Reviewerly/exHarmony huggingface dataset:
Programming STM32 Nucleo Platform for IoT Education Using STM32duino and Mbed OS

In this article we consider STM32 Nucleo programming for the tasks of Internet of Things (IoT) education for Industry 4.0 using STM32duino and Mbed OS projects. A brief comparison of the Nucleo-64 F446RE board with popular Arduino Uno and Mega boards is done. We present programs to allow IoT device to communicate via Ethernet, Bluetooth and Wi-Fi interfaces. The assessment of the applicability of STM32duino, the Mbed library and the Mbed operating system in engineering student learning tasks is done as the main result of this article.
High-Throughput and Power-Efficient Convolutional Neural Network Using One-Pass Processing Elements

In recent decades, convolutional neural network (CNN) has become essential in many real-time applications due to its massive computational ability. But its use in portable devices is limited due to its high computation requirements. This paper proposes a novel One-Pass Processing Element (OPPE) to mitigate this limitation. The proposed OPPE removes redundant computations by eliminating those with zeros that leads to low area as well as low power consumption. The proposed OPPE model is evaluated with the help of VGG-16-based CNN accelerator. The proposed OPPE design reduces the number of four-input LUTs by 5.19%, 15.91%, 10.06% and 4.93% and the power consumption by 4.26%, 7.36%, 5.81% and 1.55% when compared with the conventional processing element (PE), activation gating PE, weight gating PE and zero gating PE, respectively. The proposed CNN accelerator design using OPPE achieves high throughput with less resource utilization.
Spin‐Chain‐Star Systems: Entangling Multiple Chains of Spin Qubits

We consider spin-chain-star systems characterized by N-wise many-body interactions between the spins in each chain and the central one. We show that such systems can be exactly mapped into standard spin-star systems through unitary transformations. Such an approach allows the solution of the dynamic problem of an XX spin-chain-star model and transparently shows the emergence of quantum correlations in the system, based on the idea of entanglement between chains.
VisHikers’ Guide to Evaluation: Competing Considerations in Study Design

In this Viewpoint article, we describe the persistent tensions between various camps on the "right" way to conduct evaluations in visualization. Visualization as a field is the amalgamation of cognitive and perceptual sciences and computer graphics, among others. As a result, the relatively disjointed lineages in visualization understandably approach the topic of evaluation very differently. It is both a blessing and a curse to our field. It is a blessing, because the collaboration of diverse perspectives is the breeding ground of innovation. Yet it is a curse, because as a community, we have yet to resolve an appreciation for differing perspectives on the topic of evaluation. We explicate these differing expectations and conventions to appreciate the spectrum of evaluation design decisions. We describe some guiding questions that researchers may consider when designing evaluations to navigate differing readers' evaluation expectations.
Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases

Electronic health records naturally contain most of the medical information in the form of doctor's notes as unstructured or semi-structured texts. Current deep learning text analysis approaches allow researchers to reveal the inner semantics of text information and even identify hidden consequences that can offer extra decision support to doctors. In the presented article, we offer a new automated analysis of Polish summary texts of patient hospitalizations. The presented models were found to be able to predict the final diagnosis with almost 70% accuracy based just on the patient's medical history (only 132 words on average), with possible accuracy increases when adding further sentences from hospitalization results; even one sentence was found to improve the results by 4%, and the best accuracy of 78% was achieved with five extra sentences. In addition to detailed descriptions of the data and methodology, we present an evaluation of the analysis using more than 50,000 Polish cardiology patient texts and dive into a detailed error analysis of the approach. The results indicate that the deep analysis of just the medical history summary can suggest the direction of diagnosis with a high probability that can be further increased just by supplementing the records with further examination results.
Blockchain mediated virtual power plant: From concept to demonstration

Large penetration of various types of distributed energy resources and new requirements in electricity markets, have facilitated creation of sophisticated distributed energy resource aggregation systems for providing grid services. Aggregators such as Virtual Power Plants enable participation of small- and medium-scale distributed energy resources in electricity markets, interacting with grid operators to collectively provide ancillary power and energy services. These interactions between grid operator, Virtual Power Plants operator and distributed energy resources are complex both in terms of contractual agreements and provision of services. On all layers, accurate tracking of distributed energy resource usage and execution of contracts is required for establishing trust between transacting participants. Digitalisation and automation of processes such as control of distributed energy resources, verification and payouts for services will add value for solving complexity of dealing with multiple parties in a Virtual Power Plants aggregation model. The authors present a blockchain-integrated Virtual Power Plants system that enables full automation of distributed energy resource controls through smart contracts, digital trust between participants and immediate payouts for services. Effectiveness of the developed blockchain mechanism is demonstrated on a pilot Virtual Power Plants system comprising distributed energy storage systems, renewables, EV charging stations and loads with a total size of 1.8 MWs. Additionally, we propose a new mechanism for managing renewable energy curtailments through blockchain.
Detecting Sentiment toward Emerging Infectious Diseases on Social Media: A Validity Evaluation of Dictionary-Based Sentiment Analysis

Despite the popularity and efficiency of dictionary-based sentiment analysis (DSA) for public health research, limited empirical evidence has been produced about the validity of DSA and potential harms to the validity of DSA. A random sample of a second-hand Ebola tweet dataset was used to evaluate the validity of DSA compared to the manual coding approach and examine the influences of textual features on the validity of DSA. The results revealed substantial inconsistency between DSA and the manual coding approach. The presence of certain textual features such as negation can partially account for the inconsistency between DSA and manual coding. The findings imply that scholars should be careful and critical about findings in disease-related public health research that use DSA. Certain textual features should be more carefully addressed in DSA.
Collection of datasets with DNS over HTTPS traffic

Recently, the Internet has adopted the DNS over HTTPS (DoH) resolution mechanism for privacy-aware network applications. As DoH becomes more disseminated, it has also become a network monitoring research topic. For comprehensive evaluation and comparison of developed classifiers, real-world datasets are needed, motivating this contribution. We created a new large-scale collection of datasets consisting of two classes of traffic: i) DoH HTTPS communication and ii) non-DoH HTTPS connections. The DoH traffic is captured for multiple DoH providers and clients to include nuances of various DoH implementations and configurations. The non-DoH HTTPS connections complement the DoH communication aiming to include a wide range of existing network applications. The dataset collection consists of network traffic generated in a controlled environment and traffic captured from a real ISP network. The resulting datasets thus provide real-world network traffic data suitable for evaluating existing classifiers and the development of new methods.
A Hybrid Regularization-Based Multi-Frame Super-Resolution Using Bayesian Framework

The prime purpose for the image reconstruction of a multi-frame superresolution is to reconstruct a higher-resolution image through incorporating the knowledge obtained from a series of relevant low-resolution images, which is useful in numerous fields.Nevertheless, super-resolution image reconstruction methods are usually damaged by undesirable restorative artifacts, which include blurring distortion, noises, and stair-casing effects.Consequently, it is always challenging to achieve balancing between image smoothness and preservation of the edges inside the image.In this research work, we seek to increase the effectiveness of multi-frame super-resolution image reconstruction by increasing the visual information and improving the automated machine perception, which improves human analysis and interpretation processes.Accordingly, we propose a new approach to the image reconstruction of multi-frame super-resolution, so that it is created through the use of the regularization framework.In the proposed approach, the bilateral edge preserving and bilateral total variation regularizations are employed to approximate a high-resolution image generated from a sequence of corresponding images with low-resolution to protect significant features of an image, including sharp image edges and texture details while preventing artifacts.The experimental results of the synthesized image demonstrate that the new proposed approach has improved efficacy both visually and numerically more than other approaches.
Dynamic resource provisioning for service-based cloud applications: A Bayesian learning approach

Deciding the correct extent of resources needed to run the various cloud services is always a challenge. Often in such dynamic environments, there is a tremendous need for accurate predictions and timely decision making methodologies to estimate the future demands within a minimal cost. This brings in a need to elucidate the research divergence for optimal dynamic resource provisioning that predicts the future enumerated resources on the support of application's type. This paper proposes a framework to provision the resources in an optimal way, by combining the concepts of autonomic computing, linear regression and Bayesian learning. The use of Bayesian learning to the proposed model helps in a proactive decision making process and provide a solid theoretical framework to estimate the future predictions using the prior information available. The autonomic resource provisioning framework proposed here is developed using CloudSim toolkit inspired by a cloud layer model. The efficacy of the proposed technique is evaluated using real world workload traces from google followed by the traces from Clarknet. The model is evaluated for various parameters namely – response time, SLA violations, virtual machine usage hours and cost. It is found that the proposed model lowers the overall cost by 31% with the increase in the usage of resources by 12% when compared with the other existing approaches.
Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networks

Existing online recruitment platforms depend on automatic ways of conducting the person-job fit, whose goal is matching appropriate job seekers with job positions. Intuitively, the previous successful recruitment records contain important information, which should be helpful for the current person-job fit. Existing studies on person-job fit, however, mainly focus on calculating the similarity between the candidate resumes and the job postings on the basis of their contents, without taking the recruiters' experience (i.e., historical successful recruitment records) into consideration. In this paper, we propose a novel neural network approach for person-job fit, which estimates person-job fit from candidate profile and related recruitment history with co-attention neural networks (named PJFCANN). Specifically, given a target resume-job post pair, PJFCANN generates local semantic representations through co-attention neural networks and global experience representations via graph neural networks. The final matching degree is calculated by combining these two representations. In this way, the historical successful recruitment records are introduced to enrich the features of resumes and job postings and strengthen the current matching process. Extensive experiments conducted on a large-scale recruitment dataset verify the effectiveness of PJFCANN compared with several state-of-the-art baselines. The codes are released at: https://github.com/CCIIPLab/PJFCANN.
A Hybrid Arabic Text Summarization Approach based on Transformers

Recently, the amount of data in the world has increased tremendously. Most of the research and efforts have focused on dealing with data in the English language. Dealing with data in other languages, such as Arabic, has become a significant challenge. In this paper, we proposed a sequential hybrid model based on a transformer to summarize Arabic articles. We used two main approaches of summarization to make our model. The First one is the extractive approach which depends on the most important sentences from the articles as it is to be the summary, so we used more than word embedding techniques to help us know which sentences are more important and by using Deep Learning techniques specifically transformers such as AraBert we make our summary, The second one is abstractive, and this approach is similar to human summarization, which means that the machine can use some words which have the same meaning but are not found in the original text. We apply this kind of summary using the MT5 Arabic pre-trained transformer model. We sequentially applied these two summarization approaches to build our A3SUT hybrid model. The output of the extractive module is fed into the abstractive module. We enhanced the summary's quality to be closer to the human summary by applying this approach. We tested our model on the ESAC dataset and evaluated the extractive summary using the Rouge score technique; we got a precision of 0.5348 and a recall of 0.5515, and an f1 score of 0.4932 and the evaluation of the abstractive model is evaluated by user satisfaction. We add some features to our summary to make it more understandable and accessible by applying the metadata generation task” data about data” and classification. By applying metadata generation, we add facilities and support to our summary, identification, and summary organization. Metadata also captures and provides essential contextual details, as not all summaries are self-describing. Also, classify the original text to determine the summary topic before reading. We acquire 97.5% accuracy by using Support Vector Machine (SVM) and trained it using NADA corpus.
What Went Wrong with the IMMUNI Contact-Tracing App in Italy? A Cross-Sectional Survey on the Attitudes and Experiences among Healthcare University Students

The adoption of digital contact-tracing apps to limit the spread of SARS-CoV-2 has been sup-optimal, but studies that clearly identify factors associated with the app uptake are still limited. In April 2021, we administered a questionnaire to healthcare university students to investigate their attitudes towards and experiences of the IMMUNI app. A multivariable logistic regression model was built to identify app download predictors. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated. We surveyed 247 students. Most respondents (65.6%) had not downloaded IMMUNI, reporting as the main reason the perceived app uselessness (32.7%). In the multivariable analysis, being advised to use the app (aOR: 3.21, 95%CI: 1.80-5.73), greater fear of infecting others (aOR: 1.50, 95%CI: 1.01-2.23), and greater trust in the institutional response to the emergency (aOR: 1.33, 95%CI: 1.00-1.76) were positively associated with the outcome, whereas greater belief in the "lab-leak theory" of COVID-19 was a negative predictor (aOR: 0.75, 95%CI: 0.60-0.93). Major technical issues were reported by app users. Targeted strategies aimed at improving awareness of digital health applications should be devised. Furthermore, institutions should invest in the development of these technologies, to minimize technical issues and make them accessible to the entire population.
Transformer Network for Semantically-Aware and Speech-Driven Upper-Face Generation

We propose a semantically-aware speech driven model to generate expressive and natural upper-facial and head motion for Embodied Conversational Agents (ECA). In this work, we aim to produce natural and continuous head motion and upper-facial gestures synchronized with speech. We propose a model that generates these gestures based on multimodal input features: the first modality is text, and the second one is speech prosody. Our model makes use of Transformers and Convolutions to map the multimodal features that correspond to an utterance to continuous eyebrows and head gestures. We conduct subjective and objective evaluations to validate our approach and compare it with state of the art.
Smart Video Survillance Based Weapon Identification Using Yolov5

Video Surveillance plays an important role in every aspect of life like theft detection, unusual happenings in crowded places, monitoring the suspicious activities of each individual to provide a secure and hassle free environment. Footage of closed circuit television (CCTV) camera is taken as an evidence to track the suspicious act. It is very tough to operate surveillance cameras with human intervention to detect abnormal activities. Fully automating surveillance with smart video capturing capabilities using deep learning technique is one of the most advanced means of remotely monitoring strange activities with exact location, time of event occurred along with facial recognition of criminal. Finding misdemeanor activity in a public place is very difficult to observe, as many objects are involved in the real time scenario. An uncommon or doubtful incidents in public places are captured in CCTV cameras which promotes police force to safeguard people before any mishap happens. It helps police to reach that spot on time and rescue victim. All these are meant to be achieved by using YOLO (You Only Look Once) object detection models and its variants like YOLO V1, V2, V3, V4 and latest V5 which is 88% faster than yolov4 in Deep Learning. This proposed system helps in identifying weapons held by a person as well as face recognition to identify the suspicious user. Using YOLO v5, it is very simple to track objects like weapons in a crowd. Low resolution images, far away and out of focus in the scene can also be captured and identified accurately
Explore the entrepreneurial process of AI start‐ups from the perspective of opportunity

Abstract Digital technologies, such as artificial intelligence (AI), big data, and the Internet of Things, are becoming increasingly mature. They have become the driving force behind a new wave of innovation and entrepreneurship activities worldwide. AI is the core of the contemporary digital technology industry. New high technologies bring greater opportunities for entrepreneurs to carry out entrepreneurial activities and bring unprecedented challenges to traditional entrepreneurial activities and theories. The existing literature on the impact of AI on entrepreneurial activities is still in the exploration stage. Based on this, this study aims to explore the entrepreneurial process of AI start‐ups, combining with existing literature, select AI unicorn start‐ups ByteDance for a case study, and apply the grounded theory method to analyse and summarise the entrepreneurial process of Internet start‐ups with AI. Thus, it can provide insights for many AI start‐ups and contribute to the existing entrepreneurial research.
A comprehensive investigation on novel center-based sampling for large-scale global optimization

During the last decade, metaheuristic algorithms have been well-established approaches which are utilized for solving complex real-world optimization problems. The most metaheuristic algorithms uses stochastic strategies in their initialization phase as well as during their new candidate generation steps when there is no a-priori knowledge about the solution, which is a common valid assumption for any black-box optimization algorithm. In recent years, researchers have introduced a new concept called center-based sampling which can be utilized in any search component of the optimization process, but so far it mainly has been used just for population initialization. This novel concept clarifies that in a search space, the center point has a higher chance to be closer to an unknown solution compared to a random point, especially when the dimension of the search space increases. Thus, this concept helps the optimizer to find a better solution in a shorter time. In this paper, a comprehensive study has been conducted on the effect of center-based sampling to solve an optimization problem using three different levels of detailed investigation. These levels are as the follows: 1) considering no specific algorithm and no specific landscape (i.e., Monte-Carlo simulation); 2) considering a specific landscape but no specific algorithm (i.e., random search vs. center-based random search), and finally, 3) considering a specific algorithm and specific landscape which includes the proposing three different schemes for using center-based sampling scheme for solving Large-scale Global Optimization (LSGO) problems effectively. Furthermore, in this study, we seek to investigate the properties and capabilities of center-based sampling during optimization, which can be extended to utilize it in machine learning too, because optimization is a key role player in search and learning models. The proposed methods in this paper are evaluated on CEC 2013 LSGO benchmark functions and a real-world optimization problem, i.e., evolving ANN on two medical data sets. The experimental results confirm that center-based sampling has a crucial impact in improving the convergence rate of optimization/search algorithms when solving high-dimensional optimization problems.