Reviewerly/exHarmony huggingface dataset:
A Large-scale Comprehensive Abusiveness Detection Dataset with Multifaceted Labels from Reddit

As users in online communities suffer from severe side effects of abusive language, many researchers attempted to detect abusive texts from social media, presenting several datasets for such detection.However, none of them contain both comprehensive labels and contextual information, which are essential for thoroughly detecting all kinds of abusiveness from texts, since datasets with such fine-grained features demand a significant amount of annotations, leading to much increased complexity.In this paper, we propose a Comprehensive Abusiveness Detection Dataset (CADD), collected from the English Reddit posts, with multifaceted labels and contexts.Our dataset is annotated hierarchically for an efficient annotation through crowdsourcing on a large-scale.We also empirically explore the characteristics of our dataset and provide a detailed analysis for novel insights.The results of our experiments with strong pre-trained natural language understanding models on our dataset show that our dataset gives rise to meaningful performance, assuring its practicality for abusive language detection.
A Novel Feature Extraction Dual DCT-DWT Image Watermarking Combined with Chaos-Based Cryptosystem

As a result of the emerging technologies such as Internet-of-Things (IoT), data analysis and automation in many aspects of identity, watermarking has become increasingly important.A majority of prior systems focused on watermark embedding and recovery techniques for binary and grey-scale images in one specified region of the cover image, making the system less dependable.This study develops a novel feature extraction based blind cryptographic watermarking mechanism which provides multi-layered protection to digital data to significantly improve the watermarking system's characteristics such as robustness, visible quality and security.A hybrid algorithm is proposed by combining DCT and multilevel DWT transformation technique to provide copyright protection and authentication to digital image over the internet.Unlike the previous model here both the host and watermark images are considered colored.Prior to embedding, a layer wise encryption is performed on watermark using Chaosbased cryptosystem to enhance the security of proposed system.It is processed by applying confusion and diffusion process on individual RBG (Red, Green and Blue) components of watermark image.Then the cover image and watermark image are directed by DCT coefficient and multiregion Discrete wavelet transformation to embed the watermark in both high and low sub band of cover image.Thus, by embedding the watermark twice in low and high regions of the cover image, the algorithm's robustness is considerably improved.For better imperceptibility, the proposed method also applies novel perception using SSIMlPT metric to extract the similar features between cover image and watermark image.This is done by dividing the cover image and watermark image into non overlapping blocks of size 8x8 respectively.Now, the best match blocks of cover image are chosen for further embedding a watermark in both low-frequency and high-frequency regions of cover image.In addition, the use of non-consecutive blocks of pixels to contain the watermark makes the system more resistant to a range of attacks.In an experimental scenario, the approach is evaluated using a variety of quality criteria and watermark removal attacks.In comparison to other existing systems, the results suggest that the model can allow invisible watermarking as well as excellent attack resistance.
A Convenient Way to Determine the Optimum Angle of Incidence of Fizeau Interferometer

In a Fizeau interferometer, off-axis illumination will lead to fringe optimization. Primarily due to the unique structure of our interferometer, we first analyze the influence of the optical properties of the parallel plate as a part of the interferometer on the optimal incident angle. Generally, the incident angle determination is mainly based on the graphing method proposed by Langenbeck and the estimation formula proposed by Kajava. However, Langenbeck’s method is cumbersome, and the error of Kajava’s estimation formula is large. Based on the predecessors, this paper proposes a modified method of determining the optimal angle of incidence and further derives more accurate optimal angle expressions than Kajava’s. By simply substituting the wedge angle of the wedge cavity and the reflectivity of the cavity, the optimum incidence angle can be obtained immediately. Thus, it eliminates the tedious and complex process of finding the optimum incident angle by graphing method and makes the formula method the simplest method to find the optimum incident angle. Finally, the comparison of the interference intensity at the optimum incidence angle calculated by the improved method and normal incidence is given. It is found that the beam has a good suppression effect on the sub-peak when it is incident at the optimum incident angle calculated by the method in this paper.
Probit Regressive Tversky Indexed Rocchio Convolutive Deep Neural Learning for Legal Document Data Analytics

Legal documents data analytics is a very significant process in the field of computational law.Semantically analyzing the documents is more challenging since it’s often more complicated than open domain documents. Efficient document analysis is crucial to current legal applications, such as case-based reasoning, legal citations, and so on. Due to the extensive growth of documents of data, several statistical machine learning methods have been developed for Legal documents data analytics. However, documents are large and highly complex, so the traditional machine learning-based classification models are inefficient for accurate data analytics with minimum time. In order to improve the accurate legal documents data analytics with minimum time, an efficient technique called Probit Regressive Tversky Indexed Rocchio Convolutive Deep Neural Learning (PRTIRCDNL) is introduced. The PRTIRCDNL technique uses the Convolutive Deep neural learning concept to learn the given input with help of many layers and provides accurate classification results. Convolutive Deep Neural Learning uses two different processing steps such as keyword extraction and classification in the different layers such as input, two hidden layers and output layer. Initially, large numbers of legal documents are collected from the dataset. Then the collected legal documents are sent to the input layer of the convolutive deep neural learning. The input legal documents are transferred into the first hidden layer where the keyword extraction process is carried out by applying the Target projective probit Regression. Then the regression function extracts the keywordsbased on frequent occurrence score.Then the extracted keywords are transferred into the second hidden layer where the document classification is performed using the Tversky similarity indexive Rocchio classifier. Likewise, all the legal documents are classified into different classes. The experimental evaluation is carried out using different performancemetrics such as accuracy, precision, recall,F-measure and computational time with respect to the number of legaldocuments collected from the dataset.The observed results confirmed that the presented PRTIRCDNL techniqueprovides the better performance interms of achieving higher accuracy, precision, recall and F-measure with minimum computation time.
Locke's Holiday: Belief Bias in Machine Reading.

I highlight a simple failure mode of state-of-the-art machine reading systems: when contexts do not align with commonly shared beliefs. For example, machine reading systems fail to answer What did Elizabeth want? correctly in the context of ‘My kingdom for a cough drop, cried Queen Elizabeth.’ Biased by co-occurrence statistics in the training data of pretrained language models, systems predict my kingdom, rather than a cough drop. I argue such biases are analogous to human belief biases and present a carefully designed challenge dataset for English machine reading, called Auto-Locke, to quantify such effects. Evaluations of machine reading systems on Auto-Locke show the pervasiveness of belief bias in machine reading.
13 A learning system for safer re-opening of K-12 schools during the COVID-19 pandemic

<h3>Background</h3> School closures due to the coronavirus disease 2019 (COVID-19) pandemic are harmful to children's education. Uncertainty in infectiousness and challenges of managing asymptomatic infection slowed school re-opening in many areas of the US. The pandemic required fast, evolving, iterative response from public health and K-12 schools. <h3>Objective</h3> Use data displays designed for improvement, coupled with expertise in learning from variation, to offer insights in a fast-paced policy environment. Provide learning support alongside data. <h3>Methods</h3> A multidisciplinary team with expertise in epidemiology, family medicine, public health, and biostatistics formed to provide support in learning methods to the second largest US K-12 school system. The team generated time series and funnel statistical process control charts, using the drill-down pathway to provide insight at multiple levels of the school system. The team identified essential capabilities in data and scientific translation to support real-time, fast-paced operational decision-making. <h3>Results</h3> Outcomes include positive COVID-19 tests identified through surveillance (figure 5), school-based secondary infection, school enrollment (figures 1 and 2), quarantine associated with being a close contact of a COVID-19 positive student or staff member, and vaccination drawing from community-level data linked with school region (figures 3 and 4). Data displays showed variation in actionable formats. <h3>Conclusions</h3> Common data displays in COVID-19 lack elements required to learn from variation, including denominators, disaggregation (granularity), and operationally relevant stratification (such as subregion and school configuration). Providing these displays offered actionable data to the school district that they employed in areas such as vaccination outreach and testing protocols. There is an ongoing need for scientific input and support of actionable data displays, for use by public health as well as school districts. Future work will include incorporating these displays into operations and generating them from developing databases.
The Effect of Corporate Governance on the Innovative Efforts of Companies in the Tehran Stock Exchange

After corporate financial scandals such as Enron, Vantel and ... corporate governance matters, and attracted the attention of the researchers. In this study, the relationship between corporate governance and innovation Efforts in the companies active in the Tehran Stock Exchange (304 companies) has been investigated. This paper attempts to identify the dimensions and indicators of corporate governance, and then the most important factors affecting enterprise innovation are extracted, and indicators such as reliance on disclosed reports and the timeliness of reported disclosures, in other Studies have not been observed. The audited financial statements from 1391 to 1396 analyzed using the data panel method. The results show that the board of directors' education, choice of CEO from within or outside the company and the premium of the board of directors have a positive and significant effect on firm innovation, but the amount of payment for the right of the board of directors, the ability to rely on disclosed reports and the number of years of activity of the firm has a negative effect on the firm's innovation. CEO's education, the outsider board of directors, the ownership concentration and the timeliness of the disclosures by the company have no significant effect on the firm's innovation
Model Data Mining dan Rasio Altman untuk Prediksi Financial Distress Perusahaan

Financial distress is the stage or situation of a decline in the company's financial condition before a business failure occurs. This failure is related to liquidation, where the company fails to run its operations for profit. Financial distress prediction methods have been long developed in the field of finance using Multiple Discriminant Analyst (MDA). The most widely used MDA method is the Altman model ratio. This method is used as an early warning to the company's financial condition. This study aims to analyze the neuro fuzzy data mining algorithm with input data from the financial ratio of the Altman model to predict the company's financial distress. Research data are from the Indonesia Stock Exchange (IDX) website at www.idx.co.id. Data population between 2012-2017 LQ45 issuers are 80 companies. Preliminary data from the company's financial statements are calculated by Altman's ratio to get zscore values ??of three types of categories. These values ??are arranged in time series of four backward periods which will be used as input data for the neuro fuzzy algorithm ANFIS model. The next step is the formation of algorithms, training, testing, simulation and model evaluation using the GUI application from the Matlab program. The results of the model are predictive values ??that are compared with the results of the Altman ratio calculation of predicted years. The final results of the study show the predicted value of the ANFIS model of the Triangle membership function looks very optimal, consistent, and efficient. It was proven that the average predicted value of 9.95% was close to the actual value of the calculated year, while the results of the ANFIS model of the Trapezoid, Gauss and G-bell membership functions seemed less than optimal.&#x0D; Keywords: Financial Distress, Neuro Fuzzy, Prediction, Altman Ratio.
Chatbot Research Paper about Student Admittance Query

Recently, developers and academics have been more attentive to the design and implementation of chatbots. Chatbots are conversational systems based on artificial intelligence (AI), able to use several approaches, such as natural language processing (NLP) and neural network language processing (NN). The major objective of this study is to sum together some of the most effective implementing strategies in recent years. This study does not just critically analyze the past works on chatbot, but also offers a technique for developing a state-of-the-art, customized chatbot application. A few technologies such as DialogFlow, TensorFlow, Android Studio, and Firebase allow the chatbot. The suggested chatbot will be developed utilizing many technologies, such as DialogFlow, TensorFlow, Android Studio and Machine Learning (ML) and Deep Learning (DL), including NMT and Deep Reinforcement (RL) models.
Synthetic hyperspectral sensing simulator: a tool for optimizing applications in mineral exploration

The uniqueness and complexity of geological settings create challenges for hyperspectral sensing applications in mineral exploration. The optimization of an airborne hyperspectral survey design is a necessary step providing constraints on spectral signatures of the target, sensors used, and best flight parameters. Herein, we present a versatile software tool called HYSIMU that can be used to simulate different flight/sensor scenarios using mineral reflectance spectra from the USGS spectral library and find the most optimum survey design for a specific surface target. Random or selectable mineral reflectance spectra were assigned to synthetic random fields to create mineral distributions on synthetic digital elevation models with added spatial noise, spectral noise, and sun shadow effect. Several flights were simulated with various flight parameters such as altitude (10-200 meters), speed (1-100 m/s), and sun elevation (0, 45, 90 degrees). Several target scenarios with different levels of complexity and a 224-band hyperspectral sensor were simulated. The synthetic hyperspectral data generated from each flight and scenario were classified using ENVI. A sensitivity study was done by comparing the classification maps obtained and the ground truth using four different methods: Hausdorff Distance, Structural Similarity Index, Equal Pixel Percentage, and Correlation Coefficient. The results show a dependency of the classification maps on altitude (best for 10-50 meters) and slow flight speeds (best for 1-10 m/s) while the sun elevations did not cause any observed change in this altitude range. The results show that this toolkit can simulate any type of mineral exploration target from various airborne platforms and different flight parameters towards optimization of hyperspectral surveys.
The Role of Organizational Culture as Moderator in the Relationship between Knowledge Management and Organizational Learning in Jordanian Universities

This study examines empirically the direct relationship between knowledge management and organizational learning in Jordanian universities. It also examines the moderating effect of organizational culture on the relationship between knowledge management and organizational learning. The study data was gathered using a questionnaire survey, which was distributed to 370 academic in Jordanian universities. Out of the total (370) distributed questionnaire copies, 266 were returned and considered valid for analysis. Accordingly, data was analyzed using Smart PLS (3.2.8), and the findings indicated that knowledge management significantly affected organizational learning, also knowledge management dimensions namely knowledge acquisition, knowledge conversion, knowledge application, and knowledge protection significantly affected organizational learning. Finally, the results show that organizational culture as a positive moderating effect between the knowledge management and the organizational learning. On the basis of the results, it is recommended that Jordanian universities focus on knowledge management when planning their strategies in order to promote influences of organizational learning. Furthermore, the consolidation of organizational culture in universities because of its impact on strengthening the relationship between knowledge management and organizational learning.
Revision of the efficacy test method set up by Air Fresheners and Deodorizers Conference and explanation of the revised content

芳香消臭脱臭剤協議会の効力試験方法は，1990年に制定され30周年を迎えている.芳香消臭脱臭剤の機能評価方法について文章化された貴重なマニュアルであり，会員企業以外からも注目を集めている.このような各方面からのニーズ，技術的進歩や新たな知見，協議会を取り巻く環境の変化に伴ってこれまでも見直しが行われてきたが，今回第3回目の見直しを行った.
Recurrent Attention for the Transformer

In this work, we conduct a comprehensive investigation on one of the centerpieces of modern machine translation systems: the encoder-decoder attention mechanism. Motivated by the concept of first-order alignments, we extend the (cross-)attention mechanism by a recurrent connection, allowing direct access to previous attention/alignment decisions. We propose several ways to include such a recurrency into the attention mechanism. Verifying their performance across different translation tasks we conclude that these extensions and dependencies are not beneficial for the translation performance of the Transformer architecture.
Team Papelo at FEVEROUS: Multi-hop Evidence Pursuit

We develop a system for the FEVEROUS fact extraction and verification task that ranks an initial set of potential evidence and then pursues missing evidence in subsequent hops by trying to generate it, with a “next hop prediction module” whose output is matched against page elements in a predicted article. Seeking evidence with the next hop prediction module continues to improve FEVEROUS score for up to seven hops. Label classification is trained on possibly incomplete extracted evidence chains, utilizing hints that facilitate numerical comparison. The system achieves .281 FEVEROUS score and .658 label accuracy on the development set, and finishes in second place with .259 FEVEROUS score and .576 label accuracy on the test set.
Context or No Context? A preliminary exploration of human-in-the-loop approach for Incremental Temporal Summarization in meetings

Incremental meeting temporal summarization, summarizing relevant information of partial multi-party meeting dialogue, is emerging as the next challenge in summarization research.Here we examine the extent to which human abstractive summaries of the preceding increments (context) can be combined with extractive meeting dialogue to generate abstractive summaries.We find that previous context improves ROUGE scores.Our findings further suggest that contexts begin to outweigh the dialogue.Using keyphrase extraction and semantic role labeling (SRL), we find that SRL captures relevant information without overwhelming the the model architecture.By compressing the previous contexts by ≈ 70%, we achieve better ROUGE scores over our baseline models.Collectively, these results suggest that context matters, as does the way in which context is presented to the model.
ECONOMIC CONSEQUENCES OF CYBERCRIME AND LEGAL REGULATION MECHANISMS IN GEORGIA

With the development of Internet technologies, cybercrime has also evolved and diversified. Much of the world's economic and business information comes from electronic information, and the need for remote work caused by the coronavirus (COVID-19) pandemic has further increased demand for e-services, which in turn has contributed to increased technology risks, threats, and incidents. The main task of the states is to actively fight against the negative socio-economic, financial, and political consequences caused by the growth of cybercrime. The main purpose of the study is to assess the negative consequences of cybercrime in Georgia, the main threats to information security, to develop recommendations for the prevention of cybercrime, to improve its legal regulation mechanisms. The fight against cybercrime in Georgia is carried out using criminal norms. The law "On Information Security" is in force in the country, Georgia's cyber security strategy has been developed, but this process needs constant development. The main task of the state is to gradually improve the legislation and bring it in line with modern technologies, to ensure close cooperation between the state and society, to raise the awareness of civil society. It is essential to constantly inform the public and companies about cyber threats, as effective prevention is the best form of crime prevention policy.
Real-Time Precise Human-Computer Interaction System Based on Gaze Estimation and Tracking

Noncontact human-computer interaction has an important value in wireless sensor networks. This work is aimed at achieving accurate interaction on a computer based on auto eye control, using a cheap webcam as the video source. A real-time accurate human-computer interaction system based on eye state recognition, rough gaze estimation, and tracking is proposed. Firstly, binary classification of the eye states (opening or closed) is carried on using the SVM classification algorithm with HOG features of the input eye image. Second, rough appearance-based gaze estimation is implemented based on a simple CNN model. And the head pose is estimated to judge whether the user is facing the screen or not. Based on these recognition results, noncontact mouse control and character input methods are designed and developed to replace the standard mouse and keyboard hardware. Accuracy and speed of the proposed interaction system are evaluated by four subjects. The experimental results show that users can use only a common monocular camera to achieve gaze estimation and tracking and to achieve most functions of real-time precise human-computer interaction on the basis of auto eye control.