Reviewerly/exHarmony huggingface dataset:
THE STATE OF GLOBAL HARMFUL CONTENT REGULATION: EMPIRICAL OBSERVATIONS

Online intermediaries have always been regulated, locked in heated battles around intermediary liability for copyright or privacy reasons (Tusikov, 2016; Gorwa 2019). But a notable trend is the rapidly growing use of policy to try and govern user-generated content with a host of other perceived social or individual harms, such as disinformation, hate speech, and terrorist propaganda (Kaye, 2019; York 2019; Suzor 2019). Even as increasing academic and policy attention is paid to the global ‘techlash’, and leading voices outlining the various ways in which expression online is currently under threat, our understanding of the overall policy landscape remains ad hoc and incomplete. The goal of this paper is thus to present some initial observations on the state of harmful content regulation around the world, drawing upon a new original dataset that seeks to capture the global universe of harmful-content regulatory initiatives for user-generated content online. The first part of the paper presents descriptive results, showing the evolution (and notable increase) in policy development in the past two decades. The second half of the paper provides insight into which specific issue areas have attracted the most formal and informal regulatory arrangements, and assesses the scope (what kind of actors are seen as being a ‘platform,’ and how that is defined), key policy mechanisms (takedown regimes, transparency rules, technical standards), and sanctioning procedures (fines, criminal liability) enacted in these regulations.
Arguing against Graphic Ambivalence: What Earth Modeling Reveals about Visualization in Scientific Computing

Computation in the sciences does not sufficiently account for aesthetics, which prevents information from being corroborated. This argument is made through a comparison between virtual earth modeling communities and epistemic culture and an elaboration on modeling methods as exemplary of the negotiations between scholarly knowledge, aesthetics, and computer resources necessary for visualization. The salience of these negotiations to institutionalized epistemic practice is reinforced through three histories of visualization in the sciences: computation in ecology, empirical modeling and physically based rendering, and visualization in scientific computing. The article flags the significance of this argument for its role in shaping public policy.
Improving the Efficiency of the Method of Stochastic Gradient Identification of Objects in Binary and Grayscale Images Using Their Preprocessing

In this paper, we consider ways to improve the stochastic gradient method efficiency of object identification for binary and grayscale images using methods of image preprocessing. Identification of an object is understood as the recognition of an object on the image with its parameters estimation. Low-pass filtering and image equalization are considered as preliminary processing. The identification parameters convergence rate is investigated. The optimal sizes of Gaussian filter mask for binary and grayscale images were found based on COIL-20 images.
Datacenter Selection in Cloud Framework for Efficient Load Distribution Using a Fuzzy Approach

Cloud architecture delivers fast response to users using multi-tasking in several datacenters. Datacenter executes user query with virtual machine which is configured inside a host. Load balancing in datacenter is depended on utilization of cpu, mips, memory by host, and virtual machine. Prediction of resource utilization with dynamic load improves task scheduling/distribution and load balancing. We propose a cloud architecture to predict load in datacenters using fuzzy reasoning. Fuzzy based datacenter prediction analysis is used to estimate availability of datacenters with dynamic task load execution. Datacenter schedules task load in virtual machines for completing executions. Datacenters and virtual machines load distribution and physical resource utilization have been accomplished using scheduling algorithms.
Image stitching algorithm based on combined feature detection

The image stitching technology breaks through the limitation of camera angle of view, so that high resolution and large field of view can be guaranteed at the same time. In recent years, the image stitching technology has been widely used in aerial photography, medical treatment, cultural relics protection, VR, industrial and agricultural fields. This article mainly studies the image registration problem, based on the OpenCV library function, separately analyzes and uses several existing feature point extraction algorithms, and on this basis, explores the extraction based on oFAST + BRISK, and uses KNN algorithm and RANSAC algorithm to match and filter the key points. Finally the two images are spliced by image projection. Experiments show that this method has better rotation invariance and scale invariance, and has better adaptability in various environments.
A QoE and Visual Attention Evaluation on the Influence of Audio in 360° Videos

360° video, also known as immersive video, is the recording of video content which simultaneously captures scene information in every direction, using an omnidirectional camera. Due to their immersive nature, the popularity of 360° videos has grown significantly. Understanding user Visual Attention when watching 360° videos is very important. This knowledge can help develop effective techniques for processing, encoding, distributing, and rendering 360° content. Whilst major efforts have concentrated on the visual element of immersive experiences, recently there has been growing interest in different forms of audio and in particular high-quality spatial audio. Spatial audio allows listeners to experience sound in all directions. Ambisonics or 3D audio is one such technique which offers a complete 360° soundscape. Although several models of visual and audio-visual attention have been proposed, very few have investigated the role of spatial audio in guiding attention in 360° videos. This demo shows our dataset and our methodological approach to understanding the user's audio-visual attention and QoE when experiencing 360° videos enhanced with spatial sound (first and third order ambisonic). Our research focus is to understand how audio affects Visual Attention in 360° videos and to evaluate its impact on the user's Quality of Experience (QoE).
ANFIS-Hammerstein Model for Nonlinear Systems Identification Using GSA

The nature of many real problems in the world is nonlinear type, and identifying their plants and processes symbolizes a challenging task. Nowadays, the block-structure systems, such as the Hammerstein model, are among the most current nonlinear systems. The main characteristic of a Hammerstein model is that its architecture is made up of two blocks; a linear dynamic model preceded by a static nonlinear. The adaptive neuro-fuzzy inference system (ANFIS) is a robust scheme that incorporates a two parts structural design; a nonlinear rule-based and a linear system. In this chapter, it is proposed a new scheme based on the Hammerstein block-structure model for nonlinear system identification. The methodology introduced takes benefit of the correspondence between the ANFIS and Hammerstein structure to couple them and model nonlinear systems. The Gravitational Search Algorithm (GSA) is incorporated to the methodology to identify the model system parameters. The GSA, compared to similar optimization algorithms, achieves more reliable performance in multimodal problems, avoiding being trapped in premature solutions that are not optimal. To test and validate the effectiveness of the methodology, it has been tested over several models and compared with related works in literature showing a higher accuracy in the results.
FAHPBEP: A Fuzzy Analytic Hierarchy Process Framework in Text Classification

With the availability of websites and the growth of comments, reviews of user-generated content published on the Internet. Sentiment Classification is one of the most common problems in text mining, which applies to categorize reviews into positive and negative classes. Pre-processing has an important role when these textual contexts employed by machine learning techniques. Without efficient pre-processing methods, unreliable results will achieve. This research probes to investigate the performance of pre-processing for the Sentiment Classification problem on three popular datasets. We suggest a high-performance framework to enhance classification performance.  First, features of user's opinions are extracted based on three methods: (1) Backward Feature Selection; (2) High Correlation Filter; and (3) Low Variance Filter. Second, the error rate of the primary classification for each method calculated through the perceptron. Finally, the best method selected through the fuzzy analytic hierarchy process. This framework is beneficial for companies to observe people's comments about their brands and for many other applications. The current authors have provided further evidence to confirm the superiority of the proposed framework. The obtained results indicate that on average this proposed framework outperformed its counterparts. This framework yields 90.63 precision, 90.89 accuracy, 91.27 recall, and 91.05% f-measure.
The B-Subtle framework: tailoring subtitles to your needs

Large amounts of subtitles, from movies and TV shows, can be easily found on the web, for free, in almost every language. Several corpora, built from subtitles, with different annotations and purposes, are currently available. Considering that new sets of subtitles are constantly being released, we propose B-Subtle, an open source framework that allows the automatic creation of corpora constituted of sequential pairs of dialogue turns, gathered from subtitles. With the help of a configuration file, the B-Subtle framework permits to enrich subtitles and dialogue turns with extra information (such as movie genre or the polarity of an utterance); in addition, it allows different types of filtering to be applied to both subtitle files and dialogue turns. Therefore, with B-Subtle, each one can create his/her own corpus, tailored to his/her needs. Moreover, in order to replicate the process in a future experiment, the user just needs to save the configuration file. In this paper, we describe B-Subtle and demonstrate how to build different corpora with it.
Research on the Early Warning of College Online Public Opinion under the Big Data Environment

College online public opinion which is based on social public opinion is a kind of self-expression of college students on some hot issues in the current campus and in the society. The college students through it expressing their opinions by giving vent to their feelings. However, according to the current situation, due to the anomie of college students' online public opinion and the proliferation of false public opinions in the network, as well as the absence of public opinion warning and network guidance. This leads to the problem of college online public opinion increasingly serious. The early warning research of college online public opinion has also been paid more and more attention by the academic circle. Based on the analysis of the current situation of college online public opinion, this paper analyzes and designs three functional modules of early warning of college online public opinion: collection, analysis and early warning. At the same time, the organization system of online and offline is put forward to guarantee the efficient operation of college online public opinion warning mechanism.
CoExe: An Efficient Co-execution Architecture for Real-Time Neural Network Services

End-to-end latency is sensitive for user-interactive neural network (NN) services on clouds. For periods of high request load, co-locating multiple NN requests has the potential to reduce end-to-end latency. However, current batch-based accelerators lack request-level parallelism support, leaving the queuing time non-optimized. Meanwhile, naively partitioning resources for simultaneous requests suffers from longer execution time as well as lower resource efficiency because different applications utilize separate resources without sharing. To effectively reduce the end-to-end latency for real-time NN requests, we propose CoExe architecture, equipped with a pipeline implementation of a sparsity-driven real-time co-execution model. By leveraging the non-trivial amount of sparse operations during concurrent NNs execution, the end-to-end latency is decreased by up to 12.3× and 2.4× over Eyeriss-like and SCNN at peak workload mode. Besides, we propose row cross (RC) dataflow to reduce data movement cost, and avoid memory duplication.
A Novel Deep Learning Method Based on Modified Recurrent Neural Network for Sports Posture Recognition

ABSTRACTIn the process of studying the sports posture recognition algorithm, due to the influence of the shadow noise generated in the movement, when the current algorithms are used for the sports posture recognition, the extracted sports posture features have limitations. And there is a problem that it cannot fully reflect the dynamic characteristics of the posture. To solve the above problems, through the deep transfer learning experience in image recognition, we propose a novel deep end-to-end neural network model based on the Inception neural network and recurrent neural network. The new model adopts the 1×1 convolution to combine the multi-channel data. The convolution of different scales extracts the waveform characteristics of different scales. Maximum pooling filters false positives caused by minor perturbations. Time feature extraction is used to model time series features. It makes full use of data characteristics to complete the classification task. Compared with the state-of-the-art neural network models, the results show that sports posture recognition accuracy is improved by nearly 3 %.
ORGANIZING AND STAGING EXTRACURRICULAR CROSS-CULTURAL EVENTS AT A UNIVERSITY WITH THE PARTICIPATION OF INTERNATIONAL STUDENTS AS A WAY TO CREATE MULTICULTURAL EDUCATIONAL ENVIRONMENT

The paper studies one of the priority challenges for the modern Russian higher education system - to integrate into the international educational environment, the main characteristic of which is multiculturalism. One of the ways to create a multicultural environment in a university is to organize and stage cross-cultural events with the participation of international students. The analysis of scientific and reference literature demonstrates that the problem needs more detailed and thorough research at the level of a regional university. The purpose of the research is to study the experience on how to organize cross-cultural events with the participation of international students and develop practical recommendations on how to arrange these activities at a university. The study describes the main prerequisites for organizing events aimed at improving the quality of intercultural communication within a university, acquiring social skills to interact in a multicultural group, harmonizing inter-ethnic emotional relations, developing tolerance and respect to “an alien” culture. The study also reveals the potential of such events to solve problems related to the training and adaptation of international students in a new educational environment. The novelty of the research lies in defining the main thematic areas and stages of organizing and staging extracurricular cross-cultural events, describing the mission of a teacher. The results of the study present the assessment of the positive dynamics of changes in cross-cultural knowledge and skills of the participants in one of the cross-cultural projects. To determine these changes among the participants of the event before and after its staging, a survey was conducted using the author's questionnaire designed based on the Intercultural Development Inventory. It allowed identifying the ability of Russian and international students to perceive cultural differences and change their behavior model in accordance with them.
Examining the Effect of Knowledge Acquisition and Information Distribution on Employees’ Performance in Classified Hospitality Firms in Kenya

Performance at employees' level is critical to the overall performance of the organization since employees have the first sight of service and product delivery.Knowledge acquisition and information distribution have been acknowledged in literature as fundamental constructs of organizational learning with significant impact on performance.However, most studies have focused on performance at organizational level with limited attention to employees' performance.Empirical literature is inconclusive on the effect of knowledge acquisition and information distribution on employees' performance in different contexts.Therefore, the objective of this study was to examine the effect of knowledge acquisition and information distribution on employees' performance in the hospitality sector in Kenya.Using purposive and stratified random sampling, a sample of 225 respondents was selected from 75 classified hospitality firms in Kenya.Data was collected using a self-administered questionnaire and analyzed using multiple regression and correlation analyses.The results indicated that knowledge acquisition and information distribution were significantly positively correlated with employees' performance and had higher effect on employees' performance dimensions of service delivery and efficiency.The findings underscore the significance of acquiring new knowledge, exploiting the existing knowledge, sharing knowledge and distributing information to improve service delivery and efficiency in the hospitality sector The findings emphasis the need to align new knowledge to firm's strategy and processes in order to enhance impact on employees' performance.
Synergy Conformal Prediction for Regression

Large and distributed data sets pose many challenges for machine learning, including requirements on computational resources and training time. One approach is to train multiple models in parallel ...
Statistical Thinking in Problem Solving

This article studies the role of Statistical Thinking in Problem Solving., where the problem is considered with its wide meaning (not mathematical problems only). Particular emphasis is given to Bayesian Reasoning, whose importance in everyday life and science applications has been only recently fully recognized. Critical and Computational Thinking, the other two main modes of thinking used in Problem Solving, are also discussed and examples are presented illustrating our conclusions.
Using Multiple-Variable Matching to Identify EFL Ecological Sources of Differential Item Functioning

Context is a vague notion with numerous building blocks making language test scores inferences quite convoluted. This study has made use of a model of item responding that has striven to theorize the contextual infrastructure of differential item functioning (DIF) research and help specify the sources of DIF. Two steps were taken in this research: first, to identify DIF by gender grouping via logistic regression modeling, an inventory of mostly cited DIF sources was prepared, based on which a list of demographic items was appended to the TOEFL reading paper only to be administered to the intermediate Iranian undergraduates; second, using multiple-variable matching regression (Wu & Ercikan, 2006), a built-in sequence was followed to let every potential DIF source be considered as a covariate, over and above the conditioning variable, and specify whether a particular ecological variable could reduce DIF value/status. Then, all significant variables were analyzed together to show the final DIF predictors. The same procedures, i.e., individual/collective analyses, were employed after the purification of the test. The results indicated three ecological predictors affecting DIF before and after purification: income, administration convenience, and SES. The ultimate predictors helped create an EFL configuration of the ecological model of item responding. The implications for validity arguments are also discussed.
Eyebrow Recognition for Identifying Deepfake Videos

Deepfake imagery that contains altered faces has become a threat to online content. Current anti-deepfake approaches usually do so by detecting image anomalies, such as visible artifacts or inconsistencies. However, with deepfake advances, these visual artifacts are becoming harder to detect. In this paper, we show that one can use biometric eyebrow matching as a tool to detect manipulated faces. Our method could provide an 0.88 AUC and 20.7% EER for deepfake detection when applied to the highest quality deepfake dataset, Celeb-DF.
Argument from Old Man’s View: Assessing Social Bias in Argumentation

Social bias in language - towards genders, ethnicities, ages, and other social groups - poses a problem with ethical impact for many NLP applications. Recent research has shown that machine learning models trained on respective data may not only adopt, but even amplify the bias. So far, however, little attention has been paid to bias in computational argumentation. In this paper, we study the existence of social biases in large English debate portals. In particular, we train word embedding models on portal-specific corpora and systematically evaluate their bias using WEAT, an existing metric to measure bias in word embeddings. In a word co-occurrence analysis, we then investigate causes of bias. The results suggest that all tested debate corpora contain unbalanced and biased data, mostly in favor of male people with European-American names. Our empirical insights contribute towards an understanding of bias in argumentative data sources.
Detection of Children with Personality Through Fingerprint Random Forest And Maximum Entropy Method

every child has a personality and learning style that can be known from birth, now has found the latest method that is by analyzing fingerprint patterns. A person's fingerprint is a very unique characteristic, because no one has the same fingerprint pattern. Some have learned that fingerprint patterns are related to a person's character.Then it will be discussed the design and discussion of a person's character system based on fingerprint patterns using the gray co-occence matrix (GLCM) for feature extraction and classified with the Random Forest and Maximum Entropy Method. The dataset in this study was taken directly to elementary school children, ranging in age from 7 to 8 years. To find out the child's personality and learning style, fingerprint patterns were taken on the middle finger, ring finger and little finger taken with ink. In this case the class will be divided into three Loop, Whorl, and Arch. From that experiment has been done, of the 123 student data or datasets that have the best accuracy results is with 95% obtained using the random forest method while for the maximum entropy method only gets an accuracy of 44%.
Research on Application Scenarios of Interoperability for Urban Rail Transit

The interoperability of urban rail transit has become a new operation scenario on account of resources sharing and network operation. This paper presents three main application scenarios of interoperability, which are network scenario of interoperability based on operation planning, scenario of interoperability based on depot sharing, scenario of interoperability based on construction by phases. According to the three scenarios, corresponding application requirements and key points for independent safety assessment of project are analyzed, which provides reference for the engineering practice of interoperability.