Reviewerly/exHarmony huggingface dataset:
An Optimized Medical Image Watermarking Approach for E-Health Applications

Background: In recent years, information and communication technologies have been widely used in the healthcare sector. This development enables E-Health applications to transmit medical data, as well as their sharing and remote access by healthcare professionals. However, due to their sensitivity, medical data in general, and medical images in particular, are vulnerable to a variety of illegitimate attacks. Therefore, suitable security and effective protection are necessary during transmission. Method: In consideration of these challenges, we put forth a security system relying on digital watermarking with the aim of ensuring the integrity and authenticity of medical images. The proposed approach is based on Integer Wavelet Transform as an embedding algorithm; furthermore, Particles Swarm Optimization was employed to select the optimal scaling factor, which allows the system to be compatible with different medical imaging modalities. Results: The experimental results demonstrate that the method provides a high imperceptibility and robustness for both secret watermark and watermarked images. In addition, the proposed scheme performs better for medical images compared with similar watermarking algorithms. Conclusion: As it is suitable for a lossless-data application, IWT is the best choice for medical images integrity. Furthermore, using the PSO algorithm enables the algorithm to be compatible with different medical imaging modalities.
Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC)

The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient’s remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient’s affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient’s data/report while saving the travel and waiting time for the hospital services. The patient’s data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network.
Fair Game of Quantum Penny Flip

The Quantum Penny Flip is a classic thought experiment in quantum mechanics that demonstrates the weird and counterintuitive nature of the quantum world. To solve the “PQ” problem, we proposed a quantum strategy selection and optimization algorithm, and designed a quantum algorithm for this problem. In terms of practical applications and Case study, this paper reviews the classic two player game – “PQ coin flipping” problem, and shows that when one of the participants adopts quantum strategies, he can defeat his classic opponent and obtain higher returns. By using quantitative methods, the entire “PQ” problem is placed under a more general and fair condition, and it is promoted and studied again from multiple aspects and perspectives. At the same time, this article provides the definitions of “incomplete fair game” and “complete fair game”, modifies the quantum coin flipping game [Meyer, Phys. Rev. lett. 82 (1999) 1052] to make the game fair, and studies the multi round versions of quantum coin flipping. We have drawn some basic conclusions about fair quantum games and explained the meaning of completely fair games in practice. We discussed the limitations and scalability of the “PQ” problem and looked forward to the future development direction of quantum game research.
Demystifying Facial Expression Recognition Using Residual Networks

Residual Networks (ResNets) have emerged as a significant breakthrough in computer vision, pattern recognition, and image processing, offering a new avenue for linking theories to practical applications. The development of ResNets Networks has greatly benefited the field of facial expression recognition (FER). ResNet incorporates the concept of skip connections and has become one of the most widely used neural networks. The paper provides a comprehensive overview of utilizing ResNet architectures to address the challenge of facial expression and emotion recognition. The authors specifically highlight several ResNet variants, including the 3D version of the Inception-ResNet layer combination, ResNet and atrous convolutions, dynamic geometrical image network with ResNet, facial expression recognition using conditional random fields and ResNet, FER using ResNet and heart rate variability observations, and ResNet with squeeze and exception networks.
EFEKTIVITAS E-LKPD BERBASIS WEB-BLOGGER BERBANTUAN FLIPHTML5 DAN QUIZIZZ UNTUK MENINGKATKAN PEMAHAMAN KONSEP MATEMATIS

The problem in this research started from the low ability of students to understand mathematical concepts in class From field research it was immediately found that students' active ability to develop and discover concepts is still low and it is still difficult to choose the right solution in solving problems. This research discusses increasing students' understanding of mathematical concepts by using web-blogger based E-LKPD assisted by Fliphtml5 and Quizizz. This type of research is RnD (Research and Development). The population of this study was 44 students at SMA Muhammadiyah Ahmad Dahlan Metro class X. The research sample was 20 students. This research was conducted in 2023. Data collection in this research used a learning outcomes test in the form of 3 essay questions. Research data was obtained through a test of understanding mathematical concepts. The data analysis technique uses the t test and N-gain. In the experimental class, the highest post-test score was 92, the lowest score was 58, the average was 79, with a standard deviation of 8.518. In the control class, the highest post-test score was 83, the lowest score was 42, the average was 63, with a standard deviation of 9.179. In the process, students in the experimental class showed the ability to understand concepts better than students in the control class. The results of the effectiveness test showed that E-LKPD based on Web-Blogger assisted by Fliphtml5 and Quizizz could improve the ability to understand mathematical concepts with an average N-gain of 0, 70. Based on the hypothesis test, it was found that the use of Web-Blogger based E-LKPD assisted by Fliphtml5 and Quizizz in mathematics learning was proven to be effective in improving students' ability to understand mathematical concepts.
Aplikasi Pembelajaran Kosa Kata Untuk Anak Berkebutuhan Khusus Tunarungu Menggunakan Metode Algoritma Fisher-Yates

Deafness is a type of special need that is of concern to the government in terms of equality in obtaining education as stated in Law of the Republic of Indonesia No. 20 of 2003 concerning National Education Chapter IV Article 5 paragraph (1) states that "Every citizen has the same right to obtain quality education" and paragraph (2) "Citizens who have physical, emotional, mental, intellectual and /or social rights to obtain special education”. The problem faced by deaf student is the difficulty of capturing the information conveyed by the teacher during the learning process. These limitations make it difficult for deaf children to master good and correct vocabulary. The aim of this research is to help student with special needs learn vocabulary through learning media.&#x0D; With this application, it is hoped that it can meet the vocabulary learning needs, especially for Deaf SMPLB students at SLB Karya Mandiri Kijang.
Simulation of the Quantum Key Distribution Algorithm Using the Intel Quantum SDK

Information security is an important fabric of our world today. Technologies used in the development of information security have constantly been evolving in response to new threats. Cryptographic algorithms like the RSA, DSA, AES etc. are currently being used in securing information even over insecure channels. The coming of quantum computers has now posed a great threat to classical cryptosystems which can be broken in polynomial time thanks to Shor’s algorithm. A good hacker with good quantum computing power can break into encrypted messages easily. The Quantum Key Distribution (QKD) algorithm based on the BB84 protocol provides a solution to this problem. It makes it possible for communicating parties to exchange secret keys securely before a conversation can ensue. Verifying if these keys have been tampered with is a good first step to start before any other. QKD technology has made it possible to exchange these keys via an insecure channel and lets communicating parties know if an eavesdropper has tempered with the message. In this work, we discuss the QKD algorithm and simulated it on the newly released Intel quantum SDK.
Design and Implementation of an Online Power Analysis Attack Platform for HMAC Based on SM3

A method for an online power analysis attack platform against SM3-based HMAC was proposed in the paper. The method adopts a front-end and back-end approach. The front-end implements functions such as uploading power traces, displaying power traces, and inputting power analysis attack parameters. The back-end completes correlation power analysis attack based on the power traces and attack parameters uploaded by the front-end, and returns the attack results to the front-end for display. This design fully utilizes the powerful computing capabilities of the backend server, while also supporting the simultaneous use of multiple detecting users. It has broad application prospects and promotion value.
Text Recognition of Irregular Scence Based on the Encoder-Decoder Framework

Scene text text recognition is the recognition of text instances in natural scenes, although STR methods have made great progress, but arbitrary shapes, severely bent and rotated texts are still difficult to recognize. To solve the problem that irregular texts in complex natural scenes is difficult to identify, this paper proposes a scene text recognition method based on encoder-decoder framework, which combines self-attention mechanism to form a text recognition network in transformer encoder-decoder framework. The model improves the recognition rate of low-quality images by using super-resolution unit, describes the two-dimensional spatial correlation of characters in scene text images by self-attention mechanism, and can recognize random irregular texts. The average performance of the proposed model in the “irregular texts” benchmark is better than that of most existing STR models, and the average recognition accuracy is more than 84.3%.
Derivation of numerical criteria for the occurrence of natural phenomena in video sequences

In this paper are presented the results from an analysis of the information, obtained from dataset of one year video records. The results show that some natural phenomena could be detected by simple numerical criteria. The records have been taken by outdoor surveillance cameras. The applied data processing aims to determine criteria for the occurrence of events affecting the visibility in images. The technique uses dark channel prior images (DCPI) and correlated color temperature (CCT) for limited areas located at identical positions within consecutive frames.
User Satisfaction SentimentAnalysis For Mass Transportation Infrastructure (Case Study of Manggarai Station)

Evaluation of transportation infrastructure development is important considering that this is a solution from the government regarding the increasing mobility needs of urban communities. Social media and machine learning technologies enable an analytical approach to identify customer satisfaction in transportation infrastructure through sentiment analysis to accelerate policy evaluation analysis. The problem that arises is that it is necessary to provide specific data sources and appropriate analytical methods. To answer this, research was carried out which aimed to develop a sentiment analysis model and compare the accuracy of the Naïve Bayes, SVM, and KNN classifier algorithms in the sentiment analysis of Manggarai Station users as a case study. Data collection was carried out and sourced from Google Review. About 1453 review data were obtained which had gone through the preprocessing stage. The result of the analysis shows that the SVM classifier algorithm (RBF kernel) has the highest average accuracy with a value of 82%, followed by the KNN algorithm (k=12) with a value of 79%, and the Multinomial Naïve Bayes algorithm with a value of 75%. In the last three years, it was found that 64.28% of visitors to Manggarai Station had a negative impression of the station’s infrastructure facilities, another 27.53% had a positive impression, and 8.19% had a neutral impression.
The Influence of Knowledge Management Systems in Corporate University in Triggering Knowledge Innovation in Higher Education: A Case Study Approach

In the Industry 4.0 era, employees were demanded to perform more than repetitive tasks since the complexity and dynamic of products and processes in the organization increased. The demands require employees with superior competencies to generate innovation so that the organization can remain competitive in its industry. Answering those challenges, this paper aims to investigate how Knowledge Management System implementation inside Corporate University will trigger knowledge innovation in the organization. Quantitative methods were implemented to gain results in this study. A questionnaire with a Likert scale via Google form was distributed to 129 team leaders in the innovation award at Bina Nusantara University. Structural Equation Modelling Partial Least Square (SEM PLS) through the SmartPLS application was utilized to analyze and test the hypothesis of this study. The result of this study found there was a positive relationship between the implementation of a Knowledge Management System and knowledge innovation in the organization.
Multi-hop quantum teleportation via general distinct EPR pairs and its application in quantum network

Quantum teleportation (QT) provides a useful way to securely transfer unknown quantum state between distant nodes without physically sending the states themselves. In this paper, we present a teleportation scheme of the most general hybrid multi-qudit state via EPR pairs with different dimensions in the chain network. In this structure, the EPR channels shared between neighbor nodes could be distinct, which means that the shared EPR pairs could be different in dimension and the parameters with each other, demonstrating a universality scenario in realistic transmission. In addition, we discuss the condition that can ensure the integrity of the transmitted information with available channel resource, and our scheme could provide a method to solve the resemble problem in the construction of future quantum network.
Research on Unsupervised Fault Diagnosis based on Sparse Autoencoder and Locally Linear Embedding

In this paper, we address the shortcomings of traditional unsupervised fault diagnosis methods in handling high-dimensional, nonlinear, and highly complex fault diagnosis problems, and propose an unsupervised fault diagnosis method based on sparse autoencoder and local linear embedding. This method combines the characteristics of sparse autoencoder and manifold learning methods to reduce dimensionality and extract features from high-dimensional data while preserving the local linear relationships within the data. The proposed model was experimentally validated on a three-phase flow equipment dataset, demonstrating the effectiveness of the model.
Design of an Industrial Workshop Equipment Maintenance System Based on HoloLens

Following the modern information technology revolution, traditional manual operation and maintenance in the industrial field face challenges such as intricate and complex equipment, low technical proficiency of workers in maintenance, and increased intensity and costs of maintenance due to the addition of workshop equipment. To address these issues, this paper proposes an industrial workshop equipment maintenance system based on HoloLens. The system utilizes low-cost mature technologies to reduce deployment expenses and lower the entry barriers, thus simplifying the complexity of the maintenance system. Additionally, the system employs data reading and a graphical user interface to facilitate the understanding and use of front-line workers. Through this maintenance system, workers can efficiently carry out daily inspections and equipment maintenance tasks. In the event of unresolved issues, workers can use the system to communicate with experts remotely via video, enabling experts to provide real-time guidance for on-site problem-solving.
Optimization Study of KNN Classification Algorithm on Large-Scale Datasets: Real-Time Optimization Strategy Based on Balanced KD Tree and Multi-threaded Parallel Computing

The K-Nearest Neighbors (KNN) algorithm is a classical supervised learning method widely used in classification and regression problems. However, the KNN algorithm faces serious challenges when dealing with high-dimensional large-scale datasets, mainly in terms of high computational complexity, low search efficiency, and difficulty in real time. In this study, an optimization strategy based on balanced KD tree and multithreaded parallel computing is proposed to address this problem. Experimental results show that the optimized KNN algorithm significantly improves the search speed while maintaining classification accuracy, realizes real-time classification of large-scale datasets, and provides a powerful tool for pattern recognition, data mining, and other fields in the era of big data.
Attention Module Based on Feature Normalization

Attention mechanism is an essential component in convolutional neural networks. Although attention modules like SE and CBAM have achieved tremendous success in fields such as image classification and object detection, most existing attention modules can only perform feature fusion in either channel or spatial dimensions. This greatly limits the flexibility of attention mechanisms to adapt in both channel and spatial dimensions, leading to underutilization of feature information. To address these issues, we propose a Feature Normalization-based Attention Module (FNAM) in this paper. This module generates a feature normalization weight map from the input feature map. During the normalization process, the scale and shift parameters for each channel are derived from the mean and standard deviation of the feature map. Consequently, this approach effectively utilizes feature information from all dimensions. To demonstrate the feasibility and effectiveness of this attention module, we conducted ablation experiments. In the image classification task, the FNAM module shows a significant improvement in the performance of classification networks on the CIFAR dataset compared to other mainstream attention modules. In the object detection task on the VOC dataset, the FNAM module outperforms most other attention modules. Particularly, concerning small and medium-sized objects in the dataset, its detection accuracy reaches the highest, with improvements of 1.6% and 1.5% respectively.
Research on Preprocessing Method for Massive Operations and Maintenance Data Based on Fuzzy Correlation

The trend of diversified, multi-dimensional, and complex development of power operation and maintenance data has led to the traditional processing methods for massive operation and maintenance data being unable to meet the current needs and development requirements of the power system. This paper studies an intelligent processing system for massive power operation and maintenance data based on fuzzy correlation, and combines the application of data label system in data centers to deeply mine data features, Discovering the interconnection relationship between basic data labels and deep data labels, forming fuzzy data labels and fault prediction labels on this basis, facilitating the storage and analysis of a large amount of operation and maintenance data, reducing system load rate, and achieving data interconnection between different systems.
Research on Information Fusion Interface of Human-Computer Interaction based on Mixed Reality Technology

In order to solve the problem that high-dimensional mixed reality interface brings low task performance and heavy cognitive load to users in cross-layer operation, we study the information fusion interface of human-computer interaction and propose two kinds of fusion interfaces. The experimental results show that the deep fusion interface performs better than the combined interface in cognitive difficulty, cognitive load, operational efficiency and subjective evaluation. The research conclusion can guide the design of human-computer interaction interface in mixed reality.
Research on Model Shape Optimization based on NURBS Modeling

Target recognition technology based on synthetic aperture radar (SAR) image plays an important role in several fields. Simulation images are needed as a supplement to achieve high recognition rate of SAR images. To expand the database based on the SAR image, it is necessary to optimize the shape of the target model to improve the similarity between the simulation image and the corresponding SAR image. This paper proposes a method of model shape optimization based on NURBS modeling. This method uses non-uniform rational B-spline (NURBS) to construct target model. Hummingbird optimization algorithm (HOA) is applied to optimize the shape of the target model, and complex wavelet structure similarity (CW-SSIM) is used as an evaluation index to compare the similarity between the simulation image and the target image. An example is given to show that this method can expand the image database effectively.