Reviewerly/exHarmony huggingface dataset:
Synthetic Training Data for Semantic Segmentation of the Environment from UAV Perspective

Autonomous unmanned aircraft need a good semantic understanding of their surroundings to plan safe routes or to find safe landing sites, for example, by means of a semantic segmentation of an image stream. Currently, Neural Networks often give state-of-the-art results on semantic segmentation tasks but need a huge amount of diverse training data to achieve these results. In aviation, this amount of data is hard to acquire but the usage of synthetic data from game engines could solve this problem. However, related work, e.g., in the automotive sector, shows a performance drop when applying these models to real images. In this work, the usage of synthetic training data for semantic segmentation of the environment from a UAV perspective is investigated. A real image dataset from a UAV perspective is stylistically replicated in a game engine and images are extracted to train a Neural Network. The evaluation is carried out on real images and shows that training on synthetic images alone is not sufficient but that when fine-tuning the model, they can reduce the amount of real data needed for training significantly. This research shows that synthetic images may be a promising direction to bring Neural Networks for environment perception into aerospace applications.
Language-related stereotype threat, customers’ well-being and its outcome

Purpose While language is vital for a successful service exchange, it can also become a source of vulnerability if one party is a non-native speaker in an inter-culture service encounter (ICSE). Hence, the purpose of this study is to understand the relationship between language-related stigma that non-native customers perceive in an ICSE and the associated psychological and behavioral responses. Design/methodology/approach A survey-based research method and an experimental study was used to collect data from non-native speakers in the USA with English as their second language. Structural equation modeling procedure was used to test the hypothesized relationships. Findings The findings suggest that the customers who perceive language-related stigmatization in an ICSE context experience intergroup anxiety and lack of social belonging. In turn, intergroup anxiety influences their interaction comfort with the service provider. In the end, these experiences shape their future buying behavior, i.e. they tend to avoid direct interactions with the servers and prefer smart services. Research limitations/implications Future research is needed to explore the focal phenomenon in other service contexts and cultures to enrich knowledge on language vulnerabilities. Practical implications The study highlights the importance of technology, not just from a convenience perspective, but also as an accommodation mechanism for linguistically vulnerable customers. Originality/value To the best of the authors’ knowledge, this study is the first to empirically examine the language-related stigmatization and associated psychological and behavioral responses from the non-native customers’ perspective in a services exchange setting.
A Novel Chaotic Image Encryption Algorithm Based on Propositional Logic Coding

This paper proposes a new chaotic system 2D-HLM, which is a combination of Henon map and logistic map. SHA-256 algorithm based on the plaintext image produces the initial value, which enhances the correlation with the plaintext. Therefore, the algorithm avoids the disadvantages of being easily cracked by selected plaintext attacks. The chaotic sequence generated by 2D-HLM is adopted to scramble an image, and the bit plane is extracted and reorganized on the scrambled image. Based on the relationship between two mathematical propositions of the logistic map operations, a novel propositional logic coding algorithm is proposed. The simulation results show that the algorithm has large key space and high key sensitivity, and can resist common attacks such as differential attack.
Adversarial attacks on cooperative multi-agent deep reinforcement learning: a dynamic group-based adversarial example transferability method

Abstract Existing research shows that cooperative multi-agent deep reinforcement learning (c-MADRL) is vulnerable to adversarial attacks, and c-MADRL is increasingly being applied to safety-critical domains. However, the robustness of c-MADRL against adversarial attacks has not been fully studied. In the setting of c-MADRL, unlike the single-agent scenario, an adversary can attack multiple agents or all agents at each time step, but the attacker needs more computation to generate adversarial examples and will be more easily detected. Therefore, how the attacker chooses one or several agents instead of all agents to attack is a significant issue in the setting of c-MADRL. Aiming to address this issue, this paper proposes a novel adversarial attack approach, which dynamically groups the agents according to relevant features and selects a group to attack based on the group’s contribution to the overall reward, thus effectively reducing the cost and number of attacks, as well as improving attack efficiency and decreasing the chance of attackers being detected. Moreover, we exploit the transferability of adversarial examples to greatly reduce the computational cost of generating adversarial examples. Our method is tested in multi-agent particle environments (MPE) and in StarCraft II. Experimental results demonstrate that our proposed method can effectively degrade the performance of multi-agent deep reinforcement learning algorithms with fewer attacks and lower computational costs.
Innovative Applications and Implementation Challenges of Blockchain Technology in the Financial Sector

Blockchain use in various industries improves the standard of services provided to end users and benefits society as a whole. The chapter discusses the innovative use of blockchain technology in the financial sector, including know your customer, cross-border payments, clearing and settlements in the insurance sector, trade finance platforms, and digital identity verification. On the other hand, while blockchain technology has the potential to be a very competitive and “imaginative” technology, it is important to address concerns with its implementation, such as the cost of adoption, energy consumption, cybersecurity, interoperability, scalability, latency, and so on. The findings of this study will give the required information to the government, decision-makers, and consumers to help them understand the applications and difficulties of implementing blockchain technology in the financial sector.
AI in Robotics: Advancements, Applications and Challenges

The development, uses, and difficulties of artificial intelligence (AI) in the field of robotics are examined in this research study. Robotics has been transformed by the use of AI approaches, which allow robots to carry out difficult tasks in an adaptive and independent manner. This article gives a general overview of the many AI methods are used in robotics, including planning/control, computer vision, natural language processing, and machine learning. The numerous uses of AI in robotics, including industrial automation, service robots, autonomous cars, space exploration, and agriculture, are further discussed. The report also discusses issues related to safety and ethics, collaboration between humans and robots, adaptation and uncertainty, data constraints, and explainability/transparency. This research study seeks to contribute to the comprehension and development of AI in robotics by highlighting the current state, prospective future directions, and opportunities in the field.
Design Thinking Approach in The Development of Cirgeo's World Media

The circle concept is one of the contents in mathematics that has high complexity and is still a problem in visualization and concept exploration. This study aims to develop interactive digital media on circular material based on material characteristics, learning styles, user needs, and ease of learning in various contexts which are hereinafter named Cirgeo's World media. Media Cirgeo's World was developed using the design thinking method which has five stages, namely: 1) recognize and empathize; 2) defined; 3) ideate; 4) prototypes, and; 5) tests. Based on user responses obtained using questionnaires, the results obtained are that Cirgeo's World media is able to provide convenience for users in understanding learning objectives, learning by accommodating diverse learning styles, exploring abstract concepts more dynamically, measuring learning progress, and can be directly operated on the user's smartphone. From the results of user responses, it was also found that there were still things that were not optimal in Cirgeo's World media, namely in the user's ease of understanding the instructions for using the media. This can be followed up by improving things related to the instructions for using media that are easily accessible to users based on the results of interviews with users.
LANGUAGE PATTERNS IN ENGLISH LANGUAGE TEACHING: VIRTUAL EDUCATIONAL DISCOURSE

Distance learning with its virtual settings has launched the transformation of education delivery, pedagogy, and classroom management. Consequently, it could cause educational discourse transformation in higher education as well. The research is focused on revealing educational discourse transformation in distance learning. Besides, the paper answers the following research questions: how university teachers communicate with students in virtual classrooms; what language patterns they use for giving instructions; whether verbal communication between teachers and students has crucially changed in digitally-based education. A mixed method approach (discourse analysis of 12 video-recorded lessons of ESL university teachers and the survey of students (n = 45)) makes it possible to shed light on the state of play in virtual educational discourse. The paper reveals that 1) communication in virtual classrooms depends on pedagogical activities which allows classifying educational language models into the following categories – preparation of teaching material presentation online, student engagement in an activity online, giving instruction on how to use EdTech, and expressing emotions or attitude; 2) university teachers use special language patterns for giving instructions related to using ICT tools and apps, e.g. screen sharing, chatting and emotional modality; 3) verbal communication between teachers and students except for linguistic and extra-linguistic features includes informal modes of communication – chat emojis and signs. The findings highlight that educational discourse has crucially changed in digitally-based education and has transformed into virtual educational discourse. The insights of the paper can contribute to the investigation of virtual educational discourse analysis.
GestureWorks—One Stop Solution

In recent years, and especially in the last year there is a shift in technology toward hands-free navigation, particularly during the COVID-19 era. As technology has advanced, there has been a shift from buttons to touch technology, and in recent years, there has been a significant advancement in gesture controlled mechanisms. These technologies make it possible for even the most inexperienced user to navigate and use everyday technologies such as cell phones, iPads, computers, and so on. Working on these steps, GestureWorks is introduced, a touchless solution for all of your navigation needs. Currently, two modules are added, i.e., Gesture Controlled Virtual Mouse and OS-based Voice Assistant, and are looking forward to additional development in the future.
Design and Implementation of S-Box Using Galois Field Approach Based on LUT and Logic Gates for AES-256

In present days, the demand for protection increases. To prevent the unauthorised disclosure or alteration of the information, security is absolutely necessary. Due to the rapid advancement in technology currently available, a variety of multimedia data, including our own, is being transferred, making it susceptible to modification, editing, and duplication. Security is required when exchanging digital documents since hackers could steal the information. So, for security purpose, it needs cryptography. Cryptography is a form of secret writing that authenticates data, secures messages, and defends against attacking systems. Advanced encryption standard (AES) is one of the most popular effective security algorithms for ensuring data security. The AES algorithm is using input data which is 128 bits/16 bytes/4 words, cryptographic secret keys-128 bits/16 bytes/4 words, 192 bits/24 bytes/6 words, and 256 bits/32 bytes/8 words to encryption and decryption. The number of rounds that AES uses can be adjusted depending on the key length. For keys having 128 bits, 192 bits, and 256 bits, AES employs 10 rounds, 12 rounds, and 14 rounds, respectively. There are 14 rounds that are repeated when a key with 256 bits is utilised. In this paper, it represents implementation of S-box using Galois field approach based on LUT and logic gate. This approach computes Addition, squaring, and multiply using the Galois Field GF(28) constants. Design AES algorithm in Verilog and Simulation in XILINX ISE Design Suite 14.7 Software Tool.
Challenges and Solutions for Arabic Natural Language Processing in Social Media

The widespread use of social media has created a wealth of Arabic textual data, presenting challenges for NLP researchers due to the complexity and characteristics of the Arabic content. Despite the ambiguity of unstructured data and the peculiarity of the Arabic language and its writing style, there have been numerous studies in the field. These studies have achieved progress in areas such as sentiment analysis, topic classification, and named entity recognition. Hence, this systematic review aimed to review studies published on Arabic NLP that are focused on social media to offer a thorough understanding of what are the issues and discuss the main challenges along with solutions across various applications. The results indicated solutions. One solution is to increase annotated data for training NLP models and utilize transfer learning techniques. Another solution is to improve the text preprocessing techniques to improve the performance of the Arabic NLP computational model. Arabic NLP models can be improved with further research to better understand the rich and diverse Arabic content on social media, having significant implications for a variety of applications.
Optimal Endorsement for Network-Wide Distributed Blockchains

Blockchains offer trust and immutability in nontrusted environments, but most are not fast enough for latency-sensitive applications. Hyperledger fabric (HF) is a common enterprise-level platform that is being offered as blockchain-as-a-service by cloud providers. In HF, every new transaction requires a preliminary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">endorsement</i> by multiple mutually untrusted parties called organizations, which contributes to the delay in storing the transaction in the blockchain. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">endorsement policy</i> is specific to each application and defines the required approvals by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">endorser peers</i> (EPs) of the involved organizations. In this article, given an input <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">endorsement policy</i> , we studied the optimal choice to distribute the endorsement requests to the proper EPs. We proposed the OPtimal ENdorsement (OPEN) algorithm, devised to minimize the latency due to both network delays and the processing times at the EPs. By extensive simulations, we showed that OPEN can reduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">endorsement latency</i> up to 70% compared to the state-of-the-art solution and approximated well the introduced optimal policies while offering a negligible implementation overhead compared to them.
Indoor Semantic Location Privacy Protection With Safe Reinforcement Learning

The rapid growth and evolution of indoor location-based services (LBS) increase the risk of location privacy breaches. Additionally, the semantic tag of locations can easily expose users’ sensitive private information. This paper studies the safe reinforcement learning (RL)-based semantic location privacy protection mechanism (LPPM) in three-dimensional (3D) spaces, such as multi-story buildings. This provides a unique opportunity to derive the optimal location privacy protection (LPP) policy with safe exploration in dynamic 3D environments without knowing the accurate attack model. Safe exploration avoids selecting high-risk perturbation policies by evaluating each state-action pair’s risk level. We propose a safe Dueling Double DQN (D3QN)-based 3D LPPM (SDLPPM) to adaptively explore the perturbation policy, avoiding the overestimation of Q-values and reducing the redundant exploration in similar 3D states. 3D geo-indistinguishability is used to randomly perturb users’ locations. Besides, we further develop a safe A3C-based LPPM (SALPPM) to continuously select the perturbation policies to avoid discretization errors of perturbation angles and privacy budgets. This mechanism uses multi-thread technology to interact with the environment independently to accelerate the policy selection in complex 3D LPP scenarios. Simulation results demonstrate that the proposed mechanisms increase privacy and reduce QoS loss compared to the benchmark mechanisms.
ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis

In geosciences, micropaleontology studies the evolution of microfossils (e.g., foraminifera) throughout geological records and utilizes such information to reconstruct past environmental and climatic conditions. This field depends primarily on visual recognition of various features in microfossils which makes it ideal to apply computer vision technology, specifically deep convolutional neural networks (CNNs), to automate and optimize different microfossil identification and classification. In addition, unlabeled, low-resolution micropaleontological dataset is often available in a large volume when compared with another geosciences dataset. While the application of deep learning in micropaleontology is rapidly growing, these efforts have been severely hampered by (i) the limited availability of high-quality and high-resolution labeled fossil images and (ii) significant effort in manual labeling of various fossils by subject matter experts. Furthermore, previous works mostly exploited CNN with transfer learning to obtain high-accuracy prediction which may reduce the explainability and reproducibility of the model. To overcome this issue, we propose a novel deep learning workflow that couples hierarchical vision transformers with style-based generative adversarial network algorithms to efficiently acquire and synthetically generate realistic high-resolution labeled datasets of micropaleontology in a huge volume. Our study demonstrates that the proposed workflow was able to generate high-resolution images with a high signal-to-noise ratio, achieving 39.1 dB, and realistic synthetic images with Fréchet inception distance similarity score of 14.88. In addition, our proposed workflow could provide a huge volume of a self-labeled dataset that can be used for model benchmarking and various downstream visual tasks, including fossil classification and segmentation. We further performed, for the first time, few-shot semantic segmentation of different foraminifera chambers on both the generated and synthetic images with high accuracy. This novel meta-learning approach is only possible when a high-resolution and high-volume labeled dataset is available. It is, therefore, evident that our proposed deep learning-based workflow is promising and shows a potential to advance and optimize micropaleontological research and other visual-dependent geological analysis.
In<sub>2</sub>O<sub>3</sub> Nanofiber Neuromorphic Transistors for Reservoir Computing

In this letter, we propose neuromorphic transistors employing indium oxide (In2O3) nanofibers as the channel layers. Basic synaptic function, such as short-term memory can be emulated by one nanofiber neuromorphic transistor. Nonlinear synaptic function and short-term memory characteristic of such neuromorphic transistors are favorable for reservoir computing (RC) system with high energy-efficiency. Ultra-low energy consumption (15 pJ per reservoir state) and ultra-high accuracy (100%) of speech digital recognition are realized based on such nanofiber neuromorphic transistors, proving a great potential of the RC system for intelligent processing tasks.
Improving Cancer Detection Classification Performance Using GANs in Breast Cancer Data

Breast cancer is one of the most prevalent cancers in women.In recent years, many studies have been conducted in the breast cancer domain.Previous studies have confirmed that timely and accurate breast cancer detection allows patients to undergo early treatment.Recently, Generative Adversarial Networks have been applied in the medical domain to synthetically generate image and non-image data for diagnosis.However, the development of an effective classification model in healthcare is difficult owing to the limited datasets.To address this challenge, we propose a novel K-CGAN method trained in different settings to generate synthetic data.This study applied five classification methods and feature selection to non-image Wisconsin Breast Cancer data of 357 malignant and 212 benign cases for evaluation.Moreover, we used recall, precision, accuracy, and F1 Score on the synthetic data generated by the K-CGAN model to verify the classification performance of our proposed K-CGAN.The empirical study shows that K-CGAN performed well with the highest stability compared to the other GAN variants.Hence, our findings indicate that the synthetic data generated by K-CGAN accurately represent the original data.
An Adaptive Threshold for the Canny Edge With Actor-Critic Algorithm

We propose a method to automatically select proper values of three thresholds in the Canny edge algorithm. Edge detection is widely used for object recognition, detection, and segmentation. Due to its good performance, the Canny edge algorithm is still widely used among many edge detection algorithms. But, it requires manually selecting three appropriate thresholds for the given image. Some approaches have been proposed for automatically setting thresholds in the Canny edge algorithm. But, they either deal with partial among three entries or only show their performance in a limited range of variation. In natural scenes, images are acquired under various illumination, pose, and weather conditions. This paper proposes a method that can operate in various environments. We formulate the given problem by adopting an actor-critic algorithm. We propose an actor and critic network to solve the problem with an actor-critic algorithm. Also, we suggest a reward configuration based on an edge evaluation network and measure to prevent the reversal between high and low thresholds. The edge evaluation network uses an original image and an edge image as input. We set a negative reward when reversing the high and low thresholds occur. The proposed algorithm can adapt to unseen environments using images without requiring ground truth labels. Experimental results using diverse datasets show the feasibility of the proposed algorithm.