Reviewerly/exHarmony huggingface dataset:
Design and Implementation of AES-SHA Security Hardware using FPGA

In cryptography, hash functions are one of the most fundamental types of cryptographic primitives. They are applied in various contexts, including signature authentication and maintaining the integrity of communications. These functions are used to obtain a fixed-size unique fingerprint or hash value for an arbitrary message length. The message length can be anything. Everyone knows the importance of data encryption to maintain the privacy and confidentiality of data transmitted over various computer networks. Advanced Encryption Standard (AES) is one of the methods used to achieve this purpose. Still, an efficient design must be implemented at high speed to keep up with technology development. The Secure Hash Algorithm-3 (SHA-3) family of binary data functions is defined in this standard. NIST's choice for the SHA-3 Cryptographic Hash Algorithm Competition was an implementation of the KECCAK algorithm, from which all SHA-3 functions are derived. This standard also specifies the KECCAK-p family of mathematical permutations, including those that underpin KECCAK. The Advanced Encryption Standard (AES) and Hash can be implemented in software or hardware. However, AES and Hash are implemented on hardware. Because hardware implementation satisfies the speed, throughput and security requirements of the complex communication systems used in modern applications, it chooses the 128-bit for this architecture in this design. There are six different algorithms in the SHA-3 family: the cryptographic hash functions SHA3-256, SHA3-384, and SHA3-512 it chooses the SHA3-256 while the throughput of the system is 64 Gbps with 50 MHz FPGA.
Mimicking 2-DOF Human Eye Movement using an IR Camera-Based Wearable Eye-Gaze Tracking Controller

This study presents the real-time human-robot mimicry of the human eye two degrees-of-freedom (2-DOF) movements. The control system uses an IR camera-based wearable eye-gaze tracking controller attached to the human head to control the robotic eye. Building upon previous studies, this study addresses limitations of existing eye gaze tracking systems for robotic control in horizontal and vertical eye movements including attempts to compensate for head movements using a wearable controller. The researchers employ image processing through IR camera in eye-gaze tracking system in the wearable controller, applying the Pupil Center Corneal Reflection (PCCR) concept and polynomial regression that enables to replicates the eye movements made by humans into the robotic eye. After a statistical analysis done in the data gathered through Wilcoxon Test, the p-values obtained for horizontal and vertical movements are higher than the alpha level. This means that the observations indicate no statistically significant difference between the angles of the human eye and the robotic eye. The results demonstrate that the wearable eye-gaze tracking controller’s control system allows the robotic eye to mimic human eye movements, with a highly accurate horizontal movement of 99.07% and vertical movement of 98.91%, resulting in an overall accuracy of 98.99% in mimicking human eye movements.
A Novel Structure to Mine High Utility Items Based on Transactional Weights

Association Rule Mining finds the frequent pattern rules in a dataset but association rule mining does not gives any information about the profit of the item. High utility itemset mining finds the high utility itemsets from the dataset, the high itemset discovers the profitable and useful patterns. Mining high utility itemset is challenging compared to mining association rule mining algorithm since the downward closure property is not satisfied in high utility itemset mining. Various state-of-the algorithms have been proposed to mine high utility itemset. In this paper a novel tree based data structure to mine high utility itemset is proposed and different pruning strategies is employed to effectively mine high utility itemset. The proposed idea is evaluated on 4 real time datasets. The result shows that the proposed algorithm performs better than the state-of –the-art algorithms in terms of memory and execution time.
An Optimized Image Annotation Method Utilizing Integrating Neural Networks Model and Slantlet Transformation

The inconceivable volume of online images produced by websites and personal collections has made it difficult to retrieve images from vast databases accurately. Practically, consumers need help to get the relevant information they seek. Humans are able to identify images according to their apparent characteristics. In addition, the image pixels cannot offer enough detail for creating semantic ideas, rendering image annotation a difficult endeavor. A particularly more challenging assignment in Computer Vision (CV) and multimedia studies is image annotation (IA), in which the method assigns keywords from the dictionary to a given digital image. The major aim of IA is to convert the given digital image into single or different labels. To address this issue, this study focused on the extraction of automatic features to improve image annotation. Different techniques have been used with the suggested method such as YCbCr, Local Binary Pattern (LBP), and Slantlet Transform (ST) to improve a Feature Extraction (FE). In addition, the most widely used datasets, such as Flickr8k, ESP-Game, and Corel-5k, were used to test the proposed study. The evaluation results demonstrated superior annotation flexibility, enhanced accuracy, and decreased computation cost.
A State-of-the-art High-Resolution Network based Dipolar Detector for Occluded Pedestrian Detection

Applications including robotics, autonomous driving, assisted living, and surveillance all depend extensively on pedestrian detection. Despite the fact that several computer vision researchers have attempted to tackle the pedestrian detection problem, it remains unresolved. Scale, position, occlusion, lighting, and many other similar elements have an impact on how well the procedures work. So a novel dipolar modelling method based on the High Resolution Network (HRNet) has been proposed for an efficient pedestrian detector that resolves the occlusion issue. The two centre prediction branches built for the pedestrian detection head specifically changed the training data loss or ground truth. In order to improve the detection performance,the end-to-end optimise network's is designed as detection head and make use of the supportive outputs for the two focus projection branches. The network is compeled to focus more on the occurrences of occluded pedestrians by the proposed dipolar prediction technique. The experimental findings on the KITTI benchmark support the proposed dipolar detector's effectiveness in controlling occlusion in pedestrian identification
Analysis of Post and Previously JPEG Compressed Contrast Enhanced Images

Digital images are widely used in various fields, such as computer-aided diagnosis, handwriting recognition, medical CT, and forensic investigations. Forgers can easily alter these images by addressing several image processing operations and methods. This makes it necessary to keep them secure as they convey a lot of useful information. Detection of these enhancement operations become very important in today’s digital era in order to obtain authentic information. Contrast enhancement redistributes the image pixel in a linear or non-linear fashion by improving the separation of structural variations in image pixel intensity into a more visually differentiable distribution. This paper proposed an effective analysis for the classification of the post and previously compressed contrast-enhanced images. The analytical outcomes demonstrate the effectiveness of the presented approach and achieve good accuracy.
Automatic Dual-Axis Solar Tracking System for Enhancing the Performance of a Solar Photovoltaic Panel

This study demonstrates an automatic dual-axis solar tracking system that can improve the efficiency of a solar photovoltaic panel by tracking the sun's movement across the sky. The purpose of this study is to evaluate the efficiency of a dual-axis solar panel and compare it to the efficiency of a single-axis solar panel. The device employs a dual-axis solar tracking mechanism that utilizes four light-dependent resistors (LDRs) to monitor the sun's rays. Based on the findings from this study, the dual-axis solar tracker's energy generation capacity is significantly greater than the single-axis solar tracker. It also has a significantly greater energy generation capacity than the single-axis solar tracker. By installing tracking equipment with two axes, it is possible to increase the efficiency of solar photovoltaic panels. Compared to a system with a single-axis, the dual-axis system is more efficient and results in an 18% increase in energy production and efficiency.
SimulSilat: A Deep Learning-Based Simulator for Silat Training

This paper presents an investigation into the application of deep learning techniques and a simulated user interface system for Silat movement recognition. Silat, a traditional martial art originating from Southeast Asia, offers numerous physical and mental health benefits. To enhance the training experience and improve skill development, this study developed graphical user interfaces using MATLAB App Designer and employed a web camera connected to a laptop as the main hardware setup. The SimulSilat simulator facilitated video recording and registration of specific Silat stances, while various convolutional neural network (CNN) architectures, namely ResNet-18, ResNet-50, ResNet-101, DarkNet-19 and DarkNet-53, were evaluated for movement recognition. The simulation setup involved a white screen as the training area background, with portable LED lights strategically placed for optimal ambient lighting. The system operated in two modes: Live mode, which identified Silat movements and provided real-time accuracy, and Demo mode, which performed classification on previously saved video recordings. A dataset of 1,500 images captured from 50 Silat practitioners was utilized, with 70% allocated for training and 30% for validation. The results demonstrated the performance and generalization capabilities of the CNN architectures. DarkNet-53 emerged as the most promising architecture, exhibiting superior accuracy and generalization over the training epochs. ResNet-50 and ResNet-101 also showcased competitive performance, capturing relevant features from Silat movements. The findings emphasize the potential of deep learning and simulated user interfaces in advancing Silat training methodologies.
Blockchain-based Security for Cloud Data Storage

Data Cloud Storage is a service that allows data to save data on offsite storage system and managed by third-party and is made accessible by a web services user. It has become one of the most convenient and efficient methods to store data online. However, like other technologies and services, cloud storage has its advantages and disadvantages, but the challenge of security is the big issue. In this project, an investigation of the use of blockchain technology to improve the security of cloud data storage. The focus to address in this project is the vulnerability of cloud data storage systems to cyber-attacks and data breaches. The design and implementation of a proof-of-concept blockchain-based data storage system, and an evaluation of the system’s security and performance will be presented. A proposal system to secure cloud storage could be an alternative for traditional methods. It is proposed to use blockchain as a data protection system.
Augmented Reality in Education Application

The project aims to develop Augmented Reality (AR) technology in education, majoring in electrical engineering dedicated to electrical system wiring for single-phase. The use of AR applications has the effective potential to be used in the classroom as an experimental tool, and to stimulate student engagement in class activities. In education, students will experience a few issues especially the teaching method depending on the provided lecturer's notes where the traditional way of using a whiteboard or blackboard can cause a lack of interest in students to learn a topic and understanding the lesson. As a consequence, there exists a disparity in the level of maturity among students, with certain individuals demonstrating a greater capacity for comprehension while others may struggle to grasp the subject matter. In order to address this issue, a proposed solution is the development of an educational application utilising Augmented Reality technology. This application will be designed using Unity3D and Vuforia. The anticipated outcome has the potential to enhance pedagogical approaches in the classroom, foster student involvement in augmented reality (AR) applications, and facilitate a more efficient comprehension of single-phase wiring.
Effect of High Frequency Noise Using DCMs in FPGA on Power Analysis Attack

As the IoT society advances, the leakage of cryptographic keys through side-channel attacks has become a realistic threat. This paper proposes a method to improve the resistance of a system to power analysis attacks when implementing a cryptographic system on an FPGA by utilizing a DCM, which is an unused hard macro. The proposed method achieves a high level of resistance to tampering at a low cost, with only the cost of a small Xorshift pseudo-random number generator.
Integration of OpenCV and Cyclone V Hybrid ARM and FPGA SoC for Face Detection Application

This paper presents a Hybrid ARM and FPGA-based Face Detection System design powered by the openCV computer vision library and the SoCKit Altera Cyclone V System-on-Chip FPGA Development Board. An integrated system was designed for compatibility with the Cyclone V SoC SoCKit Development Board using Altera QSys and Altera Quartus. A custom version of the Linux operating System Kernel from GitHub was then developed to support the Development Board's specifications and the System Requirements, such as USB Video Class Kernel Modules for USB Web Cam Support of the Integrated Face Detection System, which was compiled using the Linaro toolchain. OpenCV was then compiled within the Linux System, and a face detection program using OpenCV face detection functions was developed to be compatible with the integrated system. The Integrated Face Detection System was compared to a CISC-based setup with Intel(R) Core(TM)i7-2670QMCPU@2.20GHz.The results showed that the SoC has 43% less time than the Intel Core i7 setup in detecting a face from the standard Lena.jpg input file.
Privacy-preserving Gaussian Process Latent Variable Model for Dimensional Reduction

In this paper, we propose a privacy-preserving Gaussian Process Latent Variable Model (GPLVM) for scrambled data generated on the basis of a random unitary transform. The GPLVM is a flexible Bayesian non-parametric modeling method that has been extensively studied and applied in many machine learning tasks. The proposed privacy-preserving GPLVM reduces the dimension of high-dimensional data in the scrambled domain in consideration of its use in the edge/cloud. In addition, we propose a dimensional extension method to improve security strength. We prove, theoretically, that the proposal has exactly the same estimation performance as the GPLVM for non-scrambled data. Finally, we perform numerical demonstrations on multiphase oil flow data. The effectiveness of the proposed method is verified.
A Novel Deep Learning Framework for Efficient Automatic Modulation Recognition of Sub-Nyquist Sampled Signals

Automatic Modulation Recognition (AMR) is an exciting technology that empowers intelligent communication receivers to identify different signal modulation schemes. In recent times, deep learning (DL) research has led to significant advancements in high-performance DL-AMR techniques, particularly in the context of traditional Nyquist sampling. However, in practical applications, the cost, complexity, and power consumption of analog-to-digital converters (ADCs) pose significant challenges to the implementation of real-time wideband spectrum sensing. Therefore, a sub-Nyquist sampling mechanism has been proposed to solve the challenging problem of meeting the high Nyquist sampling rate required by 6G ultra-wideband technology. Nevertheless, sub-Nyquist sampling may lead to non-linear distortion of the original signal, which can greatly increase the difficulty of signal modulation recognition and result in a significant decrease in the recognition accuracy of traditional DL-based methods. In this paper, we propose a novel deep learning framework for sub-Nyquist signal modulation recognition, which consists of two parts: (1) signal reconstruction based on Simultaneous Orthogonal Matching Pursuit (SOMP), and (2) time-frequency feature-based modulation recognition network including shorttime discrete Fourier transform (STFT) and efficient convolutional neural networks (CNN), called STFT-AMCNet. The proposed approach's performance is thoroughly evaluated and compared with previous state-of-the-art methods on a publicly available dataset to showcase its exceptional efficiency.
Deep Multimodal-based Number Finger Spelling Recognizer for Thai Sign Language

The use of video-based sign language recognition is an important technique for increasing communication and accessibility for the deaf and hard-of-hearing communities. However, developing and maintaining high-quality sign language datasets from video input is difficult, especially in Thai, because of the lack of a standard Thai finger spelling video dataset. To overcome this challenge, this article must focus on accumulating a larger total of 24 primary numbers in Thai Finger Spelling from 43 signers with various backgrounds, genders, and appearances. We conduct six deep learning-based architectures: RGB-sequencing-based CNNLSTM and VGG-LSTM for video-only modality, a sequence of coordinates of joints in human’s body using LSTM, BiLSTM, and GRU models, and the structure of human’s joints modality using ST-GCN, as well as their combinations. Our results reveal that combining the RGB-sequencing modality from VGG-LSTM with the joint’s structure modality on ST-GCN has the greatest performance on both in-sample and out-of-sample test sets.
Design Across Layers: Achieving More by Joining Hardware, Software, and Cryptography

Modern computer systems are built out of many different layers: protocols, software, and hardware all work together to make a larger system. Developers use each layer to build the next layer above it. Security engineers can incorporate defences at different layers. Each layer has its own limitations: cryptography can’t be trusted if the software implementing it is vulnerable, and hardware can’t change to adapt to new situations. By combining primitives from different layers, we can produce security mechanisms that are more than the sum of their parts. In this talk, we will show how cryptographic techniques, embedded into software, can protect against memory corruption attacks, and demonstrate how this can protect the return address stack and user data structures even in a vulnerable program.
Mechanical Design of a Device for Automatic Implementation of Security Features on Passports

In general, biometrics is concerned with the identification and verification of a person's identity. The biometric passport is one of the forms of identification for establishing an individual's identity in their home nation and abroad. Various national and international regulatory and security requirements govern the size, content, and structure of biometric passports. The mechanical design of a biometric passport marking device is the subject of this research article. The purpose of this research is to generate a practical design that meets all legal and safety standards. The best option is chosen from a variety of design concepts.
Application of container terminal virtual reality in student education

The continuous emergence of digital technologies and the strong trend of digitization in numerous segments of human activity imposes the need to devote additional attention to better adaptation of the educational process. The special focus is on improving competencies of the new generation of employees through the modernization of educational methods. This article contributes to the wider debate of bigger involvement of virtual reality (VR) as modern pedagogical tool in student engineering education. VR as computer-generated environment allows its users better understanding of real or imaginary scenarios, processes and interaction like one in real conditions. Numerous research on the application of VR for educational purposes is devoted mainly to the detection of the final effects on the mental or physical performance of the examinee. However, insufficient attention is paid to the basic precondition for the successful application of VR technology in teaching, namely the way of its use and the credibility of the replicated content. Therefore, the article explains in more detail the process of creating realistic VR models for managing container terminal equipment designed using equipment technical data provided by Adriatic Gate Container Terminal in Rijeka, the strategically most important and largest seaport in the Republic of Croatia. In the end, potential directions of future research with the developed VR model among different interest groups and interviewees are listed.
Potential use of RFID and QR code in the supply chain based on Blockchain and Smart contract

Nowadays, the discovery of new technologies is more intense than ever before. Terms such as Blockchain technology or Smart Contracts are becoming more and more familiar. Thanks to the decentralized system and the distribution of immutable records of transactions among all network participants, Blockchain finds application in almost any field, whether industry or society. Supply chain is one such area. In this paper, a theoretical model of supply chain based on Blockchain is presented. The participants of the presented model are the manufacturer with suppliers, the carrier, the retailer, and the final consumer. The model describes the process from the production of components to the sale of the final product to the final consumer. By implementing RFID and IoT, the participants can track the goods in real time. The end consumer enters the chain by scanning the QR code of the product. Contractual relationships are stored in the Blockchain in the form of Smart Contracts, which automatically execute the action based on the agreed terms. In addition, the paper describes selected Blockchain platforms that are used in the real world. Finally, we also present tools for simulating and testing Blockchain in a local network.
Survey of E-Mail Phishing Preparedness Within a Selected Group

Cyber-attacks have been in the cyber space for a long time, but in recent years they have come to the forefront. This is mainly due to organizations and individuals who are increasingly moving their activities to the online space and beginning to realize threats that can damage or misuse their data and information. One of the most common and simplest cyber-attacks is a phishing attack, which can be aimed at individuals, organization, and elements of critical infrastructure such as transportation, healthcare, energy, industry, and others. Phishing attacks have various types with various characteristics and can be sophisticated or less sophisticated. Therefore, this article discusses the definition of fundamental theoretical knowledge about phishing attacks, types of email phishing attacks, their distinctive characteristics, and a testing the preparedness of a selected group for an email phishing attack.