Reviewerly/exHarmony huggingface dataset:
Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0

.Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to Internet opens the cloud manufacturing to the massive attack surface of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this goal by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with the predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks such that the blockchain peers can timely detect high deviations and take actions. The research defined the constraints related to physical boundaries and weight lifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This research followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, booster risk management, strengthen cybersecurity, and advance sustainability efforts.
DECISION-MAKING METHOD FOR TEMPERATURE CONTROL IN THE SMART HOME

The current challenge is to provide automatic decision support in a smart home. A study of the top solutions of well-known smart homes has shown that existing solutions usually do not provide for fully automatic control in a smart home, but are focused either on automatic control in conjunction with manual control or user-controlled control. Therefore, the goal of this study is to support decision-making for fully automatic temperature control in a smart home. Human well-being and performance depend on the meteorological conditions of the environment in which a person is located. The most important condition for high performance, rest, and health is the creation and maintenance of an optimal home microclimate. One of the main parameters of the indoor microclimate is temperature. The room temperature control subsystem ensures the optimal temperature level and allows for individual adjustment for each family member. The developed rules for determining the optimal room temperature allow you to evaluate the existing temperature parameters for further automatic operation of the smart home temperature control subsystem in residential premises of various types. The purpose of the temperature control subsystem is to provide comfortable conditions in residential premises of various types in terms of their temperature regime. The developed decision-making method for temperature control in a smart home, which is the basis of the smart home temperature control subsystem, provides a comfortable and optimal (taking into account building and sanitary and hygienic standards) temperature in the corresponding living space. The results of the functioning of the smart home temperature control decision-making method have shown that the developed method provides for the recognition of situations (optimal temperature, low temperature, high temperature) and support for decision-making on the temperature regime in a certain type of residential space (turning on heating devices, turning on cooling devices, no action, etc.).
Digital Twin-assisted Space-Air-Ground Integrated Networks for Vehicular Edge Computing

In this paper, we present a framework that integrates digital twin (DT) technology into space-air-ground integrated networks (SAGINs) to enhance vehicular edge computing (VEC). Our objective is to efficiently offload tasks in ultra-reliable low-latency communications (URLLC)-enabled vehicular networks, focusing on minimizing overall latency for requested tasks by reducing transmission time for task offloading and edge processing requirements. The proposed framework leverages DT-assisted SAGINs to minimize task offloading latency, expand network coverage, and reduce energy consumption. Key components of our framework include partial task offloading, distributed edge computing, latency modeling, energy consumption analysis, mobility, and channel modeling. We formulate a non-convex optimization problem considering various network constraints to achieve the system objective. To solve this optimization problem, we develop a novel multi-agent deep reinforcement learning (DRL) algorithm, enabling intelligent decision-making by individual agents. Through extensive simulations, we validate the effectiveness of our proposed system in advancing VEC by integrating DT technology into SAGINs.
Multi-exposure Image Fusion Based on Improved Exposure Weight and Adaptive Edge-Preserving Smooth Pyramids

Multi-exposure image fusion (MEF) is an efficient method to fuse multiple differently exposed low dynamic range (LDR) images of a high dynamic range (HDR) scene into a high-quality LDR image with enriched information. However, the existing multi-scale MEF algorithms have poor adaptability to the brightness of the input images, and cannot preserve more image details while effectively suppressing the halo artifacts. To solve these problems, we first propose an improved exposure weight algorithm. The algorithm evaluates the exposure quality of pixels according to the global brightness and relative brightness of the input images, and uses adaptive factors to adjust the exposure weight to better adapt to the brightness of the input images. Then we introduce the proposed adaptive edge-preserving smoothing (AES) pyramids to the weight maps to fuse the LDR images with different exposures. With the AES pyramids, more details of the brightest and darkest regions in HDR scene are preserved and the halo artifacts are also effectively suppressed. Experimental results on 16 sets of multi-exposure image sequences prove the superiority of the proposed method over the other seven existing state-of-the-art methods in terms of both subjective and objective evaluation.
Pseudonymous Decentralised Reputation System

In this paper, we propose a novel pseudonymous decentralized reputation system that aims to establish trust within decentralized networks. The system allows users to maintain pseudonymous identities while ensuring their reputation scores are transparent, trustworthy, and resistant to manipulation. We review existing reputation systems and discuss their limitations. Our proposed system incorporates cryptographic techniques, a consensus algorithm, and distributed data storage to ensure data integrity, privacy, and scalability, specifically designed for the Decentralized Finance (DeFi) ecosystem. We present an implementation of our system, focusing on its application in DeFi platforms, and evaluate its performance against various attack vectors. Finally, we discuss the implications of our system on decentralized financial applications, such as lending and borrowing protocols, and future research directions. Lastly, we explore the broader implications of our decentralized reputation system for the DeFi ecosystem, identifying potential applications in areas such as decentralized exchanges, insurance, and asset management.
Examination System Automation Using Natural Language Processing

Numerous examination portals have been created and distributed over several servers for a number of objectives, including the administration of tests in connection withadmission exams, Olympic competitions at national and worldwide levels, and the administration of placement tests. It turns out that practically all portals are set up for multiple-choice examinations, as we found. Our goal here is not to work on current technology, but rather to work on technology that is very uncommon. The descriptive online examination method is discussed here. Simple to use, multiple choice questions are composed of a question, many possible answers to the same question as well as a field to record the correct response in the database. Descriptive queries, on the other hand, do not fall within this category. To award marks to replies, it incorporates or employs Natural Language Processing (NLP) ideas. The model's responsibility is to lead procedure on the response string so it can give the right stamps to the examinee's responses. The data is generally assembled utilizing an enlightening web-based appraisal procedure. It is likewise inspected, and the made model honors right stamps to the inquiry's answers. With respect to the front-end, HTML 5, CS S 3, Bootstrap, and JavaS cript are utilized. The back-end was created in Python using Django, NLTK for Natural Language Processing, and SQLite version 3 as the database.
Goods Moving Robot Prototype Using 3-Wheel Omni-Wheel System

In light of the recent global crisis, especially the public health emergency, traditional goods distribution systems are vulnerable to inefficiencies and gaps. Therefore, it is important to overcome this deficiency, the need for a solution, namely making technological innovations. To achieve this goal, this research introduces a revolutionary Goods Moving Robot, powered by a 3-Wheel Omni-Wheel System. Leveraging a powerful mathematical framework of kinematic and trigonometric algorithms, these autonomous robots represent a paradigm shift in goods distribution logistics. The research includes a thorough examination of wheel performance, empirical testing, and analytical models, which reveal strong evidence for the application of robots in various sectors, including healthcare logistics.
Careful When You Click? How the Dark Triad of Personality Can Influence the Likelihood of Online Crime Victimization

Cybercrime is a growing problem, with increasing numbers of people reporting they have been a victim. However, the literature has tended to focus on the characteristics of the perpetrator and has often neglected to examine how the individual differences of victims may have an impact. This paper investigates how the Dark Triad – Machiavellianism, narcissism, and psychopathy – may increase the chances of being a victim of online crime. To do this, the Cyber Routine Activities Theory was applied, which suggests victimization is a result of two things: 1) a user’s routine online activity which may bring them into contact with nefarious others and/or makes them an attractive target, and 2) the lack of a “capable guardian” who can defend against such nefarious others. Using an online survey (N = 328), we measured Internet users’ Dark Triad traits, along with their engagement in various online activities and the preventative measures used against potential criminals. Findings demonstrated that narcissism and psychopathy increased the likelihood of victimization, but Machiavellianism did not. These relationships were moderated by gender. However, contrary to other work using the Cyber-RAT, preventative measures (e.g. knowledge of computers, presence of anti-virus programs) did not seem to impact on the likelihood of victimization. The challenges of using these findings to reduce cybercrime and future work are then discussed.
Enhancing Odometry Estimation for Mobile Ground Vehicles: Leveraging Wheel Velocity Measurements and the Kalman Filter

This paper delves into the initial phase of developing an autonomous navigation system for mobile platforms. It examines the impact of wheel velocity measurements on odometry estimation, considering the transition from a reliable and precise signal to noisy and less dependable information. The study investigates how this range of available data influences the accuracy of pose estimation for a ground vehicle. Additionally, the paper explores the incorporation of the Kalman Filter as a supplementary tool to enhance the input data quality, resulting in a consistent and reliable estimation of the ground vehicle’s position and orientation. The findings presented herein contribute to the advancement of autonomous navigation systems, providing valuable insights into improving odometry estimation for mobile ground vehicles.
Low Cost and Precise Jitter Measurement Method for TRNG Entropy Assessment

Random number generators and specifically true random number generators (TRNGs) are essential in cryptography. TRNGs implemented in logic devices usually exploit the time instability of clock signals generated in freely running oscillators as source of randomness. To assess the performance and quality of oscillator-based TRNGs, accurate measurement of clock jitter originating from thermal noise is of paramount importance. We propose a novel jitter measurement method, in which the required jitter accumulation time can be reduced to around 100 reference clock periods. Reduction of the jitter accumulation time reduces the impact of the flicker noise on the measured jitter and increases the precision of the estimated contribution of thermal noise. In addition, the method can be easily embedded in logic devices. The fact that the jitter measurement can be placed in the same device as the TRNG is important since it can be used as a basis for efficient embedded statistical tests. In contrast to other methods, we propose a thorough theoretical analysis of the measurement error. This makes it possible to tune the parameters of the method to guarantee a relative error smaller than 12% even in the worst cases.
All You Need Is Fault: Zero-Value Attacks on AES and a New λ-Detection M&amp;M

Deploying cryptography on embedded systems requires security against physical attacks. At CHES 2019, M&amp;M was proposed as a combined countermeasure applying masking against SCAs and information-theoretic MAC tags against FAs. In this paper, we show that one of the protected AES implementations in the M&amp;M paper is vulnerable to a zero-value SIFA2-like attack. A practical attack is demonstrated on an ASIC board. We propose two versions of the attack: the first follows the SIFA approach to inject faults in the last round, while the second one is an extension of SIFA and FTA but applied to the first round with chosen plaintext. The two versions work at the byte level, but the latter version considerably improves the efficiency of the attack. Moreover, we show that this zero-value SIFA2 attack is specific to the AES tower-field decomposed S-box design. Hence, such attacks are applicable to any implementation featuring this AES S-box architecture.Then, we propose a countermeasure that prevents these attacks. We extend M&amp;M with a fine-grained detection-based feature capable of detecting the zero-value glitch attacks. In this effort, we also solve the problem of a combined attack on the ciphertext output check of M&amp;M scheme by using Kronecker’s delta function. We deploy the countermeasure on FPGA and verify its security against both fault and side-channel analysis with practical experiments.
A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities

With the popularity of location services and the widespread use of trajectory data, trajectory privacy protection has become a popular research area. k-anonymity technology is a common method for achieving privacy-preserved trajectory publishing. When constructing virtual trajectories, most existing trajectory k-anonymity methods just consider point similarity, which results in a large dummy trajectory space. Suppose there are n similar point sets, each consisting of m points. The size of the space is then mn. Furthermore, to choose suitable k- 1 dummy trajectories for a given real trajectory, these methods need to evaluate the similarity between each trajectory in the space and the real trajectory, leading to a large performance overhead. To address these challenges, this paper proposes a k-anonymity trajectory privacy protection method based on the similarity of sub-trajectories. This method not only considers the multidimensional similarity of points, but also synthetically considers the area between the historic sub-trajectories and the real sub-trajectories to more fully describe the similarity between sub-trajectories. By quantifying the area enclosed by sub-trajectories, we can more accurately capture the spatial relationship between trajectories. Finally, our approach generates k-1 dummy trajectories that are indistinguishable from real trajectories, effectively achieving k-anonymity for a given trajectory. Furthermore, our proposed method utilizes real historic sub-trajectories to generate dummy trajectories, making them more authentic and providing better privacy protection for real trajectories. In comparison to other frequently employed trajectory privacy protection methods, our method has a better privacy protection effect, higher data quality, and better performance.
Intrusion Detection based on Incremental Learning

Machine learning and deep learning have become essential in enhancing the performance of intrusion detection systems. While existing research on intrusion detection systems utilizing data mining and machine learning has shown effectiveness, it typically involves training static batch classifiers that identify intrusions without considering the time-varying characteristics of the regular data stream. This paper aims to propose an ensemble adaptive approach for online intrusion detection using stream-oriented learning, which can effectively adapt to concept drift in real-world environments. The technique involves the utilization of an ensemble Adaptive Random Forest classifier and Support Vector Regression (SVR) with the ADWIN change detector to detect changes in a data stream and dynamically adjust to drift detection in the streamed data. This approach enables agile adaptation against unknown intrusions and eliminates the need for frequent model retraining over time.
Central Bank Digital Currency: A Bibliometric Analysis of Scopus Database

Central Bank Digital Currency (CBDC) is a type of digital money that has a central bank as its issuer. CBDCs, in contrast to conventional physical currencies, are digital and run on either a centralized database or a decentralized blockchain network. They can completely transform the world’s financial system because they are made to work as a safe and effective form of payment. The current state of research on central bank digital currencies is reviewed using bibliometric analysis in this paper. 221 peer-reviewed journal papers published between 2018 and 2023 were analyzed using the Scopus database and VOS viewer software to find trends, patterns, and emerging areas in CBDC research. The data showed that there has been a consistent rise in publications on CBDCs since 2018, with a notable increase in 2021 and 2022. The paper concludes with a discussion of the result and significance of the study for decision-making.
Face Sketch Recognition Using Image Synthesis And Deep Learning

Criminal investigations heavily rely on crime scene sketches. To appropriately categorize sketch photos from crime scenes, this research suggests a deep learning based sketch image classification method. Convolutional Neural Network (CNN) is the algorithm utilized to classify each sketch image in the proposed deep learning based system. This method uses a custom image dataset that includes images of various people’s facial sketches. Moreover, the suggested system takes both color and sketch images. Several systems that automatically recognize subjects shown in sketches based on eyewitness descriptions have been developed recently however, when real-world forensic sketches are used their performance frequently suffers. Despite its effectiveness in many application domains, such as conventional face recognition use deep learning for face photo sketch recognition. This is primarily because there aren’t enough available sketch images to adequately train big networks. In order to address significant issues with the current face recognition system, a deep learning face synthesis and recognition system is proposed in this study. For face identification in the suggested system, the input can be either a color image or a sketch image. Images of various people’s face sketches will be utilized to train the recognition algorithm. The training algorithm is a deep neural network. The system may be able to recognize input face images once training is completed. This system comprises two steps for face recognition and synthesis. If the new input is a sketch image, the recognition stage receives the input right away. The GAN network is used to produce equivalent sketch images if the input is a color image. In this instance, the input to the CNN for classification is a synthesized image. Thus, the proposed system is a combination of image synthesis and deep learning.
Generic Components based Expert System Shell for Avionics Applications

This paper focuses on the construction of a Generic Components Based Expert System Shell for various avionics applications. Traditional Expert Systems have been constructed using a single monolithic software program for a specific application. In traditional/commercial Expert System, either the Knowledgebase is hard-coded into the program or the linking of the Expert System modules with the knowledge bases takes place during compilation time. These commercial Expert Systems can’t be used for complex applications where multiple knowledge bases have to be handled at run time, because the coupling of any particular knowledge base needs either the alteration of program code and/or recompilation. Secondly all these commercial Expert Systems are working in interactive mode during the inference process. To overcome these problems, ADE (Aeronautical Development Establishment) is currently involved in the design and development of a complex competent shell i.e. A Generic Components Based Expert System Shell for Avionics applications. This shell works in automatic mode and the system engineer can easily configure these generic components into a domain-oriented solution for any complex application including applications where multiple knowledge bases are handled. In this paper, we are discussing the role and generic nature of this shell along with its components and how they are configured as a domain-oriented solution for different kinds of applications at ADE.