Reviewerly/exHarmony huggingface dataset:
Few‐shot object detection based on global context and implicit knowledge decoupled head

Authors:
raw_author_name: Guan Yang
raw_affiliation_string: Henan Key Laboratory on Public Opinion Intelligent Analysis, Zhenzhou, People's Republic of China; School of Computer Science, Zhongyuan University of Technology, Zhenzhou, People's Republic of China
raw_author_name: Xiaoming Liu
raw_affiliation_string: Henan Key Laboratory on Public Opinion Intelligent Analysis, Zhenzhou, People's Republic of China; School of Computer Science, Zhongyuan University of Technology, Zhenzhou, People's Republic of China
raw_author_name: Kekun Huang
raw_affiliation_string: School of Mathematics, Jiaying University, Meizhou, People's Republic of China
raw_author_name: Yang Liu
raw_affiliation_string: Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou, Guangxi, People's Republic of China; School of Telecommunications Engineering, Xidian University, Xian, Shaanxi, People's Republic of China; State Key Lab for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, People's Republic of China

Abstract: Abstract The acquisition cycle of remote sensing images is slow, and the labelling process encounters challenges, which have become prominent with the rapid development of remote sensing image object detection research. Therefore, this article provides a way to make the model better capture the diversity and contextual relationships in the data, and solve this problem by more than just data augmentation. Specifically, this method is a few‐shot object detection method for remote sensing based on global context combined with implicit knowledge decoupled head (GC‐IKDH). This method first uses a segmentation strategy to convert high‐resolution images into low‐resolution images and expands the sample size through a generative model. Secondly, GC attention is introduced to generate a GC vector by weighting and averaging the information of each position in the input sequence, which helps the model better understand the semantics of the input sequence. Finally, an IKDH is added to improve the model head, which is used to learn specific features in the data so that the model can better handle the diversity in the data. Experimental results show that GC attention and IKDH boosting provide a good performance boost to the baseline model. Compared with other few‐shot samples, this method achieves state‐of‐the‐art performance under different shot settings and highly competitive results on two benchmark datasets (NWPU VHR‐10 and DIOR).
Leaving no one behind: Information (re)packaging for rural dwellers in South Africa

Authors:
raw_author_name: Mamotshabo Boloka
raw_affiliation_string: University of South Africa
raw_author_name: Mpho Ngoepe
raw_affiliation_string: University of South Africa

Abstract: In most rural communities in South Africa, there is inadequate access to information due to a lack of public facilities and infrastructure, such as libraries and internet connections. This has a negative impact on the day-to-day activities of rural dwellers as it deprives them of the opportunity to access information, which is a fundamental right. This qualitative study used the Delphi technique, interviewing five experts in the Library and Information Services (LIS) fraternity in three rounds. The format in which information is accessed, as well as available information sources and resources, need (re)packaging in order to ensure free and equal access to information on a daily basis. This can be done through the provision of free Wi-Fi by rural municipalities in churches, gathering places, and royal houses to bridge the digital chasm. Provision of free Wi-Fi would give rural dwellers an opportunity to have access to online information without any hurdles as most rural dwellers are unemployed and lack money to buy data bundles for information access. With technological advancements, lot of information is shared online be it job advertisements, tertiary education applications, news, among others. The study provides a framework for addressing issues of information inaccessibility in remote rural areas. There is a need for increasing high-speed internet access for those in rural communities.
A survey on blockchain deployment for biometric systems

Authors:
raw_author_name: Surbhi Sharma
raw_affiliation_string: Department of Computer Science and Engineering Netaji Subhas University of Technology (formerly NSIT) New Delhi India
raw_author_name: Rudresh Dwivedi
raw_affiliation_string: Department of Computer Science and Engineering Netaji Subhas University of Technology (formerly NSIT) New Delhi India

Abstract: Abstract Blockchain technology has become an emerging area in recent years due to its capacity to improve the security, dependability, and resilience of distributed systems. Research based on this technique has impacted several firms, including banking, healthcare, data processing, remote sensing, and many others. The key characteristics of blockchain technology that make it appealing are data immutability, transparency, privacy, decentralization, and distributed ledgers. However, there is a chance of a privacy breach with sensitive biometric data. The purpose of this investigation is to examine blockchain‐based biometric applications research. It begins by determining the myriad ways that biometrics and blockchain may work together, including the storage and protection of biometric templates, identity management, and biometric authentication systems. Different biometric applications with respect to blockchain technology are also identified, along with the types of biometric data taken into account, features and capabilities of blockchain technology exploited, and blockchain technology frameworks employed. Finally, the authors seek to investigate blockchain concepts in the biometric domain by evaluating their pros and cons and summarizing the methods developed on blockchain for diverse biometric applications. Additionally, the applications of blockchain‐based biometric systems are highlighted before moving on to open research questions and potential future research areas.
Spectral telescope: Convergence rate bounds for random-scan Gibbs samplers based on a hierarchical structure

Authors:
raw_author_name: Qian Qin
raw_affiliation_string: School of Statistics, University of Minnesota
raw_author_name: Guanyang Wang
raw_affiliation_string: Department of Statistics, Rutgers University

Abstract: Random-scan Gibbs samplers possess a natural hierarchical structure. The structure connects Gibbs samplers targeting higher-dimensional distributions to those targeting lower-dimensional ones. This leads to a quasi-telescoping property of their spectral gaps. Based on this property, we derive three new bounds on the spectral gaps and convergence rates of Gibbs samplers on general domains. The three bounds relate a chain's spectral gap to, respectively, the correlation structure of the target distribution, a class of random walk chains, and a collection of influence matrices. Notably, one of our results generalizes the technique of spectral independence, which has received considerable attention for its success on finite domains, to general state spaces. We illustrate our methods through a sampler targeting the uniform distribution on a corner of an n-cube.
Analysis of the ensemble Kalman–Bucy filter for correlated observation noise

Authors:
raw_author_name: Sebastian W. Ertel
raw_affiliation_string: Institut für Mathematik, Technische Universität Berlin
raw_author_name: Wilhelm Stannat
raw_affiliation_string: Institut für Mathematik, Technische Universität Berlin

Abstract: Ensemble Kalman–Bucy filters (EnKBFs) are an important tool in data assimilation that aim to approximate the posterior distribution for continuous time filtering problems using an ensemble of interacting particles. In this work we extend a previously derived unifying framework for consistent representations of the posterior distribution to correlated observation noise and use these representations to derive an EnKBF suitable for this setting as a constant gain approximation of these optimal filters. Existence and uniqueness results for both the EnKBF and its mean field limit are provided. The existence and uniqueness of solutions to its limiting McKean-Vlasov equation does not seem to be covered by the existing literature. In the correlated noise case the evolution of the ensemble depends also on the pseudoinverse of its empirical covariance matrix, which has to be controlled for global well-posedness. These bounds may also be of independent interest. Finally the convergence to the mean field limit is proven. The results can also be extended to other versions of EnKBFs.
SPCTRE: sparsity-constrained fully-digital reservoir computing architecture on FPGA

Authors:
raw_author_name: Kota Ando
raw_affiliation_string: Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan
raw_author_name: Tetsuya Asai
raw_affiliation_string: Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan

Abstract: This paper proposes an unconventional architecture and algorithm for implementing reservoir computing on FPGA. An architecture-oriented algorithm with improved throughput and architecture designed to reduce memory and hardware resource requirements are presented. The proposed architecture exhibits good performance in terms of benchmarks for reservoir computing. A prediction accelerator for reservoir computing that operates on 55.45 mW at 450 K fps with <3000 LEs is realized by implementing the architecture on FPGA. The proposed approach presents a novel FPGA implementation of reservoir computing focussing on both algorithms and architecture that may serve as a basis for applications of AI at network edge.
ARGo: augmented reality-based mobile Go stone collision game

Authors:
raw_author_name: Jiwon Kim
raw_affiliation_string: Department of Industrial Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811, South Korea
raw_author_name: Hyuk-Yoon Kwon
raw_affiliation_string: Department of Industrial Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811, South Korea

Abstract: Abstract In this study, we present a mobile Go stone collision game based on augmented reality, which we call ARGo, inspired by the traditional Korean board game, Alkkagi. ARGo aims to resolve two main issues: (1) the portability and space constraints of the original Alkkagi and (2) the limited sense of reality due to the touchscreen-based interface of the existing mobile Alkkagi games. To improve a sense of the reality of the game, ARGo provides a gameplay interface similar to the original Alkkagi by recognizing the user‘s hand motion based on AR. Additionally, it provides a customization mechanism for each user to improve the recognition of the hand motion and the strength of the attack considering each user‘s characteristics. Finally, we make the following three main contributions. First, we employ the automata theory to design the game and collision scenarios between stones. Consequently, we can clearly define the complicated states incurred by AR-based motion recognition and collisions between virtual objects. Second, we propose a collision equation based on Continuous Collision Detection tailored to ARGo, i.e., Go stones and their collisions. Through experimental studies, we demonstrate that the collision equation enables the simulation of the exact collision effects. Third, through user experience studies, we verify the effectiveness of ARGo by showing the effects of the functions implemented in ARGo and its superiority over the existing mobile game Alkkagi Mania.
Single-photon scattering and quantum entanglement of two giant atoms with azimuthal angle differences in a waveguide system

Authors:
raw_author_name: Hong-Wu Huang
raw_affiliation_string: School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, CHINA
raw_author_name: Yan-Ling Li
raw_affiliation_string: School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, CHINA

Abstract: Abstract We theoretically investigate coherent scattering of single photons and quantum entanglement of two giant atoms with azimuthal angle differences in a waveguide system. Using the real-space Hamiltonian, analytical expressions are derived for the transport spectra scattered by these two giant atoms with four azimuthal angles. Fano-like resonance can be exhibited in the scattering spectra by adjusting the azimuthal angle difference. High concurrence of the entangled state for two atoms can be implemented in a wide angle-difference range, and the entanglement of the atomic states can be switched on/off by modulating the additional azimuthal angle differences from the giant atoms. This suggests a novel handle to effectively control the single-photon scattering and quantum entanglement.
Evolution of automated weakness detection in Ethereum bytecode: a comprehensive study

Authors:
raw_author_name: Monika di Angelo
raw_affiliation_string: TU Wien and INESC-ID Lisbon, Vienna, Austria
raw_author_name: Thomas Durieux
raw_affiliation_string: TU Delft, Delft, Netherlands
raw_author_name: João F. Ferreira
raw_affiliation_string: INESC-ID and Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
raw_author_name: Gernot Salzer
raw_affiliation_string: TU Wien and INESC-ID Lisbon, Vienna, Austria

Abstract: Abstract Blockchain programs (also known as smart contracts) manage valuable assets like cryptocurrencies and tokens, and implement protocols in domains like decentralized finance (DeFi) and supply-chain management. These types of applications require a high level of security that is hard to achieve due to the transparency of public blockchains. Numerous tools support developers and auditors in the task of detecting weaknesses. As a young technology, blockchains and utilities evolve fast, making it challenging for tools and developers to keep up with the pace. In this work, we study the robustness of code analysis tools and the evolution of weakness detection on a dataset representing six years of blockchain activity. We focus on Ethereum as the crypto ecosystem with the largest number of developers and deployed programs. We investigate the behavior of single tools as well as the agreement of several tools addressing similar weaknesses. Our study is the first that is based on the entire body of deployed bytecode on Ethereum’s main chain. We achieve this coverage by considering bytecodes as equivalent if they share the same skeleton. The skeleton of a bytecode is obtained by omitting functionally irrelevant parts. This reduces the 48 million contracts deployed on Ethereum up to January 2022 to 248 328 contracts with distinct skeletons. For bulk execution, we utilize the open-source framework SmartBugs that facilitates the analysis of Solidity smart contracts, and enhance it to accept also bytecode as the only input. Moreover, we integrate six further tools for bytecode analysis. The execution of the 12 tools included in our study on the dataset took 30 CPU years. While the tools report a total of 1 307 486 potential weaknesses, we observe a decrease in reported weaknesses over time, as well as a degradation of tools to varying degrees.
Mitigating uncertainties in mineral exploration targeting: Majority voting and confidence index approaches in the context of an exploration information system (EIS)

Authors:
raw_author_name: Mahyar Yousefi
raw_affiliation_string: Faculty of Engineering, Malayer University, Malayer, Iran
raw_author_name: Mark D. Lindsay
raw_affiliation_string: ARC Centre for Data Analytics for Resources and Environments (DARE), Perth and Sydney, Australia; CSIRO Mineral Resources, Kensington, WA 6151, Australia; School of Earth Sciences, The University of Western Australia, Crawley, WA 6009, Australia
raw_author_name: Oliver Kreuzer
raw_affiliation_string: Corporate Geoscience Group, PO Box 5128, Rockingham Beach, WA 6969, Australia; Economic Geology Research Centre (EGRU), College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia

Abstract: Various mineral prospectivity modelling (MPM) approaches are available for targeting mineral deposits, each method capable of predicting areas of high prospectivity. Given the diversity of MPM approaches, the modelled areas of high prospectivity can differ across different MPMs. However, rather than a negative, different MPM outputs can benefit mineral exploration targeting because each method has its advantages. Rather, the problem lies in the lack of consensus over how to best select and delimit mineral exploration targets from different MPM results. Here we aim to address the challenges outlined above whilst quantifying and mitigating the effects of inherent uncertainties. We first generate eleven different prospectivity models utilising deep learning, machine learning, fuzzy logic, and geometric average integration methods. Then, we adopt a majority voting ensemble technique to incorporate and combine the predictions of each prospectivity model. Next, we propose a confidence index designed to mitigate uncertainty associated with our multi-technique approach to MPM. The confidence index quantifies variation in prospectivity values for each cell of the MPM target area. The conjunction of a confidence index and majority voting model facilitates consistent and robust algorithm-driven extraction of exploration targets based on an ensemble of prospectivity models.
A multi‐label social short text classification method based on contrastive learning and improved <scp>ml‐KNN</scp>

Authors:
raw_author_name: Gang Tian
raw_affiliation_string: College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China
raw_author_name: Jiachang Wang
raw_affiliation_string: College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China
raw_author_name: Rui Wang
raw_affiliation_string: College of Energy and Mining Engineering Shandong University of Science and Technology Qingdao China
raw_author_name: Guangxin Zhao
raw_affiliation_string: College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China
raw_author_name: Cheng He
raw_affiliation_string: College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

Abstract: Abstract Short texts on social platforms often have the problems of diverse categories and semantic sparsity, making it challenging to identify the diverse intentions of users. To address this issue, this article proposes a multi‐label social short text classification method (IML‐CL) based on contrastive learning and improved ml‐KNN. First, a contrastive learning approach is employed to train a multi‐label text classification model. This approach improves semantic sparsity by leveraging the knowledge from the existing samples to enrich the feature representation of short texts. Simultaneously, an improved ml‐KNN algorithm is developed to enhance the accuracy of label prediction. This algorithm utilizes a two‐layer nearest neighbor rule and introduces a penalty function and weight optimization. Next, the model generates the feature representation for the test sample and predicts its label. Additionally, the improved ml‐KNN algorithm retrieves neighbors of the test sample and uses their label information for prediction. Finally, the two predictions are combined to obtain the final prediction, which accurately identifies the user's intention. The experimental results demonstrate that, on the dataset constructed in this article, the IML‐CL method effectively boosts the performance of the baseline model.
One-way semi-quantum private comparison protocol without pre-shared keys based on unitary operations

Authors:
raw_author_name: Li-Hua Gong
raw_affiliation_string: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, People's Republic of China
raw_author_name: Chao Liu
raw_affiliation_string: Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, Jiangxi, People's Republic of China

Abstract: Abstract Semi-quantum private comparison is a method for private comparison with fewer quantum resources, enabling classical participants to collaborate with a semi-honest third party possessing complete quantum capabilities. A one-way quantum private comparison protocol is devised only by unitary operations. The protocol facilitates one-way transmission between third party (TP) and classical participants in quantum communication, where the classical participants only need to perform unitary operations and measurement operations on the transmitted qubits. In addition, classical participants do not require pre-shared keys. It is shown that the qubit efficiency of this protocol is 12.5%. Finally, security analysis and the simulation results on the IBM Quantum Experience demonstrate the security and the feasibility of this protocol.
Hand gesture recognition based on micro‐Doppler radar using graph neural network

Authors:
raw_author_name: Zhangjin Xiong
raw_affiliation_string: School of Microelectronic, Tianjin University, Tianjin, China

Abstract: Abstract Hand gesture recognition based on micro‐Doppler (MD) radar has garnered considerable attention from researchers as a potential method for human–computer interaction (HCI). However, two significant challenges, that is, obvious differences in MD map size and lots of redundant information contained in the MD maps, are encountered. Here, a multi‐scale graph‐based hand gesture recognition framework is proposed. First, a MD graph representation method is developed, which is adapted to arbitrary‐sized frames and enables to map the key features in a sparse manner. Then, the multi‐scale information from MD graph is fully extracted for hand gesture recognition. Experimental results show that the proposed framework achieves a state‐of‐the‐art accuracy of 96.73% on the four‐class radar hand gesture dataset, while reducing up to 99% of the redundant information in the MD maps. This framework requires only a little amount of memory storage with good hand gesture recognition capability, demonstrating its high potential in the HCI field.