Reviewerly/exHarmony huggingface dataset:
Cross-Policy Compliance Detection via Question Answering

Policy compliance detection is the task of ensuring that a scenario conforms to a policy (e.g. a claim is valid according to government rules or a post in an online platform conforms to community guidelines).This task has been previously instantiated as a form of textual entailment, which results in poor accuracy due to the complexity of the policies.In this paper we propose to address policy compliance detection via decomposing it into question answering, where questions check whether the conditions stated in the policy apply to the scenario, and an expression tree combines the answers to obtain the label.Despite the initial upfront annotation cost, we demonstrate that this approach results in better accuracy, especially in the cross-policy setup where the policies during testing are unseen in training.In addition, it allows us to use existing question answering models pre-trained on existing large datasets.Finally, it explicitly identifies the information missing from a scenario in case policy compliance cannot be determined.We conduct our experiments using a recent dataset consisting of government policies, which we augment with expert annotations and find that the cost of annotating question answering decomposition is largely offset by improved interannotator agreement and speed.
Effects of Teaching and Learning Through Zoom Application

The COVID-19 pandemic, which started in 2020, has inadvertently revolutionized the modes of teaching and learning, from the traditional face-to-face to the “new normal” online classes. As a result, there has been drastic increase in the number of online applications. This chapter is focused on Zoom application. It has adopted library research to analyze the impact of teaching and learning through Zoom application. A critical review of the literature reveals that Zoom application has several advantages. It is simple to use, easy to plan a meeting, can be accessed by a one-click notification or invitation link, has a telephone option for audio, has interactive features, has a video recording option, and offers a free basic package. Notwithstanding these merits, the application also has several challenges that impact teaching and learning. These include privacy and security, supervision in the case of children’s education, and internet connection. The study has recommended that future research could be extended to technical issues related to security, privacy, database and storages, and ways of preventing hackers from interfering with the application.
Automatic Microseismic Event Detection With Variance Fractal Dimension via Multitrace Envelope Energy Stacking

Surface monitoring of microseismic monitoring events is generally challenging because microseismic data have a low signal-to-noise ratio (SNR). Traditional event-detection methods struggle to detect weak microseismic events. A variance fractal dimension (VFD) method for automatic microseismic event detection via multitrace energy envelope stacking (MTEES) is introduced. In the first stage, we propose a processing microseismic data method based on the MTEES method. It increases the energy of weak microseismic data to avoid missed and false microseismic detection. Furthermore, it can greatly improve computational efficiency to satisfy real-time processing requirements. In the second stage, the VFD algorithm is applied to the data processed in the first stage to improve the feasibility and validity of microseismic event detection. A simulation test with perforation data shows the reliability of the new method in the automatic detection of microseismic events. In addition, we demonstrate that analogous results can be obtained when perforation data are not available by introducing a novel approach based on synthetic correction time. The new approach is particularly useful when perforation data are not recorded, representing a significant advantage over previous approaches. We describe the application of the novel method to a real microseismic data example from monitoring hydraulic fracture treatments in Shanxi Province, China, with and without perforation data. The new method yields improvement in microseismic event detection for microseismic monitoring. Therefore, we find a wide range of applications requiring analysis of microseismic data.
INTEGRATION OF PRACTICE-ORIENTED AND THEORETICAL APPROACHES AS A NECESSARY CONDITION FOR THE TRAINING OF MODERN PERSONNEL IN VOCATIONAL AND PEDAGOGICAL EDUCATION

The article deals with the problem of improving the quality of training of teachers of vocational training by integrating practice-oriented and theoretical approaches for the implementation of the experimental project «Professionalism» as a priority direction in the development of secondary vocational education. It is noted that the professional activity of a teacher becomes more multitasking and requires the formation of professional skills while developing fl exibility of thinking, intelligence, and the ability to quickly respond to the demands of the labor market. The professional training of teachers provides for the integration of practice-oriented and theoretical approaches to form a competitive specialist. At the same time, the educational environment should diff er in the variability of approaches, educational technologies, didactic means and teaching methods. The article describes some forms of work with students that allow modeling the future professional activity of a teacher of vocational training and imply deepening and expanding the content of theoretical material and its refraction when performing practical tasks of a professional orientation. Noting the practice-oriented approach as an active form of organizing professional training of students, the authors consider the experience of working on the basis of the South Ural State Humanitarian Pedagogical University.
Review of Data Integrity Attacks and Mitigation Methods in Edge Computing

In recent years, edge computing has emerged as a promising technology due to its unique feature of real-time computing and parallel processing. They provide computing and storage capacity closer to the data source and bypass the distant links to the cloud. The edge data analytics process the ubiquitous data on the edge layer to offer real-time interactions for the application. However, this process can be prone to security threats like gaining malicious access or manipulating sensitive data. This can lead to the intruder's control, alter, or add erroneous data affecting the integrity and data analysis efficiency. Due to the lack of transparency of stakeholders processing edge data, it is challenging to identify the vulnerabilities. Many reviews are available on data security issues on the edge layer; however, they do not address integrity issues exclusively. Therefore, this paper concentrates only on data integrity threats that directly influence edge data analysis. Further shortcomings in existing work are identified with few research directions.
Non-uniform Region Based Features for Automatic Language Identification

An audio utterance can be identified as being spoken in a particular language by using automatic language identification (LID). Each language has its own phoneme set. Hence combination of these phonemes governed by phonotactics will help in distinguishing languages. In this work, we propose an automatic language identification system utilizing features derived from non-uniform speech regions to represent phonotac-tic differences among 4 Indian languages, namely Malayalam, Marathi, Assamese, and Kannada. For this, broad phoneme labels, namely approximant (A), closure (C), fricatives (F), nasals (N), plosive/stop (P), voiced stop (B), vowels (V), and silence (S) are obtained automatically by a broad phoneme classifier (BPC). It is a DNN-based classifier which uses hand-crafted features and Mel-frequency cepstral coefficients (MFCC). In order to automatically segment speech to smaller regions, first it is chopped at every silence regions using the labels obtained from BPC. Later, it is split again at the end of each vowel. Hence, small non-uniform regions are obtained which contain phoneme combinations that may be specific to the language of the utterance. From each region, only a fixed number of frames containing certain combination of phonemes are selected. A DNN classifier is trained using 13-dimensional MFCC features of 12 fixed frames of non-uniform regions for performing LID. An average accuracy of 97.03% is obtained for test utterances of 10 sec duration belonging to 4 languages.
Co-occurrence based texture synthesis

Abstract As image generation techniques mature, there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate. In this work, we turn to co-occurrence statistics, which have long been used for texture analysis, to learn a controllable texture synthesis model. We propose a fully convolutional generative adversarial network, conditioned locally on co-occurrence statistics, to generate arbitrarily large images while having local, interpretable control over texture appearance. To encourage fidelity to the input condition, we introduce a novel differentiable co-occurrence loss that is integrated seamlessly into our framework in an end-to-end fashion. We demonstrate that our solution offers a stable, intuitive, and interpretable latent representation for texture synthesis, which can be used to generate smooth texture morphs between different textures. We further show an interactive texture tool that allows a user to adjust local characteristics of the synthesized texture by directly using the co-occurrence values.
Source Free Domain Adaptation by Deep Embedding Clustering

Domain adaptation is devised to enhance model performance on target domain when distribution shift occurred. The current domain adaptation methods tend to cause significantly transmission consumption in practical applications because they require both source and target data in training, especially for decentralized training. This paper explores a different setting, source data free domain adaptation, only target data and source model are accessible during knowledge transfer. This work proposes a simple solution deep embedding clustering domain adaptation (DECDA) to this task, which use source model to generate pseudo labels for target data and clusters target data towards the cluster centers of high-confidence target samples in feature space by deep embedding clustering algorithm. In this way, the target data can be classified by source classifier in feature space. Experiments have been evaluated in Office-31 and VisDA2017 dataset and achieved comparable results to the best source data required domain adaptation methods recently.
Real Conversation with Human-Machine 24/7 COVID-19 Chatbot Based on Knowledge Graph Contextual Search

The outbreak of the COVID-19 pandemic has changed the whole world scenario and made researchers innovate on the corona virus. Researchers are working on information that includes symptoms, Infection spreading, preventive measures, health and travel advisories, and help lines for further assistance. During this pandemic scenario, the health assistant Chatbot is a very useful conversation tool for COVID-19, which provides preliminary medical advice and preventive measure suggestions. The paper proposes an Artificial Intelligence-based Re-Co Chatbot to provide information about the corona virus and also assist with customer queries. The goal is to build a 24/7 COVID Chatbot capable of answering user questions and to emphasize and stress the concept of contextual semantic search and Knowledge Graph to serve as the FAQ for Corona information. Natural Language Processing (NLP) is used to process the user question and the SpaCy library is used for text processing. Once the question is processed, entities (the subject of question) and relations (predicate of the question) are recognized and extracted. The Chatbot is designed for about 100 question-answers pairs in the CSV file and will create about 575 relationships in the Knowledge Graph.
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection-Fold Ensemble for Out-Of-Distribution Detection

Out-of-Distribution (OOD) detection is an important problem in natural language processing (NLP).In this work, we propose a simple yet effective framework kFolden, which mimics the behaviors of OOD detection during training without the use of any external data.For a task with k training labels, kFolden induces k sub-models, each of which is trained on a subset with k -1 categories with the left category masked unknown to the sub-model.Exposing an unknown label to the sub-model during training, the model is encouraged to learn to equally attribute the probability to the seen k -1 labels for the unknown label, enabling this framework to simultaneously resolve in-and out-distribution examples in a natural way via OOD simulations.Taking text classification as an archetype, we develop benchmarks for OOD detection using existing text classification datasets.By conducting comprehensive comparisons and analyses on the developed benchmarks, we demonstrate the superiority of kFolden against current methods in terms of improving OOD detection performances while maintaining improved in-domain classification accuracy. 1
Application of Constructive Modeling and Process Mining Approaches to the Study of Source Code Development in Software Engineering Courses

We present an approach of constructing a source code history for a modern code review. Practically, it is supposed to be used in programming training, especially within initial stages. The developed constructor uses constructive-synthesizing modeling tools to classify a source code history by fine-grained changes and to construct an event log file aimed to provide information on students' coding process. Current research applies Process Mining techniques to the software engineering domain to identify software engineering skills. By better understanding of the way students design programs, we will help novices learn programming. This research provides an innovative method of using code and development process review in teaching programming skills and is aimed to encourage using code review and monitoring coding practice in educational purposes. The standard method of evaluation takes into consideration only a final result, which doesn't meet modern requirements of teaching programming.
Choosing a video conferencing service and its adaptation for educational institution

Objectives. The authors aimed to demonstrate the need for implementation of video conferencing service into the learning process, to select a video conferencing service, and to conduct a computer experiment with the selected BigBlueButton video conferencing service. Methods. The problems of choosing a video conferencing service from the list of video conferencing and video conferencing software are considered. At the stage of software selection, the features of its operation, requirements for hardware and for integration into internal information systems are indicated. Load testing of the video conferencing service was carried out by the method of volume and stability testing. Results. The load graphs for hardware components of the virtual server in the long term period are presented. The article describes the results of the graphs analysis in order to identify the key features of the video conferencing service during the test and trial operations. Conclusion. Taking into account the cost of licensing, as well as integration into the e-learning system, a choice of video conferencing service was made. A computer experiment was carried out with the selected BigBlueButton video conferencing service. The features of the hardware operation of the virtual server (on which the BigBlueButton system is located) have been determined. The load graphs for the central processing unit, random access memory and local computer network are presented. Problems of service operation at the stage of load increase are formulated.
The problem of "clip thinking" in the context of the choice of teaching methods for students of higher educational institutions.

The article examines the problem of the presence of clip thinking in a social group of young people-students. The novelty of the research lies in the question of the need for an interdisciplinary study of clip thinking as a biological, social and humanitarian phenomenon, and the priority in determining the essence of this phenomenon should be given to the cognitive sciences. The analysis of the results of methodological innovations of modern researchers reveals the charm of clip thinking as a new cognitive style and the presence of imperative recommendations on the transformation of the format of educational technologies in the direction of its adaptation to this mass phenomenon. The author of the article questions the thesis about the need to switch to teaching methods in higher education that take into account the peculiarities of clip thinking. The methodological basis for the study was: the concept of the theory of generations by N. Howe and W. Strauss, the concept of conceptual thinking by L.S. Vygotsky, the concept of cognitive styles by M.A. Kholodnaya, the concept of clip thinking by K.G. Frumkin. The results of the study (on the example of students of the FEFU Law School) show that it is wrong to apply the clip thinking model as the leading basic generational characteristic to Russian students a priori. Students are a differentiated community in which there is a variety of cognitive styles in individuals, including a wide distribution of the most productive of them – the conceptual-logical. In this regard, the author comes to the conclusion that educational technologies in higher education should combine methods that accumulate ways of reflecting reality in visual-figurative and verbal-logical expression, create an educational space in which to demonstrate ways of translating clip thinking into conceptual thinking and teach such refocusing, while it is necessary to teach students to use both cognitive styles and effectively switch between them, depending on the type of cognitive tasks associated with working with information.
Cervical Cancer Detection and Classification in Cytology Images Using a Hybrid Approach

Abstract The high incidence of cervical cancer in women has prompted the research of automatic screening methods. This work focuses on two of the steps present in such systems, more precisely, the identification of cervical lesions and their respective classification. The development of automatic methods for these tasks is associated with some shortcomings, such as acquiring sufficient and representative clinical data. These limitations are addressed through a hybrid pipeline based on a deep learning model (RetinaNet) for the detection of abnormal regions, combined with random forest and SVM classifiers for their categorization, and complemented by the use of domain knowledge in its design. Additionally, the nuclei in each detected region are segmented, providing a set of nuclei-specific features whose impact on the classification result is also studied. Each module is individually assessed in addition to the complete system, with the latter achieving a precision, recall and F1 score of 0.04, 0.20 and 0.07, respectively. Despite the low precision, the system demonstrates potential as an analysis support tool with the capability of increasing the overall sensitivity of the human examination process.
Dağıtık Defter Teknolojileri ve Uygulama Alanları Üzerine Bir İnceleme

Distributed ledger technology offers a new approach in which data is stored and managed in distributed.Centralized systems are problem in terms of security, reliability and uninterrupted operation.Data keeping in single point causes center servers to become open to cyber attacks.In addition administrators of central servers or attackers can manipulate data.With the increase in the number of devices connected to the Internet, it is increasingly difficult for central servers to answer all requests.In order to solve all these problems, studies using distributed ledger technology are increasing.Distributed ledger technology, there is no single point of failure because the data is kept in a distributed and data can't be changed by any participant.The system can operate uninterruptedly, no matter how high the transaction density as the participants can transact between each other without a central server.The first article describing the distributed ledger technology technically was published in 1991.The first widespread use is Bitcoin, which emerged in 2008.Bitcoin is without intermediary and secure payment system developed by Satoshi Nakamoto in 2008, when the global financial system collapsed.The popularization of Bitcoin cryptocurrency, studies on different sectors related to blockchain which is distributed ledger technology behind it have began.Blockchain technology due to the fact that is not sufficient in terms of speed and scalability, Hashgraph, Tangle, Holochain and Tempo distributed ledger technologies have been developed.In this article explains with existing distributed ledger technologies and defence, cybersecurity, insurance, supply chain, digital rights management, healthcare, energy and finance application areas.
A DEEP DIVE INTO THE BASICS OF DEEP LEARNING

Deep learning is a type of machine learning (ML) that is growing in importance in the medical field. It can often perform better than traditional ML models on different metrics, and it can handle non-linear problems due to activation functions. Activation functions are different non-linear functions that are used to restrict the values propagated to an interval. In deep learning, information propagates forward, passing through different layers of weights and activation functions, before reaching the final layer. Then a cost function is evaluated and propagated back through the network to adjust weights. A convolutional neural network (CNN) is a form of deep learning that is used primarily in imaging. CNNs perform significantly well with grid-like inputs because they learn shapes well. CNNs compute dot products between layers and kernels in a convolutional layer, prior to pooling, which outputs summary statistics. CNNs are better than trivial neural networks for imaging due to a number of reasons, like sparse interaction and equivariance of translation