Reviewerly/exHarmony huggingface dataset:
Development of android-based learning module in metal fabrication techniques for vocational high school 2 Klaten

Abstract This study aims to develop an android-based learning module and find out the level of feasibility of learning module development in vocational high school 2 Klaten. This study uses ADDIE models, which are analysis, design, development, implementation, and evaluation. This study involved 37 respondents (students) to determine the feasibility of the android-based learning module that was developed. Data was obtained by the questionnaire and analysed by changing the average score into a percentage. The result of this study was android based learning module products on metal fabrication techniques subject for XI grade students. The feasibility test showed that the feasibility level of learning module validated by media experts obtained 77.90% with the category of eligible. The feasibility validated by material experts showed an overall average of 86.39% with the category very eligible, and from the results of the user obtained an overall average of 78.53% with the eligible category. From the feasibility test it could be concluded that the android module development could be used as one of the learning media in metal fabrication subject.
Machine Learning based Approach for Detection of Cyberbullying Tweets

In today's technologically sound world the use of social media is inevitable.Along with benefits of social media there are serious negative impacts as well.An important issue that needs to be addressed here is cyberbullying.An effective solution for resolving this issue is the detection of the cyberbullying content by Machine Learning.This manuscript aims to put forward ideas regarding cyber-bullying detection on the social media platform twitter.The outcome of this manuscript is that whichever tweet is a bully tweet that is represented by the value 1, thus all the bully tweets are detected.The Twitter dataset is equally distributed into bully and non-bully tweets and fed to different machine learning models.The logistic regression classifier provides accurate classification of bully and non-bully tweets with precision of 91%, recall 94% and F1-score 93%.This work will help curb cyber-bullying, so that the users can stay at bay from victimization.
Hybrid Max-Min Genetic Algorithm for Load Balancing and Task Scheduling in Cloud Environment

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.
On the Faithfulness for E-commerce Product Summarization

In this work, we present a model to generate e-commerce product summaries.The consistency between the generated summary and the product attributes is an essential criterion for the ecommerce product summarization task.To enhance the consistency, first, we encode the product attribute table to guide the process of summary generation.Second, we identify the attribute words from the vocabulary, and we constrain these attribute words can be presented in the summaries only through copying from the source, i.e., the attribute words not in the source cannot be generated.We construct a Chinese e-commerce product summarization dataset, and the experimental results on this dataset demonstrate that our models significantly improve the faithfulness.
Blockchain Structure and Cryptocurrency Prices

I present a model of cryptocurrency price formation that endogenizes both the financial market for coins and the fee-based market for blockchain space. A cryptocurrency has two distinctive features: a price determined by the extent of its usage as money, and a blockchain structure that restricts settlement capacity. Limited settlement space creates competition between users of the currency, so speculative activity can crowd out monetary usage. This crowding-out undermines the ability of a cryptocurrency to act as a medium of payment, lowering its value. Higher speculative demand can reduce prices, contrary to standard economic models. Crowding-out also raises the riskiness of investing in cryptocurrency, explaining high observed price volatility.
Detection of Common Foreign Objects on Power Grid Lines Based on Faster R-CNN Algorithm and Data Augmentation Method

Abstract Foreign objects hanging on the electrical equipment are big threats to the safety of the power grid. In this paper, a high-precision object detection model designed for common foreign objects in power grid based on Faster R-CNN algorithm is proposed. The data augmentation methods such as rotation, adjusting brightness and colour saturation, adding Gaussian noise are carried out to expand the data in the training set. The accuracy rate and the recall rate of the model reach 93.09% and 94.33% respectively. The object detection model trained by the training set with data augmentation has achieved better detection results.
Digital Image Correlation as an Experimental Modal Analysis Capability

Digital image correlation (DIC) is an established test technique in several fields including quasi-static displacement measurements. Recently there has been growing interest in using DIC to measure structural dynamic response and even extract modal parameters from that information. While high-speed cameras have become more ubiquitous, there are no commercial end-to-end packages for modal analysis based on image data, particularly when combined with traditional data acquisition systems. As such, the practitioner is left to develop several key data processing capabilities, hardware interface equipment, and testing practices themselves. This work highlights several practical aspects that have been encountered while establishing DIC as a viable modal testing capability in a laboratory environment.
AQAD: 17,000+ Arabic Questions for Machine Comprehension of Text

Current Arabic Machine Reading for Question Answering datasets suffer from important shortcomings. The available datasets are either small-sized high-quality collections or large-sized low-quality datasets. To address the aforementioned problems we present our Arabic Question-Answer dataset (AQAD). AQAD is a new Arabic reading comprehension large-sized high-quality dataset consisting of 17,000+ questions and answers. To collect the AQAD dataset, we present a fully automated data collector. Our collector works on a set of Arabic Wikipedia articles for the extractive question answering task. The chosen articles match the articles used in the well-known Stanford Question Answering Dataset (SQuAD). We provide evaluation results on the AQAD dataset using two state-of-the-art models for machine-reading question answering problems. Namely, BERT and BIDAF models which result in 0.37 and 0.32 F-1 measure on AQAD dataset.
Conceptualization of Digital Competence

The aim of the present research is to study the essence, structure, implementation, and indicators of the concept of the digital transversal competence of university students for its improvement in the study process. This article includes the relevance of transversal competences and the essence of the concept of digital competence. This concept is then developed, the methodology of digital competence assessment and measurement is adapted, and the content analysis method was used to classify and analyze the expression of digital competence in eight professional areas.
Dec-PPCPP: A Decentralized Predator–Prey-based Approach to Adaptive Coverage Path Planning Amid Moving Obstacles

Enabling multiple robots to collaboratively perform coverage path planning on complex surfaces embedded in R3 in the presence of moving obstacles is a challenging problem that has not received much attention from researchers. As robots start to be practically deployed, it is becoming important to address this problem. A novel decentralized multirobot coverage path planning approach is proposed that is adaptive to unexpected stationary and moving obstacles while aiming to achieve complete coverage with minimal cost. The approach is inspired by the predator-prey relation. For a robot (a prey), a virtual stationary predator enforces spatial ordering on the prey, and dynamic predators (other robots) cause the prey to be repelled resulting in better task allocation and collision-avoidance. The approach makes the best use of both worlds: offline global planning for tuning of model parameters based on a prior map of the surface, and real-time local planning for adaptive and swift decision making amid moving obstacles and other robots while preserving global behavior. Comparisons with other approaches and extensive testing and validation using different number of robots, different surfaces and obstacles, and various scenarios are conducted.
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission

In this paper, we present a novel approach for conformal prediction (CP), in which we aim to identify a set of promising prediction candidates---in place of a single prediction. This set is guaranteed to contain a correct answer with high probability, and is well-suited for many open-ended classification tasks. In the standard CP paradigm, the predicted set can often be unusably large and also costly to obtain. This is particularly pervasive in settings where the correct answer is not unique, and the number of total possible answers is high. We first expand the CP correctness criterion to allow for additional, inferred admissible answers, which can substantially reduce the size of the predicted set while still providing valid performance guarantees. Second, we amortize costs by conformalizing prediction cascades, in which we aggressively prune implausible labels early on by using progressively stronger classifiers---again, while still providing valid performance guarantees. We demonstrate the empirical effectiveness of our approach for multiple applications in natural language processing and computational chemistry for drug discovery.
Meta-Heuristic Feature Optimization for ontology-based data security in a campus workplace with robotic assistance

BACKGROUND: For campus workplace secure text mining, robotic assistance with feature optimization is essential. The space model of the vector is usually used to represent texts. Besides, there are still two drawbacks to this basic approach: the curse and lack of semantic knowledge. OBJECTIVES: This paper proposes a new Meta-Heuristic Feature Optimization (MHFO) method for data security in the campus workplace with robotic assistance. Firstly, the terms of the space vector model have been mapped to the concepts of data protection ontology, which statistically calculate conceptual frequency weights by term various weights. Furthermore, according to the designs of data protection ontology, the weight of theoretical identification is allocated. The dimensionality of functional areas is reduced significantly by combining standard frequency weights and weights based on data protection ontology. In addition, semantic knowledge is integrated into this process. RESULTS: The results show that the development of the characteristics of this process significantly improves campus workplace secure text mining. CONCLUSION: The experimental results show that the development of the features of the concept hierarchy structure process significantly enhances data security of campus workplace text mining with robotic assistance.
A Geometric Analysis of Deep Generative Image Models and Its Applications

Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These networks are trained to map random inputs in their latent space to new samples representative of the learned data. However, the structure of the latent space is hard to intuit due to its high dimensionality and the non-linearity of the generator, which limits the usefulness of the models. Understanding the latent space requires a way to identify input codes for existing real-world images (inversion), and a way to identify directions with known image transformations (interpretability). Here, we use a geometric framework to address both issues simultaneously. We develop an architecture-agnostic method to compute the Riemannian metric of the image manifold created by GANs. The eigen-decomposition of the metric isolates axes that account for different levels of image variability. An empirical analysis of several pretrained GANs shows that image variation around each position is concentrated along surprisingly few major axes (the space is highly anisotropic) and the directions that create this large variation are similar at different positions in the space (the space is homogeneous). We show that many of the top eigenvectors correspond to interpretable transforms in the image space, with a substantial part of eigenspace corresponding to minor transforms which could be compressed out. This geometric understanding unifies key previous results related to GAN interpretability. We show that the use of this metric allows for more efficient optimization in the latent space (e.g. GAN inversion) and facilitates unsupervised discovery of interpretable axes. Our results illustrate that defining the geometry of the GAN image manifold can serve as a general framework for understanding GANs.
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning

Post-hoc multi-class calibration is a common approach for providing high-quality confidence estimates of deep neural network predictions. Recent work has shown that widely used scaling methods underestimate their calibration error, while alternative Histogram Binning (HB) methods often fail to preserve classification accuracy. When classes have small prior probabilities, HB also faces the issue of severe sample-inefficiency after the conversion into K one-vs-rest class-wise calibration problems. The goal of this paper is to resolve the identified issues of HB in order to provide calibrated confidence estimates using only a small holdout calibration dataset for bin optimization while preserving multi-class ranking accuracy. From an information-theoretic perspective, we derive the I-Max concept for binning, which maximizes the mutual information between labels and quantized logits. This concept mitigates potential loss in ranking performance due to lossy quantization, and by disentangling the optimization of bin edges and representatives allows simultaneous improvement of ranking and calibration performance. To improve the sample efficiency and estimates from a small calibration set, we propose a shared class-wise (sCW) calibration strategy, sharing one calibrator among similar classes (e.g., with similar class priors) so that the training sets of their class-wise calibration problems can be merged to train the single calibrator. The combination of sCW and I-Max binning outperforms the state of the art calibration methods on various evaluation metrics across different benchmark datasets and models, using a small calibration set (e.g., 1k samples for ImageNet).
Dynamic Membership Functions for Context-Based Fuzzy Systems

In fuzzy systems, membership functions determine the groups to which a variable can belong to, and these groups are static or only have one setting in some aspect. However, fuzzy systems typically require to model the dynamic environment they represent. Still, this behavior does not reflect the membership groups in a conventional way. Thus, conventional fuzzy systems are not capable of reflecting the dynamics of the real-time context. The approach presented consists of a fuzzy system where the membership functions can have dynamic transformations, according to contextual variables that influence them, to have a model that adjusts in real time. The membership functions' dynamism is achieved because the form in the sets can be transformed; the maximum degree of membership of a set is in a range of zero to one; and, the location of the sets in the discourse universe can vary dynamically. The results show the feasibility of a context-based fuzzy system with dynamic membership functions built-in real time, that has been influenced by contextual variables. Therefore, unlike other proposals, this approach allows modeling the influence of the context on a fuzzy system, making it more adjusted to reality. To illustrate our proposed approach, a case study is presented where a fuzzy system estimates the heat stress in a work environment that uses data acquired from wearable devices. This system automatically generates the following indicators: (i) energy level wasted while performing a physical activity, (ii) personalized measurement of workload level, and (iii) measurement of Occupational Heat Stress (OHS).
Block-RAS: A P2P Resource Allocation Scheme in 6G Environment with Public Blockchains

Blockchain technology has emerged to provide immense security solutions and create trust between the stakeholders. In a multi-application scenario, fair resource allocation is complex and challenging. Various Resource Allocation Schemes (RAS) have been proposed by the researchers across the globe, but these solutions are not sufficient to handle the security, trust, latency, and bandwidth issues in the network, which introduces vulnerabilities in the system. Motivated from the aforementioned issues, this paper proposes Block-RAS, a blockchain-based RAS to manage the demand-supply of resources between the users and resource providing companies (RPC) in a secured and trusted environment. Block-RAS provides a highly reliable, low-latency, and bandwidth optimum communication between users and RPC with embedded 6G network infrastructure. In Block-RAS, the security, trust, and transparency are achieved using ethereum blockchain, whereas the cost-effective and optimum bandwidth utilization is achieved using the Interplanetary File System (IPFS). Finally, the performance evaluation of Block-RAS is done by a comparative analysis of the proposed approach with traditional approaches that are dependent on centralized 5G based schemes where the Block-RAS outperforms in terms of delay, packet-loss, blockchain block-size, scalability, and network bandwidth utilization.
Breast Cancer Survival Rate Prediction In Mammograms Using Machine Learning

Breast cancer has produced an immense data set such as electronic medical records, metabolomics analysis and report which includes patient health history and disorders. However, the data ought to be analyzed and integrated if needed to produce models about physiological mechanisms of pathogens, the use of machine learning along with Image Analysis is to predict the survival rate among the breast cancer patients. The mammographic image of the breast is taken among cancer patients and normal person to diagnose the diff between the cancerous cell and normal cell using the image analysis. This will help to detect the early sign of breast cancer among the patient and also the analysis of mammographic image at different stage of breast cancer will help in depicting the survival rate among the patients. The mammographic data set is analyzed through various filters of image search like median, average and Gaussian filter. The methodologies are used on mammographic data set.
Steel: proof-oriented programming in a dependently typed concurrent separation logic

Steel is a language for developing and proving concurrent programs embedded in F ⋆ , a dependently typed programming language and proof assistant. Based on SteelCore, a concurrent separation logic (CSL) formalized in F ⋆ , our work focuses on exposing the proof rules of the logic in a form that enables programs and proofs to be effectively co-developed. Our main contributions include a new formulation of a Hoare logic of quintuples involving both separation logic and first-order logic, enabling efficient verification condition (VC) generation and proof discharge using a combination of tactics and SMT solving. We relate the VCs produced by our quintuple system to solving a system of associativity-commutativity (AC) unification constraints and develop tactics to (partially) solve these constraints using AC-matching modulo SMT-dischargeable equations. Our system is fully mechanized and implemented in F ⋆ . We evaluate it by developing several verified programs and libraries, including various sequential and concurrent linked data structures, proof libraries, and a library for 2-party session types. Our experience leads us to conclude that our system enables a mixture of automated and interactive proof, making it productive to build programs foundationally verified against a highly expressive, state-of-the-art CSL.
Fast and Efficient Visibility Restoration Technique for Single Image Dehazing and Defogging

Poor weather conditions are detrimental to transportation systems and increase the likelihood of road accidents. Haze and fog are the most responsible atmospheric parameters affecting visibility and hence the traffic performance. The intelligent driving assistance systems developed for automatic vehicles use clear vision for various smart applications like keeping within the correct lane and recognize traffic signs. Bad weather decreases the visibility significantly based on the intensity of fog and haze. So, there is a need for the restoration of clear visibility. This paper introduces a novel visibility restoration approach based on thresholding and gamma transformation method. A step-by-step process for the proposed method is as follows. First, the proper selection of an atmospheric light value is responsible for the validation of the color and contrast of the recovered images. The threshold process makes it possible to estimate atmospheric light. Then, accurately estimating the transmission depth from object to object in small time is the most challenging aspect due to unequal distribution. To solve the problem, the gamma transformation method is used to correctly estimate the depth. Finally, restore the scene radiance from the hazy and foggy images. The experimental results show that the proposed measurement ensures good uniformity about qualitative and quantitative evaluation using eight performance metrics: contrast gain, percentage of saturated pixels, blind contrast assessment, structural similarity index measure (SSIM), image visibility measurement (IVM), mean square error (MSE), visual contrast measure (VCM), and peak signal-to-noise ratio (PSNR). The proposed algorithm excels in previous works in terms of processing time and visibility restoration results comparable to sophisticated state-of-the-art techniques.