Reviewerly/exHarmony huggingface dataset:
Advancement of Online Learning at DOBA Business School on the Basis of the OOFAT Model

The purpose of this paper is to present an example of good practice in comprehensive and theoretically and professionally grounded planning and implementation of further development of online learning at DOBA Business School. We decided for the OOFAT model as the starting point. Based on the gap between the achieved and target (desired) levels of subcategories of flexibility and openness of the OOFAT model and taking into account the trends in e-learning and institutional and legal formal constraints, we have prepared five sets of concrete activities to advance the online learning model by 2025. These are: mobile learning, learning analytics, multimedia and interactive learning materials, open education, including open educational resources, and recognition (accreditation). The revised model of online learning will represent a shift towards the third (semantic) generation of e-learning, which will place DOBA Business School close to the leading educational institutions in the field of technology-enhanced education at the global level.
A Pervasive Achievement of Iterative Mean Filter for An Outlier Extinguish Objective on FVIN

For outlier extinguish investigating population, especially on the impulsive outlier, the outlier extinguish approach is a prevalent process, which is first executed before other progress operations therefore the iterative mean filter (IMF) is originated for FVIN (Fix Value Impulsive Noise) and turned into one of the most achievement outlier extinguish approach. This academic article addresses to prospect the outlier extinguish approach established on IMF, which is originated from mean filter in order to carry out the deficiency of the foregoing outlier extinguish approach, for FVIN at omnipresent scattering of outlier consistency (5%-90%). In the experimental analysis, abundant outlier figures, which are unified of F16, Girl, Lena and Pepper at omnipresent scattering of outlier consistency, are examined for analyzing the achievement of an outlier extinguish approach established on iterative mean filter. From these results, the outlier extinguish approach established on IMF has superior achievement, in contrast with SMF, MF, AMF.
Handwritten Digit Recognition for Two Digits Using CNN &amp; Image Processing

The hand-written recognition of digits is a maj or step in many document processing and analysis applications using digital image processing technology. Image processing and Machine Learning are fast-growing domain with growing applications in engineering and computer science. Pre-processing and analysis of documents are becoming more popular in many pattern recognition applications. The aim of this paper is to propose a system which will take image as an input and will automatically detect and display the digit present in it. This will save the time and efforts and will reduce the chances of involvement of human error in the system. This system can be further used in many applications like reading postal addresses, bank check amount, forms etc. In the proposed system input image with handwritten digit will be taken as input and after pre-processing it the image will be given to the model for further prediction. The output will be saved in a text file for future use.
An improved sparrow search algorithm for mobile robot path planning

This paper proposes an improved sparrow search algorithm for robot path planning. The improved heuristic algorithm introduces Tent map and opposition-based learning scheme into sparrow search algorithm. The Tent map is applied to initialize population for greater diversity, and opposition-based learning enhances the diversity of the population in evolution process, so as to avoid the premature convergence of the algorithm. The proposed algorithm is adopted to mobile robot path planning. Experiment results show that the improved algorithm can guide the mobile robot to find the optimal path quickly, and also prove the superiority and effectiveness of the proposed algorithm over the original algorithm.
Investigation of Subword-Based Bilingual Automatic Speech Recognition for Indian Languages

Modelling Indian languages is difficult as the number of word forms they present is high. In such cases, prior work has proposed using subwords instead of words as the basic units to model the language. This provides the potential to cover more word forms. Additionally, previous work revealed that all Indian languages have several phonetic similarities. Consequently, multilingual acoustic models have been proposed to counter the data scarcity issue most Indian language datasets have. However, these models use monolingual language models (LM). They do not exploit the common basic tokens that certain Indian languages have. This motivated us to implement a bilingual subword-based Automatic Speech Recognition (ASR) system for Hindi and Marathi. Further, we try a combination of word-based monolingual LM with bilingual acoustic model to examine the reason for degradation in word-based multilingual ASRs. Our experiments show that multilingual subword-based ASR models outperform their word-based counterparts by upto 9.77% and 26.35% relative Character Error Rate (CER) in the case of Hindi and Marathi respectively.
A Diversified Attention Model for Interpretable Multiple Clusterings

Multiple clusterings can explore the same set of data from different perspectives by discovering different and meaningful clusterings. However, most, if not all, of the existing approaches overwhelmingly focus on the diversity between clustering subspaces, and pay much less attention on the salience of the subspaces. As a consequence, the quality of the produced clusterings is an understudied aspect of the problem. Furthermore, existing methods cannot explain the unique internal subspace structure of each clustering, and cannot incorporate multi-facet knowledge to generate different clusterings. In this paper, we propose a solution named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iMClusts</b> ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</u> nterpretable <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ultiple <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Clust</u> ering <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u> by diversified attention). iMClusts makes use of the expressive representational power of deep autoencoders and multi-head attention to generate multiple salient embedding matrices, and multiple clusterings therein. In addition, it leverages multi-facet knowledge and enhances the diversity between heads to boost the quality and diversity of multiple clusterings. Experimental results on benchmark datasets show that iMClusts can generate multiple clusterings with quality, interpretability, and diversity.
Harnessing Energy of M-ary Hopfield Neural Network for Connectionist Temporal Sequence Decoding

Sequence decoding is the core component of systems that deal with sequence alignment problems like continuous speech recognition, visual scene labelling, multimedia storage and retrieval. In this paper, we address the problem of connectionist temporal sequence decoding in the context of movie sequence data. We present a novel decoding algorithm and associated system which harnesses the energy of an extended M-ary Hopfield Neural Network (MHNN) to decode movie sequence data. This is a first of its kind approach using the Hopfield associative memory based connectionist framework to solve the sequence alignment problem. We demonstrate with experiments the robustness of the proposed algorithm and system to handle temporal variability of the input sequence which is very crucial for connectionist sequence decoding system.
Robust Self-Tuning Semiparametric PCA for Contaminated Elliptical Distribution

Principal component analysis (PCA) is one of the most popular dimension reduction methods. The usual PCA is known to be sensitive to the presence of outliers, and thus many robust PCA methods have been developed. Among them, the Tyler's M-estimator is shown to be the most robust scatter estimator under the elliptical distribution. However, when the underlying distribution is contaminated and deviates from ellipticity, Tyler's M-estimator might not work well. In this article, we apply the semiparametric theory to propose a robust semiparametric PCA. The merits of our proposal are twofold. First, it is robust to heavy-tailed elliptical distributions as well as robust to non-elliptical outliers. Second, it pairs well with a data-driven tuning procedure, which is based on active ratio and can adapt to different degrees of data outlyingness. Theoretical properties are derived, including the influence functions for various statistical functionals and asymptotic normality. Simulation studies and a data analysis demonstrate the superiority of our method.
Assessment of static type checking compliance of EOLANG

The EOLANG programming language is a novel technology relying on formal phi-calculus similar to lambda-calculus for functional programming languages, and on design choices declared as mitigating most weak points of mainstream object-oriented programming languages. EO is under active development up to date and has some obvious development vectors. One of these vectors is development of compilation time type checker. With dynamic type checking provided by now, writing and testing programs in EO takes long and is vulnerable to bugs. In this paper the assessment of static type checking compliance of EOLANG is performed. The results reveal serious weak points in EOLANG implementation and theoretical basis making development of static type checker impossible.
Exploring Kernel-Based Texture Transfer for Pose-Guided Person Image Generation

Pose-guided person image generation that aims to transfer the pose of a given person to a target pose has recently received lots of research attention. Due to the spatial misalignment and occlusions of different local body parts by pose variations, this task is still challenging especially in maintaining high-fidelity textures and body structures in generated images. Besides, most works also suffer from the limited number of texture styles in the given person datasets, restricting the diversity of generated persons' appearances. To solve these problems, we design a Kernel-based Texture-Fusion Joint Refinement Network (TFJR-Net) to jointly refine the structure and texture information of generated images. First, we leverage a bone-map representation to guide the generation of human parsing maps, which has more structure priors and richer context information than traditional key-point maps, thus reduce the uncertainty of generated body structures. Next, a Texture-Kernel Injection Normalization module (TKIN) is proposed to inject the per-region texture-kernel into the corresponding semantic region from the human parsing map, which decouples the texture and shape information, and also preserves fine-grained features for complex textures. Furthermore, we are the first to introduce external texture patterns outside of the dataset in human semantic regions such as the upper clothes. We fuse the two texture domains in a shared texture space through our designed texture-fusion TKIN modules. Extensive experiments are conducted on the Deepfashion dataset, with the DTD dataset as an external texture source. The experimental results demonstrate the superiority of our proposed method in generating persons of better textures and structures than state-of-the-art works, and also show the generalization ability of our proposed method to absorb diversified external textures for generating person images. The source codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pilgrim00/TKIN</uri> .
Websites of ICSSR Research Institutes: An Evaluation

In this digital era, the user's communities are increasingly becoming more web-savvy and using the new emerging technologies for information communication, sharing, and collaboration which is reflected in users' information seeking, searching, and retrieval behaviors. In such a scenario, the websites emerged as an important tool for the dissemination of information to the remotely located users' and play an important role in the information dissemination process. The present study was intended to evaluate the contents and design of 29 ICSSR research institutes' websites. The research data was collected by survey and observation methods using a checklist and some web analyzer tools. The study shows that Delhi has the highest number of ICSSR research institutes (7). About half of the ICSSR institutes i.e. 13(44.83%) were providing the link for job information/vacancy on their website and Facebook was the most common social media tool used by ICSSR institutes and 16 (55%) research institutes have given Facebook links on their website followed by Twitter- 14(48.28%) and YouTube.The interlinking pattern was very weak among ICSSR research institutes' websites and the Institute of Public Enterprise (IPE) scored the highest 150.1 in website performance in the Nibbler test.
How Does This New Developer Test Fit In? A Visualization to Understand Amplified Test Cases

Developer testing, the practice of software engineers programmatically checking that their own components behave as they expect, has become the norm in today’s software projects. With the constantly growing size and complexity of software projects and with the rise of automated test generation tools, understanding a test case is becoming more and more important compared to writing test cases from scratch.This holds especially in the area of developer-centric test amplification, where a tool automatically generates new test cases to improve a developer-maintained test suite. To investigate how visualization can help developers understand and judge test cases, we present the TESTIMPACTGRAPH, a visualization of the call tree and coverage impact of a JUnit test case proposed for amplification. It empowers the developer to drill down into the behavior of a test case, as well as providing them a clear view on how the proposed test case contributes to the coverage of the overall test suite. In a think-aloud study we investigate which information developers seek from the TESTIMPACTGRAPH, how its features can support them in accessing this information, and observations regarding the coverage impact of test cases. We infer ten actionable recommendations on how developer tests can be visualized to help developers understand their behavior and impact.
Multiresolution Decomposition Analysis via Wavelet Transforms for Audio Deepfake Detection

Voice and face recognition are becoming omnipresent, and the need for secure biometric technologies increases as technologies like deepfake are making it increasingly harder to spot fake generated content. To improve current audio spoofing detection, we propose a curated selection of wavelet transforms based-models where, instead of the widely employed acoustic features, the Mel-spectrogram image features are decomposed through multiresolution decomposition analysis to better handle spectral information. For that, we adopt the use of median-filtering harmonic percussive source separation (HPSS), and perform a large-scale study on the application of several recent state-of-the-art computer vision models on audio anti-spoofing detection. These wavelet transforms are experimentally found to be very useful and lead to a notable performance of 4.8% EER on the ASVspoof2019 challenge logical access (LA) evaluation set. Finally, a more adversarialy robust WaveletCNN-based model is proposed.
Communication-efficient Federated Learning Design with Fronthaul Awareness in NG-RANs

Next Generation Radio Access Networks (NG-RANs) have become a promising paradigm to meet the strict demands of the 5G and beyond applications by distributively pushing the radio and computing functionalities in the close approximate to end-users. With the emergence of new technologies, network densification, and richer and more demanding applications, the limited capacity of the fronthaul links and privacy concerns poses a severe constraint on realizing NG-RAN systems in the real environment. To tackle these challenges, we propose a Federated Learning (FL)-based NG-RAN algorithm, named FedNG, in which the User Equipment (UEs), as well as NG-RAN infrastructures, collaborate throughout the learning process and the sharing prediction model to ensure privacy and relieve the burden on fronthaul interface. Specifically, our proposed scheme enables Distributed Units (DUs) to cooperatively learn a shared predictive model by taking the first-phase training models of the DUs as the initial input of the local training and then uploading sub-optimal DU models to the Central Unit (CU) to involve in the next phase of global training. Finally, numerical results are provided to evaluate our proposed scheme in terms of accuracy, service latency, and traffic size. The convergence of our proposed algorithm confirms that our approach significantly outperforms the existing state-of-the-art solution based on Federated Averaging (FedAvg).
Effective and Interpretable Information Aggregation with Capacity Networks

How to aggregate information from multiple instances is a key question in several machine learning problems such as multiple instance learning. Prior neural models implement different variants of the well-known encoder-decoder strategy according to which all input features are encoded a single, high-dimensional embedding which is then decoded to generate an output. In this work, inspired by Choquet capacities, we propose Capacity networks. Unlike encoder-decoders, Capacity networks generate multiple interpretable intermediate results which can be aggregated in a semantically meaningful space to obtain the final output. Our experiments show that implementing this inductive bias leads to improvements over different encoder-decoder architectures. Moreover, the interpretable intermediate results make Capacity networks interpretable by design, which allows a semantically meaningful inspection, evaluation, and regularization of the network internals.
Internet Security: Threats and Its Preventive Measures

Internet security is a broad issue that encompasses security for all transitions made over the Internet. Internet security provides authentication when data is exchanged over the Internet. Internet security is becoming a top priority, especially for financial and government institutions, as lack of security is a serious threat to the integrity and confidentiality of any organization. Since the Internet is an insecure channel for exchanging private data on messages and is susceptible to intrusion or frauds such as phishing. Therefore, it is necessary to implement certain methods to protect your data messages. In this paper, we provide an analysis of various security threats and the protective measures that can help the users to protect their data from unwanted access.
Digital Currency Supervision Based on Blockchain Technology: A literature review

The 21 st century has witnessed the revolutionary changes that Bitcoin brought. Notably, the application of digital transactions is not limited to the financial field. Yet the research on its regulation policy seems disproportionally little, and even fewer studies have mentioned the system behind the virtual currency – blockchain technology. Security matters of online transactions have gradually come into people’s vision recently. The focuses come down to what blockchain technology is, how it can add to digital currency regulation and the potential of blockchain on refining current regulation policies. This paper helps provide a guideline for virtual currency supervision policy and emphasizes the practical use of blockchain technology. A literature review underpins the study; the research methodology is based on a qualitative perspective. Existing regulatory policies in the USA and UK are assessed to find out their strengths and challenges and examine the use of blockchain technology to alleviate currently implemented policies.
Robust Multivariate Modelling for Heterogeneous Data Sets with Mixtures of Multivariate Skew Laplace Normal Distributions

Modelling multivariate heterogeneous data with taking into account skewness and thick-tailedness is a challenging problem. Finite mixture model of multivariate normal distributions is often considered for modelling heterogeneous data sets in multivariate settings. However, in real-life problems, besides heterogeneity of the data sets, they may have skewed and/or heavy-tailed empirical distributions so that modelling with finite mixture of normal distributions may not provide an adequate model to represent all the features of data. Finite mixtures of more flexible multivariate distributions have been introduced in the literature to simultaneously overcome heterogeneity, skewness and heavy-tailedness in multivariate data sets. In this study, using the favourable properties of the multivariate skew Laplace normal (MSLN) distribution proposed by [13, 14], we introduce finite mixtures of MSLN distributions that can be considered as an alternative mixture model to deal with skewness and heavy-tailedness simultaneously in multivariate heterogeneous data sets. We will propose the maximum likelihood (ML) estimation method to estimate the parameters of finite mixtures of MSLN distributions via the expectation-maximization (EM) algorithm proposed by [10]. We will provide a simulation study and a real data example to demonstrate the performance and the applicability of proposed mixture model.
Design and Implementation of a Security Baseline Check System for UOS

The Unity Operating System (UOS) has been launched in the process of localization in China, but the security of the operating system needs further study. To realize the terminal security management, China proposed the “Chinese Government Desktop Core Configuration Specifications” in 2008, referring to the federal desktop core configuration plan of the United States. Therefore, this paper designs and implements a security baseline check system for UOS based on browser/server architecture based on the CIS benchmark. This system consists of four modules: system baseline check module, middleware baseline check module, database baseline check module, and vulnerability check module. The UOS V20 (1050a) with initial configuration was tested using this system. A total of 77 items were checked, among which 41 items failed, and the final score was 45 points, which proved the system’s effectiveness for all basic system checks, baseline checks of different servers and databases, and partial detection of publicly available vulnerabilities. However, this system has limitations for checking more complex operating systems, so there is still a need to optimize the security baseline check algorithm to check more types of operating systems.