Reviewerly/exHarmony huggingface dataset:
An Authenticated Enrolment Scheme of Nodes using Blockchain and Prevention of Collaborative Blackhole Attack in WSN

Security is indispensable concern part of the WSN. The reliable mechanisms and routing schemes are facing different set of encounters in the era of authentic status quo and it is also very knotty. Problem is identified in the recognition of untrusted nodes and routes followed from source to destination along with the restraint of battery status in WSN. No effective technique is there to avert the vindictive node attack. The current research article provides an algorithm of the nodes by employing the blockchain technology as far as solution to the other persisting drawbacks like identification of untrusted nodes and untrusted routes were tackled out by the help of secure AODV using blockchain. The outcomes of this research article by performing the simulation and experimental validation of the existing systems denotes the successful identification and occurrence of malicious nodes, end-to-end delay, packet delivery ratio and through put performance evaluation. The registration of the nodes in blockchain database and behaviour of black hole attack in AODV protocol was also simulated using NS2 blockchain algorithm.
An Improved Continuous Tabu Search Algorithm with Adaptive Neighborhood Radius and Increasing Search Iteration Times Strategies

Based on Continuous Tabu Search with Sine map and Staged strategy (CTS-SS) algorithm, an improved CTS-SS (iCTS-SS) algorithm is proposed to achieve rapid convergence and enhance the robustness of the CTS-SS algorithm with strategies of adaptive neighborhood radius and awarding search iterations. Unlike a fixed neighborhood radius used in the CTS-SS algorithm, the iCTS-SS algorithm utilizes the adaptive neighborhood radius to improve the search capabilities. According to whether the optimal result is replaced, it could be narrowed for centralized search or expanded for decentralized search. By this way, the result can be better than using regular iterations. The experiments adopt various typical functions as numerical experiment cases to evaluate the robustness, accuracy and efficiency of iCTS-SS. And for comparison, these functions are also solved by CTS-SS and Modified Staged Continuous Tabu Search (MSCTS), which is one of the latest continuous tabu search algorithms. Experimental results show that iCTS-SS outperforms both CTS-SS and MSCTS in statistical significance.
Shape-Based Features for Optimized Hand Gesture Recognition

Gesture recognition is the most intuitive form of human-computer interface. Hand gestures provide a natural way for humans to interact with computers to perform a variety of different applications. However, factors such as complexity of hand gesture structures, differences in hand size, hand posture, and environmental illumination can influence the performance of hand gesture recognition algorithms. Considering the above factors, this paper aims to present a real time system for hand gesture recognition on the basis of detection of some meaningful shape-based features like orientation, center of mass, status of fingers, thumb in terms of raised or folded fingers of hand and their respective location in image. The internet is growing at a very fast pace. The use of web browser is also growing. Everyone has at least two or three most frequently visited website. Thus, in this paper, effectiveness of the gesture recognition and its ability to control the browser via the recognized hand gestures are experimented and the results are analyzed.
Globalization – a Cause or a Solution for Corona Economic Crisis

Research background: Since the extension of the Corona virus there are people saying that all this is happening because of globalisation, phenomena that diminished the control of states at the borders, lack of strong internal security rules, loose of political responsibility and decisions, etc. In this paper we investigated the situation from different economical perspectives and we clearly show the existence of positive effects of globalization. We will never know certainly enough at which percentage the current crisis results from a mismatch between the intensity of economic and human exchanges and the inability of public authorities to be prepared and take fast and appropriate decisions. It is well accepted that in a globalized world, interdependence prevails over sovereignty. Purpose of the article: We will analyse if this reality is really one of the main problems which cause the current disaster. Nevertheless, it would be too easy to blame only the lacks of effective globalization, much complicated is to identify the problem and solve it. Searching if the globalization is the cause or the solution may be interesting since from the political side, we will see a wave of critics and sceptics, many politicians will try to reposition in order to gain visibility and votes. Methods: Using descriptive and comparative analysis we demonstrate that societies may obtain greater benefits from cooperation, collaboration and integration, even in pandemic times. Findings &amp; Value added: In a global world, we can see that borders do not protect against a pandemic and that all the major challenges facing humanity can only be solved by multilateral systems and by multilateral negotiation. We clearly demonstrated that global society has greater benefits from cooperation, collaboration and integration.
Leveraging Contextual Embeddings and Idiom Principle for Detecting Idiomaticity in Potentially Idiomatic Expressions

The majority of studies on detecting idiomatic expressions have focused on discovering potentially idiomatic expressions overlooking the context. However, many idioms like blow the whistle could be interpreted idiomatically or literally depending on the context. In this work, we leverage the Idiom Principle (Sinclair et al., 1991) and contextualized word embeddings (CWEs), focusing on Context2Vec (Melamud et al., 2016) and BERT (Devlin et al., 2019) to distinguish between literal and idiomatic senses of such expressions in context. We also experiment with a non-contextualized word embedding baseline, in this case word2Vec (Mikolov et al., 2013) and compare its performance with that of CWEs. The results show that CWEs outperform the non-CWEs, especially when the Idiom Principle is applied, as it improves the results by 6%. We further show that the Context2Vec model, trained based on Idiom Principle, can place potentially idiomatic expressions into distinct ‘sense’ (idiomatic/literal) regions of the embedding space, whereas Word2Vec and BERT seem to lack this capacity. The model is also capable of producing suitable substitutes for ambiguous expressions in context which is promising for downstream tasks like text simplification.
Image Inpainting using Patch Consensus and DCT Priors

We present an implementation of the PACO-DCT inpainting algorithm.This method is based on maximizing the likelihood of image patches in terms of their DCT coefficients, while requiring consensus on the overlapping patches.The resulting problem is solved as an instance of the PACO framework.
E-commerce content and collaborative-based recommendation using K-Nearest Neighbors and enriched weighted vectors

In this paper, we present two productive and functional recommender methods to improve the ac- curacy of predicting the right product for the user. One proposal is a survey-based recommender system that uses k-nearest neighbors. It recommends products by asking questions from the user, efficiently applying a binary product vector to the product attributes, and processing the request with a minimum error. The second proposal uses an enriched collaborative-based recommender system using enriched weighted vectors. Thanks to the style rules, the enriched collaborative- based method recommends outfits with competitive recommendation quality. We evaluated both of the proposals on a Kaggle fashion-dataset along with iMaterialist and, results show equivalent performance on binary gender and product attributes.
Learning Negation Scope from Syntactic Structure

We present a semi-supervised model which learns the semantics of negation purely through analysis of syntactic structure. Linguistic theory posits that the semantics of negation can be understood purely syntactically, though recent research relies on combining a variety of features including part-of-speech tags, word embeddings, and semantic representations to achieve high task performance. Our simplified model returns to syntactic theory and achieves state-of-the-art performance on the task of Negation Scope Detection while demonstrating the tight relationship between the syntax and semantics of negation.
The QMUL/HRBDT contribution to the NADI Arabic Dialect Identification Shared Task

We present the Arabic dialect identification system that we used for the country-level subtask of the NADI challenge. Our model consists of three components: BiLSTM-CNN, character-level TF-IDF, and topic modeling features. We represent each tweet using these features and feed them into a deep neural network. We then add an effective heuristic that improves the overall performance. We achieved an F1-Macro score of 20.77% and an accuracy of 34.32% on the test set. The model was also evaluated on the Arabic Online Commentary dataset, achieving results better than the state-of-the-art.
Sentiment Analysis and Text Classification for Social Media Contents Using Machine Learning Techniques

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Credit Card Fraud Detection Framework - A Machine Learning Perspective

The short improvement withinside the E-Commerce enterprise has caused a dramatic enlargement withinside the usage of credit score playing cards for on-line buys and thusly they had been flooded with the fraud diagnosed with it. As of late, for banks has gotten extraordinarily tough for figuring out the fraud with inside the credit card framework. Machine getting to know assumes an essential component in distinguishing credit card fraud withinside the transactions. For foreseeing those transactions banks make use of specific system getting to know methodologies, beyond data has been accrued and new highlights are being applied for enhancing the prescient force. The exhibition of possible threats identification in credit card instances is highly prompted through the analysing technique at the informational collection, the dedication of factors, and discovery strategies applied. This paper explores the presentation of K-Nearest Neighbor, Decision Trees, Support Vector Machine (SVM), Logistic Regression, Random Forest, and XGBoost for credit card fraud detection. Dataset of credit card transactions is accrued from Kaggle and it includes a sum of 2,84,808 credit card transactions of an EU financial institution dataset. It depicts doubtful transactions as fraud &amp; labels it "high-quality class" and actual ones as the "poor class". The dataset is relatively imbalanced, it has approximately 0.172% of fraud cases and the relaxations are actual transactions. These methods are implemented for the dataset and work is carried out in Python. The presentation of the methods is classed relying on the accuracy and F1 rating and confusion matrix. Results display that every set of rules may be used for credit card fraud detection with excessive precision. The proposed version may be helpful for the invention of numerous anomalies.
Hypnotized by Lines of Code

Lines of code (LOC) have been among the most popular measures for predicting software defects, but so far, their ultimate use has not been adequately understood. This article illustrates that LOC-based defect predictions are misleading, irrespective of how good a predictor LOC is.
DNNs for multi-map semantic segmentation

Modern vehicles include a vast number of intellectual functions such as lane-keeping assist (LKA), vehicle, pedestrian and obstacle recognition (FCW, PPS) which are implemented in the advanced driver assistance system (ADAS). These functions allow a vehicle to localize itself correctly within the road lane and to increase the overall system safety. It is also critical for vehicle motion and planning the target trajectory. Previously, algorithms implementing ADAS functions were based on classical computer vision approaches (e.g. edge detection, morphology, Hough transform), which did well only on a rather simple road scene. Modern state-of-the-art systems are based on semantic segmentation networks, it is the unquestionable trend. With a more thorough study of the road scene segmentation issues we face the problem that the existing benchmark suites such as MOTS, KITTI as well as recent DNNs for the road/lane semantic segmentation employ only mutually exclusive classes i.e. in this case, a pixel can belong to a single class only. But if we recognize the road scene, a pixel can easily refer to several classes, e.g. to ego-lane and crosswalk. The classical approach with mutually exclusive classes will give preference to only one class in this case and we will get an ego-lane consisting of two components. As a result, it may be difficult to restore the ego-lane at the stage of post-processing, see Figure 1. To overcome this problem, in the paper we propose the approach with multiple segmentation maps as an output of the DNN architecture, as well as a multi-map loss function. In this case, each pixel is referred to several classes at the same time (depending on the number of layers) and we don’t have the restriction to use mutually exclusive classes. The DNN classifier for each segmentation map has a separate activation branch and loss function.
Formation of professional reflection of future specialists in physical therapy, ergotherapy

The purpose of the study is to develop a model for the formation of professional reflection of future specialists in physical therapy, ergotherapy and to study the impact of its implementation in the process of professional training. Based on the theoretical analysis of the scientific literature, we have developed a model for the formation of professional reflection of future specialists in physical therapy and ergotherapy. The model developed is based on a reflexive approach, which assumes that a person has the capacity to take an active research position in relation to his activity and to himself as his subject, in order to critically analyze, understand and evaluate its effectiveness for its development. The approach to reflective activity allows us to consider reflection as a mechanism for the development and implementation of activity, in turn, activity is the object of reflection. The essence of this approach is most consistent with the understanding of the professional reflection of specialists in physical therapy, ergotherapy as an integral part of professional activity, which led to the development of a program of work with students on its basis. Psychological methods, a specially designed questionnaire and an expert evaluation method were used to evaluate the effectiveness of the model. Formation of professional reflection of future specialists in physical therapy, ergotherapy is presented in the form of three successive interrelated stages. The components of professional reflection of specialists in physical therapy and ergotherapy are covered. We used the methods of diagnostics of reflexivity and examination twice, before the beginning of the experimental work and after its completion. The analysis of the experimental work showed that the students of the experimental group significantly increased the level of formation of each of the components of professional reflection of specialists in physical therapy and ergotherapy. The results of the study make it possible to outline a number of unresolved problems, the development of which is dictated by the needs of the theory and practice of the entire education system. According to the results of the study, the introduction of the developed model in the process of professional training of future specialists in physical therapy and ergotherapy will contribute to the formation of professional reflection.
A compact representation of the bone fracture area. Application to fractured bones of clinical cases

The extraction of the main features of a fractured bone area enables a posterior virtual reproduction of the same fracture on other bones. The utilisation of the fracture zone for other applications is almost an unexplored field of research. Recreating a given fracture on other areas or bones can be directly applied to medical training programmes of traumatologists or to automatic bone fracture reduction algorithms. This paper is focused on the process of generating a fracture pattern taking computed tomography scans as a starting point. A set of alternative representations, for different purposes, is presented and discussed. A study of several clinical cases is analysed. Finally, the potential usages of bone fracture patterns are described.
The CogALex shared task on monolingual and multilingual identification of semantic relations

The shared task of the CogALex-VI workshop focuses on the monolingual and multilingual identification of semantic relations. We provided training and validation data for the following languages: English, German and Chinese. Given a word pair, systems had to be trained to identify which relation holds between them, with possible choices being synonymy, antonymy, hypernymy and no relation at all. Two test sets were released for evaluating the participating systems. One containing pairs for each of the training languages (systems were evaluated in a monolingual fashion) and the other proposing a surprise language to test the crosslingual transfer capabilities of the systems. Among the submitted systems, top performance was achieved by a transformer-based model in both the monolingual and in the multilingual setting, for all the tested languages, proving the potentials of this recently-introduced neural architecture. The shared task description and the results are available at https://sites.google.com/site/cogalexvisharedtask/.
Low-Cost Design and Implementation of Cloud SCADA System

In Industries, it’s always look forward to reducing their operational cost and their Critical infra structures. Therefore, companies search for solutions to reduce their cost and ensure the system efficiency. One of that solutions is the use of a Cloud computing system which offers the advantage of integrating CPSs such as SCADA systems. This paper proposed a cost effective, simple, reliable and secured Cloud SCADA system. The proposed Cloud SCADA System based on secured nodes was implemented, which connects each other through VPN channel “encrypted’’ to ensure the isolation of our system form external attacks. The remote terminal unit (RTU) also connected to master terminal unit (MTU) through unit encrypted VPN channel, so in our system, when the end user need to connect to MTU the firewall of our system protect on MTU from un-recognized end point users. Comparing the cost of our system with pervious work, it’s lower than Arduino + WI-FI method by five times and 13 times lower than Raspberry-PI method. Also comparing with other server options the proposed server is easy for using, available for adding new components and also has high security.
MOBILE LEARNING DALAM PEMBELAJARAN

This research aims to identify the characteristics, objectives, benefits, advantages and limitations of mobile learning, to analyze the opportunities and challenges of mobile learning utilization during the covid-19 pandemic, and to describe the application of mobile learning in primary education. This is a qualitative library research with descriptive-analytic analysis. The finding of study show that mobile learning as a part of e-learning has many advantages to support learning process without time, space and place limitation. Although has some limitations, mobile learning briefly has good opportunities to be utilized as virtual media during covid-19 pandemic to replace the face-to-face learning. Tablet, smartphone and laptop can be used as personal digital assistant devices to support mobile learning implementation. To perform it optimally, it takes teachers’ skills to operate devices and to design digital-based teaching materials properly . Keywords : e-learning, mobile learning, learning resources , pandemic covid-19
METHODS FOR INCREASING THE CLASSIFICATION ACCURACY BASED ON MODIFICATIONS OF THE BASIC ARCHITECTURE OF CONVOLUTIONAL NEURAL NETWORKS

Object of research: basic architectures of deep learning neural networks. Investigated problem: insufficient accuracy of solving the classification problem based on the basic architectures of deep learning neural networks. An increase in accuracy requires a significant complication of the architecture, which, in turn, leads to an increase in the required computing resources, as well as the consumption of video memory and the cost of learning/output time. Therefore, the problem arises of determining such methods for modifying basic architectures that improve the classification accuracy and require insignificant additional computing resources. Main scientific results: based on the analysis of existing methods for improving the classification accuracy on the convolutional networks of basic architectures, it is determined what is most effective: scaling the ScanNet architecture, learning the ensemble of TreeNet models, integrating several CBNet backbone networks. For computational experiments, these modifications of the basic architectures are implemented, as well as their combinations: ScanNet + TreeNet, ScanNet + CBNet. The effectiveness of these methods in comparison with basic architectures has been proven when solving the problem of recognizing malignant tumors with diagnostic images – SIIM-ISIC Melanoma Classification, the train/test set of which is presented on the Kaggle platform. The accuracy value for the area under the ROC curve metric has increased from 0.94489 (basic architecture network) to 0.96317 (network with ScanNet + CBNet modifications). At the same time, the output compared to the basic architecture (EfficientNet-b5) increased from 440 to 490 seconds, and the consumption of video memory increased from 8 to 9.2 gigabytes, which is acceptable. Innovative technological product: methods for achieving high recognition accuracy from a diagnostic signal based on deep learning neural networks of basic architectures. Scope of application of the innovative technological product: automatic diagnostics systems in the following areas: medicine, seismology, astronomy (classification by images) onboard control systems and systems for monitoring transport and vehicle flows or visitors (recognition of scenes with camera frames).