Reviewerly/exHarmony huggingface dataset:
Making sense of tweets using sentiment analysis on closely related topics

Microblogging has taken a considerable upturn in recent years, with the growth of microblogging websites like Twitter people have started to share more of their opinions about various pressing issues on such online social networks. A broader understanding of the domain in question is required to make an informed decision. With this motivation, our study focuses on finding overall sentiments of related topics with reference to a given topic. We propose an architecture that combines sentiment analysis and community detection to get an overall sentiment of related topics. We apply that model on the following topics: shopping, politics, covid19 and electric vehicles to understand emerging trends, issues and its possible marketing, business and political implications.
Performance Analysis of Cryptographic Hash Functions Suitable for Use in Blockchain

A blockchain, or in other words a chain of transaction blocks, is a distributed database that maintains an ordered chain of blocks that reliably connect the information contained in them. Copies of chain blocks are usually stored on multiple computers and synchronized in accordance with the rules of building a chain of blocks, which provides secure and change-resistant storage of information. To build linked lists of blocks hashing is used. Hashing is a special cryptographic primitive that provides one-way, resistance to collisions and search for prototypes computation of hash value (hash or message digest). In this paper a comparative analysis of the performance of hashing algorithms that can be used in modern decentralized blockchain networks are conducted. Specifically, the hash performance on different desktop systems, the number of cycles per byte (Cycles/byte), the amount of hashed message per second (MB/s) and the hash rate (KHash/s) are investigated. The comparative analysis of different hashing algorithms allows us to choose the most suitable candidates for building decentralized systems type of blockchain.
Social Media Identity Deception Detection

Social media have been growing rapidly and become essential elements of many people’s lives. Meanwhile, social media have also come to be a popular source for identity deception. Many social media identity deception cases have arisen over the past few years. Recent studies have been conducted to prevent and detect identity deception. This survey analyzes various identity deception attacks, which can be categorized into fake profile, identity theft, and identity cloning. This survey provides a detailed review of social media identity deception detection techniques. It also identifies primary research challenges and issues in the existing detection techniques. This article is expected to benefit both researchers and social media providers.
Competitive photonic neural networks

Photonics offers high hopes for next-generation neural network processors. Now it has been shown that even entirely using off-the-shelf photonics allows surpassing speed and energy efficiency of cutting-edge GPUs.
Smartpathk: a platform for teaching glomerulopathies using machine learning

Abstract Background With the emergence of the new coronavirus pandemic (COVID-19), distance learning, especially that mediated by information and digital communication technologies, has been adopted in all areas of knowledge and at all levels, including medical education. Imminently practical areas, such as pathology, have made traditional teaching based on conventional microscopy more flexible through the synergies of computational tools and image digitization, not only to improve teaching-learning but also to offer alternatives to repetitive and exhaustive histopathological analyzes. In this context, machine learning algorithms capable of recognizing histological patterns in kidney biopsy slides have been developed and validated with a view to building computational models capable of accurately identifying renal pathologies. In practice, the use of such algorithms can contribute to the universalization of teaching, allowing quality training even in regions where there is a lack of good nephropathologists. The purpose of this work is to describe and test the functionality of SmartPath k , a tool to support teaching of glomerulopathies using machine learning. The training for knowledge acquisition was performed automatically by machine learning methods using the J48 algorithm to create a computational model of an appropriate decision tree. Results An intelligent system, SmartPath k , was developed as a complementary remote tool in the teaching-learning process for pathology teachers and their students (undergraduate and graduate students), showing 89,47% accuracy using machine learning algorithms based on decision trees. Conclusion This artificial intelligence system can assist in teaching renal pathology to increase the training capacity of new medical professionals in this area.
An optimal gradient method for smooth strongly convex minimization

We present an optimal gradient method for smooth strongly convex optimization. The method is optimal in the sense that its worst-case bound on the distance to an optimal point exactly matches the lower bound on the oracle complexity for the class of problems, meaning that no black-box first-order method can have a better worst-case guarantee without further assumptions on the class of problems at hand. In addition, we provide a constructive recipe for obtaining the algorithmic parameters of the method and illustrate that it can be used for deriving methods for other optimality criteria as well.
On the Development and Performance Evaluation of Improved Radial Basis Function Neural Networks

This article deals with the development of four modified radial basis function neural network (RBFNN) models. The corresponding learning algorithms associated with the updating of internal parameters of the models are derived. The conventional inputs are used in the first and second modified RBFNN models (models 3 and 4) whereas exponential nonlinear inputs are used in the fifth and sixth RBFNN models to provide additional nonlinearity for achieving a better solution of nonlinear classification, and direct and inverse modeling problems. To assess and compare the performance potentiality of the proposed four new RBFNN models, one classification problem, one direct modeling problem, and one inverse modeling problem are solved through computer simulation-based experiments. For comparison and to assign the performance rank of each of the four modified RBFNN models, two conventional and commonly used RBFNN models (models 1 and 2) are also simulated. To access the performance of different models during the training phase of Examples 1 and 2, the root mean-square error (RMSE) value, mean absolute deviation (MAD), and the number of iterations required to achieve convergence are obtained. For the third example, only the first two performance measures are found. During the testing or validation phase, the output responses of the different models of Example 2 are compared with the desired response analysis. For Example 3, the bit-error rate (BER) plots are compared. The observation of all the results demonstrates consistent ranks of all models in the case of all three examples. It is, in general, found that the ranks of the models 1–6 are 6, 4, 3, 2, 5, and 1, respectively. In essence, in terms of all performance measures, model M-6 with an exponential version of inputs with weights on both layers occupies the first position whereas model M-4 with conventional inputs as the second position.
Digital media investigators: challenges and opportunities in the use of digital forensics in police investigations in England and Wales

Purpose In-depth knowledge about specific national approaches to using digital evidence in investigations is scarce. A clearer insight into the organisational barriers and professional challenges experienced, alongside a more detailed picture of how digital evidence can help police investigations are required to empirically substantiate claims about how digital technologies are changing the face of criminal investigations. The paper aims to focus on the introduction of digital media investigators to support investigating officers with the collection and interpretation of digital evidence. Design/methodology/approach Drawing on ethnographic and interview data collected as part of an Economic and Social Research Council-funded project on the application of digital forensics expertise in policing in England and Wales, this paper examines the changing face of investigations in relation to escalating digital demand. Findings The analysis presents the national and regional organisational parameters of deploying digital expertise in criminal investigation and examines some of the challenges of being a digital media investigator (DMI). Through testimonies from DMIs, digital forensic practitioners, investigating and senior officers and forensic managers, the analysis explores the organisational tensions in the collection, processing, interpretation and use of information from digital devices for evidential purposes. Research limitations/implications The paper offers an empirical basis for the comparative study of how the DMI role has been implemented by law enforcement agencies and its fit within broader institutional considerations and processes. Practical implications The development of the DMI role has raised questions about the supply of digital expertise, especially to volume crime investigations, and tensions around occupational divisions between scientific and operational units. Social implications The findings show that while the introduction of the DMI role was much needed, the development of this valuable provision within each force and the resources available require sustained and coordinated support to protect these professionals and retain their skills. Originality/value This study contributes to the growing sociological and criminological literature with an ethnographically based perspective into the organisational and occupational tensions in the identification and processing of digital evidence in England and Wales.
Using virtual reality treadmill as a locomotion technique in a navigation task: Impact on user experience – case of the KatWalk

This paper reports empirical results from a study on the impact of the KatWalk virtual reality omnidirectional treadmill on the user experience. The omnidirectional treadmill is a mechanical device, that allows the user to perform locomotive motion in any direction, allowing for 360 degrees of horizontal movement. This locomotion method appeared as a potential solution of the locomotion problem in virtual reality after the emergence and the democratization of the new generation of head-mounted display systems such as HTC Vive and Oculus Rit. However, little empirical work has been done to test the efficiency of the tool as a locomotion technique in virtual environments. Twenty-four subjects (13 females, mean age = 30.38, SD = 6.32) participated in the experiment. Results showed that the tool is suitable for traveling in virtual environments seen through head-mounted display systems, whether they are composed of plan or bumpy ground, with or without obstacles.
Techno-economic and environmental assessment of utilizing campus building rooftops for solar PV power generation

The academic institute, a huge consumer of energy, is still depending upon the grid and/or conventional fuel despite having potential areas of a solar PV installation. No such study has been reported in Nepal which has techno-economically discussed the potential use of academic institute rooftops for solar PV installation. In this study, we have investigated the techno-economic and environmental assessment of a 1 megawatt (MW) rooftop solar PV using PVsyst for Purwanchal Campus, Nepal. It has been estimated the plant will produce about 1,660 MWh of usable AC electricity annually and out of which 95% can be supplied to the grid. The remaining can fulfill the demand of the campus at sunshine hours. For this, a capital investment of about USD 961,404 is needed for the plant with an annual operating cost of USD 8,008. This project gives a 190% return of investment within its life of 25 years with a Levelized cost of energy (LCOE) of 0.069 USD/kWh. The performance ratio and payback period of the project are calculated at about 0.765 and 8.4 years, respectively. The system will produce about 97% less emission than that of diesel generator, which is currently being used for power backup. It is concluded that a 1 MW grid-connected solar PV system is feasible for the studied campus.
Conditional generation of medical images via disentangled adversarial inference

Synthetic medical image generation has a huge potential for improving healthcare through many applications, from data augmentation for training machine learning systems to preserving patient privacy. Conditional Adversarial Generative Networks (cGANs) use a conditioning factor to generate images and have shown great success in recent years. Intuitively, the information in an image can be divided into two parts: 1) content which is presented through the conditioning vector and 2) style which is the undiscovered information missing from the conditioning vector. Current practices in using cGANs for medical image generation, only use a single variable for image generation (i.e., content) and therefore, do not provide much flexibility nor control over the generated image. In this work we propose DRAI—a dual adversarial inference framework with augmented disentanglement constraints—to learn from the image itself, disentangled representations of style and content, and use this information to impose control over the generation process. In this framework, style is learned in a fully unsupervised manner, while content is learned through both supervised learning (using the conditioning vector) and unsupervised learning (with the inference mechanism). We undergo two novel regularization steps to ensure content-style disentanglement. First, we minimize the shared information between content and style by introducing a novel application of the gradient reverse layer (GRL); second, we introduce a self-supervised regularization method to further separate information in the content and style variables. For evaluation, we consider two types of baselines: single latent variable models that infer a single variable, and double latent variable models that infer two variables (style and content). We conduct extensive qualitative and quantitative assessments on two publicly available medical imaging datasets (LIDC and HAM10000) and test for conditional image generation, image retrieval and style-content disentanglement. We show that in general, two latent variable models achieve better performance and give more control over the generated image. We also show that our proposed model (DRAI) achieves the best disentanglement score and has the best overall performance.
FAST CAT

Descriptive and empirical sciences, such as History, are the sciences that collect, observe and describe phenomena in order to explain them and draw interpretative conclusions about influences, driving forces and impacts under given circumstances. Spreadsheet software and relational database management systems are still the dominant tools for quantitative analysis and overall data management in these these sciences, allowing researchers to directly analyse the gathered data and perform scholarly interpretation. However, this current practice has a set of limitations, including the high dependency of the collected data on the initial research hypothesis, usually useless for other research, the lack of representation of the details from which the registered relations are inferred, and the difficulty to revisit the original data sources for verification, corrections or improvements. To cope with these problems, in this paper we present FAST CAT, a collaborative system for assistive data entry and curation in Digital Humanities and similar forms of empirical research. We describe the related challenges, the overall methodology we follow for supporting semantic interoperability, and discuss the use of FAST CAT in the context of a European (ERC) project of Maritime History, called SeaLiT, which examines economic, social and demographic impacts of the introduction of steamboats in the Mediterranean area between the 1850s and the 1920s.
Development of a Virtual Ecological Environment for Learning the Taipei Tree Frog

The learning objectives of environmental education emphasize investigation in real life to enhance students’ skills and experiences in solving practical problems. This study used the virtual reality (VR) technology to develop a virtual ecological environment for learning about the Taipei tree frog, supported by situated learning and game-based learning design to enhance students’ learning interest and motivation. Users can wear the head-mounted display (HMD) to explore the virtual environment for learning the Taipei tree frog’s ecological behavior, such as foraging and mating as well as its habitats and predators. A teaching experiment was conducted to investigate students’ learning effectiveness and the senses of presence and anxiety after using the virtual ecological environment. The experimental group (wearing the HMD) contained 40 students, the control group (using the desktop VR) contained 40 students, and both groups were used as samples to learn about the Taipei tree frog. The results indicated that using HMD VR and desktop VR could both enhance learning achievements, but the learning effectiveness of the former was significantly higher than that of the latter. The levels of anxiety for both groups were about the same, but the level of presence for the experimental group was higher than that of the control group because the HMD VR provided a more immersive experience than the desktop VR. The virtual ecological environment can save the time and effort of travelling to the natural habitat for observing the Taipei tree frog, and the design of role-playing game (RPG) can enhance learners’ interest and motivation. Therefore, it is a useful tool for promoting environmental education.
Characterizing key agents in the cryptocurrency economy through blockchain transaction analysis

Abstract The cryptocurrency economy provides a comprehensive digital trace of human economic behavior: almost all cryptocurrency users’ activities are faithfully recorded in transactions on public blockchains. However, the user identifiers in the transaction records, i.e., blockchain addresses, are anonymous. That is, they cannot be associated with any real “off-chain” identify of actual users. Nonetheless, identifying the economic roles of the addresses from their past behaviors is still feasible. This paper analyzes Ethereum token transactions, characterizes key economic agents’ behavior from their transaction patterns, and explores their identifiability through interpretable machine learning models. Specifically, six types of most active economic agents are considered, including centralized cryptocurrency exchanges, decentralized exchanges, cryptocurrency wallets, token issuers, airdrop services, and gaming services. Transaction patterns such as trading volume, transaction tempo, and structural properties of transaction networks are defined for individual blockchain addresses. The results showed that cryptocurrency exchanges and online wallets have signature behavior patterns and hence can be accurately distinguished from other agents. Token issuers, airdrop services, and gaming services can sometimes be confused. Moreover, transaction networks’ features provide the richest information in the economic agent’s identification.
Effectiveness of a Cloud-Based Telepathology System in China: Large-Sample Observational Study

Whole-slide imaging allows the entire slide to be viewed in a manner that simulates microscopy; therefore, it is widely used in telepathology. However, managing the large digital files needed for whole-slide imaging is difficult. To solve this problem, we set up the Chinese National Cloud-Based Telepathology System (CNCTPS). CNCTPS has been running for more than 4 years and has accumulated a large amount of data.The main purpose of this study was to comprehensively evaluate the effectiveness of the CNCTPS based on a large sample. The evaluation indicators included service volume, turnaround time, diagnosis accuracy, and economic benefits.Details of 23,167 cases submitted to the CNCTPS from January 2016 to December 2019 were collected to analyze the service volume, turnaround time, and economic benefits. A total of 564 patients who visited the First Affiliated Hospital of Zhengzhou University and obtained final diagnoses were followed up to analyze the diagnostic accuracy of the CNCTPS.From 2016 to 2019, the service volume of the CNCTPS increased from 2335 to 9240, and the number of participating hospitals increased from 60 to 74. Consultation requests from county-level hospitals accounted for 86.57% (20,287/23,167). A total of 17,495 of 23,167 cases (75.52%) were confirmed, including 12,088 benign lesions, 5217 malignant lesions, and 190 borderline lesions. Of the cases, 3.85% (893/23,167) failed to be diagnosed for reasons such as poor slice quality and incomplete sampling. The median turnaround time was 16.93 hours and was shortened yearly (between 2018 and 2019: adjusted P=.01; other groups: adjusted P<.001); 82.88% cases were diagnosed in 48 hours. There was a discrepancy between the diagnosis and final diagnosis for 11 cases, including 4 false-positive cases and 7 false-negative cases. The sensitivity and specificity were 97.66% and 98.49%, respectively. The diagnostic accuracy of the system was 98.05%, with no statistical difference from the final diagnosis in the hospital (P=.55). By using this system, a total of US $300,000 was saved for patients every year.The novel cloud-based telepathology system has the potential to relieve the shortage of pathologists in primary hospitals. It can also simultaneously reduce medical costs for patients in China. It should, therefore, be further promoted to enhance the efficiency, quantity, and quality of telepathology diagnoses.
Linearly-implicit schemes for collisions in musical acoustics based on energy quadratisation

Collision modelling represents an active field of research in musical acoustics. Common examples of collisions include the hammer-string interaction in the piano, the interaction of strings with fretboards and fingers, the membrane-wire interaction in the snare drum, reed-beating effects in wind instruments, and others. At the modelling level, many current approaches make use of conservative potentials in the form of power-laws, and discretisations proposed for such models rely in all cases on iterative root-finding routines. Here, a method based on energy quadratisation of the nonlinear collision potential is proposed. It is shown that there exists a suitable discretisation of such a model that may be resolved in a single iteration, while guaranteeing stability via energy conservation. Applications to the case of lumped as well as fully distributed systems will be given, using both finite-difference and modal methods.
HyQuas

Quantum computing has shown its strong potential in solving certain important problems. Due to the intrinsic limitations of current real quantum computers, quantum circuit simulation still plays an important role in both research and development of quantum computing. GPU-based quantum circuit simulation has been explored due to GPU's high computation capability. Despite previous efforts, existing quantum circuit simulation systems usually rely on a single method to improve poor data locality caused by complex quantum entanglement. However, we observe that existing simulation methods show significantly different performance for different circuit patterns. The optimal performance cannot be obtained only with any single method.