Reviewerly/exHarmony huggingface dataset:
Neural radiance fields for multi-scale constraint-free 3D reconstruction and rendering in orchard scenes

Authors:
raw_author_name: Xin Wang
raw_affiliation_string: College of Engineering, China Agricultural University, Beijing 100083, China
raw_author_name: Longrunmiao Tang
raw_affiliation_string: School of Management Engineering, Capital University of Economics and Business, Beijing 100070, China
raw_author_name: Jiahui Sun
raw_affiliation_string: School of Management Engineering, Capital University of Economics and Business, Beijing 100070, China
raw_author_name: Ye Wang
raw_affiliation_string: School of Management Engineering, Capital University of Economics and Business, Beijing 100070, China

Abstract: Efficiently, accurately, and realistically reconstructing large-scale 3D orchard scenes in a virtual world is an immensely challenging task. This complexity stems from the intricate and expansive of real orchard scenes. Traditional 3D reconstruction and rendering methods have encountered limitations in terms of modeling efficiency and computational costs, hindering the ability to provide users with immersive experiences. In response to these challenges, this study introduces a strategy for 3D scene reconstruction and rendering grounded in implicit neural representation: the NeRF-Ag model. Building upon the baseline NeRF, this model integrates a multi-resolution latent feature encoding technique, notably heightening training efficiency and elevating modeling precision. Furthermore, by means of environmental factor embedding, the model's robustness and practical applicability are further enhanced. The experimental outcomes illustrate that NeRF-Ag attains photo-realistic rendering outcomes across small, medium, and large scales. Moreover, it surpasses NeRF concerning the evaluation metrics of PSNR, SSIM, and LPIPS. Notably, the training speed of NeRF-Ag is roughly 39 times faster than NeRF. In 3D reconstruction tasks, NeRF-Ag showcases enhanced texture detail representation and higher modeling accuracy compared to the COLMAP-based 3D reconstruction method. Additionally, this study accomplishes free-viewpoint rendering of 3D scenes employing NeRF-Ag and provides evidence substantiating the connection between the quantity of training images and the precision of 3D rendering. The conclusions of this study will contribute to supporting and referencing the implementation of immersive visual interactive features within agricultural digital twin systems.
Privacy-preserving recommendation system based on social relationships

Authors:
raw_author_name: Hao Wang
raw_affiliation_string: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
raw_author_name: Ye Su
raw_affiliation_string: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
raw_author_name: Ziyu Niu
raw_affiliation_string: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
raw_author_name: Zhi Li
raw_affiliation_string: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China

Abstract: In the era of internet-based data, recommendation systems are crucial for helping users access personalized content and facilitating business promotion and sales. Recommendation systems based on social relationships have gained popularity due to their ability to use social influence and enhance the impact and credibility of recommendations. However, building a successful recommendation system faces the challenge of protecting user data privacy, as it requires a significant amount of sensitive user data. For this challenge, we propose a privacy-preserving recommendation system based on social relationships in the dual-cloud model. To achieve the secure computation of this system, we also design and improve some underlying secure protocols such as secure equality protocol and secure comparison protocol. Our underlying protocol has the advantages of high efficiency and provable security. As a result, our recommendation system can provide excellent recommendations while ensuring privacy protection. Experiments show that our system can perform secure recommendation calculations on 128-bit data with semi-honest security in approximately 5.5 s.
Statistically Significant Duration-Independent-based Noise-Robust Speaker Verification

Authors:
raw_author_name: Asmita Nirmal
raw_affiliation_string: Department of Electronics and Telecommunication Engineering,  Datta Meghe College of Engineering, Navi Mumbai, Maharashtra, India.
raw_author_name: Deepak Jayaswal
raw_affiliation_string: Department of Electronics and Telecommunication Engineering,  St. Francis Institute of Technology, Mumbai, Maharashtra, India.
raw_author_name: Pramod H. Kachare
raw_affiliation_string: Department of Electronics and Telecommunication Engineering,  Ramrao Adik Institute of Technology, Navi Mumbai, Maharashtra, India.

Abstract: A speaker verification system models individual speakers using different speech features to improve their robustness. However, redundant features degrade the system's performance. This paper presents Statistically Significant Duration-Independent Mel frequency Cepstral Coefficients (SSDI-MFCC) features with the Extreme Gradient Boost classifier for improving the noise-robustness of speaker models. Eight statistical descriptors are used to generate signal duration-independent features, and a statistically significant feature subset is obtained using a t-test. A redeveloped Librispeech database by adding noises from the AURORA database to simulate real-world test conditions for speaker verification is used for evaluation. The SSDI-MFCC is compared with Principal Component Analysis (PCA) and Genetic Algorithm (GA). The comparative results showed average equal error rate improvements by 4.93 % and 3.48 % with the SSDI-MFCC than GA-MFCC and PCA-MFCC in clean and noisy conditions, respectively. A significant reduction in verification time is observed using SSDI-MFCC than the complete feature set.
Towards Feature-based ML-enabled Behaviour Location

Authors:
raw_author_name: Sophie Fortz
raw_affiliation_string: NADI - Namur Digital Institute ( 61 rue de Bruxelles, 5000 Namur - Belgium)
raw_author_name: Paul Temple
raw_affiliation_string: DiverSe - Diversity-centric Software Engineering (Campus de Beaulieu, 35042 Rennes cedes - France)
raw_author_name: Xavier Devroey
raw_affiliation_string: NADI - Namur Digital Institute ( 61 rue de Bruxelles, 5000 Namur - Belgium)
raw_author_name: Gilles Perrouin
raw_affiliation_string: NADI - Namur Digital Institute ( 61 rue de Bruxelles, 5000 Namur - Belgium)

Abstract: Mapping behaviours to the features they relate to is a prerequisite for variability-intensive systems (VIS) reverse engineering. Manually providing this whole mapping is labour-intensive. In black-box scenarios, only execution traces are available (e.g., process mining). In our previous work, we successfully experimented with variant-based mapping using supervised machine learning (ML) to identify the variants responsible of the production of a given execution trace, and demonstrated that recurrent neural networks (RNNs) work well (≥ 80% accuracy) when trained on datasets in which we label execution traces with variants. However, this mapping (i) may not scale to large VIS because of combinatorial explosion and (ii) makes the internal ML representation hard to understand. In this short paper, we discuss the design of a novel approach: feature-based mapping learning.
<i>q</i>-analog qudit Dicke states

Authors:
raw_author_name: David Raveh
raw_affiliation_string: Physics, University of Miami - Coral Gables Campus, PO Box 248046, Coral Gables, Florida, 33124-6914, UNITED STATES
raw_author_name: Rafael I Nepomechie
raw_affiliation_string: Department of Physics, University of Miami - Coral Gables Campus, P.O. Box 248046, Coral Gables, Florida, 33124-6914, UNITED STATES

Abstract: Abstract Dicke states are completely symmetric states of multiple qubits (2-level systems), and qudit Dicke states are their d -level generalization. We define here q -deformed qudit Dicke states using the quantum algebra <?CDATA $su_q(d)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>s</mml:mi> <mml:msub> <mml:mi>u</mml:mi> <mml:mi>q</mml:mi> </mml:msub> <mml:mo stretchy="false">(</mml:mo> <mml:mi>d</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> . We show that these states can be compactly expressed as a weighted sum over permutations with q -factors involving the so-called inversion number, an important permutation statistic in Combinatorics. We use this result to compute the bipartite entanglement entropy of these states. We also discuss the preparation of these states on a quantum computer, and show that introducing a q -dependence does not change the circuit gate count.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="double-struck">F</mml:mi></mml:math>-valued trace of a finite-dimensional commutative <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="double-struck">F</mml:mi></mml:math>-algebra

Authors:
raw_author_name: R. Sarma
raw_affiliation_string: Department of Mathematics, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi-110016, India

Abstract: A non-zero F-valued F-linear map on a finite-dimensional commutative F-algebra is called an F-valued trace if its kernel contains no non-zero ideals. However, given an F-algebra, such a map may not always exist. We find an infinite class of finite-dimensional commutative F-algebras which admit an F-valued trace. In fact, in these cases, we explicitly construct a trace map. An F-valued trace on a finite-dimensional commutative F-algebra induces a non-degenerate bilinear form on the F-algebra which may be theoretically and computationally helpful. In this article, we suggest a couple of applications of an F-valued trace map of an F-algebra to algebraic coding theory.
An optimized Q-Learning algorithm for mobile robot local path planning

Authors:
raw_author_name: Qian Zhou
raw_affiliation_string: School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing, 210003, Jiangsu, China
raw_author_name: Yang Lian
raw_affiliation_string: School of Computer Science, School of Software and School of Cyberspace Security, Nanjing University of Posts and Telecommunications, Nanjing, 210037, Jiangsu, China
raw_author_name: Jiayang Wu
raw_affiliation_string: School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing, 210003, Jiangsu, China
raw_author_name: Haiyong Wang
raw_affiliation_string: School of Computer Science, School of Software and School of Cyberspace Security, Nanjing University of Posts and Telecommunications, Nanjing, 210037, Jiangsu, China
raw_author_name: Jinli Cao
raw_affiliation_string: Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia

Abstract: The Q-Learning algorithm is a reinforcement learning technique widely used in various fields such as path planning, intelligent transportation, penetration testing, among others. It primarily involves the interaction between an agent and its environment, enabling the agent to learn an optimal strategy that maximizes cumulative rewards. Most non-agent-based path planning algorithms face challenges in exploring completely unknown environments effectively, lacking efficient perception in unfamiliar settings. Additionally, many Q-Learning-based path planning algorithms suffer from slow convergence and susceptibility to getting stuck in local optimal solutions. To address these issues, an optimized version of the Q-Learning algorithm (Optimized Q-Learning, O-QL) is proposed and applied to local path planning of mobile robots. O-QL introduces novel Q-table initialization methods, incorporates a new action-selection policy, and a new reward function, and adapts the Root Mean Square Propagation (RMSprop) method in the learning rate adjustment. This adjustment dynamically tunes the learning rate based on gradient changes to accelerate learning and enhance path planning efficiency. Simulation experiments are carried out in three maze environments with different complexity levels, and the performance of the algorithm in local path planning is evaluated using steps, exploration reward, learning rate change and running time. The experimental results demonstrate that O-QL exhibits improvements across all four metrics compared to existing algorithms.
Safe path planning and adjustable zonotope-tube model predictive tracking control for autonomous vehicle

Authors:
raw_author_name: Xiwei Wu
raw_affiliation_string: School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
raw_author_name: Bing Xiao
raw_affiliation_string: School of Automation, Northwestern Polytechnical University, Xi'an 710072, China

Abstract: The path planning and tracking control problem of autonomous vehicle with model matched uncertainty and external disturbance is studied in this paper. A safe lane change zone is designed by considering lane change time, distance, and driving speed. Meanwhile, the candidate paths are generated using cubic quasi-uniform B-spline curve, and the optimal path is selected based on feasibility, comfort, and efficiency criteria. The proposed planning method ensures an efficient and comfortable lane change path without collision with surrounding vehicles. Model uncertainty and external disturbance may affect lane change tracking control of autonomous vehicle. Hence, a fault-tolerant path tracking controller is implemented by using deep neural networks and adjustable zonotope-tube model predictive control. A deep neural network-based controller is proposed to compensate for model matched uncertainty, thereby enhancing the fault tolerance of the control system. Then, an adjustable zonotope-tube model predictive controller is designed to enclose the actual trajectory within a zonotope-tube with the nominal state as the center and the disturbance set as the radius by using the feedback control to achieve the asymptotic stabilization of the closed-loop system. The effectiveness of this path planning and tracking control method is finally verified by numerical simulation.
Unlabeled scene adaptive crowd counting via meta-ensemble learning

Authors:
raw_author_name: Jia Zeng
raw_affiliation_string: School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 611756, China
raw_author_name: Anyong Qing
raw_affiliation_string: School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 611756, China
raw_author_name: Yang Wang
raw_affiliation_string: Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3H 2L9, Canada

Abstract: The objective of unlabeled scene adaptive crowd counting (USACC) is to adapt the crowd counting model to a particular scene by utilizing only a handful of unlabeled images from that scene, rather than considering all the diverse scenarios that may occur in the unknown environment at once. The resolution of this problem facilitates the fast widespread deployment of crowd counting models, mitigating the issue of performance deterioration caused by domain shift. To tackle the USACC problem, we propose a novel method called meta-ensemble learning that incorporates ensemble learning into the meta-learning paradigm. Specifically, we pass the input data through the network with stochasticity multiple times, implicitly creating an ensemble of multiple models, to produce multiple distinct outputs which can be averaged as pseudo labels to adapt the model. In an iteration of offline training, the scene-specific parameters are learned by minimizing the consistency loss between the actual predictions and the pseudo labels generated from a few unlabeled images belonging to that scene. Then, we optimize the model using the remaining labeled images from the same scene to alleviate error accumulation caused by pseudo labels, and thus improve the accuracy of the pseudo labels in subsequent iterations. The training process explicitly simulates the process of adapting to a particular scene during the test. Therefore, the model is able to adapt to the target scene using a handful of unlabeled images by minimizing the consistency loss during the test. Extensive experiments on several benchmarks demonstrate our method surpasses both baselines and state-of-the-art methods.
Low-complexity adaptive reconciliation protocol for continuous-variable quantum key distribution

Authors:
raw_author_name: Xueqin Jiang
raw_affiliation_string: Communication Engineering, Donghua University, North Renmin Road, Songjiang District, Shanghai, 201620, CHINA
raw_author_name: Jiahao Tang
raw_affiliation_string: Communication Engineering, Donghua University, North Renmin Road, Songjiang District, Shanghai, 201620, CHINA
raw_author_name: Peng Huang
raw_affiliation_string: State Key Lab of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, Shanghai 200240, Shanghai, 200240, CHINA
raw_author_name: Guihua Zeng
raw_affiliation_string: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, Shanghai, 200240, CHINA

Abstract: Abstract In continuous-variable quantum key distribution systems, reconciliation is a crucial step that significantly affects the secret key rate (SKR). The rateless protocol based on Raptor codes can achieve high reconciliation efficiency at low signal-to-noise ratios (SNRs). However, the high complexity of low-density parity-check (LDPC) codes used for the precoding in Raptor codes limits the speed of reconciliation. In this paper, we propose an adaptive reconciliation protocol by modifying Raptor codes. The length of random binary sequences is increased because we remove the LDPC precoding that adds redundancy. The modified Raptor codes reduce the complexity of encoding with better performance. The proposed protocol gives a reconciliation efficiency higher than 98.1% in the SNR below −20 dB and maintains a certain SKR in long-distance transmission.
Scaled local gate controller for optically addressed qubits

Authors:
raw_author_name: Jeff Thompson
raw_affiliation_string: Department of Electrical and Computer Engineering, Princeton University, Princeton, New Jersey 08544, USA
raw_author_name: Bichen Zhang
raw_affiliation_string: Department of Electrical and Computer Engineering, Princeton University, Princeton, New Jersey 08544, USA
raw_author_name: Pai Peng
raw_affiliation_string: Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina 27708, USA

Abstract: Scalable classical controllers are a key component of future fault-tolerant quantum computers. Neutral atom quantum computers leverage commercially available optoelectronic devices for generating large-scale tweezer arrays and performing parallel readout, but implementing massively parallel, locally addressed gate operations is an open challenge. In this work, we demonstrate an optical modulator system based on off-the-shelf components, which can generate a two-dimensional array of over 10,000 focused spots with uniform frequency and amplitude, and switching them on and off individually in arbitrary configurations at rates of up to 43 kHz. Through careful control of aberrations, the modulator achieves an extinction ratio of 46 dB, and nearest-neighbor crosstalk of −44dB with a beam spacing of 4.6 waists. The underlying components can operate at wavelengths from the UV to the NIR, and sustain high laser intensities. This approach is suitable for local addressing of gates with low cross-talk error rates in any optically addressed qubit platform, including neutral atoms, trapped ions, or solid-state atomic defects.
Intelligent path planning by an improved RRT algorithm with dual grid map

Authors:
raw_author_name: Rui Zhang
raw_affiliation_string: School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China
raw_author_name: He Guo
raw_affiliation_string: School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China
raw_author_name: Darius Andriukaitis
raw_affiliation_string: Department of Electronics Engineering, Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
raw_author_name: Yongbo Li
raw_affiliation_string: School of Aeronautics, Northwestern Polytechnical University, Xi’an, China
raw_author_name: Grzegorz Królczyk
raw_affiliation_string: Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland
raw_author_name: Zhixiong Li
raw_affiliation_string: Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland

Abstract: This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly-exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two-level map' approach, where a lower-resolution grid map is derived from a high-resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target-biased strategy and dual-direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes.