Reviewerly/exHarmony huggingface dataset:
Challenges and Strategies Taken by English Language Educators in Tertiary Institutions during Emergency Remote Teaching and Learning (ERTL)

The strike of COVID-19 pandemic has affected various sectors tremendously, including the education sector around the globe. This situation has forced the closure of most educational institutions, which include higher learning institutions. Since the Movement Control Order announcement, most teaching and learning processes in higher learning institutions were transferred online. The shift to online learning brings different challenges for teachers in higher learning institutions, including English teachers, regarding technological skills and assessment. In this study, the researchers adopted a multiple case study to explore the strategies of 34 English teachers from nine universities in Malaysia, Indonesia and Maldives to overcome the challenges during Emergency Remote Teaching and Learning. The data was collected through focused group interview sessions, individual open-ended responses via Google Forms and Whatsapp discussion threads. The recorded sessions were transcribed verbatim and analysed using NVIVO software to identify the common categories and themes. The findings showed that the teachers of English overcome problems concerning their technological skills by seeking help from their colleagues, attending courses, training and webinars and choosing suitable platforms for online learning, also conducting better online assessments. The faculty could also provide more webinars, online training, and courses to help teachers to perform better during online learning. This study is believed to shed some light on aspects that need to be considered while planning and executing a meaningful ESL online teaching and learning experience in higher learning institutions, particularly in developing countries.
Close Contact Individual Monitoring Based on Prescribed Social Distance Using Computer Vision

Contact tracing is one of the methods used by the government and organizations for controlling viral diseases like COVID-19, which claimed many human lives. Social distancing is advised to everyone to minimize the virus from spreading. This study aims to build a contact tracing tool that monitors social distancing individually using computer vision in real-time. Object tracking by detection is used for individual monitoring with YOLOv4 (You Only Look Once) as the object detector and SORT (Simple Online and Real-time Tracking) as the object tracker. The combination gained an average streaming and detection frame rate of 26 FPS and 10 FPS on NVIDIA's GTX 1650, respectively. It is expected to have more frame rate when used in a more powerful device. Moreover, the system obtained 98.2% accuracy in measuring the distance between individuals. Furthermore, the performance of the QR scanner used in the study attains a 100% success rate and a 98% accuracy in allocating the QR code to the correct owner from the video stream.
An Entity of the Ocean Data Prediction System Using Machine Learning Techniques

The tropical cyclone is one of the most powerful and destructive meteorological systems on Earth. Researchers note tropical cyclone data every few seconds, but utilizing all of the data with the appropriate accuracy values is difficult. In this system, we predict the various elements' status accuracy and loss in the ocean data set. The use of machine learning methods has developed a lot, and the prediction of the value of the ocean data follows the new enhanced term to give the status of the elements in the data. The LSTM (long short-term memory neural network excavation model) of the historical track's helpful information is more profound and more precise. Bi-LSTM goes the both forward and backward directions, and Adam optimizer, two updated machine learning techniques, are utilized to assess the status of the ocean element in the data set. It goes beyond the existing system to offer an opportunity for a different system result. The data set with a large number of values will also perform accurately. The project's ultimate objective is to give oceanographers a tool to anticipate the quality of ocean data in real-time, which can increase the precision of climate models and help with improved ocean-related decision-making.
Cohort Identification from Free-Text Clinical Notes Using SNOMED CT's Hierarchical Semantic Relations.

In this paper, a new cohort identification system that exploits the semantic hierarchy of SNOMED CT is proposed to overcome the limitations of supervised machine learning-based approaches. Eligibility criteria descriptions and free-text clinical notes from the 2018 National NLP Clinical Challenge (n2c2) were processed to map to relevant SNOMED CT concepts and to measure semantic similarity between the eligibility criteria and patients. The eligibility of a patient was determined if the patient had a similarity score higher than a threshold cut-off value. The performance of the proposed system was evaluated for three eligibility criteria. The performance of the current system exceeded the previously reported results of the 2018 n2c2, achieving the average F1 score of 0.933. This study demonstrated that SNOMED CT alone can be leveraged for cohort identification tasks without referring to external textual sources for training.
Multi-objective Symbolic Regression to Generate Data-driven, Non-fixed Structure and Intelligible Mortality Predictors using EHR: Binary Classification Methodology and Comparison with State-of-the-art.

Symbolic Regression (SR) is a data-driven methodology based on Genetic Programming, and it is widely used to produce arithmetic expressions for modelling learning tasks. Compared to other popular statistical techniques, SR outcomes are given by an arbitrary set of mathematical operations, representing arbitrarily complex linear and non-linear functions without a predefined fixed structure. Another advantage is that, unlike other machine learning algorithms, SR produces interpretable results. In this paper, we explore the qualities and limitations of this technique in a novel implementation as a binary classifier for in-hospital or short-term mortality prediction in patients with Covid-19. Our results highlight that SR provides a competitive alternative to popular statistical and machine learning methodologies to model relevant clinical phenomena thanks to good classification performance, stability in unbalanced dataset management, and intrinsic interpretability.
Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.

Federated learning (FL) is a privacy preserving approach to learning that overcome issues related to data access, privacy, and security, which represent key challenges in the healthcare sector. FL enables hospitals to collaboratively learn a shared prediction model without moving the data outside their secure infrastructure. To do so, after having sent model updates to a central server, an update aggregation is performed, and the model is sent back to the sites for further training. Although widely applied on neural networks, the deployment of FL architectures is lacking scalability and support for machine learning techniques such as decision tree-based models. The latter, when embedded in FL, suffer from costly encryption techniques applied for sharing sensitive information such as the splitting decisions within the trees. In this work, we focus on predicting hemodynamic instability on ICU patients by enabling distributed gradient boosting in FL. We employ a clinical dataset from 25 hospitals generated based on the Philips eICU database and we design a FL pipeline that supports neural-based boosting models as well as conventional neural networks. This enhancement enables decision tree models in FL, which represent the state-of-the-art approach for classification tasks involving tabular clinical data. Comparable performances in terms of accuracy, precision, recall and F1 score have been reached when detecting hemodynamic instability in FL, and in a centralized setup. In summary, we demonstrate the feasibility of a scalable FL for detecting hemodynamic instability in ICU data, which preserves privacy and holds the deployment benefits of a neural-based architecture.
A Noise reduction in the medical images using hybrid combination of filters with nature-inspired Black Widow Optimization Algorithm

This paper proposes an image filtering method to remove the noises in medical images in a controlled manner. To achieve this goal, the optimal parameters of the conventional filters are determined using the nature-inspired black widow (BWO) optimization algorithm to remove the noise efficiently. The BWO algorithm is chosen over other optimization algorithms because it quickly explores the optimal parameter values due to its procreate and cannibalism steps. The procreate step explores new solutions, whereas the cannibalism steps remove the inappropriate solutions while exploring the optimal solution. In the proposed method, speckle and sharpening filters are considered. In the proposed method, initially, medical images are read. After that, they are enhanced using the power law method because images are either low or high contrast. In the power law method, the gamma value plays an important role. Therefore, the optimal gamma value is determined using the BWO algorithm as done for the filter values. After that, noise addition is performed on them and removed them using the speckle filter. Further, the edges of the image are filtered using the sharpening filter. The proposed method is validated on the standard dataset images downloaded from Kaggle. It is found that the proposed method enhances the image and removes the noise in a controlled manner. Besides that, it achieves better Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) in the output.
A Computation of Frequent Itemset using Matrix Based Apriori Algorithm

gunasekaran.cse@drmgrdu.ac.in&#x0D; The Apriori Algorithm is a traditional method for determining the frequent itemsets from a lot of data. Association rules can be generated based on frequently occurring item sets. The Apriori algorithm has two bottlenecks, namely that it generates a large number of candidate sets and that it repeatedly examines the database. It takes a long time to execute and takes up a lot of space. We provide a novel strategy called Matrix Based Apriori Algorithm to get beyond the limitations of Apriori Algorithm. We don't need to constantly scan the database because all operations are first applied to the matrix after which the database is converted back into its original form. In addition, we have reduced the potential itemsets by using several pruning techniques. The Matrix Based Apriori algorithm outperforms the standard Apriori algorithm in terms of time, with an average rate of time reduction of 71.5% with the first experiment and 86% with the second. In a similar vein, we contrasted our Matrix Based Apriori with an effective alternative known as improved apriori. We discovered that our method outperforms the upgraded apriori by 20%.
Smartphone Authentication with Lightweight Deep Learning

&lt;p&gt;Over the past decade, machine learning methods have found their way into a large variety of computer security applications, including accurate spam detection, scalable discovery of new malware families, identifying malware download events in vast amounts of web traffic, detecting software exploits, blocking phishing web pages, and preventing fraudulent financial transactions, just to name a few. At the same time, machine learning methods themselves have evolved. In particular, Deep Learning methods have recently demonstrated great improvements over more “traditional” learning approaches on a number of important tasks, including image and audio classification, natural language processing, machine translation, etc. Deep learning has faced an exponential growth over the last decade. Today, it has found use in a wide range of applications, security being one of those. In this project we try to exploit deep learning methodologies to facilitate user authentication on smartphones. Smartphone authentication has explored a variety of domains like iris recognition (latest Samsung Galaxy S8), face recognition (iPhoneX), fingerprints (traditional iPhones), PINs and pattern recognitions (traditional smartphones). In this project, we try to develop a lightweight authentication scheme based on sensor data which continually authenticates the user at regular intervals of time, thus providing greater security.&lt;/p&gt;
Evaluation of GPU Logic Reconfiguration after Service Start

In order to make full use of heterogeneous hardware, it is necessary to have a technical skill of hardware such as CUDA, and the current situation is that the barrier is high. Based on this background, I have proposed environment-adaptive software that enables high-performance operation by automatically converting application code written for normal CPUs by engineers according to the deployed environment and setting appropriate amount of resources. Until now, I only considered conversions and settings before operation. In this paper, I verify that the logic is reconfigured according to the usage characteristics during operation. I confirm that the application running on the GPU is reconfigured into other loops or applications offloading according to the usage trends.
DIGITAL DOUBLES IN MANAGEMENT

The work considers some digital models of real devices that have interface means by which the simulated object interacts with the external environment. Such models are called «digital twins». Two types of models used in creation of digital twins are considered. These are time series models and domain models. Domain models use the laws of a given domain. They use mathematical, logical and probabilistic properties inherent in a particular domain. Time series models are universal for various domains and do not depend on the physical nature of variables of a specific series. Such models can be used to predict the behavior of a simulated object. As examples, a delay prediction model in a video traffic control system and a model for recharging batteries of wireless sensor networks are considered.
Fire product traceability system based on blockchain and IPFS

The effective traceability of fire products is an important guarantee to prevent potential safety hazards. Aiming at the problems of unverifiable authenticity of fire product information and insecure data storage, this paper constructs a fire product traceability model and implements a traceability system. The traceability model uses the Internet of Things technology to ensure the credibility of the data source, and stores fire data in combination with blockchain and IPFS (Inter Planetary File System). Meanwhile, the traceability system is designed based on the Hyperledger Fabric architecture to achieve efficient information sharing and traceability management of fire products. The experimental results show that compared with the traditional traceability system, the traceability system has low design and maintenance cost and improved security.
Inverse Consistency by Construction for Multistep Deep Registration

Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a consequence of its structure, as long as it parameterizes its output transform by a Lie group. We extend this technique to multi-step neural registration by composing many such networks in a way that preserves inverse consistency. This multi-step approach also allows for inverse-consistent coarse to fine registration. We evaluate our technique on synthetic 2-D data and four 3-D medical image registration tasks and obtain excellent registration accuracy while assuring inverse consistency.
Reasonable AI and Other Creatures: What Role for AI Standards in Liability Litigation?

Standards play a vital role in supporting policies and legislation of the European Union. The regulation of artificial intelligence (AI) makes no exception as made clear by the AI Act proposal. Particularly, Articles 40 and 41 defer to harmonised standards and common specifications the concrete definition of safety and trustworthiness requirements, including risk management, data quality, transparency, human oversight, accuracy, robustness, and cybersecurity. Besides, other types of standards and professional norms are also relevant to the governance of AI. These include European non-harmonised standards, international and national standards, professional codes and guidelines, and uncodified best practices. This contribution casts light on the relationship between standards and private law in the context of liability litigation for damage caused by AI systems. Despite literature’s commitment to the issue of liability for AI, the role of standardisation in this regard has been largely overlooked hitherto. Furthermore, while much research has been undertaken on the regulation of AI, comparatively little has dealt with its standardisation. This paper aims to fill this gap. Building on previous scholarship, the contribution demonstrates that standards and professional norms are substantially normative in spite of their private and voluntary nature. In fact, they shape private relationships due to normative and economic reasons. Indeed, these private norms enter the courtrooms by explicit or implicit incorporation into contracts as well as by informing general clauses such as reasonableness and duty of care. Therefore, they represent the yardstick against which professionals’ performance and conduct are evaluated. Hence, a link between standards, safety, and liability can be established. Against this backdrop, the role of AI standards in private law is assessed. To set the scene, the article provides a bird’s-eye view of AI standardisation. The European AI standardisation initiative is analysed along with other institutional and non-institutional instruments. Finally, it is argued that AI standards contribute to defining the duty of care expected from developers and professional operators of AI systems. Hence, they might represent a valuable instrument for tackling the challenges posed by AI technology to extracontractual and contractual liability.
MSMC-TTS: Multi-Stage Multi-Codebook VQ-VAE based Neural TTS

This paper aims to improve neural TTS with vector-quantized, compact speech representations. We propose a Vector-Quantized Variational AutoEncoder (VQ-VAE) based feature analyzer to encode acoustic features into sequences with different time resolutions, and quantize them with multiple VQ codebooks to form the Multi-Stage Multi-Codebook Representation (MSMCR). The TTS system, MSMC-TTS, is proposed to predict better speech via this representation. In prediction, the multi-stage predictor is trained to map the input text sequence to MSMCRs in stages, by minimizing Euclidean distance and “triplet loss”. In synthesis, the neural vocoder converts ground-truth or predicted MSMCRs into speech waveforms. The proposed system is trained with single-speaker TTS datasets and tested in various scenarios for comprehensive evaluation. In TTS evaluation, MSMC-TTS obtains MOS of 4.34 and 4.10 on English and Chinese datasets, which significantly outperforms VITS with scores of 3.78 and 3.90. Meanwhile, compared with Mel-Spectrograms, the domain discrepancy between prediction and ground truth is lower in MSMCRs with the higher Domain-classification Error Rate (DER). Furthermore, this system shows lower modeling complexity and data size requirements, preserving excellent performance even with fewer model parameters or training data. The noticeable improvement in analysis-synthesis and TTS from multiple codebooks and stages also validate them as vital components in seeking a more profitable speech representation and building high-performance neural TTS.
Online Learning With Incremental Feature Space and Bandit Feedback

Online learning is a fundamental paradigm for learning from continuous data stream. Tradition online learning approaches usually assume that the feature space of data stream is fixed and the incoming instance can always get the true label after making its prediction. However, in many real-world applications, such as the personalized recommender systems, the feature space may keep expanding due to the accumulation of user behaviors. Besides, we may only get bandit feedback, i.e., we only know whether the prediction is correct or not. To solve this important but rarely studied problem, we propose a novel algorithm LIFBF, together with its two variants LIFBF-I and LIFBF-II, to learn from data stream with incremental feature space and bandit feedback. Specifically, when an instance arrives with augmented features, we first utilize the exploration-exploitation strategy to guess its best label, then, a new loss function considering both bandit feedback and guessed label is proposed. Finally, we design a highly dynamic multi-class classifier, which updates the shared and augmented features by adopting the passive-aggressive rule and structural risk minimization principle, respectively. We theoretically analyze the cumulative loss bound of LIFBF. Besides, empirical studies on various datasets further validate the effectiveness of our proposed algorithms.
Classification of Hand Movements Based on EMG Signals using Topological Features

Hand movement classification based on Electromyography (EMG) signals has been extensively investigated in the past decades as a promising approach used for controlling upper prosthetics or robotics.Topological data analysis is a relatively new and increasingly popular tool in data science that uses mathematical techniques from topology to analyze and understand complex data sets.This paper proposes a method for classifying hand movements based on EMG signals using topological features crafted with the tools of TDA.The main findings of this work on hand movement EMG classification are as follows: (1) topological features are effective in classifying EMG signals and outperform other time domain features tested in the experiments; (2) the 0-th Betti numbers are more effective than the 1-st Betti numbers; (3) Betti amplitude is a more stable and powerful feature than other topological features discussed in this paper.Additionally, Betti curves were used to visualize topological patterns for hand movement EMG.