Reviewerly/exHarmony huggingface dataset:
Deep aspect extraction and classification for opinion mining in e‐commerce applications using convolutional neural network feature extraction followed by long short term memory attention model

Abstract Users of e‐commerce websites review different aspects of a product in the comment section. In this research, an approach is proposed for opinion aspect extraction and recognition in selling systems. We have used the users' opinions from the Digikala website ( www.Digikala.com ), which is an Iranian e‐commerce company. In this research, a language‐independent framework is proposed that is adjustable to other languages. In this regard, after necessary text processing and preparation steps, the existence of an aspect in an opinion is determined using deep learning algorithms. The proposed model combines Convolutional Neural Network (CNN) and long‐short‐term memory (LSTM) deep learning approaches. CNN is one of the best algorithms for extracting latent features from data. On the other hand, LSTM can detect latent temporal relationships among different words in a text due to its memory ability and attention model. The approach is evaluated on six classes of opinion aspects. Based on the experiments, the proposed model's accuracy, precision, and recall are 70%, 60%, and 85%, respectively. The proposed model was compared in terms of the above criteria with CNN, Naive Bayes, and SVM algorithms and showed satisfying performance.
Information About Ourselves from Ourselves: Young Users of Wearable Technologies in Secondary School

Abstract Several researchers have recognized the value of self-tracking technologies used to personally obtain data about ourselves. The aim of the present study was to assess the barriers associated with the “technologies of existence”, so-called wearables, including lack of knowledge of these devices, lack of information on their correct use, as well as difficulties regarding data integration and interpretation. To help to overcome these barriers we investigated a project involving two self-tracking activities in an Italian secondary school, performing a quantitative and qualitative analysis of the students using these technologies for educational purposes. Thanks to the project and its contextualized practical and theoretical activities, students were able to become “enhanced users” in terms of their knowledge, autonomy and awareness as regards wearable technologies. Our findings regarding the application of wearable technologies in a scholastic setting may also be step forward in addressing a well-known common pitfall of self-tracking: insufficient scientific rigor. Our quantitative and qualitative analysis showed how the use of wearable devices in educational settings had a range of beneficial effects, above all, eliciting satisfaction among students, but also yielding positive outcomes regarding the acquisition of scientific knowledge perceived both in terms of device applications and data interpretation. In conclusion, our findings may have broad implications in the future design and development of wearable technologies.
SwarmAd: A decentralised content management system

Abstract Online presence is becoming an important part of everyday's life and online communities may represent a significant source of engagement for the elderlies. Nevertheless, many may struggle to be online due to a lack of expertise, and a decentralised architecture may provide a solution by removing intermediaries, such as a webmaster, while not requiring expensive cloud solutions. However, issues concerning accessibility, security, and user experience have to be tackled. The paper focuses mainly on three issues: providing a human‐readable domain, moderating content, and creating a reward system based on user reputation. An architecture is proposed based on Ethereum and Swarm. Smart contracts provide an automated set of rules to handle enterprise registration, content creation, and decision‐making process, while Swarm serves both as distributed storage and the web host. Besides, in combination with Ethereum Name Service, Swarm provides a secure, distributed, and human‐readable point of access to the web interface. The paper also describes an innovative two‐token system where one token is meant to be a trustworthy reputation metre and the other is a spendable coin to get rewards. The final result is a fully decentralised, authenticated and moderated platform where users can aggregate and share their content presentations on the Internet.
Outlier Elimination Technique Using Deletion‐Imputation Iteration for Fault‐Prone Module Detection

In software development, improving the efficiency of a testing process is important to ensure reliability during a limited development period. One approach to improving the testing process is identifying modules that are likely to contain faults (fault‐prone modules) and allocate effort toward resolving them. For this purpose, many fault‐prone module detection models have been proposed in previous studies. However, an appropriate fault‐prone module detection model cannot be constructed if outliers, such as modules with a significantly large number of source code lines and branches, but no faults, are included. In this study, we propose a new outlier elimination technique that creates missing values (deleted values) to complete the data artificially and applies a missing value imputation technique using a regression approach. If the imputed value differs significantly from the actual (recorded) value, the proposed technique treats the values as outliers. We name the proposed technique Outlier Elimination Technique using Deletion‐Imputation Iteration (OEdii), and its performance is verified experimentally. In general, our experimental results are more accurate than the previous outlier elimination techniques in the area under the ROC curve. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Empowering ultrasound image filtering precision by reducing speckles and preserving edge cues

Abstract Ultrasound speckle is an interference pattern that reduces the quality of medical ultrasound images and decreases the capability of interpreting the medical information included in these images. Therefore, speckle suppression is vital whenever ultrasound imaging is used. In this research, an efficient adaptive ultrasound image filter is proposed to reduce ultrasound speckle while maintaining the edge cues in the image. In particular, the pixels that correspond to tissue boundaries are identified by computing the edge map of ultrasound image, and the noisy pixels are determined by calculating the image radiation. The proposed filtering method is designed to adaptively apply different window sizes and bandwidths based on the edge map and image radiation. The proposed method is employed to process a set of ultrasound images and the filtering performance is assessed using a set of broadly accepted filtering metrics. These metrics aim to evaluate the filtering performance, including the capability of the filter to preserve the edges and maintain the image quality. The performance of the proposed filtering method is compared with eight existing image filtering methods. The experimental results indicated that the proposed filtering method enables effective edge preservation and retains image quality. These results suggest the potential of the proposed filtering method to achieve effective speckle suppression in ultrasound images.
Tangent hybrid leader coronavirus herd optimization for the foreground and background image segmentation using multilevel thresholding

ABSTRACTIn this paper, multilevel thresholding with Tangent Hybrid Leader Coronavirus Herd Optimization (THLCHO) is introduced for the image segmentation of foreground and background, The input image is passed to the pre-processing that is done by the adaptive wiener filtering (AWF) technique and the Region of Interest (RoI) extraction. Multilevel thresholding approaches such as threshold and threshold levels are employed to segment the images by entropy-based Kapur, and ostu thresholding, masi entropy and Renyi’s entropy, then the threshold values are optimally selected by THLCHO and considered as the output of the techniques. Secondly, the pre-processed image is subjected to the background and foreground image segmentation utilizing the entropy-based Fuzzy C-Means (EFCM) algorithm, which is assumed as an output of EFCM. Lastly, both outcomes are merged to produce the resultant output. The evaluation metrics employed for Multithresholding- THLCHO, namely, dice co-efficient, peak signal-to-noise-ratio (PSNR) and uniformity measure acquired the maximal values of 0.900, 38.274 dB and 0.918, respectively.KEYWORDS: Foreground-background image segmentationcoronavirus herd immunity optimizer (CHIO)hybrid leader-based optimization (HLBO)tangent search algorithm (TSA)fuzzy C–means (FCM) algorithmadaptive wiener filtering (AWF)Renyi's entropyMultilevel thresholding Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsR. SowmiyaR. Sowmiya received the B.E Degree from Maamallan Institute Of Technology Sriperumbudur in 2015. She received her M.E in Communication System in 2021 in Annamalai University Chidambaram. Currently she is doing her Research studies in Annamalai University Chidambaram. Her area of interests are Image Processing, Wireless Communication And Networking. She has presented papers in many conferences.P. D. SathyaP. D. Sathya received the B.E degree in electronics and communication from Periyar University, in 2003, the M.E degree in applied electronics from Anna university, in 2005, and the ph.D degree from Annamalai University, India, in 2012. She is currently an Assistant professor with the department of Electronics and communication Engineering, Annamalai University.she has 17 years of experience In teaching and research with specialization in signal processing, image and video processing and antenna design. She has published more than 50 research articles in reputed international journals.
Evaluation of an automated microscope using machine learning for the detection of malaria in travelers returned to the UK

Introduction Light microscopy remains a standard method for detection of malaria parasites in clinical cases but training to expert level requires considerable time. Moreover, excessive workflow causes fatigue and can impact performance. An automated microscopy tool could aid in clinics with limited access to highly skilled microscopists, where case numbers are excessive, or in multi-site studies where consistency is essential. The EasyScan GO is an automated scanning microscope combined with machine learning software designed to detect malaria parasites in field-prepared Giemsa-stained blood films. This study evaluates the ability of the EasyScan GO to detect, quantify and identify the species of parasite present in blood films compared with expert light microscopy. Methods Travelers returning to the UK and testing positive for malaria were screened for eligibility and enrolled. Blood samples from enrolled participants were used to make Giemsa-stained smears assessed by expert light microscopy and the EasyScan GO to determine parasite density and species. Blood samples were also assessed by PCR to confirm parasite density and species present and resolve discrepancy between manual microscopy and the EasyScan GO. Results When compared to light microscopy, the EasyScan GO exhibited a sensitivity of 88% (95% CI: 80-93%) and a specificity of 89% (95% CI: 87-91%). Of the 99 samples labelled positive by both, manual microscopy identified 87 as Plasmodium falciparum ( Pf ) and 12 as non- Pf . The EasyScan GO correctly reported Pf for 86 of the 87 Pf samples and non- Pf for 11 of 12 non- Pf samples. However, it failed to distinguish between non- Pf species, reporting all as P. vivax . The EasyScan GO calculated parasite densities were within +/-25% of light microscopy densities for 33% of samples between 200 and 2000 p/µL, falling short of WHO level 1 (expert) manual microscopy competency (50% of samples should be within +/-25% of the true parasitemia). Discussion This study shows that the EasyScan GO can be proficient in detecting malaria parasites in Giemsa-stained blood films relative to expert light microscopy and accurately distinguish between Pf and non- Pf species. Performance at low parasite densities, distinguishing between non- Pf species and accurate quantitation of parasitemia require further development and evaluation.
ANALYZING USER PASSWORDS WORLDWIDE IN TERMS OF CYBER THREATS

Analysing user passwords used around the world is a very broad topic and involves a variety of factors. Password analysis is usually based on detailed studies by security experts, cyber security researchers and encryption experts. Weak passwords, repeated passwords, simple patterns, short passwords, personal information are critical contents that create vulnerabilities and are ignored by users. Attackers or attack tools working for password interception can easily intercept passwords that contain security measures below a certain level, that is, passwords that are not complex. The purpose of this study is to analyse global password usage, to increase users' awareness about password security and to encourage users to use stronger passwords. Small program snippets and new tools have been coded to achieve this goal. In future studies, it is aimed to improve the current application by automating the control of the distance of the texts from each other as a new password suggestion method and hybridising machine learning methods.
Impediments during the execution of a search and seizure warrant for digital information by forensic investigators in South Africa

Purpose This paper aims to identify impediments, discuss impediments and make recommendations for the impediments during the execution of a search and seizure warrant for digital evidence in South African criminal cases. Design/methodology/approach The discussion of this article, the second article of two, focuses on a literature review of international and local impediments identified in case law and published research literature and how it is approached in various jurisdictions. Findings This study found that impediments identified and addressed internationally during the execution of a search and seizure warrant for digital evidence are relevant to South African criminal cases and still need to be addressed during the execution of a search and seizure warrant for digital evidence in South African criminal cases. Research limitations/implications Although searches and seizures for digital evidence are relevant to civil, regulatory and criminal investigations, this study focuses on the search and seizure for digital evidence in criminal matters with an emphasis on the provisions of the Criminal Procedure Act 51 of 1977 and the Cybercrimes Act 19 of 2020. Originality/value The originality of this paper lies in the procedures followed during the physical search and seizure of digital information during the execution of search and seizure warrants for digital information in South Africa. If the South African Police Service follows the recommended procedures, it will contribute to the success of the South African Police Service, which would result in the improved quality of investigations and successful prosecution of crime in South Africa.
Characteristics of quantum channels through evolution of quantumness quantifiers

Non-locality and non-classicality are two aspects of quantum theory which delineates it from classical counterpart. There are several ways by which non-locality and non-classicality can be quantified. In general, we call them as quantifiers of quantumness. Quantum discord and measurement induced non-locality(MIN) are examples of such kind of quantumness quantifiers in composite quantum systems. In this paper, we will geometrically investigate the evolution of these quantifiers under different quantum channels. The motivation is to characterize quantum channels, analytically, through the evolution of quantumness quantifiers.
The perceived credibility of human-like social robots: virtual influencers in a luxury and multicultural context

Purpose Social robots are invading our daily lives. Recently, thanks to artificial intelligence, humanoid social robots have been developed and influence individuals on social media. This paper aims to understand the perception of luxury consumers regarding human-like virtual influencers (VI) in a multicultural context. Design/methodology/approach Conducting a qualitative method, the authors interviewed 32 Chinese and French consumers of luxury products from the Gen-Z and millennial generation about their perception of human influencers and human-like VI after following them on social media specific platforms. Findings Using source credibility theory, this research unveils the different ways in which consumers perceive human-like VI according to their physical or content attractiveness, expertise, similarities and trustworthiness. The results suggest that the perception of human-like VI by millennials and Gen-Z is closely related to their cultural setting and their familiarity of the technology in a luxury context. Originality/value Considering Gen-Z and millennials' willing for para-social interactions and given the importance to the credibility of the source or the emotions displayed by VI, the intercultural empirical setting of this study introduces the ambivalence of the perception of social robots versus human-like influencers in a luxury digital context.
The Path of Information Philosophy to Solve the Defect of Einstein’s Integration of Space–Time

Einstein’s concept of spatiotemporal integration only involves the spatialization of time without the temporization of space. Therefore, there is an inevitable and important issue, which is the need for time to cancel its own characteristics to adapt to the rules of space, leading to the dilemma of the antinomy between time and space, which is both integrated and not fully integrated. This kind of integration is not only incomplete spatiotemporal integration but also faces the situation of being misinterpreted in the meaning of time itself so that the meaning of time itself tends to disappear, and space cannot balance the dual meanings of time and space. The solution of information philosophy proposes a possibility to solve this problem. In the sense of information evolution, the spatialization of time and the spatialization of space are carried out simultaneously, thus achieving complete spatiotemporal integration.
Reflections on Human Subjectivity in the Information Society from the Body Philosophy

In the information society, people can think about the existence of subjectivity through the philosophy of the body. Body philosophy breaks the dominance of consciousness and constructs human subjectivity from the body, so that people can realize the unity of body and mind through the body. People understand each other through physical perception, behavior, and speech. Finally, body philosophers such as Merleau-Ponty explained the intercorporate as “flesh” from the dimension of ontology, and the homology based on “flesh” completely broke the barrier of the dichotomy between subject and object and established an inseparable connection with the world. Body philosophy solves the problem of how to deal with the relationship between humans and others and nature in the information society, which helps people not to lose themselves in the information society and determine their own existence and value in the world.
Controlling purity, indistinguishability, and quantum yield of an incoherently pumped two-level system by spectral filters

Dephasing processes significantly impact the performance of deterministic single-photon sources. Dephasing broadens the spectral line and suppresses the indistinguishability of the emitted photons, which is undesirable for many applications, primarily for quantum computing. We consider a light emitted by a two-level system with a pulsed incoherent pump in the presence of the spectral filter. The spectral filter allows control of the second-order autocorrelation function, indistinguishability, and quantum yield. We show that narrow spectral filters can increase the indistinguishability of the emitted light while undermining the quantum yield. The influence of the spectral filter on the second-order autocorrelation function depends on the duration of the pump. When the pump pulse is long compared to the lifetime of the two-level system, the narrow spectral filters lead to a rapid increase in the second-order autocorrelation function. In this limit, the statistics of the light from the two-level system inherit the statistics of the incoherent pump. In the case of the short duration of the pump pulse, it is possible to preserve single-photon properties to some degree for the sublifetime width of the spectral filter. Moreover, the single-photon properties of the light manifest themselves differently, depending on the response time of the quantum system affected by this light. In particular, in the case of long response time, the spectral filter with a sublifetime width can provide the near-zero second-order autocorrelation function.
Taming over-smoothing representation on heterophilic graphs

Graph Neural Networks (GNNs) have shown expressive modeling ability on graphs. An inherent assumption is that those connected graph nodes exhibit strong homophily, passing consistent label information into the associated central node representation. However, heterophilic graphs may hold inconsistent labels for those connected nodes, degenerating the performance of the typical message passing and exacerbating its over-smoothing issue. In this paper, to tame heterophilic graphs for GNNs, a novel label estimation-based message passing scheme is presented, which can further relieve the over-smoothing and lead the learned representations to be more distinguishable on graphs with either homophily or heterophily. Specifically, we optimize the edge weights of the graph by invoking the label estimation with the goal of returning a more effective adjacency matrix. With this matrix, a novel message passing scheme is presented, aggregating neighbor information with consistent labels to their central nodes. Meanwhile, a symmetric cross entropy is also employed to mitigate negative effects from noisy labels. With such guarantees in our new message passing, a uniform framework termed Label Estimation-based GNN (LE-GNN) is finally established. Extensive experiments demonstrate the ability of our model to prevent over-smoothing and show its effectiveness for node classification on both homophilic and heterophilic graphs.