Reviewerly/exHarmony huggingface dataset:
Assessing Hazard Potential of Select Chemicals using Computational Toxicology Models

Release of potentially persistent, bioaccumulative and inherently toxic chemicals into the environment may cause adverse effects to plants and animals. Hundreds of thousands of chemicals are manufactured each year and experimentally assessing them for these parameters is costly, time consuming and requires animal testing. To address these concerns there has been a paradigm shift in adoption of computational chemistry and toxicology methods for assessing environmental risks posed by such chemicals. These computer-based methods harness the power of fast processors, high speed internet, statistical methods, and curated toxicological databases to fulfil this need. In this work a summary of selected publicly available computational models and databases is presented. Global regulatory agencies apply these predictive models to support their decisions. Indian regulatory bodies too could benefit from this exercise in identifying chemicals of ecotoxicological concern and in taking an appropriate regulatory decision thereby protecting the environment.
Reversible Logic based Cryptography Design Algorithm using Random Keys

Reversible computations, besides quantum computing, have various applications in digital signal processing, nanotechnology and bioinformatics. They are particularly useful in designing low-power devices and improving computational efficiency. Cryptography is vital for protecting sensitive information in fields such as bioinformatics and digital signal processing where private data is frequently exchanged. However, cryptographic algorithms can consume significant power and require large areas, particularly when implemented in hardware. Reversible logic gates offer a potential solution by being more power-efficient and potentially reducing implementation area. Using random numbers as keys for both encryption and decryption in a reversible logic gate-based cryptographic algorithm can enhance security. LSB watermarking is a technique to embed additional metadata into digital media, improving data security. To evaluate the performance of the Field Programmable Gate Array for the Reversible Logic Gate Cryptography Design architecture, comparing it to the other state-of-the-art approach is necessary.
Health performance and economic growth in sub-Saharan Africa: new evidence based on quantile regressions

Purpose The present study investigates the nexus between health performance dynamics and economic growth in 43 countries in sub-Saharan Africa for the period 2004–2018. Design/methodology/approach Four health performance dynamics are used, notably: total life expectancy, male life expectancy, female life expectancy and risk of maternal death. The empirical evidence is based on quantile regressions (QRs) in order to put into perspective the conditional distribution of economic growth. Findings The following findings are established: (1) total life expectancy and male life expectancy increase economic growth exclusively in the 10th and 90th quantiles of economic growth; (2) female life expectancy boosts economic growth in the 90th quantile of economic growth and (3) the risk of maternal death reduces economic growth in the 75th and 90th quantiles of economic growth. Policy implications are discussed. Originality/value The study complements the literature on the nexus between health performance and economic growth by assessing the nexuses throughout the conditional distribution of economic growth.
High-Performance RNS Modular Exponentiation by Sum-Residue Reduction

With rapid development and application of artificial intelligence and block chain, the requirement of information and data security is also increased, in which the public-key cryptography, such as Rivest-Shamir-Adleman (RSA) cryptography, plays a significant role. Modular exponentiation is fundamental in computer arithmetic and is widely applied in cryptography, such as ElGamal cryptography, Diffie–Hellman key exchange protocol, and RSA cryptography. The implementation of modular exponentiation in a residue number system leads to high parallelism in computation and has been applied in many hardware architectures. While most residue number system (RNS)-based architectures utilize RNS Montgomery algorithm with two residue number systems, the recent modular multiplication algorithm with sum residues performs modular reduction in only one residue number system with about the same parallelism. In this work, it is shown that the high-performance modular exponentiation and RSA cryptography can be implemented in RNS. Both the algorithm and architecture are improved to achieve high performance with extra area overheads, where a 1024-bit modular exponentiation can be completed in 0.567 ms in Xilinx XC6VLX195t-3 platform, costing 26 489 slices, 87 357 LUTs, 363 dedicated multipilers of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18$</tex-math> </inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $</tex-math> </inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18$</tex-math> </inline-formula> bits, and 65 block RAMs.
Embedded Systems Multimedia Framework for Microcontroller Devices

The presented paper attempts to establish a generalized approach to the development of embedded systems multimedia applications. It is formalized in the form of a framework that defines rules and recommenda- tions for a developer on how to implement specific pieces of software that work with multimedia data. The basis for the development process is the division of the system’s func- tionality into stages with the following development of each stage. The framework also defines how touch sensor events may be elaborated. The proposed framework has been tested in a test scenario in an application with multiple stages. The results proved that the solution is feasible for multimedia applications (specifically, with graphics proc- essing) and can be regarded as a generalized approach to the development of embedded systems with multimedia functionality.
Speech Models Training Technologies Comparison Using Word Error Rate

The main purpose of this work is to analyze and compare several technologies used for training speech models, including traditional approaches as Hidden Markov Models (HMMs) and more recent methods as Deep Neural Networks (DNNs). The technologies have been explained and compared using word error rate metric based on the input of 1000 words by a user with 15 decibel background noise. Word error rate metric has been ex- plained and calculated. Potential replacements for com- pared technologies have been provided, including: Atten- tion-based, Generative, Sparse and Quantum-inspired models. Pros and cons of those techniques as a potential replacement have been analyzed and listed. Data analyzing tools and methods have been explained and most common datasets used for HMM and DNN technologies have been described. Real life usage examples of both methods have been provided and systems based on them have been ana- lyzed.
Multi-objective Variable Neighborhood Search for Improving Software Modularity

Search-Based Software Engineering is a research area that aims to tackle software engineering tasks as optimization problems. Among the problems in this area, we can find the Software Module Clustering Problem (SMCP). This problem, which has been proved to be $$\mathcal{N}\mathcal{P}$$ -hard, focuses on finding the best organization of a software project in terms of modularity. Since modular code is easier to understand, the result is an increase of the quality of software projects and thus a reduction of the costs associated to their maintenance. To tackle the SMCP, software projects are often modeled as graphs that represent the dependencies between different components. In this work, we study two well-known multi-objective approaches for the SMCP: the Maximizing Cluster Approach (MCA) and the Equal-size Cluster Approach (ECA). Each of these variants is composed of 5 different objectives. We propose a heuristic algorithm based on the Multi-Objective Variable Neighborhood Descent (MO-VND) schema to tackle the aforementioned variants and we introduce three neighborhoods to be explored within the algorithm. Finally, we compare the performance of our proposal with the performance of NSGA-III over a dataset of real software projects. The results show that the proposed algorithm is competitive when tackling the MCA, and some ideas are given to increase its efficiency when tackling the ECA.
Detecting thermal anomalies in buildings using frequency and temporal domains analysis

Identifying and modifying areas in a building where thermal anomalies occur will help to maintain a comfortable indoor thermal environment while reducing energy waste. In this study, a Fourier transform-based method was developed to detect places with thermal anomalies by considering the periodic characteristics of the temperature time series. Results were compared with anomalies detected by a support vector regression-based method. Both methods were applied to six days of temperature data obtained from sensors placed in various locations in two office buildings, each building having 60 sensors placed within them during the test period. Results showed that both Fourier transform and support vector regression based methods are capable of identifying abnormal time series without prior knowledge of the series. This developed Fourier transform-based anomaly detection method can be used effectively for anomaly detection in time series with periodic characteristics, which will make it easier to identify poorly-performing areas within an internal building environment using non-permanent sensors.
Transformer Based Hope Speech Comment Classification in Code-Mixed Text

Our research aims to describe Natural Language Processing (NLP) tasks aimed at hope speech detection in social media code-mixed comments. Commentaries on social media often violate strict grammar rules and contain code-mixed text written in non-native scripts. Detecting hope speech from these code-mixed sentences will be challenging in three languages: Tamil, English, and Malayalam. First, we remove unnecessary content from noisy texts as a pre-processing and set a baseline for detecting hope speech. Then we implemented pre-trained transformer models and different traditional machine-learning models with feature extraction methods. In the test set, our best configurations achieved macro F1 scores of 0.5498, 0.5447, and 0.7202 for Tamil, English, and Malayalam, while in the development set, we achieved macro F1 scores of 0.5825, 0.4779, and 0.7202 for Tamil, English, and Malayalam.
Question Answering System for Tamil Using Deep Learning

Tamil, a Dravidian language family member, is widely spoken in numerous Indian states. But languages like Tamil, are under- represented on the web. Many NLP models perform worse with these languages when compared to English, the effects of which lead to subpar experiences in many web applications for most of the users. The number of Tamil websites has grown and continues to expand. Tamil newspapers, periodicals, and e-journals are examples. Therefore Tamil Question Answering System will be beneficial to many people who are in the rural areas and also for those who prefer our native Tamil Language. This paper aims to create a Question Answering System in Tamil using deep learning techniques, starting from question processing through an interface, retrieving the relevant context for the given question and finally the answer is displayed after appropriate processing. The model is evaluated using a manually created test dataset and the metrics are analyzed.
Multi-view Consistent Generative Adversarial Networks for Compositional 3D-Aware Image Synthesis

Abstract This paper studies compositional 3D-aware image synthesis for both single-object and multi-object scenes. We observe that two challenges remain in this field: existing approaches (1) lack geometry constraints and thus compromise the multi-view consistency of the single object, and (2) can not scale to multi-object scenes with complex backgrounds. To address these challenges coherently, we propose multi-view consistent generative adversarial networks (MVCGAN) for compositional 3D-aware image synthesis. First, we build the geometry constraints on the single object by leveraging the underlying 3D information. Specifically, we enforce the photometric consistency between pairs of views, encouraging the model to learn the inherent 3D shape. Second, we adapt MVCGAN to multi-object scenarios. In particular, we formulate the multi-object scene generation as a “decompose and compose” process. During training, we adopt the top-down strategy to decompose training images into objects and backgrounds. When rendering, we deploy a reverse bottom-up manner by composing the generated objects and background into the holistic scene. Extensive experiments on both single-object and multi-object datasets show that the proposed method achieves competitive performance for 3D-aware image synthesis.
An object detection approach with residual feature fusion and second‐order term attention mechanism

Abstract Automatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging research. Since the boundary box location is not sufficiently accurate and it is difficult to distinguish overlapping and occluded objects, the authors propose a network model with a second‐order term attention mechanism and occlusion loss. First, the backbone network is built on CSPDarkNet53. Then a method is designed for the feature extraction network based on an item‐wise attention mechanism, which uses the filtered weighted feature vector to replace the original residual fusion and adds a second‐order term to reduce the information loss in the process of fusion and accelerate the convergence of the model. Finally, an objected occlusion regression loss function is studied to reduce the problems of missed detections caused by dense objects. Sufficient experimental results demonstrate that the authors’ method achieved state‐of‐the‐art performance without reducing the detection speed. The mAP@ .5 of the method is 85.8% on the Foggy_cityscapes dataset and the mAP@ .5 of the method is 97.8% on the KITTI dataset.
Game-Theoretically Secure Protocols for the Ordinal Random Assignment Problem

We study game-theoretically secure protocols for the classical ordinal assignment problem (aka matching with one-sided preference), in which each player has a total preference order on items. To achieve the fairness notion of equal treatment of equals, conventionally the randomness necessary to resolve conflicts between players is assumed to be generated by some trusted authority. However, in a distributed setting, the mutually untrusted players are responsible for generating the randomness themselves. In addition to standard desirable properties such as fairness and Pareto-efficiency, we investigate the game-theoretic notion of maximin security, which guarantees that an honest player following a protocol will not be harmed even if corrupted players deviate from the protocol. Our main contribution is an impossibility result that shows no maximin secure protocol can achieve both fairness and ordinal efficiency. Specifically, this implies that the well-known probabilistic serial (PS) mechanism by Bogomolnaia and Moulin cannot be realized by any maximin secure protocol. On the other hand, we give a maximin secure protocol that achieves fairness and stability (aka ex-post Pareto-efficiency). Moreover, inspired by the PS mechanism, we show that a variant known as the OnlinePSVar (varying rates) protocol can achieve fairness, stability and uniform dominance, which means that an honest player is guaranteed to receive an item distribution that is at least as good as a uniformly random item. In some sense, this is the best one can hope for in the case when all players have the same preference order.
Study of Verbal Bullying in Early Adolescents

Verbal bullying is increasingly common among students and sometimes tends to result in serious problems, so it is important to know its forms and causes so that preventive actions can be carried out more quickly by the management of educational institutions. This study aims to find out the forms of verbal bullying and the factors that lead to verbal bullying behavior among the santri of two modern Islamic boarding schools. It uses descriptive qualitative approach, in which the data were taken from santri and teachers at the boarding schools. Words or expressions that contain verbal bullying and factors that cause the behavior of verbal bullying in the schools become the data of this research, which were carried out using observation and interview techniques. The results found that there are 43 verbal bullying expressions, which can be classified by 10 data of dubbing, 9 data of insulting, 9 data of cursing, and 15 data of mocking. The expressions used in verbal bullying are various words or phrases that are often used by children of their age, which are considered normal, because very view expressions that really violate cultural ethics in this area. While factors that cause the verbal bullying were classified into five factors, that were economic ability, seniority tradition, seniority, school disharmony or situation and individual or group characteristics. This shows that the factors that cause verbal bullying in public schools also occur in these pesantren, and the nobel goals that want to achieve by the institutions have not been achieved properly.
Detection of Internet‐wide traffic redirection attacks using machine learning techniques

Abstract Internet‐wide traffic redirection attacks have been reported for long, and are mainly caused by Border Gateway Protocol route hijacking. Such attacks can be quite harmful, impairing access to popular Internet sites for long periods. This work addresses the use of machine learning techniques (both unsupervised and supervised) leveraging from a distributed monitoring infrastructure of probes that measure the round trip time to Internet sites under surveillance. The detection process is separated into two stages: per‐probe classification and a combination of individual probe decisions. Our results show that the best strategy is to classify using an unsupervised technique based on Tukey's method and to combine using Hidden Markov Models, due to its performance and adaptability to different attack types.
Development of Deacidification Methods in Paper Preservation: Systematic Review

Systematic review is a method for research synthesis to summarize the results of primary research based on comprehensive and balanced facts.The development of the deacidification method is an interesting and important theme to be reviewed with a systematic review.So that new knowledge is obtained and in order to consider the right method to be used in information institutions in paper preservation.This study aims to investigate the development of deacidification including the substance used, the method applied, its novelty, its measurement and testing as well as assessment in determining the success of the process.
Optimal security parameter for encrypted control systems against eavesdropper and malicious server

A sample identifying complexity and a sample deciphering time have been introduced in a previous study to capture an estimation error and a computation time of system identification by adversaries. The quantities play a crucial role in defining the security of encrypted control systems and designing a security parameter. This study proposes an optimal security parameter for an encrypted control system under a network eavesdropper and a malicious controller server who attempt to identify system parameters using a least squares method. The security parameter design is achieved based on a modification of conventional homomorphic encryption for improving a sample deciphering time and a novel sample identifying complexity, characterized by controllability Gramians and the variance ratio of identification input to system noise. The effectiveness of the proposed design method for a security parameter is demonstrated through numerical simulations.
SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysis

Abstract The steganalysis for JPEG image is an important research topic, as the enormous popularity of JPEG image on Internet. However, the stego noise feature extraction process of the existing deep learning‐based steganalytic methods are not adaptive enough to the content of the image, which may lead to suboptimal steganalysis performance. In order to solve this issue, an adaptive stego noise extraction network, named SNENet, for JPEG image steganalysis is proposed. The stego noise extraction module of the network is specifically designed for steganalysis, which consists of parallel dilated convolutional layer and inverted bottleneck layer. This specific design expands the receptive field of the network, which makes the extraction of the stego noise more global and adaptive to the content of the image. The experimental results indicate that proposed network outperforms the state‐of‐the‐art steganalytic method by as much as 6.25% for UED‐JC and 3.35% for J‐UNIWARD. The design of the network is also justified in the extensive ablation experiments.
Design and Implementation of a Digital Donation Box with Automated Coin Sorting System

The digital donation box is a new and innovative approach to the traditional method of collecting donations for charities and non-profit organizations. This system utilizes an Arduino microcontroller, which serves as the brain of the donation box, to automate the donation process and provide greater transparency and security to donors. The system is designed to accept various forms of payment, including all types of coins. The donation box also incorporates an LCD that shows real-time updates on the donation, and a switch is used to display the total amount collected in the box. The entered coins will be separated and collected in different boxes, which will help the management monitor the system. This project can potentially revolutionize how charitable organizations collect donations, making the process more efficient, secure, and transparent