Reviewerly/exHarmony huggingface dataset:
I Can’t Escape Myself: Cloud Inter-Processor Attestation and Sealing using Intel SGX

SGX enclaves protect the code and data within from untrusted software, but do not retain their state when destroyed. Sealing allows preserving this data for future use by storing it outside the enclave boundary: the data is encrypted with a fresh secret key, and this key is bound to the processor that sealed the data and, either to the enclave measurement, or the public key of the enclave author. However, in a cloud environment, customers do not choose in which processor their code executes: the enclave that seals some data (for backup or future use) may be destroyed and later instantiated on a different processor or migrated to another processor. In those cases, the new processor would not be able to unseal the data, since the secret key is bound to the sealing processor.This paper presents Inter-Processor Attestation and Sealing (IPAS), a novel sealing mechanism for cloud environments and Intel SGX. In IPAS, sealed data is no longer bound to the sealing processor, but only to the enclave measurement or the public key of the enclave author, thus enabling other processors to unseal the sealed data. This is achieved without exporting the sealing secret key outside the enclave and without trusting third parties.
Intelligent Metaverse Museum Based on the Universal Design to Enhance Immersive Learning Experiences

The rapid growth of information technology and communication has led to a transformation in the traditional concept of museums that is familiar to everyone. Technology has the potential to create channels for users to acquire new knowledge and experiences. Consequently, researchers have shown interest in designing museum architecture that is engaging, captivating to visitors, efficient in imparting knowledge, and equally accessible to all individuals. The objective of this research is twofold: 1) to study the process of creating immersive learning experiences for an intelligent universe museum based on universal design principles, and 2) to design an intelligent metaverse museum architecture based on universal design principles to enhance immersive learning experiences.
Predictive Maintenance Using Machine Learning

The aims of this study are to find faults in component machine before its breakdown which focus on pump and motor by using Machine learning as “classifier” and to determine the most effective algorithm that can be identify faults for machine across five different datasets that are based on predictive maintenance and enhance the model by using cross validation then compare performance of our model to previous research studied by using the accuracy and the area under the curve receiver operating characteristics (AVC_ROC) as performance evaluation metric.
Recognizing and Understanding Sport Activities Based on Wearable Sensor Signals Using Deep Residual Network

The prevalence of intelligent wearable devices has significantly increased, offering advantages to individuals of all age groups. In the field of human activity recognition (HAR) research, wearable sensor data plays a crucial role in classifying various human actions. Sensor-based assessments are commonly employed in hazard analysis and risk assessments. However, traditional machine learning (ML) algorithms have faced challenges when applied to sports activities due to their unpredictable nature. These challenges have prompted the need for manual feature extraction, limiting the efficacy of ML methodologies in data categorization. To address this, our study introduces ResNet, a deep residual network, for effectively categorizing sports activities using wearable sensor data. To evaluate our proposed approach, we utilized a publicly available benchmark dataset consisting of sensor data collected from the dominant wrist, neck, and thigh of each study participant. The experimental results demonstrated the exceptional performance of the ResNet model, achieving an impressive accuracy rate of 99.83% in recognizing sport activities. Additionally, we compared the efficacy of our suggested model with five fundamental deep learning models, namely CNN, LSTM, BiLSTM, GRU, and BiGRU. The comparative analysis revealed that the ResNet model outperformed the other deep learning models, demonstrating superior performance in sport activity recognition.
Digital Culture Leadership for Smart Schools

The purpose of this study is to develop a conceptual framework for digital culture leadership in smart schools based on international education standards. Through content analysis and synthesis of relevant theories, documents, and research on digital culture, digital leadership, smart schools, and international educational standards, the study identifies key components. These components include digital culture and digital leadership, elements of smart schools, smart school personnel, smart schools' technology, and international education standards criteria encompassing leadership, strategy, measurement, analysis and knowledge management, workforce, and operations. The findings of this research are used to establish criteria for evaluating the digital culture leadership of smart school personnel in alignment with international education standards.
Eye-Tracking Technology and its Application in Neuroscience

A novel approach has been developed that utilizes information models of the oculo-motor system (OMS) for diagnosing neuropsychological states of an individual. This approach is based on employing Volterra polynomial models of the “input-output” type along with eye-tracking technology. It aims to enhance the precision of OMS modeling and provides a more robust diagnosis within the proposed heuristic feature space. Integral and differential transformations of multidimensional transition characteristics of the OMS are employed in this methodology, greatly streamlining the determination of features and the practical implementation of a Bayesian classifier. The derived heuristic features contribute to heightened diagnostic accuracy by analyzing eye movements and reflecting the neuropsychological states of the individual. This research holds significant potential applications in fields such as psychology, neurology, and human-computer interaction, offering a non-invasive and potentially more accurate avenue for comprehending an individual's internal states.
Meta-Learning with Evolutionary Strategy for Resilience Optimization of Image Recognition System

The problem of optimizing the resilience of image recognition systems to destructive disturbances has not yet been fully solved and is quite relevant for safety- critical applications. The task of optimizing the resilience of image recognition system to disturbing influences is a high- level task in relation to accuracy optimization, which determines the prospects of using the ideas and methods of meta-learning to solve it. Stated research goal is to develop an architectural add-ons and the meta-learning method for optimizing the resilience of an image recognition system to destructive disturbances. Meta-updating with evolutionary strategy is proposed for direct maximization of the expected value of resilience criterion. The experiments were conducted on a model with the ResNet-18 architecture, with an add-on in the form of convolutional adapters and meta- adapters for parallel correction of frozen pretrained modules. It has been experimentally confirmed that the proposed method provides a better resilience to random bit- flipinjection compared to training with fault injection by an average of 5.32%. Also, the proposed method provides a better resilience to adversarial evasion attacks compared to adversarial training by an average of 5.42%.
Artificial Intelligence and Internet of Things Intersection in Green Energy

A great emphasis has been given regarding the Sustainable Development Goals (SDGs) that the UN proposed in 2015. They emphasized the safety, security, and sustainability of the environment by providing 17 such goals. Clean energy at an affordable level, innovation, and infrastructure are some of the goals that are applicable in the green energy sector. The technologies of Industry 4.0 have the capability to transform the energy sector. Internet of Things (IoT) and Artificial Intelligence (AI) can aid the performance of green energy sources. Keeping this in mind the article aims to review the application of IoT in wind energy, solar energy, and AI models of Support Vector Regression (SVR) and Artificial Neural Network (ANN) in the DC microgrids through various research papers. The study aims to provide the challenges faced in the application of these technologies such as energy efficiency, security, accuracy, etc., and provide suitable suggestions like carbon mitigation strategies, open data frameworks, etc for the development of technologies for a better and sustainable tomorrow.
Personal Recognition System Based on EMG

In this research, we aim to design a highly accurate personal recognition system using electromyograph (EMG). A general method of personal recognition based on EMG utilizes muscle potential during certain gestures. The key idea of the general method of personal recognition based on EMG is to construct a discriminator that identify the gesture from the EMG generated when a certain gesture is performed. The principle of the discriminator is that when a series of gestures is performed, the discriminator identifies the correct gesture if the EMG are generated by the true user, but does not from the EMG generated by other users, even if they perform the same gesture. In previous studies, there has been a problem of high misidentification rate, i.e., the system does not recognize the true user or misidentifies other users as the true user. Therefore, this study strives to improve the accuracy of personal regcognition systems based on EMG by using deeplearning to find personal features, and based on features to construct a discriminator that can identify the gesture from the EMG generated.
Decision Making Strategy for COVID-19 Risk Minimization Using Data-Driven Model

During a pandemic outbreak, it is crucial to build socially resilient and sustainable communities. Data-driven solutions that account for the requirements of both exist military applications and global strategies are necessary for this task. There has been a lot of interest in the smartphone-based Digital Proximity Tracing Technology (DPTT) for reducing, confining, and tracking the ongoing COVID-19 epidemic with population acceptability insights and the effectiveness of the function. This study contrasts DPTTs with Data-Driven Epidemic Intelligence Strategies (DDEIS) to emphasize their distinctions and provide a new perspective on how to deal with them. DDEIS's core features might be used to provide a social as well as technological solution for stopping the spread of disease, protecting the public's health, and getting cities back to normal as soon as possible. The influence of human behavior is considered while weighing the benefits and drawbacks of society decision making and individual choice. The SEIR (Susceptible-Exposed-Infectious-Recovered) epidemiology model offers early information on user preferences in a DPTT. The suggested model's influence on the Covid-19 dissemination dynamics is assessed, and the findings are given.
Artificial Intelligence and Law: A Bibliometric Insight into Academic Publications and Research Trends

In this study, the analysis of artificial intelligence, machine learning, and deep learning studies in the field of law over the past 20 years was conducted. A total of 2161 data were obtained from the Scopus database, with Social Sciences selected as the subject area. The data consisted of various types such as articles, conference papers, book chapters, and books from the years 2003 to 2022. Several analyses were employed in the data analysis, including the number of studies per year, distribution of studies by subject areas, distribution of studies by country, distribution of studies by number of authors, and distribution of trend topics. The research findings revealed a significant increase in the number of studies after 2017, with a focus on topics such as big data, robots, ethics, rights, data protection, autonomous weapons, and natural language processing. In terms of the number of publications, Italy, Australia and Brazil draw attention after the USA, China and England. While the focus of the relations between the authors was the EU countries, there was an intense cooperation between China-USA and Australia-EU countries. The growing number of studies in this area suggests that artificial intelligence will make further progress and become more prominent within the legal field in the near future.
Deep Learning Based Cryptanalysis on SLIM Cipher

This paper provides an in-depth exploration of neural distinguishers in cryptanalysis with a focus on the SLIM Cipher. Building upon the work of Gohr, we aim to explain why neural distinguishers outperform traditional differential attacks and enhance contemporary methodologies. Our experiments centered on SLIM Cipher's fifth round and aimed to understand the influence of input difference, output difference, and previous round differentials on the accuracy of the neural distinguisher. Our investigations revealed that the choice of input difference was significant, and only focusing on maximizing the differential probability might not always yield the best results. Neural distinguisher appears to observe not just about the distribution of differences from the ciphertext but also additional cryptanalytic features or properties, which was a key insight from our research. Furthermore, we found that the output difference in the ciphertext played a vital role in the neural distinguisher's performance. By partially decrypting ciphertext pairs, we discovered that the output difference from the penultimate round was also a major factor impacting the accuracy of the neural distinguisher. This led to an improved methodology, yielding impressive accuracy results. These findings present valuable insights that can guide the future development and application of these tools in cryptanalysis.
Central Security Incident Management Platform in Industry 4.0 with Threat Intelligence Interface

The emergence of Industry 4.0 has tightly integrated Information Technologies (IT) and Operational Technologies (OT), enabling enhanced manufacturing capabilities and cost reduction. However, this integration also expands the attack surface and introduces new cyber vulnerabilities and threats, often with more significant impacts than before. Consequently, there is a pressing need for a comprehensive approach that ensures the security of both IT infrastructures and products. In response to this challenge, the ZenSIM project was initiated, aiming to develop a platform-based solution for identifying security vulnerabilities in the product and production environments of manufacturers. The project's objectives include detecting cyber-attacks and anomalies, generating Cyber Threat Intelligence (CTI), and sharing it with relevant stakeholders for further investigation and the creation of publicly available CTI and security advisories. This paper only addresses the architectural design of the proposed solution for protecting Industry 4.0-enabled manufacturing environment by utilizing open-source knowledge about known asset vulnerabilities and exchanging incident information. The experimental validation of the platform is beyond the scope of this paper.
Human Activity Recognition by BiLSTM Recurrent Neural Networks and Support Vector Machine

In the last years of this decade, the recognition of human activity has become important to a wide range of researchers in pattern recognition and human-computer interaction as a result of its wide range of real-world applications, such as gesture recognition, biometric user identification, surveillance by authorities, behavior analysis and health monitoring of the elderly. Human Activity Recognition (HAR) has become a significant topic in mobile and ubiquitous computing as a result of the widespread use of wearable sensor devices and the Internet of Things (loT). Deep Learning (DL) is one of the most commonly used problem-solving techniques in the HAR system. Nevertheless, there are major challenges in applying HAR to problems in recognizing various human activities. In this paper, presented and showed the activities of implementing a new combination of DL methods for multi-class user activity identification to HAR. Using DL methods can be extracting discriminative features automatically from raw sensor data. Specifically, in this work, we proposed a hybrid architecture that features a combination of Bidirectional Long Short-Term Memory (BILSTM) networks and support vector Machines (SVM) for the HAR task. The UCI HAR dataset is used to test the model, it consists of accelerometer and gyroscope data obtained from smartphones. The dataset is split into 30 % for testing and 70% for training. The results for the (BILSTM-SVM) model, showed that the highest accuracy for all users was 98.74 %, higher than all previous models using the same dataset.
Bimodal Author Identification on Famous Political Discourses Based on Text and Speech

In this investigation, we present a new method of Author Identification (AI) based on two different modalities: Transcript Text (TT) and Speech Signal (SS). This research work is performed on a corpus of several discourses given by Famous Politicians in English (FPOL corpus). The originality of this work is that we use both Speech Signal and its Text Transcript during the classification process. That is, 14 famous politicians are considered, where 6 are female politicians and 8 are male politicians. The first analysis uses the text modality based on three classifiers and three features: Character 3Grams, Character 4Grams and Words, while the second analysis is based on those textual features in association with the average Pitch (Fo) of the speech signal modality. The proposed association uses a logical filtering based on the average Pitch range in order to correct the errors of identification if any. The experiments of author identification conducted on the FPOL corpus show that the association of the two modalities enhances considerably the identification performances. The average accuracy improvement brought by the speech modality is about 5% for character 3Grams and 4Grams, and it is about 12% of improvement for words.
Role of Artificial Intelligence in Haematological Disorder

Artificial intelligence (AI) is a branch of computer science that uses a computational framework to replicate human brain functions. The primary non-natural neuron was created in 1943, and the primary non-natural neural system (ANN) was applied to hereditary procedures soon afterward. Researchers and scientists have already recognized the potential for a comparable technology in medicine. The capability of this technology to retain and analyses altogether medicinal information consumes it very alluring to complement or perhaps replace clinician in creation a designs. The routine of skilled or knowledge-based organizations in dull scientific use for analysis, healing management, and predictive assessment are examples of requests of artificial intelligence in medicine. New samples contain procedures practical to analysis in, psychiatry, heart illnesses etc. Biomedical applications include protein structure prediction, analysis and clustering of gene expression data, genome sequencing The first AI-based gadgets have been used in hematology to manage routine laboratory data. Created on neural systems expert by information since marginal plasma study, new methods for the differential diagnosis of certain diseases, such as anemias, thalassemia, and leukemia, are now available.
Image-to-Video Person Re-Identification Using Semantic Information

Person re-identification is at ask that aims to identify a subject from a video sequence captured by multiple cameras with a non-overlapping field o f view. It has many applications in surveillance, public safety, smart cities and especially in context-aware holographic communication. The multiple frames in a video sequence can be used to find temporal r elations and t heir representation can be utilized for improving the re-identification task. Furthermore, semantic information related to the person can be used to improve the performance and deliver better representations of the video sequences and images. In this work, we propose a novel approach for the utilization of semantic information in learning non-local weights by capturing the relations among features extracted from video sequences. The semantic information is represented by the human body and extracted using a dedicated neural network. Experimental validation using popular datasets confirms the validity of the proposed approach.
Seamless Transition: Migrating from Relational Databases to Document-Oriented Databases

The exponential growth in data volume has presented significant challenges in storing and analyzing big data. Relational databases, traditionally used for data management, have struggled to meet the demands of big data, particularly in terms of velocity and variety. This limitation hinders scalability as data volume increases. In contrast, NoSQL databases offer flexible schemas that cater to the requirements of big data. To overcome the inefficiencies of relational databases and leverage the benefits of NoSQL, this paper proposes an algorithm for migrating from a relational database to a document-oriented database. The study also applies transformation rules to a case study, evaluating the performance of migration time across various database properties. The results demonstrate that the proposed migration method successfully handles migration time and encompasses all relevant database properties.
Exploring Renewable Energy Solutions for Indonesia's New Capital: A PVsyst Analysis for the Construction of a Floating PV System on Merdeka Reservoir Balikpapan

Indonesia, blessed with abundant solar resources and its unique archipelagic nature, presents significant potential for renewable energy, notably solar power. This study offers a comprehensive analysis of a proposed Floating Photovoltaic (FPV) system on Balikpapan's Merdeka Reservoir in Kalimantan. It covers technical, financial, and environmental aspects of the project, taking advantage of the region's average of 12 daily hours of sunshine, which provides an optimal environment for solar energy generation. Employing PV syst software, we model the FPV system's performance, factoring in regional solar irradiance and reservoir characteristics. Our findings reveal that the FPV system at Merdeka Reservoir delivers valuable insights into its overall performance, achieving an average normalized production of 4.13 kWh/kWp/day. The system encounters various losses, including array soiling, thermal factors, and DC wiring losses. Despite these challenges, financial analysis demonstrates promising economic viability, with a Net Present Value (NPV) of 67.83 trillion IDR and an Internal Rate of Return (IRR) of 13.30%. The projected payback period stands at 9.1 years, indicating a relatively swift return on investment. Additionally, environmental assessment highlights the FPV system's substantial contribution to CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions reduction, with a total savings of 180,556 tons over its lifetime. This research enriches our understanding of renewable energy solutions for Indonesia's new capital and aligns with the nation's commitment to achieving net-zero emissions (NZE) by 2060.