Reviewerly/exHarmony huggingface dataset:
Ceramic art image design based on neural network and particle image algorithm

In recent years, scientists have developed a new type of algorithm called convolutional neural network algorithm in the field of neural networks. This algorithm not only has a powerful image recognition function, but also can distinguish and arrange data images. At the same time, the algorithm's recognition and processing functions are also very powerful, are able to identify relatively hidden images and process a very large image library in a short time. The research content of this article is the application and development of ceramic image creation based on the classification effect of neural network and the characteristics of quantum particle swarm algorithm. And according to the principles, standards, characteristics of neural network classification and the characteristics and technology of particle swarm algorithm, the traditional LB G algorithm and an improved LB G algorithm are discussed, and simulation experiments are carried out. During the experiment, the staff analyzed and optimized the specific process of the quantum particle swarm algorithm through a large number of calculations and simulation experiments. And according to the classification of neural network and quantum particle swarm algorithm, the researchers also proposed a set of practical ceramic image design methods. Through the inspection and comparison of the design results, the researchers preliminarily judged that the design method is not only practical, but also has many advantages such as recognition, high accuracy and good visual experience. At the same time, the staff also optimized the method based on the preliminary design results.
A survey on blockchain sharding

As a promising technology, blockchain has found widespread application in numerous decentralized systems. However, the scalability problem of blockchain has drawn considerable criticism. Sharding, an effective technology, offers a solution to enhance blockchain scalability by enabling parallel validation and confirmation of transactions or new block generation. Although extensive research has been conducted on sharding, the existing literature still lacks a thorough review on its current state of arts with comprehensive analysis and evaluation. In this paper, we propose a series of evaluation criteria regarding scalability, applicability, and reliability. Additionally, we classify the cutting-edge sharding schemes based on blockchain type and sharding techniques. We then provide a comprehensive overview of these existing schemes by analyzing their respective advantages and disadvantages according to the proposed criteria. At the end of the survey, we highlight open issues and suggest future research directions based on the results of our meticulous analysis.
A practical-quantum differential attack on block ciphers

Differential attack is a basic cryptanalysis method for block ciphers that exploits the high probability relations between the input and output differences. The existing work in quantum differential cryptanalysis of block ciphers is focused on resource estimation to recover the last round subkeys on the basis of relations constructed using classical methods. To find such relations using quantum computing, we propose a method to search the high probability differential and impossible differential characteristics. The method explores all possible input and output difference pairs using superposition of qubits. The proposed method is used to design the quantum circuit to search the differential characteristics for a toy cipher smallGIFT. We execute the quantum circuit on a quantum simulator to get differential and impossible characteristics. These characteristics are validated with the characteristics obtained using branch-and-bound based method. The differential characteristic is used to mount quantum key recovery attack using Grover’s search. We provide the estimation of quantum resources to search the differential characteristics of lightweight block cipher GIFT-64 on a quantum computer.
<scp>CHAN</scp>: Skeleton based action recognition by multi‐level feature learning

Abstract Skeleton‐based action recognition has been continuously and intensively studied. However, dynamic 3D skeleton data are difficult to be popularized in practical applications due to the restricted data acquisition conditions. Although the action recognition method based on 2D pose information extracted from RGB video can effectively avoid the influence of complex background, it is susceptible to factors such as video jitter and joint overlap. To reduce the interference of the aforementioned factors, we use two‐dimensional skeletal joint coordinate modal information to represent the changes in human body posture. First, we use a target detector and pose estimation algorithm to obtain the joint coordinates of each frame sample from RGB video. Then the feature extraction network is combined to perform multi‐level feature learning to establish correspondence between actions and corresponding multi‐level features. Finally, the hierarchical attention mechanism is introduced to design the model named CHAN. By calculating the association between elements, the weight of the action classification is redistributed. Extensive experiments on three datasets demonstrate the effectiveness of our proposed method.
#WhatIsDemocracy: finding key actors in a Chinese influence campaign

Abstract The rapid increase in China’s outward digital presence on western social media platforms highlights China’s priorities for promoting pro-Chinese narratives and stories in recent years. Simultaneously, China has increasingly been accused of launching information operations using bot activity, puppet accounts, and other inauthentic activity to amplify its messaging. This paper provides a comprehensive network analysis characterization of the hashtag influence campaign China promoted against the US-hosted Summit on Democracy in December 2021, in addition to methods to identify different types of actors within this type of influence campaign. China uses layers of state-sponsored accounts, bots, and non-bot accounts to promote its messaging. Lastly, we describe how China uses localized campaigns under a more extensive umbrella campaign for information diffusion toward targeted audiences.
A Novel Method for Solving Universum Twin Bounded Support Vector Machine in the Primal Space

In supervised learning, the Universum, a third class that is not a part of either class in the classification task, has proven to be useful. In this study we propose (N $$ \mathfrak {U} $$ TBSVM), a Newton-based approach for solving in the primal space the optimization problems related to Twin Bounded Support Vector Machines with Universum data ( $$ \mathfrak {U} $$ TBSVM). In the N $$ \mathfrak {U} $$ TBSVM, the constrained programming problems of $$ \mathfrak {U} $$ TBSVM are converted into unconstrained optimization problems, and a generalization of Newton’s method for solving the unconstrained problems is introduced. Numerical experiments on synthetic, UCI, and NDC data sets show the ability and effectiveness of the proposed N $$ \mathfrak {U} $$ TBSVM. We apply the suggested method for gender detection from face images, and compare it with other methods.
Addressing the semi-open set dialect recognition problem under resource-efficient considerations

This work presents a resource-efficient solution for the spoken dialect recognition task under semi-open set evaluation scenarios, where a closed set model is exposed to unknown class inputs. We have primarily explored the task 2 of the OLR 2020 challenge for our experiments. In this task, three Chinese dialects Hokkien, Sichuanese, and Shanghainese, are to be recognized. For evaluation, along with the three target dialects, utterances from other unknown classes are also included. We find that the top-performing submissions and the baseline system did not propose solutions that explicitly address the semi-open set scenario. This work pays special attention to the semi-open set nature of the problem and analyzes how the unknown utterances can potentially degrade the overall performance if not treated separately. We train our main dialect classifier with the ECAPA-TDNN architecture and 40-dimensional MFCC from the training data of three dialects. We propose a confidence-assessment algorithm and combine the TDNN performance from both end-to-end and embedding extractor approaches. We then frame the semi-open set scenario as a constrained optimization problem. By solving it, we prove that the performance degradation by the unknown utterances is minimized if the corresponding softmax prediction is equally confused among the target outputs. Based on this criterion, we develop different feedback modules in our system. These modules work on the novelty detection principles and flag unknown class utterances as anomaly. The prediction score of the corresponding utterance is then penalized by flattening. The proposed system achieves Cavg(×100) score of 8.50 and EER (%) of 9.77. Averaging both metrics, the score for our system outperforms the winning submission. Due to the proposed semi-open set adaptations, our system achieves this performance using much less training data and computation resources than the top-performing submissions. Additionally, to verify the broader applicability of the proposed semi-open set solution, we experiment with two other dialect recognition tasks covering English and Arabic languages and larger database sizes.
Implementation of Least Square Algorithm to Predict Monthly Revenue (Case Study: Djuju’s Grocery Store)

Business owners need to estimate their revenue, which is crucial for the sustainability of their operations. Thus, entrepreneurs such as micro, small, and medium-sized business owners, as well as owners of grocery stores, leverage technological advancements to maximize their sales operations. However, manual sales activities can pose challenges for managing sales data, such as disorganized sales record keeping, failure to record sales of high-volume customers, and time-consuming manual reporting for revenue predictions. To address these issues, researchers have developed a revenue prediction information system. In this study, revenue and profit predictions for the following period were calculated using the Least Square algorithm with the Mean Absolute Percentage Error (MAPE). An example calculation for a 12-month period resulted in a revenue forecast of Rp. 2,837,687.76 for the month of June 2023 with a MAPE of 12.71%.
MEDIA DIGITAL DALAM MENINGKATKAN KEMAMPUAN BERPIKIR KRITIS PEMBELAJARAN IPA SISWA SEKOLAH DASAR

Because teachers continue to dominate learning in the classroom, it is difficult for students to be critical of their learning. The purpose of this study is to describe digital media innovations that improve students' critical thinking skills in learning science in elementary schools. This study uses a literature study design with a qualitative approach. The secondary data used in this study were obtained by means of collecting information based on the literature, analyzing data from national and international journals published in the last five years, and reviewing books. Research findings show that innovative digital media can improve students' thinking skills in learning Natural Sciences (IPA) in Elementary Schools, such as videos, Power Point presentations, e-books, flipbooks, augmented reality, and educational websites.
Validating an Automated System of Evaluation for English Drafting Skill: A Case of Large-scale, High-stakes Selection of Entry-level Managerial Positions

The effectiveness of using a constructed response measure for assessing drafting ability is proven and has been used extensively in managerial selection in state-owned organizations. With the advent of online recruitment trends and technology-enhanced assessments, automated scoring has been conceived as a replacement for human scoring with the purpose of emulating the human scoring system. In the context of large-scale writing assessments, automated scoring could provide superior results to human scoring in terms of validity and reliability. In the present study, an attempt has been made to validate an automated essay scoring (AES) algorithm. The study was conducted on a sample of 11,497 randomly selected from a population of 54,392 shortlisted candidates for a national-level examination for the selection of entry-level executives in managerial positions in state-owned banks and a state-owned insurance company. The evaluation of the descriptive (constructed response) component of the examination (English composition) was carried out parallelly by four expert human raters and the AES algorithm. The parameters for evaluation were devised and made available to raters and the algorithm. Data were analysed using mean SD and Pearson correlation coefficient. Results show that the mean scores of human expert raters ( M = 14.648) and automated algorithm method ( M = 15.804) were similar. Further analysis was also undertaken to check the convergent validity of the features used in the algorithm by examining the relation of algorithm scores with sub scores from the objective test of the same construct. Results indicate a significant correlation. It can thus be said that the algorithm scoring method developed can be considered a replacement in that it can complement human expert raters in the evaluation of descriptive papers with consistent scoring and fairness, without inherent biases of inter-rater and intra-rater variation, in addition to practical benefits of speed and cost.
A Digitalized Voting System using Fingerprint Authentication

Electronic voting, also referred to as "e-voting," has been used since the 1970s and has several benefits over paper-based systems, including increased efficiency and reduced mistake rates. However, there are still obstacles to the widespread adoption of these systems, particularly when it comes to enhancing their susceptibility to potential defects. To promote error-free, quick, and secure voting in elections, the study proposes an authentication system and a digitalized version of the current voting system in this research and suggests a fingerprint authentication method because fingerprints serve as a user's unique and crucial form of identification. Aadhar database is stored in the system, and when a voter enters the polling booth, authentication and verification are done through a fingerprint scanner with the help of this. The voter can cast their vote digitally once the authentication and verification processes have been completed. If the voter has already voted, an error occurs and the 'voter has already voted' message is displayed. Since the system already has the entire voter's information, if there is a problem with the authentication procedure, the voter won't be able to cast their vote. The proposed design has an easy-to-understand architecture and responds quickly. It makes trouble-free, easy, and accurate counting possible. This approach allows qualified voters to cast their ballots from anywhere in India.
Video Surveillance System and Violence Detection

The Video Surveillance System is an enhanced version of modern CCTV surveillance. The system is trained using Yolo algorithm to detect the weapons and also is trained using Convolutional Neural Network (CNN) to detect Violence in public places. The system not only detects but also it sends notification to the central control of the system like Police Stations and the police will be able to take necessary actions. The users can also send complaints and use the android application in case of emergency to alert the police if the user is in danger situations. The police will be able to take quick actions using the locations available with the complaint or emergency. By combining CCTV surveillance and weapon detection the system can act as a Central Surveillance System. The system will not record the full video instead take the snapshot of the detected weapons or violence and send it to the admin saving the memory.
AMQP Protocol-Based Multilevel Security for M-commerce Transactions

Security is the main concern for E-commerce applications. Nowadays, most of the E-commerce applications are done on palmtops, laptops, and mobile phones through some applications. That is why we gave the name M-commerce. When it comes to purchasing goods and services online, the term “E-commerce” is used and if those purchases are with mobile phones, then the term M-Commerce is used. Additionally, “the ability to purchase products from any location via a wireless Internet-connected device” is used to describe M-commerce. Web-enabled wireless phones are the most popular means of mobile communication. The phrase “e-commerce in a mobile environment” might also be used to describe it. M-Payments Commerce's module is a critical issue that requires more study before practical solutions can be introduced. A number of security issues were discussed in this article. Cryptography can be accomplished using any number of conventional and public key/private key algorithms. As a result, each algorithm has its own advantages and disadvantages based on several factors, such as Key size and Data size. After testing all parameters, the suggested model will be adjusted for certain contexts, which is the next step in the development of the generalized model (Three Level Gateway Protocol for Secure M-Commerce Transactions).
Measurement techniques for aspheric surface parameters

Aspheric surfaces are widely used in advanced optical instruments.Measuring the aspheric surface parameters (ASPs) with high accuracy is vital for manufacturing and aligning optical aspheric surfaces.This paper provides a review of various techniques for measuring ASPs and discusses the advantages/disadvantages of these approaches.The aim of this review is to contribute to advancements in the fabrication and testing of aspheric optical elements and their practical applications in diverse fields.
Privacy-Preserving Data Publishing Models, Challenges, Applications, and Issues

The distribution of Electronic Health Records is highly needed for various analysis purposes and medical studies. However, the data should be disclosed to the data recipient in such a way that the privacy of the individual is ensured. Privacy-preserving data publishing is a challenging task because the distributed data should be protected against multiple privacy threats. Several privacy models, methods, techniques have been proposed and studied in earlier works, however, the study concerning privacy models considering adversary background knowledge is very limited. In this study, various privacy models and their attack models have been analyzed and depicted, the main focus of the study is the adversary’s background knowledge privacy-preserving data publishing model. Various research challenges in security and privacy have been identified and summarized. Furthermore, the various applications of privacy-preserving data publishing such as Cloud, E-health, Social Network, Agriculture, and Smart City have been studied and the need for privacy is briefed. Finally, the paper highlighted the study's intuitions regarding present unresolved challenges as well as probable future approaches in privacy-preserving data publishing.
Review of Model-Based Techniques in Augmented Reality Occlusion Handling

This paper reviews a model-based method in occlusion handling. The common methods of handling occlusion in Augmented Reality (AR) can be generally classified as depth-based or model-based. This paper presents a review of model-based methods in order to gain understanding of the current state-of-the-art literature and challenges in this field. After searching and filtering in SCOPUS database, 7 papers ranging from 2015 to 2022 were selected for this review and analyzed. From the review, the main conclusion that can be drawn is the trend evolved from researching and developing on the Kinect device to handheld devices. The main challenges are improving the lighting interaction between real and virtual objects and also reducing misalignment due to reprojection errors.
Big Data Security: Attack’s Detection Methods Using Digital Forensics

The main objective of this research is to defend big data against various cyberattacks. Today, it is commonplace to use big data security to safeguard data and analytical processes against malicious activity. Cybercriminals are increasingly using fraudulent calls, texts, and other techniques to steal users' private information. Nowadays, these attacks are increasingly frequent online. In this paper, we addressed various security technologies and strategies that are being utilized to make big data more secure from our prior research references. We also presented various digital forensics tools that may be used to identify who, where, and how an attack was conducted. The fields of data science and cybersecurity are combined in this study. Detecting attacks on big data and ensuring and mitigating data security using cybersecurity and digital forensics techniques are the key goals of this study.
Evaluation of Security of Cloud Deployment Models in Electronic Health Records

Most of the doctors have used EHR (Electronic Health Records) for patient record-keeping due to the convenience of data or record sharing at will. Additionally, it is more convenient for the patients, nurses, and other healthcare ecosystem stakeholders. The cloud is becoming the infrastructure for the majority of EHRs due to cheaper costs and application scalability, yet without compromising data protection. Using a key-control method, we have suggested a framework for storing medical records and allowing patients and doctors to access them. The situations we've thought about here are for rural and urban medical facilities, making them more suitable for Indian medical services. By creating separation between the encryption algorithms used to transmit and store data, the proposed approach provides double data security. The outcome of the experiment demonstrates that it is capable of scaling both the quantity of patients and the quantity of health record components. Cloud computing has a lot of computers and a lot of data. An essential component of cloud computing is data security. The cloud is plagued by a variety of concerns, including storage, environment and security problems including dependability and privacy. Even still, despite all the effort put into resolving these issues, there are still certain security issues. In cloud storage, ensuring data security is a crucial concern. Our paper examines the research of security issues in healthcare system deployment models. Different methods that have been developed attempt to address various security concerns for healthcare systems.
Compression in Dynamic Scene Tracking and Moving Human Detection for Life-Size Telepresence

Human detection, tracking and other forms of human identification have much practical applicability, including telepresence systems. The advent of the depth cameras, such as the Microsoft Kinect, has made human detection and tracking much faster and more efficient. With telepresence technology capabilities, it is possible for a local user to interact with a user located at a remote location freely even in a moving state. Thus, this paper describes the proposed method involving the phases in producing real-time moving human detection to be integrated with life-size telepresence. The proposed method starts with the real-time moving human detection phase followed by the data transmission phase. The paper also presents the life-size telepresence in the holographic display, which has been configured for remote communication and setting up the experimental setup for the life-size telepresence. The implementation process has been explained and the results for the amount of data in bytes transmitted per frame have been discussed. This paper aims to discuss a real-time moving human detection captured using depth sensors and it has been successfully integrated with a life-size telepresence application.