Reviewerly/exHarmony huggingface dataset:
15 Detection of Augmented Facial Landmarks-based Face Swapping

Facial images play a large part in visual communication. Such images furnish nonverbal messages such as intent, emotion and identity of a human being and have numerous applications in the fields of computer vision and graphics. Due to various image processing tools and the utilization of Deep Learning (DL) methods, it is easy even for non-experts to swap the faces in face images, aiming to construct face swap attacks in face images or videos. We focus on face swapping and its detection. Face swapping replaces a face in the destination image with a face in the source image, while maintaining image attributes. It has numerous applications, including privacy protection, computer games and entertainment. However, it could also be used for fraudulent or illicit purposes. A new method for swapping the facial images and its detection based on augmented facial landmarks and Weighted Local Magnitude Patterns (WLMP) is proposed. The 81-augmented facial landmarks are extracted and performed face swapping. The extracted WLMP features from the facial images are provided to the Support Vector Machines (SVM) classifier to detect the presence of face swap attacks. The effectiveness of the proposed approach is estimated on a real-world face image dataset. The performance of the proposed approach is evaluated with various types of SVM classifiers. The overall results show that the proposed approach effectively swaps the two facial images and detects the swapped face images from the original with accuracy of 95%.
The Paradigm Shift in Higher Education and Impact of Distance Learning in Era of Industry 4.0 and Society 5.0

The transformation happening in the Industry 4.0 and Society 5.0 era has thrown a huge challenge before the education system across the world and has given a wake-up call to introspect and align with the changes. Both industry and society have seen a few stages of development, but education is yet to compete with both. This book chapter focuses on studying the growth from Industrial Revolution 1 to 4 and the societal transformation 1 to 5 and also how remote education and its influence can advance the quality of higher education in order to match the expectations. The paradigm shift in education started by moving from only an industry focus to including an informational focus, but now, the need is to also include the society focus. Though distance learning was seen as a compulsion, initially, the benefits were then well-perceived and accepted among the stakeholders. There is a great need to align future-ready 21st-century higher education with the specifications of Industry 4.0 and requirements of Society 5.0 through blended learning. This chapter will assist the academicians and education policymakers in creating an industry-relevant and society-centric curriculum and pedagogy for students who are going to work in a smart epoch of Society 5.0 and industry 4.0. The best practices by the Japanese government prove that this will be the best solution for the society and country in the future. The influence of distance learning had accelerated the higher education system to survive and lead in Society 5.0 which is super-smart, human-centered, and with the power of Industry 4.0.
Flexible revocation and verifiability for outsourced Private Set Intersection computation

Private Set Intersection (PSI) is a fundamental cryptographic protocol with a wide range of application scenarios. It is the method that allows multiple parties who hold private data sets to calculate the intersection of their data without revealing any information other than the intersection to others. Cloud computing reduces computation and data management overhead by outsourcing such computations. However, since the cloud is not trustworthy, cryptographic methods need to be applied to maintain the confidentiality of outsourced datasets, and verifiable computing algorithms need to be used to detect the correctness of outsourced computations. But encryption reduces the availability, which creates challenges for PSI calculations. For the first time, this paper presents a revocable and verifiable private set intersection (RV-PSI) scheme, which provides an efficient verification function and a flexible authorization-revocation mechanism. RV-PSI enables data owners to exercise fine-grained access control over outsourced datasets in PSI computations. Security definitions of RV-PSI and corresponding proofs are provided in this paper. And we implement our scheme and report by performance evaluation results.
Hybrid Autonomous Vehicle (Aerial and Grounded)

This work discusses hybrid autonomous vehicles that are grounded and aerial vehicles that are utilized to select their course based on their environmental characteristics. It includes algorithms for path planning, obstacle avoidance, and trajectory planning. It also has a microcontroller, known as the PIXHAWK Flight Controller, for various transmissions and configurations. Calibration and testing are performed using Mission Planner software. This article shows the different problematic features of an autonomous vehicle with several functionalities.
Hardware-Efficient Barrel Shifter Design Using Customized Dynamic Logic Based MUX

Bidirectional barrel shifters are used in a variety of algorithms because they can rotate a bit as many times as desired within a single cycle. However, as the number of input bits increases, the number of stages required increases and, accordingly, the hardware cost increases exponentially. In this paper, we propose a dynamic logic-based barrel shifter with customized domino logic for efficient hardware design. The proposed bidirectional barrel shifter has been implemented with 28nm CMOS technology, and in 32-bit configuration, power, performance, and area are improved by 36%, 19%, and 37%, respectively, compared to the conventional domino logic based approach.
Flexible GPU-Based Implementation of Number Theoretic Transform for Homomorphic Encryption

This paper proposes a flexible implementation of Number Theoretic Transform (NTT) on GPU platforms. The proposed method introduces an adjustable number (i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$NTT_{core}$</tex> ) of butterfly units that are simultaneously implemented in each NTT computational stage. The NTT implementation of a large polynomial was experimented on an NVIDIA GeForce RTX 3070 GPU card and showed at least 21× acceleration compared with that on the CPU. The proposed approach is worthy to parallelize NTT computations of multiple polynomials in expensive homomorphic functions with high circuit depth.
Abstract model of continuous-time quantum walk based on Bernoulli functionals and perfect state transfer

In this paper, we present an abstract model of continuous-time quantum walk (CTQW) based on Bernoulli functionals and show that the model has perfect state transfer (PST), among others. Let [Formula: see text] be the space of square integrable complex-valued Bernoulli functionals, which is infinitely dimensional. First, we construct on a given subspace [Formula: see text] a self-adjoint operator [Formula: see text] via the canonical unitary involutions on [Formula: see text], and by analyzing its spectral structure we find out all its eigenvalues. Then, we introduce an abstract model of CTQW with [Formula: see text] as its state space, which is governed by the Schrödinger equation with [Formula: see text] as the Hamiltonian. We define the time-average probability distribution of the model, obtain an explicit expression of the distribution, and, especially, we find the distribution admits a symmetry property. We also justify the model by offering a graph-theoretic interpretation to the operator [Formula: see text] as well as to the model itself. Finally, we prove that the model has PST at time [Formula: see text]. Some other interesting results about the model are also proved.
Monkeypox: a model-free analysis

Monkeypox is a zoonotic disease caused by a virus that is a member of the orthopox genus, which has been causing an outbreak since May 2022 around the globe outside of its country of origin Democratic Republic of the Congo, Africa. Here we systematically analyze the data of cumulative infection per day adapting model-free analysis, in particular, statistically using the power law distribution, and then separately we use reservoir computing-based Echo state network (ESN) to predict and forecast the disease spread. We also use the power law to characterize the country-specific infection rate which will characterize the growth pattern of the disease spread such as whether the disease spread reached a saturation state or not. The results obtained from power law method were then compared with the outbreak of the smallpox virus in 1907 in Tokyo, Japan. The results from the machine learning-based method are also validated by the power law scaling exponent, and the correlation has been reported.
CARDYNET: Deep Learning Based Navigation for Car-Like Robots in Dynamic Environments

Abstract Recently, there has been an increased interest in learning based navigation solutions for robots operating in dynamic environments. Navigation in such environments is particularly challenging due to several reasons. The decisions made by other agents may only be partially observable to the robot, and they may have conflicting objectives. The robot may have imperfect or no knowledge of other agents’ goals and planned routes. Several of the recent studies focus on differential drive robots. However, unlike the differential drive, car-like robots are subjected to additional motion constraints that make the problem harder. In this work, we first utilize an optimization based planner, namely TEB local planner, as the expert to generate high quality motion commands for a car-like robot operating in a simulated dynamic environment. We utilize these labels to train a deep neural network that learns to navigate. The deep learning based planner is further augmented with safety margins to enhance its effectiveness in collision avoidance. Finally, we experimentally evaluated the performance of our proposed method by comparing it with deployable TEB planner based local planners in the stage_ros simulator as well as real world experiments. Our method demonstrated superior performance in terms of obstacle avoidance as well as successful mission completion.
VR and EEG Combined Self-monitoring Platform of Cognitive Care

VR has the potential as a digital healthcare solution as much as its entertainment function. Continuous monitoring and early diagnosis of cognitive decline is the only way to avoid cognitive deterioration where the risk increases with chronological aging. Backed up by real-time neural signals of EEG, the combination of VR and EEG could be a useful method to manage one’s cognitive status. Here, we provide supportive results to the usability of the all-in-one VR-EEG cognitive care platform in generations including a growing population of older adults.
Proposed Website Architecture with Embedded Augmented Reality to Discover Cultural Heritage

Mobile-based augmented reality is the type of augmented reality that runs on our mobile devices and has exploded in popularity over the last couple of years in many fields. However, there is always the sticky point that the users have to install an application to enjoy the Augmented Reality experience. WebAR or web-based augmented reality is a digital technology that allows us to share augmented reality experiences over the internet with two main characteristics; platform-independent, and no mobile application installation is required. Embedded augmented reality, or inline augmented reality considered a type of WebAr where the user can enjoy the experience of augmented reality without leaving the parent site. The main objective of this study is to propose a structure to embed augmented reality in a website using ar.js API. The proposed website contains information about Al-Hadba Minaret, which was destroyed by a terrorist operation in 2014. The research methodology is divided into three main parts. Firstly, we propose an architecture of both client-server request and response and the architecture of ar.js over other Entity Component Systems. Secondly, we discuss the structure of the proposed system. Finally, we simulate the result of the proposed architecture via appcircle.io. The total website performance is 79% and the accuracy of WebAr recognition is 60%. We hope this study will contribute to adopting the promising web-based augmented reality instead of mobile-based augmented reality.
Utilization TikTok application for learning oral English

The development of technologies has revolutionized English language communication competence. Social media have swept the world within a short period of time. Teenagers are inevitably enthralled at all ages. Since TikTok is the most popular social application inside and outside China, supplying many English-speaking short videos benefits learning English as the Foreign Language (EFL)students by improving their sensitivity to English. This paper utilizes a questionnaire to investigate the effectiveness of applying TikTok to oral English learning from student’s point of view. The expected findings show students are eager to improve their competence in speaking English. Meanwhile, students categorize the existing problems of oral English education into three kinds: Pedagogy, teaching materials, and pronunciation. More than 70% of students think taking advantage of TikTok helps settle these existing problems. Therefore, teachers are suggested to combine useful short videos on TikTok with the speaking missions in English-speaking classes.
Improved RRT<sup>*</sup> Algorithm-Based Path Planning for Unmanned Aerial Vehicle in a 3D Metropolitan Environment

The unmanned aerial vehicles (UAV’s) are widely used in smart logistic application. The optimal route prediction, however, is a fundamental prerequisite for UAV in commercial applications. This paper introduces an Improved Rapid random tree (IRRT * ) algorithm with triangular inequality rewiring technique for finding collision free path for UAVs in a three-dimensional (3D) environment. The 3D building environments for navigation were developed using MATLAB/Simulink 2021, a virtual occupancy grid model. By considering UAV variable elements such as roll angle, air speed, flight path angle, and boundary threshold parameters, the suggested work aims to provide a comparative analysis of sampling algorithm-based optimal path. The proposed route planning control strategy is to identify the violation free path to locate the destination in 3D environment at variable altitude and air speed. Compared to the standard RRT and RRT * algorithms, the proposed IRRT * algorithm can shorten the planning time, reduce the cost distance and improve the algorithm’s applicability in the formation path planning problem. Simulation experiments with two environments and their different situations are carried out to determine the efficiency and performance of the proposed IRRT * algorithm. Statistical investigation supported the effectiveness of the IRRT * approach, which has low computational cost and a smooth travel trajectory that significantly resolves the unmanned aerial vehicle path planning issues in logistic applications.
Stepwise Sample Generation

Increasingly more industrial applications incorporate artificial intelligence based systems, which require a lot of data, e.g. captured from real machines or digital twin simulations, to train their models. But the data sets available or captured are often small, imbalanced or for specific use cases only. The most used approach to solve this problem efficiently are virtual sample generators. This type of program is commonly used to artificially construct new data from a small original data set, but tend to ignore weak correlation between features or even introduce false new correlations. Therefore, we propose a novel stepwise synthetic sample generation approach which allows engineers and domain experts to include their domain knowledge directly in the sample generation process. They can judge how features depend on each other, which value range is plausible or acceptable and which features aren’t relevant at all for getting better AI model approximation results and generating suitable synthetic data samples. In addition, we propose using simple machine learning models, such as support vector machines, to verify the synthetic data.
A Relative Value Function Based Learning Beam Search for the Longest Common Subsequence Problem

Beam search (BS) is a well-known graph search algorithm frequently used to heuristically find good or near-optimal solutions to combinatorial optimization problems. Its most crucial component is a heuristic function that estimates the best achievable length-to-go from any state to a goal state. This function usually needs to be developed specificially for a problem at hand, which is a manual and time-consuming process. Building on previous work, we propose a Relative Value Function Based Learning Beam Search (RV-LBS) to automate this task at least partially by using a multilayer perceptron (MLP) as heuristic function, which is trained in a reinforcement learning manner on many randomly created problem instances. This MLP predicts the difference of the expected solution length from a given state to the expected average solution length of all states at a current BS level. To support the training of the MLP on the longest common subsequence (LCS) problem, a compact fixed-size encoding of the distribution of differences of remaining string lengths to the average string length at the current level is presented. Tests show that a MLP trained by RV-LBS on randomly created small-size problem instances is able to guide BS well also on larger established LCS benchmark instances. The obtained results are highly competitive to the state-of-the-art.
Design and Implementation of the CNN Accelator Based on Multi-streaming SIMD Mechanisms

In this paper, we propose a SIMD architecture for CNN model computing based on multi-streaming SIMD concepts. The proposed processor is called the Basic Multiple CNN Operation Cell (BMCOC). With BMCOC, we can expand as the many BMCOC architecture to achieve massive parallelism for execution of CNN models. Some designed philosophies are following such as 1) building fixed-point operation flow with adaptive quantization for the lossless CNN computing, 2) constructing the double local buffer to provide the overlapped computing model, 3) taking scalability mechanisms to design BMCOC module to suitable module scaling distribution to get high data parallel performance through the adaptive data set arrangement. In the simulation and verification results, Under the TSMC 90 nm process, the operating frequency can reach 100 MHz, the area is about 305,613 μm2, and the power consumption is about 2.98 mW. Based on this operating frequency, the performance in comparison, using MobileNet designed for embedded environments as the operating model, under the Multi-Streaming SIMD architecture with 32 BMCOC cores, the computing speed is about 30.6 times higher than that of Cortex-M55, and 4.15 times higher than that of PC. Compared with the GPU, the speed is about 3.12 times faster.
Analysing the suitability of blockchain consensus algorithms in swarm robotics

Swarm robotics integrates robotics with swarm intelligence. Consensus achievement is one of the major behaviours of swarm robotics. Strategies that solve the consensus-achievement problem have been shown to be vulnerable to Byzantine robots. Blockchain-based solutions have been shown to improve Byzantine fault tolerance. However, these solutions suffer from poor performance, excessive resource consumption, and vulnerability to the 51% attack due to the use of the proof-of-work consensus algorithm. This study surveys existing blockchain consensus algorithms and their suitability to swarm robotics. Consequently, a taxonomy is introduced consisting of three categories, namely proof of resource, proof of authority, and Byzantine agreement. The proof-of-resource algorithms are vulnerable to the 51% attack and consume excess resources. Proof of authority algorithms creates permissioned blockchains, which contravenes the principles of swarm robotics. Byzantine agreement algorithms introduce centralisation. Consequently, the need for a new consensus algorithm to be used in swarm robotics is postulated.
Elevating the organisational innovation and knowledge sharing capability through soft TQM practices

The development of human capital skills and expertise is needed to transform the nation's manufacturing industry into one that is smart, innovative, and resilient. Developing a human capital strategy (soft TQM practices) that places a premium on knowledge sharing (KS) and organisational innovation (OI) is crucial for the manufacturing sector's digital transformation. The purpose of this study is to provide a comprehensive understanding of the significance and connections between soft TQM practices, KS, and OI among manufacturers. Two hundred twenty two valid responses from manufacturing firms were analysed, the study discovered a significant and positive relationship between these three-dimensional variables using partial least squares-structural equation modelling (PLS-SEM). Meanwhile, KS acts as a mediator between soft TQM practices and OI. By advancing theories of knowledge-based dynamic capabilities (KBDC), this research demonstrates the great influence of soft TQM practices on both KS and OI, which is important for manufacturing firms redesigned pivotal management strategy.
myBeST—A Web-Based Survival Prognostic Tool for Women with Breast Cancer in Malaysia: Development Process and Preliminary Validation Study

Women with breast cancer are keen to know their predicted survival. We developed a new prognostic model for women with breast cancer in Malaysia. Using the model, this study aimed to design the user interface and develop the contents of a web-based prognostic tool for the care provider to convey survival estimates. We employed an iterative website development process which includes: (1) an initial development stage informed by reviewing existing tools and deliberation among breast surgeons and epidemiologists, (2) content validation and feedback by medical specialists, and (3) face validation and end-user feedback among medical officers. Several iterative prototypes were produced and improved based on the feedback. The experts (n = 8) highly agreed on the website content and predictors for survival with content validity indices ≥ 0.88. Users (n = 20) scored face validity indices of more than 0.90. They expressed favourable responses. The tool, named Malaysian Breast cancer Survival prognostic Tool (myBeST), is accessible online. The tool estimates an individualised five-year survival prediction probability. Accompanying contents were included to explain the tool's aim, target user, and development process. The tool could act as an additional tool to provide evidence-based and personalised breast cancer outcomes.