Reviewerly/exHarmony huggingface dataset:
Characterizing Social Media Contents for Regulating Hate Crimes and Cyber Racism against Marginalized and Dalits In India

This article is an attempt to identify the role of social media in regulating hate crimes and cyber racism in India. The freedom of speech is given to the citizens in many countries but a few disturbing elements are misusing this freedom and suppressing the voice of marginalized and Dalits. It is observed that the Fake news contents, hate crimes and cyber racism are increasing in India. The vigilance of internet contents and digital media is in nascent phase and need to be governed in the light of human rights and freedom of speech [1].One side network of haters and fake news bibliophiles are expanding their network in digital media at a faster rate. On the other side cohort of advocates dealing in International agreements, Human Rights and International Laws are establishing a sound framework for raising voice of marginalized on all possible decisive platforms. User generated contents are future of social media therefore, sketching the characters and identifying cyber crimes in advance must be an integral feature of the programming on social networking sites. This article is intended to identify overall impact of hate crimes on the society in general and on the Dalit and marginalized sections more specifically. The article will suggest some proactive measures for characterizing social media contents in order to regulate these contents by designated authorities or popular social networks.
Computer network quality of experience enriched analysis using fuzzy logic model

Two important Quality of Experience (QoE) parameters in computer networks service-related performance metrics are throughput and end-to-end delay. A slow delay indicates high network efficiency thus high throughput. Similarly, the entire four parameters linked to the integrity of service (Delay, Jitter, Packet Loss, and Throughput) are deliberated to be primary factors affecting almost all computer networks. The study's objective is to identify the vital primary parameters with preeminent results to be kept into consideration when analyzing Network QoE by using a fuzzy logic methodology. The tools used in the study were Linux MTR (my trace-route) tool for data extraction, Ms. Excel for data cleaning and presentation, MATLAB(matrix laboratory) software for plotting of functions/data, and creation of interfaces. A Fuzzy logic methodology was implemented using an experimental research design. The analysis of results using independent sample T-Test technique compared four primary parameters affecting any network to the two important QoE parameters. The results specified a significant consequence using the entire four Integrity of service parameters model compared to using a combination of two parameters model for consideration of network QoE whereby the p-value obtained was 0.017 which is less than 0.05. The target population for this model is the service providers enabling them to have the finest approachable measures to deal with network service providers to achieve QoS (Quality of service) in line with Service Level Agreement (SLAs) thus achieving finest network QoE.
Machine Learning Cancer Diagnosis Based on Medical Image Size and Modalities

Nowadays, machine learning (ML) is one of the significant technology in the new era. It almost applies to all the fields in our lives. ML has a notable impact on medical care when dealing with medical images. Medical images are used to investigate the internal-parts of the body of human. The type and size of medical images vary from one scan to another. Besides, medical images are not like natural images. While natural images can perform object detection and classification easily, even if the images are re-sized to smaller images. Resizing medical images is not an efficient method because they interact with pixel-level. In this chapter, medical imaging modalities and histopathology are explained. Furthermore, the best medical image type and size for classification and detection of medical diagnoses are explained. Moreover, specific methods are considered in medical images such as image compression, image format, image resize, and other essential aspects. Finally, we also give a brief summary of deep learning algorithms that are used with medical images.
The Need for Multicultural and Multilingual Sensitivity in Transforming Graphic Design Curriculum in a University of Technology

Graphic design is a learning area that relies on the use of visual representation, involving images and/or text, to convey meaning. By its very nature, visual communication is a language that is vulnerable to an unintended array of misinterpretations because of the differing semiotic backgrounds of its consuming audiences. Thus, students need to be equipped with the necessary cultural awareness to design communication that is sensitive to the varying needs of these audiences. This study employs a case study approach with a view to interrogating the curriculum in relation to multiculturalism in the graphic design curriculum. Data for this study were obtained through participant observation, semi-structured informal interviews and document analysis. From a theoretical perspective, the paper draws on Vygotsky’s (1978) sociocultural learning to examine the role of culture in the teaching and learning of culturally diverse students, as well as Phillion’s (2002) narrative multiculturalism to understand the narratives of the respondent students, lecturers and an industry expert. The findings point to a lack of a cohesive and coordinated approach to teaching and learning and reflect a lack of sensitivity to cultural and linguistic diversity in the Graphic Design Department.
Modern Interactive Educational Technologies in Higher Education (on the Example of Teaching Political Science at the University)

The author of the paper uses the example of teaching political science at the University to focus on the types of interactive educational technologies and mentions current distance learning technologies. The study presents the characteristics of various approaches that classify educational technologies into simulation and non-simulation, dialogue and training, game and discussion, and have the potential to improve the effectiveness of classes. According to the typology of educational technologies, it is said that it is appropriate to use them in the educational process to activate the activities of students studying in the Humanities, in particular political science. The study demonstrates one of the options for preparing for a lesson in political science, shows practical examples of the implementation of educational technologies that could be useful in the study of both theoretical and applied areas of political science. Based on the results of the research, the author comes to conclusions about the prospects for further development and increasing the role of distance education and the need to combine traditional and modern teaching techniques and methods.
Cloud-Assisted Curriculum Teaching Activity Design and Implementation

Abstract The development of “Internet +” technology has greatly promoted the transformation of the education field. This article takes the course “Cloud Data Centre Architecture and Security Management” as an example, attempts to employ the resources in the existing teaching resource library deployed on the cloud computing platform to improve the traditional teacher-centred teaching model by using advanced teaching methods such as flip classrooms. Which changed the learning status of teaching materials with textbooks or reference books as the main learning resources. Through a year of teaching practice, it is shown that this curriculum teaching design based on the teaching resource library can not only change the teachers’ teaching concepts and teaching methods, but also fully mobilize the enthusiasm and initiative of students to achieve satisfactory teaching effect.
Community Preparation in Tangse District in Flash Flood Disaster Mitigation

This study aims to determine the Tangse community preparedness in mitigation of flood disasters. The research design used in this study is a qualitative research design . Data analysis is performed using three activities that occur simultaneously, namely data reduction, data presentation, and drawing conclusions or verification, or can also use words to describe facts and observed phenomena. Based on the results of research and discussion, it can be concluded that in terms of the knowledge and attitude of the Tangse community it is already good where the community already knows the signs of flash floods and the environment is vulnerable to disasters. But it is still very lacking in terms of regulations and policies. Regulations that have not been implemented properly, as well as sanctions for illegal loggers involving law enforcement officials.
Sequential Multi-fusion Network for Multi-channel Video CTR Prediction

In this work, we study video click-through rate (CTR) prediction, crucial for the refinement of video recommendation and the revenue of video advertising. Existing studies have verified the importance of modeling users' clicked items as their latent preference for general click-through rate prediction. However, all of the clicked ones are equally treated in the input stage, which is not the case in online video platforms. This is because each video is attributed to one of the multiple channels (e.g., TV and MOVIES), thus having different impacts on the prediction of candidate videos from a certain channel. To this end, we propose a novel Sequential Multi-Fusion Network (SMFN) by classifying all the channels into two categories: (1) target channel which current candidate videos belong to, and (2) context channel which includes all the left channels. For each category, SMFN leverages a recurrent neural network to model the corresponding clicked video sequence. The hidden interactions between the two categories are characterized by correlating each video of a sequence with the overall representation of another sequence through a simple but effective fusion unit. The experimental results on the real datasets collected from a commercial online video platform demonstrate the proposed model outperforms some strong alternative methods.
Student Session: Comparative Analysis of Live VM Migration with SGX Enclave

For secure and flexible utilize of computing resources, virtual SGX and EPC migration schemes have been proposed. This paper analyzes the proposed EPC migration schemes under live VM context. According to our analysis, each of VM migration schemes has advantages and disadvantages in terms of VM downtime and security aspects.
AMTICS: Aligning Micro-clusters to Identify Cluster Structures

OPTICS is a popular tool to analyze the clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based clustering specified by the height as the density threshold for clusters. However, in very dynamic and rapid changing applications a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial cluster instances. The offline phase reveals the current data clustering structure with low complexity and at any time.
A Framework for Extracted View Maintenance

When information extraction programs (extractors) are applied to documents, they create relations that store facts found in the documents. In this work, we formalize and address the problem of keeping such extracted relations consistent with source documents that are arbitrarily updated. We define three classes of document updates, namely those that are irrelevant, autonomously computable, and pseudo-irrelevant with respect to a given extractor. Finally, we propose algorithms to detect pseudo-irrelevant document updates with respect to extractors that are expressed as document spanners, a model of information extraction inspired by SystemT.
Fast Computation of Laser Vibrometer Alignment Using Photogrammetric Techniques

Laser vibrometry has become a mature technology for structural dynamics testing, enabling many measurements to be obtained in a short amount of time without mass-loading the part. Recently multi-point laser vibrometers consisting of 48 or more measurement channels have been introduced to overcome some of the limitations of scanning systems, namely the inability to measure multiple data points simultaneously. However, measuring or estimating the alignment (Euler angles) of many laser beams for a given test setup remains tedious and can require a significant amount of time to complete and adds an unquantified source of uncertainty to the measurement. This paper introduces an alignment technique for the multipoint vibrometer system that utilizes photogrammetry to triangulate laser spots from which the Euler angles of each laser head relative to the test coordinate system can be determined. The generated laser beam vectors can be used to automatically create a test geometry and channel table. While the approach described was performed manually for proof of concept, it could be automated using the scripting tools within the vibrometer system.
E-books and changes in collection management at Leeds University Library

The case study describes the steps taken by Leeds University Library to improve the curation of its e-books and to improve access to these resources for users. The challenges faced are examined, and the implementation of the improvements is explained.
Algebraic semantics for propositional superposition logic

We provide a new semantics and a slightly different formalisation for the propositional logic with superposition (PLS) introduced and studied in Tzouvaras [(2018). Propositional superposition logic. Logic Journal of the IGPL, 26(1), 149–190]. PLS results from Propositional Logic (PL) by adding a new binary connective | construed as the ‘superposition operation’ and a few axioms about it. The original semantics used in the above paper was the so-called sentence choice semantics (SCS), based on choice functions for all pairs of classical formulas of PL. In contrast, the algebraic or Boolean-value choice semantics (BCS) developed in this paper is based on choice functions for pairs of elements of a Boolean algebra B in which the classical sentences take truth values. The Boolean-value choice functions can be subject to similar constraints as those imposed on sentence choice functions. The new axiomatisation is based on the same set of axioms as the previous one but uses a new inference rule, called Rule of Analogy (RA), in place of the rule Salva Veritate (SV) of the previous systems. The Deduction Theorem fails in the systems containing the new rule. As a consequence the completeness theorems for them hold conditionally again, namely the systems are complete with respect to BCS if and only if every consistent set of sentences is extended to a consistent and complete set. Finally connections are established between tautologies of the semantics SCS and those of BCS.
Motorbike Generator Using Bidirectional Generative Adversarial Networks

This paper proposes a Bidirectional Generative Adversarial Network (BiGAN) to generate fake images for Motorbike Generator Challenge. Experiment results, Motorbike Generator task demonstrate that this method not only stabilizes GAN training but also achieves significant improvements over several state-of-the-art GAN methods.
El impacto de las redes sociales en el proceso de enseñanza aprendizaje de la termodinámica

The development and use of social networks in the teaching-learning process that students develop, makes it an important and almost essential tool in the educational process. This entails changes, of various absolutely inevitable characteristics when it comes to launching innovative proposals in the teaching of thermodynamics. Hence the training processes in universities demand new models of pedagogical strategy capable of responding to the needs of the new society. However, in practice, during the teaching process of thermodynamics, it has been detected that traditionalist teaching approaches are still assumed, which does not meet the expectations demanded by the new training models. The research carried out is aimed at studying the evaluation of the teaching-learning process in its formative and meaningful training, with the objective of promoting formative evaluation through the use of virtual training environments.
Text to Realistic Image Generation Using Stackgan

Generative Advertising Networks (GAN), which results in the synthesis of real-world images. For given text definitions, conditional GANs are ready to create pictures that are more associated with text semantics. Stacked GAN is used to train the effective GANs to obtain high quality of naturalistic images from text explanations. Initially, the properties are evoking from the text explanations. The hidden thought behind GAN is that it comprises of two neural systems that contend with one another during the game structure, to be specific the generator and the discriminator. The generator accepts arbitrary commotion as information and produces tests as yield. Truth is told the objective of the generator is to get models that resemble genuine pictures that befuddle segregation with the goal that they look like genuine pictures when taking a gander at copies. The discriminator takes genuine pictures from the info dataset and fax pictures from the generator and gives a judgment on whether a given picture is real. Besides, the picture created can be improved by constriction estimation in repetitive multi-model misfortune. Making photograph reasonable pictures from text has brilliant applications including CAD for inside/mechanical plan, sacks and material things. In more established computer games, GAN was utilized in the game advancement area to reproduce 4K or lower goal 2D surfaces through picture preparing, and afterward down-example them to the fitting neighborhood goal.
A System for Risk Assessment of Privacy Disclosure

The wide spread and sharing of considerable information promotes the development of many industries such as the health care and so on. However, data owners should pay attention to the problem of privacy preservation during the sharing of data. A risk assessment system is presented in this paper. The system can assess the risk of privacy disclosure due to the sharing of data, and help data owners to evaluate whether it is safe or not to share the data.
Types of Internal Group Reference in the Educational University Environment and Their Characteristics

One of the key functions of the professional educational environment of the university is to satisfy the need for social orientation of each member of the educational and professional community (group, course, creative association). The person who is integrated into the new social conditions is faced with the task of forming ideas about the profession, about the image of a professional, about a model for mastering professional competencies. In this regard, the social environment and the nature of relations in it are of great importance. The carrier of social environment determinants is a social group that has a system of values, standards of behavior, attitudes that will not be equally significant for each member of the group – group reference characteristics are refracted through the prism of personal choice, the experience of each member of the group, who has mastered patterns of behavior and interaction. Each social group has a certain degree of reference. The article analyzes the types of intragroup reference based on a combination of indicators of group cohesion and the type of interpersonal behavior, predicts the dynamics of the development of relations in a group with one or another type of reference and describes the effect of each type on the effectiveness of educational and professional activities.
Predicting the Political Polarity of Tweets Using Supervised Machine Learning

With the advent of social media; politicians, media outlets, and ordinary citizens alike are routinely turning to Twitter to share their thoughts and feelings. Discerning politically biased tweets from neutral ones can assist in determining the propensity of an elected official or a media outlet in engaging in political rhetoric. This paper presents a supervised machine learning approach to predict whether a tweet is politically biased or neutral. The approach uses a labeled data set available at Crowdflower, where each tweet is tagged with a partisan/neutral label plus its message type and audience. The approach considers a combination of linguistic features including Term Frequency-Inverse Document Frequency (TF-IDF), bigrams, and trigrams along with metadata features including mentions, retweets, and URLs, as well as the additional labels of message type and audience. It trains both simple and ensemble classifiers and assesses their performance using precision, recall, and F1-score. The results demonstrate that the classifiers can predict the polarity of a tweet accurately when trained on a combination of TF-IDF and metadata features that can be extracted automatically from the tweets, eliminating the need for additional tagging which is manual, cumbersome and error prone.
Aspect-Level Attributed Network Embedding via Variational Graph Neural Networks

Attributed information network embedding (AINE) has been widely used in network analysis. Existing AINE methods mainly focus on preserving network proximities and minimizing the reconstruction loss of node attribute information from a single aspect. However, complex network data may stem from different aspects. For example, a social network may consist of working relationship networks, alumni associations and so on. In this paper, we propose a novel model, called Aspect-level Attributed Network Embedding (AANE), to embed nodes by learning different aspect-level information. Specifically, we use a transform matrix to model aspect-level network topological structure and node attributes. Then, we leverage graph neural networks to learn aspect-level embedding. To learn a robust representation, we aggregate different aspect-level embeddings via the attention mechanism in a variational manner. Experimental results on four real-world network datasets demonstrate that AANE outperforms the state-of-the-art network embedding methods.
Semantic Convolutional Neural Machine Translation Using AMR for English-Vietnamese

Semantic representation can help in enforcing meaning preservation and handling data sparsity of neural machine translation models. This paper presents an extension of the convolutional neural machine translation model to incorporate Abstract Meaning Representation as a kind of semantic representation to reduce language ambiguity or alleviate data sparseness problems. Evaluating on translating from English to Vietnamese with a low resource setting in the domain of TED talks, we obtain promising results in terms of both perplexity reductions and improved BLEU scores over the baseline method.