Reviewerly/exHarmony huggingface dataset:
Cross-source Data Error Detection Approach Based on Federated Learning

With the emergence and accumulation of massive data, data governance has become an important manner to improve data quality and maximize data value.Specifically, data error detection is a crucial step to improve data quality, which has attracted wide attention from both industry and academia.At present, various detection methods tailored for a single data source have been proposed.However, in many real-world scenarios, data are not centrally stored or managed.Data from different sources but highly correlated can be employed to improve the accuracy of error detection.Unfortunately, due to privacy/security issues, cross-source data are often not allowed to be integrated centrally.To this end, this paper proposes FeLeDetect, a cross-source data error detection method based on federated learning, so as to improve the error detection accuracy by using cross-source data information on the premise of data privacy.First, a Graph-based Error Detection Model, namely GEDM, is presented to capture sufficient data features from each data source.On this basis, the paper then designs a federated co-training algorithm, namely FCTA, to collaboratively train GEDM by using different cross-source data without privacy leakage of data.Furthermore, the paper designs a series of optimization methods to reduce communication costs during federated learning and manual labeling efforts.Finally, extensive experiments on three real-world datasets demonstrate that (1) GEDM achieves an average improvement of 10.3% and 25.2% in terms of the F 1 score in the local and centralized scenarios, respectively, outperforming all the five existing state-of-the-art methods for error detection; (2) the F 1 score of the error detection by FeLeDetect is 23.2% on average higher than that by GEDM in the local scenario.
A Non-contact Interaction Method for 3D Display Devices

We propose a new interaction method to deal with the defects of traditional hardware in 3D interaction. The proposed method accurately captures human motion using the depth camera, obtains the RGB and depth images of the human body, and calculates the corresponding joints’ 2D and 3D point coordinates. It estimates the human head orientation posture combined with the PnP function in OpenCV to control the presentation change of the scene in the three-dimensional display device. The method proposed in this paper is non-contact, does not need specific 3D glasses, and solves the problem that a monocular camera cannot be positioned. The experimental results show the precise control of the method proposed in this paper.
Real-time text detection and recognition for blind people

The number of visually handicapped or blind persons worldwide are constantly increasing for many reasons such as diabetes, ageing, traffic accidents, etc. In addition to mobility, they need help in reading different boards, text labels, and other items in their everyday life. Text detection and recognition is an important part of blind assistance systems that help them become more economically and socially independent. We proposed an efficient system for text detection and recognition form natural scenes and a system is divided into three parts, first part mainly comprises input text image and preprocessing, Second part includes segmentation and text matching and last part includes text recognition and convert text to speech. The blind person is then given speech output. System accuracy 96.97%.
Bureaucracy Entrepreneurship Strategy Waste Management in East Priangan, Indonesia

The limited quantity of human resources and inadequate service and bureaucracy in waste management services in East Priangan have made waste management in the area very ineffective.The lack of facilities and infrastructure, public awareness, and the absence of rules and strategies in waste management then exacerbate this.This study then aims to see how the entrepreneurial strategy of waste management bureaucracy can be implemented in East Priangan, Indonesia.This research is descriptive qualitative research using SWOT analysis.The data used in this study came from interviews and literature studies regarding studies that still relate to the research discussion.The results of this study then found that waste management in East Priangan uses five strategies, where the Core Strategy and cultural strategy are going well, while the Consequence Strategy, Customer Strategy, and Control Strategy can be said to be not optimal.The strategy for entrepreneurship in the waste management bureaucracy that is effective in East Priangan is an Aggressive Strategy (Growth Oriented Strategy), meaning that the organization is in a steady state by maintaining the previous strategy and seeking necessary improvements to be further optimized through internal strengthening or future innovation.
Image Segmentation and Counting of Red Blood Cells in Hepatitis B Patients

Human blood contains RBCs, WBCs, platelets, and plasma. The counting of blood cells is important since it shows one's overall health. Counting Blood Cells encompasses all of the cells required to evaluate a person's health and diagnose illnesses. A lack of red blood cells (RBC), which account for 99 percent of all blood cells, causes a number of blood disorders. The typical wait time for blood results and reports in today's hospitals and clinical laboratories is currently 24 hours in cases of high-severity diseases with high mortality rates. For high-risk diseases such as hepatitis B, doctors and healthcare technicians advise patients to wait as little as possible and to begin treatment as soon as possible. The process starts with picture acquisition and image enhancement, which reduces noise in photos while preserving the edges, before converting them to binary images and separating the region of interest from the background. RBC is separated from the other blood components during the segmentation process. The blood image is morphologically treated before RBC counting is performed using the Hough transform, a powerful image segmentation technique.
Feature reduction techniques based code smell prediction

Code Smell refers to the telltale signs of poor code design that leads to software quality issues. Developers require specific methods to measure the complexity of Code Smells in order to resolve the problem quickly. Recent research has examined the problem of predicting Code Smell using various detection methods. However, the accuracy of machine learning-based Code Smell detectors is still at a normal level. One of the main objective of this paper is to assess how well dimensionality reduction methods can predict Code Smells. This paper uses three machine learning techniques with feature reduction techniques, such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Linear Discriminate Analysis (LDA). Ten-fold cross-validation is used to ensure that the model is well-trained. Datasets are balanced using the Synthetic Minority Oversampling Technique (SMOTE) to ensure an equal number of classes in each dataset. The experimental result concluded that the AdaBoost method with LDA performs better in both the Long Parameter List and Switch Statement datasets, with an accuracy of 92.72% and 91.24%, respectively.
Design and Implementation of Indoor Patrol Robot

In this paper, the depth camera and lidar are used in indoor patrol robot, and the Gmapping algorithm of particle filter algorithm is used to construct map, which realizes the automatic navigation of Move_base algorithm combining global path planning and local path planning. In this paper, the adaptive Monte Carlo localization algorithm is used for localization, and the real-time analysis of obstacles to achieve automatic obstacle avoidance, to improve the actual use of the robot security. The feasibility of the robot is verified by simulation experiments in this paper. Through experimental debugging, the robot can accurately realize autonomous positioning and navigation, and achieve the basic function of indoor patrol.
Cryptography of Blockchain

With the development of digital currencies and 5G technology, blockchain has gained widespread attention and is being used in areas such as healthcare, industry and smart vehicles. Many security issues have also been exposed in the course of blockchain applications. Cryptography can ensure the security of data on the blockchain, the integrity and validity of data as well as the ability to authenticate users and anonymize them. This article therefore examines the cryptography underlying blockchain security issues, providing an overview of cryptographic homomorphic encryption, zero-knowledge proofs and secure multi-party computation commonly used in blockchains. At the same time, the development of quantum computing is bound to affect existing cryptographic systems, and blockchains applying these cryptographic systems are bound to be hit hard, so this article discusses four of the most promising post-quantum cryptography techniques available: hash-based public key cryptography, code-based public key cryptography, multivariate public key cryptography, and lattice-based public key cryptography.
Differential Fault Attack on AES Using Maximum Four Bytes Faulty Ciphertexts

The Internet of Things connects various types of devices and hardware for the connection are built-in. Symmetric key-based encryption may be embedded in the chip according to the environment. Recently, security problems have increased since the development of IoT devices. In particular, as the use of these devices has increased dramatically, the possibility of accessing and stealing cryptographic devices is also increasing. According to such physical accessibility, the issue of side-channel analysis of the safety of IoT devices is increasing. Differential Fault Attack (DFA) is based on intentionally injecting a fault at a specific time in the process of the cryptographic operation in an embedded device with a chip to cause a malfunction desired by the attacker. The Piret & Quisquarter DFA (P &Q DFA) is a differential fault attack method proposed under the assumption that single-byte fault are injected into the input of the AES 9th round MixColumn. However, single-byte faults occur in an environment where actual faults are injected, and the fault ciphertext with faults of two or more bytes can be collected. This paper proposes an analysis technique that can utilize defective ciphertext data containing two to four byte faults by extending the existing P &Q DFA, and optimizing the table called D that needs to be calculated in advance. In addition, this paper presents the results of recovering four bytes of the 10-round key by applying it to the multi-byte faults data obtained through the electromagnetic wave fault injection attack experiment on real devices.
Graph Data

Graph data have been widely observed in real-world applications, e.g., knowledge bases and social networks can be modeled as graphs. In such scenarios, entities are represented by the vertexes in the graphs, each of which has one class tag such as persons, attribute values and connections with other entities. With the consideration over three main components, class, relation and attribute, metalanguage for graph models is introduced to define how graph data is serialized and compiled in files or databases, e.g., eXtensible Markup Language (XML) and Resource Description Frameworks (RDFs).
Bibliometric Analysis of the Three-Volume Labor “Development of the Concepts of Modern Psychology” (2018–2021)

The article presents a bibliometric analysis of the concepts analyzed by scientists from the Institute of Psychology of the Russian Academy of Sciences and disclosed in three volumes of the work “Development of modern concepts of psychology” (2018, 2019 and 2021). The generalization carried out for all the presented concepts made it possible to put forward a hypothesis about the existence of metaconcepts — the subject and the collective subject, which pull together the network organization of conceptual fields, setting the architecture of their system-network organization. The conducted bibliometric analysis of the concepts of three volumes showed that the mapping of concepts makes it possible to identify clusters of network organization, among which there are two, which can be conditionally designated as “individual subject” and “collective subject”. Only the morpheme “psychologist-”, outstripping all the others in frequency of occurrence. These results may support the hypothesis of the existence of “meta-concepts” and their role in the system-network organization. Comparative analysis of the concept map based on the author&amp;apos;s keywords of publications indexed in Scopus for five years, “contraction” of terms into complexes that can be designated as individual and collective subjects could not be found, while the analysis of articles in the same database by the same authors discovered two centers in 20 years. The individual and collective subject become central concepts, with multiple connections. The question of hierarchical conceptual organization remains open due to the limited possibilities of bibliometric analysis, which does not allow revealing such relationships between concepts. Bibliometric analysis made it possible to show the network organization of modern concepts, the existence of thematic conceptual fields and leave open the question of their system-network organization.
Deep Learning Object Detection

Object detection techniques are a major part of computer vision research, with large-scale applications in industrial, scientific and other scenarios. Technologies such as face detection, medical image detection, autonomous driving, and traffic detection have played a significant role in people’s lives. With the rapid development of deep learning, many application areas, such as image classification, text classification, machine translation, etc., have achieved breakthrough success in combination with deep learning. R-CNN brings object detection into the era of deep learning, and its advantage compared with traditional methods is that the former requires personnel to extract features manually, while the latter uses deep learning to extract features automatically, which greatly improves efficiency, simplifies operation, and opens a new era of object detection research. First, this paper provides an overview of deep learning-based object detection backbone networks, reviews and analyzes milestone object detection algorithms, compares commonly used datasets, summarizes applications, and finally concludes the paper.
Research on Electricity Customer Complaint Risk Based on Improved Grey Wolf Optimization Fuzzy C-means Clustering Algorithm

The frequent occurrence of electricity customer complaints has always been the focus of the electricity sector. This paper proposes a fuzzy C-means clustering algorithm based on the improved gray wolf optimization. First, an improved PCA method is proposed to reduce the complexity of data analysis. Then this paper proposes the mirror search strategy, the random difference mutation strategy and the leader wolf variable weight strategy, which are used to improve the search accuracy and to avoid falling into the local solution. The simulation results show that the improved grey wolf optimization fuzzy C-means clustering algorithm has the best clustering effect, it effectively improves the clustering accuracy and realizes the accurate division of user complaint risk levels.
CCF-Net: A Cascade Center-Based Framework Towards Efficient Human Parts Detection

Human parts detection has made remarkable progress due to the development of deep convolutional networks. However, many SOTA detection methods require large computational cost and are still difficult to be deployed to edge devices with limited computing resources. In this paper, we propose a lightweight Cascade Center-based Framework, called CCF-Net, for human parts detection. Firstly, a Gaussian-Induced penalty strategy is designed to ensure that the network can handle objects of various scales. Then, we use Cascade Attention Module to capture relations between different feature maps, which refines intermediate features. With our novel cross-dataset training strategy, our framework fully explores the datasets with incomplete annotations and achieves better performance. Furthermore, Center-based Knowledge Distillation is proposed to enable student models to learn better representation without additional cost. Experiments show that our method achieves a new SOTA performance on Human-Parts and COCO Human Parts benchmarks(The Datasets used in this paper were downloaded and experimented on by Kai Ye from Shenzhen University.).
Bayesian Causal Mediation Analysis with Longitudinal Data

Mediation analysis was concerned with the decomposition of the total effect of exposure on the outcome into the indirect effects and the remaining indirect effects, through a given mediation. However, when longitudinal data including time varying exposure and mediator variables, the estimated causal effects are affected by time varying confounders. Standard generalized linear equations did not give unbiased estimates. In this paper, we introduced inverse probability weighting technique to adjust such time varying confounders. Considering that the amount of data may be small and the distribution is not uniform, we decide to use Bayesian Inference to estimate the Structural Equation Model (SEM) parameters, and finally estimates the causal effect through counterfactual thought. This paper summarized the relevant theoretical knowledge of this method, verified the feasibility of this method by using the simulated data, and compared the performance of different methods.
Practical Analyses of How Common Social Media Platforms and Photo Storage Services Handle Uploaded Images

The research done in this study has delved deeply into the changes made to digital images that are uploaded to three of the major social media platforms and image storage services in today’s society: Facebook, Flickr, and Google Photos. In addition to providing up-to-date data on an ever-changing landscape of different social media networks’ digital fingerprints, a deep analysis of the social networks’ filename conventions has resulted in two new approaches in (i) estimating the true upload date of Flickr photos, regardless of whether the dates have been changed by the user or not, and regardless of whether the image is available to the public or has been deleted from the platform; (ii) revealing the photo ID of a photo uploaded to Facebook based solely on the file name of the photo.
CONTINUOUS INTENTION TO USE ONLINE LEARNING DURING COVID-19 PANDEMIC BASED ON THREE DIFEERENT THEORITICAL MODELS (TAM, SVT, TOE)

The novel COVID-19 pandemic has impacted educational systems in almost all countries worldwide. Traditional classes have been canceled or shifted to online mode through the affected countries. Resuming traditional face-to-face instruction might be delayed. This unexpectedly fast and mandatory shifting to online education, along with the significant challenges that face learners and instructors, has led to uncertainty regarding its future. This study aims to inspect students’ continuous intention (CI) towards online education during COVID-19, by incorporating different constructs from three theoretical models: first, conservation values( Security(SEC), Conformity(CON)) of Schwartz Value Theory(SVT), organizational support factors (Training(TR), Top management support(TS)) in Technology-Organizational-Environmental (TOE) , and the Technology acceptance model(TAM ) main factors (perceived usefulness(PU), perceived ease of use(PEU)). To achieve the research goal, a research model was developed referring to previous strong literature. The data was gathered from 310 students from Imam Abdulrahman Bin Faisal university (IAU) in Saudi Arabia, and analyzed with Structural Equation Modelling SEM-PLS. Findings show that TAM factors (PU, PEU), conservation values factors (SEC, CON), and organizational support factors (TR, TS) are important determinants for online learning adoption during COVID19 pandemic . The study provides directions for designers and developers to establish a more effective online learning environment, which is more suited for the new digitized generation during unexpected conditions.
THE WHATSAPP APPLICATION USE AS A SUPPORT SERVICE IN DISTANCE EDUCATION: A CASE ANALYSIS

This study aims to reveal the strengths and limitations of the WhatsApp application as a support service in distance education, conducted through a learning management system during the Covid-19 pandemic, and make suggestions on improving its use in this capacity. Despite the lack of readiness on the part of the students and the lack of sufficient knowledge regarding distance education during the pandemic, many universities had to revert to emergency remote teaching. This situation necessitated the intensive use of various support services for the students to get the most out of their emergency remote teaching However, their lack of experience with distance education prevented them from using the support services offered by learning management systems. Due to the increasingly widespread use of mobile technologies, one of the support services used by course instructors became the WhatsApp application. The following research is a qualitative case study. The sample for the study was determined through purposive sampling. The data was analyzed using the content analysis method. Nvivo qualitative data analysis software was used to contribute to the detailed and versatile data analysis. The most important results of the study were that the application supported and strengthened distance learning by allowing instant communication, but that the absence of a moderator caused various limitations in this communication. In addition, it was concluded that incorporating natural language processing techniques into learning management systems and clearly stated rules by course instructors improved the use of such instant communication applications as a support service for distance education.
AUTOMATIC TEXT SUMMARIZATION AND KEYWORD EXTRACTION USING NATURAL LANGUAGE PROCESSING

The process of gaining and absorbing knowledge from various sources is a time-consuming process where people, mainly youth spend time surfing the internet for relevant information.The proposed system mainly focuses on scrapping the data from websites and providing the summary as well as keywords from the information extracted from various websites giving the user flexibility to select the websites of their choice.The proposed system for text summarization and keyword extraction undergoes a sequence of steps from data extraction from a website.For the selection of relevant information from the extracted data, it is necessary to use a natural language process.The proposed project helps its users to reduce their surfing time and gives a summary prepared from multiple website links and documents or keywords from a particular website or document
Supplementary Figure 1 from Relationship of Mammographic Density and Gene Expression: Analysis of Normal Breast Tissue Surrounding Breast Cancer

&lt;p&gt;Supplementary Figure 1 - PDF file 385K, Representative pictures of breast composition by digital pathological image analysis&lt;/p&gt;
Supplementary Figure 2 from Relationship of Mammographic Density and Gene Expression: Analysis of Normal Breast Tissue Surrounding Breast Cancer

&lt;p&gt;Supplementary Figure 2 - PDF file 138KB, The Creighton method to evaluate the association of gene expression profile of each patient with pre-defined biological signatures&lt;/p&gt;
Video 1 from The Digital Slide Archive: A Software Platform for Management, Integration, and Analysis of Histology for Cancer Research

&lt;p&gt;Video&lt;/p&gt;
Supplemental Figure 3 from A Novel Breast Cancer Index for Prediction of Distant Recurrence in HR&lt;sup&gt;+&lt;/sup&gt; Early-Stage Breast Cancer with One to Three Positive Nodes

&lt;p&gt;Supplemental Figure 3 shows the discriminative performance of BCIN+ by ROC analysis for 15-year distant recurrence in patients with 1-3 positive nodes.&lt;/p&gt;