Reviewerly/exHarmony huggingface dataset:
TAME: Attention Mechanism Based Feature Fusion for Generating Explanation Maps of Convolutional Neural Networks

The apparent "black box" nature of neural networks is a barrier to adoption in applications where explainability is essential. This paper presents TAME (Trainable Attention Mechanism for Explanations) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a method for generating explanation maps with a multi-branch hierarchical attention mechanism. TAME combines a target model’s feature maps from multiple layers using an attention mechanism, transforming them into an explanation map. TAME can easily be applied to any convolutional neural network (CNN) by streamlining the optimization of the attention mechanism’s training method and the selection of target model’s feature maps. After training, explanation maps can be computed in a single forward pass. We apply TAME to two widely used models, i.e. VGG-16 and ResNet-50, trained on ImageNet and show improvements over previous top-performing methods. We also provide a comprehensive ablation study comparing the performance of different variations of TAME’s architecture.
Image enhancement in wavelet domain using histogram equalization and median filters

Image enhancement is one of the challenging but crucial methods that is employed in image processing technology for enhancing the visual appearance of images. This paper presents an effective and efficient image enhancement model in which Non Local mean (NLM) filter is used along with Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE) and Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) techniques. The primary objective of the proposed image enhancement model is to enhance the quality of images by reducing the noise effect in them. To do so, we have selected four different images of Barbara, camera, Lena and Hand whose quality is increased and analyzed on three different noise levels of 15, 20 and 25 respectively. Here, we have used NLM filter which is an advanced filtration technique for denoising the images. Also, MMBEBHE and BPDFHE techniques have been implemented for enhancing the quality of images on different noise levels. The efficiency and usefulness of proposed image enhancement model is examined and validated in MATLAB software under Mean Square Error (MSE) and Peak to Signal Ratio (PSNR) values.
Research on innovative strategies of college students’ English teaching under the background of artificial intelligence

Abstract Today, corresponding to the new wave of internationalisation and integration, education is also subject to rapid and innovative changes. Accordingly, detailed research is undertaken in the present study on innovative teaching strategies that can be developed, under the background of artificial intelligence, to familiarise college students with the ability to fluently express their ideas in English. The research shows that among the factors affecting the quality of English teaching, teachers’ teaching methods, English teaching environment and students’ independent learning ability account for high weightage, which are 54%, 31% and 11%, respectively. Through the model of college students’ English teaching innovation strategy, it can be seen that the teaching method, teaching environment and autonomous learning method are the most feasible methods that can be employed in crafting a students’ English teaching innovation strategy.
Petrogenesis and geochemical characteristics of pyroxenite dykes in and around Salem Mafic–Ultramafic complex, southern India: an arc-related origin of Alaskan-type

Arc-related origin of pyroxenites in association withAlaskan-type tectonics has been described in many mafic-ultramafic complexes across the globe.The Salem Mafic-Ultramafic Complex (SMUC) is one such Neoproterozoic Alaskan-type complex exposed at the northern margin of the Cauvery Suture Zone (CSZ), Southern Granulite Terrane, south India.The Complex consists of mafic and ultramafic sequences along with several occurrences of pyroxenite intrusions of varied thickness in the form of dykes.Similar pyroxenite dykes were also observed in and around the Complex at several locations within the basement hornblende gneiss, trending in the NE-SW and E-W directions.Petrography of these dykes indicated websterite variety with cumulate textures and reveals the dominance of clinopyroxene along with orthopyroxene, primary amphibole, minor plagioclase and oxide minerals like magnetite, ilmenite and spinels.The whole-rock chemistry of 10 representative samples showed enrichment of LIL elements (Sr, K, Rb, Th) and depletion of HFSE (Hf, Ti, Y, Yb) with normalized primitive mantle and N-MORB.The clinopyroxene mineral chemistry represented tholeiitic signatures with high Mg# values (Mg/(Mg + Fe)) up to 0.91, and the two-pyroxene thermobarometry of these pyroxenites yielded re-equilibrium crystallization temperatures of 820-932C with moderate pressures at 11-12 kbar.Various tectonic discrimination plots of clinopyroxene mineral chemistry together with wholerock chemistry favoured their origin under arc settings with the interactions of fluid-related subduction zone metasomatism relevant to Neoproterozoic Alaskan-type tectonics.
Assessing the Effectiveness of Extradition and the Enforcement of Extra-territorial Jurisdiction in Addressing Trans-national Cybercrimes

Cyberspace operates on a geographically borderless platform, thus often rendering national laws ineffective in regulating the impact of cyber-related activities outside South African borders. Recognising this issue, South Africa adopted the Cybercrimes Act, which permits the exercise of extra-territorial jurisdiction over trans-national cyber-related offences. The enforcement and effectiveness of extra-territorial jurisdiction and extradition law have, however, proven to be challenging and controversial in the international sphere. Issues such as internet fragmentation, contrasting municipal laws, and uncoordinated regulatory actions across state boundaries have undermined existing provisions regulating trans-national cybercrimes. These issues are furthered by the increased recognition of human rights, such as the right to privacy, which has deterred international cooperation and collaboration as states are subsequently required to subject their own citizens and entities to increased interception and scrutiny. The main thesis of this investigation is aimed at reviewing the practical implications surrounding the enforcement of extra-territorial jurisdiction and extradition law over trans-national cybercrimes. To this end, states are implored to develop both domestic and multilateral cybercrime laws and to improve existing enforcement mechanisms outlined in extradition law and mutual assistance agreements.
PLM-AS: Pre-trained Language Models Augmented with Scanpaths for Sentiment Classification

Recent research demonstrated that deep neural networks could generate meaningful feature representations from both eye-tracking data and sentences without designing handcrafted features, which achieved competitive performance across cognitive NLP tasks, such as sentiment classification over gaze datasets, but the previous works mainly encode the text and gaze data separately without considering the interaction between these two modalities or applying large-scaled pre-trained models. To address these challenges, we introduce PLM-AS, a novel framework to take full advantage of textual and eye-tracking features by sequence modeling in a highly interactive way for multimodal fusion. It is also the first attempt to combine large-scaled pre-trained models with eye-tracking features in the cognitive reading task. We show that PLM-AS captures cognitive signals from eye-tracking data and shows improved performance on sentiment classification within and across three datasets of different domains.
Automatic Consistency Checking of Table and Text in Financial Documents

A company's financial documents use tables along with text to organize the data containing key performance indicators (KPIs) (such as profit and loss) and a financial quantity linked to them. The KPI’s linked quantity in a table might not be equal to the similarly described KPI's quantity in a text. Auditors take substantial time to manually audit these financial mistakes and this process is called consistency checking. As compared to existing work, this paper attempts to automate this task with the help of transformer-based models. Furthermore, for consistency checking it is essential for the table's KPIs embeddings to encode the semantic knowledge of the KPIs and the structural knowledge of the table. Therefore, this paper proposes a pipeline that uses a tabular model to get the table's KPIs embeddings. The pipeline takes input table and text KPIs, generates their embeddings, and then checks whether these KPIs are identical. The pipeline is evaluated on the financial documents in the German language and a comparative analysis of the cell embeddings' quality from the three tabular models is also presented. From the evaluation results, the experiment that used the English-translated text and table KPIs and Tabbie model to generate table KPIs’ embeddings achieved an accuracy of 72.81% on the consistency checking task, outperforming the benchmark, and other tabular models.
Hyperchaotic image encryption using DNA coding and discrete cosine transform

Abstract The article presents hyperchaos image encryption using DNA (deoxyribonucleic acid) coding and the chaotic maps in discrete cosine transform. The proposed multilayered security system comprises of advanced levels of confusion and diffusion. Initially, the diffusion among the pixels is attained by employing DNA XOR operation between pixel values and the unpredictable sequence generated from hyperchaotic system. Secondly, the pixels of the partially encrypted image is scrambled by Baker map to permute the image pixels. Finally, the partially encrypted image is diffused through the discrete cosine transform. The significant contribution of this algorithm is to improve the quality of encryption through a DNA encoded scheme. The demonstration results and experimental values are in support the robustness of the proposed scheme and have shown resistance against underlying cryptanalytic attacks.
Persuasive Strategies in Donald Trump's Political Speeches

This study analyses persuasive strategies on Donald Trump’s political speeches. This study uses qualitative research as the design and Content Analysis as the approach. The researcher explores persuasive strategies used by Donald Trump using Aristotle’s Theory to see what persuasive strategies are used and how Trump’s political speech could affect audiences to vote for him. The researcher selected three speeches such as campaign speech (2015),victory speech (2016) and inauguration speech (2017) as the object of study. These speeches are selected based on some considerations, such as lexical density, political concepts described by Trump, academic scrutiny and also to make this research more manageable. Furthermore, the researcher found that Trump has used persuasive strategies in order to convince Americans to vote him as President. According to Aristotle , there are three types of Persuasive Strategies such as Logos, Ethos and Pathos. The researcher found thirty one statements indicated of containing pathos, thirteen statements of Logos and fifteen statements as Ethos. Pathos encompasses the emotional influence on the audience. Based on the analysis, the researcher concludes that persuasive strategies played crucial part to evoke audience’s emotions and feelings.
PickMail: A Serious Game for Email Phishing Awareness Training

Phishing threats are on the rise, especially through Business Email Compromise (BEC).Despite having several tools for phishing email detection, the attacks are becoming smarter and personal, targeting individuals to gain access to personal and organizational information.Game-based cybersecurity training methods are found to have positive results in educating users.Along this line, we introduce PickMail, an anti-phishing awareness game that simulates typical real-life email scenarios to train an organization's employees.In PickMail, we train participants to judge the legitimacy of an email by inspecting its various parts, such as the sender's email domain, hyperlinks, attachments, and forms.The game also records participants' decision-making steps that lead to their final judgment.Our study with 478 participants shows how the serious game-based training helped the participants make better judgments on emails, with the correctness in identifying email legitimacy reaching 92.62%.The study also provided us with insights that could help develop better training methods and user interfaces.
Strategies in Language Learning: A Survey Among Undergraduate Japa-nese Language Students in USIM

It is crucially important to know what strategies are used by language learners in learning the targeted language, so that teachers will have better insights to prepare classes that equally suit learners’ behaviour. This is a pilot study aiming to identify what are the most and less frequent learning strategies used by Japanese language learners at Universiti Sains Islam Malaysia (USIM). A set of questionnaires adapted from the Oxford’s Strategy Inventory for Language Learning (SILL) has been distributed to 259 USIM Japanese learners at the end of semester academic year of 2021/2022. 54 students volunteered to participate and answer the questionnaire. The average frequency for each category in SILL has been identified. As for the results, Japanese language learners at USIM are moderate users of metacognitive categories as the average frequency is at the highest. In contrast, strategies from affective categories have the lowest frequency of usage. It is a hope, that in the future, learners are introduced with a set of learning strategies before or during the classes in order to help them find the best way of acquiring the target language.
A MOOC-Based Hybrid Teaching Model of College English

In the era of intelligence, Internet + technology is widely used in various fields, and English Teaching in the education industry of colleges and universities gradually tends to be an online and offline mixed teaching mode. However, under the MOOC model, the feedback of College Students’ English learning and the recognition of their knowledge level has become new difficulties. Aiming at the feedback of students’ learning situation under the mixed mode of College English teaching, this paper uses the optimized Bayesian knowledge tracking model (BKTM) to predict students’ English learning situation and introduces students’ learning behavior and forgetting behavior to optimize parameters. Finally, a performance verification experiment is carried out by analyzing the students’ answer performance in College English mixed teaching. The results show that the prediction errors of the four knowledge points of 60 students in the two classes are all about 7%, and the maximum error is 11%. Experiments show that the model has high accuracy and stable performance in predicting the probability of mastering knowledge points.
Predicting Customer Satisfaction through Sentiment Analysis on Online Review

User-generated content, such as user reviews, posts, tags, ratings, and opinions on the internet, can be used as a business indicator if collected and appropriately analyzed. One of the examples is predicting customer satisfaction through implementing big data analytics on online reviews. In analyzing the user-generated content to predict customer satisfaction, the author implements machine learning approach using the Sentiment Analysis method. Five-fold cross-validation was performed to train the classification model. The training was performed with a combination of tokenization methods: term frequency-inverse document frequency (tf-idf) and bag-of-words; n-gram types: unigram, bigram, trigram, and combination of unigram, bigram, and trigram; and machine learning algorithms: linear support vector classification (LinearSVC) and multinomial naïve bayes (MultinomialNB). The result was then evaluated using classification performance metrics such as precision, recall, F1 measure, and AUC score. The result shows that the tf-idf vectorizer performs similarly to the bag-of-words method. A similar result was also observed for machine learning algorithm selection. Both MultinomialNB and LinearSVC produce the same performance. Low-level n-grams (such as unigrams and bigrams) tended to have higher precision, recall, F1 measure, and AUC score than high-order n-grams (such as trigrams). The best results were achieved by combining unigrams, bigrams, and trigrams, resulting in an average performance score of 0.94 for all measurements. From the result and analysis, the author finds that predicting customer satisfaction using text and sentiment analysis methods on user-generated content is possible. The model’s performance in this experiment is decent, with high precision, recall, F1, and AUC score.
3D Object Detection with Attention: Shell-Based Modeling

LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box (BBox).However, under the three-dimensional space of autonomous driving scenes, the previous object detection methods, due to the pre-processing of the original LIDAR point cloud into voxels or pillars, lose the coordinate information of the original point cloud, slow detection speed, and gain inaccurate bounding box positioning.To address the issues above, this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++, which effectively preserves the original point cloud coordinate information.To improve the detection accuracy, a shell-based modeling method is proposed.It roughly determines which spherical shell the coordinates belong to.Then, the results are refined to ground truth, thereby narrowing the localization range and improving the detection accuracy.To improve the recall of 3D object detection with bounding boxes, this paper designs a self-attention module for 3D object detection with a skip connection structure.Some of these features are highlighted by weighting them on the feature dimensions.After training, it makes the feature weights that are favorable for object detection get larger.Thus, the extracted features are more adapted to the object detection task.Extensive comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.
Hephaestus: Modelling, Analysis, and Performance Evaluation of Cross-Chain Transactions

&lt;p&gt;Ecosystems of multiple blockchains are now a reality. Multi-chain applications and protocols are perceived as necessary to enable scalability, privacy, and composability. Despite being a promising emerging research area, we recently have witnessed many attacks that have caused billions of dollars in losses. Attacks against bridges that connect chains are at the top of such attacks in terms of monetary cost, and no apparent solution seems to emerge from the ongoing chaos. &lt;/p&gt; &lt;p&gt;In this paper, we present our contribution to minimizing bridge attacks. In particular, we explore the concepts of &lt;em&gt;cross-chain transaction, cross-chain logic, &lt;/em&gt;and the&lt;em&gt; cross-chain state&lt;/em&gt; as the enablers of the cross-chain model. We propose &lt;strong&gt;Hephaestus&lt;/strong&gt;, the first cross-chain model generator that captures the operational complexity of cross-chain applications. &lt;strong&gt;Hephaestus&lt;/strong&gt; can generate cross-chain models from local transactions on different ledgers realizing arbitrary use cases and allowing operators to monitor their cross-chain applications. Monitoring helps identify outliers and malicious behavior, which can help programmatically to stop bridge hacks and other attacks. We conduct a detailed evaluation of our system, where we implement a cross-chain bridge use case. Our experimental results show that &lt;strong&gt;Hephaestus &lt;/strong&gt;can process 600 cross-chain transactions in less than 5.5 seconds in an environment with two blockchains and requires sublinear storage.&lt;/p&gt;
DDCAL: Evenly Distributing Data into Low Variance Clusters Based on Iterative Feature Scaling

Abstract This work studies the problem of clustering one-dimensional data points such that they are evenly distributed over a given number of low variance clusters. One application is the visualization of data on choropleth maps or on business process models, but without over-emphasizing outliers. This enables the detection and differentiation of smaller clusters. The problem is tackled based on a heuristic algorithm called DDCAL (1d distribution cluster algorithm) that is based on iterative feature scaling which generates stable results of clusters. The effectiveness of the DDCAL algorithm is shown based on 5 artificial data sets with different distributions and 4 real-world data sets reflecting different use cases. Moreover, the results from DDCAL, by using these data sets, are compared to 11 existing clustering algorithms. The application of the DDCAL algorithm is illustrated through the visualization of pandemic and population data on choropleth maps as well as process mining results on process models.
An Intelligent Framework for Person Identification Using Voice Recognition and Audio Data Classification

Abstract The paper proposes a framework to record meeting to avoid hassle of writing points of meeting. Key components of framework are “Model Trainer” and “Meeting Recorder”. In model trainer, we first clean the noise in audio, then oversample the data size and extract features from audio, in the end we train the classification model. Meeting recorder is a post-processor used for sound recognition using the trained model and converting the audio into text. Experimental results show the high accuracy and effectiveness of the proposed implementation.
Aspect-based Sentiment Analysis and Location Detection for Arabic Language Tweets

Abstract The research examines the accuracy of current solution models for the Arabic text sentiment classification, including traditional machine learning and deep learning algorithms. The main aim is to detect the opinion and emotion expressed in Telecom companies’ customers tweets. Three supervised machine learning algorithms, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF), and one deep learning algorithm, Convolutional Neural Network (CNN) were applied to classify the sentiment of 1098 unique Arabic textual tweets. The research results show that deep learning CNN using Word Embedding achieved higher performance in terms of accuracy with F1 score = 0.81. Furthermore, in the aspect classification task, the results reveal that applying Part of Speech (POS) features with deep learning CNN algorithm was efficient and reached 75 % accuracy using a dataset consisting of 1277 tweets. Additionally, in this study, we added an additional task of extracting the geographical location information from the tweet content. The location detection model achieved the following precision values: 0.6 and 0.89 for both Point of Interest (POI) and city (CIT).