Reviewerly/exHarmony huggingface dataset:
Security Order of Gate-Level Masking Schemes

Masking schemes have been introduced to thwart side-channel attacks. In software applications, attackers can measure leakage at several points in time and combine them to defeat the masking. In hardware gate-level masking, all shares of a masked variable are manipulated at the same time in a nanoscale circuit. In this article, we focus on setups where the attacker uses one mesoscopic probe, which measures an aggregated leakage of all shares. We consider masking schemes where each bit is randomly split (by XOR) into so-called shares (two or more). We analyze two interesting case studies about the interrelationship of attack order vs. the number of shares. First of all, we show that when the unique probe is measuring the sum of each share’s individual leakage (so-called Hamming weight model), one measurement can reveal the sensitive unshared value, provided the attacker is able to determine the leakage’s least significant bit. Second, we analyze a hardware masking belonging to threshold schemes. Such schemes require fulfilling a so-called incompleteness property, whereby some input shares must be absent from output shares. We analyze a first-order incomplete scheme, i.e., where the number of missing input shares is equal to one. In schemes such as threshold implementation, this requires the number of shares to be strictly more than two. Hence the natural question is whether such a scheme would resist highorder attacks of order also strictly more than two? We answer by the negative, and show that the lowest attack order is two: the security of such a masking scheme is governed by the order of incompleteness and not by the number of shares. We verify our findings using four different sets of experiments including theoretical analysis, digital simulation, HSpice simulation and also real-silicon (FPGA emulation).
Blockchain health sensor network performance analysis on low powered microcontroller devices

The development of the Internet of Things (IoT) industry has enhanced the use of sensor-based electronic devices and improved the performance of low-powered microcontroller devices significantly. Low-powered microcontroller devices such as Raspberry Pi and Arduino amplified the use of IoT products in various industries. As an emerging technology, blockchain technology holds considerable potential for securing low-powered IoT devices. Blockchain technology encompasses information se-curity features such as decentralization, encryption, timestamps, and ledger system. The integration of IoT and blockchain may address most of the security issues in low-power IoT network systems. However, the integration of IoT and blockchain raises a number of research issues. Blockchain performance is one of the most significant. In this paper, we investigate blockchain performance in a wireless sensor network environment using low-powered microcontroller devices. We use three commonly used blockchain algorithms to examine blockchain performance on microcontroller devices. This blockchain performance evaluation may unlock a wide range of research avenues related to blockchain technology and low-powered IoT sensor networks.
Preventing Cryptographic Attacks Using AI-hard Password Authentication

Contemporary adversaries are incapable of cracking a hash function within reasonable time. However, advancements in quantum computing would empower such adversaries to perform more computations in a shorter time span, leading to a major security crisis. Integrating artificial intelligence based approaches that are hard problems such as natural language understanding or CAPTCHA can help with future-proofing security. Hence, this work proposes a CAPTCHA-like challenge that adds AI-Hardness to authenticate the client password. In the proposed system, no information about the password is directly sent in client-server communication and cannot be captured by adversaries. Server generates a challenge text and sends it to the client, which is then hashed with the client password. The output is embedded into an image and is XORed twice with the hash generated with the client password and sent to the server. This introduces randomness in the image. The server reverses the process using its copy of the password. The server retrieves the challenge text from the image and compares it with the original challenge text sent by the server to the client earlier. If the two challenge texts match, the client is authenticated. The system was evaluated based on the number of iterations required to perform a brute-force attack, correlation between the encrypted values of similar passwords and success rate of authentication. The system produced better results. As the challenge text is embedded in the image, it is difficult for bots to read. Though the challenge text is human readable, it is secured under many layers of a salted image. The size of the image and the randomness provided drastically increase the time required for an adversary to succeed in his attack.
Assessing The Performance Of Advanced Object Detection Techniques For Autonomous Cars

A fundamental yet challenging problem in general image analysis is object detection. It has recently generated a lot of interest and is essential for many applications. Despite the fact that there are numerous methods, a comprehensive study of the current identity research is still necessary. This study offers a thorough overview of recent advancements in discernible image retrieval that are based on deep learning.1) Techniques for identifying objects in a region, including Fast R-CNN (fast region-based convolutional neural network), R-CNN (region-based convolutional neural network), and Mask R-CNN (mask region-based convolutional neural network) are investigated.2) Classifications in addition to regression-based object identification methods like YOLO (you only look once), SSD (single-shot detector), and Retina Net. Several benchmark sets of data from free sources that include their usual evaluation metrics. We primarily focus on deep learning algorithms in core applications, such as object identification in monitoring, combat, transport, healthcare and quotidian life.In the scrutiny, we look closely at a number of challenges, such as limited storage space and computational power, an extensive range of denominations and based on discrepancy. The survey’s conclusion is reached by demonstrating how object detection can be applied to autonomous vehicles and by enhancing the current findings in the ensuing years.
A new basic probability assignment generation and combination method for conflict data fusion in the evidence theory

Dempster-Shafer evidence theory is an effective method to deal with information fusion. However, how to deal with the fusion paradoxes while using the Dempster's combination rule is still an open issue. To address this issue, a new basic probability assignment (BPA) generation method based on the cosine similarity and the belief entropy was proposed in this paper. Firstly, Mahalanobis distance was used to measure the similarity between the test sample and BPA of each focal element in the frame of discernment. Then, cosine similarity and belief entropy were used respectively to measure the reliability and uncertainty of each BPA to make adjustments and generate a standard BPA. Finally, Dempster's combination rule was used for the fusion of new BPAs. Numerical examples were used to prove the effectiveness of the proposed method in solving the classical fusion paradoxes. Besides, the accuracy rates of the classification experiments on datasets were also calculated to verify the rationality and efficiency of the proposed method.
Multimedia display of wushu intangible cultural heritage based on interactive system and artificial intelligence

With the rapid development of the information age, various forms of digitalization have facilitated people’s various lives, but digitalization has also brought crisis to people. The most obvious is that in the digital era, the original living environment of intangible cultural heritage of martial arts and its traditional development and protection mode have become less and less compatible with the digital era. Therefore, its development is currently facing difficulties, even endangered problems. Based on this phenomenon, this paper combines artificial intelligence technology and interactive system to establish an intelligent multimedia display interactive system for intangible cultural heritage of martial arts. In this system, the martial artist's moving image can be captured by the action image acquisition device, and the collected image sequence can be calculated and analyzed. The system host “remembers” the martial artist’s action. The main functions of the system, such as random flow, click and zoom, multi-point movement and printing, are all in the charge of the picture control module; Video playback, full screen display and ring button control functions are mainly completed by the video control module. The experimental results show that the task time of using interactive system to locate visual objects with massive information in icon materials is significantly faster than that of using no interactive highlighting system. Therefore, we can preliminarily determine that the interactive system can improve the performance of target positioning in massive information. In this paper, the interactive system and artificial intelligence technology are applied to the intangible cultural heritage of martial arts, which has played a role in promoting its development.
Offline Handwritten Amharic Character Recognition Using Few-Shot Learning

Few-shot learning is a demanding and challenging problem in machine learning aimed at learning from only fewer labeled training examples. It has become an active area of research due to deep learning requiring huge amounts of labeled dataset, which is not easy practically. Learning from fewer examples is also an important attempt towards learning like humans. Few-shot learning has proven a very good promise in different areas of machine learning applications, particularly in image classification. As it is a recent technique, most researchers focus on understanding and solving the issues related to its concept by focusing only on common image datasets like Mini-ImageNet and Omniglot. Few-shot learning also opens an opportunity to address low resource languages like Amharic. Hence in the current study, it is used to address offline handwritten character recognition for Amharic language. Particularly, prototypical networks, the popular and simpler type of few-shot learning, is implemented as a baseline. Using the opportunities explored in the nature of Amharic alphabet having columnwise and rowwise characters' similarities, a novel way of augmenting the training episodes is proposed. According to the study's experimental result, it was observed that the proposed method outperformed the baseline method in 1-shot setting. This study has implemented few-shot learning for Amharic characters for the first time. More importantly, the findings of the study open new ways of examining the influence of training episodes in few-shot learning, which is one of the critical issues requiring further exploration. The datasets used for this study are collected from native Amharic language writers using an Android App developed as a part of this study.
Stochastic gradient descent for optimization for nuclear systems

The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challenging due to their stochastic nature. ADAM is a gradient descent method that accounts for gradients with a stochastic nature. This analysis uses challenge problems constructed to verify if ADAM is a suitable tool to optimize k-eigenvalue nuclear systems. ADAM is able to successfully optimize nuclear systems using the gradients of k-eigenvalue problems despite their stochastic nature and uncertainty. Furthermore, it is clearly demonstrated that low-compute time, high-variance estimates of the gradient lead to better performance in the optimization challenge problems tested here.
A quantum circuit to generate random numbers within a specific interval

Abstract Random numbers are of vital importance in fields such as cyptography and scientific simulations. However, it is well known how difficult it is for classical computers to generate random numbers. This is not the case for quantum computers, which are able to genuinely generate random numbers thanks to the property of superposition and their counter-intuitive concept of measurement. However, despite the simplicity of designing a circuit that generates a random number between 0 and $2^{N}-1$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>N</mml:mi> </mml:msup> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:math> (being N the number of available qubits), designing a quantum circuit to generate a number within a specific interval is far from trivial. This paper proposes a customizable circuit design to generate random numbers. The circuit is non- hardware dependent, it allows fault-tolerance, and it can be used by current quantum devices. Therefore, it is a valuable tool for all those quantum applications and algorithms that need to work with random numbers. Moreover, a comparator circuit has also been designed as part of this work. This comparator is the best currently available in the literature in terms of qubits, T-count, and T-depth. It is therefore a useful tool for any other circuit or algorithm where this operation is needed.
Designing Virtual Reality and Augmented Reality Microlearning Activities

Using virtual and augmented reality in the ESL/EFL classroom can create an authentic and immersive learning experience. This chapter will discuss the potential benefits of using these methods and introduce strategies, platforms, and activities that teachers can use to create learning environments that improve their learners’ language proficiency.
People's Perceptions (and Bias) Toward Creative Content Generated by Ai (ChatGPT-4), Human Experts, and Human-AI Collaboration

With the wide availability of Large Language Models (e.g., ChatGPT-4) nowadays, there are four primary paradigms for Human-AI collaboration: human only, AI only, a human making the final decision with AI output as reference, or an AI making the final decision with human input as reference. In partnership with one of the world’s leading consulting firms (over $175 billion USD market capitalization), we enlist professional content creators and ChatGPT-4 to create advertising content for products and persuasive content for campaigns following the paradigms outlined above. When participants were asked to evaluate the quality of the content without any knowledge of the creation paradigm (i.e., they are not informed of AI’s potential involvement at all), we find that content generated by ChatGPT-4 making the sole or final decision on the content is perceived as higher quality. This result remains true when participants are partially informed of the content creation process (e.g., they are briefed about the four potential content creation paradigms upfront, but they do not know exactly how each piece of content they evaluate is created.) However, the performance gap between human and AI becomes narrower when participants are informed of the creation paradigm for each type of content; this bias is mostly driven by human favoritism rather than AI aversion. Our findings suggest that knowing the same content is created by a human expert increases its perceived quality, but knowing that AI is involved in the creation process does not reduce its perceived quality. However, even considering this human favoritism, the professional content creators do not outperform ChatGPT-4. We also examine how the results vary with the task categories.
Negation and redistribution with a preference — An information theoretic analysis

Yager (2014) defined the negation of a probability distribution using the idea that one can oppose the occurrence of any uncertain event by redistributing its probability equally among the other outcomes in a finite sample space. Equal redistribution signifies that probabilities are reallocated without preference to any particular outcome. The more the iterations of negation, the more uncertain the probability distribution becomes eventually converging to the maximum entropy state. However to apply the concept of negation in any uncertain environment, a detailed examination of the underlying structure of negation in probabilistic frameworks is required. In the present work, the definitions of negation of a discrete finite probability distribution proposed by various researchers has been reviewed and the way the concept of negation redefines probabilistic frameworks has been discussed. Also a generalized framework for redistribution of probabilities has been proposed. Finally the generalized framework has been compared with the original one(Yager's Model).
Dual cross knowledge distillation for image super-resolution

The huge computational requirements and memory footprint limit the practical deployment of super resolution (SR) models. Knowledge distillation (KD) allows student networks to obtain performance improvement by learning from over-parameterized teacher networks. Previous work has attempted to solve SR distillation problem by using feature-based distillation, which ignores the supervisory role of the teacher module itself. In this paper, we introduce a cross knowledge distillation framework to compress and accelerate SR models. Specifically, we propose to obtain supervision by cascading the student into the teacher network for directly utilizing teacher’s well-trained parameters. This not only reduces the difficulty of optimization for students but also avoids designing alignment with obscure feature textures between two networks. To the best of our knowledge, we are the first work to explore the cross distillation paradigm on the SR tasks. Experiments on typical SR networks have shown the superiority of our method in generated images, PSNR and SSIM.
Computer Vision-Based Autonomous Method for Quantitative Detection of Loose Bolts in Bolted Connections of Steel Structures

In this study, an autonomous computer vision-based method is presented to quantitatively detect loose bolts. The method integrates keypoint detection via YOLOv5 and PIPNet, distortion correction via perspective transformation, and rotation angles quantification via geometric imaging. Distortion correction is incorporated to address skewed angles and improve the accuracy of rotation angles. A representative experiment on bolted connection of steel structures is conducted to evaluate the presented approach. The effects of the focal distance, skewed angle, and lighting conditions on the detection and quantification performance are evaluated by varying the imaging conditions. The results demonstrate that the presented approach automatically detects all bolts and their corners, irrespective of the imaging conditions. No false detection occurs, and the quantification errors are lower than 1°. The proposed method can be deployed for automatic detection of loose bolts and quantification of rotation angles in bolted connections under different imaging conditions.
Exploring the effects of virtual hand appearance on midair typing efficiency

Summary Midair typing has gradually become the potential mainstream of virtual reality (VR) typing, as it eliminates the setup of additional hardware devices and provides convenience for portable head‐mounted displays (HMDs). In midair typing, users type on a virtual keyboard while using their virtual hands. In addition to the lack of a dominant method for VR‐based midair typing, we still do not know the impact that different appearances of virtual hands could have on text input in VR. Thus, we picked a widely used midair typing method (i.e., the VR‐rendered QWERTY keyboard) and conducted a VR study to explore the effects of three hand appearances (i.e., abstract, mannequin, realistic hands) on typing efficiency in terms of typing accuracy, typing speed, and task load. Our within‐group study revealed that (1) the mannequin hand caused significantly higher performance on typing speed than the abstract and realistic hands, (2) the abstract hand caused significantly lower performance (more typing errors) on typing accuracy compared to the mannequin and realistic hands, and (3) participants rated the task load of using the abstract hand significantly higher than the mannequin and realistic hands.
AdamR-GRUs: Adaptive momentum-based Regularized GRU for HMER problems

Handwritten Mathematical Expression Recognition (HMER) is essential to online education and scientific research. However, discerning the length and characters of handwritten mathematical expressions remains a formidable challenge. In this study, we propose an encoder–decoder architecture incorporating a novel Adaptive momentum-based Regularized Gated Recurrent Unit (AdamR-GRU) to solve this problem. It incorporates a novel gate within the GRU framework to maintain input histories, enabling extended memory retention and faster convergence. To assess the performance of AdamR-GRU, we conducted a series of comprehensive experiments on the CROHME 2014, 2016, and 2019 datasets. The results indicate that the AdamR-GRU model exhibits competitive performance with existing state-of-the-art methods regarding Expression Recognition Rate (exprate). Moreover, our model achieves state-of-the-art accuracy concerning 1, 2, and 3 symbol-level errors (top 1, 2, 3 error accuracy) on the CROHME 2014 and 2016 datasets. For the CROHME 2019 dataset, the model attains state-of-the-art performance in top 2 and 3 error accuracy. AdamR-GRU demonstrates reduced computational complexity and memory usage compared to alternative contemporary models.
New coding scheme to compile circuits for Quantum Approximate Optimization Algorithm by genetic evolution

Compiling quantum circuits on target quantum hardware architectures is one of the key issues in the development of quantum algorithms, and the related problem is known as the Quantum Circuit Compilation Problem (QCCP). This paper presents a genetic algorithm for solving QCCP instances for Quantum Approximate Optimization Algorithms (QAOA). In particular, such instances represent quantum circuits for the resolution of both MaxCut and Graph-Coloring combinatorial problems. The presented algorithm represents a significant improvement over an already existing genetic algorithm called Decomposition Based Genetic Algorithm (DBGA), and is characterized by a completely new coding scheme that allows to reduce the number of SWAP gates introduced in the decoding step, consequently reducing the circuit depth. After providing a description of the problem, this paper presents the newly produced genetic algorithm (termed DBGA-X) in detail, especially focusing on the new coding/decoding scheme. Subsequently, a set of results will be presented that demonstrate the superior performance of the new method compared with the results obtained from recent literature against the same benchmark. In addition, new benchmarks characterized by larger quantum architectures and by a higher number of compilation passes are proposed in this paper, to the aim of testing the scalability of the proposed method in more realistic scenarios.