Reviewerly/exHarmony huggingface dataset:
Use of Augmented Reality Application in E-learning System During COVID-19 Pandemic

During the COVID-19 pandemic, several educational institutions face critical challenges in conducting online classes. E-learning system through augmented reality application has several dynamic features that would enhance teachers’ and students’ intention to learn during the COVID-19 pandemic. Nevertheless, the implementation of augmented reality applications in e-learning systems relies on the advancement of technologies, technical training, educating teachers and students on how to use the novel technology in the online class environment. Thus, lack of agreement about the core challenges and factors that shape the successful implementation of augmented reality applications in the e-learning system environment during the COVID-19 pandemic. An empirical gap has been identified in developing the knowledge on the challenges and indicators on implementing augmented reality applications in e-learning systems during the COVID-19 pandemic. However, the current study explores the influence of augmented reality applications in the e-learning system environment during the COVID-19 pandemic. It investigates the key challenges and factors that create barriers to implementing augmented reality applications. A qualitative research method was applied to obtain the aim of this study. We employed the interview method using thematic analysis through NVivo software. We conducted ten interviews with faculty members in the University of Business and Technology, Jeddah, Saudi Arabia. The respondents were selected based on their previous experience in the e-learning system. Overall, the findings of the current paper offer dynamic recommendations for educators, policymakers, developers, and researchers, which will significantly enable them to understand more about the usage of augmented reality applications in e-learning systems with the key indicators during the COVID-19 pandemic.
The Interactive Effects of Leadership Styles on Counterproductive Work Behavior: An Examination Through Multiple Theoretical Lenses

This study aimed to investigate the effects of leadership styles on counterproductive work behavior, quality of working life, and psychological empowerment.The target population was employees in SOE Islamic banks in Malang Raya, Indonesia.A proportional random sampling was used to obtain a sample size of 160 from the 310 bank employees.SEM-PLS, a measurement and structural equation model was employed to explain the relationship between variables.The results showed that transformational leadership could reduce employee counterproductive work behavior through quality of work-life, while transactional leadership increases this behavior.Furthermore, the quality of work-life cannot mediate the effect of transactional leadership on counterproductive work behavior, while psychological empowerment cannot moderate the impact of transformational leadership on quality of work-life.
Performance analysis of reshaped Gabor filter for removing the Rician distributed noise in brain MR images

Magnetic Resonance Imaging (MRI) is an essential clinical tool for detecting the abnormalities such as tumors and clots in the human brain. The brain MR images are contaminated by artifacts and noise that follow Rician distribution during the acquisition process. It causes the loss of fine details information, distortion, and a blurred vision of the image. A reshaped Gabor filter-based denoising technique is proposed to overcome these issues. To develop the reshaped Gabor filter, the range of reshaping parameters of the filter is initially obtained by a random search method. Further, to evaluate the better performance of the proposed filter, a manual search is used to find the optimal parametric values and tested on T1, T2, and PD weighted MR data sets one by one. Also, the proposed technique is compared with the existing state of the art filtering methods such as Wiener, Median, Partial differential equation (PDE), Anisotropic diffusion filter (ADF), Non-local means filter (NLM), Modified complex diffusion filter (MCD), Multichannel residual learning of CNN (MRL), Maximum a posteriori (MAP), Adaptive non-local means algorithm (ADNLM), and Advance NLM filtering with non-sub sampled (AVNLMNS) on the basic reference and no reference parameter. The parameters such as mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index metric (SSIM), perception-based image quality evaluator (PIQE), and blind/referenceless image spatial quality evaluator (BRISQE) are evaluated on T1, T2, and PD weighted MR images with different noise variances such as 1%, 3%, 5%, 7%, and 9%. The proposed method may be used as a better denoising scheme for Rician distributed noise, edge preservation, fine details restoration, and enhancement of abnormalities.
Deep Learning Based Multi-Target Multi-Camera Tracking System

Multi-target multi-camera tracking has lately received great attention for computer vision. However, the traditional methods are difficult to be applied to the industry due to their low efficiency and high cost. In order to overcome the above problem, we design and implement a novel deep learning based multi-target multi-camera tracking system, termed MTMCT, which combines three sub-modules, i.e., person detection, single-camera tracking (SCT), and person re-identification (Re-ID) to achieve multi-camera pedestrian tracking and cross-camera pedestrian retrieval. In this system, we adopt the tracking-by-detection strategy and introduce the coordinate attention mechanism in the person detection sub-module, which not only considers the relationship between the channels but also pays attention to the position information in the feature space. In addition, we employ a fast inner product search based on Möbius transformation to improve the efficiency of this system. Experiments demonstrate the effectiveness of our system in real applications.
Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues

It is a common practice for recent works in vision language cross-modal reasoning to adopt a binary or multi-choice classification formulation taking as input a set of source image(s) and textual query.In this work, we take a sober look at such an "unconditional" formulation in the sense that no prior knowledge is specified with respect to the source image(s).Inspired by the designs of both visual commonsense reasoning and natural language inference tasks, we propose a new task termed "Premise-based Multi-modal Reasoning" (PMR) where a textual premise is the background presumption on each source image.The PMR dataset contains 15,360 manually annotated samples which are created by a multi-phase crowd-sourcing process.With selected high-quality movie screenshots and human-curated premise templates from 6 pre-defined categories, we ask crowdsource workers to write one true hypothesis and three distractors (4 choices) given the premise and image through a cross-check procedure.Besides, we generate adversarial samples to alleviate the annotation artifacts and double the size of PMR.We benchmark various stateof-the-art (pretrained) multi-modal inference models on PMR and conduct comprehensive experimental analyses to showcase the utility of our dataset.
Multimodal Metronome — Rhythm game for musical instruments

The goal of this project is to create an application that uses auditory, visual, and tactile elements to assist and guide musical instrument learners, using features available on the Nintendo Switch device and its paired “Joy-Con” detachable interface, to increase the novelty as well as intuitiveness of musical performance while stimulating user experience. By integrating visual music, spectral analysis, and motion capture, we obtain a metronome system that synchronizes visual, acoustic, and haptic display.
Advanced Incremental Attribute Learning Clustering Algorithm for Medical and Healthcare Applications

Data-driven science and its consequences in the extensive field of artificial intelligence and especially in machine learning have the potential to drive important changes in medicine. Therewith, new instances including new incoming mixed features are unceasingly emerged at high rate as data streams. Hence, big data promises immense advantages for medical and healthcare research. Aiming to analyse these medical data streams in the lastly mentioned fields, this paper provides a better insight into an advanced incremental attribute and object learning k-prototypes algorithm for healthcare and medical applications. Experiments performed on various real mixed healthcare data sets show that the proposed real-time healthcare/medical application is efficient and may cover different medical case studies such as patient monitoring, disease control, and clinical support systems for better prediction of diseases. The measured evaluation criteria accentuate the efficiency of the proposed method compared to the conventional k-prototypes method.
Analysing the Nutritional Facts in Mc. Donald’s Menu Items Using Exploratory Data Analysis in R

A quantitative data analytic tradition is the Exploratory Data Analysis (EDA) based on the original work of John Tukey. In Statistics, EDA is the process of cleaning data and using appropriate visualizations to extract insights and summarize the data in the given dataset. The computational and core conceptual tools of EDA cover the use of interactive data display and graphics, diagnosing, evaluating, an emphasis on model building, and addressing the issues of the fundamental measurements that are consorted with several distributions and also undertaking some of the procedures that are resistant to mislead or flawed results because of the unpredictable change of real world data. EDA can be further classified as Graphical or non-graphical and Univariate or multivariate data. EDA can be helpful for expressing what does the data refers before the modeling task. It is not easy to see at a large dataset or a whole spreadsheet and to decide important characteristics of the data. It may be typical to obtain insights by seeing at plain numbers. So, EDA techniques have been evolved as an assist in this type of situation. In this article we are going to take a nutritional dataset and visualize it using EDA. This visualization contains Bar chart, Histogram, Boxplot, Scatterplot, Barcode and Violin plot.
The HW-TSC’s Speech to Speech Translation System for IWSLT 2022 Evaluation

The paper presents the HW-TSC's pipeline and results of Offline Speech to Speech Translation for IWSLT 2022.We design a cascade system consisted of an ASR model, machine translation model and TTS model to convert the speech from one language into another language(en-de).For the ASR part, we find that better performance can be obtained by ensembling multiple heterogeneous ASR models and performing reranking on beam candidates.And we find that the combination of context-aware reranking strategy and MT model fine-tuned on the in-domain dataset is helpful to improve the performance.Because it can mitigate the problem that the inconsistency in transcripts caused by the lack of context.Finally, we use VITS model provided officially to reproduce audio files from the translation hypothesis.
Sentimental Analysis of Twitter Data on Online Learning During Unlock Phase of COVID-19

The coronavirus-induced lockdown has brought everyone’s life to a standstill and negatively impacted sentiments worldwide. It has also made online learning a compulsion for most students. The world is now following work from home and the e-learning boom. Almost all the schools and colleges have leveraged distance learning, thus continuing education from home and learning processes. Thus, online learning has a significant effect on education and has become a viable option for offline classes. The new norm has opened doors for blended learning, which will likely stay in the future. Our study analyses the sentiments of students on online learning. The proposed technique has been used to analyze tweets are subjectivity and polarity and later performed statistical analysis on cleaned tweets to know students’ sentiments on online learning.
Optimal Prediction of Bitcoin Prices Based on Deep Belief Network and Lion Algorithm with Adaptive Price Size

This paper introduces a new bitcoin predictin model that includes three major phases: data collection, Feature Extraction and Prediction. The initial phase is data collection, where Bitcoin raw data are collected, from which the features are extracted in the Features Extraction phase. The feature extraction is a noteworthy mechanism for detecting the bitcoin prices on day-by-day and minute-by –minute. Such that the indexed data collected are computed regarding certain standard indicators like Average True Range (ATR), Exponential Moving Average (EMA), Relative Strength Index (RSI) and Rate of Change (ROC). These technical indicators based features are subjected to prediction phase. As the major contribution, the prediction process is made precisely by deploying an improved DBN model, whose weights and activation function are fine-tuned using a new modified Lion Algorithm referred as Lion Algorithm with Adaptive Price Size (LAAPS). Finally, the performance of proposed work is compared and proved its superiority over other conventional models.
CAQ: Toward Context-Aware and Self-Adaptive Deep Model Computation for AIoT Applications

Artificial Intelligence of Things (AIoT) has recently accepted significant interests. Remarkably, embedded artificial intelligence (e.g., deep learning) on-device transforms IoT devices into intelligent systems that robustly and privately process data. Quantization technique is widely used to compress deep models for narrowing the resource gap between computation demands and platform supply. However, existing quantization schemes induce unsatisfaction for IoT scenarios since they are oblivious to dynamic changes of application context (e.g., battery and hierarchical memory availability) during the long-term operation. Subsequently, they will mismatch the user-desired resource efficiency and application lifetime. Also, to adapt to the dynamic context, we can neither accept the latency for model retraining with existing hand-crafted quantization nor the overhead for quantization bit width researching with prior on-demand quantization. This article presents a context-aware and self-adaptive deep model quantization (CAQ) system for IoT application scenarios. CAQ integrates a novel switchable multigate quantization framework, optimizing the quantized model accuracy and energy efficiency in diverse contexts. Based on the learned model, CAQ can switch among different gating networks in a context-aware manner and then adopt it to automatically capture the representation importance of various layers for optimal quantization bit-width selection. The experimental results show that CAQ achieves up to 50% storage savings with even 2.61% higher accuracy than the state-of-the-art baselines.
Smear character recognition method of side-end power meter based on PCA image enhancement

Abstract Since it is difficult for manual recording to track the rapid change of indication of the power meter, the power meter images are collected by the camera and automatically recognized and recorded to effectively overcome the disadvantages of manual recording. However, the complex scene lighting environment and smearing character shadows make it difficult to transfer captured images directly to convolutional neural networks for character recognition. A smear character recognition method of side-end power meter under complex lighting conditions is proposed in this article. First, the uneven illumination image enhancement algorithm is studied. Through the estimation of the illumination component of the image, the fusion weight is calculated by the principal component analysis for multiscale fusion, and the up-sampling and down-sampling are adopted to reduce the calculation of the algorithm and achieve the rapid image enhancement. A convolution neural network framework based on deep learning is proposed to realize the segmentation of smear characters, and the final segmented individual characters are fed into a network to identify the meter readings. The experimental results show that the proposed smear character recognition method has fast recognition speed, and the recognition rate of samples with the smear character and complex illumination reaches 99.8%, which meets the requirements of power meter character recognition and is better than other algorithms.
Empathy and Distress Prediction using Transformer Multi-output Regression and Emotion Analysis with an Ensemble of Supervised and Zero-Shot Learning Models

This paper describes the participation of the SINAI research group at WASSA 2022 (Empathy and Personality Detection and Emotion Classification).Specifically, we participate in Track 1 (Empathy and Distress predictions) and Track 2 (Emotion classification).We conducted extensive experiments developing different machine learning solutions in line with the state of the art in Natural Language Processing.For Track 1, a Transformer multi-output regression model is proposed.For Track 2, we aim to explore recent techniques based on Zero-Shot Learning models including a Natural Language Inference model and GPT-3, using them in an ensemble manner with a fine-tune RoBERTa model.Our team ranked 2 nd in the first track and 3 rd in the second track.
Modelling and analysis of an interval type-2 Mamdani fuzzy PID controller

From the last decade, modelling of Interval Type-2 (IT2) fuzzy controllers has become an exciting area of research in the field of control systems engineering. The input-output relationship of the fuzzy controller gives insights into the exact structure of the controller. From the literature, it is observed that IT2 fuzzy controllers handle plant uncertainty in a better way than their Type 1 (T1) counterparts due to the presence of additional Degree of Freedom (DoF) provided by the Footprint of Uncertainty (FoU) present in the membership functions of Interval Type-2 Fuzzy Sets (IT2FSs). Further, it is also noticed that controllers obtained with non-uniformly distributed fuzzy sets control the uncertain and nonlinear plants in a better way than their uniformly distributed counterparts. To take advantage of these two, in this study, the authors have made an attempt to unveil the exact input-output relationship of an Interval Type-2 Fuzzy PID (IT2FPID) controller, where the modelling is done using a particular type of FoU and non-uniformly distributed fuzzy sets. Apart from modelling, the properties of the newly derived controller are analysed. Moreover, to justify the theoretical development made in this manuscript, the applicability of the controller is shown through simulation, where an unstable chemical reactor plant with a large time delay is controlled. Finally, the effectiveness of the proposed controller is delineated by comparing its performance with that of some recently developed controllers.
BertOdia: BERT Pre-training for Low Resource Odia Language

Odia language is one of the 30 most spoken languages in the world. It is spoken in the Indian state called Odisha. Odia language lacks online content and resources for natural language processing (NLP) research. There is a great need for a better language model for the low resource Odia language, which can be used for many downstream NLP tasks. In this paper, we introduce a Bert-based language model, pre-trained on 430,000 Odia sentences. We also evaluate the model on the well-known Kaggle Odia news classification dataset (BertOdia: 96%, RoBERTaOdia: 92%, and ULMFit: 91.9% classification accuracy), and perform a comparison study with multilingual Bidirectional Encoder Representations from Transformers (BERT) supporting Odia. The model will be released publicly for the researchers to explore other NLP tasks.
Supplies for Type 1 Diabetes management during COVID-19 social distancing in Brazil

This study analyzed socioeconomic factors related with the acquisition of supplies for blood glucose management by people with Type 1 Diabetes Mellitus during social distancing due to the COVID-19 Pandemic in Brazil. This was a cross-sectional study with data collected during 21 days in July 2020, by an online form on socioeconomic data and acquisition of supplies for glycemic monitoring. This research applied Pearson's Chi-Squared test with adjusted residual analysis (p&lt;0.05). 472 adults of both sexes participated. Relationships were found between the type of device used for blood glucose monitoring (glucometer or Flash system) and income (p&lt;0.000), education (p=0.007), macro-regions (p=0.049), and type of city (p=0.043); between insulin acquisition and income (p&lt;0.000), macro-region (p=0.027) and type of neighborhood (p=0.003); between acquisition of reagent strips and income (p&lt;0.000); between acquisition of lancets and income (p=0.001), type of city (p=0.035) and neighborhood (p=0.010); between the use of Flash System and income (p&lt;0.000) and type of neighborhood (p=0.006). The results expose the social inequalities in the acquisition of supplies for blood glucose management by people with Type 1 Diabetes during the Pandemic in Brazil.
Automatic Key-Phrase Extraction: Empirical Study of Graph-Based Methods

Key-phrases in a document are phrases that provide a high-level description of its content without reading it completely. In some research articles, authors specify key-phrases in the articles they have written. However, the vast majority of books, articles, and web pages published every day, lack key-phrases. The manual extraction of these phrases is a tedious task and takes a long time. For this reason, automatic key-phrase extraction (AKE), which is an area of Text Mining, remains the best solution to overcome these difficulties. Because they are used in many Natural Language Processing (NLP) applications, such as text summarization and text classification. This article presents a comparison of some methods of extracting key-phrases from documents. Especially the graph-based approaches. These approaches are evaluated by their abilities to extract key-phrases. Our work focuses on the study of the performance of these methods in extracting key-phrases, whether from short or long texts, with the aim of providing information that contributes to improving their efficiency.
Five Phases Algorithm for Global Optimization

Wang, Xiaopeng Chu, Shu-Chuan Pan, Jeng-ShyangIn this work, a novel meta-heuristic algorithm named five phases algorithm (FPA) is proposed. FPA is inspired by five phases (wood, fire, earth, metal, water) scheme in traditional Chinese thought. FPA maintains a balance between exploitation and exploration in search space, which is modelled based on the generating and overcoming strategy of five phases and learning strategy from the agent with the same label. FPA has a simple structure but excellent performance. It also does not have any predefined control parameters, only two common parameters are required: population size and terminal condition. This provides flexibility to users to solve different optimization problems. The comprehensive performance of FPA is evaluated on the CEC2013 and CEC2017 test suites. The solutions obtained by FPA are compared with 5 state-of-the-art meta-heuristic algorithms including particle swarm optimization (PSO), grey wolf optimizer (GWO), multi-verse optimizer (MVO), sine cosine algorithm (SCA) and slime mould algorithm (SMA). The experimental results confirm that the proposed FPA is highly competitive compared to the counterparts.