Reviewerly/exHarmony huggingface dataset:
Decision tree algorithm for multi-label hate speech and abusive language detection in Indonesian Twitter

Hate speech and abusive language are easily found in written communications in social media like Twitter. They often cause a dispute between parties, the victims, and the first who write the tweet. However, it is also difficult to distinguish whether a tweet contains hate speech and/or abusive language for those who take sides. This research aims to develop a method to classify the tweets into abusive and/or contain hate speech classes. If hate speech is detected, then the system will measure the hardness level of hatred. The dataset includes 13,126 real tweets data. Word embeddings are used for featuring text input. For the tweets classification, we use a Decision Tree algorithm. Some engineering of features and parameters tuning has improved the classification of the three classes: hate speech class, abusive words, and hate speech level. The lexicon feature in the Decision Tree classification produces the highest accuracy for detecting the three classes rather than engineering special features and textual features. The average accuracy of the three classes increased from 69.77 % to 70.48 % for the training-testing composition of 90:10, and another 69.35 % to 69.54 % for 80:20 respectively.
Predictive Constructions Based on Measure-Valued Pólya Urn Processes

Measure-valued Pólya urn processes (MVPP) are Markov chains with an additive structure that serve as an extension of the generalized k-color Pólya urn model towards a continuum of possible colors. We prove that, for any MVPP (μn)n≥0 on a Polish space X, the normalized sequence (μn/μn(X))n≥0 agrees with the marginal predictive distributions of some random process (Xn)n≥1. Moreover, μn=μn−1+RXn, n≥1, where x↦Rx is a random transition kernel on X; thus, if μn−1 represents the contents of an urn, then Xn denotes the color of the ball drawn with distribution μn−1/μn−1(X) and RXn—the subsequent reinforcement. In the case RXn=WnδXn, for some non-negative random weights W1,W2,…, the process (Xn)n≥1 is better understood as a randomly reinforced extension of Blackwell and MacQueen’s Pólya sequence. We study the asymptotic properties of the predictive distributions and the empirical frequencies of (Xn)n≥1 under different assumptions on the weights. We also investigate a generalization of the above models via a randomization of the law of the reinforcement.
Keyphrase Extraction with Incomplete Annotated Training Data

Extracting keyphrases that summarize the main points of a document is a fundamental task in natural language processing.Supervised approaches to keyphrase extraction(KPE) are largely developed based on the assumption that the training data is fully annotated.However, due to the difficulty of keyphrase annotating, KPE models severely suffer from incomplete annotated problem in many scenarios.To this end, we propose a more robust training method that learns to mitigate the misguidance brought by unlabeled keyphrases.We introduce negative sampling to adjust training loss, and conduct experiments under different scenarios.Empirical studies on synthetic datasets and open domain dataset show that our model is robust to incomplete annotated problem and surpasses prior baselines.Extensive experiments on five scientific domain datasets of different scales demonstrate that our model is competitive with the state-of-the-art method.
HUMAN ACTIVITY RECOGNITION AND FALL DETECTION

Personalized monitoring and its application is increasing with the advancement of technology. And during pandemics its become very essential to keep an eye on the prone area and one of the area to identify was old age home. Dizziness, unconsciousness, and others are the common problems associated with elderly people due to weakness and this was also the symptoms of covid. So an unusual activity of falling of elderly people was very difficult to identify and also to monitor. The technology was updated till now to identify posture of normal activity such as running, walking, jumping and many but revert to that falling was an area need to explore. During the fall of an elderly person, the injuries are very fatal, and to void this case we proposed a design to identify the fall and try to notify the system about its fall. Although we try to predict the fall so that it becomes easy to monitor and provide medical help as soon as possible. The main theme is to identify the posture activity and once identify we will compare the activity with trained datasets and if it’s normal in vision them no notification occurred and if the percentage of falls was high then we can predict the system as fall video
Learning Cross-lingual Representations for Event Coreference Resolution with Multi-view Alignment and Optimal Transport

We study a new problem of cross-lingual transfer learning for event coreference resolution (ECR) where models trained on data from a source language are adapted for evaluations in different target languages.We introduce the first baseline model for this task based on XLM-RoBERTa, a state-of-the-art multilingual pre-trained language model.We also explore language adversarial neural networks (LANN) that present language discriminators to distinguish texts from the source and target languages to improve the language generalization for ECR.In addition, we introduce two novel mechanisms to further enhance the general representation learning of LANN, featuring: (i) multi-view alignment to penalize cross coreference-label alignment of examples in the source and target languages, and (ii) optimal transport to select close examples in the source and target languages to provide better training signals for the language discriminators.Finally, we perform extensive experiments for cross-lingual ECR from English to Spanish and Chinese to demonstrate the effectiveness of the proposed methods.
FaBULOUS: Fact-checking Based on Understanding of Language Over Unstructured and Structured information

As part of the FEVEROUS shared task, we developed a robust and finely tuned architecture to handle the joint retrieval and entailment on text data as well as structured data like tables. We proposed two training schemes to tackle the hurdles inherent to multi-hop multi-modal datasets. The first one allows having a robust retrieval of full evidence sets, while the second one enables entailment to take full advantage of noisy evidence inputs. In addition, our work has revealed important insights and potential avenue of research for future improvement on this kind of dataset. In preliminary evaluation on the FEVEROUS shared task test set, our system achieves 0.271 FEVEROUS score, with 0.4258 evidence recall and 0.5607 entailment accuracy.
Morton Filter-Based Security Mechanism for Healthcare System in Cloud Computing

Electronic health records contain the patient's sensitive information. If these data are acquired by a malicious user, it will not only cause the pilferage of the patient's personal data but also affect the diagnosis and treatment. One of the most challenging tasks in cloud-based healthcare systems is to provide security and privacy to electronic health records. Various probabilistic data structures and watermarking techniques were used in the cloud-based healthcare systems to secure patient's data. Most of the existing studies focus on cuckoo and bloom filters, without considering their throughputs. In this research, a novel cloud security mechanism is introduced, which supersedes the shortcomings of existing approaches. The proposed solution enhances security with methods such as fragile watermark, least significant bit replacement watermarking, class reliability factor, and Morton filters included in the formation of the security mechanism. A Morton filter is an approximate set membership data structure (ASMDS) that proves many improvements to other data structures, such as cuckoo, bloom, semi-sorting cuckoo, and rank and select quotient filters. The Morton filter improves security; it supports insertions, deletions, and lookups operations and improves their respective throughputs by 0.9× to 15.5×, 1.3× to 1.6×, and 1.3× to 2.5×, when compared to cuckoo filters. We used Hadoop version 0.20.3, and the platform was Red Hat Enterprise Linux 6; we executed five experiments, and the average of the results has been taken. The results of the simulation work show that our proposed security mechanism provides an effective solution for secure data storage in cloud-based healthcare systems, with a load factor of 0.9. Furthermore, to aid cloud security in healthcare systems, we presented the motivation, objectives, related works, major research gaps, and materials and methods; we, thus, presented and implemented a cloud security mechanism, in the form of an algorithm and a set of results and conclusions.
Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution

Masked language models (MLMs) have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR).To further improve this approach, in this study, we made two proposals.The first is a new pretraining task that trains MLMs on anaphoric relations with explicit supervision, and the second proposal is a new finetuning method that remedies a notorious issue, the pretrainfinetune discrepancy.Our experiments on Japanese ZAR demonstrated that our two proposals boost the state-of-the-art performance, and our detailed analysis provides new insights on the remaining challenges.
Who Makes You More Disappointed? The Effect of Avatar Presentation, Company Market Status, and Agent Identity on Customers’ Perceived Service Quality and Satisfaction of Online Chatting Services

Recent advances in interactive communication have allowed companies to introduce new digital technologies to differentiate their online customer chatting services, allowing companies to leverage both human and AI agents in their chatting services.In addition, the presentation of chatting agents is also varied including human pictures, human image, and so on.However, little is known about how these changes in online chatting service affect customer responses.To answer this call, the current research examines the effects of avatar presentation, company market status, and agent identity on customers' perception of service quality and satisfaction in the context of minor service failure that customers encounter through online chatting services.The results of two studies show that customers perceive better service quality and demonstrate lower satisfaction if served by an artificial intelligence [AI] agent (vs. a human-agent), represented by a human picture (vs. a human image).However, this pattern was found only for a company with high marketstatus, not for a company with low market-status.These results will help marketing practitioners strategically design their chatting service environments according to the nature of services they offer.
Multi-task Learning in Argument Mining for Persuasive Online Discussions

We utilize multi-task learning to improve argument mining in persuasive online discussions, in which both micro-level and macro-level argumentation must be taken into consideration.Our models learn to identify argument components and the relations between them at the same time.We also tackle the low-precision which arises from imbalanced relation data by experimenting with SMOTE and XGBoost.Our approaches improve over baselines that use the same pre-trained language model but process the argument component task and two relation tasks separately.Furthermore, our results suggest that the tasks to be incorporated into multi-task learning should be taken into consideration as using all relevant tasks does not always lead to the best performance.
Enhancing Document Ranking with Task-adaptive Training and Segmented Token Recovery Mechanism

In this paper, we propose a new ranking model DR-BERT, which improves the Document Retrieval (DR) task by a task-adaptive training process and a Segmented Token Recovery Mechanism (STRM).In the task-adaptive training, we first pre-train DR-BERT to be domain-adaptive and then make the two-phase fine-tuning.In the first-phase fine-tuning, the model learns query-document matching patterns regarding different query types in a pointwise way.Next, in the second-phase finetuning, the model learns document-level ranking features and ranks documents with regard to a given query in a listwise manner.Such pointwise plus listwise fine-tuning enables the model to minimize errors in the document ranking by incorporating ranking-specific supervisions.Meanwhile, the model derived from pointwise fine-tuning is also used to reduce noise in the training data of the listwise fine-tuning.On the other hand, we present STRM which can compute OOV word representation and contextualization more precisely in BERT-based models.As an effective strategy in DR-BERT, STRM improves the matching perfromance of OOV words between a query and a document.Notably, our DR-BERT model keeps in the top three on the MS MARCO leaderboard since May 20, 2020.
Bayesian inference on the number of recurrent events: A joint model of recurrence and survival

The number of recurrent events before a terminating event is often of interest. For instance, death terminates an individual's process of rehospitalizations and the number of rehospitalizations is an important indicator of economic cost. We propose a model in which the number of recurrences before termination is a random variable of interest, enabling inference and prediction on it. Then, conditionally on this number, we specify a joint distribution for recurrence and survival. This novel conditional approach induces dependence between recurrence and survival, which is often present, for instance, due to frailty that affects both. Additional dependence between recurrence and survival is introduced by the specification of a joint distribution on their respective frailty terms. Moreover, through the introduction of an autoregressive model, our approach is able to capture the temporal dependence in the recurrent events trajectory. A non-parametric random effects distribution for the frailty terms accommodates population heterogeneity and allows for data-driven clustering of the subjects. A tailored Gibbs sampler involving reversible jump and slice sampling steps implements posterior inference. We illustrate our model on colorectal cancer data, compare its performance with existing approaches and provide appropriate inference on the number of recurrent events.
Comparative analysis of the earthquake disaster risk of cities in Eastern China based on lethal levels – a case study of Yancheng City, Suqian City and Guangzhou City

Earthquake disaster risk analyses provide significant scientific guidance for reducing earthquake disaster losses etc. The current commonly used method is based on the vulnerability of different types of buildings to evaluate, and may ignore the difference between the seismic resistance of the same type of buildings in different areas. The lethal level considers a series of reasons such as different types of buildings, resulting in different capacities for casualties, the lethal levels of the cities in the same subregion are considerably different and exhibit obvious distribution characteristics. In Yancheng and Suqian, the lethal levels in urban areas are lower than those in rural areas, but the lethal levels in Guangzhou show the opposite trend (the lethal levels are higher in urban areas than in other towns and rural areas). The average lethal level in Guangzhou is 0.3856, that in Suqian is 0.5844, and that in Yancheng is 0.5912. This study based on the lethal levels, conduct a comparative analysis of the earthquake disaster risk of cities throughout eastern China, and a map of the earthquake disaster risk in each city is obtained. The overall lethal level and risk in Guangzhou is much lower than those in Yancheng and Suqian. The main influencing factors in the different cities also diverge. One reason for the lower lethal levels and risk in Guangzhou is that most of the brick-concrete structures are equipped with ring beams and structural columns; conversely, Yancheng and Suqian exhibit higher lethal levels and risk because the brick-concrete structures have ring beams but no structural columns. These maps provide a technical reference and solid scientific and technological support for further earthquake disaster risk analyses and for disaster prevention, mitigation and disaster relief planning, and this method improves the accuracy of rapid postearthquake assessment.
PERPUSTAKAAN DIGITAL SEBAGAI PENUNJANG PEMBELAJARAN JARAK JAUH

This paper aims to describe the role of digital libraries as support for distance learning during a pandemic. This research uses the type of literature study conducted to prepare a research framework and make use of general literature sources to obtain research data. The stages of research carried out in this library research are determining the topics to be studied, searching for and selecting data by the focus of the study, conducting source analysis, and developing data by the focus of the research and concluding it. The result is that the library as a support and learning resource must adapt to distance learning patterns so that libraries that were originally only a pile of books and are still traditional have to change their service patterns to a digital library model so that it makes it easier for users to take advantage of the library during the Covid-19 pandemic. To maximize the role of digital libraries, students must also actively increase their learning independence and must have awareness of students and the role of parents in directing, helping, guiding, and motivating students so that good collaboration between students and parents can be created. and teachers in optimizing the role of digital libraries in distance learning. Thus it will create a reading habit for students from their awareness and will further increase the effectiveness of learning at distance during the Covid-19 pandemic.
THE DEVELOPMENT OF THE SUPERSERVICE"ENTERING THE UNIVERSITY ONLINE" IN THE CONTEXT OF THE DIGITAL TRANSFORMATION OF HIGHER EDUCATION

Digital transformation covers all areas of the economy. The analysis in the article showed that this mechanism of filing documents has a number of serious shortcomings related to technical and organizational shortcomings. one of 49 pilot universities in the country that have implemented this project in the admissions campaign. that the proportion of applicants who submitted documents through the super service is insignificant, so it is necessary to identify the main reasons for the low popularity of this mechanism. To do this, the authors conducted a sociological survey among the applicants of the Russian Federation, which reflected the attitude of respondents to the peculiarities of applying to universities and identified the difficulties they had. In order to determine the main areas of improvement of the superservice "Getting to the university online" the main criteria for competitiveness of each of the mechanisms of filing documents to the admissions commission were defined and an expert assessment on them was made. As a result of the study, concrete measures to improve the implementation of the superservice in Kazan GAU, which can be extrapolated to other universities in the country, have been proposed
Automatic Classification Method of Quaternary Lithology in Vegetation Cover Area Combining Spectral, Textural, Topographic, Geothermal, and Vegetation Features

To explore the automatic classification method of Quaternary lithology in vegetation covered areas is significantly helpful to improve the efficiency of Quaternary lithology mapping. Due to the vegetation cover and human modification effects, the traditional lithology identification methods based on image spectra and textures are often challenging to be effective. This paper uses multi-source remote sensing data such as OLI, TIRS, and ASTER GDEM to extract multiple types of spectral (SPEC), textural (TEX), topographic (TOPO), geothermal (TEM), and vegetation (VEG) based on principal component transform, gray co-occurrence matrix, topographic factor calculation, thermal radiation transport model and vegetation index in the Quaternary distribution area of Viet Chi, Vietnam. Remote sensing features were selected and combined to form 16 kinds of classification datasets. The lithological units was automatically classified using the random forest method, The method’s accuracy was evaluated to study the effectiveness of multi-type remote sensing features on the automatic classification of Quaternary lithology in vegetation cover area. The results show that the geothermal, textural, and topographic features can effectively improve the lithological classification accuracy, and the overall classification accuracy is improved by 0.32%, 0.87%, and 2.25%, respectively, compared with the use of spectral data alone. Among the 16 classification datasets constructed, the dataset combining spectral, textural, topographic, and geothermal features (SPEC+ TEX+ TOPO+ TEM) obtained the highest automatic lithology classification accuracy of 80.99%. This study can provide a technical idea for rapid differentiation of regional Quaternary surface sediment lithologies.
Analisis SWOT Pelaksanaan Perkuliahan Daring Pada Masa Pandemi COVID-19 di STAI Yogyakarta

The Covid-19 pandemic has brought major changes in various fields of life, including the field of education. One of the changes is about the implementation of lectures, namely the existence of online lectures (in the network). The author conducted research on the implementation of online lectures at STAI Yogyakarta using the SWOT Analysis method which aims to analyze the advantages, disadvantages, challenges and threats in the online lecture process at STAI Yogyakarta. The results of the research conducted by the authors found various internal and external factors, which were then analyzed to find various strategies to overcome the weaknesses and improve the strengths of STAI Yogyakarta. These strategies are divided into four types, namely Strength-Opportunities (SO), Weaknesses-Opportunities (WO), Strength-Threats (ST) and Weaknesses-Threats (WT) strategies.
Text-based Steganography using Huffman Compression and AES Encryption Algorithm

In every system of security, to keep important data confidential, we need a high degree of protection. Steganography can be defined as a way of sending confidential texts through a secure medium of communications as well as protecting the information during the process of transmission. Steganography is a technology that is used to protect users' security and privacy. Communication is majorly achieved using a network through SMS, e-mail, and so on. The presented work suggested a technology of text hiding for protecting secret texts with Unicode characters. The similarities of glyphs provided invisibility and increased the hiding capacity. In conclusion, the proposed method succeeded in securing confidential data and achieving high payload capacity by using the Huffman compression algorithm, which was implemented on an unlimited text length. In addition, this approach has the ability to hide a single bit in every digit or letter in the cover file. Also, the approach meets the cognitive transparency and does not make the modifications apparent on the original data. The method suggested in this work increases the security level through coding a secret message before embedding it within the cover text, with the use of the Advanced Encryption Standard (AES) algorithm.