Reviewerly/exHarmony huggingface dataset:
A Solution Paper on Cyber Security Awareness Campaign Lacking in Rural and Urban Area of India

The present paper focuses on Cyber Security Awareness Campaign’s lacking in many rural and urban area of India and Changing behavior requires more information security awareness programs and we extract essential components from Social media and with appropriate procedural and technological work. We can use it for an awareness campaign. Large amount of raw data that has been viewed by users in the form of text, videos, photos and audio. The outcome showed that the delivery methods improves the awareness on the general population.
PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning

The application of deep learning to medical image segmentation has been hampered due to the lack of abundant pixel-level annotated data. Few-shot Semantic Segmentation (FSS) is a promising strategy for breaking the deadlock. However, a high-performing FSS model still requires sufficient pixel-level annotated classes for training to avoid overfitting, which leads to its performance bottleneck in medical image segmentation due to the unmet need for annotations. Thus, semi-supervised FSS for medical images is accordingly proposed to utilize unlabeled data for further performance improvement. Nevertheless, existing semi-supervised FSS methods has two obvious defects: (1) neglecting the relationship between the labeled and unlabeled data; (2) using unlabeled data directly for end-to-end training leads to degenerated representation learning. To address these problems, we propose a novel semi-supervised FSS framework for medical image segmentation. The proposed framework employs Poisson learning for modeling data relationship and propagating supervision signals, and Spatial Consistency Calibration for encouraging the model to learn more coherent representations. In this process, unlabeled samples do not involve in end-to-end training, but provide supervisory information for query image segmentation through graph-based learning. We conduct extensive experiments on three medical image segmentation datasets (i.e. ISIC skin lesion segmentation, abdominal organs segmentation for MRI and abdominal organs segmentation for CT) to demonstrate the state-of-the-art performance and broad applicability of the proposed framework.
Blasphemy and minorities

Abstract ‘Blasphemy and minorities’ describes how ideas about blasphemy have changed with the increasingly visible presence of religious and ethnic minorities. Modern blasphemy laws tended to limit blasphemy to a crime that could only be committed by those educated as Christians against Christianity—with the result that Jews could not blaspheme, nor could Judaism be blasphemed against. Nevertheless, ‘Jews’ have been very present as images in blasphemy controversies. Today it is also very common for blasphemy to be represented as a Muslim problem. The first modern ‘blasphemous’ cartoon of Muhammad and Salman Rushdie’s novel The Satanic Verses (1988), as well as the Danish Cartoon Affair of 2005/2006, are fascinating examples of blasphemy and creativity.
Social Media and Its Impact on Growth and Performance of Business

Television, newspapers and radio were only form of media available for businesses till late 1990’s after that change came into it and social media took its place. Social media is a communication tool. It is used for social interaction. It uses advanced communication techniques. Social media is beneficial for both company and its customers. Social media has become important tool for marketers. It requires very small amount of investment. Online presence has become important for companies. Social media helps companies to make its social presence. Social media has become strong tool for marketing at international level. Its role in India is also increasing day by day.  This study focuses on advantages, significance and effect of social media on business and its growth.
Digital Resilience and the Continuance Use of Mobile Payment Services

The use of mobile payment services is an essential contributor to financial inclusion in emerging markets. Unfortunately, the service has become a platform for fraud. Mobile payment users need to be digitally resilient to continue using the service after adverse events. However, there is scant literature on users' continuance use of mobile payment services in the post-event of fraud. The focal point of prior literature has been on technology adoption or threat avoidance to implement policies that protect users. Analysing the relationship between individual digital resilience and post-adoption behavioural patterns will enable service providers to support individual digital resilience to promote users' continuance use of the service. This research aims to develop and empirically validate a conceptual model to examine individual digital resilience in the context of the continuance use of mobile payments. The model will be based on protection motivation theory. Survey data will be obtained from victims of mobile payment fraud and other users who continue using the service despite their knowledge of mobile payment fraud. The results from this study are expected to make key contributions to theory, practice, and policy in the areas of digital resilience, mobile payments, and ICT4D.
Electronic Crimes against genetics in databases

Genetics information includes private databases of human genes or the results of the genetic researchers. Such information shall be protected to avoid hacking and unauthorized entry. The owner samples and his personal information might be threatened when someone hacked and disclosed his information. Accordingly, Egypt issued a new Law No.175 of the year 2018 concerning Anti-cyber and Information Technology Crimes law. Such Law consists of four Parts, the first part consists of general provisions, the second one is provisions and the rules of procedures, the third part is crimes and penalties and the last one is concluding provisions.  Hacking the personal information and databases are one of the crimes stated by the law. The abovementioned law defined Electronic data and information, and personal data as follows: 1. Electronic data and information: Everything that can be created, stored, processed, synthesized, transmitted, shared or copied by IT, such as numbers, codes, cyphers, letters, symbols, signs, images, sounds, etc. 2. Personal data: Any data relating to an identified or identifiable natural person, directly or indirectly by linking them to other data.  In addition, Article 17 of the law Anti-cyber and Information Technology Crimes states that. “Anyone who damages, disables or modifies a route or cancels, in whole or in part, intentionally and unlawfully, the programs, data or information stored, processed, generated or synthesized on any information system and the like, regardless of the means used in the crime, shall be punished by imprisonment for a period of not less than two years and a fine not less than one hundred thousand pounds and not exceeding five hundred thousand pounds or either of those two penalties.”
Super Bowl LIV banks on Verizon 5G for security, former New York City police commissioner says

As fans flock to Miami for Super Bowl LIV, law enforcement is employing the latest 5G technology to keep them safe.
Enhancing entanglement and total correlations dynamics via local unitaries

The interaction with the environment is one of the main obstacles to be circumvented in practical implementations of quantum information tasks. The use of local unitaries, while not changing the initial entanglement present in a given state, can enormously change its dynamics through a noisy channel, and consequently its ability to be used as a resource. This way, local unitaries provide an easy and accessible way to enhance quantum correlations in a variety of different experimental platforms. Given an initial entangled state and a certain noisy channel, what are the local unitaries providing the most robust dynamics? In this paper we solve this question considering two qubits states, together with paradigmatic and relevant noisy channels, showing its consequences for teleportation protocols and identifying cases where the most robust states are not necessarily the ones imprinting the least information about themselves into the environment. We also derive a general law relating the interplay between the total correlations in the system and environment with their mutual information built up over the noisy dynamics. Finally, we employ the IBM Quantum Experience to provide a proof-of-principle experimental implementation of our results.
Woody Allen sues Amazon for $68M, alleges breach of contract

Allen, 83, reached an agreement with Amazon in August 2017 to produce four movies for its â€œPrime Videoâ€ streaming service.
A Sparse Structure Learning Algorithm for Bayesian Network Identification from Discrete High-Dimensional Data

This paper addresses the problem of learning a sparse structure Bayesian network from high-dimensional discrete data. Compared to continuous Bayesian networks, learning a discrete Bayesian network is a challenging problem due to the large parameter space. Although many approaches have been developed for learning continuous Bayesian networks, few approaches have been proposed for the discrete ones. In this paper, we address learning Bayesian networks as an optimization problem and propose a score function that satisfies the sparsity and the DAG property simultaneously. Besides, we implement a block-wised stochastic coordinate descent algorithm to optimize the score function. Specifically, we use a variance reducing method in our optimization algorithm to make the algorithm work efficiently in high-dimensional data. The proposed approach is applied to synthetic data from well-known benchmark networks. The quality, scalability, and robustness of the constructed network are measured. Compared to some competitive approaches, the results reveal that our algorithm outperforms the others in evaluation metrics.
Blueprint: walking as mapping between the real and virtual city

Blueprint is a digital artwork which invites walkers to trace a map on the streets  of Yekaterinburg with their body, made by  two artists working together despite being 2,400 miles apart.  While wandering through city streets to take part in an artwork seems a distant luxury, the concept of a map that mixes the physical and virtual worlds and the idea of making art by remote working has taken on a fresh relevance.
Cosmological Data Generation with Generative Adversarial Networks

Author(s): Gupta, Kapil | Advisor(s): Forbes, Angus G | Abstract: Cosmological data is comprised of dark matter and ordinary matter forming halos, filaments, sheets and voids under the influence of gravity over large periods of time, termed as the Cosmic Web. Direct observations and experiments over the cosmic web are not possible, making computational models of the underlying processes extremely important in studying evolution of the universe. N-body simulations, which are the ubiquitously used model, are computationally expensive, having to evolve the position of millions of particles of matter over cosmic time while considering effects of various physical interactions. Thus, N-body simulations are a major bottleneck in such cosmological experiments. Generative Adversarial Networks (GANs), which have been successfully used to generate synthetic data from sparse inputs, are proposed as a possible alternative to such simulations in this thesis. We perform the training of GANs in three separate experiments, first training over normalized 2D slices, then over 2D slices of high-dynamic range data, and finally training over 3D voxels of data. We use a 1024^3 block of data of size 180 Mpc generated using Bolshoi-Planck simulation as the input to train all the models. All the trained models take much less amount of time to generate synthesized data with high visual quality. We also discuss several metrics that can be used to compare summary statistics of synthetic data with the input.
School geography: problems of scientific and methodological support of the concept

The article deals with the problems of scientific and methodological support of the Concept of development of geographical education in the Russian Federation at school and ways to solve them in modern conditions. It is shown that both organizational and substantive issues are closely related to the position of the subject in the Standards and curricula of schools. A special place is given to the system of additional professional training for teacher training and retraining in the conditions of digitalization and distance learning.
Semantically Coherent Out-of-Distribution Detection

Current out-of-distribution (OOD) detection benchmarks are commonly built by defining one dataset as in-distribution (ID) and all others as OOD. However, these benchmarks unfortunately introduce some unwanted and impractical goals, e.g., to perfectly distinguish CIFAR dogs from ImageNet dogs, even though they have the same semantics and negligible covariate shifts. These unrealistic goals will result in an extremely narrow range of model capabilities, greatly limiting their use in real applications. To overcome these drawbacks, we re-design the benchmarks and propose the semantically coherent out-of-distribution detection (SC-OOD). On the SC-OOD benchmarks, existing methods suffer from large performance degradation, suggesting that they are extremely sensitive to low-level discrepancy between data sources while ignoring their inherent semantics. To develop an effective SC-OOD detection approach, we leverage an external unlabeled set and design a concise framework featured by unsupervised dual grouping (UDG) for the joint modeling of ID and OOD data. The proposed UDG can not only enrich the semantic knowledge of the model by exploiting unlabeled data in an unsupervised manner, but also distinguish ID/OOD samples to enhance ID classification and OOD detection tasks simultaneously. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on SC-OOD benchmarks. Code and benchmarks are provided on our project page: this https URL.
Covid-19 and the year-long internet restrictions in Jammu & Kashmir

In August 2019, the Indian Parliament abrogated Article 370 of the Constitution of India, and snapped internet and cellular services in Jammu and Kashmir in apprehension of civil unrest. High-speed internet services were suspended in the region for 18 months which was in gross violation of citizens’ rights. Kashish Khandelwal examines the immediate and long-term impact of denial of access to high-speed internet for the people of Jammu & Kashmir.
DATASET: Characteristic features of ‘buy, sell or trade’ file-sharing websites - Student as Partners Summer Research Project 2020-2021

This research was completed as part of a students as partners summer research project over 2020-2021.  Student use of peer-to-peer file-sharing websites is common across higher education. Learning artifacts are reduced to transactional exchanges either uploaded for money or credit to download someone else’s material.  It is commonly agreed that while some sharing practices are benign, students are encouraged to upload assessment tasks with or without answers. File-sharing or ‘buy, sell or trade’ site usage by students, however, has proved difficult to define. This study examines the characteristic features of five commonly used file-sharing websites to ascertain how to attract students to use their services.
Flexible approaches to the development of software: evolution and prospects of development

The article is devoted to the study of fiexible approaches (Agile) to the design of information systems software. Much attention is paid to the comparative analysis of currently popular fiexible methodologies, including the DevOps (Development and Operation) methodology [4], [5], which covers the entire life cycle of creating a software product. In conclusion, based on the conducted research, the author reveals the global and Russian trends in the development of Agile and formulates their development prospects.
A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number (a.k.a. shanten number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly, respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.
Artificial Intelligence and the Evolution of Managerial Skills: An Exploratory Study

This article investigates how the rise of Artificial Intelligence (AI) in organizations may affect managerial skills. We conducted qualitative and semi-structured interviews of 40 experts around the world who work with AI in a variety of disciplines and sectors. Using thematic content analysis on the data, we identify the trends showing how AI may replace, augment, or not affect managerial skills. In addition, our results highlight the technical and non-technical skills that managers should develop for successful implementation of AI. This study contributes to the scholarship in its depiction of these trends and in its empirical exploration of the links between managerial skills and AI. We also supplement existing taxonomies of managerial skills, provide future research proposals, and discuss the theoretical as well as the practical implications of our study.
Exploring the potential of flow-based programming for machine learning deployment in comparison with service-oriented architectures.

Despite huge successes reported by the field of machine learning, such as speech assistants or self-driving cars, businesses still observe very high failure rate when it comes to deployment of ML in production. We argue that part of the reason is infrastructure that was not designed for activities around data collection and analysis. We propose to consider flow-based programming with data streams as an alternative to commonly used service-oriented architectures for building software applications. To compare flow-based programming with the widespread service-oriented approach, we develop a data processing application, and formulate two subsequent ML-related tasks that constitute a complete cycle of ML deployment while allowing us to assess characteristics of each programming paradigm in the ML context. Employing both code metrics and empirical observations, we show that when it comes to ML deployment each paradigm has certain advantages and drawbacks. Our main conclusion is that while FBP shows great potential for providing infrastructural benefits for deployment of machine learning, it requires a lot of boilerplate code to define and manipulate the dataflow graph. We believe that with better developer tools in place this problem can be alleviated, establishing FBP as a strong alternative to currently prevalent SOA-driven software design approach. Additionally, we provide an insight into the trend of prioritising model development over data quality management.
Public Sentiments & Performance of Industrial Indices: Evidence from Twitter Happiness Index

In this study we analyzed sentiments of the people in terms of Happiness Index calculated based on twitter discussions. We used most liquid composite stock indices from India - Nifty50, CNX Midcap, CNX Smallcap and many sectoral indices. The study was conducted for the period – 2014-2018, on daily data. We used bivariate Vector Auto-Regressive (VAR) framework to test Granger causality of the Sentiments measured as Happiness Index with the next day’s market returns. Key findings of the study suggest that SmallCap and Nifty50 show marginal relationship with previous day’s happiness scores, while amongst sectoral indices only Financial Services and Automotive sector are significantly prone to get impacted by sentiments.
EMPIRICAL STUDY OF PERSONAL DETERMINANTS OF THE CHOICE OF COPING STRATEGIES OF SERVICEMEN

Today, the activities of military units become especially important in conditions of instability in the country. Consequently, the study of the psychological characteristics of such people becomes expedient. Modern types of combat are requirements that often exceed the capabilities and resources of the military. This, in turn, creates a need for a special type of personality - stable, socially active, with a high level of self-regulation and the ability to withstand pressure from adverse factors [9].In modern psychology, the individual way in which a person can cope with a complex, crisis situation is determined by the concept of coping behavior. The study of this concept is especially relevant for persons working in the Armed Forces of Ukraine. Coping is a strategy of behavior in complex stressful situations, which is chosen by servicemen to deal with stress. This is a kind of response of the psyche, which is an adaptive mechanism at the time of negative impact. The choice of coping strategies is determined by a number of personality traits that determine the dominant pattern of behavior of a particular serviceman during decompression.
On culture and Cultural Studies: reflections of a scientist

The article raises the problems of ontology and social essence of culture, its definition from the author's scientific point of view, features of modern culture, the place of art in culture, as well as the issues of general prospects of cultural science and education. The original author's definition of culture as a program of group adaptive behavior of people is given.
Analysis of Webometric Term in Scopus Database during 2000-2019

It is a dynamic area of research in which majority of research work is being done. Scopus is a leading research platform, helping to find, analyze and share information in the sciences, social sciences, arts, and humanities. The present study discusses the “Webometric” as reflected in SCOPUS for the period from 2000–2019. This study investigates the highly productive authors, document type etc.
STRATEGI PEMASARAN PRODUK INDUSTRI KREATIF MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING BERBASIS PARTICLE SWARM OPTIMIZATION

The existence of abundant UMKM data sources can be used to dig up information. Classification is one of the techniques to explore hidden data owned by data mining. Data mining classification methods, one of which is the Support Vector Machine (SVM) algorithm. The SVM algorithm has proven better results than the KKN, Decision Tree and Linear Regression algorithms. In the classification process, the accuracy and time efficiency results obtained are very important. So optimization is needed in order to increase accuracy and time efficiency during the classification process. The optimization of the SVM algorithm was carried out using the K-Means algorithm for the clustering and continuous process on UMKM data and the feature selection process using Particle Swarm Optimization (PSO). This paper aims to optimize the accuracy of the data in the form of type of business, business and turnover. From the results of the discussion of the SVM method using K-Means and PSO, it gives an average accuracy of 55% but 0.12% lower than SVM just using PSO. Keywords: UMKM, Clustering, K-Means, SVM, PSO