Reviewerly/exHarmony huggingface dataset:
Maximal curves and Tate-Shafarevich results for quartic and sextic twists

We study elliptic surfaces corresponding to an equation of the specific type y2=x3+f(t)x, defined over the finite field Fq for a prime power q≡3mod4. It is shown that if s4=f(t) defines a curve that is maximal over Fq2 then the rank of the group of sections defined over Fq on the elliptic surface is determined in terms of elementary properties of the rational function f(t). Similar results are shown for elliptic surfaces given by y2=x3+g(t) using prime powers q≡5mod6 and curves s6=g(t). Finally, for each of the forms used here, existence of curves with the property that they are maximal over Fq2 is discussed, as well as various examples.
Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow

PurposeTo evaluate the stand-alone diagnostic performances of AI-CAD and outcomes of AI-CAD detected abnormalities when applied to the mammographic interpretation workflow.MethodsFrom January 2016 to December 2017, 6499 screening mammograms of 5228 women were collected from a single screening facility. Historic reads of three radiologists were used as radiologist interpretation. A commercially-available AI-CAD was used for analysis. One radiologist not involved in interpretation had retrospectively reviewed the abnormality features and assessed the significance (negligible vs. need recall) of the AI-CAD marks. Ground truth in terms of cancer, benign or absence of abnormality was confirmed according to histopathologic diagnosis or negative results on the next-round screen.ResultsOf the 6499 mammograms, 6282 (96.7%) were in the negative, 189 (2.9%) were in the benign, and 28 (0.4%) were in the cancer group. AI-CAD detected 5 (17.9%, 5 of 28) of the 9 cancers that were intially interpreted as negative. Of the 648 AI-CAD recalls, 89.0% (577 of 648) were marks seen on examinations in the negative group, and 267 (41.2%) of the AI-CAD marks were considered to be negligible. Stand-alone AI-CAD has significantly higher recall rates (10.0% vs. 3.4%, P < 0.001) with comparable sensitivity and cancer detection rates (P = 0.086 and 0.102, respectively) when compared to the radiologists’ interpretation.ConclusionAI-CAD detected 17.9% additional cancers on screening mammography that were initially overlooked by the radiologists. In spite of the additional cancer detection, AI-CAD had significantly higher recall rates in the clinical workflow, in which 89.0% of AI-CAD marks are on negative mammograms.
An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis

Google Translate has been prominent for language translation; however, limited work has been done in evaluating the quality of translation when compared to expert translators. Sanskrit, one of the oldest written languages in the world was added to the Google Translate engine in 2022. Sanskrit is known as the mother of North Indian languages such as Hindi, Punjabi, and Bengali. Sanskrit has been used for composing sacred Hindu texts such as the Bhagavad Gita and the Upanishads. In this study, we present a framework that evaluates Google Translate for Sanskrit using the Bhagavad Gita. We first published a translation of the Bhagavad Gita in Sanskrit using Google Translate and then compared it with selected prominent translations by experts. Our framework features a BERT-based language model that implements sentiment and semantic analysis. The results indicate that there is a low level of similarity between the Bhagavad Gita by Google Translate and expert translators in terms of sentiment and semantic analysis and the translation of philosophical terms and metaphors. We further implemented a qualitative evaluation and found that Google Translate was unsuitable for the translation of certain Sanskrit words and phrases due to the poetic nature, contextual significance, metaphor and imagery. The mistranslations are not surprising since the Bhagavad Gita is known as a difficult text not only to translate but also to interpret since it relies on contextual, philosophical and historical information. It is difficult to distinguish different names and philosophical concepts such as karma and dharma without knowing the context and having a background in Hindu philosophy. Our framework lays the foundation for the automatic evaluation of other languages translated by Google Translate.
Current status and implications of legislation related to digital therapeutics

With the development of the digital healthcare sector, the software itself has now become a medical device. Digital Therapeutics(DTx) are defined as “providing evidence-based treatment interventions driven by high-quality software programs to prevent, manage, or treat medical disorders or diseases.” With the spread of digital therapeutics(DTx), major countries are expanding government-level support and investment, and overhauling related laws.&#x0D; This study examined the current status of domestic and foreign digital therapeutics and identified related laws and regulatory trends. Based on this, domestic legal and institutional improvement measures were proposed.&#x0D; According to the results of the study, major countries, including Korea, defined medical devices based on the Medical Device Act and regulated medical devices by classifying them according to the degree of risk.&#x0D; In the case of digital therapeutics, they are classified into low-risk grades and stipulate processes such as licensing and test evaluation of devices. In addition, a separate fast track was applied or the digital therapeutics was supported with individual guidelines. However, for the safe development of digital therapeutics in the future, it is necessary to systematically reorganize the regulations of digital therapeutics by reflecting the core contents of the guidelines in the law or enacting separate laws.&#x0D; In addition, the compensation method and level for the application of the health insurance system should be improved. In other words, doctor fees and patient compensation methods should be considered by reflecting the characteristics of digital therapeutics. Various discussions are needed in consideration of health insurance finance issues.&#x0D; Finally, due to the nature of digital therapeutics, it is necessary to strengthen data security measures and establish a management system for this.
Convolutional neural network-based recognition method for volleyball movements

With the development of network technology and computer intelligent monitoring technology, a large number of video data came into being. In view of the analysis of specific targets in video, the traditional artificial analysis method can not meet the existing needs. In volleyball teaching in college physical education, because each student has different movements, intelligent processing of video data has become a key issue. Through collecting and sorting out the relevant research results on behavior recognition, it is found that in the research of deep learning, the algorithm structure applied in this paper is representative, and its strong learning ability, especially compared with the traditional algorithm, can more accurately identify human movements, natural language processing and so on, but the research on volleyball action recognition is still less. Therefore, this paper constructs a data set, improves the convolution neural network model, subsequently, new models are constructed through the neural network structure to improve the accuracy of the nonlinear expression and optimize the content of the input data. In order to more accurately analyze the effectiveness of this algorithm, the new data are obtained by grouping the volleyball games in college sports courses. Compared with the original paper, the accuracy of the improved 3D network is improved by 3.3%-88.5%, and the complexity is reduced by 33.6%.
Deep Hybrid Multimodal Biometric Recognition System Based on Features-Level Deep Fusion of Five Biometric Traits

The need for information security and the adoption of the relevant regulations is becoming an overwhelming demand worldwide. As an efficient solution, hybrid multimodal biometric systems utilize fusion to combine multiple biometric traits and sources with improving recognition accuracy, higher security assurance, and to cope with the limitations of the uni-biometric system. In this paper, three strategies for dealing with a feature-level deep fusion of five biometric traits (face, both irises, and two fingerprints) derived from three sources of evidence are proposed and compared. In the first two proposed methodologies, each feature vector is mapped from the feature space into the reproducing kernel Hilbert space (RKHS) separately by selecting the appropriate reproducing kernel. In this higher space, where the result is the conversion of nonlinear relations to linear ones, dimensionality reduction algorithms (KPCA, KLDA) and quaternion-based algorithms (KQPCA, KQPCA) are used for the fusion of the feature vectors. In the third methodology, the fusion of feature spaces based on deep learning is administered by combining feature vectors in in-depth and fully connected layers. The experimental results on 6 databases in the proposed hybrid multibiometric system clearly show the multimodal template obtained from the deep fusion of feature spaces; while being secure against spoof attacks and making the system robust, they can use the low dimensionality of the fused vector to increase the accuracy of a hybrid multimodal biometric system to 100%, showing a significant improvement compared with uni-biometric and other multimodal systems.
Dimensional measurement of anti-stress cone of HV cable joints based on 3D point cloud

Abstract To solve the problem of automatic measurement of anti-stress cone of high voltage cable joints, a measurement algorithm based on 3D point cloud is proposed. Firstly, denoising and coordinate adjustment of point cloud of a cable joint are realized. Then the characteristic curve model of fitting circle radius indicating structure characteristic of cable joints is constructed by fitting circles with r andom s ample c onsensus algorithm. After that, the discrete points on the characteristic curve are extracted according to the characteristics such as the fluctuation range of the characteristic curve for straight line fitting and the intersection of the fitted lines is calculated to complete the segmentation of the anti-stress cone area of the cable joint. Finally, the parameter measurement of the anti-stress cone was completed according to the structure of the anti-stress cone, and the error correction post-processing was performed. Two types of cable joint point clouds with standard dimensions and surface defects were used for measurement experiments, the absolute errors of the measurement results are less than 0.2 mm and 1.0 mm respectively, and the relative errors are less than 0.25% and 2% respectively, indicating that the proposed method has high robustness and measurement accuracy, which meet the measurement requirements in engineering practice.
Manticore: A Framework for Efficient Multiparty Computation Supporting Real Number and Boolean Arithmetic

We propose a novel framework, $$\texttt{Manticore}$$ , for multiparty computations, with full threshold and semi-honest security model, supporting a combination of real number arithmetic (arithmetic shares), Boolean arithmetic (Boolean shares) and garbled circuits (Yao shares). In contrast to prior work (Mohassel and Zhang, in 2017 IEEE symposium on security and privacy (SP), 2017; Mohassel and Rindal, in Proceedings of the 2018 ACM SIGSAC conference on computer and communications security, 2018), $$\texttt{Manticore}$$ mitigates overflows, which is of paramount importance for machine learning applications, without compromising efficiency or security. Compared to other overflow-free recent techniques such as MP-SPDZ (Escudero et al., in 40th annual international cryptology conference, CRYPTO. Lecture notes in computer science, 2020) that convert arithmetic to Boolean shares, $$\texttt{Manticore}$$ uses an efficient modular lifting/truncation method that allows for scalable high numerical precision computations with optimal numerical windows and hence, highly efficient online phases. We adapt basic MPC operations such as real-valued polynomial evaluation, division, logarithms, exponentials, Fourier series evaluations and oblivious comparisons to $$\texttt{Manticore}$$ by employing our modular lift in combination with existing efficient conversions between arithmetic, Boolean and Yao shares. We also describe a highly scalable computations of logistic regression models with real-world training data sizes and high numerical precision through PCA and blockwise variants (for memory and runtime optimizations) based on second-order optimization techniques. On a dataset of 50 M samples and 50 features distributed among two players, the online phase completes in 14.5 h with at least 10 decimal digits of precision compared to plaintext training. The setup phase of $$\texttt{Manticore}$$ is supported in both the trusted dealer and the interactive models allowing for tradeoffs between efficiency and stronger security. The highly efficient online phase makes the framework particularly suitable for MPC applications where the output of the setup phase is part of the input of the protocol (such as MPC-in-the-head or Prio).
Age-dependent face diversification via latent space analysis

Facial age transformation methods can change facial appearance according to the target age. However, most existing methods do not consider that people get older with different attribute changes (e.g., wrinkles, hair volume, and face shape) depending on their circumstances and environment. Diversifying such age-dependent attributes while preserving a person’s identity is crucial to broaden the applications of age transformation. In addition, the accuracy of age transformation to childhood is limited due to dataset bias. To solve these problems, we propose an age transformation method based on latent space analysis of StyleGAN. Our method obtains diverse age-transformed images by randomly manipulating age-dependent attributes in a latent space. To do so, we analyze the latent space and perturb channels affecting age-dependent attributes. We then optimize the perturbed latent code to refine the age and identity of the output image. We also present an unsupervised approach for improving age transformation to childhood. Our approach is based on the assumption that existing methods cannot sufficiently move a latent code toward a desired direction. We extrapolate an estimated latent path and iteratively update the latent code along the extrapolated path until the output image reaches the target age. Quantitative and qualitative comparisons with existing methods show that our method improves output diversity and preserves the target age and identity. We also show that our method can more accurately perform age transformation to childhood.
Technological Solutions for Digital Identity

Digital transformation of enterprises is driving the need for a digital identity to recognize people for delivering services. The implementation of digital identity is complex, requiring several technological solutions and much coordination. Capturing and processing data is challenging because biometric issues may arise due to imaging errors. This article addresses this issue and proposes a computer vision-based framework for contactless recognition process using a focus group discussion approach for inputs from experts. The proposed framework enhances image capturing process, extraction of high-quality features from captured images, image processing, contactless face detection, and authentication. The study also derives lessons for other biometric-based identity projects based on image analysis. The proposed framework can be used as a reference for understanding multidimensional perspectives, scalability, and adoption of technological solutions in other similar projects in developing countries in future.
Empirically evaluating the effect of security precautions on cyber incidents

Available data on firm cybersecurity often exhibits a positive correlation between investment in security precautions and cyber attacks since investments are often made after a firm has been breached. Using survey data from Israeli firms about their cyber defenses, we overcome the endogeneity obstacle using an instrumental variable (IV) drawn from questions about a cybersecurity directive. The resulting regressions examine the causal relationship between security precautions potentially undertaken by enterprises and the likelihood of experiencing a cyber incident. Once suitably instrumented and controlling for characteristics that make some firms more attractive attack targets than others, we find robust evidence that increased adoption of security controls does in fact reduce the likelihood of being breached.
VoteChain - A Blockchain-Based Voting Platform

Due to increasingly widespread electoral corruption, citizens are slowly starting to lose trust in the fairness of democratic elections. The main objective of VoteChain is the elimination of the aspect of trust from the electoral process, in order to make voting more secure, transparent, and easily accessible. This paper proposes and implements a robust system that enhances voting efficiency by creating an electronic platform on top of a distributed Bitcoin Cash blockchain ledger. Blockchain represents a time-stamped series of immutable data records shared across a distributed network. When utilized in the context of voting, it guarantees full anonymity, vote integrity, and a fair, incontrovertible ledger with verifiable election results to all voters. Moreover, the system offers the ability to vote via any Internet-enabled computer or smartphone, dramatically decreasing the overall election organization costs. The system is envisioned as an application that connects to the Bitcoin Cash blockchain network via a custom feature-rich library. After discussing the system's characteristics, design, and underlying technology, this paper presents an example election scenario explaining how VoteChain works in-depth. In the end, the system's possible shortcomings are outlined, along with its prospective evolution and potential improvements that can be implemented.
Parallel DNA sequencing with exact and approximate string-matching algorithms

String-matching algorithms are concerned with lo-cating specific patterns within larger strings or text files. This problem of exact sequence matching is studied in various fields of molecular biology, data compression, and information retrieval. Therefore, the goal of this paper is to address problems that string matching algorithms have when used on DNA sequences. The main obstacles are the pattern and text lengths that are used as input data. The main content and the whole goal is to adapt the parallel string matching problem to a larger size of text and adapt exact brute force to fit the idea of a DNA matching that can not have 100% matching. To achieve that idea is to implement naive k-mismatches that allow matching to 99% which is acceptable when talking about DNK sequences.
A Study on the Improvement of Weapon System T&amp;E performance System

The purpose of this study is to derive the need to improve the test and evaluation(T&amp;E) performance system of the weapon systems to which advanced science and technology is applied, evaluate priorities, and present development plans. T&amp;E of Al-based weapon systems through a literature research and case analysis on changes in the T&amp;E environment for weapon system, 11 detailed evaluation items were derived from the in terms of the T&amp;E system, structure, and technology. Al-based weapon system test evaluation should be performed in parallel with data-based performance evaluation and actual T&amp;E, and performance measurement using a separate T&amp;E data set is required for AI models performance evaluation. As a result of analyzing the importance of T&amp;E through AHP analysis, the order of T&amp;E system-technology-structure was evaluated, and the priority of detailed evaluation items was evaluated in the order of T&amp;E result judgment-T&amp;E organization and expert training-scientific T&amp;E. For evaluation items with high priority, measures to improve the T&amp;E performance system were presented.
Impact of tool integration on the efficiency of test execution

Testing is a crucial part of software development. Testing uses a combination of tools to execute test cases and record their results. This paper addresses the integration process of such tools (Windchill and JIRA) into a single application that can be used for overall test case management, as well as exploring the potential for shortening the testing process duration. In addition, test execution and reporting times for tests executed using the integrated test management tool and for tests executed without the aforementioned tool will be analyzed to determine if test execution and reporting times have been reduced as a result of integrating multiple tools.
Representing Multifunctional Devices in Voice-Controlled Systems

As the number of smart devices continues to grow, it is becoming increasingly required by the consumers to integrate them into voice-controlled systems like Google Home and Amazon Alexa. However, many device types are not fully supported on these platforms, and the integration often require modifications to fit the voice assistant platform requirements, which is sometimes not possible. In particular, if multifunctional devices have to be represented in a way supported by Google Home and Amazon Alexa APIs, many features are not visible within the assistant and it is not possible to control them. This paper proposes a solution for representing complex devices within a voice-controlled system, by using unique templates specific to the device model and manufacturer. The template contains the necessary information, which allows to split the functionality of one physical device into multiple logical devices so that each logical device can be controlled by the voice assistant.
A Secure Cloud-Based Video Recording and Sharing Scheme for Home Security Applications

Security and privacy in the Internet of things (IoT) ecosystems are very serious concerns for significant applications. Today, the use of video surveillance systems to oversee and increase the security of homes and other premises has become widespread. In many of these systems, a copy of the captured videos is transmitted to the cloud storage server, where it is archived and stored. However, the data collected by these systems can be very sensitive and private, so that the owners may not want to trust the cloud in terms of video access. In some applications, such as smart home security, homeowners need to give access to several trusted users to retrieve stored videos from the cloud server in an emergency. How to manage the video access control without trusting the cloud service is a challenging problem.In order to solve the above challenge, we present a secure scheme which allows users to encrypt video footage and securely store it on the cloud server. Furthermore, by employing this scheme, a user can add other users to the system to share recorded videos with them. Moreover, the user can remove other users’ access to the videos whenever he/she wants without trusting the cloud service provider. Our security assessment shows that the proposed scheme is robust and resists various malicious attacks. Furthermore, we demonstrate that the proposed protocol is efficient and well-designed through performance analysis.