Reviewerly/exHarmony huggingface dataset:
Few-fringe-based phase-shifting profilometry employing hilbert transform

In optical measurement techniques, when the projector works at a constant projection rate, reducing the number of projected fringe patterns is an effective way to reduce the projection time. In this paper, we propose a few-fringe-based phase-shifting profilometry employing Hilbert transform. We project only two fringe patterns for each frequency of fringes, with the phase shift between the fringes designed to be π. The Hilbert transform makes these two captured fringes phase shifted by π/2, thus transforming the original two fringes into four, and the wrapped phase can be obtained through these four fringes with a phase difference of π/2. To improve the accuracy and robustness of the method, we adopt three frequencies of fringes for phase unwrapping. Further, since different phase unwrapping methods can be used for the different frequencies of the fringes, we propose a heterodyne 2H+2 M+2 L method and a hierarchical 2H+2 M+2 L respectively. Multiple experimental results have confirmed the feasibility and effectiveness of the two 2H+2 M+2 L methods, and the different characteristics of the reconstructed shapes obtained by the two 2H+2 M+2 L methods are compared to provide useful guidance in the selection of the required method for different application scenarios. The experimental results demonstrate that when using the heterodyne and hierarchical methods for phase unwrapping, the RMSE of the 2H+2 M+2 L method is only higher than that of the three-frequency four-step phase-shifting method by 0.0056 and 0.0024 respectively, but this 2H+2 M+2 L method improves the efficiency of the three-frequency four-step phase-shifting method by 50%.
New Approach to Design and Assess Metaverse Environments for Improving Learning Processes in Higher Education: The Case of Architectural Construction and Rehabilitation

This research paper has defined and applied a new approach to develop and optimize augmented reality-based metaverse environments for learning construction and rehabilitation in architectural studies. This research paper is part of a broader project that aims to improve the learning process of architectural students by approaching construction and rehabilitation realities to the classroom in a feasible and pedagogical way. The approach has been applied successfully to develop a new environment with augmented reality that allows teachers to carry out activities using computers, tablets, cellphones and smartphone headsets in classrooms. Students’ satisfaction regarding the new environment is high, though there is room for improvement. The assessment of this new environment has involved both questionnaires and a multicriteria decision-making method which have holistically evaluated the new proposal by achieving complementary results. The main advantages of this new environment are in terms of application and inclusion, while its weaknesses will be solved in future steps that will introduce both mixed reality, to enlarge students’ perception and comprehension, as well as neuroeducation, to diminish students’ potential annoyances and dissatisfaction.
The Cost of Training Machine Learning Models Over Distributed Data Sources

Federated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning model without sharing their raw data.However, it requires a central server to coordinate the learning process, thus introducing potential scalability and security issues.In the literature, server-less federated learning approaches like gossip federated learning and blockchain-enabled federated learning have been proposed to mitigate these issues.In this work, we propose a complete overview of these three techniques, proposing a comparison according to an integral set of performance indicators, including model accuracy, time complexity, communication overhead, convergence time, and energy consumption.An extensive simulation campaign permits to draw a quantitative analysis considering both feedforward and convolutional neural network models.Results show that gossip federated learning and standard federated solution are able to reach a similar level of accuracy, and their energy consumption is influenced by the machine learning model adopted, the software library, and the hardware used.Differently, blockchain-enabled federated learning represents a viable solution for implementing decentralized learning with a higher level of security, at the cost of an extra energy usage and data sharing.Finally, we identify open issues on the two decentralized federated learning implementations and provide insights on potential extensions and possible research directions on this new research field.
Learning the Relation Between Similarity Loss and Clustering Loss in Self-Supervised Learning

Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations of one image based on contrastive learning. By utilizing the consistency of two augmentations, the burden of manual annotations can be freed. Contrastive learning exploits instance-level information to learn robust features. However, the learned information is probably confined to different views of the same instance. In this paper, we attempt to leverage the similarity between two distinct images to boost representation in self-supervised learning. In contrast to instance-level information, the similarity between two distinct images may provide more useful information. Besides, we analyze the relation between similarity loss and feature-level cross-entropy loss. These two losses are essential for most deep learning methods. However, the relation between these two losses is not clear. Similarity loss helps obtain instance-level representation, while feature-level cross-entropy loss helps mine the similarity between two distinct images. We provide theoretical analyses and experiments to show that a suitable combination of these two losses can get state-of-the-art results.
Interpretable machine learning for psychological research: Opportunities and pitfalls.

In recent years, machine learning methods have become increasingly popular prediction methods in psychology. At the same time, psychological researchers are typically not only interested in making predictions about the dependent variable, but also in learning which predictor variables are relevant, how they influence the dependent variable, and which predictors interact with each other. However, most machine learning methods are not directly interpretable. Interpretation techniques that support researchers in describing how the machine learning technique came to its prediction may be a means to this end. We present a variety of interpretation techniques and illustrate the opportunities they provide for interpreting the results of two widely used black box machine learning methods that serve as our examples: random forests and neural networks. At the same time, we illustrate potential pitfalls and risks of misinterpretation that may occur in certain data settings. We show in which way correlated predictors impact interpretations with regard to the relevance or shape of predictor effects and in which situations interaction effects may or may not be detected. We use simulated didactic examples throughout the article, as well as an empirical data set for illustrating an approach to objectify the interpretation of visualizations. We conclude that, when critically reflected, interpretable machine learning techniques may provide useful tools when describing complex psychological relationships. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Improving Visual Object Tracking Using General UFIR and Kalman Filters Under Disturbances in Bounding Boxes

A well-known problem of visual object tracking is the difficulty of accurately estimating the object trajectory under conditions of environmental disturbances in the bounding box (BB) of a video camera. In this paper, we consider BB variations as Gaussian-Markov colored measurement noise (CMN). In order to perform accurate tracking in the presence of CMN, we use measurement differencing and develop a robust general unbiased finite impulse response (GUFIR) filter and use the general Kalman filter (GKF) as a benchmark. The GUFIR and GKF algorithms are tested by the “Car4” benchmark. It is shown that, in terms of the tracking precision and under the heavy disturbance with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.65 \leqslant \Psi \leqslant 0.95$ </tex-math></inline-formula> coloredness factor, the best tracking performance is achieved using the robust GUFIR filter. When <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Psi &lt; 0.6$ </tex-math></inline-formula> , the GUFIR and GKF algorithms perform near equally. In the extreme point of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Psi = 1.0$ </tex-math></inline-formula> , where the Gauss-Markov CMN loses the stationarity, both algorithms provide zero precision and become inefficient. In general, it is concluded that the GUFIR filter, which ignores any zero mean disturbance and initial values, is much more suitable for applications in visual object tracking than Kalman-like algorithms relying on complete object information.
Text and data mining exceptions in the development of generative AI models: What the EU member states could learn from the Japanese “nonenjoyment” purposes?

Abstract The European Union (EU) text and data mining (TDM) provisions are a progressive move, but the horizon is still uncertain for both generative artificial intelligence (GenAI) models researchers and developers. This article suggests that to drive innovation and further the commitment to the digital single market, during the national implementation, EU Member States could consider taking the Japanese broad, all‐encompassing and “nonenjoyment‐based” TDM as an example. The Japanese “nonenjoyment” purposes, however, are not foreign to the European continental view of copyright. A similar concept can be found under the German concept of “Freier Werkgenuss” or enjoyment of the work. A flexible TDM exception built upon the German notion of nonenjoyment purposes could become an opening clause to foster innovation and creativity in the age of GenAI. Moreover, the article argues that an opening clause allowing TDM with “nonenjoyment” purposes could be permissible under the so‐called three‐step test. This article further suggests, if there is no political will to safeguard “the right to read should be the right to mine” and to provide a welcoming environment for GenAI researchers and developers, when shaping the legal interpretation through national case law, the EU Member States could consider the following: (1) advocate for 72 h of response if technological protection measures (TPMs) are preventing TDM, and (2) Robot Exclusion Standard (robot.txt) as a warning when TDM is not allowed on a website. It is now in the hands of the EU Member States, whether to protect the interests of rightholders or to create a balance between safeguarding “the right to read should be the right to mine,” protecting rightholders exclusivity, and creating a supportive environment for the GenAI models researcher and developers.
Cosmic coding and transfer storage (COSMOCATS) for invincible key storage

Abstract Thus far, a perfectly secure encryption key storage system doesn’t exist. As long as key storage is connected to a network system, there is always a chance that it can be cracked. Even if storage is not continually connected to a network system; it is repeatedly necessary for an individual to access storage to upload and download the data; hence there is always a loophole with every conventional encryption key storage system. By utilizing the penetrative nature of cosmic-ray muons, the COSMOCAT (Cosmic coding and transfer) technique may tackle this problem by eliminating the requirement for any network connection to data storage. COSMOCAT was invented as a post quantum key generation and distribution technique for wireless near field communication. However, in its first stage of development, COSMOCAT relied on standard comparators and Global Positioning System (GPS) or other Global Navigation Satellite Systems (GNSS) for key generation. Temporal jitters of the signals outputted from comparators and frequency fluctuations in GPS-disciplined oscillators degraded the key strength and the efficiency of both the key generation and distribution. New strategies are tested in this paper to improve these factors. As a result, the key strength and the key authenticating rate limit are respectively improved by 4 orders of magnitude and more than 5 orders of magnitude. As a consequence, it has become possible to propose a practical methodology for a new key storage and authentication strategy which has the potential to be an impregnable defense against any kind of cyber/physical attack to data storage. Practical applications of COSMOCATS-based symmetric-key cryptosystems to an electronic digital signing system, communication, and cloud storage are also discussed.
Visual Navigation of Mobile Robots in Complex Environments Based on Distributed Deep Reinforcement Learning

The increasingly popular method of deep reinforce- ment learning can not only help mobile robots output accurate actions in complex environments but can also search for collisionfree paths. In this paper, a robot visual navigation model in complex environments based on distributed deep reinforcement learning is proposed. According to the characteristics of different regions in the complex environment, the environment is divided into several regions, and we proposed method can realize visual navigation in large scene complex environments. In these regions, we combine long-short term memory (LSTM) and proximal policy optimization (PPO) algorithms as a local visual navigation model and design a new reward function that trains the target through factors such as the action of mobile robots, the distance between robots and the target, and the running time of robots. We create respective experience pool independently through model training. The model of robot visual navigation via distributed deep reinforcement learning uses the RGB-D image obtained from the first perspective of mobile robots and the polar coordinates of the target in mobile robots coordinate system as input, and the continuous motion of mobile robots as output to realize the task of end-to-end visual navigation without maps. Our model can complete accurately robot visual navigation in large complex scenes without maps and human intervention. In our experiments, we verify our proposed model by performing the promising navigation tasks in virtual environments.
Clustering Algorithms

The advantages of the clustering algorithm and the classical clustering algorithm are discussed in this chapter. For the problem of distinguishing adjacent objects in the highway traffic scene, the classical clustering algorithm is improved, and the main axis-based density peak fuzzy clustering (SDPFC) algorithm is proposed.
Recursive greedy initialization of the quantum approximate optimization algorithm with guaranteed improvement

The quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm, where a quantum computer implements a variational ansatz consisting of $p$ layers of alternating unitary operators and a classical computer is used to optimize the variational parameters. For a random initialization, the optimization typically leads to local minima with poor performance, motivating the search for initialization strategies of QAOA variational parameters. Although numerous heuristic initializations exist, an analytical understanding and performance guarantees for large $p$ remain evasive. We introduce a greedy initialization of QAOA which guarantees improving performance with an increasing number of layers. Our main result is an analytic construction of $2p+1$ transition states---saddle points with a unique negative curvature direction---for QAOA with $p+1$ layers that use the local minimum of QAOA with $p$ layers. Transition states connect to new local minima, which are guaranteed to lower the energy compared to the minimum found for $p$ layers. We use the Greedy procedure to navigate the exponentially increasing with $p$ number of local minima resulting from the recursive application of our analytic construction. The performance of the Greedy procedure matches available initialization strategies while providing a guarantee for the minimal energy to decrease with an increasing number of layers $p$.
The effects of the blockchain technology and big data analytics on supply chain performance: The mediating effect supply chain risk management

The purpose of this paper is to investigate potential links between Blockchain technology (BCT) and big data analytics (BDA) with supply chain risk management (SCRM) and supply chain performance (SCP) in the Jordanian Chemical and Cosmetic Industries Sector. Additionally, the paper tests a conceptual model that links SCRM to indirect effects. To test our proposition, data were collected from 364 employees working in Jordanian Chemical and Cosmetic Industries Sector. The data were analyzed using structural equation modeling with aid of the Lavaan R package. The results show that the influences of blockchain technology and big data analytics on supply chain performance do occur directly, and indirectly through the cascading of a supply chain risk management.
Potential cyber threats of adversarial attacks on autonomous driving models

Autonomous Vehicles (CAVs) are currently seen as a viable alternative to traditional vehicles. However, CAVs will face serious cyber threats because many components of the driving system are based on machine learning models and are vulnerable to adversary attacks. We have reviewed the scientific literature and highlighted the main types of disruptive attacks on autonomous driving models that pose potential threats to CAVs. In this paper, we have compiled a dataset with traffic sign images obtained from public sources. We made experiments in which we distorted the original images and used them to train deep neural network-based classification models. The experiments demonstrated a possible threat to traffic sign recognition by autonomous vehicles. This work can give researchers and engineers a better understanding of the current state and trends in CAV security for their future use.
HISTORY LEARNING BASED ON WORDWALL APPLICATIONS TO IMPROVE STUDENT LEARNING RESULTS CLASS X IPS IN MA AS'ADIYAH KETAPANG

The development of historical learning evaluation instruments is a development in the form of questions in Class X with the subject of History of Hindu-Buddhist influences in Indonesia, the sub-subject of learning Acculturation of Local Culture and Hindu-Buddhist which is validated by the validator before being tested on students. Testing questions to students was carried out using an evaluation tool in the form of a wordwall web application to improve student learning outcomes for class X social studies at MA As'adiyah Ketapang. The purpose of this research and development is to (1) measure the validity of the evaluation instrument for history learning based on the wordwall web application in class X IPS MA As'adiyah Ketapang students, (2) find out the increase in learning outcomes before and after using the wordwall web application-based learning evaluation instrument., (3) knowing the student's response to the attractiveness of the wordwall web application-based learning evaluation tool. This type of research is Research and Development (RnD) with the ADDIE model consisting of Analysis, Design, Development, Implementation and Evaluation. The results showed that the learning evaluation instrument was feasible to use based on the assessment of material experts, it was obtained a score of 76% in the fairly good category, design experts, which amounted to 98% in the very good category, and history teachers, namely 81.3% in the good category. The results of the response to attractiveness in the trial showed an average result of 90% and in the field trial there was an increase in learning outcomes with the results of the N-Gain trial with results of 44% in the high category, 50% in the medium category and 18.6% in the low category.
Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

Pandemics have always been a nightmare for humanity, especially in developing countries. Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics. Still, developing countries cannot afford such solutions because these may severely damage the country’s economy. Therefore, this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly. The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things (IIoT) and blockchain-enabled technologies. Compared to existing studies, the immutable and tamper-proof contact tracing and quarantine management solution is proposed. The use of advanced technologies and information security is a critical area for practitioners in the internet of things (IoT) and corresponding solutions. Therefore, this study also emphasizes information security, end-to-end solution, and experimental results. Firstly, a wearable wristband is proposed, incorporating 4G-enabled ultra-wideband (UWB) technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization (WHO). Secondly, distributed ledger technology (DLT) omits the centralized dependency for transmitting contact tracing data. Thirdly, a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication mechanism. Lastly, based on geofencing techniques, blockchain-enabled machine-to-machine (M2M) technology is proposed for quarantine management. The step-by-step methodology and test results are proposed to ensure contact tracing and quarantine management. Unlike existing research studies, the security aspect is also considered in the realm of blockchain. The practical implementation of the proposed solution also obtains the results. The results indicate the successful implementation of blockchain-enabled contact tracing and isolation management using IoT and geo-fencing techniques, which could help battle pandemic situations. Researchers can also consider the 5G-enabled narrowband internet of things (NB-IoT) technologies to implement contact tracing solutions.