Reviewerly/exHarmony huggingface dataset:
When Wireless Technologies Faces COVID-19: via Apps using to Combat the Pandemic and Save the Economy

As the number of smartphone users grows, the smartphone's function in healthcare has evolved from a device that is used to arrange doctor appointments via the internet rather than the traditional approach. Mobile apps are a convenient way to track and collect data in order to combat the spread of COVID-19. To ensure that the right to privacy and civil liberties are maintained, we report on our investigation of 50 COVID -19- related apps, including their access to and use of personally identifiable information. Reservations are made at the doctor over the Internet, and an appointment is arranged for the visit and medical examination at a certain time, resulting in a reduction in the spread of the virus, especially in pandemic conditions such as Covid-19. Additionally, this provides a service for saving data and health files in the cloud on the Internet to retrieve information when needed, and not wasting these files and health papers while providing protection for them in terms of patient privacy. Designing an application (App) functioning on IOS and Android systems which works in mobile devices easily to provide the ability to book a doctor appointment from App. The App is a link between the patient and the doctor to reduce the patient’s presence in health centers and medical facilities which are good centers for transmission of infection, especially in COVID-19. During the design of app, we have considered the low speed of data throughput especially for IRAQ.
LiCAM: Long-Tailed Instance Segmentation with Real-Time Classification Accuracy Monitoring

Recently, deep neural networks have achieved remarkable progress in class balancing instance segmentation. However, most applications in the real world have a long-tailed distribution, i.e., limited training examples in the majority of classes. The long-tailed challenge leads to a catastrophic drop in instance segmentation because the gradient of the head classes suppresses the gradient of the tail classes, leading to a bias towards the major classes. We propose LiCAM, a novel framework for long-tailed segmentation. It features an adaptive loss function named Moac Loss, which is adjustable during the training according to the monitored classification accuracy. LiCAM also cooperates with an oversampling technique named RFS, which alleviates the severe imbalance between head and tail classes. We conducted extensive experiments on the LVIS v1 dataset to evaluate LiCAM. With a coherent end-to-end training pipeline, LiCAM significantly outperforms other baselines.
Rare minerals of noble metals in the collection of the Mining Museum: new data

Modern analytical methods (optical and electron microscopy, X-ray microanalysis) were used to study the unique samples of sulfide ores from the Norilsk ore field from the Mining Museum collections of Saint Petersburg Mining University. Samples containing rare minerals of silver and platinum-group metals (sobolevskite, urvantsevite, sperrylite, argentopentlandite, froodite, kotulskite, and others) were studied. The chemical composition, grain sizes, aggregates, and mineral associations of more than ten noble metal minerals have been refined. The efficiency of combining various methods of electron microscopy and X-ray microanalysis for studying samples of this type is shown. The results of the work made it possible to obtain high-quality images of rare minerals, to detail information on museum objects, and to compile their scientific description. The conducted research showed the relevance of studying museum objects from known deposits of complex genesis and mineral composition in order to find and describe the samples with rare minerals.
Repeated experience or a virtual nose to reduce simulator sickness? – Investigating prediction of the sensorial conflict theory and the rest-frame hypothesis in two virtual games

This paper presents two experiments measuring the impact of a virtual nose on simulator sickness and game experience in two virtual reality games. The pilot study contrasts a design implementation from the sensorial conflict theory (habituation) to one design suggestion from the rest-frame hypothesis (virtual nose). Habituation contributed significantly to the reduction of simulator sickness while the low fidelity prototype of the virtual nose did not. The main study presents an improved nose model and investigated the impact of awareness toward the virtual nose. The improved nose indicated that the usage of a virtual nose reduces simulator sickness while it does not affect the game experience. Further results showed no significant impact of the rest-frame's awareness, neither on simulator sickness nor on the game experience. In sum, the outcome of the study supports the thesis that a rest-frame in form of a stable virtual nose can be used in demanding virtual reality applications to reduce simulator sickness while not affecting the game experience.
Provenance-Based SPARQL Query Formulation

We present in this paper a novel solution for assisting users in formulating SPARQL queries. The high-level idea is that users write "semi-formal SPARQL queries", namely, queries whose structure resembles SPARQL but are not necessarily grounded to the schema of the underlying Knowledge Base (KB) and require only basic familiarity with SPARQL. This means that the user-intended query over the KB may differ from the specified semi-formal query in its structure and query elements. We design a novel framework that systematically and gradually refines the query to obtain candidate formal queries that do match the KB. Crucially, we introduce a formal notion of provenance tracking this query refinement process, and use the tracked provenance to prompt the user for fine-grained feedback on parts of the candidate query, guiding our search. Experiments on a diverse query workload with respect to both DBpedia and YAGO show the usefulness of our approach.
Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords, account identifiers, bank details, etc.In general, these kinds of cyberattacks are made at users through phone calls, emails, or instant messages.The anti-phishing techniques, currently under use, are mainly based on source code features that need to scrape the webpage content.In third party services, these techniques check the classification procedure of phishing Uniform Resource Locators (URLs).Even though Machine Learning (ML) techniques have been lately utilized in the identification of phishing, they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing offenses.The tremendous growth and evolution of Deep Learning (DL) techniques paved the way for increasing the accuracy of classification process.In this background, the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification (HSOHDL-PDC) model.The presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website URLs.In addition, SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no importance.Moreover, a hybrid Convolutional Neural Network-Long Short Term Memory (HCNN-LSTM) technique is also applied for identification and classification of phishing.The hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved outcomes.The performance of the proposed HSOHDL-PDC model was validated using CMC, 2022, vol.73, no.3 different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.
MCBC-SMOTE: A Majority Clustering Model for Classification of營mbalanced Data

Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms.In supervised learning, dealing with the problem of class imbalance is still considered to be a challenging research problem.Various machine learning techniques are designed to operate on balanced datasets; therefore, the state of the art, different undersampling, over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets, but highly skewed datasets still pose the problem of generalization and noise generation during resampling.To overcome these problems, this paper proposes a majority clustering model for classification of imbalanced datasets known as MCBC-SMOTE (Majority Clustering for balanced Classification-SMOTE).The model provides a method to convert the problem of binary classification into a multi-class problem.In the proposed algorithm, the number of clusters for the majority class is calculated using the elbow method and the minority class is over-sampled as an average of clustered majority classes to generate a symmetrical class distribution.The proposed technique is cost-effective, reduces the problem of noise generation and successfully disables the imbalances present in between and within classes.The results of the evaluations on diverse real datasets proved to provide better classification results as compared to state of the art existing methodologies based on several performance metrics.
Optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using Whale Algorithm

The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector.The capability of online fuzzy tracking systems is maximum power, resistance to radiation and temperature changes, and no need for external sensors to measure radiation intensity and temperature.However, the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing.The controller used in the maximum power point tracking (MPPT) circuit must be able to adapt to the new radiation conditions.Therefore, in this paper, to more accurately track the maximum power point of the solar system and receive more electrical power at its output, an adaptive fuzzy control was proposed, the parameters of which are optimized by the whale algorithm.The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm (P&O) method, which is a practical and high-performance method.To evaluate the performance of the proposed algorithm, the particle swarm algorithm optimized the adaptive fuzzy controller.The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system.Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method, which distinguishes it from other methods.On the other hand, the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm, which confirms the higher accuracy of the proposed algorithm.
Accelerated Parallel Hybrid GPU/CPU Hash Table Queries with String Keys

GPUs can accelerate hash tables for fast storage and look-ups utilizing their massive parallelism. State-of-the-art GPU hash tables use keys with fixed length like integers for optimal performance. Because strings are often used for structures like dictionaries, it is useful to support keys with variable length as well. Modern GPUs enable the combination of CPU and GPU compute power and we propose a hybrid approach, where keys on the GPU have a maximum length and longer keys are processed on the CPU. Therefore we develop a GPU accelerated approach of robin-map and libcuckoo based on string keys. We use OpenCL for GPU acceleration and threads for the CPU. Furthermore, we integrate the GPU approach into our benchmark framework $$H^2$$ and evaluate it against the CPU variants and the GPU approach adapted for the CPU. We evaluated our approach in the hybrid context by using longer keys on CPU and shorter keys on GPU. In comparison to the original libcuckoo algorithm our GPU approach achieves a speed-up of 2.1 and in comparison to the robin-map a speed-up of 1.5. For hybrid workloads our approach is efficient if long keys are processed on the CPU and short keys are processed on the GPU. By processing a mixture of 20% long keys on CPU and 80% short keys on GPU our hybrid approach has a 40% higher throughput than the CPU only approach.
CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition

Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding.Such sceneunderstanding task is a demanding part of several technologies, like augmented reality-based scene integration, robotic navigation, autonomous driving, and tourist guide.Incorporating visual information in contextually unified segments, convolution neural networks-based approaches will significantly mitigate the clutter, which is usual in classical frameworks during scene understanding.In this paper, we propose a convolutional neural network (CNN) based segmentation method for the recognition of multiple objects in an image.Initially, after acquisition and preprocessing, the image is segmented by using CNN.Then, CNN features are extracted from these segmented objects, and discrete cosine transform (DCT) and discrete wavelet transform (DWT) features are computed.After the extraction of CNN features and computation of classical machine learning features, fusion is performed using a fusion technique.Then, to select the minimal set of features, genetic algorithm-based feature selection is used.In order to recognize and understand the multi-objects in the scene, a neuro-fuzzy approach is applied.Once objects in the scene are recognized, the relationship between these objects is examined by employing the object-to-object relation approach.Finally, a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the image.The experimental results over complex scene datasets including SUN Red Green Blue-Depth (RGB-D) and Cityscapes' demonstrated a remarkable performance.
CoK: A Survey of Privacy Challenges in Relation to Data Meshes

The growing volumes of data that appear on multiple distributed platforms raise the question of how to compose data meshes that can be published and/or shared safely amongst multiple cooperating parties. Data meshes are composed of subsets (or whole sets) of data repositories that are owned by autonomous parties. This raises new challenges in terms of guaranteeing privacy across various data mesh compositions. In this paper, we present a survey of the issues that emerge in guaranteeing the privacy of distributed mesh data. We discuss the limitations of existing solutions in handling personal data privacy with respect to meshed data. Finally, we postulate that identifying personal data in such datasets must be handled with a performance efficient algorithm that can determine (on-the-fly), potential linkages across various data repositories, that could be exploited to subvert privacy.
An Improved Transfer-Learning for Image-Based Species Classification of Protected Indonesians Birds

This research proposed an improved transfer-learning bird classification framework to achieve a more precise classification of Protected Indonesia Birds (PIB) which have been identified as the endangered bird species.The framework takes advantage of using the proposed sequence of Batch Normalization Dropout Fully-Connected (BNDFC) layers to enhance the baseline model of transfer learning.The main contribution of this work is the proposed sequence of BNDFC that can be applied to any Convolutional Neural Network (CNN) based model to improve the classification accuracy, especially for image-based species classification problems.The experiment results show that the proposed sequence of BNDFC layers outperform other combination of BNDFC.The addition of BNDFC can improve the model's performance across ten different CNN-based models.On average, BNDFC can improve by approximately 19.88% in Accuracy, 24.43% in F-measure, 17.93% in G-mean, 23.41% in Sensitivity, and 18.76% in Precision.Moreover, applying fine-tuning (FT) is able to enhance the accuracy by 0.85% with a smaller validation loss of 18.33% improvement.In addition, MobileNetV2 was observed to be the best baseline model with the lightest size of 35.9 MB and the highest accuracy of 88.07% in the validation set.
Design of a Peer-to-Peer Energy Trading Platform Using Multilayered Semi-Permissioned Blockchain

A secured and scalable Peer-to-Peer (P2P) energy trading platform can facilitate the integration of renewable energy and thus contribute to building sustainable energy infrastructure.The decentralized architecture of blockchain makes it a befitting candidate to actualize an efficient P2P energy trading market.However, for a sustainable and dynamic blockchain-based P2P energy trading platform, few critical aspects such as security, privacy and scalability need to be addressed with high priority.This paper proposes a blockchain-based solution for energy trading among the consumers which ensures the systems' security, protects users' privacy, and improves the overall scalability.More specifically, we develop a multilayered semi-permissioned blockchain-based platform to facilitate energy transactions.The practical byzantine fault tolerant algorithm is employed as the underlying consensus for verification and validation of transactions which ensures the system's tolerance against internal error and malicious attacks.Additionally, we introduce the idea of quality of transaction (QoT)-a reward system for the participants of the network that eventually helps determine the participant's eligibility for future transactions.The resiliency of the framework against the transaction malleability attack is demonstrated with two uses cases.Finally, a qualitative analysis is presented to indicate the system's usefulness in improving the overall security, privacy, and scalability of the network.
Finding Asymptomatic Spreaders in a COVID-19 Transmission Network by Graph Attention Networks

In the COVID-19 epidemic the mildly symptomatic and asymptomatic infections generate a substantial portion of virus spread; these undetected individuals make it difficult to assess the effectiveness of preventive measures as most epidemic prevention strategies are based on the detected data. Effectively identifying the undetected infections in local transmission will be of great help in COVID-19 control. In this work, we propose an RNA virus transmission network representation model based on graph attention networks (RVTR); this model is constructed using the principle of natural language processing to learn the information of gene sequence and using a graph attention network to catch the topological character of COVID-19 transmission networks. Since SARS-CoV-2 will mutate when it spreads, our approach makes use of graph context loss function, which can reflect that the genetic sequence of infections with close spreading relation will be more similar than those with a long distance, to train our model. Our approach shows its ability to find asymptomatic spreaders both on simulated and real COVID-19 datasets and performs better when compared with other network representation and feature extraction methods.
Do mathematicians and undergraduates agree about explanation quality?

Abstract Offering explanations is a central part of teaching mathematics, and understanding those explanations is a vital activity for learners. Given this, it is natural to ask what makes a good mathematical explanation. This question has received surprisingly little attention in the mathematics education literature, perhaps because the field has no agreed method by which explanation quality can be reliably assessed. In this paper, we explore this issue by asking whether mathematicians and undergraduates agree with each other about explanation quality. A corpus of 10 explanations produced by 10 mathematicians was used. Using a comparative judgement method, we analysed 320 paired comparisons from 16 mathematicians and 320 from 32 undergraduate students. We found that both mathematicians and undergraduates were able to reliably assess the quality of a set of mathematical explanations. Furthermore, the assessments were largely consistent across the two groups. Implications for theories of mathematical explanation are discussed. We conclude by arguing that comparative judgement is a promising technique for exploring explanation quality.
Auxiliary information: the raking-ratio empirical process

We study the empirical measure associated to a sample of size $n$ and modified by $N$ iterations of the raking-ratio method. This empirical measure is adjusted to match the true probability of sets in a finite partition which changes each step. We establish asymptotic properties of the raking-ratio empirical process indexed by functions as $n\rightarrow +\infty $, for $N$ fixed. We study nonasymptotic properties by using a Gaussian approximation which yields uniform Berry-Esseen type bounds depending on $n,N$ and provides estimates of the uniform quadratic risk reduction. A closed-form expression of the limiting covariance matrices is derived as $N\rightarrow +\infty $. In the two-way contingency table case the limiting process has a simple explicit formula.
Datalog in Wonderland

Modern data analytics applications, such as knowledge graph reasoning and machine learning, typically involve recursion through aggregation. Such computations pose great challenges to both system builders and theoreticians: first, to derive simple yet powerful abstractions for these computations; second, to define and study the semantics for the abstractions; third, to devise optimization techniques for these computations. In recent work we presented a generalization of Datalog called Datalog, which addresses these challenges. Datalog is a simple abstraction, which allows aggregates to be interleaved with recursion, and retains much of the simplicity and elegance of Datalog. We define its formal semantics based on an algebraic structure called Partially Ordered Pre-Semirings, and illustrate through several examples how Datalog can be used for a variety of applications. Finally, we describe a new optimization rule for Datalog, called the FGH-rule, then illustrate the FGH-rule on several examples, including a simple magic-set rewriting, generalized semi-naïve evaluation, and a bill-of-material example, and briefly discuss the implementation of the FGH-rule and present some experimental validation of its effectiveness.
From healthy communities to toxic debates: Disqus’ changing ideas about comment moderation

This article examines how the commenting platform Disqus changed the way it speaks about commenting and moderation over time. To understand this evolving self-presentation, we used the Internet Archive Wayback Machine to analyse the company's website and blog between 2007 and 2021. By combining interpretative close-reading approaches with computerised distant-reading procedures, we examined how Disqus tried to advance online discussion and dealt with moderation over time. Our findings show that in the mid-2000s, commenting systems were supposed to help filter and surface valuable contributions to public discourse, while ten years later their focus had shifted to the proclaimed goal of protecting public discourse from contamination with potentially harmful ("toxic") communication. To achieve this, the company developed new tools and features to keep communities "healthy" and to facilitate and semi-automate active and interventive forms of moderation. This rise of platform interventionism was fostered by a turn towards semantics of urgency in the company's language to legitimise its actions.