Reviewerly/exHarmony huggingface dataset:
Analisis Konten Food Vlogger Pada Media Sosial Instagram Di Marriott Cafe Hotel JW Marriott Medan

This research was conducted because researchers observed that food vlogger content on Instagram social media at the Marriott Cafe Hotel JW Marriott Medan was not effective. The main problem is that the uploaded content still has little insight or interaction such as the number of viewers, likes, and those who share the content, as well as a lack of detail and enthusiasm in explaining the content reviewed by food vloggers. The aim of this research is to analyze the content carried out by 5 food vlogger on social media Instagram Marriott Cafe Hotel JW Marriott Medan. The type of research used was descriptive with a qualitative approach, by interviewing three (3) informants using a purposive sampling technique. Data collection techniques through interviews, observation and documentation., observation, documentation. The data is analyzed by reduction, namely summarizing, selecting and focusing on important things and analyzing the data obtained with the problem being studied. The results of this research show that the content uploaded by food vloggers at the Marriott Cafe is visually and audio and editing is good, but the content, namely the way the food vlogger conveys the message, is less interesting, namely the food vlogger does not use an original voice such as a good, unique, distinctive narrative. in conducting reviews, this is what causes Instagram users to be less interested in watching food vlogger content which causes little engagement on Instagram social media when food vloggers at the Marriott Cafe Hotel JW Marriott Medan.
Hierarchical Federated Transfer learning and Digital Twin Enhanced Secure Cooperative Smart Farming

The agriculture industry is extensive utilizing AI and data-driven systems for efficiency and automation, with the goal to meet the rising food demand. Individual farm owners can leverage agricultural cooperatives to consolidate resources, exchange data, and share domain knowledge. These cooperatives can enable the generation of AI-supported insights for their member farmers. However, this collaborative approach has raised concerns among individual smart farm owners regarding cybersecurity threats, and privacy. A cybersecurity breach not only endangers the farm attacked but can also risks the entire network of smart farms members within the cooperative. In this research, we emphasize security challenges within cooperative smart farming and introduce a multi-layered architecture incorporating Digital Twins (DT). Further, we introduce a hierarchical federated transfer learning framework designed to address and mitigate the security threats in collaborative smart farming. Our approach leverages Federated Learning (FL) based Anomaly Detection (AD), which operate on edge servers, enabling the execution of AD models locally without exposing the farm’s data. This localization also has excellent generalization ability, which can highly improve the detection of unknown cyber attacks. We employ a hierarchical FL structure that supports aggregation at various levels, fostering multi-party collaboration. Furthermore, we have devised an approach that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, complemented by transfer learning. The objective is to expedite training duration while upholding high accuracy levels. To illustrate the efficiency of our proposed architecture, we present a use case to demonstrate our model’s capabilities. Furthermore, we also present a proof-of-concept implementation of our proposed architecture within Amazon Web Services (AWS) environment, reflecting real-world feasibility.
Research on the Path of Innovation and Entrepreneurship Education in Local Applied Universities under the "Cloud" Mode

The"cloud"modelisoneoftheimportantexplorationdirectionsofinnovationandentrepreneurshipeducation（"InnovationAndEntrepreneurshipEducation"canbewrittenasI&amp;EE）inuniversities.LocalapplieduniversitiesareconstantlytryingtoreformtheI&amp;EEmodel.ThisstudyaimstoexplorethepathofI&amp;EEinlocalapplieduniversitiesunderthe"cloud"model,inordertoimprovetheeffectivenessandeffectivenessofI&amp;EE.ThroughthepracticalcaseandqualitativeanalysisofI&amp;EEinuniversities,thisstudydeeplyanalyzesthepracticalachievementsandbottlenecksofI&amp;EEinlocalapplieduniversitiesunderthe"cloud"mode.Onthebasisofsummarizing,thispaperputsforwardthepracticalmodeofI&amp;EEpathinlocalapplieduniversitiesunderthe"cloud"modewith"production,teaching,researchandsocialservice"asthefulcrum,thatis,throughthedevelopmentofproductivepracticeprojects,promotethedifferentiateddevelopmentofstudents,andsolvetheproblemofcombiningteachingpracticeandentrepreneurshipbymeansofschool-enterprisecooperation.ThisresearchhasimportantpracticalsignificanceforpromotingthecontinuousinnovationandupgradingofI&amp;EEinuniversities
Data Security Mechanism of Digital Library Based on Blockchain

Blockchain technology has unique advantages in the construction of smart libraries, but its centralized storage method and unlimited replication of dual data bring unreliable data, reader privacy, and intellectual property issues to smart libraries. This article proposes a deep integration of digital twin and blockchain technology, utilizing the decentralized and tamper resistant characteristics of blockchain to store digital twin data in intelligent libraries. The experimental testing of this study shows that the evaluation accuracy of the blockchain library data security evaluation model based on the SAE Kmeans++algorithm reaches 99.8%, which is significantly improved compared to traditional methods. This means that the security system design based on blockchain not only meets the national requirements for the internal network information security protection capability of libraries, but also meets the relevant market requirements for IDC security.
Evaluation on Power Equipment Operation Status Monitoring System Based on Data Mining

There are a lot of data information in the monitoring and control system of power equipment. Because of its large size, the traditional monitoring system is not able to monitor the running state of power equipment effectively. For the purpose of improving the monitoring efficiency of power equipment running, this paper puts forward a monitoring and control system based on data mining technique. Through the establishment of the operating state data model, people can realize the comprehensive monitoring of the operation state of the power equipment, and raise the efficiency from 75.34% to 89.8%. Therefore, the data mining technique can increase the monitoring efficiency of the monitoring system.
Delayed-choice quantum erasure using Poisson-distributed photons

Quantum superposition lies at the core of quantum mechanics and has been applied to various quantum technologies. Over the past few decades, Wheeler’s delayed-choice thought experiment has been extensively examined to explore the wave-particle duality of the complementary nature of quantum mechanics. The essence of the delayed-choice quantum eraser lies in the contradictory quantum property that violates the cause-effect relationship in classical physics. Our experiments present empirical evidence of the quantum eraser using coherent photon pairs provided in a noninteracting Mach-Zehnder interferometer for the polarization-basis control of output photons. The observed interference fringe is the direct proof of the quantum eraser, where the violation of the cause-effect relationship is due to the selective choices of the measurement events.
Categorizing the effects of knowledge management practices on SMES: a literature review

Large firms have managed to design robust knowledge management (KM) systems that help to create more effective responses to turbulent environments. On contrary, small and medium-sized enterprises (SMEs), which often suffer from the liability of smallness, usually carry out KM practices in a more informal and less planned manner. The objective of this research is to characterize the effects of KM practices in SMEs. The designed documentary review systematically analyzed 276 publications in journals indexed in Scopus in 2022. The main results suggest a tendency to study the dimensions of KM, such as dynamic technological capability, human capital, and their effects on financial and innovative performance, among others. The findings contribute to the literature by reordering information provided by scientific publications and revealing the dependent, mediating, and moderating variables linkedto KM that are currently used by SMEs.
Unsupervised Long-Term Person Re-Identification with Clothes Change

Most person re-identification methods artificially assume that each person's clothing is stationary in space and time. Since the average person often changes clothes even within a single day, this condition primarily holds true in situations involving short-term re-identification scenarios. Some recent studies have investigated re-identification of clothing changes based on supervised learning to reduce this limitation. In this paper, we remove the necessity for personal identity labels, which makes this new problem dramatically more challenging than conventional unsupervised short-term Re-ID. To surmount these obstacles, we introduce a novel approach known as the Curriculum Person Clustering (CPC) method, which exhibits the ability to dynamically modify the clustering criterion based on the clustering confidence in the clustering process. Experimental results on DeepChange show that CPC surpasses other unsupervised re-id method and even close to supervised methods.
LWSFACE: Light-Weighted Weakly-Supervised 3D Facial Texture Reconstruction From a Single Image

In recent years, digital human generation based on 3D face reconstruction has found extensive utility across various do-mains. The methods of 3D face reconstruction based on the 3D Morphable Model have achieved promising results. However, the fidelity of the reconstructed facial texture is limited due to the low-dimensional representation of 3DMM. Recently, some texture refinement works are proposed to ad-dress this problem, but they requires either high-resolution 3D dataset or complicated network structure. In this paper, we propose LWSFace, which trains a light-weighted network to recover high-fidelity facial texture from 3DMM coeffi-cients and texture extracted from image via estimated UV map in weakly-supervised manner. The results of both qual-itative and quantitative comparison experiment indicate that our LWSFace surpasses state-of-the-art approaches.
CTP: Defending Against Data Poisoning in Attack Traffic Detection Based Deep Neural Networks

Deep learning is extensively employed in attack traffic detection, exhibiting outstanding performance. To enhance model effectiveness, security personnel acquires additional traffic data from public sources for training. However, public source data may not always be reliable due to potentially inaccurate attack labels. By deliberately modifying labels, attackers can insert backdoors into neural networks, enabling undetected network attacks and posing significant risks. In this paper, we introduce CTP (Cluster and Train with Pruning), an effective method to counter data poisoning attacks and bolster the defense capabilities of attack traffic detection models. CTP consists of two components: activation layer clustering and pruning training. Firstly, activation layer clustering visually reveals poisoned data with manipulated labels, thwarting attackers' attempts to poison the model. Secondly, pruning training reduces the likelihood of model neurons being poi-soned, further mitigating the risk of poisoned data. These steps significantly strengthen deep learning models' resistance to data poisoning attacks. We conduct experiments on three popular datasets, and the results indicate that our proposed method effectively enhances the model's defense capabilities.
Research and Development of Book Query and Management System Based on OCR

As people increasingly aspire to intelligent and convenient lifestyles, the demand for text extraction in complex scenarios is growing. OCR(Optical Character Recognition) provides two methods for text extraction: text detection combined with text recognition and end-to-end recognition. However, due to the diversity and complexity of scene text, there is currently no text detection and recognition application on the market that performs exceptionally well in all scenarios. A common solution to this problem is to construct targeted models and collect targeted datasets for specific usage scenarios. In this paper, based on deep learning methods, we focus on the tasks of label recognition and book information retrieval for library books. We construct our own dataset, design, and build a text detection and recognition network. The model is deployed in an Android application to enable scanning and recognition of book labels. Additionally, it can retrieve relevant information about books corresponding to the labels by querying a database.
Investigation on Power System Dynamic Monitoring System Using Artificial Neural Networks

The power system is one of the indispensable infrastructure in modern society, and its stable operation is crucial for the normal operation of society. Therefore, how to monitor the operation status of the power system and detect abnormal situations in a timely manner is a challenge that power system managers and engineers have been striving to solve. This article introduced the application of artificial neural networks in power systems and analyzed relevant algorithms. The model mainly consisted of two parts, namely neurons and decision support vector machines. This article studied a progressive method based on the interaction of factors such as different neural structures, power distribution characteristics, and fault state classifiers. A set of nonlinear systems for describing complex problems was established by using hybrid learning method. This article used fuzzy reasoning theory to analyze the comprehensive diagnosis technology of intelligent knowledge architecture and processing capabilities, and designed a system for power system load monitoring. This article also conducted simulation tests on the performance of the system, and the test results showed that the system’s fault finding time remained within 3 to 5 seconds.
GDEMO-HAT: Knowledge-guided Medical Text Classification Using Heterogeneous Graph-based Dependency Modeling with Hierarchical Attention

The medical texts contain rich information about the health status of patients, including personal demographic details, examination items, diagnostic narratives, and corresponding outcomes. There is often an underlying dependency between a patient and multiple medical texts. Such dependency has a substantial influence on the accuracy and reliability of the patient's comprehensive diagnostic outcomes, thus necessitating the modeling of dependency. Most of the existing studies on medical text classification focused only on textual content and often overlooked the inherent dependency between the patient and medical texts, leading to the loss of relevant contextual information. To address the above problems, we propose an approach for medical text classification using heterogeneous Graph-based DEpendency MOdeling with Hierarchical ATtention (GDEMO-HAT), which accounts for patient-notes dependency and retains the contextual information. We model the dependencies between the patients and their medical texts via heterogeneous graphs. To facilitate medical text mining, we incorporate domain knowledge into the construction of a Patient-Notes-Physician (PNP) graph. Based on the constructed PNP graph, we introduce the hierarchical attention mechanism for classification that assigns different levels of importance to nodes based on distinct semantic contexts and thereby effectively captures the intricate interdependencies among these nodes. The representations of dependencies are integrated at the semantic level, thereby enabling a more comprehensive understanding of textual data. To demonstrate the effectiveness of the proposed approach, we conduct a classification task of the diagnosis of cardiovascular and cerebrovascular diseases based on medical text. The classification accuracy rate achieves 94%, which is significantly higher than the performances of the other compared models, such as Bert.
Qr-Code Berbasis Android Untuk Aplikasi Optimalisasi Presensi Asisten Laboratoriur

Laboratory assistant attendance system used to calculate lab assistant attendance. This system uses his Android based on his QR code. Operationally, the instructor can access the scans via her QR. The purpose of this study was to alleviate problems such as attendance, accumulation of attendance documents, and calculation of the number of laboratory assistants. The method of this study used the RUP (Rational Unified Process) method in the design using UML (Unified Modeling Language). The programming languages used are PHP and HTML and the use of the MYSQL database. As a result of this research, the number of commuting assistants was calculated by the system, so the calculation could be simplified. Keywords: Attendance System; Laboratory Assistant; System QR-Code; Android
Studies of Improving Drinking Water Quality in the Kalurahan Banaran Kabupaten Kulon Progo Using Porous Concrete Filter

Some citizens of the Kalurahan Banaran Kabupaten Kulon Progo everyday use water that has an iron content (Fe) of 8.8 mg/L and a turbidity of 120 NTU, which is above the limit set by the Minister of Health’s Regulation RI Number: 2 Tahun 2023 on the Quality Requirements of Drinking Water. To solve this problem, research has been conducted to reduce Fe and turbidity by using porous concrete filters. Three models of porous concrete A (porousity 0.42), B (porousity 0.44), and C (porousity 0.46), were used to filter water samples using two aerator diffusers at speeds of 3 L/h and 6 L/hour on each porous concrete filter. The results of the study showed that at an aeration rate of 3 L/hour, there had been a successive decrease in turbidity and Fe from 14.9 NTU to 1.38 NTU and 1.64 mg/L to 0.14 mg/L (filter A); 80 NTU to 0.5 NTU and 6.55 mg/L to 0.02 mg/L (filter B); 80 NTU to 0.1 NTU and 6.5 mg/L to 0 mg/L (filter C). For an aeration rate of 6 L/hour, there had been a successive decrease in turbidity and Fe from 80 NTU to 0.82 NTU and 6.55 mg/L to 0.05 mg/L (filter A); 89.8 NTU to 0.28 NTU, and 7.8 mg/L to 0.01 mg/L (filter B); 80 NTU to 0.28 NTU and 6,55 mg/L to 0 mg /L (filter C). The results show that the porous concrete filter can be considered an alternative to drinking water treatment in the Kalurahan Banaran Kabupaten Kulon Progo.
An Efficient Hash Computing Unit for Kyber Algorithm

In the post-quantum Kyber algorithm, the Hash computation directly affects the processing speed of key generation, noise polynomial, and key exchange processes. To address the issues of multiple types of Hash computations, complex variables, and high resource consumption, this paper proposes an efficient Hash computation unit. The proposed scheme first establishes independent data interaction units through the sponge construction, achieving higher parallelism by combining the logically merged Keccak-1600 permutation function. Subsequently, the central binomial distribution sampling and optimized rejection sampling are integrated into the Hash unit, resulting in a compact and highly flexible Hash computation unit. Finally, the scheme is tested and verified under the Taiwan Semiconductor Manufacturing Company (TSMC) 65nm process. The proposed method consumes only 36.4K equivalent logic gates, and experimental results show that the Hash computation cycle is reduced by up to 30%, rejection sampling cycle by 16%, making it more efficient compared to related implementations.
Deep Learning Approach for Hate and Non Hate Speech Detection in Online Social Media

People's ability to freely and anonymously share their thoughts and feelings online on social media platforms is contributing to a rising issue of hate speech. Hate speech has the potential to hurt both people and groups, contribute to the polarisation of society, and even provoke acts of physical violence. Therefore, identifying and removing hate speech from online social media is an important task for the purpose of maintaining an online environment that is healthy and respectful. A method based on deep learning is proposed in this study for differentiating between hate speech and other types of communication that may be found in online social media. The solution that has been developed makes use of a natural language processing (NLP) and long term short memory (LSTM) model that is trained on a huge dataset consisting of tweets that have been annotated. Tweets that include hate speech and tweets that do not contain hate speech were both included in the dataset and were labelled by human annotators. Python software with IDE of Spyder version 3.7 is used to carry out the simulation work. The accuracy level reached overall is 91.14%.
Pedestrian Pose Estimation for Campus Unmanned Delivery Vehicles

Pedestrian pose estimation can provide a basis for planning and control systems of campus unmanned delivery vehicles to ensure pedestrian safety and improve delivery efficiency. A lightweight pedestrian pose estimation model is built in this paper, and a model training method incorporating campus pedestrian information is proposed. In order to verify the effectiveness of the proposed method and the real-time performance of the model, a comparative experiment is conducted and the real-time performance is tested on the server side and the AGV platform. The results show that the proposed model and training method can efficiently and accurately accomplish campus pedestrian pose estimation in real scene.
Comparative Analysis on Automated Detection of Credit Card Fraud

Detection of the fraud of Credit Card is one of the maximum popular troubles in cutting-edge worldwide. that is due to the upward push of buying and selling and e-exchange structures. credit score card fraud normally takes location at the same time as a card is stolen for fraudulent capabilities or on the equal time as a fraudster makes use of credit score card information for their very own purposes. In worldwide, we are going through many credit score card issues. A credit score card fraud detection device is installed area to come upon fraudulent sports activities. This venture is broadly talking supposed to interest on machine reading algos. The algos used are RF set of on-line and Ada-boost set of online. The consequences of the 2 algos are primarily based on the accuracy, the precision, the maintain in mind, and the F1 rating. The ROC is drawn primarily based completely on the CM. We evaluate the Random wooded place set of online tips and the Ada-boost set of regulations and recall the set of on lines with awesome accuracy, precision, consider, and the F1 rating because the extraordinary set of online tips used for fraud detection.