Reviewerly/exHarmony huggingface dataset:
MAPPING DISCORD’S DARKSIDE: DISTRIBUTED HATE NETWORKS ON DISBOARD

Scholars and journalists have noted that Discord, a social application oriented around voice/video chat communities and popular amongst gamers, has a history of harboring white supremacist and toxic groups. Discord has recently undertaken a public rebranding to distance itself from white supremacist, alt-right, and hateful content through a commitment to proactive moderation (Brown, 2020). However, Discord relies extensively on third-party services (like bots and server bulletins), and current scholarship has not adequately accounted for the role of such third-party actors in facilitating hateful and white supremacist networks on private platforms like Discord. This study notes how Discord’s model for curating only popular servers offloads the ethical burden of searchability to server bulletin sites like Disboard, to deleterious effect. This study involves two parts: 1) we use critical technoculture discourse analysis to examine Discord’s blogs, moderation policies, and API (Brock, 2018) and 2) we present data scraped from publicly-available descriptions and tags of 3,600 Discord servers listed on Disboard. Our study finds that thousands of servers on Disboard use overtly white supremacist and hateful tags, often advertising their ‘edgy’ communities as racist, raiding-oriented, and deliberately toxic. These servers exploit Discord’s moderation tools and Disboard’s networked affordances to proliferate within Discord’s distributed ecology. Ultimately, we argue that Discord’s response to hate, as a platform, does not address its reliance on unmoderated third-party services or the networked practices of its toxic communities.
RPTK1: A New Thangka Data Set for Object Detection of Thangka Images

Data set is the basis of machine learning, a good data set can promote the development of various applications.Machine learning has been deeply involved in the protection and inheritance of cultural resources.However, there are few data sets about Thangka, and the types and quantity of Thangka images are relatively few.Therefore, we first establish a Thangka data set called RPTK1 (Religious Portrait Thangka Version 1), which contains 3,338 Thangka images, more than any other Thangka data set.The objects in the data set basically cover the common Thangka religious portraits, tools and headdresses, and are marked in the professional language of Buddhism.Then, on the basis of the RPTK1 data set, in order to achieve better detection of small Thangka objects (Thangka religious tools), we propose an improved Single Shot MultiBox Detector (SSD) method, called Single Shot MultiBox Detector with Improvement Feature Fusion And Loss Function (FALSSD).Finally, in order to verify the effectiveness of the FALSSD method, we conduct experiments on the RPTK1 data set.The experimental results show that the mAP of our method in the RPTK1 data set reaches 83.85%.Compared with the other ten state-of-the-art methods, the performance of our model is better.
6MapNet: Representing Soccer Players from Tracking Data by a Triplet Network

Although the values of individual soccer players have become astronomical, subjective judgments still play a big part in the player analysis. Recently, there have been new attempts to quantitatively grasp players’ styles using video-based event stream data. However, they have some limitations in scalability due to high annotation costs and sparsity of event stream data. In this paper, we build a triplet network named 6MapNet that can effectively capture the movement styles of players using in-game GPS data. Without any annotation of soccer-specific actions, we use players’ locations and velocities to generate two types of heatmaps. Our subnetworks then map these heatmap pairs into feature vectors whose similarity corresponds to the actual similarity of playing styles. The experimental results show that players can be accurately identified with only a small number of matches by our method.
Formal verification of the extension of iStar to support Big data projects

Identifying all the right requirements is indispensable for the success of anysystem. These requirements need to be engineered with precision in the earlyphases. Principally, late corrections costs are estimated to be more than 200times as much as corrections during requirements engineering (RE). EspeciallyBig data area, it becomes more and more crucial due to its importance andcharacteristics. In fact, and after literature analyzing, we note that currentsRE methods do not support the elicitation of Big data projects requirements. Inthis study, we propose the BiStar novel method as extension of iStar to under-take some Big data characteristics such as (volume, variety ...etc). As a firststep, we identify some missing concepts that currents requirements engineeringmethods do not support. Next, BiStar, an extension of iStar is developed totake into account Big data specifics characteristics while dealing with require-ments. In order to ensure the integrity property of BiStar, formal proofs weremade, we perform a bigraph based description on iStar and BiStar. Finally, anapplication is conducted on iStar and BiStar for the same illustrative scenario.The BiStar shows important results to be more suitable for eliciting Big dataprojects requirements.
Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse

Online frauds have pernicious impacts on different system domains, including waste management systems. Fraudsters illegally obtain rewards for their recycling activities or avoid penalties for those who are required to recycle their own waste. Although some approaches have been introduced to prevent such fraudulent activities, the fraudsters continuously seek new ways to commit illegal actions. Machine learning technology has shown significant and impressive results in identifying new online fraud patterns in different system domains such as e-commerce, insurance, and banking. The purpose of this paper, therefore, is to analyze a waste management system and develop a machine learning model to detect fraud in the system. The intended system allows consumers, individuals, and organizations to track, monitor, and update their performance in their recycling activities. The data set provided by a waste management organization is used for the analysis and the model training. This data set contains transactions of users’ recycling activities and behaviors. Three machine learning algorithms, random forest, support vector machine, and multi-layer perceptron are used in the experiments and the best detection model is selected based on the model’s performance. Results show that each of these algorithms can be used for fraud detection in waste managements with high accuracy. The random forest algorithm produces the optimal model with an accuracy of 96.33%, F1-score of 95.20%, and ROC of 98.92%.
Lazy Low-Resource Coreference Resolution: a Study on Leveraging Black-Box Translation Tools

Large annotated corpora for coreference resolution are available for few languages.For machine translation, however, strong black-box systems exist for many languages.We empirically explore the appealing idea of leveraging such translation tools for bootstrapping coreference resolution in languages with limited resources.Two scenarios are analyzed, in which a large coreference corpus in a high-resource language is used for coreference predictions in a smaller language, i.e., by machine translating either the training corpus, or the test data.In our empirical evaluation of coreference resolution using the two scenarios on several medium-resource languages, we find no improvement over monolingual baseline models.Our analysis of the various sources of error inherent to the studied scenarios, reveals that in fact the quality of contemporary machine translation tools is the main limiting factor.
IoT in Banking: The trends, threats, and solution

The Internet of Things is the next step of the digital revolution that will change consumers' lives. The Internet of Things promises to be a worthy representative of an open course in technology, economics, and culture. IoT, without a doubt, has a promising future. The modern consumer actions and uses represent inescapable digital transformations for banking institutions. Emerging digital world developments guide all banking services' digital transformation. However, the security threats of using IoT in banking are increasing. Cybercriminals such as hacking, corruption, and financial violence, data breaches, and financial expenditure risks will continue to trouble the use of IoT in banking. Therefore, this research aims to study the banking industry's existing IoT uses, issues, and challenges in adopting the IoT in the banking industry. IoT threats are highlighted in this paper. This article sets out a model dimension of the process monitoring framework for IoT security risk management. Other than that, this paper also studies the existing security risk management model of IoT in banking. Moreover, preventive IoT protection initiatives and approaches to enhance IoT protection by implementing blockchain technology and Control Model Information Structure are addressed in this article
Event Coreference Data (Almost) for Free: Mining Hyperlinks from Online News

Cross-document event coreference resolution (CDCR) is the task of identifying which event mentions refer to the same events throughout a collection of documents.Annotating CDCR data is an arduous and expensive process, explaining why existing corpora are small and lack domain coverage.To overcome this bottleneck, we automatically extract event coreference data from hyperlinks in online news: When referring to a significant real-world event, writers often add a hyperlink to another article covering this event.We demonstrate that collecting hyperlinks which point to the same article(s) produces extensive and highquality CDCR data and create a corpus of 2M documents and 2.7M silver-standard event mentions called HyperCoref.We evaluate a state-of-the-art system on three CDCR corpora and find that models trained on small subsets of HyperCoref are highly competitive, with performance similar to models trained on goldstandard data.With our work, we free CDCR research from depending on costly humanannotated training data and open up possibilities for research beyond English CDCR, as our data extraction approach can be easily adapted to other languages. 1
Image Compression-Aware Deep Camera ISP Network

Several recent studies have attempted to fully replace the conventional camera image signal processing (ISP) pipeline with convolutional neural networks (CNNs). However, the previous CNN-based ISPs, simply referred to as ISP-Nets, have not explicitly considered that images have to be lossy-compressed in most cases, especially by the off-the-shelf JPEG. To address this issue, in this paper, we propose a novel compression-aware deep camera ISP learning framework. At first, we introduce a new use case of compression artifacts simulation network (CAS-Net), which operates in the opposite way of commonly used compression artifacts reduction networks. Then, the CAS-Net is connected with an ISP-Net such that the ISP network can be trained with consideration of image compression. Throughout experimental studies, we show that our compression-aware camera ISP network can produce images with a better tradeoff between bit-rate and image quality compared to its compression-agnostic version when the performance is evaluated after JPEG compression.
Performance Evaluation of Cloud Database Security Algorithms

Cloud computing is a group of servers providing the on demand computing services to the users on rental basis. One of the services providing by the cloud is Data Storage as a Service. In Data Storage as a Service user will outsource their sensitive data to cloud storage environment, where the users data will be stored and whenever user needs data, they will access from the cloud storage from anywhere and anytime using any device connected to the internet. One of the critical issues in this is data security. When user outsources their sensitive data to cloud vendor, the cloud vendor may leak the user’s sensitive data to third persons because vendor is not a trusted party. To overcome these problems there are many authors suggested different security algorithms. In this research we studied and evaluating the performances of those security algorithms and proposing a better solution, which provides best security to data stored in cloud databases.
SOURCES OF INDIVIDUAL DIFFERENCES IN EDUCATION

Individual differences that need to be considered in the implementation of classroom teaching are factors related to the readiness of children to receive teaching because these differences will determine the education system as a whole. These differences with other individual approach as well, but it is still realized that education is not solely aimed at developing individuals as individuals, but also in relation to the varied patterns of community life. The factors that influence individual differences are self-concept, the anxiety experienced by students, learning motivation.
Knowledge Management System in Pharmaceutical Healthcare Sector: A Conceptual Research

Aim: Pharmaceutical healthcare is a process comprising knowledge-intensive tasks. Therefore the tools used in the management of knowledge are gaining more attention. This paper aims to investigate knowledge management systems, their implementation, tools used for decision making.&#x0D; Method: We have used three databases to research the knowledge management system in the pharmaceutical healthcare sector. PubMed, Google Scholar and journal websites were used for the search of the required key terms.&#x0D; Result: After analyzing the data, it was found that the effective utilization of knowledge management systems in the pharmaceutical health care sector has increased the quality of care. There are many opportunities; some create new advances in health care, and some even create barriers. All these help in clinical decision support.&#x0D; Conclusion: Right decision at the right time is made by evidence-based decisions in the healthcare sector. A knowledge management system is paramount in the pharmaceutical healthcare sector. Implementation of appropriate tools will significantly enhance the quality of care.
Acoustic Word Embeddings for End-to-End Speech Synthesis

The most recent end-to-end speech synthesis systems use phonemes as acoustic input tokens and ignore the information about which word the phonemes come from. However, many words have their specific prosody type, which may significantly affect the naturalness. Prior works have employed pre-trained linguistic word embeddings as TTS system input. However, since linguistic information is not directly relevant to how words are pronounced, TTS quality improvement of these systems is mild. In this paper, we propose a novel and effective way of jointly training acoustic phone and word embeddings for end-to-end TTS systems. Experiments on the LJSpeech dataset show that the acoustic word embeddings dramatically decrease both the training and validation loss in phone-level prosody prediction. Subjective evaluations on naturalness demonstrate that the incorporation of acoustic word embeddings can significantly outperform both pure phone-based system and the TTS system with pre-trained linguistic word embedding.
A Performance-Based Comparative Encryption and Decryption Technique for Image and Video for Mobile Computing

When data exchange advances through the electronic system, the need for information security has become a must. Protection of images and videos is important in today's visual communication system. Confidential image / video data must be shielded from unauthorized uses. Detecting and identifying unauthorized users is a challenging task. Various researchers have suggested different techniques for securing the transfer of images. In this research, the comparative study of these current technologies also addressed the types of images / videos and the different techniques of image / video processing with the steps used to process the image or video. This research classifies the two types of Encryption Algorithm, Symmetric and Encryption Algorithm, and provides a comparative analysis of its types, such as AES, MAES, RSA, DES, 3DES and BLOWFISH.
Pengaruh Media Pop Up Book terhadap Keterampilan Berbicara Siswa MIS Al-Asriyah Langsa

The use of media is still limited to use in classroom learning, especially such as media pop up books because the work on this media is quite complicated. The purpose of this study was to find out the influence of pop up book media on speaking skills on the beautiful theme of togetherness of the proud sub-theme of being an Indonesian child in class IV MIS Al-Asriyah Langsa. The study used a quasi-experimental approach. From the analysis of data obtained the influence of pop up book media on the speaking skills of students speaking on the theme of the beautiful togetherness of the proud sub-theme of being an Indonesian child in class IV MIS Al-Asriyah Langsa. This is evidenced by the experimental grade practice average value of 14.9 with a standard deviation of 3.9 and a control class pretest average of 14.7 with a standard deviation of 4. After learning using pop up book media the average posttest class of the experiment increased to 18.35 with a standard deviation of 4.43. While learning in the control class without using learning media obtained a posttest value of 15.7 with a standard deviation of 4.1. From the results of the tcount test obtained a value of 8.36 and a ttable value of 2,042, it can be withdrawn from the influence of pop up book media on the speaking skills of MIS Al-Asriyah Kota Langsa students. rsquare was obtained by 0.546 or 54.6%. That means that the media pop up book has an influence on the speaking skills of students in class IV MIS Al-Asriyah Langsa with an influence rate of 54.6%.
Quantum information masking of Hadamard sets

We study quantum information masking of arbitrary dimensional states. Given a set of fixed reducing pure states, we study the linear combinations of them, such that they all have the same marginal states with the given ones. We define the so-called Hadamard set of quantum states whose Gram–Schmidt matrix can be diagonalized by Hadamard unitary matrices. We show that any Hadamard set can be deterministically masked by a unitary operation. We analyze the states which can be masked together with the given Hadamard set using the result about the linear combinations of fixed reducing states. Detailed examples are given to illustrate our results.
Kalman Bayesian Neural Networks for Closed-Form Online Learning

Compared to point estimates calculated by standard neural networks, Bayesian neural networks (BNN) provide probability distributions over the output predictions and model parameters, i.e., the weights. Training the weight distribution of a BNN, however, is more involved due to the intractability of the underlying Bayesian inference problem and thus, requires efficient approximations. In this paper, we propose a novel approach for BNN learning via closed-form Bayesian inference. For this purpose, the calculation of the predictive distribution of the output and the update of the weight distribution are treated as Bayesian filtering and smoothing problems, where the weights are modeled as Gaussian random variables. This allows closed-form expressions for training the network's parameters in a sequential/online fashion without gradient descent. We demonstrate our method on several UCI datasets and compare it to the state of the art.
Factors affecting cloud data-center efficiency: a scheduling algorithm-based analysis

Nowadays, users are required to cache, scrutinise, and process massive datasets from various fields, including science, business, and research.As a result, they require data-intensive platforms with ample storage and processing power.In addition, many of these kinds of platforms must-have features like parallel processing, fault tolerance, data dissemination, scalability, availability, and load balancing.Google developed the MapReduce programming paradigm to counter this problem, which served as the foundation for Apache's open-source Hadoop project.
A Systematic Literature Review on Regression Test Case Prioritization

Test case prioritization (TCP) is deemed valid to improve testing efficiency, especially in regression testing, as retest all is costly.The TCP schedule the test case execution order to detect bugs faster.For such benefit, test case prioritization has been intensively studied.This paper reviews the development of TCP for regression testing with 48 papers from 2017 to 2020.In this paper, we present four critical surveys.First is the development of approaches and techniques in regression TCP studies, second is the identification of software under test (SUT) variations used in TCP studies, third is the trend of metrics used to measure the TCP studies effectiveness, and fourth is the stateof-the-art of requirements-based TCP.Furthermore, we discuss development opportunities and potential future directions on regression TCP.Our review provides evidence that TCP has increasing interests.We also discovered that requirement-based utilization would help to prepare test cases earlier to improve TCP effectiveness.
Readability and content assessment of patient information texts on oral cancers found on Turkish websites

Aim: Oral cancer is one of the health problems that results in high mortality in the community. It is known that, when diagnosed in the later stages (3 or 4), the disease is difficult to treat. Patient education on heredity and early diagnoses are critical for the successful treatment of oral cancer. This study aimed to evaluate Turkish texts about oral cancers found online in terms of their readability and content.&#x0D; Methodology: The first 100 websites returned from a Google search using the keywords "Ağız kanseri" (“oral cancer,” in Turkish) were examined. The patient information texts obtained were evaluated according to the Ateşman Readability Index. Their contents were also evaluated in terms of whether or not they provided sufficient information about the disease.&#x0D; Results: It was understood that the texts examined in the study were of medium difficulty according to the Ateşman Readability Index (mean 61.73± 11.81). The content of the information on the websites examined in the study was deemed sufficient and useful. Most of the websites examined were those of private clinics and hospitals.&#x0D; Conclusion: The results of the study indicated that the patient information texts found on Turkish websites were of medium reading difficulty. Although they are sufficient in terms of content, if they are not understood by the patients, their intended effects on readers will not be seen.&#x0D; &#x0D; How to cite this article: Kaya DI. Readability and content assessment of patient information texts on oral cancers found on Turkish websites. Int Dent Res 2021;11(Suppl.1):91-5. https://doi.org/10.5577/intdentres.2021.vol11.suppl1.15&#x0D; &#x0D; Linguistic Revision: The English in this manuscript has been checked by at least two professional editors, both native speakers of English.&#x0D; &#x0D;