Reviewerly/exHarmony huggingface dataset:
The Fuzzy Experiment Approach for Detection and Prevention of Phishing attacks in online Domain

Phishing is an aspect of identity theft that uses engineering and social paradigms to steal personal information from unsuspecting users for their selfish gain.Most attacks are in the form of luring the user into clicking a link that directs the user to a rogue page.The major target of phishing attacks are online customers of e-banking and payment service providers, and these groups suffer huge financial loss.Phishing is not all about sending spoofed mails to users as most people assume, rather it is a multifaceted techno-social issue for which there is no particular solution to end its reign.This has given rise to a series of research in this field as scholars are working towards creating more efficient anti-phish solutions by quantifying risks and degree of vulnerability of users.Most approaches to combat phishing are not able to make dynamic decisions to determine the risk rate of the website and this allows for a large number of false positive.The use of blacklists and whitelists has their limitations due to poor scalability and time constraint.Anti-phishing solutions are methods put in place to protect internet users from attacks aimed at defrauding them of their finances.Browser plug-ins have been the most recent methods adopted, though a lot of questions have been raised to question the effectiveness of these plug-ins.In this research we aim at developing an intelligent anti-phishing plug-in for e-banking, capable of detecting phishing attacks based on existing knowledge about features and patterns of phishing websites.The proposed system is developed to protect users from deceptive tricks used by phishers by giving them the ability to identify phishy or fraudulent websites.The proposed model uses fuzzy logic to define rules and assign linguistic indicators in the form of if-then rules to each phishing criteria.The approach adopted is a combination of fuzzy reasoning in quantifying dynamic and unclear phishing characteristics, with the proficiency to categorize the phishing rules.
Evaluation of the Coherence of Polish Texts Using Neural Network Models

Coherence evaluation of texts falls into a category of natural language processing tasks. The evaluation of texts’ coherence implies the estimation of their semantic and logical integrity; such a feature of a text can be utilized during the solving of multidisciplinary tasks (SEO analysis, medicine area, detection of fake texts, etc.). In this paper, different state-of-the-art coherence evaluation methods based on machine learning models have been analyzed. The investigation of the effectiveness of different methods for the coherence estimation of Polish texts has been performed. The impact of text’s features on the output coherence value has been analyzed using different approaches of a semantic similarity graph. Two neural networks based on LSTM layers and a pre-trained BERT model correspondingly have been designed and trained for the coherence estimation of input texts. The results obtained may indicate that both lexical and semantic components should be taken into account during the coherence evaluation of Polish documents; moreover, it is advisable to analyze corresponding documents in a sentence-by-sentence manner taking into account word order. According to the retrieved accuracy of the proposed neural networks, it can be concluded that suggested models may be used in order to solve typical coherence estimation tasks for a Polish corpus.
Performance Evolution of Face and Speech Recognition system using DTCWT and MFCC Features

Every activity in day-to-day life is required the need of mechanized automation for ensuring the security. The biometrics security system provides the automatic recognition of human by overcoming the traditional recognition methods like Password, Personal Identification Number and ID cards etc. The face recognition is a wide research with many applications. In the proposed work face recognition is carried out using DTCWT (Dual Tree Complex Wavelet Transform) integrated with predominant QFT (Quick Fourier Transform) and speech recognition is carried out using MFCC (Mel Frequency Cepstral Coefficients) algorithm. The distance formula is used for matching the test features and database features of the face and speech images. Performance variables such as EER, FRR, FAR and TSR are evaluated for person recognition
Model-Based Waste Management Handling Angkuts Application to Smart City: The Perspective of Human Security

Security today is not only defined from a military standpoint, but also human security in which it has relations to social issues, human rights, economic, and environmental. Environmental issues, one of which is on the management of waste handling. A large amount of waste that goes to the place of final processing Pontianak Batu Layang is Hundreds of tonnes per day to make some millennials led by Muhammad Hafiz Waliyyudin initiating Angkuts application to support the city of Pontianak in waste management. This research used a descriptive method for designing a management model, especially waste management planning and actualization of Angkuts towards application-based Smart City from the perspective of human security. Based on these results, the researchers concluded that the design of waste handling management is done through planning and actuating. Planning applications applied technologies based on Angkuts industrial era 4.0 toward achieving smart city. Implementation of an application-based waste handling management Angkuts millennial generation is through participation and government support. As a recommendation, the application has the potential to support Angkuts based waste handling applications in the human security perspective to be used in a wider area again, not only in the city of Pontianak.&#x0D;
Identifying the contradictions in the technology enhanced language classroom

This study takes place in the context of a federal laptop-mediated English language pre-university course in the United Arab Emirates. Despite predictions and claims from policy makers and practitioners that 1:1 classroom devices would revolutionise teaching and learning, student results remain static and student attrition remains high. Through the lens of activity theory this paper identifies ten contradictions, and their discursive manifestations, potentially causing failure and attrition. This paper contributes to the fields of technology enhanced learning, 1:1 device initiatives, English language teaching, computer assisted and mobile assisted language learning and activity theory by highlighting several problematic experiences in teachers’ practices and mapping these within the activity system context. The paper also questions the positive impact of a 1:1 laptop initiative in this particular context, with implications for future research.
Efficient Dynamic Group Signature Scheme with Verifier Local Revocation and Time-Bound Keys using Lattices

Revocation is an important feature of group signature schemes. Verifier Local Revocation (VLR) is a popular revocation mechanism which involves only verifiers in the revocation process. In VLR, a revocation list is maintained to store the information about revoked users. The verification cost of VLR based schemes islinearly proportional to the size of recvocation list. In many applications, the size of revocation list grows with time, which makes the verification process expensive. In this paper, we propose a lattice based dynamic group signature using VLR and time bound keys to reduce the size of revocation list to speed up the verification process. In the proposed scheme, an expiration date is fixed for signing key of each group member, and verifiers can find out (at constantcost) if a signature is generated using an expired key. Hence revocation information of members who are revoked before signing key expiry date (premature revocation) are kept in revocation list, and other members are part of natural revocation. This leads to a significant saving on the revocation check by assuming natural revocation accounts for large fraction of the total revocation. This scheme also takes care of non-forgeability of signing key expiry date.
Conceptual Grounding Constraints for Truly Robust Biomedical Name Representations

Effective representation of biomedical names for downstream NLP tasks requires the encoding of both lexical as well as domain-specific semantic information.Ideally, the synonymy and semantic relatedness of names should be consistently reflected by their closeness in an embedding space.To achieve such robustness, prior research has considered multi-task objectives when training neural encoders.In this paper, we take a next step towards truly robust representations, which capture more domainspecific semantics while remaining universally applicable across different biomedical corpora and domains.To this end, we use conceptual grounding constraints which more effectively align encoded names to pretrained embeddings of their concept identifiers.These constraints are effective even when using a Deep Averaging Network, a simple feedforward encoding architecture that allows for scaling to large corpora while remaining sufficiently expressive.We empirically validate our approach using multiple tasks and benchmarks, which assess both literal synonymy as well as more general semantic relatedness.Our code is open-source and available at www.github.com/clips/conceptualgrounding.
REAL-TIME MONITORING DEFORMATION OF BUILDING USING PHOTOGRAPHY DYNAMIC MONITORING SYSTEM

&#x0D; &#x0D; &#x0D; The spatial structure building is a type of building system; it is necessary to monitor deformation to determine its stability and robustness. Under the dynamic deformation of structures, it is challenging to determine appropriate zero image (the reference image) if we use the PST-IM- MP (photograph scale transformation-image matching-motion parallax) method to obtain the deformation of structures. This paper offers the Z-MP (zero-centered motion parallax) method to solve these problems and offers PDMS (Photography Dynamic Monitoring System) based on the digital photography system to monitor the dynamic deformation of the tennis stadium located in Jinan Olympic Sports Center. The results showed that the spatial structures of the tennis stadium were robust, and the deformations were elastic and within the permissible value. Compared with the PST-IM-MP method, the Z-MP method is more suitable for deformation monitoring structures under real-time deformation. This paper indicates PDMS has advantages of the simplicity of operations, automation, and the ability of non-contact dynamic deformation monitoring for multiple points in a short period. In the future, it will have broader application prospects.&#x0D; &#x0D; &#x0D;
Pointless Pose: Part Affinity Field-Based 3D Pose Estimation without Detecting Keypoints

Human pose estimation finds its application in an extremely wide domain and is therefore never pointless. We propose in this paper a new approach that, unlike any prior one that we are aware of, bypasses the 2D keypoint detection step based on which the 3D pose is estimated, and is thus pointless. Our motivation is rather straightforward: 2D keypoint detection is vulnerable to occlusions and out-of-image absences, in which case the 2D errors propagate to 3D recovery and deteriorate the results. To this end, we resort to explicitly estimating the human body regions of interest (ROI) and their 3D orientations. Even if a portion of the human body, like the lower arm, is partially absent, the predicted orientation vector pointing from the upper arm will take advantage of the local image evidence and recover the 3D pose. This is achieved, specifically, by deforming a skeleton-shaped puppet template to fit the estimated orientation vectors. Despite its simple nature, the proposed approach yields truly robust and state-of-the-art results on several benchmarks and in-the-wild data.
A Unified Feature Representation for Lexical Connotations

Ideological attitudes and stance are often expressed through subtle meanings of words and phrases.Understanding these connotations is critical to recognizing the cultural and emotional perspectives of the speaker.In this paper, we use distant labeling to create a new lexical resource representing connotation aspects for nouns and adjectives.Our analysis shows that it aligns well with human judgments.Additionally, we present a method for creating lexical representations that capture connotations within the embedding space and show that using the embeddings provides a statistically significant improvement on the task of stance detection when data is limited. AspectLexicon Example Rules Examples Social Value GI Authoritative power !valuable Related to failure !not valuable attorney ; valuable (+) aimless ; not valuable (-) Politeness GI Gain of respect !polite Loss of affection !impolite commendable ; polite (+) alienation ; impolite (-) Impact GI Virtue !positive Loss of well-being !negative adept ; positive impact (+) shock ; negative impact (-) Factuality DAL Imagery(w) > ✓ F ! factual Imagery(w) < ✓ F ! not factual rocky ; factual (+) tradition ;
Civil liability for artificial intelligence products versus the sustainable development of CEECs: which institutions matter?

The aim of this paper is to conduct a meta-analysis of the EU and CEECs civil liability institutions in order to find out if they are ready for the Artificial Intelligence (AI) race. Particular focus is placed on ascertaining whether civil liability institutions such as the Product Liability Directive (EU) or civil codes (CEECs) will protect consumers and entrepreneurs, as well as ensure undistorted competition. In line with the aforementioned, the authors investigate whether the civil liability institutions of the EU and CEECs are based on regulations that can be adapted to the new generation of robots that will be equipped with learning abilities and have a certain degree of unpredictability in their behaviour. The conclusion presented in the paper was drawn on the basis of a review of the current literature and research on national and European regulations. The primary contribution that this article makes is to advance the current of the research concerning the concepts of AI liability for damage and personal injury. A second contribution is to show that the current civil liability institutions of the EU as well as the CEECs are not sufficiently prepared to address the legal issues that will start to arise when self-driving vehicles or autonomous drones begin operating in fully autonomous modes and possibly cause property damage or personal injury.
Mobile Applications for Developing Second Language Collaborative Writing

&lt;p&gt;Developing writing skills in a foreign language is one of the demanding tasks for both teachers and students. It not only demands extensive reading and intensive practice in writing both inside and outside the classroom but also timely feedback and error-correction. The experimental study aimed at studying the impact of collaborative writing tasks using mobile applications like &lt;em&gt;WhatsApp&lt;/em&gt;, &lt;em&gt;Google Docs&lt;/em&gt;, and &lt;em&gt;Google Slides&lt;/em&gt; on enhancing the writing ability at the pre-intermediate level of Common European Frame of Reference (CEFR). The participants were tertiary level students of Cihan University who were randomly divided into two equal groups (n=28). One group used &lt;em&gt;WhatsApp&lt;/em&gt; and the other group used the &lt;em&gt;Google Docs&lt;/em&gt; and &lt;em&gt;Google Slides&lt;/em&gt; apps on their mobile devices. The comparative study analyzed the impact of each application on improving the writing skills of the students with pretest and posttest results and semi-structured interviews. The results indicated that the participants who used &lt;em&gt;Google Docs&lt;/em&gt; and &lt;em&gt;Google Slides&lt;/em&gt; apps have performed better than the participants in the &lt;em&gt;WhatsApp&lt;/em&gt; group. The results have implications for teachers teaching writing skills and students who write using mobile applications.&lt;/p&gt;
Secure Naïve Bayes Classification without Loss of Accuracy with Application to Breast Cancer Prediction

The classification and prediction accuracy of Machine Learning (ML) algorithms, which often outperform human experts of the related field, have enabled them to be used in areas such as health and disease prediction, image and speech recognition, cyber-security threats and credit-card fraud detection and others. However, laws, ethics and privacy concerns prevent ML algorithms to be used in many real-case scenarios. In order to overcome this problem, we introduce a few flexible and secure building blocks which can be used to build different privacy preserving classifications schemes based on already trained ML models. Then, as a use-case scenario, we utilize and practically use those blocks to enable a privacy preserving Naïve Bayes classifier in the semi-honest model with application to breast cancer detection. Our theoretical analysis and experimental results show that the proposed scheme in many aspects is more efficient in terms of computation and communication cost, as well as in terms of security properties than several state of the art schemes. Furthermore, our privacy preserving scheme shows no loss of accuracy compared to the plain classifier.
Optimizing Text Categorization for Indonesian Text Using Clustering Label Technique

Text Categorization plays an important role for clustering the rapidly growing, yet unstructured, Indonesian text in digital format. Furthermore, it is deemed even more important since access to digital format text has become more necessary and widespread. There are many clustering algorithms used for text categorization. Unfortunately, clustering algorithms for text categorization cannot easily cluster the texts due to imperfect process of stemming and stopword of Indonesian language. This paper presents an intelligent system that categorizes Indonesian text documents into meaningful cluster labels. Label Induction Grouping Algorithm (LINGO) and Bisecting K- means are applied to process it through five phases, namely the pre-processing, frequent phrase extraction, cluster label induction, content discovery and final cluster formation. The experimental result showed that the system could categorize Indonesian text and reach to 93%. Furthermore, clustering quality evaluation indicates that text categorization using LINGO has high Precision and Recall with a value of 0.85 and 1, respectively, compare to Bisecting K-means which has a value of 0.78 and 0.99. Therefore, the result shows that LINGO is suitable for categorizing Indonesian text. The main contribution of this study is to optimize the clustering results by applying and maximizing text processing using Indonesian stemmer and stopword.
Attackdet: Combining web data parsing and real-time analysis with machine learning

HTTP protocol Websocket
Hierarchical motion-compensated deep network for video compression

Video coding is the process of reducing the huge volume of video data to a small number of bits. High coding efficiency reduces the bandwidth required for video streaming, and the space required to store the video data on electronic devices, while maintaining the fidelity of the decompressed video signal. In recent years, deep learning has been extensively applied in the field of video coding. However, it remains challenging how to explore the intra- and inter-frame correlations in deep learning-based video coding systems to improve the coding efficiency. In this work, we propose a hierarchical motion estimation and compensation network for video compression. The video frames are tagged as intra-frames and inter-frames. While intra-frames are compressed independently, the inter-frames are hierarchically predicted by adjacent frames using a bi-directional motion prediction network, which results in highly sparse and compressible residue. The residue frames are then compressed via separately trained residue coding networks. Experimental results demonstrate that the proposed hierarchical deep video compression network offers significantly higher coding efficiencey and superior visual quality compared to prior arts.
Cybercrime: History of formation, current state and ways of counteraction

The article examines the history of the emergence and development of cybercrime, the specifics of the current situation in society, which contributes to an increase in the number of cybercrimes and ways of countering cybercrime. It has been established that cybercrime first appeared in the middle of the last century. This was due to the emergence and subsequent intensive growth in the number of computers, and then smartphones. However, cybercrime that exists today is significantly different from what it was in its early days. Today, the number of devices and services provided via the Internet is growing, the number of users is growing, and, accordingly, the number of cybercrimes and the level of their organization is growing. In the course of the study, the differences between the concepts of "cybercrime" and "computer crimes" were identified, as well as the main features of cybercrimes. The problems that law enforcement agencies face in the investigation and fight against cybercrimes were revealed. The factors contributing to the growth of cybercrime were also analyzed. Based on the analysis, the main directions of combating cybercrime and preventing the growth of the number of cybercrimes in society were developed.
Cognition-aware Cognate Detection

Automatic detection of cognates helps downstream NLP tasks of Machine Translation, Cross-lingual Information Retrieval, Computational Phylogenetics and Cross-lingual Named Entity Recognition.Previous approaches for the task of cognate detection use orthographic, phonetic and semantic similarity based features sets.In this paper, we propose a novel method for enriching the feature sets, with cognitive features extracted from human readers' gaze behaviour.We collect gaze behaviour data for a small sample of cognates and show that extracted cognitive features help the task of cognate detection.However, gaze data collection and annotation is a costly task.We use the collected gaze behaviour data to predict cognitive features for a larger sample and show that predicted cognitive features, also, significantly improve the task performance.We report improvements of 10% with the collected gaze features, and 12% using the predicted gaze features, over the previously proposed approaches.Furthermore, we release the collected gaze behaviour data along with our code and cross-lingual models.