Reviewerly/exHarmony huggingface dataset:
Why Vedic Multiplication Algorithm Works?

The speed of oral multiplication using the Vedic formula is amazing. This paper gives a basis for it. Vedic formula fully uses traditional multiplication only but it is well organized to produce quick results. In usual product finding, the result is found only after summing all multiplications. On the contrast, Vedic algorithm does the reverse engineering. It finds what partial multiplications are filling at units, tens, hundreds, etc. The moment a column is filled, summation of all partial products is immediately done and proceeds to the next column. This way oral product finding becomes amazingly speedy. In spite of this, it started losing importance due advent of computers, supercomputers, and also, very cheap calculators. But very soon computer hardware designers were allured to cash the advantageous Vedic algorithm to make the computer speedy at less power consumption and less area.
EQALM Symposium - Insights into SARS-CoV-2 associated analytics that only EQA schemes can provide

While nucleic acid amplification tests (NAATs) remain the gold standard for clinical diagnostic detection of SARS-CoV-2 due to the high sensitivity and specificity, population testing with antigen tests has become a central part of testing policies for COVID-19 control and surveillance in many countries.Several regulatory-cleared tests are commercially available.However, their performance has been evaluated with early-pandemic variants, and continuous post-market review is consequently needed to determine whether detection rates of tests might be impacted by current variants or emerging variants of SARS-CoV-2.Beginning in May 2021, several international EQA schemes (currently 7 challenges in total) have been distributed by Quality Control for Molecular Diagnostics (QCMD, Glasgow, Scotland, UK), containing inactivated SARS-CoV-2-positive samples of various genetic strains (including variants of concern, VOCs: e.g., Delta, Omicron BA.1, BA.2, BA.4,BA.5) at different concentrations and negative samples, to support testing infrastructures with high-quality proficiency testing options for their antigen tests [laboratory-based and point-of-care (PoC) tests, including lateral flow rapid antigen tests (LFTs)].The primary EQA objectives are to assess the sensitivity of antigen tests and the potential impact of circulating SARS-CoV-2 strains on their performance.All core samples were correctly reported by 76.1 to 97.1% at participant level and by 73.5 to 93.1% at dataset level within the different challenges.Sensitivity differences could be shown in viral loads and SARS-CoV-2 strains/variants including the impact on performance by a B.1.1.7-likemutant strain with a deletion in the nucleoprotein gene.EQA schemes provide valuable data to inform participants about weaknesses in their testing process or methods and support ongoing assay evaluations for regulatory approval or post-market surveillance.Therefore, the performance of COVID-19 diagnostic antigen-based testing should be continuously monitored to ensure their usefulness.
A Chinese Implicit Sentiment Analysis Model Based on Relational Weighted Graph Convolutional Network

Implicit sentiment analysis is used to deal with text corpora that are devoid of sentiment words, which is a difficult task for text sentiment analysis. Implicit sentiment statements in the Chinese corpus are dominated by euphemistic and implicit expressions, and implicit sentiment analysis research can aid in improving the performance of Chinese sentiment analysis tasks. We propose a Chinese Implicit Sentiment Analysis Based on Relational Weighted Graph Convolutional Network (CISA-RWGCN) in this paper, which builds a model with RWGCN, sets different weight mapping matrices for different types of edge relations on GCN, and calculates the weights of neighbor nodes using node representation, node labels, and edge relation types of target and neighbor nodes. On the SMP-ECISA 2019 dataset, the results of comparison and ablation experiments show that the model's full utilization of syntactic information can help the Chinese sentiment classification task achieve 87.4% on the F1 score.
Searching for the Most Probable Combination of Class Labels Using Etcetera Abduction

Many machine perception tasks require a trained model to assign class labels to multiple entities in the same context, e.g., labeling multiple objects in a single photograph. In these tasks, different combinations of labels may be more likely than others, e.g., when co-occurrence biases are considered, such that the most-confident label assigned to an individual object is not always the best choice. In this paper, we propose a new method for combining evidence from multiple class probability distributions to identify the most probable combination of labels in multi-entity contexts. Our method encodes discrete class probability distributions as literals in first-order logic, and uses probability-ranked logical abduction to identify the most likely label combination, incorporating the prior and conditional probabilities of each label. We evaluate our method on two computer vision benchmarks, first for labeling common objects in photographs of everyday contexts, and second for labeling actions of athletes in sports videos. Results indicate significant gains in classifier accuracy over systems that merely select the model's most confident class label.
Knowledge Graph Relationship Prediction Model for Intelligent Search

Intelligent search system can effectively realize the integration of multi-source heterogeneous data resources. In the intelligent search and question answering system based on knowledge graph, the questions are usually asked by some simple questions, which are processed in a pipeline way, and then the subject entity and predicate relationship of the question are selected, and then the most likely answer entity is selected by information integration. Entity relation extraction is the difficulty of knowledge graph. We found that in traditional information processing models, neural network models usually only utilize question sentences for relation classification. But such question sentences often lack context, making it difficult to fully utilize the computational power of neural networks. In response to the above problems, this paper proposes a knowledge graph relationship prediction model (BXS) for intelligent search systems, which uses sentence subject headings and entity background information as auxiliary reasoning information to obtain sufficient context for the problem. We utilize two attention mechanism models to fuse auxiliary information with questions for semantic matching. The experimental results on the data set prove that the model can effectively improve the accuracy of relation extraction, and effectively improve the accuracy of the final question and answer.
Research on Security Sandbox System Based on Computer Big Data Hyperledger Fabric Blockchain Platform

This paper designs an "institution-business-system" multi-level security sandbox system from the perspective of cross-department data sharing and combined with Fabric blockchain technology. The system makes full use of the characteristics of blockchain technology, such as decentralization, immutable and consensus mechanism, to achieve collaborative services of secure sandbox facilities. Then this paper proposes a distributed and regulated privacy protection scheme that combines group signature, private address protocol, zero-knowledge proof and attribute encryption. This algorithm improves the group manager mechanism of group signature. The experimental results show that the scheme can ensure the data traceability and realize the supervision of both sides of the transaction, and improve the level of privacy protection of data on the chain.
Playable Quotes for Game Boy Games

When discussing a work of literature, text quotes can provide access to specific pieces of content and allow them to be placed in a larger context. However, we have no obvious analog for this notion of quotes in the medium of videogames. In this paper, we introduce the concept of playable quotes. To support the needs of developers, educators, streamers, and other stakeholders, playable quotes should be playable (directly interactive), partitioned (small slices of larger works), permanent (able to outlive the original work), and performative (able to demonstrate specific styles of play). Focusing on the Game Boy hand-held game console, we describe Tenmile, a deployed prototype for creating and sharing self-contained playable quotes of Game Boy games that satisfy our key requirements. Using Tenmile’s technical design as a template, we lay out a strategy for implementing similar notions of playable quotes across other computing platforms and raise issues that should be considered before continuing beyond Game Boy.
Machine Learning for Parsing Individual Differences

The dominant case-control paradigm, for which a group of healthy individuals is compared to a group of patients, occludes individual-level differences contributing to disorder or disease. Therefore, we developed normative modelling which is inspired by approaches used in paediatrics for which a child’s height is charted against age. Here, I present novel results on mapping the heterogeneity of mental disorders against population norms which were estimated for imaging and cognitive data using models of varying complexity.
Efficient Multi-task Scheduling with Nearest Neighbor Algorithm for Multi-robot Systems in Antarctic Environments

This paper addresses the problem of multi-robot task scheduling in Antarctic environments. It is difficult to operate multiple robots in Antarctic environments due to the icy ground condition and the lack of powers. In our previous work, the multi-robot task scheduling in Antarctic environments has been solved using ant colony optimization. Although it worked successfully, it caused much computation time. This paper proposes an efficient multi-robot task scheduling approach using nearest neighbor algorithm in Antarctic environments. The proposed method was tested in both simulated environments and Antarctic environments. The proposed method showed better performance in terms of computation time and cost than ant colony optimization and genetic algorithm.
Detection of Textual Propaganda Using Passive Aggressive Classifiers

Nowadays, social media activity, particularly news that spreads over the network, is a major source of knowledge. People search out and chew up news from internet-based living because of the low effort, easy access, and rapid dissemination of information. Twitter, as one of the most wellknown continuing news sources, also happens to be one of the most dominant news disseminating media. It has already been known to wreak significant harm by disseminating snippets of gossip. Online clients are typically susceptible, and everything they do on web-based networking media is assumed to be trustworthy. As a result, automating counterfeit propaganda detection is critical to maintaining a vibrant online media and informal organization. In order to computerize propaganda news identification in Twitter datasets, this research develops a technique for recognizing propaganda text messages from tweets by figuring out how to anticipate precision evaluations. This paper proposes a supervised machine learning technique, Passive aggressive classifiers that uses Count Vectorizer and Term FrequencyInverse Document Frequency Vectorizer as feature extraction to detect propaganda news based on the polarity of the corresponding article. Finally, this algorithm uses dataset with 43000 records and shows good accuracy.
Prospects of Digital Watermarking in Providing Security, Reliability, and Privacy to Medical Images

Telemedicine is one of the most eminent terms used in the modern e-healthcare system. Digital medical image reports, along with electronic patient records, play a central role in diagnosis from distance. These reports need to be transmitted over an open communication channel with immense security and reliability, so that appropriate diagnosis can be performed. Moreover, the privacy of the patient is required to be preserved. Digital watermarking is one of the most conventional and suitable practices to serve all of these purposes. New challenges appear in the domain of watermarking with the advancement of digital signal processing; consequently, researchers are endowing more efforts to overcome these challenges. In this paper, a survey on medical image watermarking has been done, and the performance of a few state-of-the-art medical image watermarking techniques is compared. This work makes the researchers and the developers familiar with the recent trends, challenges, and scopes in this domain to facilitate them in finding out adequate research directions.
Altered Neural Net for Breast Histological Image Categorization

Manual diagnosis of histopathological breast images is both time-taking and inaccurate process. Breast-cancer Computerized Diagnosis (BCD) using the Hematoxylin-Eosin (HE) tissue attained from biopsy relies on the efficiency and accuracy of the process. Accuracy of the conventional classification methods depend on extracted features. To conquer the difficulties of traditional feature-based techniques, deep-learning approaches are preferred as an alternative in attaining accurate classification. Deep Altered-Convolutional Neural Network (A-CNN) for categorization of HE breast tissue images as Benign/Malignant is stated. Network architecture of A-CNN is intended to gain information at nuclei and overall tissue levels. Extracted features from A-CNN are used to train the Multi-Class SVM (MC-SVM) classifier. The cascaded categorization method accomplished better accuracy of 94.16 % and a best AUC of 0.913 than the individual ones.
SUBJECTIVE EXPERIENCES OF PERFORMANCE AND SATISFACTION IN THE PROCESS OF A MIXED LEARNING FORMAT

The article considers the relationship of the subjective assessment indicators for the results of learning in a mixed format, satisfaction with the process of mixed learning with awareness and tolerance to uncertainty in students of different age groups. The results showed that there was no connection between the subjective assessment of learning in a mixed format and tolerance to uncertainty, as well as between satisfaction with the process of mixed learning and awareness. Mindfulness is associated with a subjective assessment of learning outcomes, whereas interpersonal tolerance to uncertainty is associated with the perception of the variability of mixed learning. The majority of respondents demonstrated high levels of satisfaction with the mixed learning format. The study found a connection between awareness and satisfaction with the results of learning in a mixed format, which is associated with the fact that the resource of awareness helps to adapt more easily in the learning process. The flexible model of mixed education combined with high awareness allows to realize the individual educational needs of students. Awareness helps to better understand the own changes, in particular, the formation of expertise in the chosen field. The relationship between satisfaction with the process of mixed learning, characterized by variability, and interpersonal tolerance to uncertainty is revealed. Such a connection indicates rigidity in the perception of interpersonal relationships, which is a factor in the formation of discomfort due to the learning forms variability
Multiexperience-Assisted Efficient Multiagent Reinforcement Learning

Recently, multiagent reinforcement learning (MARL) has shown great potential for learning cooperative policies in multiagent systems (MASs). However, a noticeable drawback of current MARL is the low sample efficiency, which causes a huge amount of interactions with environment. Such amount of interactions greatly hinders the real-world application of MARL. Fortunately, effectively incorporating experience knowledge can assist MARL to quickly find effective solutions, which can significantly alleviate the drawback. In this article, a novel multiexperience-assisted reinforcement learning (MEARL) method is proposed to improve the learning efficiency of MASs. Specifically, monotonicity-constrained reward shaping is innovatively designed using expert experience to provide additional individual rewards to guide multiagent learning efficiently, with the invariance guarantee of the team optimization objective. Furthermore, a reward distribution estimator is specially developed to model an implicated reward distribution of environment by using transition experience from environment, containing collected samples (state–action pair, reward, and next state). This estimator can predict the expectation reward of each agent for the taken action to accurately estimate the state value function and accelerate its convergence. Besides, the performance of MEARL is evaluated on two multiagent environment platforms: our designed unmanned aerial vehicle combat (UAV-C) and StarCraft II Micromanagement (SCII-M). Simulation results demonstrate that the proposed MEARL can greatly improve the learning efficiency and performance of MASs and is superior to the state-of-the-art methods in multiagent tasks.
GIS Modeling of Solar Neighborhood Potential at a Fine Spatiotemporal Resolution

This research presents a 3D geographic information systems (GIS) modeling approach at a fine spatiotemporal resolution to assess solar potential for the development of smart net-zero energy communities. It is important to be able to accurately identify the key areas on the facades and rooftops of buildings that receive maximum solar radiation, in order to prevent losses in solar gain due to obstructions from surrounding buildings and topographic features. A model was created in ArcGIS, in order to efficiently compute and iterate the hourly solar modeling and mapping process over a simulated year. The methodology was tested on a case study area located in southern Ontario, where two different 3D models of the site plan were analyzed. The accuracy of the work depends on the resolution and sky size of the input model. Future work is needed in order to create an efficient iterative function to speed the extraction process of the pixelated solar radiation data.
Evaluating the influence of backbone network architectures for object detection in aerial images

Drones are increasingly being used in surveillance, agriculture, and delivery tasks. However, the real-life application of images collected from drones in urban manage- ment in Vietnam is still limited. Although drone images have many advantages thanks to the flexibility of the latest devices, there are still new challenges, such as top-down views, small objects, arbitrary directions, and class imbalance. In this paper, we conduct research, survey, and evaluate the performance of CNN-based network architectures on object detection in aerial images. Experiments were conducted on seven deep learning network architectures: VGG, ResNet, ResNext, Res2Net, ResNeSt, HRNet, and RegNet to bring objective judgments and conclusions based on experiments, contributing to the development of solutions for applications of determining the status of urban traffic in Vietnam.
Advances in analysis for cobalt and nickel in geological materials

早在20世纪初期,化学家就开展了比色法分析钴、镍的研究工作。由于比色法灵敏度不高且操作繁琐,人们开始追求更高效的固体样品消解方法、更简便的操作以及更高灵敏度和高精度的分析技术。在样品分解方面,逐渐发展出了酸溶和碱熔两套样品分解体系;在仪器分析方面,则发展出了原子吸收光谱法、等离子体光谱法、X射线荧光光谱法、等离子体质谱法等更加高效简洁的仪器分析技术。随着地质科学的发展,电子探针和激光剥蚀电感耦合等离子体质谱微区原位分析技术,以及多接收等离子体质谱镍同位素分析技术也逐渐发展起来。在元素分析方面,原子吸收光谱法、等离子体光/质谱法一般需要经过酸或者碱将样品分解为溶液状态,前处理流程较为繁琐;而X射线荧光光谱法采用熔片或者压片进行样品制备,前处理方法简单高效,更加受到青睐。在同位素分析方面,镍同位素逐渐应用到钴镍矿床研究中,近年有望通过典型矿床剖析明确多种成矿过程镍同位素的行为与分馏机制,如岩浆演化、热液蚀变、风化等。镍同位素的分离技术难度较高,因此,创新镍同位素的分离过程和测试方法,并建立更加简便的分析流程是未来发展的重点方向。在微区分析方面,激光剥蚀-电感耦合等离子体质谱比电子探针的样品制备简单、分析速度快、成本低,更有发展潜力。本文总结了近百年来地质样品钴、镍分析方法的演变与突破,对比了各类方法的优缺点,展望了地质样品钴、镍分析方法的发展前景。
Light field multi-scale blocked local binary pattern feature in the spatial-angular domain

Image feature detection and matching are still important to many applications in computer vision. Light field imaging captures structured multi-view data, which provides a potential capability for solving the polysemy problem of feature matching. In this paper, we propose a light field multi-scale blocked LBP feature extraction and matching algorithm based on the light field spatial-angular domain. The proposed algorithm is on the basis of the classic local binary pattern feature, which can be enhanced by adding the description of the variations of different angular views. First, the Harris feature detection is employed to select feature candidates from the light field central sub-aperture images. Then, an orientation selection is introduced to calculate the most invariant angular neighbors. We suggest extracting the local binary pattern features of the selected angular views and stitching them together, to form a novel light field multi-scale blocked LBP feature. Finally, the distance between different features is measured using the vector cosine similarity. The proposed algorithm achieves an average matching precision of 93% in both virtual and real scenes on the paired light fields matching dataset. The proposed algorithm outperforms the classical SIFT feature and the state-of-the-art light field feature. The proposed local binary features are also significantly better than SIFT features and LiFF features in terms of descriptor length.
COMMUNITY RADIO AND ITS ROLE IN AWARENESS AND DEVELOPMENT OF PEOPLE A CROSS SECTIONAL STUDY

The utilisation of community radio as an effective medium can be of tremendous assistance in the dissemination of information and the cultivation of consciousness within communities.The purpose of this experimental and cross-sectional research was to learn more about the ways in which listening to community radio can help its listeners grow as individuals and become more conscious.This investigation gathered both quantitative and qualitative data through the use of a mixed-methods approach from a population that was intended to be representative of Indian community radio listeners and listeners.According to the findings, community radio may be a significant component in increasing people's awareness of topics relating to public health, education, and societal issues.In addition, community radio serves as a forum for participation in the surrounding area, which can facilitate the expansion process and contribute to the establishment of authority.According to the findings of the research, community radio should have more financial resources, its technological underpinning should be upgraded, and more local participation should be encouraged in radio broadcasting.The findings of the research, taken together, offer significant new insights into the ways in which community radio helps to foster consciousness and development.These findings from the research have implications for improving the effectiveness of community radio programmes in India and other underdeveloped countries.