Reviewerly/exHarmony huggingface dataset:
PAC Learning and Occam’s Razor: Probably Approximately Incorrect

Computer scientists have provided a distinct justification of Occam’s Razor. Using the probably approximately correct framework, they provide a theorem that they claim demonstrates that we should favor simpler hypotheses. The argument relies on a philosophical interpretation of the theorem. I argue that the standard interpretation of the result in the literature is misguided and that a better reading does not, in fact, support Occam’s Razor at all. To this end, I state and prove a very similar theorem that, if interpreted the same way, would justify the contradictory Anti-Occam’s Razor—the principle that we should favor more complex hypotheses.
Robust Comparative Analysis of Zero-Knowledge Proofs using Discrete Logarithm Problem

Robustness features are essential for any black-box or non-black box security mechanism in cryptography. Zero-knowledge proofs is an example of non-black box security mechanism used in cryptography. In this paper, we have explored and analyzed zero-knowledge proofs using discrete logarithm problem and compared it with integer factoring problem to check its robustness and compatibility for higher security in cryptography. The upper bound of Pollard's rho algorithm for discrete logarithm is growing faster in comparison with Pollard's rho factoring algorithm. The covariance between asymptotic notations of two different algorithms for zero-knowledge proofs are computed and positive covariance result is obtained, which clearly shows that random variants used for both algorithms are growing in same direction and have similar behavior. The functional value line segment of Pollard's rho algorithm for discrete logarithm converges with the functional value line segment of Pollard's rho factoring algorithm at a point.
Noise Removal Super-resolution for Camera Images Utilizing Total Variation Regularization Method

It is necessary to remove noise from images in order to prevent deterioration due to noise during superresolution for surveillance camera images. We propose to apply Total Variation regularization for time axis direction, and it is possible to separate noise and moving objects, and by applying noise removable process to the output, it is achieved to remove noise for super-resolution images.
Current Trends on the Acquisition, Virtual Representation, and Interaction of Cultural Heritage

Cultural heritage (CH) tells us about our roots, and therefore, constitutes a rich value for the society. Its conservation, dissemination, and understanding are of utmost importance. In order to preserve CH for the upcoming generations, it needs to be documented, a process that nowadays is done digitally. Current trends involve a set of technologies (cameras, scanners, etc.) for the shape and radiometric acquisition of assets. Also, intangible CH can be digitally documented in a variety of forms. Having such assets virtualized, a proper dissemination channel is of relevance, and recently, new technologies that make use of interaction paradigms have emerged. Among them, in this chapter, the authors focus their attention in the technologies of virtual reality (VR), augmented reality (AR), and serious games (SGs). They aim to explore these technologies in order to show their benefits in the dissemination and understanding of CH. Though the work involving them is not trivial, and usually a multidisciplinary team is required, the benefits for CH make them worth it.
Does Word2Vec encode human perception of similarityƒ A study in Bangla

The quest to understand how language and concepts are organized in human mind is a never-ending pursuit undertaken by researchers in computational psycholinguistics; simultaneously, on the other hand, researchers have tried to quantitatively model the semantic space from written corpora and discourses through different computational approaches - while both of these interacts with each other in-terms of understanding human processing through computational linguistics and enhancing NLP methods from the insights, it has seldom been systematically studied if the two corroborates each other. In this paper, we have explored how and if the standard word embedding based semantic representation models represent the human mental lexicon. Towards that, We have conducted a semantic priming experiment to capture the psycholinguistics aspects and compared the results with a distributional word-embedding model: Bangla word2Vec. Analysis of reaction time indicates that corpus-based semantic similarity measures do not reflect the true nature of mental representation and processing of words. To the best of our knowledge this is first of a kind study in any language especially Bangla.
ANDRAGOGICAL CONCEPT OF THE DEVELOPMENT OF INFORMATION AND COMMUNICATION COMPETENCE OF TEACHERS IN INSTITUTIONS OF POSTGRADUATE TEACHER EDUCATION

&#x0D; &#x0D; &#x0D; &#x0D; The article discusses the issues of andragogical support for the development of information and communication competence of teachers in institutions of postgraduate Pedagogical education. The authors analyze the current socio-educational situation; emphasize the importance of continuing education and the development of andragogical competence of employees of postgraduate education institutes. The attention is focused on the diversification of the types of advanced training of pedagogical and scientific-pedagogical workers, which necessitates the modernization of content, forms, technologies in the system of postgraduate education, in particular the development of information and communication competence of pedagogical workers. The basic andragogical principles, the functions of the teacher-andragog, the methods of the andragogical approach to creating special conditions for the interaction of teachers and students are substantiated. Based on theoretical and empirical research, the state of this problem is analyzed on the example of the development of information and communication competence of methodologists of regional methodological services.&#x0D; &#x0D; &#x0D; &#x0D;
From 2.5D Bas‐relief to 3D Portrait Model

Abstract In contrast to 3D model that can be freely observed, p ortrait bas‐relief projects slightly from the background and is limited by fixed viewpoint. In this paper, we propose a novel method to reconstruct the underlying 3D shape from a single 2.5D bas‐relief, providing observers wider viewing perspectives. Our target is to make the reconstructed portrait has natural depth ordering and similar appearance to the input. To achieve this, we first use a 3D template face to fit the portrait. Then, we optimize the face shape by normal transfer and Poisson surface reconstruction. The hair and body regions are finally reconstructed and combined with the 3D face. From the resulting 3D shape, one can generate new reliefs with varying poses and thickness, freeing the input one from fixed view. A number of experimental results verify the effectiveness of our method.
On the privacy of a code-based single-server computational PIR scheme

We show that the single-server computational PIR protocol proposed by Holzbaur, Hollanti and Wachter-Zeh in [6] is not private, in the sense that the server can recover in polynomial time the index of the desired file with very high probability. The attack relies on the following observation. Removing rows of the query matrix corresponding to the desired file yields a large decrease of the dimension over $\mathbb {F}_q$ of the vector space spanned by the rows of this punctured matrix. Such a dimension loss only shows up with negligible probability when rows unrelated to the requested file are deleted.
A Security Analysis of Blockchain Based Decentralized Energy Exchange System

Energy trading using distributed smart grids is gaining popularity these days. The integration of Blockchain with distributed smart grids will provide an even better solution with features like sophisticated consumption monitoring and a robust and scalable energy trading system. Blockchain with its properties of secure recording, anonymity, transparency and distributed implementation will further strengthen the decentralized operation of energy trading among various entities of the smart grid network. However, the full potential of this new network cannot be achieved due to the inherent security and privacy issues. In this research paper, the authors have identified a security vulnerability in the transaction flow implementation. First, an overview of a decentralized energy trading network has been given. Then the process flow has been discussed in detailed at every stage. Finally, two scenarios have been presented where the poor security measures can be exploited to perform a double spending attack (DSA) with a help of a Block Delay Technique.
Performance Analysis of Whale Optimization Algorithm Based on Strategy Parameter

The performance of heuristic algorithms highly depends on the parameter values of the algorithms. Whale optimization algorithm (WOA) is a newly developed heuristic algorithm which has strategy parameter a that decreases linearly from 2 to 0 as iteration increases. In this paper, two algorithms, modified whale optimization algorithm-1 (MWOA-1) and modified whale optimization algorithm-2 (MWOA-2), have been proposed based on the variation of the strategy parameter a. The experiments are performed on a set of 23 benchmark problems. Results are compared with original WOA, gravitational search algorithm and grasshopper optimization algorithm. Based on the analysis of results, it is concluded that the overall performance of MWOA-1 and MWOA-2 is better than others on scalable unimodal function with dim = 30, scalable multimodal functions with dim = 30 and low-dimensional multimodal functions.
Interactive Assigning of Conference Sessions with Visualization and Topic Modeling

Creating thematic sessions based on accepted papers is important to the success of a conference. Facing a large number of papers from multiple topics, conference organizers need to identify the topics of papers and group them into sessions by considering the constraints on session numbers and paper numbers in individual sessions. In this paper, we present a system using visualization and topic modeling to help the construction of conference sessions. The system provides multiple automatically generated session schemes and allows users to create, evaluate, and manipulate paper sessions with given constraints. A case study based on our system on the VAST papers shows that our method can help users successfully construct coherent conference sessions. In addition to conference session management, our method can be extended to other tasks, such as event and class schedule.
Deep Listening: Early Computational Composition and its Influence on Algorithmic Aesthetics

The popularity of the program DeepDream, created by Google engineer Alexander Mordvintsev, exploded due to the open source availability of its uncanny, hallucinogenic aesthetic.Once used to synthesize visual textures, the program popularized the concept of neural network training through image classification algorithms, inspiring visual art interrogating machine learning and the training of proprietary prediction algorithms; though DeepDream has facilitated the production of many mundane examples of surreal computer art, it has also helped to produce some conceptually rich visual investigations, including MacArthur "genius" awardee Trevor Paglen's recent installation A Study o f Invisible I mages.While the significance of trained neural networks is presently considered valuable to computer vision experimentation, a medial archaeological investigation of the conceptual underpinnings of machine learning reveals the fundamental influence early sonic experiments in computational music have in its computational and conceptual framework.Early computational music works, such as Lejaren Hiller Jr. and Leonard Isaacson's Illiac Suite (1957), the first score composed by a computer, as well as Hiller and John Cage's ambitious multimedia performance HPSCHD (1969), used stochastic models to automate game-like processes, such as Giovanni Pierluigi da Palestrina's Renaissance-era polyphonic instruction, as well as the I Ching divination process of casting coins or yarrow stalks.Hiller's concerns regarding the historical use of compositional/mathematical gameplay uncovers a conceptual and performative emphasis anticipating the "training" of visual models.Through the adverse reactions of audiences to Hiller's compositions, written by what the press deemed derogatorily "An electronic brain" in 1957 parallel public reactions to the disturbing mutations of DeepDream, popular participation in the open source project signals a growing willingness to collaborate creatively with computers to interrogate both computational and cognitive processes.
Double‐weighted fuzzy clustering with samples and generalized entropy features

Summary For the dataset with different sample contributions and different feature importance, it is difficult to acquire a proper cluster structure that covers the entire features of the sample set. To improve the clustering result, a novel weighted fuzzy clustering algorithm based on both samples and generalized entropy features, called SGEF‐WFCM, is proposed in this article, among which a new objective function is developed on the basis of feature‐weighted generalized entropy regularization with a double‐weighting strategy of samples and features, the weighted coefficients of the features to each cluster, as well as the importance of the samples to the cluster are calculated dynamically, to obtain a better clustering result. Finally, experiments on both synthetic datasets and real‐world datasets from UCI are employed to verify the performance of the proposed SGEF‐WFCM algorithm. The results show that SGEF‐WFCM is superior to the conventional FCM algorithm in both the effectiveness and the usefulness during practices.
FPGA Implementation of Encryption Algorithms Based on Residual Polynomials

This paper describes an encryption algorithm based on a polynomial system of residual classes. We study the FPGA bitstream implementation on the Xilinx and 16-nm UltraScale™ ASIC architecture which enables floating-point operation, multi-processing, parallelism, pipelining, highperformance computing, etc. The software-based bitstream encryption and on-chip decryption are performed with the stored encryption key and encrypted bitstream, generated by a Vivado tool. The self-authenticating algorithms with a symmetric key are investigated. The encryption algorithms are implemented using the polynomial system of residual numbers. Data encryption in a residual number system is effectively implemented by ASICs. Design of irreducible polynomial is proposed and discussed. Our developments and findings are empowered by the low-power FinFET FPGA architecture.
Semi-Supervised Meta-Learning via Self-Training

The goal of meta-learning is to learn a learning procedure to generate a learner from only a handful of labeled datapoints. However, the learning procedure is learned by a meta-learner from an enormous amount of few-shot tasks constructed from a large amount of labeled datapoints. From this point of view, few-shot learning is also depending on huge amount of labeled data. In this paper, we present a simple and efficient method for few-shot classification in a semi-supervised setting where only a small portion of training samples are labeled. We assign these unlabeled data pseudo-labels using a classifier trained with both labeled and unlabeled data. Once the pseudo-label obtained, we can run meta-learning over tasks constructed from labeled and pseudo-labeled data. We evaluate our method on miniImagenet and tieredImagenet benchmarks whose meta-training sets are split into unlabeled portion and labeled portion further in order to adapt to our framework. Our experimental results confirm that our semi-supervised meta-learning approach acquires a considerable performance gain over meta-learning with only labeled data and significantly outperforms previous state-of-the-art semi-supervised meta-learning methods.
Authorship Verification of Yorùbá Blog Posts using Character N-grams

The task of determining whether a pair (or more) documents were written by the same author comes under authorship verification. N-grams are sequences of elements appearing in texts, they can be words, POS tags, characters, or some other elements that can be encountered one after another in texts. The tasks in authorship verification were more challenging as it focuses on whether the target author and the text to be used have a closely related style. In this paper, an authorship verification task on a Yorùbá blog posts is hereby presented. N-grams features were extracted from the corpus and inductive learning techniques was applied to build feature-based models in order to perform the automatic author identification. K-means clustering algorithm was used in the study since the supervised algorithm cannot be applied to the one-class classification of the dataset. The evaluation was done with Silhouette Coefficient algorithm which is used to evaluate unlabeled data. The result obtained is positive which indicates the data points have a strong relationship with the dataset. The obtained result signifies a yes relationship between the posts. This signifies that the posts were from the same author.
An Evening at the Ballroom: Whatever Happened to Televanilla?

This article re-introduces the submerged 1968 performance Televanilla, an improvisational theatre dance piece that deployed analogue image-processing tools to establish a dialogical system with technological media.It argues that the performance anticipated principal concepts and strategies for real-time human-machine interaction, and the "virtual" representation of participants in mediated art environments.Although the performance received reviews from renowned critics in prominent New York newspapers and magazines, it disappeared from the historical records almost entirely.The article provides a critical evaluation of the artists' conceptual and technical approaches towards the deployment of analogue media technology for creating an early example of audience participation and human-machine interaction in mediated artworks.This analysis is complemented by an evaluation of contemporary reviews of the performance to illustrate to what degree the critical reception at the time was prepared to appraise the concept of technology-based interactivity through a human-machine interface in artworks appropriately.
Design and Application of m-Mutation Operator in Genetic Algorithm to Solve Traveling Salesman Problem

This research work intends to elucidate Traveling Salesman Problem, a variant of Symmetric Hamiltonian Cycle Problems more capably. A different mutation operator called m-mutation operator for genetic algorithm is proposed to solve this problem. The efficiency of proposed mutation operator is compared with existing mutation operators by retaining the same selection, crossover and fitness function. The mutation operator is tested with data from TSPLIB dataset. The intercity distance table of cities was input to the coded C program which implemented the proposed mutation operator. The same dataset was used to compare the performance of proposed mutation operator with other existing mutation operators. The results of the experiment confirm that proposed mutation operator searches better solutions quicker compared to other mutation operators.
Research on Analysis of Power System Transient Signal by Neural Network and Genetic Algorithm

It is an important and difficult subject to acquire accurate transient signal parameters in the field of power system signal analysis. A neural network combined with genetic algorithm analysis approach for power system transient signal is proposed to improve the analysis precision of the transient signal including a period component varying in spectral waveform, a non-period component varying in exponential waveform and switching operation sequence. The signal parameters which are assembly estimated based on numeric differential and genetic algorithm are used as initial value of the neural network. The fundamental frequency is treated as weight to be adjusted to estimate the signal frequency and all harmonics' amplitudes and phases. The learning rate and the delayed frequency adjustment improve the convergence performance. The result of Matlab simulation demonstrates that the algorithm achieved high accuracy and rapid convergence.
WITHDRAWN: Extracting actionable knowledge from social networks with node attributes

The Publisher regrets that this article is an accidental duplication of an article that has already been published in Expert Systems with Applications, volume 152, 15 August 2020, 113382 http://dx.doi.org/10.1016/j.eswa.2020.113382. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
Wellbeing at Work: Four Perspectives on What User Experiences with Artifacts May Contribute

Most work involves the use of artifacts; thus, user experience (UX) is a factor in how most employees experience their work. This study revisits the tool, media, dialogue-partner, and system perspectives on artifact use to explore how UX may contribute to wellbeing at work. It is found that artifacts foster positive UX when they lend the user expressive power (tool), are transparent (media) or perceptive (dialogue partner).They foster negative UX when they break the user’s task focus or make the user a mere system component. These findings are discussed and refined by elaborating the classic concepts of ready to hand and present at hand.
Analysis on the Cost of Implementing the Flipped Classroom Model in Universities

In this paper, the cost factors of implementing the flipped classroom model are analyzed from two aspects of fixed cost and variable cost on the basis of the teaching practice research of the flipped classroom model in colleges and universities. Meanwhile, the cost factors affecting the increment of teaching value are analyzed and sorted, and some conclusions are drawn, for example, the hardware cost is the common cost factor of the flipped classroom and traditional teaching model, and the human resource cost of teachers has become the most important extra cost and plays a vital role in many links of the implementation of the new teaching model compared with the traditional teaching model. Through the research in this paper, it is expected to help university administrators and teachers to make reasonable cost investment and resource allocation, so as to realize the maximization of educational benefits under the new teaching model.
HPCCloud Seer: A Performance Model Based Predictor for Parallel Applications on the Cloud

With the continual increase in the high performance computing (HPC) market share, the need for a cheaper and widely available system rather than the expensive typical HPC systems increases. A promising alternative to HPC typical systems is the cloud computing environment which is characterised by being cheap, flexible, scalable and available. However, the cloud is based on virtualization which increases the latency to access the processing and network resources due to resource sharing. This makes the cloud an unpredictable environment to long run time programs such as HPC applications. Hence, modelling and understanding performance is essential for exploiting such environment. In this paper we propose a predictor for the execution time of the message passing interface (MPI) based applications on the cloud, as they are a major class of HPC applications. The predictor is based on an analytical performance model through considering the cloud resources as a queueing network, and the parallel applications as jobs contesting for the shared resources. The prediction based on the proposed model is measured on both a cluster of bare-metal servers and on a group of virtual machines. The overall accuracy of this prediction is 88% for 10 benchmarks, 5 from SPEC-MPI and 5 from NASA parallel benchmarks.