Reviewerly/exHarmony huggingface dataset:
SilentSign: Device-free Handwritten Signature Verification through Acoustic Sensing

Signature is one of the most prevailing identity authorization approaches. It is yet inconvenient to use in real life in the sense that a majority of existing signature verification approaches rely on additional digital signing devices. In this paper, we propose a portable device-free signature verification system named SilentSign which makes use of acoustic sensors (i.e., microphone and speaker) embedded in smart devices to enable secure and convenient signature verification service. The basic idea is to leverage acoustic signals to measure the distance variation of the tip of the pen while signing. We carefully design the signal modulation scheme, develop a phase-based distance measurement technique, and train the verification model for high performance and robustness. Compared with conventional digital signing systems, SilentSign allows users to sign more invisibly and conveniently. We conduct extensive experiments involving 35 participants to evaluate SilentSign. Results show that SilentSign can achieve 98.2% AUC and 1.25% EER.
The Effect of 'Fish Diversity' Book in Kampar District on the Learning Motivation and Obstacles of Kampar High School Students through Online Learning during the COVID-19 Period

The study aims to analyze the effect of the fish diversity book in Kampar District on the learning motivation and obstacles of Kampar High School students through online learning during the COVID-19 period. This type of research is a quasi-experimental study with research subjects in class XI SMA Kampar (Kampar High School). In its implementation, students were given a motivational questionnaire in the beginning, after that they were given the task of studying the types of fish in Kampar through the book files of fish diversity in Kampar District through online learning. After online learning, the post-test was given using a questionnaire evaluation of the motivation and online learning obstacles questionnaire. Parameters: learning motivation with five indicators, ten indicators of online obstacles. Motivation data collection using pre-test and post-test. Data is presented and analyzed descriptively. The results obtained an average score of post-test student learning motivation (77.7) increased compared to before studying diversity book (65.9) with an N-gain index of 0.34, 74% of respondents said online learning was more effective, 50% of respondents stated the ordinary school is better than online learning. The implementation of fish species diversity books through online learning is effective in increasing student learning motivation, but it is less effective in motivating desires, appreciation, and not being attractive enough. The obstacles of online learning at Kampar High School include internet network facilities and field practicum during the COVID-19 period.
Decentralized data access control over consortium blockchains

Blockchain is an emerging data management technology that enables people in a collaborative network to establish trusted connections with the other participants. Recently consortium blockchains have raised interest in a broader blockchain technology discussion. Instead of a fully public, autonomous network, consortium blockchain supports a network where participants can be limited to a subset of users and data access strictly controlled. Access control policies should be defined by the respective data owner and applied throughout the network without requiring a centralized data administrator. As a result, decentralized data access control (DDAC) emerges as a fundamental challenge for such systems. However, we show from a trust model for consortium collaborative networks that current consortium blockchain systems provide limited support for DDAC. Further, the distributed, replicated nature of blockchain makes it even more challenging to control data access, especially read access, compared with traditional DBMSes. We investigate possible strategies to protect data from being read by unauthorized users in consortium blockchain systems using combinations of ledger partitioning and encryption strategies. A general framework is proposed to help inexperienced users determine appropriate strategies under different application scenarios. The framework was implemented on top of Hyperledger Fabric to evaluate feasibility. Experimental results along with a real-world case study contrasted the performance of different strategies under various conditions and the practicality of the proposed framework.
Entropy-Based Analysis of the Impact of Environmentally Sensitive Elements on Groundwater Quality of the Ameka Region of Southeast Nigeria: Medical Geology Implications

Understanding the impact of natural and modified environment on human health is a subject within the scope of medical geology. Ameka and its environs in southeastern Nigeria is known for intensive mining and agricultural activities that frequently predispose water resources to pollution by environmentally sensitive elements (ESEs). The present study analyzed the groundwater quality and associated probabilistic human health risks due to ESEs in Ameka area, using multiple indices and a novel health risk assessment framework, HHRISK. Entropy-weighted water quality index and other geochemical evaluation indices (including pollution index, enrichment factor, index of geoaccumulation, pollution index of groundwater, and quantification of contamination) revealed that all the groundwater samples from hand-dug wells and boreholes were extremely polluted and hence unsuitable for drinking. The HHRISK model was utilized for ingestion and dermal exposure routes assessments. Results showed that all the samples pose very high carcinogenic and non-carcinogenic health risks. With respect to aggregated non-carcinogenic HHRISK, the order of health threat impact of the ESEs for children and adult populations is predicted as Cd > As > Se > Co > Ni > Pb > Mn > Zn > Cu > Fe. However, aggregated cancer risk for both populations followed the trend Ni > Cd > As > Pb. Furthermore, cumulative non-carcinogenic risk (HRQcum) and carcinogenic risk (CRcum) scores (for children and adults) exceeded standard limits in all the samples. However, the scores obtained for the adults were generally lower, implying that the children population is more vulnerable to both non-carcinogenic and carcinogenic health risks. Thus, there is urgent need to adopt environmentally-friendly pollution mitigation measures in the Ameka area.
Distributed forward-backward (half) forward algorithms for generalized Nash equilibrium seeking

We present two distributed algorithms for the computation of a generalized Nash equilibrium in monotone games. The first algorithm follows from a forward-backward-forward operator splitting, while the second, which requires the pseudo-gradient mapping of the game to be cocoercive, follows from the forward-backward-half-forward operator splitting. Finally, we compare them with the distributed, preconditioned, forward-backward algorithm via numerical experiments.
Hunting Algorithm for Multi-AUV Based on Dynamic Prediction of Target Trajectory in 3D Underwater Environment

In the research of multi-robot systems, multi-AUV (multiple autonomous underwater vehicles) cooperative target hunting is a hot issue. In order to improve the target hunting efficiency of multi-AUV, a multi-AUV hunting algorithm based on dynamic prediction for the trajectory of the moving target is proposed in this article. Firstly, with moving of the target, sample points are updated dynamically to predict the possible position of a target in a short period time by using the fitting of a polynomial, and the safe domain of the moving target, which is a denied area for the hunting AUVs, is built to avoid the target's escape when it detects AUVs. Secondly, the method of negotiation is adopted to allocate appropriate desired hunting points for each AUV. Finally, the AUVs arrive at desired hunting points rapidly through deep reinforcement learning (DRL) algorithm to achieve hunting the moving target. The simulations show that hunting AUVs can surround the moving target of which the trajectory is unknown rapidly and accurately by the algorithm in the 3D environment with complex obstacles and results obtained is satisfactory.
Handwriting Recognition using Artificial Intelligence Neural Network and Image Processing

Due to increased usage of digital technologies in all sectors and in almost all day to day activities to store and pass information, Handwriting character recognition has become a popular subject of research.Handwriting remains relevant, but people still want to have Handwriting copies converted into electronic copies that can be communicated and stored electronically.Handwriting character recognition refers to the computer's ability to detect and interpret intelligible Handwriting input from Handwriting sources such as touch screens, photographs, paper documents, and other sources.Handwriting characters remain complex since different individuals have different handwriting styles.This paper aims to report the development of a Handwriting character recognition system that will be used to read students and lectures Handwriting notes.The development is based on an artificial neural network, which is a field of study in artificial intelligence.Different techniques and methods are used to develop a Handwriting character recognition system.However, few of them focus on neural networks.The use of neural networks for recognizing Handwriting characters is more efficient and robust compared with other computing techniques.The paper also outlines the methodology, design, and architecture of the Handwriting character recognition system and testing and results of the system development.The aim is to demonstrate the effectiveness of neural networks for Handwriting character recognition.
ExGAN: Adversarial Generation of Extreme Samples

Mitigating the risk arising from extreme events is a fundamental goal with many applications, such as the modelling of natural disasters, financial crashes, epidemics, and many others. To manage this risk, a vital step is to be able to understand or generate a wide range of extreme scenarios. Existing approaches based on Generative Adversarial Networks (GANs) excel at generating realistic samples, but seek to generate typical samples, rather than extreme samples. Hence, in this work, we propose ExGAN, a GAN-based approach to generate realistic and extreme samples. To model the extremes of the training distribution in a principled way, our work draws from Extreme Value Theory (EVT), a probabilistic approach for modelling the extreme tails of distributions. For practical utility, our framework allows the user to specify both the desired extremeness measure, as well as the desired extremeness probability they wish to sample at. Experiments on real US Precipitation data show that our method generates realistic samples, based on visual inspection and quantitative measures, in an efficient manner. Moreover, generating increasingly extreme examples using ExGAN can be done in constant time (with respect to the extremeness probability τ), as opposed to the O(1/τ) time required by the baseline approach.
Bridging the Skill Gap in Robotics: Global and National Environment

This article focuses on the demand for skills of highly qualified scientific and technical professionals (engineers and researchers) in robotics, on both a global and national level. Information is collected using the text-mining of open-access vacancies for understanding the global trends and in-depth interviews with experts for a more detailed study of national trends. The study explores the combination of hard and soft skills, as well as interdisciplinary skills. Soft skill requirements play an important role in the demanded skill set of the specialist, but the claims for hard skills (including digital) are not becoming less strict. Programming and the knowledge of specialized software packages are the most important skills, but must be combined with practical skills (assembly, welding, soldering). The broad range of application areas for robotic systems creates demand for new multidisciplinary skills (knowledge of artificial intelligence, new materials, and biology). Rapid technological development underlines the growing importance of soft skills, such as communication skills, self-motivation, and a willingness to learn. Lists of the most demanded skills in different countries principally coincide. Results can be applied for developing policies aimed at eliminating the skill gap in prospective technological areas.
Decoding Strategies for Improving Low-Resource Machine Translation

Pre-processing and post-processing are significant aspects of natural language processing (NLP) application software. Pre-processing in neural machine translation (NMT) includes subword tokenization to alleviate the problem of unknown words, parallel corpus filtering that only filters data suitable for training, and data augmentation to ensure that the corpus contains sufficient content. Post-processing includes automatic post editing and the application of various strategies during decoding in the translation process. Most recent NLP researches are based on the Pretrain-Finetuning Approach (PFA). However, when small and medium-sized organizations with insufficient hardware attempt to provide NLP services, throughput and memory problems often occur. These difficulties increase when utilizing PFA to process low-resource languages, as PFA requires large amounts of data, and the data for low-resource languages are often insufficient. Utilizing the current research premise that NMT model performance can be enhanced through various pre-processing and post-processing strategies without changing the model, we applied various decoding strategies to Korean–English NMT, which relies on a low-resource language pair. Through comparative experiments, we proved that translation performance could be enhanced without changes to the model. We experimentally examined how performance changed in response to beam size changes and n-gram blocking, and whether performance was enhanced when a length penalty was applied. The results showed that various decoding strategies enhance the performance and compare well with previous Korean–English NMT approaches. Therefore, the proposed methodology can improve the performance of NMT models, without the use of PFA; this presents a new perspective for improving machine translation performance.
(Re) Defining the High-Power Field for Digital Pathology

The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs).Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at "×40" equivalent HPF on different sized monitors with varying display resolutions (1900 × 1080-4500 × 3000) and aspect ratios (e.g., Food and Drug Administration [FDA]-cleared 27" Philips PS27QHDCR, FDA-cleared 24" Dell MR2416, 24" Hewlett Packard Z24n G2, and 28" Microsoft Surface Studio). Digital and microscopic HPF areas were calculated and compared.A significant variation of HPF area occurred between differing monitor size and display resolutions with minor differences between WSI viewers. No differences were identified by scanner or WSIs scanned at different resolutions (e.g., 0.5, 0.25, 0.24, and 0.12 μm/pixel).Glass slide HPF at × 400 magnification with conventional light microscopy was not equivalent to "×40" digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.
Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win

Sparse Neural Networks (NNs) can match the generalization of dense NNs using a fraction of the compute/storage for inference, and have the potential to enable efficient training. However, naively training unstructured sparse NNs from random initialization results in significantly worse generalization, with the notable exceptions of Lottery Tickets (LTs) and Dynamic Sparse Training (DST). In this work, we attempt to answer: (1) why training unstructured sparse networks from random initialization performs poorly and; (2) what makes LTs and DST the exceptions? We show that sparse NNs have poor gradient flow at initialization and propose a modified initialization for unstructured connectivity. Furthermore, we find that DST methods significantly improve gradient flow during training over traditional sparse training methods. Finally, we show that LTs do not improve gradient flow, rather their success lies in re-learning the pruning solution they are derived from — however, this comes at the cost of learning novel solutions.
RSTrace+: Reviewer suggestion using software artifact traceability graphs

Various types of artifacts (requirements, source code, test cases, documents, etc.) are produced throughout the lifecycle of a software. These artifacts are connected with each other via traceability links that are stored in modern application lifecycle management repositories. Throughout the lifecycle of a software, various types of changes can arise in any one of these artifacts. It is important to review such changes to minimize their potential negative impacts. To make sure the review is conducted properly, the reviewer(s) should be chosen appropriately. We previously introduced a novel approach, named RSTrace, to automatically recommend reviewers that are best suited based on their familiarity with a given artifact. In this study, we introduce an advanced version of RSTrace, named RSTrace+ that accounts for recency information of traceability links including practical tool support for GitHub. In this study, we conducted a series of experiments on finding the appropriate code reviewer(s) using RSTrace+ and provided a comparison with the other code reviewer recommendation approaches. We had initially tested RSTrace+ on an open source project (Qt 3D Studio) and achieved a top-3 accuracy of 0.89 with an MRR (mean reciprocal ranking) of 0.81. In a further empirical evaluation of 40 open source projects, we compared RSTrace+ with Naive-Bayes, RevFinder and Profile based approach, and observed higher accuracies on the average. We confirmed that the proposed reviewer recommendation approach yields promising top-k and MRR scores on the average compared to the existing reviewer recommendation approaches. Unlike other code reviewer recommendation approaches, RSTrace+ is not limited to recommending reviewers for source code artifacts and can potentially be used for recommending reviewers for other types of artifacts. Our approach can also visualize the affected artifacts and help the developer to make assessments of the potential impacts of change to the reviewed artifact.
EnsVAE: Ensemble Variational Autoencoders for Recommendations

Recommender systems are information software that retrieves relevant items for users from massive sources of data. The variational autoencoder (VAE) has proven to be a promising approach for recommendation systems, as it can explore high-level user-item relations and extract contingencies from the input effectively. However, the previous variants of VAE have so far seen limited application to domain-specific recommendations that require additional side information. Hence, The Ensemble Variational Autoencoder framework for recommendations (EnsVAE) is proposed. This architecture specifies a procedure to transform sub-recommenders' predicted utility matrix into interest probabilities that allow the VAE to represent the variation in their aggregation. To evaluate the performance of EnsVAE, an instance - called the “Ensemblist GRU/GLOVE model” - is developed. It is based on two innovative recommender systems: 1-) a new “GloVe content-based filtering recommender” (GloVe-CBF) that exploits the strengths of embedding-based representations and stacking ensemble learning techniques to extract features from the item-based side information. 2-) a variant of neural collaborative filtering recommender, named “Gate Recurrent Unit-based Matrix Factorization recommender” (GRU-MF). It models a high level of non-linearities and exhibits interactions between users and items in latent embeddings, reducing user biases towards items that are rated frequently by users. The developed instance speeds up the reconstruction of the utility matrix with increased accuracy. Additionally, it can switch between one of its sub-recommenders according to the context of their use. Our findings reveal that EnsVAE instances retain as much information as possible during the reconstruction of the utility matrix. Furthermore, the trained VAE's generative trait tackles the cold-start problem by accurately estimating the interest probabilities of newly-introduced users and resources. The empirical study on real-world datasets proves that EnsVAE significantly outperforms the state-of-the-art methods in terms of recommendation performances.
Hierarchical Fuzzy Logic for Multi-Input Multi-Output Systems

Fuzzy logic has created a high impact on research and development in almost all engineering applications. Recently, there has been an increasing interest in various offshoots of fuzzy logic approach and hierarchical fuzzy logic is one such area of research development and applications. With the increase in volume of data, hierarchical fuzzy logic has emerged as a highly suitable candidate for research. The objective of this paper is to develop methodology for multi-input multi-output hierarchical fuzzy systems. In particular, a system to be designed is broken into a number of sub-subsystems, where each subsystem is designed separately and then connected in hierarchical structure. The strategy used in this paper is to avoid the repetition of common terms across different subsystems of multi-input multi-output systems. This strategy has not been presented hitherto by any other author. This paper first discusses in detail the implementation of a multi-input single-output hierarchical system. It then extends the approach to multi-input multi-output hierarchical systems.