Reviewerly/exHarmony huggingface dataset:
The Navigation of Home Service Robot Based on Deep Learning and Machine Learning

Authors:
raw_author_name: Yupei Yan
raw_affiliation_string: Zhuhai City Polytechnic College, Zhuhai 519090, China
raw_author_name: Sengfat Wong
raw_affiliation_string: Faculty of Science and Technology, University of Macau, Macau 999078, China
raw_author_name: Qiang Pan
raw_affiliation_string: Zhuhai City Polytechnic College, Zhuhai 519090, China
raw_author_name: Xiaoxin Lin
raw_affiliation_string: Zhuhai City Polytechnic College, Zhuhai 519090, China

Abstract: This paper discusses how to improve the accuracy of navigation for home service robot based on the deep learning and machine learning. First, the crawling programing is applied to collect enough images of fridge and washing machine on the web; a deep learning framework is proposed that can distinguish fridge and washing machine more accurately. Following, the data come from the robot operating system topics are collected and cleaned, the linear regression, decision tree, and linear SVR algorithms are applied and compared to predict the power consumption of the robot, and a conclusion is obtained that liner movement will consume more power, which provides a reference for the path planning of the robot. Lastly, the conclusions are proposed that a novel methodology is applied to distinguish different home appliances, which is useful for the accurate navigation of the robot; the liner movement will consume more power compared to turning left or right, which supplies a reference for the optimized path planning for the robot.
Understanding security behaviour among healthcare professionals by comparing results from technology threat avoidance theory and protection motivation theory

Authors:
raw_author_name: Barbara Hewitt
raw_affiliation_string: Health Information Management, Texas State University, San Marcos, TX, USA

Abstract: We investigate security behaviour among healthcare professionals while comparing upon two prevalent theories in information security (IS) research: Protection Motivation Theory (PMT) and Technology Threat Avoidance Theory (TTAT). Although IS research often uses these models, very few studies compare the two models using a diverse sample of 245 healthcare professionals. The data were analysed using partial least squares regression - structural equation modelling (PLS-SEM). Results indicated that both models were similarly robust at predicting security behaviour. Perceived severity and response efficacy positively influenced avoidance motivation (AM) for PMT. All factors of the threat appraisal arm of TTAT and safeguard efficacy played an influencing role on AM in TTAT. Response cost and self-efficacy were not supported in either model, providing us with potentially new insights into this specific population's security intentions. With the addition of Perceived Threat, TTAT appears to be slightly more robust model with 64% of the variance explained by the given model while PMT explains only 60% of the variance. Given the ever-increasing risk of data breaches in healthcare, practical applications and implications of the study's findings to health information technology (HIT) policy and infrastructure are also discussed.
eyeScrollR: A software method for reproducible mapping of eye-tracking data from scrollable web pages

Authors:
raw_author_name: Jacob L. Orquin
raw_affiliation_string: Reykjavik University, Reykjavik, Iceland

Abstract: The Internet has become an important part of our lives and an increasing number of researchers use eye-tracking technology to examine attention and behavior in online environments. Researchers, however, face a significant challenge in mapping eye-tracking data from scrollable web pages. We describe the R package eyeScrollR for mapping eye-tracking data from scrollable content such as web pages. The package re-maps eye-tracking gaze coordinates to full-page coordinates with a deterministic algorithm based on mouse scroll data. The package includes options for handling common situations, such as sticky menus or ads that remain visible when the user scrolls. We test the package's validity in different hardware and software settings and on different web pages and show that it is highly accurate when tested against manual coding. Compared to current methods, eyeScrollR provides a more reproducible and reliable approach for mapping eye-tracking data from scrollable web pages. With its open code and free availability, we recommend eyeScrollR as an essential tool for eye-tracking researchers, particularly those who adhere to open-science principles. The eyeScrollR package offers a valuable contribution to the field of eye-tracking research, facilitating accurate and standardized analysis of eye-tracking data in web scrolling contexts.
Maximizing solar radiations of PV panels using artificial gorilla troops reinforced by experimental investigations

Authors:
raw_author_name: Ashraf K. Abdelaal
raw_affiliation_string: Department of Electric Power and Machine, Faculty of Technology, Suez University, Suez, 43512, Egypt
raw_author_name: Attia A. El-Fergany
raw_affiliation_string: Department of Electric Power and Machine, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt

Abstract: Abstract This article's main objective is to maximize solar radiations (SRs) through the use of the gorilla troop algorithm (GTA) for identifying the optimal tilt angle (OTA) for photovoltaic (PV) panels. This is done in conjunction with an experimental work that consists of three 100 W PV panels tilted at three different tilt angles (TAs). The 28°, 30°, and 50° are the three TAs. The experimental data are collected every day for 181-day and revealed that the TA of 28° is superior to those of 50° and 30°. The GTA calculated the OTA to be 28.445°, which agrees with the experimental results, which show a TA of 28°. The SR of the 28o TA is 59.3% greater than that of the 50° TA and 4.5% higher than that of the 30° TA. Recent methods are used to compare the GTA with the other nine metaheuristics (MHTs)—the genetic algorithm, particle swarm, harmony search, ant colony, cuckoo search, bee colony, fire fly, grey wolf, and coronavirus disease optimizers—in order to figure out the optimal OTA. The OTA is calculated by the majority of the nine MHTs to be 28.445°, which is the same as the GTA and confirms the experimental effort. In only 181-day, the by experimentation it may be documented SR difference between the TAs of 28° and 50° TA is 159.3%. Numerous performance metrics are used to demonstrate the GTA's viability, and it is contrasted with other recent optimizers that are in competition.
Rapid trajectory generation for quadrotor with limited motor speed

Authors:
raw_author_name: Shikai Shao
raw_affiliation_string: School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, China
raw_author_name: Yi Guo
raw_affiliation_string: School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, China
raw_author_name: Yuanjie Zhao
raw_affiliation_string: School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, China

Abstract: Planning a safe and dynamic flyable trajectory for unmanned aerial vehicle (UAV) is the precondition for autonomous flight. Considering environmental complexity and flight safety under limited motor speed, we proposed a new time-segmented trajectory planning method, which can ensure motor speed within the allowable range and achieve safe flight. Firstly, the candidate trajectory for quadrotor UAV is expressed by high-order polynomials. Herein, polynomial is used as the expression of trajectory, and nonlinear programming (NLP) is used to calculate polynomial coefficient. Then, two strategies and an initial value calculation method are proposed to improve the efficiency of NLP. Furthermore, aiming at the problem that the initial trajectory of UAV motor cannot meet the current speed limit constraint after the motor speed drops, an adjustment strategy based on feasible trajectory is designed. The method presented in this paper not only enhances flexibility of the flight process, improves solving efficiency of the algorithm but also enables UAV to continue flying when the motor speed is limited. Finally, simulation analysis and comparison illustrate the effectiveness and superiority of the proposed method.
Constructing immersive toy trial experience in mobile augmented reality

Authors:
raw_author_name: Lingxin Yu
raw_affiliation_string: School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University Nanjing, China
raw_author_name: Siru Chen
raw_affiliation_string: School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University Nanjing, China
raw_author_name: Zhifei Ding
raw_affiliation_string: School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University Nanjing, China
raw_author_name: Jiahao Han
raw_affiliation_string: School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University Nanjing, China

Abstract: When consumers purchase toys from retail stores, the majority of toys are packaged, making it difficult for them to observe the toys comprehensively. This limitation may hinder their ability to make informed purchase decisions. To address this challenge, this paper introduces an immersive toy experience program utilizing augmented reality (AR) technology. The program utilizes the camera on mobile devices to scan and identify the toy's cover image, subsequently showcasing corresponding virtual toy models in a simulated environment. Additionally, interactive controls enable users to manipulate the viewing angles. In terms of methodology, we have specifically designed an expandable collection of toy images, allowing the recognition of recently introduced toys by adding them to the database, enhancing the scalability of our application. In comparison to previous research, our work transcends the constraints of traditional toy shopping, providing a more intuitive, interactive, and personalized experience through AR technology.
Table tennis motion recognition based on the bat trajectory using varying-length-input convolution neural networks

Authors:
raw_author_name: Jun Zhang
raw_affiliation_string: School of Exercise and Health, Shanghai University of Sport, Shanghai, 200438, China
raw_author_name: Yu Xing
raw_affiliation_string: School of Sport Communication and Information Technology, Shandong Sport University, Jinan, 250102, Shandong, China
raw_author_name: Jie Ren
raw_affiliation_string: China Table Tennis College, Shanghai University of Sport, Shanghai, 200438, China

Abstract: Action recognition has been applied in fields such as smart homes, gaming, traffic management, and security monitoring. Motion recognition is helpful for biomechanical analysis, auxiliary training systems, table tennis robots, motion-sensing games, virtual reality and other fields. In our study, we collected data on table tennis skill motion, created the TTMD6 dataset, and analyzed the characteristics of table tennis paddle trajectories. We propose a motion recognition algorithm to recognize paddle trajectories. Other research has used multijoint data to identify actions, while we use only the paddle trajectory to recognize table tennis skill motions, accelerating the speed of motion recognition. Therefore, it is feasible to use paddle trajectories to recognize table tennis skill motions.
Numerically Stable Locality-Preserving Partial Least Squares Discriminant Analysis for Efficient Dimensionality Reduction and Classification of High-dimensional Data

Authors:
raw_author_name: Noor Atinah Ahmad
raw_affiliation_string: School of Mathematical Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia.

Abstract: Dimensionality reduction plays a pivotal role in preparing high-dimensional data for classification and discrimination tasks by eliminating redundant features and enhancing the efficiency of classifiers. The effectiveness of a dimensionality reduction algorithm hinges on its numerical stability. When data projections are numerically stable, they lead to enhanced class separability in the lower-dimensional embedding, consequently yielding higher classification accuracy. This paper investigates the numerical attributes of dimensionality reduction and discriminant subspace learning, with a specific focus on Locality-Preserving Partial Least Squares Discriminant Analysis (LPPLS-DA). High-dimensional data frequently introduce singularity in the scatter matrices, posing a significant challenge. To tackle this issue, the paper explores two robust implementations of LPPLS-DA. These approaches not only optimize data projections but also capture more discriminative features, resulting in a marked improvement in classification accuracy. Empirical evidence supports these findings through numerical experiments conducted on synthetic and spectral datasets. The results demonstrate the superior performance of the proposed methods when compared to several state-of-the-art dimensionality reduction techniques in terms of both classification accuracy and dimension reduction.
A novel state transition algorithm with adaptive fuzzy penalty for multi-constraint UAV path planning

Authors:
raw_author_name: Xiaojun Zhou
raw_affiliation_string: School of Automation, Central South University, Changsha, 410083, China
raw_author_name: Zhouhang Tang
raw_affiliation_string: School of Automation, Central South University, Changsha, 410083, China
raw_author_name: Nan Wang
raw_affiliation_string: College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China
raw_author_name: Chunhua Yang
raw_affiliation_string: School of Automation, Central South University, Changsha, 410083, China
raw_author_name: Tingwen Huang
raw_affiliation_string: Texas A&M University at Qatar, Doha, 10587, Qatar

Abstract: Unmanned aerial vehicles (UAVs) require pre-planned flight paths that are energy-efficient, safe and smooth across their wide range of application scenarios. In this study, a novel UAV path planning method is proposed. Firstly, the UAV path planning under numerous obstacles is modelled as a continuous constrained optimization problem. The cost function is formulated as a linear combination of length, height variation, and smoothness, while the constraints include obstacle avoidance, height limitation, and the maneuverability of UAV. Subsequently, a novel constrained state transition algorithm with adaptive fuzzy penalty (AFSTA) is proposed to solve the optimization problem. In AFSTA, a novel adaptive fuzzy penalty function is designed to leverage expert knowledge to establish a reasonable mapping relationship from the fitness value and the degree of constraint violation to the penalty factor for a candidate solution. Meanwhile, the state transition algorithm (STA) is used as the search engine for both global and local search. Experimental results illustrate that the proposed method can find energy-efficient, safe, and maneuverable flight paths successfully with the superiority over other state-of-the-art metaheuristic algorithms.
Efficient learning of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>t</mml:mi></mml:math>-doped stabilizer states with single-copy measurements

Authors:
raw_author_name: Nai-Hui Chia
raw_affiliation_string: Department of Computer Science, Rice University, TX 77005-1892, United States
raw_author_name: Ching-Yi Lai
raw_affiliation_string: Institute of Communications Engineering, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
raw_author_name: Han-Hsuan Lin
raw_affiliation_string: Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan

Abstract: One of the primary objectives in the field of quantum state learning is to develop algorithms that are time-efficient for learning states generated from quantum circuits. Earlier investigations have demonstrated time-efficient algorithms for states generated from Clifford circuits with at most <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>log</mml:mi><mml:mo>&amp;#x2061;</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> non-Clifford gates. However, these algorithms necessitate multi-copy measurements, posing implementation challenges in the near term due to the requisite quantum memory. On the contrary, using solely single-qubit measurements in the computational basis is insufficient in learning even the output distribution of a Clifford circuit with one additional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math> gate under reasonable post-quantum cryptographic assumptions. In this work, we introduce an efficient quantum algorithm that employs only nonadaptive single-copy measurement to learn states produced by Clifford circuits with a maximum of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>log</mml:mi><mml:mo>&amp;#x2061;</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> non-Clifford gates, filling a gap between the previous positive and negative results.
A Survey about Post Quantum Cryptography Methods

Authors:
raw_author_name: Jency Rubia J
raw_affiliation_string: Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
raw_author_name: Ezhil E Nithila
raw_affiliation_string: Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
raw_author_name: Sherin Shibi C
raw_affiliation_string: SRM Institute of Science and Technology
raw_author_name: Rosi A
raw_affiliation_string: Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

Abstract: Cryptography is an art of hiding the significant data or information with some other codes. It is a practice and study of securing information and communication. Thus, cryptography prevents third party intervention over the data communication. The cryptography technology transforms the data into some other form to enhance security and robustness against the attacks. The thrust of enhancing the security among data transfer has been emerged ever since the need of Artificial Intelligence field came into a market. Therefore, modern way of computing cryptographic algorithm came into practice such as AES, 3DES, RSA, Diffie-Hellman and ECC. These public-key encryption techniques now in use are based on challenging discrete logarithms for elliptic curves and complex factorization. However, those two difficult problems can be effectively solved with the help of sufficient large-scale quantum computer. The Post Quantum Cryptography (PQC) aims to deal with an attacker who has a large-scale quantum computer. Therefore, it is essential to build a robust and secure cryptography algorithm against most vulnerable pre-quantum cryptography methods. That is called ‘Post Quantum Cryptography’. Therefore, the present crypto system needs to propose encryption key and signature size is very large.in addition to careful prediction of encryption/decryption time and amount of traffic over the communication wire is required. The post-quantum cryptography (PQC) article discusses different families of post-quantum cryptosystems, analyses the current status of the National Institute of Standards and Technology (NIST) post-quantum cryptography standardisation process, and looks at the difficulties faced by the PQC community.
Effective prime factorization via quantum annealing by modular locally-structured embedding

Authors:
raw_author_name: Jingwen Ding
raw_affiliation_string: Department of Computer Science and Engineering, University of Trento, Trento, Italy
raw_author_name: Giuseppe Spallitta
raw_affiliation_string: Department of Computer Science and Engineering, University of Trento, Trento, Italy
raw_author_name: Roberto Sebastiani
raw_affiliation_string: Department of Computer Science and Engineering, University of Trento, Trento, Italy

Abstract: This paper investigates novel techniques to solve prime factorization by quantum annealing (QA). First, we present a very-compact modular encoding of a multiplier circuit into the architecture of current D-Wave QA devices. The key contribution is a compact encoding of a controlled full-adder into an 8-qubit module in the Pegasus topology, which we synthesized using Optimization Modulo Theories. This allows us to encode up to a 21 × 12-bit multiplier (and a 22 × 8-bit one) into the Pegasus 5760-qubit topology of current annealers. To the best of our knowledge, these are the largest factorization problems ever encoded into a quantum annealer. Second, we investigated the problem of actually solving encoded PF problems by running an extensive experimental evaluation on a D-Wave Advantage 4.1 quantum annealer. In the experiments we introduced different approaches to initialize the multiplier qubits and adopted several performance enhancement techniques. Overall, 8,219,999 = 32,749 × 251 was the highest prime product we were able to factorize within the limits of our QPU resources. To the best of our knowledge, this is the largest number which was ever factorized by means of a quantum annealer; also, this is the largest number which was ever factorized by means of any quantum device without relying on external search or preprocessing procedures run on classical computers.
Stabilizing two-qubit entanglement with dynamically decoupled active feedback

Authors:
raw_author_name: Sacha Greenfield
raw_affiliation_string: Center for Quantum Information Science and Technology, University of Southern California, Los Angeles, California 90089, USA; Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, USA; Institute for Quantum Studies, Chapman University, Orange, California 92866, USA
raw_author_name: Leigh Martin
raw_affiliation_string: Berkeley Center for Quantum Information and Computation, Berkeley, California 94720, USA; Department of Physics, University of California, Berkeley, California 94720, USA
raw_author_name: Felix Motzoi
raw_affiliation_string: Berkeley Center for Quantum Information and Computation, Berkeley, California 94720, USA; Department of Chemistry, University of California, Berkeley, California 94720, USA; Peter Grünberg Institute of Quantum Control (PGI-8), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich 52428, Germany
raw_author_name: K. Birgitta Whaley
raw_affiliation_string: Berkeley Center for Quantum Information and Computation, Berkeley, California 94720, USA; Department of Chemistry, University of California, Berkeley, California 94720, USA
raw_author_name: Justin Dressel
raw_affiliation_string: Institute for Quantum Studies, Chapman University, Orange, California 92866, USA; Schmid College of Science and Technology, Chapman University, Orange, California 92866, USA
raw_author_name: Eli M. Levenson-Falk
raw_affiliation_string: Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, USA

Abstract: We propose and analyze a protocol for stabilizing a maximally entangled state of two noninteracting qubits using active state-dependent feedback from a continuous two-qubit half-parity measurement in coordination with a concurrent, noncommuting dynamical decoupling drive. We demonstrate that such a drive can be simultaneous with the measurement and feedback, while also playing a key part in the feedback protocol itself. We show that robust stabilization with near-unit fidelity can be achieved even in the presence of realistic nonidealities, such as time delay in the feedback loop, imperfect state-tracking, inefficient measurements, dephasing from $1/f$-distributed qubit-frequency noise, and relaxation. We mitigate feedback-delay error by introducing a forward-state-estimation strategy in the feedback controller that tracks the effects of control signals already in transit. More generally, the steady state is globally attractive without the need for ancillas, regardless of the error state, in contrast to most known feedback and error-correction schemes.