Reviewerly/exHarmony huggingface dataset:
Detecting Anomalies in Daily Activity Routines of Older Persons in Single Resident Smart Homes: Proof-of-Concept Study

One of the main challenges of health monitoring systems is the support of older persons in living independently in their homes and with relatives. Smart homes equipped with internet of things devices can allow older persons to live longer in their homes. Previous surveys used to identify sensor-based data sets in human activity recognition systems have been limited by the use of public data set characteristics, data collected in a controlled environment, and a limited number of older participants.The objective of our study is to build a model that can learn the daily routines of older persons, detect deviations in daily living behavior, and notify these anomalies in near real-time to relatives.We extracted features from large-scale sensor data by calculating the time duration and frequency of visits. Anomalies were detected using a parametric statistical approach, unusually short or long durations being detected by estimating the mean (μ) and standard deviation (σ) over hourly time windows (80 to 355 days) for different apartments. The confidence level is at least 75% of the tested values within two (σ) from the mean. An anomaly was triggered where the actual duration was outside the limits of 2 standard deviations (μ-2σ, μ+2σ), activity nonoccurrence, or absence of activity.The patterns detected from sensor data matched the routines self-reported by users. Our system observed approximately 1000 meals and bathroom activities and notifications sent to 9 apartments between July and August 2020. A service evaluation of received notifications showed a positive user experience, an average score of 4 being received on a 1 to 5 Likert-like scale. One was poor, two fair, three good, four very good, and five excellent. Our approach considered more than 75% of the observed meal activities were normal. This figure, in reality, was 93%, normal observed meal activities of all participants falling within 2 standard deviations of the mean.In this research, we developed, implemented, and evaluated a real-time monitoring system of older participants in an uncontrolled environment, with off-the-shelf sensors and internet of things devices being used in the homes of older persons. We also developed an SMS-based notification service and conducted user evaluations. This service acts as an extension of the health/social care services operated by the municipality of Skellefteå provided to older persons and relatives.
A Comparative Review on Applications of Different Sensors for Sign Language Recognition

Sign language recognition is challenging due to the lack of communication between normal and affected people. Many social and physiological impacts are created due to speaking or hearing disability. A lot of different dimensional techniques have been proposed previously to overcome this gap. A sensor-based smart glove for sign language recognition (SLR) proved helpful to generate data based on various hand movements related to specific signs. A detailed comparative review of all types of available techniques and sensors used for sign language recognition was presented in this article. The focus of this paper was to explore emerging trends and strategies for sign language recognition and to point out deficiencies in existing systems. This paper will act as a guide for other researchers to understand all materials and techniques like flex resistive sensor-based, vision sensor-based, or hybrid system-based technologies used for sign language until now.
Mobile Augmented Reality Learning Media with Metaverse to Improve Student Learning Outcomes in Science Class

The development of information and communication technology affects the learning methods and media used. Augmented Reality technology allows students to experience learning with objects that can be seen in person. The purpose of the study was to see how students learned when they used the metaverse app, which is mobile augmented reality software for science courses. The population group consists of 92 students from several schools in cluster I, Depok Subdistrict. Then the sample was taken using the slovin formula until it obtained 75 samples of grade 5 students. This study employs experimental research techniques and a single group pre- and post-test. A t-test is used to analysed the collected data. According to the findings, using that metaverse applications has a favourable impact on students' learning outcomes. Students can use the Metaverse app to see better learning outcomes. Students are also more interested in learning and can easily understand and discover what is being learned. In addition, students find it more fun to learn using the Metaverse app, which is a mobile augmented reality.
A Fault Tolerant Elastic Resource Management Framework Toward High Availability of Cloud Services

Cloud computing has become inevitable for every digital service which has exponentially increased its usage. However, a tremendous surge in cloud resource demand stave off service availability resulting into outages, performance degradation, load imbalance, and excessive power-consumption. The existing approaches mainly attempt to address the problem by using multi-cloud and running multiple replicas of a virtual machine (VM) which accounts for high operational-cost. This paper proposes a Fault Tolerant Elastic Resource Management (FT-ERM) framework that addresses aforementioned problem from a different perspective by inducing high-availability in servers and VMs. Specifically, (1) an online failure predictor is developed to anticipate failure-prone VMs based on predicted resource contention; (2) the operational status of server is monitored with the help of power analyser, resource estimator and thermal analyser to identify any failure due to overloading and overheating of servers proactively; and (3) failure-prone VMs are assigned to proposed fault-tolerance unit composed of decision matrix and safe box to trigger VM migration and handle any outage beforehand while maintaining desired level of availability for cloud users. The proposed framework is evaluated and compared against state-of-the-arts by executing experiments using two real-world datasets. FT-ERM improved the availability of the services up to 34.47% and scales down VM-migration and power-consumption up to 88.6% and 62.4%, respectively over without FT-ERM approach.
WSN-BASED MONITORING SYSTEMS FOR THE SOLAR POWER STATIONS OF THE TELECOMMUNICATION DEVICES

Renewable energy sources are an increasingly popular way to generate electrical energy for telecommunications systems. The use and effectiveness of remote monitoring systems in such systems has led to growing interests relatively to it. As with any new technological systems, new problems have arisen in the use of renewable energy sources in telecommunications systems and the use of IoT-based remote monitoring systems in them. In particular, one of these problems is the decentralization of remote monitoring systems for renewable power plants of telecommunication systems. The paper details the stages of development and modelling of open-source centralized monitoring systems for solar power station of telecommunication systems. In this paper, a real-time remote monitoring system of solar power sources was modelled and investigated by wireless sensor networks of telecommunications devices. Proteus software environment was obtained for modelling. Before modelling the system, a system structure and a block diagram were developed, each of the elements that are part of the system is described. Software for the system was created. The developed structure and software were tested by modelling. The results of the modelling were presented in a virtual terminal, an oscillography, and a local web browser. ABSTRAK: Sumber tenaga boleh baharu semakin meningkat popular dalam menghasilkan tenaga elektrik bagi sistem telekomunikasi. Penggunaan dan keberkesanan sistem pemonitoran jarak jauh dalam jaringan ini telah membawa kepada peningkatan minat kepada tenaga boleh baharu. Walau bagaimanapun, melalui sistem teknologi baru ini, masalah baru timbul dalam penggunaan punca tenaga boleh baharu dalam sistem telekomunikasi dan penggunaan bersama sistem pemonitoran berdasarkan IoT. Khususnya dalam desentralisasi sistem pemonitoran jarak jauh bagi loji kuasa sistem telekomunikasi. Kajian ini merincikan peringkat pembinaan dan model sumber terbuka berpusat sistem pemonitoran bagi sistem janakuasa solar sistem telekomunikasi. Kajian ini turut memodelkan sistem pemonitoran jarak jauh secara langsung bagi sumber kuasa solar dan dikaji dengan rangkaian sensor tanpa jaringan bagi alatan telekomunikasi. Perisian Proteus telah dihasilkan bagi model ini. Sebelum model sistem dibina, struktur sistem dan gambar rajah blok dibina. Setiap unsur dalam sistem ini diperihalkan. Perisian bagi sistem ini turut dibina. Struktur yang dibina dan perisian ini diuji melalui model. Hasil dapatan model dibentangkan dalam pangkalan maya, oscilograf dan pelayar web tempatan.
Fast, Furious and Insecure: Passive Keyless Entry and Start Systems in Modern Supercars

The security of immobiliser and Remote Keyless Entry systems has been extensively studied over many years. Passive Keyless Entry and Start systems, which are currently deployed in luxury vehicles, have not received much attention besides relay attacks. In this work we fully reverse engineer a Passive Keyless Entry and Start system and perform a thorough analysis of its security.Our research reveals several security weaknesses. Specifically, we document the use of an inadequate proprietary cipher using 40-bit keys, the lack of mutual authentication in the challenge-response protocol, no firmware readout protection features enabled and the absence of security partitioning.In order to validate our findings, we implement a full proof of concept attack allowing us to clone a Tesla Model S key fob in a matter of seconds with low cost commercial off the shelf equipment. Our findings most likely apply to other manufacturers of luxury vehicles including McLaren, Karma and Triumph motorcycles as they all use the same system developed by Pektron.
Big Data in Auditing for the Future of Data Driven Fraud Detection

Industry revolution 4.0 makes people face change, the auditor profession is no exception. Auditors no longer conduct audits using the manual method but use computerized systems such as big data analytics. Our research aims to find out how auditors must change, when facing new technology approach audit. Very complicated and various data kinds that was too large to be audited manually. The research method in this research is descriptive qualitative, the method of data collection uses interviews with informants. The informant is the auditor partner of the public accounting firm. Big data has advantage and disadvantage to audit and fraud detection profession. The results of this study state that audit firm must be aware of the obstacles that come from internal and external for the implementation of big data analytics audit. Obstacles will hinder the adoption of technology for the auditor, while the auditor must change rapidly following the demands of technological development
Financial Development and Economic Growth in Selected Asian Economies: A Dynamic Panel ARDL Test

The present endeavor measures the extent of the nexus between financial development and economic growth by utilizing annual macroeconomic panel data for selected 14 Asian economies.The study focuses on the link between the indicators of financial development and economic growth.The results of panel cointegration analysis suggest that there is two-way cointegration relationship from GDP to GCF and BM in short-run as well as in long-run however, the relationship is one-way, that is, from GDP to DCPS as well as from GDP to DCBS.The findings of the present study establish strong indications of the positive long-run relationship among all the selected indicators of financial development and economic growth.Moreover, the present attempt also indicates that gross capital formation and broad money are critical for economic growth and suggests that upliftment of economic growth of the economies improves the development of financial sector.
Cloud-based email phishing attack using machine and deep learning algorithm

Cloud computing refers to the on-demand availability of personal computer system assets, specifically data storage and processing power, without the client's input. Emails are commonly used to send and receive data for individuals or groups. Financial data, credit reports, and other sensitive data are often sent via the Internet. Phishing is a fraudster's technique used to get sensitive data from users by seeming to come from trusted sources. The sender can persuade you to give secret data by misdirecting in a phished email. The main problem is email phishing attacks while sending and receiving the email. The attacker sends spam data using email and receives your data when you open and read the email. In recent years, it has been a big problem for everyone. This paper uses different legitimate and phishing data sizes, detects new emails, and uses different features and algorithms for classification. A modified dataset is created after measuring the existing approaches. We created a feature extracted comma-separated values (CSV) file and label file, applied the support vector machine (SVM), Naive Bayes (NB), and long short-term memory (LSTM) algorithm. This experimentation considers the recognition of a phished email as a classification issue. According to the comparison and implementation, SVM, NB and LSTM performance is better and more accurate to detect email phishing attacks. The classification of email attacks using SVM, NB, and LSTM classifiers achieve the highest accuracy of 99.62%, 97% and 98%, respectively.
T-MOOC for Initial Teacher Training in Digital Competences: Technology and Educational Innovation

Massive open online courses (MOOCs) are perceived as emerging technologies for training and innovation in the educational context. They have become approaches for distance education in the face of the new challenges, changes, and crises experienced by the COVID-19 pandemic. They represent, in turn, new emerging opportunities as a response to the United Nations recommendations for open education and the development of sustainable goals. The presence of technologies in the development of educational tasks means that the acquisition of Digital Competences (DC) by teachers and students in training goes beyond the mere mastery of content and teaching methodologies. The research presented aims to analyze the educational possibilities of T-MOOCs for the development of DC in teachers, and as resources that favor autonomous and collaborative learning in innovative scenarios. The study sample is made up of 313 students of the Primary Education Degree at the University of Seville (Spain). For this purpose, two online questionnaires (Google Forms) were applied at the beginning of the course: the Digital Teaching Competence Questionnaire (DigCompEdu), and the Content Questionnaire: Digital Resources and Digital Pedagogy. The results obtained show that the students’ level of both digital competences and subject content is low to medium, so that training in educational technology is required for the acquisition of key digital competences. Based on the data obtained, the following actions are proposed: (a) The concretion of the contents structured by means of a learning guide and e-activities to be developed by the student body, taking into account the United Nations guidelines with regard to the Development of Sustainable Objectives; (b) The creation of a training and innovative environment under the T-MOOC architecture, based on open and distance learning due to the current health situation of COVID-19, which, on the one hand, empowers students to use digital tools, and on the other hand, facilitates the acquisition of the SDGs; and (c) The evaluation of the T-MOOC designed as a resource for autonomous, collaborative, guided learning in emerging contexts in which technologies and educational innovation play an important role for sustainable development.
Light Weighted Healthcare CNN Model to Detect Prostate Cancer on Multiparametric MRI

The SEMRCNN model is proposed for autonomously extracting prostate cancer locations from regions of multiparametric magnetic resonance imaging (MP-MRI). Feature maps are explored in order to provide fine segmentation based on the candidate regions. Two parallel convolutional networks retrieve these maps of apparent diffusion coefficient (ADC) and T2W images, which are then integrated to use the complimentary information in MP-MRI. By utilizing extrusion and excitation blocks, it is feasible to automatically increase the number of relevant features in the fusion feature map. The aim of this study is to study the current scenario of the SE Mask-RCNN and deep convolutional network segmentation model that can automatically identify prostate cancer in the MP-MRI prostatic region. Experiments are conducted using 140 instances. SEMRCNN segmentation of prostate cancer lesions has a Dice coefficient of 0.654, a sensitivity of 0.695, a specificity of 0.970, and a positive predictive value of 0.685. SEMRCNN outperforms other models like as V net, Resnet50-U-net, Mask-RCNN, and U network model for prostate cancer MP-MRI segmentation. This approach accomplishes fine segmentation of lesions by recognizing and finding potential locations of prostate cancer lesions, eliminating interference from surrounding areas, and improving the learning of the lesions' features.
Compressed-Encoding Particle Swarm Optimization with Fuzzy Learning for Large-Scale Feature Selection

Particle swarm optimization (PSO) is a promising method for feature selection. When using PSO to solve the feature selection problem, the probability of each feature being selected and not being selected is the same in the beginning and is optimized during the evolutionary process. That is, the feature selection probability is optimized from symmetry (i.e., 50% vs. 50%) to asymmetry (i.e., some are selected with a higher probability, and some with a lower probability) to help particles obtain the optimal feature subset. However, when dealing with large-scale features, PSO still faces the challenges of a poor search performance and a long running time. In addition, a suitable representation for particles to deal with the discrete binary optimization problem of feature selection is still in great need. This paper proposes a compressed-encoding PSO with fuzzy learning (CEPSO-FL) for the large-scale feature selection problem. It uses the N-base encoding method for the representation of particles and designs a particle update mechanism based on the Hamming distance and a fuzzy learning strategy, which can be performed in the discrete space. It also proposes a local search strategy to dynamically skip some dimensions when updating particles, thus reducing the search space and reducing the running time. The experimental results show that CEPSO-FL performs well for large-scale feature selection problems. The solutions obtained by CEPSO-FL contain small feature subsets and have an excellent performance in classification problems.
Personalized Fashion Compatibility Modeling via Metapath-guided Heterogeneous Graph Learning

Fashion Compatibility Modeling (FCM) is a new yet challenging task, which aims to automatically access the matching degree among a set of complementary items. Most of existing methods evaluate the fashion compatibility from the common perspective, but overlook the user's personal preference. Inspired by this, a few pioneers study the Personalized Fashion Compatibility Modeling (PFCM). Despite their significance, these PFCM methods mainly concentrate on the user and item entities, as well as their interactions, but ignore the attribute entities, which contain rich semantics. To address this problem, we propose to fully explore the related entities and their relations involved in PFCM to boost the PFCM performance. This is, however, non-trivial due to the heterogeneous contents of different entities, embeddings for new users, and various high-order relations. Towards these ends, we present a novel metapath-guided personalized fashion compatibility modeling, dubbed as MG-PFCM. In particular, we creatively build a heterogeneous graph to unify the three types of entities (i.e., users, items, and attributes) and their relations (i.e., user-item interactions, item-item matching relations, and item-attribute association relations). Thereafter, we design a multi-modal content-oriented user embedding module to learn user representations by inheriting the contents of their interacted items. Meanwhile, we define the user-oriented and item-oriented metapaths, and perform the metapath-guided heterogeneous graph learning to enhance the user and item embeddings. In addition, we introduce the contrastive regularization to improve the model performance. We conduct extensive experiments on the real-world benchmark dataset, which verifies the superiority of our proposed scheme over several cutting-edge baselines. As a byproduct, we have released our source codes to benefit other researchers.
<b>modelsummary</b>: Data and Model Summaries in <i>R</i>

modelsummary is a package to summarize data and statistical models in R. It supports over one hundred types of models out-of-the-box, and allows users to report the results of those models side-by-side in a table, or in coefficient plots. It makes it easy to execute common tasks such as computing robust standard errors, adding significance stars, and manipulating coefficient and model labels. Beyond model summaries, the package also includes a suite of tools to produce highly flexible data summary tables, such as dataset overviews, correlation matrices, (multi-level) cross-tabulations, and balance tables (also known as
CoCo-BERT

BERT-type structure has led to the revolution of vision-language pre-training and the achievement of state-of-the-art results on numerous vision-language downstream tasks. Existing solutions dominantly capitalize on the multi-modal inputs with mask tokens to trigger mask-based proxy pre-training tasks (e.g., masked language modeling and masked object/frame prediction). In this work, we argue that such masked inputs would inevitably introduce noise for cross-modal matching proxy task, and thus leave the inherent vision-language association under-explored. As an alternative, we derive a particular form of cross-modal proxy objective for video-language pre-training, i.e., Contrastive Cross-modal matching and denoising (CoCo). By viewing the masked frame/word sequences as the noisy augmentation of primary unmasked ones, CoCo strengthens video-language association by simultaneously pursuing inter-modal matching and intra-modal denoising between masked and unmasked inputs in a contrastive manner. Our CoCo proxy objective can be further integrated into any BERT-type encoder-decoder structure for video-language pre-training, named as Contrastive Cross-modal BERT (CoCo-BERT). We pre-train CoCo-BERT on TV dataset and a newly collected large-scale GIF video dataset (ACTION). Through extensive experiments over a wide range of downstream tasks (e.g., cross-modal retrieval, video question answering, and video captioning), we demonstrate the superiority of CoCo-BERT as a pre-trained structure.