Reviewerly/exHarmony huggingface dataset:
The Global Financial System’s New Tool: Digital Money

The developments in information and internet technology have led to profound changes in the global financial system. In the new financial system, modern financial instrument are used more than conventional money and financial instruments. With the emergence of new financial instruments, economic and financial crises were experienced in both money and capital markets and significant structural problems were observed in the economy. Those who believed that the financial system caused the crisis of 2008 were in search of a new financial system. Satoshi Nakamoto, about one and a half months after the crisis with encryption technique invented a highly reliable currency that eliminates intermediary service as a solution to many problems caused by real money, which allows monetary parties to conduct their transactions directly. Cryptocurrency technology aims to make transactions reliable and provide money control with the encryption technique. Due to the high security encryption technique of the network structure it is not possible to infiltrate into the system. In addition to being reliable, the new currency recently has been more effective as an investment tool rather than being a medium of exchange in daily life anywhere in the world. In order to increase the use of cryptocurrencies serious infrastructural preparation is needed. Therefore, its use for investment purposes has become widespread. The use of cryptocurrencies as a financial instrument and their impact on the money and capital markets remain to be seen.
A voting approach of modulation classification for wireless network

Radio signal modulation has been a key task for a wireless communication system. In this paper, a novel voting approach has been introduced for the identification of radio signal modulations for wireless network systems using the statistical Machine Learning (ML) based method. Several well-known ML classifiers have been used here before using it for the voting approach. The most effective ML algorithms have been utilized in Ensemble Voting Classifier (EVC). In the end, we found that this voting approach provides over 90% accuracy for the high SNR values for eight different wireless network modulation schemes. This model has also performed better in case of low SNR values and provided negligible loss for each individual modulations compared to the existing methods.
Wireless Multicast of Virtual Reality Videos With MPEG-I Format

Deploying virtual reality (VR) streaming systems over wireless networks can provide an immersive environment for users without behavioral restrictions, and thus has drawn considerable attentions recently.MPEG-I as a new panorama video standard just targets such immersive multimedia applications.Although multiple VR delivery schemes have been proposed, MPEG-I has not been sufficiently researched, especially for multicast scenarios.Therefore, in this paper, we would like to design a multicast scheme to deliver 360 VR videos with MPEG-I format.Specifically, based on the prediction of users' viewports, we formulate an optimization problem of user grouping and bandwidth allocation to maximize user utilities.To solve the problem, we first propose a method which is composed of an optimal bandwidth allocation algorithm and a two-level hierarchical user grouping scheme.Furthermore, we consider the channel feedback overhead in multicast scenarios with a large number of users.Based on a reasonable assumption, we propose a simplified grouping method with low feedback overhead.Simulation results demonstrate the effectiveness of our proposed method, which yields similar performance compared to an exhausting search method when serving a large number of users with high SNRs.
Impact on the Information Security Management Due to the Use of Social Networks in a Public Organization in Ecuador

It analyzed the management for the dissemination of government information in social networks and security management models applied to communicate with society. The problem is the lack of a model that integrates a security scheme to expose information from a government organization on social networks. The objective is to define a generic algorithm prototype for security policies application. It used the deductive method and exploratory research to analyze the information of the reference articles. It turned out the following: Guidelines for defining information security policies, General scheme of information security policies, Mathematical description to measure impact, and Generic algorithm prototype for the security policies application using flowchart techniques. It was concluded that for the exposure and socialization of information from a government organization to society, it is recommended to have alternatives that allow mitigating the risks, threats, and vulnerabilities of information such as integrated security models, security policies, responsible, technologies, and adequate cryptographic algorithms to improve the confidentiality, integrity, and authenticity of the information based on its mission, vision, strategic objectives of each government organization, and legal provisions such as the LOTAIP 2017 among others.
Quantum robots perturbed by Levy processes: Stochastic analysis and simulations

In the present work, the problem of quantization of a robot via the Schrodinger equation with Jerk Levy noise and the wave function plots is analyzed. Quantum average and quantum fluctuation of the trajectory positions are displayed. It is observed that the quantum robots are molecules whose link angles can be varied resulting in changing chemical properties, thus justifying the importance of this work also in physics of biological engineering. we have also made computations on the statistics of the wave function as well as transition probabilities between two stationary states. The quantum analysis has been based on using time independent perturbation theory to evaluate the approximate evolution operator of the unperturbed robot.
Bug types fixed by API-migration

It is broadly known that adaptive maintenance improves software quality by making it accessing to new features and technology that may be included in the new version of an underlying API. This work presents a case study of six large C++ open source systems to investigate the role of API-migrations during bug-fixing activities. The case study involves mining the adaptive commits that were bug fixing revisions and then categorizing the relevant fixed bugs into a set of defined kinds. The study shows that only eight kinds covered 78.95% to 88.89% of the total fixed bugs in the examined systems. Also, the frequency of occurrence of each uncovered kind has been computed for each examined system. Correlation analysis results show that the uncovered kinds have very similar frequencies across all examined systems. Overall, the undertaken study illustrates the true benefits of an adaptive maintenance in the process of bug fixing.
Test Case Selection for All-Uses Criterion-Based Regression Testing of Composite Service

Composite services evolve for various reasons.Test case selection in the regression testing is an effective technique to ensure the correctness of modified versions meanwhile to reduce the cost of testing.However, few work has studied the test case selection problem based on the data flow testing criteria.In addition, there are three observable kinds of changes during the evolution, including Process change, Binding change and Interface change, which all bring impact to the data flow.To address these issues, a test case selection approach is proposed for regression testing of BPEL (Business Process Execution Language) composite service where all-uses criterion is satisfied and all the three change types are involved.BPEL composite service is modeled with a two-level model in which XCFG (eXtended Control Flow Graph) describes the behavior of BPEL process in the first level and WSDM (Web Service Description Model) depicts the interface information of composite service and partner services in the second level.Change impact analysis is performed to identify the affected definition-use pairs by comparing and analyzing two-level models of the baseline and evolved versions.And testing paths are generated to cover the affected definition-use pairs and select test cases based on the path condition analysis.Empirical result shows that the proposed approach is effective.
Detecting Hypernymy Relations Between Medical Compound Entities Using a Hybrid-Attention Based Bi-GRU-CapsNet Model

Named entities composed of multiple continuous words frequently occur in domain-specific knowledge graphs. In general, these named entities are composable and extensible, such as names of symptoms and diseases in the medical domain. Unlike the general entities, we address them as compound entities, and try to identify hypernymy relations between them. Hypernymy detection between compound entities plays a critical role in domain-specific knowledge graph construction. Traditional hypernymy detection approaches do not perform well on compound entities for two reasons. One is the lack of contextual information, and the other is the absence of compound entities, i.e. out-of-vocabulary (OOV) problem. In this paper, we propose a hybrid-attention-based method called Bi-GRU-CapsNet for the detection of hypernymy relations. The hybrid attention mechanism consists of heuristic attention and self-adaptive attention, which are used for the lack of contextual information. The attentions focus on the differences of two compound entities on the lexical and semantic level, respectively. For OOV problem, the English words or Chinese characters in compound entities are fed into bidirectional gated recurrent units (Bi-GRUs). Additionally, we use capsule network (CapsNet) to determine the existence of hypernymy relations under different cases. Experimental results show that our proposed method outperforms other baseline methods on both English and Chinese corpora of symptom and disease pairs.
Designing a Conversational Requirements Elicitation System for End-Users

Context: Digital transformation impacts an ever-increasing degree of everyone's business and private life. It is imperative to incorporate a wide audience of user requirements in the development process to design successful information systems (IS). Hence, requirements elicitation (RE) is increasingly performed by end-users that are novices at contributing requirements to IS development projects. Objective: We need to develop RE systems that are capable of assisting a wide audience of end-users in communicating their needs and requirements. Prominent methods, such as elicitation interviews, are challenging to apply in such a context, as time and location constraints limit potential audiences. Research Method: The presented dissertation project utilizes design science research to develop a requirements self-elicitation system, LadderBot. A conversational agent (CA) enables end-users to articulate needs and requirements on the grounds of the laddering method. The CA mimics a human interviewer's capability to rephrase questions and provide assistance in the process and allows users to converse in their natural language. Furthermore, the tool will assist requirements analysts with the subsequent aggregation and analysis of collected data. Contribution: The dissertation project makes a practical contribution in the form of a ready-to-use system for wide audience end-user RE and subsequent analysis utilizing laddering as cognitive elicitation technique. A theoretical contribution is provided by developing a design theory for the application of conversational agents for RE, including the laboratory and field evaluation of design principles.
A Protocol Independent Approach in Network Covert Channel Detection

Network covert channels are used in various cyberattacks, including disclosure of sensitive information and enabling stealth tunnels for botnet commands. With time and technology, covert channels are becoming more prevalent, complex, and difficult to detect. The current methods for detection are protocol and pattern specific. This requires the investment of significant time and resources into application of various techniques to catch the different types of covert channels. This paper reviews several patterns of network storage covert channels, describes generation of network traffic dataset with covert channels, and proposes a generic, protocol-independent approach for the detection of network storage covert channels using a supervised machine learning technique. The implementation of the proposed generic detection model can lead to a reduction of necessary techniques to prevent covert channel communication in network traffic. The datasets we have generated for experimentation represent storage covert channels in the IP, TCP, and DNS protocols and are available upon request for future research in this area.
The Challenges for Regulation and Control in an Environment of Rapid Technological Innovations

Currently, amplified use of the ITC-technologies and digitalization in almost all industries has changed the value and significance of the information. The use of these new technologies offer tremendous opportunities for innovation and development, but at the same time ask for regulation and control policies to ensure appropriate storage and use of information and avoid illicit utilization of data. Moreover, use of innovative technologies such as blockchain-based technology, artificial intelligence, cloud technology, and others has complicated and disrupted the landscape of the financial services providers and their ancillary service providers such as auditors, underwriters, advisors, actuaries, lawyers, and regulators. The insurance services industry as well is substantially influenced by these current developments especially through the intensive adoption of InsurTech and RegTech solutions. With this paper, we aim through a review of the literature, to highlight the challenges in regulation and control in an environment of rapid technological innovation, specifically focusing on InsurTech and RegTech, offering logical solutions to insurance companies. We also provide an analysis of the essence of the main technologies used or potentially used by the insurance services industry as big data, blockchain-based technology, artificial intelligence, and cloud technology, identifying the benefits and risks of these innovative technologies. The analysis results in the proposed strategy to develop and calibrate controls and regulations. To make suggestions for the use of the technological innovative potential in the insurance services industry, we integrate the basic utility model of behavior, as well as the underlying regulatory and control principles as a benchmark. The basic dilemma thereby refers to the control versus innovation dilemma, as all of the strategies for control of innovation due to technology in insurance markets are imperfect. Therefore, the efficiently managed controls are required to be flexible enough to allow for quick recalibration, whenever this is necessary, possibly with Artificial Intelligence or other RegTech solutions.
Optimizing Super Resolution for Face Recognition

Face Super-Resolution is a subset of Super Resolution (SR) that aims to retrieve a high-resolution (HR) image of a face from a lower resolution input. Recently, Deep Learning (DL) methods have improved drastically the quality of SR generated images. However, these qualitative improvements are not always followed by quantitative improvements in the traditional metrics of the area, namely PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index). In some cases, models that perform better in opinion scores and qualitative evaluation have worse performance in these metrics, indicating they are not sufficiently informative. To address this issue we propose a task-based evaluation procedure based on the comparative performance of face recognition algorithms on HR and SR images to evaluate how well the models retrieve high-frequency and identity defining information. Furthermore, as our face recognition model is differentiable, this leads to a novel loss function that can be optimized to improve performance in these tasks. We successfully apply our evaluation method to validate this training method, yielding promising results.
Design of Fuzzy Ensemble Architecture Realized With the Aid of FCM-Based Fuzzy Partition and NN With Weighted LSE Estimation

Neural networks (NNs) with least square error (LSE) estimation form a certain type of single hidden layer feed-forward NNs. In this class of networks, the input connections (weights) and the biases of hidden neurons are generated randomly and fixed after being generated. The output connections are estimated by the LSE method rather than the back-propagation method. The random generation of the input connection weights and the hidden biases results in the larger number of hidden neurons to assure the quality of classification performance. To reduce the number of neurons in the hidden layer while maintaining the classification performance, we apply a “divide and conquer” strategy in this article. In other words, we divide an overall input space into several subspaces by using information granulation technique (Fuzzy C-Means clustering algorithm) and determine the local decision boundaries among related subspaces. A decision boundary defined in the input space can be considered as being composed of several decision boundaries defined in subspaces that form the entire input space. For the decision boundaries defined in the subspaces, their nonlinearity becomes lower in comparison with the one being encountered when considering the entire input space. Through the weighted LSE estimation instead of using the LSE estimation method, the connections of several NNs can be estimated without interfering with each other. After estimating the weights, the decision boundaries defined in the related subspaces are merged to a single decision boundary by using fuzzy ensemble technique. Several machine learning datasets and one real world application dataset are used to evaluate and validate the proposed fuzzy ensemble classifier. Based on the experimental results, the proposed classifier shows better classification performance when compared with the performance of some selected classifiers.
Out of control online? A combined examination of peer-offending and perceived formal and informal social control in relation to system-trespassing

Compared to the offline world, the online environment is more anonymous and therefore less easily controlled. Criminological theories generally show that people are more likely to commit crime when they experience or perceive less formal or informal social control. Consequently, one might assume that people are more likely to commit crime online than offline. As formal social control is lacking, informal social control and peer-offending may be important predictors of online offending. In this paper, we examine to what extent differences in perceived formal and informal online control are related to the likelihood that a person will commit a system-trespassing offense (i.e. malicious hacking). Additionally, we examine peer system-trespassing, and how this may interact with perceptions of formal and informal social control. In the logistic regression analyses we employ eight different measures for perceptions of formal and informal social control, in addition to peer-offending, and control variables. Both direct effects and interaction effects are examined. Our results show that peer-offending is an important predictor of system-trespassing and interaction effects show that perceived formal and informal online social control are related to this type of offending but only for specific groups. Implications will be discussed.
A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items

Content-Based recommender systems (CB) filter relevant items to users in overloaded search spaces using information about their preferences. However, classical CB scheme is mainly based on matching between items descriptions and user profile, without considering that context may influence user preferences. Therefore, it cannot achieve high accuracy on user preference prediction. This paper aims to handle context-awareness (CA) to improve quality of recommendation taking contextual information as the trend in current trend interest, in which a stream of status updates can be analyzed to model the context. It proposes a novel CA-CB approach that recommends question/answer items by considering context awareness based on topic detection within current trend interest. A case study and related experiments were developed in the big data framework Spark to show that the context integration benefits recommendation performance.
A CMA-ES-Based Adversarial Attack on Black-Box Deep Neural Networks

Deep neural networks(DNNs) are widely used in AI-controlled Cyber-Physical Systems (CPS) to controll cars, robotics, water treatment plants and railways. However, DNNs have vulnerabilities to well-designed input samples that are called adversarial examples. Adversary attack is one of the important techniques for detecting and improving the security of neural networks. Existing attacks, including state-of-the-art black-box attack have a lower success rate and make invalid queries that are not beneficial to obtain the direction of generating adversarial examples. For these reasons, this paper proposed a CMA-ES-based adversarial attack on black-box DNNs. Firstly, an efficient method to reduce the number of invalid queries is introduced. Secondly, a black-box attack of generating adversarial examples to fit a high-dimensional independent Gaussian distribution of the local solution space is proposed. Finally, a new CMA-based perturbation compression method is applied to make the process of reducing perturbation smoother. Experimental results on ImageNet classifiers show that the proposed attack has a higher success-rate than the state-of-the-art black-box attack but reduce the number of queries by 30% equally.
Improved Model of Quantum Deterministic Protocol Implementation in Channel with Noise

Traditional cryptographic methods have persistence, which are based on the alleged impossibility of solving a certain mathematical problem for a polynomial time. As an alternative quantum cryptography technologies don't depend on the computational capabilities of the intruder, because they use the unique properties of quantum particles, and are based on the principles of quantum physics laws inviolability. There are many methods and approaches used to solve the tasks of securing the privacy of message transmission without the use of encryption. One of the most advanced quantum cryptography technology is quantum direct secure communication, which allows information transmitting by an open channel without prior encryption. For this purpose, model of quantum deterministic protocol (for two modes of work) was created. Also the experimental studies of a deterministic protocol model in a channel with noise were carried out.
Privacy-Preserving Location-Based Query Over Encrypted Data in Outsourced Environment

The location-based services in the outsourced environment has become a research hotspot with the rise of cloud computing. Meanwhile, various privacy issues have been increasingly prominent. We propose an efficient spatial query protocol and another novel secure spatial query protocol using fully homomorphic encryption to accomplish privacy-preserving query processing on outsourced data. We adopt the Moore curve to transform the spatial data into one-dimensional sequence and utilize the random permutation to protect the location privacy. Furthermore, the proposed efficient scheme provides the considerable performance with data privacy preservation. Meanwhile, the secure spatial query protocol is robust against various attacks. Experiments show that the proposed schemes achieve high accuracy while improving the security when compared with the existing related approaches.