Reviewerly/exHarmony huggingface dataset:
Managing User-Centered Design Activities in Distributed Agile Development

Abstract The integration of user-centered design (UCD) activities into agile information systems development has become more popular recently. Despite the fact that there are many ways the merging of UCD activities into agile development can be carried out, it has been widely recognized that coordinating design activities with development activities is one of the most common problems, especially in distributed environments where designers, developers and users are spread over several sites. The main approach to coordinate UCD activities with distributed agile development is the use of informal methods (e.g. communication through using video conference tools). In addition to the temporal, geographical and socio-cultural barriers associated with this type of methods, a major limitation is a lack of awareness of how UCD activities and development activities affect each other. Furthermore, some agile project management tools are integrated with design platforms but fail to provide the necessary coordination that helps team members understand how the design and development activities affect their daily work. This research aims to support the effective management of integrating UCD activities into distributed agile development by (i) identifying the key activity dependencies between UX design teams and development teams during distributed UCD/agile development and (ii) designing a computer-based system to provide coordination support through managing these activity dependencies. In order to achieve these objectives, two case studies are carried out. Our findings revealed 10 main dependencies between UCD design teams and development teams as shown by six types of activity. In addition, the participatory design approach shows that developing a computer-based system to manage seven of these selected dependencies is achievable.
A hybrid model to secure the exchange of DH keys

The Diffie-Hellmann(DH) algorithm is an algorithm that allows the exchange of secret keys, shared via a channel that is not necessarily secure to ensure communication between two users. When exchanging DH keys, none of the users have proof that they are communicating with the right interlocutor, because the shared keys can be detected by a “Man in the middle”. In this article, we propose a model called DRA which combines the three encryption algorithms DH, RSA (Rivest Shamir and Ademann) and AES (Advanced Encryption Standard), it aims to improve the security of DH key exchange especially with the evolution of computing power and computer speeds. The results obtained in this article show the efficiency of our model compared to the S-SEJAD model which itself has brought a real improvement compared to existing solutions such as S-WANE, New-Two-Pass, STS, HMQV.
Argument parsing via corpus queries

Abstract We present an approach to extracting arguments from social media, exemplified by a case study on a large corpus of Twitter messages collected under the #Brexit hashtag during the run-up to the referendum in 2016. Our method is based on constructing dedicated corpus queries that capture predefined argumentation patterns following standard Walton-style argumentation schemes. Query matches are transformed directly into logical patterns, i. e. formulae with placeholders in a general form of modal logic. We prioritize precision over recall, exploiting the fact that the sheer size of the corpus still delivers substantial numbers of matches for all patterns, and with the goal of eventually gaining an overview of widely-used arguments and argumentation schemes. We evaluate our approach in terms of recall on a manually annotated gold standard of 1000 randomly selected tweets for three selected high-frequency patterns. We also estimate precision by manual inspection of query matches in the entire corpus. Both evaluations are accompanied by an analysis of inter-annotator agreement between three independent judges.
Show Me the Money! Identity Fraud Losses, Capacity to Act, and Victims’ Efforts for Reimbursement

In the Internet age, identity fraud has become quite a popular way to steal money—resulting in substantial numbers of victims as evidenced by victimization surveys. This article focuses on people who have been illegally debited from their bank account, one of the most common forms of identity fraud. For this group, we examine their patterns of action to report the incident to banks and police, and their success in achieving reimbursement of the money they lost. Following the argument of the Netherlands Council for Government Policy, it is assumed here that the “capacity to act” varies between people. In this context, it may lead to differential outcomes for victims after ID fraud. More specifically, we expect that groups in a socially disadvantaged position (low-educated) as well as impulsive people will more often refrain from contacting formal agencies (bank, police), and therefore more frequently lack reimbursement and remain with larger financial losses. Gottfredson and Hirschi’s self-control theory and Black’s theory on the behavior of law offer additional insight in support of this argument. We use data from 636 victims that were surveyed in the LISS panel, which is based on a Dutch representative population sample. Most of our hypotheses are confirmed by the data.
Enhanced Squeezing and Entanglement in Nondegenerate Three-Level Laser Coupled to Squeezed Vacuum Reservoir

Squeezing and entanglement of a two-mode cascade laser, produced by a three-level atom which is initially prepared by a coherent superposition of the top and bottom levels then injected into a cavity coupled to a two-mode squeezed vacuum reservoir is discussed. I obtain stochastic differential equations associated with the normal ordering using the pertinent master equation. Making use of the solutions of the resulting differential equations, we determined the mean photon number for the cavity mode and their correlation, EPR variables, smallest eigenvalue of the symplectic matrix, intensity difference fluctuation, and photon number correlation. It is found that the squeezed vacuum reservoir increases the degree of the statistical and nonclassical features of light produced by the system. Furthermore, using the criteria developed by logarithm negativity and Hillery-Zubairy criteria, the quantum entanglement of the cavity mode is quantified. It is found that the degree of the entanglement for the system under consideration increases with the squeezing parameter of the squeezed vacuum reservoir.
Anecdotes From Turkish Online Learning During the COVID-19 Pandemic

Emergency remote teaching, on the one hand, constitutes the ground for equality of opportunity for students; but on the other hand, it can result in inequality becoming even more pronounced for disadvantaged groups. This chapter aims to present anecdotal evidence of inequalities from the perspectives of students, educators, parents, support personnel, and administrators during pandemic in Turkey. The study is based on the embedded single-case design and was applied with a total of 250 participants. According to the findings, lack of hardware, and/or issues of an infrastructural nature were some of the most cited forms of inequality. The stakeholders also noted problems having been experienced during live sessions, unclear measurement and evaluation grading systems, and educational materials applied to students with disabilities as forms of inequality. For the future of the next generation, there is a requirement for conscious state policies to be developed and implemented during the upcoming period as countries attempt to deal with the ongoing pandemic crisis.
2D Discrete Mirror Transform for Image Non-Linear Approximation

In this paper, a new 2D transform named Discrete Mirror Transform (DMT) is presented. The DMT is computed by decomposing a signal into its even and odd parts around an optimal location in a given direction so that the signal energy is maximally split between the two components. After minimizing the information required to regenerate the original signal by removing redundant structures, the process is iterated leading the signal energy to distribute into a continuously smaller set of coefficients. The DMT can be displayed as a binary tree, where each node represents the single (even or odd) signal derived from the decomposition in the previous level. An optimized version of the DMT (ODMT) is also introduced, by exploiting the possibility to choose different directions at which performing the decomposition. Experimental simulations have been carried out in order to test the sparsity properties of the DMT and ODMT when applied on images: referring to both transforms, the results show a superior performance with respect to the popular Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) in terms of non-linear approximation.
Research on Spectrum Intelligent Recognition Technology Based on an Edge Processing Framework KubeEdge

In recent years, artificial intelligence technology has developed rapidly, spectrum recognition presents the characteristics of intelligence. However, with the increase of the amount of radio spectrum data, problems such as the large amount of computation of artificial intelligence algorithm and the shortage of storage and computing resources of spectrum recognition terminal become more and more prominent, resulting in low efficiency of spectrum recognition. Combined with edge computing technology, an spectrum intelligent recognition framework based on edge processing framework KubeEdge is proposed in this paper. The architecture uses KubeEdge framework to co-manage cloud server and edge spectrum recognition node. The training of spectrum intelligent recognition algorithm is realized in the cloud, and the spectrum of input signals is recognized and classified at the edge node, so as to achieve the purpose of cloud and edge collaborative recognition and improve the efficiency of spectrum recognition.
MARGIN: Uncovering Deep Neural Networks Using Graph Signal Analysis

Interpretability has emerged as a crucial aspect of building trust in machine learning systems, aimed at providing insights into the working of complex neural networks that are otherwise opaque to a user. There are a plethora of existing solutions addressing various aspects of interpretability ranging from identifying prototypical samples in a dataset to explaining image predictions or explaining mis-classifications. While all of these diverse techniques address seemingly different aspects of interpretability, we hypothesize that a large family of interepretability tasks are variants of the same central problem which is identifying relative change in a model’s prediction. This paper introduces MARGIN, a simple yet general approach to address a large set of interpretability tasks MARGIN exploits ideas rooted in graph signal analysis to determine influential nodes in a graph, which are defined as those nodes that maximally describe a function defined on the graph. By carefully defining task-specific graphs and functions, we demonstrate that MARGIN outperforms existing approaches in a number of disparate interpretability challenges.
A Usability Study of the Central-Bantu Multilingual Keyboards

Central-Bantu speakers usually use the predominant QWERTY keyboard on a combination of some local and European languages, such as Chichewa and English. There has been no research that looks into usability issues of Central-Bantu speakers typing text in English and in local languages on QWERTY. This article presents our experiments that assess the usability of multilingual keyboards for Central-Bantu language speakers. We first obtained a Central-Bantu layout that is partially optimized from QWERTY, based on the German-QWERTZ keyboard, and a highly optimized version based on the French-AZERTY keyboard. The two keyboard layouts (Entry and advanced-level) were optimized for both English and Chichewa, for a two-finger text-entry mode. Simulation tests measured text-entry rates for Chichewa and English. The usability experiments show that the text-entry rate of English is about 13% higher than that of Chichewa, which is close to the prediction of the simulation test. The Bantu keyboards reduced this imbalance to about 4%. While the simulation tests predicted an increase in text-entry rate, the experimental tests show a decrease in performance. Typing-error rates almost double from QWERTY to the Advanced-Bantu keyboard. In this study, we show that the Central-Bantu keyboards have high practical-performance potential for both Chichewa and English, at balanced usability levels.
Convergence of Non-Convex Non-Concave GANs Using Sinkhorn Divergence

Sinkhorn divergence is a symmetric normalization of entropic regularized optimal transport. It is a smooth and continuous metrized weak-convergence with excellent geometric properties. We use it as an alternative for the minimax objective function in formulating generative adversarial networks. The optimization is defined with Sinkhorn divergence as the objective, under the non-convex and non-concave condition. This work focuses on the optimization’s convergence and stability. We propose a first order sequential stochastic gradient descent ascent (SeqSGDA) algorithm. Under some mild approximations, the learning converges to local minimax points. Using the structural similarity index measure (SSIM), we supply a non-asymptotic analysis of the algorithm’s convergence rate. Empirical evidences show a convergence rate, which is inversely proportional to the number of iterations, when tested on tiny colour datasets <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cats</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CelebA</monospace> on the deep convolutional generative adversarial networks and ResNet neural architectures. The entropy regularization parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> is approximated to the SSIM tolerance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> . We determine that the iteration complexity to return to an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -stationary point to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}\left ({\kappa \, \log (\epsilon ^{-1})}\right)$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> is a value that depends on the Sinkhorn divergence’s convexity and the minimax step ratio in the SeqSGDA algorithm.
Properties of the network of semantic relations in the Russian language based on the RuWordNet data

Abstract It was previously shown that the WordNet thesaurus has a small-world structure. We obtained a similar result for RuWordNet, a recently created thesaurus of the Russian language, and determined the main characteristics of the network of semantic relations in the Russian language. They are the average length of the path between the vertices, the maximum path length between the vertices, the cluster coefficient and the assortativity coefficient. The distribution of the vertices degrees was also considered. We compared the values of the structural parameters of the networks for WordNet and RuWordNet. A significant difference between RuWordNet and WordNet is that RuWordNet has many semantic relations of the ‘POS-synonymy’ type (synonymy between words of different parts of speech) that are hardly found in WordNet. The values of structural constants were determined for RuWordNet both regarding this type of relations and without taking into account these relations, which allows one to estimate the influence of relations between parts of speech on the structure of the words network.
A Blockchain-Based Hierarchical Authentication Scheme for Multiserver Architecture

In a multiserver architecture, authentication schemes play an important role in the secure communication of the system. In many multiserver authentication schemes, the security of the mutual authentications among the participants is based on the security of the registration center’s private key. This centralized architecture can create security risks due to the leakage of the registration center’s private key. Blockchain technology, with its decentralized, tamper-proof, and distributed features, can provide a new solution for multiserver authentication schemes. In a lot of multiserver authentication schemes, users’ permission is generally controlled by the registration center (RC), but these permission control methods cannot be applied in the decentralized blockchain system. In this paper, a blockchain-based authentication scheme for multiserver architecture is proposed. Our scheme provides a hierarchical authentication method to solve the problems of user permission control and user revocation caused by no registration center. The security of our scheme is formally proved under the random oracle model. According to our analysis, our scheme is resistant to attacks such as impersonation attacks and man-in-the-middle attacks. In addition, our performance analysis shows that the proposed scheme has less computation overhead.
Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud

In recent years, smartphone users are interested in large volumes to view live videos and sharing video resources over social media (e.g., Youtube, Netflix). The continuous streaming of video in mobile devices faces many challenges in network parameters namely bandwidth estimation, congestion window, throughput, delay, and transcoding is a challenging and time-consuming task. To perform these resource-intensive tasks via mobile is complicated, and hence, the cloud is integrated with smartphones to provide Mobile Cloud Computing (MCC). To resolve the issue, we propose a novel framework called delay aware bandwidth estimation and intelligent video transcoder in mobile cloud. In this paper, we introduced four techniques, namely, Markov Mobile Bandwidth Cloud Estimation (MMBCE), Cloud Dynamic Congestion Window (CDCW), Queue-based Video Processing for Cloud Server (QVPS), and Intelligent Video Transcoding for selecting Server (IVTS). To evaluate the performance of the proposed algorithm, we implemented a testbed using the two mobile configurations and the public cloud server Amazon Web Server (AWS). The study and results in a real environment demonstrate that our proposed framework can improve the QoS requirements and outperforms the existing algorithms. Firstly, MMBCE utilizes the well-known Markov Decision Process (MDP) model to estimate the best bandwidth of mobile using reward function. MMBCE improves the performance of 50% PDR compared with other algorithms. CDCW fits the congestion window and reduces packet loss dynamically. CDCW produces 40% more goodput with minimal PLR. Next, in QVPS, the M/M/S queueing model is processed to reduce the video processing delay and calculates the total service time. Finally, IVTS applies the M/G/N model and reduces 6% utilization of transcoding workload, by intelligently selecting the minimum workload of the transcoding server. The IVTS takes less time in slow and fast mode. The performance analysis and experimental evaluation show that the queueing model reduces the delay by 0.2 ms and the server's utilization by 20%. Hence, in this work, the cloud minimizes delay effectively to deliver a good quality of video streaming on mobile.
A Design of Learning based Human Positioning Algorithm using Images

In this paper, a learning based human positioning algorithm using image was proposed to overcome the disadvantages of fingerprint map-based localization on hardware devices, and the human positioning accuracy was also verified through an experiment. The core parts of proposed algorithm are the learning phase of human positioning map (HPM) and human positioning phase. As a result of the experiment, the human recognition accuracy of proposed algorithm showed a high recognition accuracy of over 99.74% when a person moves between 0m and 6m.
T-Growth Stochastic Model: Simulation and Inference via Metaheuristic Algorithms

The main objective of this work is to introduce a stochastic model associated with the one described by the T-growth curve, which is in turn a modification of the logistic curve. By conveniently reformulating the T curve, it may be obtained as a solution to a linear differential equation. This greatly simplifies the mathematical treatment of the model and allows a diffusion process to be defined, which is derived from the non-homogeneous lognormal diffusion process, whose mean function is a T curve. This allows the phenomenon under study to be viewed in a dynamic way. In these pages, the distribution of the process is obtained, as are its main characteristics. The maximum likelihood estimation procedure is carried out by optimization via metaheuristic algorithms. Thanks to an exhaustive study of the curve, a strategy is obtained to bound the parametric space, which is a requirement for the application of various swarm-based metaheuristic algorithms. A simulation study is presented to show the validity of the bounding procedure and an example based on real data is provided.
Unsupervised latent event representation learning and storyline extraction from news articles based on neural networks

Storyline extraction aims to generate concise summaries of related events unfolding over time from a collection of temporally-ordered news articles. Some existing approaches to storyline extraction are typically built on probabilistic graphical models that jointly model the extraction of events and the storylines from news published in different periods. However, their parameter inference procedures are often complex and require a long time to converge, which hinders their use in practical applications. More recently, a neural network-based approach has been proposed to tackle such limitations. However, event representations of documents, which are important for the quality of the generated storylines, are not learned. In this paper, we propose a novel unsupervised neural network-based approach to extract latent events and link patterns of storylines jointly from documents over time. Specifically, event representations are learned by a stacked autoencoder and clustered for event extraction, then a fusion component is incorporated to link the related events across consecutive periods for storyline extraction. The proposed model has been evaluated on three news corpora and the experimental results show that it outperforms state-of-the-art approaches with significant improvements.
Power Spectrum Based Wi-Fi RF Fingerprint Extraction Method

Wi-Fi signals have broader spectrum and are more susceptible to multipath interference, resulting in the decline of the recognition accuracy based on radio frequency fingerprint (RFF) features. This paper proposes a novel power spectrum based Wi-Fi RF fingerprint extraction method which can eliminate the impact of multipath channels and achieve purer RFF characteristics. The experimental results show that the recognition rate of 27 devices can reach 93.3% when random forest model is used.