Reviewerly/exHarmony huggingface dataset:
Digital image stabilization method based on variational mode decomposition and sampling fluctuation analysis

Unintentional motions often cause cameras to produce shaky images, which is a significant source of inter-frame blur and video quality decline. To ad-dress this issue, we present a digital image stabilization approach based on variational mode decomposition (VMD) and sampling fluctuation analysis (SFA) to generate stable video sequences. Our method first estimates the global motion vector (GMV) from a video sequence using the speeded up robust features (SURF) algorithm. We then decompose the GMV into various modes using VMD to separate jitter motions from intentional ones. Here, SFA is applied to distinguish different modes based on their unique structural characteristics. We evaluate our proposed method in complex scenarios by comparing it with several existing methods. Our experimental results demonstrate that VMD outperforms other stabilization techniques under comparable conditions.
A Walk-through towards Network Steganography Techniques

2D and 3D digital multimedia files offer numerous benefits like excellent quality, compression, editing, reliable copying, etc. These qualities of the multimedia files, on the other hand, are the cause of fear including the fear of getting access to data during communication. Steganography plays an important role in providing security to the data in communication. Changing the type of cover file from digital multimedia files to protocols improve the security of the communication system. Protocols are an integral part of the communication system and these protocols can also be used to hide secret data resulting in low chances of detection. This paper is intended to help improve existing network steganography techniques by enhancing bandwidth and decreasing detection rates through reviewing previous related work. Recent papers of the last 21 years on network steganography techniques have been studied, analyzed, and summarized. This review can help researchers to understand the existing trends in network steganography techniques to pursue further work in this area for algorithms’ improvement. The paper is divided according to the layers of the OSI model.
Human Motion Pattern Recognition Based on Nano-sensor and Deep Learning

A human motion pattern recognition algorithm based on Nano-sensor and deep learning is studied to recognize human motion patterns in real time and with high accuracy. First, human motion data are collected by micro electro mechanical system, and the noise in such data is filtered by smoothing filtering method to obtain high-quality motion data. Second, key time-domain features are extracted from high-quality motion data. Finally, after fusing and processing the key time-domain features, it is input into the deep long and short-term memory (LSTM) neural network to build a deep LSTM human motion pattern recognition model and complete human motion pattern recognition. The results show that the proposed algorithm can realize the recognition of various motion patterns with high accuracy of data acquisition, the average recognition accuracy is 94.8%, the average recall reaches 89.7%, and the F1 score of the algorithm are high, and the recognition time consuming is short, which can realize accurate and efficient human motion pattern recognition and provide guarantee for effective monitoring of the target human motion health.
PHEP: Paillier Homomorphic Encryption Processors for Privacy-Preserving Applications in Cloud Computing

• Cloud computing has evolved into the key infrastructure of emerging applications, storing massive amounts of data. Yet, how to safely handle this sensitive data in a shared cloud is a major concern. Paillier homomorphic encryption is an important privacy protection approach that permits arithmetic operations on ciphertext without first decrypting it, offering a viable solution to the privacy dilemma. • The Paillier approach has a significant computational overhead compared to plaintext computation because computing in the ciphertext domain requires expensive large integer modular operations that are inefficient for CPUs. As a result, it is preferable to create domain-specific processors for Paillier. Paillier computing patterns are divided into two types, both of which are extensively employed in Paillier applications: independent vector operations and multiply-and-accumulate (MAC) operations. The former is primarily employed in applications such as private information retrieval and on the client side for privacy-preserving AI. In contrast, the latter is required for cloud-side AI inference, particularly computing convolution in neural networks. • We introduce PHEP: Paillier Homomorphic Encryption Processors for cloud-based privacy-preserving applications. PHEP is built on two Paillier acceleration chips: Paillier engine-1 and Paillier engine-2, both produced on the same wafer. Paillier engine-1 focuses on vector operations and attempts to increase computation as much as feasible. It contains 80 processing elements (PE) and can provide 480 TOPS (INT8) for a 16-chip Full-Height-Full-Length (FHFL) PCle card. Paillier engine-2 is designed for MAC operations and has 16 high-performance bit-serial sparse PEs. It only has 192 TOPS (INT8) for an 8-chip FHFL PCle board. However, it is specialized for matrix operations like convolutions. Both engine chips have the same hardware interface, allowing them to use the same PCB board, FPGA scheduler, and software framework design. The PHEP accelerator card also contains a host FPGA. The host FPGA schedules both data transfers and computation among these engine chips. To manage these engines, we use a complex software stack. The software stack includes an offline compiler and an online task scheduler for automatically balancing compute workload across multiple cards on the same server and even across multiple servers. The findings of the end-to-end evaluation reveal that PHEP can perform Paillier-based machine learning workloads 1–2 orders of magnitude faster than state-of-the-art CPUs (Intel Xeon Platinum 8260M with 192 cores), making these privacy-preserving applications practical.
Improving Narrative Writing Skills with the CTL Approach and Illustrated Image at SMA Negeri 5 Semarang

Writing is a language skill with the highest difficulty level compared to other language skills. Writing skills at SMA Negeri 5 Semarang based on the results of interviews conducted by researchers with English teachers of class X and the results of the initial survey test found a problem, namely the low ability to write stories in English subjects. They don't have self-confidence, are afraid of making mistakes, and don't know what to write and how to start. For this reason, the teacher conducts Classroom Action Research by applying a contextual approach (Contextual Teaching and Learning (CTL)) and illustrated media for Class X Students of SMA Negeri 5 Semarang. The common goal of learning writing skills is for students to express ideas, ideas, opinions, and knowledge systematically and in writing and have a penchant for writing
21. Cybercrime: A social ecology

This chapter outlines the social ecology of cybercrime. The national and international contours of power, crime, and harm emerging in Internet societies lies at the heart of many crucial areas of contemporary criminological debate. Despite the increasing prominence of cybercrime, its integration into mainstream criminology remains fragmented and piecemeal. Moreover, cybercrime studies are breaking new frontiers in novel forms of empirical work after drawing on the very large datasets made available in online forums, chat channels, and social media. The chapter explains how collaborations from different disciplines might form the seeds of an optimistic future for the criminology of cybercrime.
The Implementation Of Rewards And Punishments Towards Students Motivation In English Learning

This research uses descriptive qualitative research as a research methodology located at SMP Negeri 3 Pitu Riase. This research was conducted to determine the implementation process of giving rewards and punishments to increase students' learning motivation and to find out the results of the implementation of giving rewards and encouragement to increase students' learning motivation in English subjects. Data collection techniques used are observation, interviews and documentation. While data analysis used data reduction, data presentation and data verification. The data obtained from this study is divided into two, namely data in the form of verbal (words) for example "Good" and nonverbal (actions) for example thumbs up, applause, additional points, advice, reprimand, vocabulary memorization, deductions and pinches. This data was obtained from the treatment and words of the teacher to his students during the learning process. The results of the research from the implementation of reward and punishment to increase students' motivation to learn in English subjects at SMP Negeri 3 Pitu Riase show that: the process of implementing reward and punishment is carried out spontaneously in the classroom and sometimes there is an agreement with students if there are students who violate or Failure to do the work will be punished. Rewards are given in the form of praise, applause, thumbs up and additional points. The punishment is in the form of advice, standing in front of the class and memorizing vocabulary. The results of the implementation of reward and punishment are: (1) The level of student attention to learning increases; (2) The level of student confidence in the ability to do learning tasks; (3) The level of student satisfaction with the learning process that has been carried out; and (4) Determining actions which must be done.
The application of intelligent algorithms in word discrimination

Words play a huge role in people's communication and transmission of information. The LSTM model is first established in this paper to analyze the changing trend of the number of people in the time series of the data set. According to the model, the linear regression model was used to process the word characteristic values and put them into the least square model for fitting through linear regression, and the MAPE value was obtained, and the comparative test effect was conducted on the value. At the same time, the F statistic was used to test the significance of the regression equation, and the Prob value was obtained. After the comparison of standard values, word attributes did not affect the percentage of the number of people registered in the difficult mode. The characteristic values of 5 words were divided by analysis and input into LR linear regression, XGB, random forest, GA-BP neural network, and Bayesian classifier models for training. It was found that the XGB determination coefficient of the simulated annealing model was 0.506. Finally, BP neural network learning based on a genetic algorithm is used to predict, and the percentage of correct answers to each word is subject to normal distribution results.
Introduction

This introductory chapter sets the foundation for this book, providing essential information about the two complementary encryption paradigms: Homomorphic Encryption and Searchable Encryption. Homomorphic encryption enables operations on encrypted data without decryption, offering the opportunity for privacy-preserving computation, while searchable encryption allows specific searches to be performed on encrypted data, retaining confidentiality. The chapter outlines their importance in today’s digitized world with growing privacy concerns and illustrates their potential in different domains.
System Design Process

This chapter marks the beginning of Part 3. Thus far, we have reviewed the underlying iOS fundamentals (Part 1) and techniques to build scalable applications (Part 2). However, being able to write quality code and architect applications well is only a portion of what is necessary to deliver valuable software in a large company. In this environment, engineering and business teams can have competing priorities causing conflict and adding risk to project success. Additionally, engineering teams themselves can have competing priorities and conflicting launches. These added risk factors result in significant cross-functional communication requirements, resulting in additional planning and launch scheduling requirements.
PREDIKSI KEPUASAN PELAYANAN PERPUSTAKAAN MENGGUNAKAN ALGORITMA DECISION TREE (C4.5)

Prediction of library service satisfaction is important in determining library growth and progress. To determine which attributes are prioritized by library managers is very useful for improving library services going forward. The results of the analysis using the Confusion Matrix can be concluded that the accuracy value of the determinant attribute on Y '(2 types of assessment) is better than Y (5 type of assessment) with an accuracy value of 75.50%. Based on the analysis of the two decision trees on Y and Y ', the attributes that equally affect the satisfaction of library services are Environmental Comfort (X13), Officer Clarity (X3), Collection Fulfillment (X2) and Courtesy (X9). Based on the combination of the decision tree image of Y 'with the weighting value table of each attribute, it can be concluded that the Collection Fulfillment Attribute (X2) is the most influential attribute on the library service satisfaction decision.Keywords: Prediction, Library, Accuracy, Algorithm, Decision Tree (C4.5)
Imbalanced data classification using improved synthetic minority over-sampling technique

In data mining, deep learning and machine learning models face class imbalance problems, which result in a lower detection rate for minority class samples. An improved Synthetic Minority Over-sampling Technique (SMOTE) is introduced for effective imbalanced data classification. After collecting the raw data from PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases, the pre-processing is performed using min-max normalization, cleaning, integration, and data transformation techniques to achieve data with better uniqueness, consistency, completeness and validity. An improved SMOTE algorithm is applied to the pre-processed data for proper data distribution, and then the properly distributed data is fed to the machine learning classifiers: Support Vector Machine (SVM), Random Forest, and Decision Tree for data classification. Experimental examination confirmed that the improved SMOTE algorithm with random forest attained significant classification results with Area under Curve (AUC) of 94.30%, 91%, 96.40%, and 99.40% on the PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases.
A-381 Laboratory-based Reflexive Algorithm for Identification of Falciparum and Non-Falciparum-Species and its Clinical Utility in the Diagnosis of Malaria in Southern Ghana

Abstract Background Malaria presents a public health menace to millions of people in LMICs, killingmore than 600 000 people with the highest number in children living in sub-Saharan Africa.Laboratory testing provides the basis for &amp;gt;70% of clinical decisions, although their accessibilityand affordability are limited in LMICs and results in syndromic diagnosis and self-medication.The National Malaria Control Program guidelines specify that healthcare facilities perform RapidDiagnostic Testing (RDT) or microscopy for the diagnosis of malaria. This “one size fits all”approach has the unfortunate potential of resulting in a higher number of false positives and falsenegatives. Although Plasmodium falciparum is the predominant species in Ghana, emergingevidence suggests that non-falciparum species (P. vivax, P. ovale, P. malariae, and P. knowlesi)contribute to morbidity and mortality, and complicates the diagnosis and treatment for a subset ofpatients. The rationale for this study is the development of a reflexive, stratified diagnosticapproach that combines the use of RDTs and Microscopy/NAAT for the identification offalciparum and non-falciparum species in malaria endemic areas. Method Venous blood was collected from participants and five different RDTs on the Ghanaianmarket were used to screen the blood for the presence of malaria parasites along with microscopyand the LAMP method. The diagnostic performance of the RDTs was evaluated by utilizingmicroscopy as the gold standard. For microscopy, a volume of 6 µL and 2 µL of the blood sampleswere pipetted onto labeled frosted- end slides for the preparation of thick and thin blood films,respectively. Lastly, DNA was extracted from venous blood (QIAamp DNA Mini Kit, Qiagen,Germantown, MD, USA) and analyzed using a modified version of the Nested Malaria PCR assaythat can identify the five species of the Plasmodium parasite including P. knowlesi. Results Preliminary data from results from 71 participants showed an overall sensitivity of 90%for the identification of Plasmodium infection among all the RDTs examined in this investigation.The Wondfo® One Step Malaria HRP2/pLDH (P.f/Pan) test was the only RDT that passed theWHO recommended specificity of 90% when compared to microscopy. The WHO recommends aminimum sensitivity of 95%; however, none of the five RDTs utilized in this study met thisrequirement. Conclusion Although microscopy remains the gold standard for the identification of Plasmodiumspp for malaria diagnosis, it is becoming increasing popular for most healthcare facilities in LMICsto use RDTs for screening of the huge patient numbers that present with febrile illness. From ourpreliminary results, we have shown that positive cases by RDTRs are usually confirmed positiveby microscopy while most RDT negatives are false negatives by microscopy. Our combinatorialapproach with the molecular testing assay using LAMP increases the specificity of the testingprocess that guides treatment for malaria patients.
Correction to: Use of machine learning algorithms to predict life-threatening ventricular arrhythmia in sepsis

[This corrects the article DOI: 10.1093/ehjdh/ztad025.].
Technology embodiment and the immersion aesthetics—Comments on the VR reset of “The Legend of Sword and Fairy I”

&lt;p&gt;Virtual reality departs from and transcends reality, and the VR reset of “The Legend of Sword and Fairy I” aggregates the complete industrial ecology formed by the extended development of old game IP and the VR blue ocean commercial market. From two-dimensional to VR, the digital technology field has shaped the virtual embodied world where traditional culture and modernity are intertwined, and constructed the “Immortal Sword World” that gathers, condenses and reshapes the national complex, cultural consciousness and modern discourse. A series of physical operations enables players to interact with the “Immortal Sword World” in real time and in a natural way. The absolute and unique rules and mechanisms of “The Legend of Sword and Fairy I” VR create a 3D community discourse field with an entire new social system + technical system. The open interactive architecture of “Immortal Sword World” provides a meta-statement for free exploration, ideally presenting the pre-determined sequence of the immersive “Immortal Sword World” and allowing players to open up an imaginary future dreaming space in a step beyond reality.&lt;/p&gt;
Data structures

This chapter presents the minimal setup of data structures required to follow the rest of the book in a self-contained manner. Balanced binary trees are enhanced to solve dynamic range minimum queries. Bitvector rank and select data structures and their extensions to larger alphabets with wavelet tree are covered. Then a special structure for solving static range minimum queries is derived. The chapter ends with a concise description of hashing primitives, such as perfect hashing, Bloom filters, minimizers, and the Rabin–Karp rolling hash.
Retracted: Pharmaceutical Reagent Inventory Strategy Based on Contract Shelf Life and Patient Demand

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Retracted: Application of Intelligent Exercise Training Equipment in Clinical Nursing of Neurology Department

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Retracted: A Case-Control Study of Continuous Veno-Venous Hemofiltration Combined with Xuebijing Injection in the Treatment of Severe Sepsis

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Retracted: Study on the Correlation between the Prevalence of Venous Thromboembolism in Kazak Pregnant and Lying-In Women in Xinjiang

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Retracted: Risk Factors for Pulmonary Metastasis in Differentiated Thyroid Carcinoma Patients and the Significance of Changes in Matrix Metalloproteinase 13 and microRNA-142 Levels

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Retracted: Digital Construction of Vocal Music Teaching Resource Base Using Data Mining Technology

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Retracted: Perception Research of Artificial Intelligence in Environmental Public Health Physiotherapy Nursing for the Elderly

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Retracted: Intelligent Classification Model of Music Emotional Environment Using Convolutional Neural Networks

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Retracted: Design of Chinese Grammar Recognition and Error Correction Model Based on the Deep Neural Network

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Retracted: Construction of College English Teaching Environment Assessment Model Based on BP Neural Network and Multiple Intelligence Theory

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Retracted: Research on Teaching Design of Geochemistry and Biology under the Background of Ecological Environment and Information Technology

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Retracted: Prediction and Analysis of Corporate Financial Risk Assessment Using Logistic Regression Algorithm in Multiple Uncertainty Environment

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Retracted: Influence and Analysis of Music Teaching Environment Monitoring on Students’ Mental Health Using Data Mining Technology

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Retracted: Higher Education Environment Monitoring and Quality Assessment Model Using Big Data Analysis and Deep Learning

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Retracted: Research on Organization Design of College Chinese Teaching under Big Data Environment

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Retracted: Construction of Smart Higher Education Teaching Resources Using Data Analysis Technology in Unbalanced Data Environment

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Retracted: A Deep Learning-Based TE Method for MSs’ Mental Health Analysis

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Retracted: Risk Management of Sports Venues and Olympic Sports Cooperation Spirit under Complex Environment

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Retracted: Optimal Development Model of College Music Curriculum Based on Psychology and Big Data Analysis in a Quantitative Environment

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Retracted: Entrepreneurial Mentality Analysis for College Students Based on the Improved BP Neural Network

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Retracted: Construction and Analysis of College English Teaching Path under the Multiple Network Teaching Environment and Credit System

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Retracted: Big Data Analysis of Benign Interaction of Great Power Relations and New International Relations Based on Deep Learning

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