Reviewerly/exHarmony huggingface dataset:
Named entity disambiguation in short texts over knowledge graphs

The ever-growing usage of knowledge graphs (KGs) positions named entity disambiguation (NED) at the heart of designing accurate KG-driven systems such as query answering systems (QAS). According to the current research, most studies dealing with NED on KGs involve long texts, which is not the case of short text fragments, identified by their limited contexts. The accuracy of QASs strongly depends on the management of such short text. This limitation motivates this paper, which studies the NED problem on KGs, involving only short texts. First, we propose a NED approach including the following steps: (i) context expansion using WordNet to measure its similarity to the resource context. (ii) Exploiting coherence between entities in queries that contain more than one entity, such as "Is Michelle Obama the wife of Barack Obama?". (iii) Taking into account the relations between words to calculate their similarity with the properties of a resource. (iv) the use of syntactic features. The NED solution approach is compared to state-of-the-art approaches using five datasets. The experimental results show that our approach outperforms these systems by 27% in the F-measure. A system called Welink, implementing our proposal, is available on GitHub, and it is also accessible via a REST API.
A Deep Neural Network Model for the Detection and Classification of Emotions from Textual Content

Emotion-based sentimental analysis has recently received a lot of interest, with an emphasis on automated identification of user behavior, such as emotional expressions, based on online social media texts. However, the majority of the prior attempts are based on traditional procedures that are insufficient to provide promising outcomes. In this study, we categorize emotional sentiments by recognizing them in the text. For that purpose, we present a deep learning model, bidirectional long-term short-term memory (BiLSMT), for emotion recognition that takes into account five main emotions (Joy, Sadness, Fear, Shame, Guilt). We use our experimental assessments on the emotion dataset to accomplish the emotion categorization job. The datasets were evaluated and the findings revealed that, when compared to state-of-the-art methodologies, the proposed model can successfully categorize user emotions into several classifications. Finally, we assess the efficacy of our strategy using statistical analysis. This research’s findings help firms to apply best practices in the selection, management, and optimization of policies, services, and product information.
Topophilia, Placemaking, and Boundary Work

The COVID-19 pandemic fundamentally changed the nature of work by shifting most in-person work to a predominantly remote modality as a way to limit the spread of the coronavirus. In the process, the shift to working-from-home rapidly forced the large-scale adoption of groupware technologies. Although prior empirical research examined the experience of working-from-home within small-scale groups and for targeted kinds of work, the pandemic provides HCI and CSCW researchers with an unprecedented opportunity to understand the psycho-social impacts of a universally mandated work-from-home experience rather than an autonomously chosen one. Drawing on boundary theory and a methodological approach grounded in humanistic geography, we conducted a qualitative analysis of Reddit data drawn from two work-from-home-related subreddits between March 2020 and January 2021. In this paper, we present a characterization of the challenges and solutions discussed within these online communities for adapting work to a hybrid or fully remote modality, managing reconfigured work-life boundaries, and reconstructing the home's sense of place to serve multiple, sometimes conflicting roles. We discuss how these findings suggest an emergent interplay among adapted work practice, reimagined physical (and virtual) spaces, and the establishment and continual re-negotiation of boundaries as a means for anticipating the long-term impact of COVID on future conceptualizations of productivity and work.
Pedestrian Target Tracking Based On DeepSORT With YOLOv5

Pedestrian target tracking is an important problem in the field of computer vision. To address low tracking accuracy and tracking errors in pedestrian target tracking. This paper developed a YOLOv5-based DeepSORT pedestrian target tracking algorithm (YOLOv5-DeepSORT), which introduces the high-performing YOLOv5 algorithm into the DeepSORT algorithm, which detects the tracking video frame by frame, and then predicts the target position using Kalman filtering while matching the same target using the Hungarian algorithm. To verify the performance of the YOLOv5-DeepSORT algorithm, this paper compares it with the YOLOv3-DeepSORT algorithm for experiments. The results show that the YOLOv5-DeepSORT algorithm can detect and track pedestrians well. Compared with the YOLOv3-DeepSORT algorithm, the MOTA of the YOLOv5- DeepSORT algorithm is improved by 9.9%, the number of false detections and missed detections is significantly reduced, and the number of target ID switching is decreased by 23 times, which proves the effectiveness of the method in this paper.
Automating data science

Given the complexity of data science projects and related demand for human expertise, automation has the potential to transform the data science process.
A Decentralized Privacy Preserving Healthcare Blockchain for IoT, Challenges, and Solutions

Healthcare industry operation needs resources and information sharing between business partners. Internet of things (IoT) aims to simplify distributing data collection in the healthcare business, sharing and processing information across collaborative business partners using appropriate information system architecture. However, a large portion of existing IoT-based healthcare systems leveraged for managing data is centralized, posing potential risks of a single point of failure in natural disasters. The medical data privacy and security problems could result from a delay in treatment progress, even endangering the patient's life. This chapter describes the use of blockchain-enabled secure management of healthcare systems. Blockchain technology contributes to transactional data's intelligent and flexible handling through appropriate convergence with IoT technology in supporting data integration, processing, and providing data privacy and security-related issues. Finally, the chapter presents challenges and solutions on blockchain-based electronic healthcare record (EHR) systems.
Evaluating factors contributing to the failure of information system in the banking industry

The increasing use of Information Technology (IT) has led to many security and other related failures in the banks and other financial institutions in Bangladesh. In this paper, we investigated the factors contributing to the failurein the IT system of the banking industry in Bangladesh. Based on the experts' opinions and weight on the specified evaluating criteria, an empirical test was conducted using a rough set theory to produce a framework for the IT system failure factors. In this study, an extended approach involving the integration of rough set theory based flexible Failure Mode and Effect Analysis (FMEA) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has beenapplied to help the managers of the corresponding field to identify the factors responsible for the failure of the IT system in the banking industries and then prioritize them accordingly, for the ease of decision-making.In this research, eleven such failure factors were identified, which were then quantitatively analyzed to facilitate managers in crucial decision-making. It was observed that cyber-attack, database hack risks, server failure, network interruption, broadcast data error, and virus effect were the most significant factors for the failure of the IT system. The framework developed in this research can be utilized to assist in efficient decision-makingin other serviceindustries where IT systems play a key role. To the best of the knowledge, this is the first study thatempirically tested key failure factors of the IT system for the banking sector using an integrated method.
Measurement Uncertainty, Purity, and Entanglement Dynamics of Maximally Entangled Two Qubits Interacting Spatially with Isolated Cavities: Intrinsic Decoherence Effect

In a system of two charge-qubits that are initially prepared in a maximally entangled Bell's state, the dynamics of quantum memory-assisted entropic uncertainty, purity, and negative entanglement are investigated. Isolated external cavity fields are considered in two different configurations: coherent-even coherent and even coherent cavity fields. For different initial cavity configurations, the temporal evolution of the final state of qubits and cavities is solved analytically. The effects of intrinsic decoherence and detuning strength on the dynamics of bipartite entropic uncertainty, purity and entanglement are explored. Depending on the field parameters, nonclassical correlations can be preserved. Nonclassical correlations and revival aspects appear to be significantly inhibited when intrinsic decoherence increases. Nonclassical correlations stay longer and have greater revivals due to the high detuning of the two qubits and the coherence strength of the initial cavity fields. Quantum memory-assisted entropic uncertainty and entropy have similar dynamics while the negativity presents fewer revivals in contrast.
A Deep Learning-based Approach for Real-time Facemask Detection

The COVID-19 pandemic is causing a global health crisis. Public spaces need to be safeguarded from the adverse effects of this pandemic. Wearing a facemask becomes one of the effective protection solutions adopted by many governments. Manual real-time monitoring of facemask wearing for a large group of people is becoming a difficult task. The goal of this paper is to use deep learning (DL), which has shown excellent results in many real-life applications, to ensure efficient real-time facemask detection. The proposed approach is based on two steps. An off-line step aiming to create a DL model that is able to detect and locate facemasks and whether they are appropriately worn. An online step that deploys the DL model at edge computing in order to detect masks in real-time. In this study, we propose to use MobileNetV2 to detect facemask in real-time. Several experiments are conducted and show good performances of the proposed approach (99% for training and testing accuracy). In addition, several comparisons with many state-of-the-art models namely ResNet50, DenseNet, and VGG16 show good performance of the MobileNetV2 in terms of training time and accuracy.
DoTA: Unsupervised Detection of Traffic Anomaly in Driving Videos

Video anomaly detection (VAD) has been extensively studied for static cameras but is much more challenging in egocentric driving videos where the scenes are extremely dynamic. This paper proposes an unsupervised method for traffic VAD based on future object localization. The idea is to predict future locations of traffic participants over a short horizon, and then monitor the accuracy and consistency of these predictions as evidence of an anomaly. Inconsistent predictions tend to indicate an anomaly has occurred or is about to occur. To evaluate our method, we introduce a new large-scale benchmark dataset called Detection of Traffic Anomaly (DoTA)containing 4,677 videos with temporal, spatial, and categorical annotations. We also propose a new VAD evaluation metric, called spatial-temporal area under curve (STAUC), and show that it captures how well a model detects both temporal and spatial locations of anomalies unlike existing metrics that focus only on temporal localization. Experimental results show our method outperforms state-of-the-art methods on DoTA in terms of both metrics. We offer rich categorical annotations in DoTA to benchmark video action detection and online action detection methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The DoTA dataset has been made available at: <uri>https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly</uri></i>
Design and Development of Smart Solar Grass Cutter

From the time immemorial, the sun is the major source of energy for life on earth used for heat and lighting. Nowadays, solar energyhas been known as a renewable energy source. It is an alternative energy to that of fossil fuel and it can be collected from the renewable resources such as sun, wind and hydro. This paper introduces a new development of grass cutter, named as Smart Solar Grass Cutter, by usingsolar irradiance as a primary energy source with the presence of a solar panel. This grass cutter prototype is developed to reduce air pollutant and improve the current design specifically the blade position based on the previous studies. With current technology, this new prototype is designed as remotely controlled grass cutter using Arduino UNO. Smartphone is used as the remote controller. After developing an established prototype, the design analysis is carried out to bevalidate with the theoretical values to ensure that the prototype can be safely used. The Smart Solar Grass Cutter can operate more than two hours when the used battery is fully charged. Based upon the results, the Smart Solar Grass Cutteris reliable with high efficiency of the system compared to the previous studies. Therefore, it can be concluded that the prototype is reliable and environmentally friendly.
The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their linguistic information, and convert them to easy-to-process representations, such as lists of features or embedding-based vector representations. These NLP-based representations are usually used at a later stage as inputs for machine learning techniques or rule-based methods. Thus, requirements representations play a major role in determining the accuracy of different approaches. In this paper, we conducted a survey in the form of a systematic literature mapping (classification) to find out (1) what are the representations used in RE tasks literature, (2) what is the main focus of these works, (3) what are the main research directions in this domain, and (4) what are the gaps and potential future directions. After compiling an initial pool of 2,227 papers, and applying a set of inclusion/exclusion criteria, we obtained a final pool containing 104 relevant papers. Our survey shows that the research direction has changed from the use of lexical and syntactic features to the use of advanced embedding techniques, especially in the last two years. Using advanced embedding representations has proved its effectiveness in most RE tasks (such as requirement analysis, extracting requirements from reviews and forums, and semantic-level quality tasks). However, representations that are based on lexical and syntactic features are still more appropriate for other RE tasks (such as modeling and syntax-level quality tasks) since they provide the required information for the rules and regular expressions used when handling these tasks. In addition, we identify four gaps in the existing literature, why they matter, and how future research can begin to address them.
Netherite

Serverless is a popular choice for cloud service architects because it can provide scalability and load-based billing with minimal developer effort. Functions-as-a-service (FaaS) are originally stateless, but emerging frameworks add stateful abstractions. For instance, the widely used Durable Functions (DF) allow developers to write advanced serverless applications, including reliable workflows and actors, in a programming language of choice. DF implicitly and continuosly persists the state and progress of applications, which greatly simplifies development, but can create an IOps bottleneck. To improve efficiency, we introduce Netherite, a novel architecture for executing serverless workflows on an elastic cluster. Netherite groups the numerous application objects into a smaller number of partitions, and pipelines the state persistence of each partition. This improves latency and throughput, as it enables workflow steps to group commit, even if causally dependent. Moreover, Netherite leverages FASTER's hybrid log approach to support larger-than-memory application state, and to enable efficient partition movement between compute hosts. Our evaluation shows that (a) Netherite achieves lower latency and higher throughput than the original DF engine, by more than an order of magnitude in some cases, and (b) that Netherite has lower latency than some commonly used alternatives, like AWS Step Functions or cloud storage triggers.
Exploring the Impacts of COVID-19 on Digital and Metaverse Games

The coronavirus (COVID-19) pandemic has markedly changed the ways we live and the ways we play games. This is because the situations disrupted by the virus have forced so many people to quarantine and self-isolate. These can lead to many serious problems such as mental and physical health. At the same time, the metaverse, particularly virtual reality and augmented reality, has been very popular in the fields of human-computer interaction and computer science in recent years. Therefore, in this paper, we propose a new research work for exploring recent digital games inspired during the virus outbreak to support people in various aspects, such as entertaining people during the pandemic period, enhancing awareness among people about the virus, suggesting preventive measures against the spread of the virus. Next, more specifically, the impacts of COVID-19 on recent games from virtual and augmented realities are examined. We investigate metaverse games to support people during COVID-19 and the post-pandemic period, including enhancing the healthcare education toolkit, simulating health protection protocols, and supporting healthcare workers.
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context

We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey scene content well but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises 10, 000 freehand scene vector sketches with per point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description. Using our dataset, we study for the first time the problem of fine-grained image retrieval from freehand scene sketches and sketch captions. We draw insights on: (i) Scene salience encoded in sketches using the strokes temporal order; (ii) Performance comparison of image retrieval from a scene sketch and an image caption; (iii) Complementarity of information in sketches and image captions, as well as the potential benefit of combining the two modalities. In addition, we extend a popular vector sketch LSTM-based encoder to handle sketches with larger complexity than was supported by previous work. Namely, we propose a hierarchical sketch decoder, which we leverage at a sketch-specific “pretext” task. Our dataset enables for the first time research on freehand scene sketch understanding and its practical applications. We release the dataset under CC BY-NC 4.0 license: FS-COCO dataset ( https://github.com/pinakinathc/fscoco ).
Phase-change materials for energy-efficient photonic memory and computing

Abstract Neuromorphic algorithms achieve remarkable performance milestones in tasks where humans have traditionally excelled. The breadth of data generated by these paradigms is, however, unsustainable by conventional computing chips. In-memory computing hardware aims to mimic biological neural networks and has emerged as a viable path in overcoming fundamental limitations of the von Neumann architecture. By eliminating the latency and energy losses associated with transferring data between the memory and central processing unit (CPU), these systems promise to improve on both speed and energy. Photonic implementations using on-chip, nonvolatile memories are particularly promising as they aim to deliver energy-efficient, high-speed, and high-density data processing within the photonic memory with the multiplexing advantages of optics. In this article, we overview recent progress in this direction that integrates phase-change material (PCM) memory elements with integrated optoelectronics. We compare performances of PCM devices using optoelectronic programming schemes and show that energy consumption can be significantly reduced to 60 pJ using picosecond (ps) optical pulse programming and plasmonic nanogap devices with a programming speed approaching 1 GHz. With these energy-efficient waveguide memories, concepts of in-memory photonic computing are implemented based on crossbar arrays. Compared with digital electronic accelerators: application-specific integrated circuits (ASICs) and graphics processing units (GPUs), photonic cores promise 1−3 orders higher compute density and energy efficiency, although much more work toward commercialization is still required. Graphical abstract
Contribution of Blockchain in Development of Metaverse

Metaverse is becoming the new standard for social networks and 3D virtual worlds when Facebook officially rebranded to Metaverse in October 2021. Many relevant technologies are used in the metaverse to offer 3D immersive and customized experiences at the user’s fingertips. Despite the fact that the metaverse receives a lot of attention and advantages, one of the most pressing concerns for its users is the safety of their digital material and data. As a result of its decentralization, immutability, and transparency, blockchain is a possible alternative. Our goal is to conduct a comprehensive assessment of blockchain systems in the metaverse to properly appreciate its function in the metaverse. To begin with, the paper introduces blockchain and the metaverse and explains why it’s necessary for the metaverse to adopt blockchain technology. Aside from these technological considerations, this article focuses on how blockchain-based approaches for the metaverse may be used from a privacy and security standpoint. There are several technological challenegs that need to be addressed for making the metaverse a reality. The influence of blockchain on important key technologies with in metaverse, such as Artifical Intelligence, big data and the Internet-of-Things (IoT) is also examined. Several prominent initiatives are also shown to demonstrate the importance of blockchain technology in the development of metaverse apps and services. There are many possible possibilities for future development and research in the application of blockchain technology in the metaverse.