationship between the vibration and the reactive power and the excitation current of the rotor rotor should be monitored, and the correlation analysis should be done.
Real Time Face Recognition on an Edge Computing Device

Face recognition systems have vast applications in surveillance systems and human-computer interactions. Different approaches such as Principal Component Analysis, Fisher linear discriminant analysis, Convolutional Neural Networks (CNN) have been commonly used for face recognition. However, in the recent times, CNN's have shown quite promising results in various face recognition systems. But, deep learning based CNNs have many limitations such as they require extensive training data, have excessively high computational and cooling requirements, and lack flexibility in deployment. Fields such as robotics and embedded systems that deploy face recognition systems have significantly less power on board and limited heat dissipation capacity. Therefore, it becomes difficult to deploy deep learning models on them but edge computing based devices like the Intel Neural Stick bridge this gap as they have certain advantages. In this paper, we review different applications of face recognition systems and various algorithms used for face recognition. We then elaborate the limitations of deep learning based face recognition systems and examine how edge-computing devices can solve these problems. We then present a flowchart to deploy a CNN based face recognition model on an edge-computing device.
Research on Building Exploitable Vulnerability Database for Cloud-Native App

Due to issues such as static unverifiability and lack of effective application in vulnerability databases. This paper proposes a method of building Exploitable Vulnerability Database for Cloud-Native App (CNA). In this paper, a new machine-oriented structured vulnerability intelligence format is designed to store vulnerability information to automate the processing of vulnerability information. A large number of cloud-native application vulnerabilities have been tested and a modularized test process has been proposed as a form of prototype that automatically builds a Deliberated Vulnerable Environment (DVE) in a pipelined manner using the DevOps toolchain. This prototype system can directly access the vulnerability database to build vulnerability intelligence and extract key information from it. The affected software versions from the vulnerability intelligence are used to fetch all vulnerable software base images by version comparison. A CNA image containing the desired vulnerability can be generated from the base image with post-installation configurations. A Vulnerability Auto-Validation Framework is used to verify whether it is exploitable or not. After this process, a CNA image with the DVE, is automatically pushed to an image registry. This DVE image can be widely used in different cases, such as sandbox, honeypot, cyber range, etc. Experiments have been conducted to demonstrate that it can build and deliver DVE images from the proposed vulnerability intelligence. This paper uses real vulnerabilities in National Vulnerability Database (NVD) to experiment on the prototype system. The experiment uses the machine-oriented structured vulnerability intelligence designed in this paper as the prototype system input, and successfully releases the DVE image when the vulnerability intelligence is accurate. What's more, the building process and result of the DVE image can be inspected and visualized through CI/CD pipeline so that to increase the confidence of the veracity of the vulnerability intelligence.
FRACTAL DIMENSION OF FRACTIONAL BROWNIAN MOTION BASED ON RANDOM SETS

The fractal dimension of fractional Brownian motion can effectively describe random sets, reflecting the regularity implicit in complex random sets. Data mining algorithms based on fractal theory usually follow the calculation of the fractal dimension of fractional Brownian motion. However, the existing fractal dimension calculation methods of fractal Brownian motion have high time complexity and space complexity, which greatly reduces the efficiency of the algorithm and makes it difficult for the algorithm to adapt to high-speed and massive data flow environments. Therefore, several existing fractal dimension calculation methods of fractional Brownian motion are summarized and analyzed, and a random method is proposed, which uses a fixed memory space to quickly estimate the associated dimension of the data stream. Finally, a comparison experiment with existing algorithms proves the effectiveness of this random algorithm. Second, in the sense of two different measures, based on the principle of stochastic comparison, the stability of the stochastic fuzzy differential equations is derived using the stability of the comparison equations, and the practical stability criterion of two measures according to probability is obtained. Then, the stochastic fuzzy differential equations are discussed. The definition of stochastic exponential stability is given and the stochastic exponential stability criterion is proved.