Reviewerly/exHarmony huggingface dataset:
Prediction of addiction to drugs and alcohol using machine learning: A case study on Bangladeshi population

Nowadays addiction to drugs and alcohol has become a significant threat to the youth of the society as Bangladesh’s population. So, being a conscientious member of society, we must go ahead to prevent these young minds from life-threatening addiction. In this paper, we approach a machinelearning-based way to forecast the risk of becoming addicted to drugs using machine-learning algorithms. First, we find some significant factors for addiction by talking to doctors, drug-addicted people, and read relevant articles and write-ups. Then we collect data from both addicted and nonaddicted people. After preprocessing the data set, we apply nine conspicuous machine learning algorithms, namely k-nearest neighbors, logistic regression, SVM, naïve bayes, classification, and regression trees, random forest, multilayer perception, adaptive boosting, and gradient boosting machine on our processed data set and measure the performances of each of these classifiers in terms of some prominent performance metrics. Logistic regression is found outperforming all other classifiers in terms of all metrics used by attaining an accuracy approaching 97.91%. On the contrary, CART shows poor results of an accuracy approaching 59.37% after applying principal component analysis.
ThickBrick: optimal event selection and categorization in high energy physics. Part I. Signal discovery

A bstract We provide a prescription called ThickBrick to train optimal machine-learning-based event selectors and categorizers that maximize the statistical significance of a potential signal excess in high energy physics (HEP) experiments, as quantified by any of six different performance measures. For analyses where the signal search is performed in the distribution of some event variables, our prescription ensures that only the information complementary to those event variables is used in event selection and categorization. This eliminates a major misalignment with the physics goals of the analysis (maximizing the significance of an excess) that exists in the training of typical ML-based event selectors and categorizers. In addition, this decorrelation of event selectors from the relevant event variables prevents the background distribution from becoming peaked in the signal region as a result of event selection, thereby ameliorating the challenges imposed on signal searches by systematic uncertainties. Our event selectors (categorizers) use the output of machine-learning-based classifiers as input and apply optimal selection cutoffs (categorization thresholds) that are functions of the event variables being analyzed, as opposed to flat cutoffs (thresholds). These optimal cutoffs and thresholds are learned iteratively, using a novel approach with connections to Lloyd’s k-means clustering algorithm. We provide a public, Python implementation of our prescription, also called ThickBrick, along with usage examples.
CIC-FL: Enabling Class Imbalance-Aware Clustered Federated Learning over Shifted Distributions

Federated learning (FL) is a distributed training framework where decentralized clients collaboratively train a model. One challenge in FL is concept shift, i.e. that the conditional distributions of data in different clients are disagreeing. A natural solution is to group clients with similar conditional distributions into the same cluster. However, methods following this approach leverage features extracted in federated settings (e.g., model weights or gradients) which intrinsically reflect the joint distributions of clients. Considering the difference between conditional and joint distributions, they would fail in the presence of class imbalance (i.e. that the marginal distributions of different classes vary in a client's data). Although adopting sampling techniques or cost-sensitive algorithms can alleviate class imbalance, they either skew the original conditional distributions or lead to privacy leakage. To address this challenge, we propose CIC-FL, a class imbalance-aware clustered federated learning method. CIC-FL iteratively bipartitions clients by leveraging a particular feature sensitive to concept shift but robust to class imbalance. In addition, CIC-FL is privacy-preserving and communication efficient. We test CIC-FL on benchmark datasets including Fashion-MNIST, CIFAR-10 and IMDB. The results show that CIC-FL outperforms state-of-the-art clustering methods in FL in the presence of class imbalance.
Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

With the development of science and technology, the middle volume and neural network in the semantic image segmentation of the codec show good development prospects. Its advantage is that it can extract richer semantic features, but this will cause high costs. In order to solve this problem, this article mainly introduces the codec based on a separable convolutional neural network for semantic image segmentation. This article proposes a codec based on a separable convolutional neural network for semantic image segmentation research methods, including the traditional convolutional neural network hierarchy into a separable convolutional neural network, which can reduce the cost of image data segmentation and improve processing efficiency. Moreover, this article builds a separable convolutional neural network codec structure and designs a semantic segmentation process, so that the codec based on a separable convolutional neural network is used for semantic image segmentation research experiments. The experimental results show that the average improvement of the dataset by the improved codec is 0.01, which proves the effectiveness of the improved SegProNet. The smaller the number of training set samples, the more obvious the performance improvement.
FACTORS CONTRIBUTING STUDENTS’ SPEAKING ANXIETY

Anxiety is a dimension of foreign language speaking that is heavily investigated in the EFL contexts. The anxiety in speaking needs to find out factors making students feel anxiety in speaking. This study aims to find out the factors contributing students’ speaking Anxiety in speaking at MTs. Darul Ishlah Ireng Lauq Lombok Barat. This research is classified as case study using descriptive qualitative method. The research object covers factors contributing students’ speaking anxiety. To find out the data, questionnaire and interview activities are carried out. The questionnaire is used to know the factors making students’ anxiety in speaking. The results are in the form of percentage. Meanwhile, the interview activities are oriented to strengthen the students’ responses based on the results of questionnaire. The data are then analyzed using qualitative ways. The steps of qualitative data analysis cover data condensation, data display, and conclusion drawing or verification. Based on the result of analysis, there are 10 (ten) factors contributing students’ anxiety, such as being afraid to speak in English, being afraid with teacher’s consequence, lack of self-confident, fear of being less competent than others students, embarrassment, insufficient preparation, fear of making mistakes, limited vocabulary, habit in using English language, and language test. The detail explanation of the findings is elaborated in discussion part of this article.
LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content

The conceptualization of a claim lies at the core of argument mining.The segregation of claims is complex, owing to the divergence in textual syntax and context across different distributions.Another pressing issue is the unavailability of labeled unstructured text for experimentation.In this paper, we propose LESA, a framework which aims at advancing headfirst into expunging the former issue by assembling a source-independent generalized model that captures syntactic features through part-of-speech and dependency embeddings, as well as contextual features through a fine-tuned language model.We resolve the latter issue by annotating a Twitter dataset which aims at providing a testing ground on a large unstructured dataset.Experimental results show that LESA improves upon the state-of-the-art performance across six benchmark claim datasets by an average of 3 claim-F1 points for in-domain experiments and by 2 claim-F1 points for general-domain experiments.On our dataset too, LESA outperforms existing baselines by 1 claim-F1 point on the in-domain experiments and 2 claim-F1 points on the general-domain experiments.We also release comprehensive data annotation guidelines compiled during the annotation phase (which was missing in the current literature).
Universal quantum gates, artificial neurons, and pattern recognition simulated by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>L</mml:mi><mml:mi>C</mml:mi></mml:mrow></mml:math> resonators

We propose to simulate quantum gates by LC resonators, where the amplitude and the phase of the voltage describe the quantum state. By controlling the capacitance or inductance of resonators, it is possible to control the phase of the voltage arbitrarily. A set of resonators acts as the phase-shift, the Hadamard, and the CNOT gates. They constitute a set of universal quantum gates. We also discuss an application to an artificial neuron. As an example, we study pattern recognition of numbers and alphabets by evaluating the similarity between an input and the reference pattern. We also study colored pattern recognition by using a complex neural network.8 MoreReceived 10 December 2020Revised 3 March 2021Accepted 22 March 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.023051Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasGeometric & topological phasesQuantum algorithmsQuantum computationQuantum gatesQuantum simulationQuantum InformationCondensed Matter, Materials & Applied Physics
A meta-analysis of channel switching approaches for reducing zapping delay in internet protocol television

Channel zapping delays are inconveniences that are often experienced by the subscribers of Internet protocol television (IPTV). It is a major bottleneck in the IPTV channels switching system that affect the quality of experience of users. Consequently, numerous channels switching approaches to minimize zapping delay in IPTV have been suggested. However, there is little knowledge reported in the literature on the determination of the strength of the evidence presented on the approaches of reducing zapping delay in IPTV, which is the prime purpose of this study. The extraction of the relevant articles was designed following the technique of preferred reporting items for systematic reviews and meta-analyses (PRISMA). All the included research articles were searched from the widely used databases of Google Scholar, and Web of Science. All statistical analyses were performed with the aid of the random-effects model implementation in Stata version 15. The overall pooled estimated delay component was presented in forest plots. Overall, thirteen studies were included in the meta-analysis and the overall pooled estimate was 10% (95% CI: 7%, 30%)). Experimental studies have shown that virtual elimination of IPTV zapping delay is possible for a relevant chunk of channel switching requests.
Signature Verification Using Support Vector Machine and Convolution Neural Network

Signature is used for recognition of an individual. Signature is considered as a mark that an individual write on a paper for his/her identity or proof. It is used as a unique feature for identifying an individual. It is highly used in social and business functions which gives rise to verification of signature. There are chances of signature getting forged. Hence, the need to identify signature as genuine of forged is utmost important. In this paper, identification of signature as genuine or forged is done using two approaches. First approach is using SVM and second is using CNN. For SVM, pre-processing of signature image is done and feature extraction is performed. Features extracted are histogram of gradient, shape, aspect ratio, bounding area, contour area and convex hull area. Further, SVM is applied to classify signature as genuine or forged and accuracy is determined. In the second approach, signature image is pre-processed, CNN is used to classify signature as genuine or forged and accuracy is determined. Dataset used here is ICDAR Dutch dataset along with 80 signatures taken from 4 people.Dutch dataset consists of 362 signature imagesand signature images taken from 4 people consists 10 genuine and 10 forged signatures which sums to 442 signature images. The proposed system provides accuracy of 86.39% using SVM and around 83.78% using CNN.
Disambiguatory Signals are Stronger in Word-initial Positions

Psycholinguistic studies of human word processing and lexical access provide ample evidence of the preferred nature of word-initial versus word-final segments, e.g., in terms of attention paid by listeners (greater) or the likelihood of reduction by speakers (lower).This has led to the conjecture-as in Wedel et al. ( 2019b), but common elsewhere-that languages have evolved to provide more information earlier in words than later.Informationtheoretic methods to establish such tendencies in lexicons have suffered from several methodological shortcomings that leave open the question of whether this high word-initial informativeness is actually a property of the lexicon or simply an artefact of the incremental nature of recognition.In this paper, we point out the confounds in existing methods for comparing the informativeness of segments early in the word versus later in the word, and present several new measures that avoid these confounds.When controlling for these confounds, we still find evidence across hundreds of languages that indeed there is a cross-linguistic tendency to front-load information in words. 1
Reducing Discontinuous to Continuous Parsing with Pointer Network Reordering

Discontinuous constituent parsers have always lagged behind continuous approaches in terms of accuracy and speed, as the presence of constituents with discontinuous yield introduces extra complexity to the task.However, a discontinuous tree can be converted into a continuous variant by reordering tokens.Based on that, we propose to reduce discontinuous parsing to a continuous problem, which can then be directly solved by any off-the-shelf continuous parser.To that end, we develop a Pointer Network capable of accurately generating the continuous token arrangement for a given input sentence and define a bijective function to recover the original order.Experiments on the main benchmarks with two continuous parsers prove that our approach is on par in accuracy with purely discontinuous state-of-the-art algorithms, but considerably faster.
WEBINAR STUDENT PRESENCE SYSTEM BASED ON REGIONAL CONVOLUTIONAL NEURAL NETWORK USING FACE RECOGNITION

World health organization announce Covid-19 as a pandemic so On March 15th 2020, the social distancing has been established with working, learning, and praying from home. Webinar is one of the solutions so those activities still can be done face to face and conference-based. With webinar, users can interact each other in an online meeting from home. Student presence is part of a webinar. The purpose of this research is to design an accurate student presence with a face recognition system using R-CNN method. The object of this research is a human face with sufficient light, medium, and the face must be facing the camera. This research proposed for a webinar student presence system is using face recognition with Regional Convolutional Neural Network (R-CNN). With object detection and several scenarios used in this method, the webinar student presence system using R-CNN will be more accurate than the methods that have ever been used before. This research has done four scenarios to obtain the best parameters like 45 of total layers, test data of the whole dataset percentage as 10%, RMSProp as model op- timizer, and 0.0001 learning rate. With those parameters, it have resulted the best system performance including 99.6% accuration, 1 × 10-4 loss, 100% precision, 99% recall, and 99.5% F1 Score.
Design of grip strength measuring system using FSR and flex sensors using SVM algorithm

This paper proposes a design of a complete system to identify weak grip strength that is caused by multiple factors like ageing, diseases, or accidents. This paper presents a grip measurement system that comprises of force sensing resistor and flex sensor to evaluate the condition of the hand. The system is tested by gripping a pencil and a cylindrical object using the glove, to determine the condition of the hand. Force sensitive resistor (FSR) evaluates the force applied by the different parts of the palm on the object being grasped. Flex sensor evaluates the bending of the fingers and thumb. The data from the sensors is then compared with existing data to evaluate the state of the hand. The data from the sensors is stored on the personal computer (PC) through serial communication. A model is trained using the data from the sensors, which determine if the grip strength of the user is weak or strong. The model is also trained to differentiate between two modes that are pen mode and object mode. The model achieved an accuracy of 90.8 percent using support vector machine (SVM) algorithm. This glove can be deployed in medical centers to assist in grip strength measurement.
Keep Learning: Self-supervised Meta-learning for Learning from Inference

A common approach in many machine learning algorithms involves self-supervised learning on large unlabeled data before fine-tuning on downstream tasks to further improve performance.A new approach for language modelling, called dynamic evaluation, further fine-tunes a trained model during inference using trivially-present ground-truth labels, giving a large improvement in performance.However, this approach does not easily extend to classification tasks, where ground-truth labels are absent during inference.We propose to solve this issue by utilizing self-training and back-propagating the loss from the model's own class-balanced predictions (pseudo-labels), adapting the Reptile algorithm from meta-learning, combined with an inductive bias towards pre-trained weights to improve generalization.Our method improves the performance of standard backbones such as BERT, Electra, and ResNet-50 on a wide variety of tasks, such as question answering on SQuAD and NewsQA, benchmark task SuperGLUE, conversation response selection on Ubuntu Dialog corpus v2.0, as well as image classification on MNIST and ImageNet without any changes to the underlying models.Our proposed method outperforms previous approaches, enables self-supervised finetuning during inference of any classifier model to better adapt to target domains, can be easily adapted to any model, and is also effective in online and transfer-learning settings.
To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning

In low-resource settings, model transfer can help to overcome a lack of labeled data for many tasks and domains.However, predicting useful transfer sources is a challenging problem, as even the most similar sources might lead to unexpected negative transfer results.Thus, ranking methods based on task and text similarity -as suggested in prior workmay not be sufficient to identify promising sources.To tackle this problem, we propose a new approach to automatically determine which and how many sources should be exploited.For this, we study the effects of model transfer on sequence labeling across various domains and tasks and show that our methods based on model similarity and support vector machines are able to predict promising sources, resulting in performance increases of up to 24 F 1 points.
AMatrix factorization technique using parameter tuning of singular value decomposition for Recommender Systems

In this article we explained the concepts of SVD and algorithm evolution. MF technique and the working of it with computational formulas. PCA withstep-by-step approach with example and A novel approach of Hyper SVD and How to fine tune it and pseudocode of the Hyper SVD with the Experimental setup using SurpriseLib and computing RMSE and MSE for the accuracy purpose and solving with the real time example which solves the cold start hassle also together and it can be seen that comparison of SVD and Hyper SVD and Random algorithm is done and types of Movies they recommended. There is far more difference between the results of the both algorithms and movie recommendations as per the results Hyper SVD is flexible and efficient and superior compared to other algorithms.
Beyond the English Web: Zero-Shot Cross-Lingual and Lightweight Monolingual Classification of Registers

We explore cross-lingual transfer of register classification for web documents.Registers, that is, text varieties such as blogs or news are one of the primary predictors of linguistic variation and thus affect the automatic processing of language.We introduce two new registerannotated corpora, FreCORE and SweCORE, for French and Swedish.We demonstrate that deep pre-trained language models perform strongly in these languages and outperform previous state-of-the-art in English and Finnish.Specifically, we show 1) that zeroshot cross-lingual transfer from the large English CORE corpus can match or surpass previously published monolingual models, and 2) that lightweight monolingual classification requiring very little training data can reach or surpass our zero-shot performance.We further analyse classification results finding that certain registers continue to pose challenges in particular for cross-lingual transfer.
State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval

Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standards, describes the main concepts involved in video coding. Video compression performs conversion of large raw bits of video sequence into a small compact one, achieving high compression ratio with good video perceptual quality. Removing redundant information is the main task in the video sequence compression. A survey on various block matching algorithms, quantization and entropy coding are focused. It is found that many of the methods having computational complexities needs improvement with optimization.