Reviewerly/exHarmony huggingface dataset:
High-Performance Hardware Implementation of the Saber Key Encapsulation Protocol

Authors:
raw_author_name: Dejian Li
raw_affiliation_string: State Grid Key Laboratory of Power Industrial Chip Reliability Technology, Beijing Smart-Chip Microelectronics Technology Co., Ltd., Beijing 100192, China
raw_author_name: Song Cheng
raw_affiliation_string: State Grid Key Laboratory of Power Industrial Chip Reliability Technology, Beijing Smart-Chip Microelectronics Technology Co., Ltd., Beijing 100192, China
raw_author_name: Yuantuo Zhang
raw_affiliation_string: College of Electronics and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
raw_author_name: Shunxian Gao
raw_affiliation_string: State Grid Key Laboratory of Power Industrial Chip Reliability Technology, Beijing Smart-Chip Microelectronics Technology Co., Ltd., Beijing 100192, China
raw_author_name: Yijun Cui
raw_affiliation_string: College of Electronics and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Abstract: Information is pivotal in contemporary society, highlighting the necessity for a secure cryptographic system. The emergence of quantum algorithms and the swift advancement of specialized quantum computers will render traditional cryptography susceptible to quantum attacks in the foreseeable future. The lattice-based Saber key encapsulation protocol holds significant value in cryptographic research and practical applications. In this paper, we propose three types of polynomial multipliers for various application scenarios including lightweight Schoolbook multiplier, high-throughput multiplier based on the TMVP-Schoolbook algorithm and improved pipelined NTT multiplier. Other principal modules of Saber are designed encompassing the hash function module, sampling module and functional submodule. Based on our proposed multiplier, we implement the overall hardware circuits of the Saber key encapsulation protocol. Experimental results demonstrate that our overall hardware circuits have different advantages. Our lightweight implementation has minimal resource consumption. Our high-throughput implementation only needs 23.28 μs to complete the whole process, which is the fastest among the existing works. The throughput rate is 10,988 Kbps and the frequency is 416 MHz. Our hardware implementation based on the improved pipelined NTT multiplier achieved a good balance between area and performance. The overall frequency can reach 357 MHz.
An Efficient Multi-Party Secure Aggregation Method Based on Multi-Homomorphic Attributes

Authors:
raw_author_name: Qi Gao
raw_affiliation_string: School of Cryptographic Engineering, Information Engineering University, Zhenzhou 450001, China
raw_author_name: Yi Sun
raw_affiliation_string: School of Cryptographic Engineering, Information Engineering University, Zhenzhou 450001, China
raw_author_name: Xingyuan Chen
raw_affiliation_string: School of Cryptographic Engineering, Information Engineering University, Zhenzhou 450001, China; State Key Laboratory of Cryptology, Beijing 100094, China
raw_author_name: Fan Yang
raw_affiliation_string: School of Cryptographic Engineering, Information Engineering University, Zhenzhou 450001, China

Abstract: The federated learning on large-scale mobile terminals and Internet of Things (IoT) devices faces the issues of privacy leakage, resource limitation, and frequent user dropouts. This paper proposes an efficient secure aggregation method based on multi-homomorphic attributes to realize the privacy-preserving aggregation of local models while ensuring low overhead and tolerating user dropouts. First, based on EC-ElGamal, the homomorphic pseudorandom generator, and the Chinese remainder theorem, an efficient random mask secure aggregation method is proposed, which can efficiently aggregate random masks and protect the privacy of the masks while introducing secret sharing to achieve tolerance of user dropout. Then, an efficient federated learning secure aggregation method is proposed, which guarantees that the computation and communication overheads of users are only O(L); also, the method only performs two rounds of communication to complete the aggregation and allows user dropout, and the aggregation time does not increase with the dropout rate, so it is suitable for resource-limited devices. Finally, the correctness, security, and performance of the proposed method are analyzed and evaluated. The experimental results indicate that the aggregation time of the proposed method is linearly related to the number of users and the model size, and it decreases as the number of dropped out users increases. Compared to other schemes, the proposed method significantly improves the aggregation efficiency and has stronger dropout tolerance, and it improves the efficiency by about 24 times when the number of users is 500 and the dropout rate is 30%.
Joint Entity and Relation Extraction Model Based on Inner and Outer Tensor Dot Product and Single-Table Filling

Authors:
raw_author_name: Ping Feng
raw_affiliation_string: College of Computer Science and Technology, Changchun University, Changchun 130022, China; College of Computer Science and Technology, Jilin University, Changchun 130012, China; Jilin Provincial Key Laboratory of Human Health State Identification and Function Enhancement, Changchun 130022, China; Jilin Rehabilitation Equipment and Technology Engineering Research Center for the Disabled, Changchun 130022, China; Ministry of Education Key Laboratory of Intelligent Rehabilitation and Barrier-Free Access for the Disabled, Changchun 130022, China
raw_author_name: Lin Yang
raw_affiliation_string: College of Computer Science and Technology, Changchun University, Changchun 130022, China
raw_author_name: Renjie Wang
raw_affiliation_string: College of Computer Science and Technology, Changchun University, Changchun 130022, China
raw_author_name: Dantong Ouyang
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun 130012, China

Abstract: Joint relational triple extraction is a crucial step in constructing a knowledge graph from unstructured text. Recently, multiple methods have been proposed for extracting relationship triplets. Notably, end-to-end table-filling methods have garnered significant research interest due to their efficient extraction capabilities. However, existing approaches usually generate separate tables for each relationship, which neglects the global correlation between relationships and context, producing a large number of useless blank tables. This problem results in issues of redundant information and sample imbalance. To address these challenges, we propose a novel framework for joint entity and relation extraction based on a single-table filling method. This method incorporates all relationships as prompts within the text sequence and associates entity span information with relationship labels. This approach reduces the generation of redundant information and enhances the extraction capability for overlapping triplets. We utilize the internal and external multi-head tensor fusion approach to generate two sets of table feature vectors. These vectors are subsequently merged to capture a wider range of global information. Experimental results on the NYT and WebNLG datasets demonstrate the effectiveness of our proposed model, which maintains excellent performance, even in complex scenarios involving overlapping triplets.
Robust Soliton Distribution-Based Zero-Watermarking for Semi-Structured Power Data

Authors:
raw_author_name: Lei Zhao
raw_affiliation_string: State Grid Jiangsu Marketing Service Center, Nanjing 210003, China
raw_author_name: Yunfeng Zou
raw_affiliation_string: State Grid Jiangsu Marketing Service Center, Nanjing 210003, China
raw_author_name: Chao Xu
raw_affiliation_string: State Grid Jiangsu Marketing Service Center, Nanjing 210003, China
raw_author_name: Yulong Ma
raw_affiliation_string: State Grid Jiangsu Marketing Service Center, Nanjing 210003, China
raw_author_name: Wen Shen
raw_affiliation_string: State Grid Smart Grid Research Institute Co., Ltd., Nanjing 210003, China
raw_author_name: Shuai Jiang
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing 210003, China
raw_author_name: Yue Yu
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Nanjing 210003, China
raw_author_name: Yubo Song
raw_affiliation_string: Purple Mountain Laboratories, Nanjing 211189, China; School of Cyber Science and Engineering, Southeast University, Nanjing 210003, China
raw_author_name: Yu Jiang
raw_affiliation_string: Purple Mountain Laboratories, Nanjing 211189, China; School of Cyber Science and Engineering, Southeast University, Nanjing 210003, China

Abstract: To ensure the security of online-shared power data, this paper adopts a robust soliton distribution-based zero-watermarking approach for tracing semi-structured power data. The method involves extracting partial key-value pairs to generate a feature sequence, processing the watermark into an equivalent number of blocks. Robust soliton distribution from erasure codes and redundant error correction codes is utilized to generate an intermediate sequence. Subsequently, the error-corrected watermark information is embedded into the feature sequence, creating a zero-watermark for semi-structured power data. In the tracking process, the extraction and analysis of the robust zero-watermark associated with the tracked data facilitate the effective identification and localization of data anomalies. Experimental and simulation validation demonstrates that this method, while ensuring data security, achieves a zero-watermark extraction success rate exceeding 98%. The proposed approach holds significant application value for data monitoring and anomaly tracking in power systems.
Research on Knowledge Graph Construction and Semantic Representation of Low Earth Orbit Satellite Spectrum Sensing Data

Authors:
raw_author_name: Ziwei Liu
raw_affiliation_string: School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
raw_author_name: Nan Yang
raw_affiliation_string: School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
raw_author_name: Huajian Xu
raw_affiliation_string: Nanjing Electronic Equipment Institute (NEEI), Nanjing 210007, China

Abstract: The growth of frequency-usage devices has made the electromagnetic spectrum posture complex, resulting in an urgent demand for frequency-usage posture cognition. However, the sensing of space-based platforms is limited by the transmission capacity of the satellite-to-ground link and the satellite processing capacity, which makes on-satellite data analysis and posture generation lack the efficient means. Facing the above issues, an idea of a knowledge graph construction and semantic representation for low Earth orbit (LEO) satellite spectrum sensing data is designed in this paper. In the designed construction process, technologies such as knowledge extraction, ontology construction, knowledge fusion and knowledge visualization are utilized to efficiently analyze on-satellite sensing data. Moreover, the constructed spectrum knowledge graph can be applied in the analysis and prediction of frequency-]usage behavior and intelligent spectrum management, which exhibits the effectiveness of the spectrum knowledge graph. Finally, the further development of the spectrum knowledge graph is foreseen.
Target Tracking Algorithm Based on Adaptive Strong Tracking Extended Kalman Filter

Authors:
raw_author_name: Feng Tian
raw_affiliation_string: College of Communication and Information Technology, Xi’an University of Science and Technology, Xi’an 710054, China; Xi’an Key Laboratory of Network Convergence Communication, Xi’an University of Science and Technology, Xi’an 710054, China
raw_author_name: Xinzhao Guo
raw_affiliation_string: College of Communication and Information Technology, Xi’an University of Science and Technology, Xi’an 710054, China
raw_author_name: Weibo Fu
raw_affiliation_string: College of Communication and Information Technology, Xi’an University of Science and Technology, Xi’an 710054, China

Abstract: Kalman filtering is a common filtering method for millimeter-wave traffic radars. The proposal is for an Adaptive Strong Tracking Extended Kalman Filter (EKF) algorithm that aims to address the issues of classic EKF’s low accuracy and lengthy convergence time. This method, which incorporates time-varying fading effects into the covariance matrix of the traditional EKF, is based on the ST algorithm. It allows the recalibration of the covariance matrix and precise filtering and state estimation of the target vehicle. By altering the fading and attenuating factors of the ST algorithm and using orthogonality principles, many fine-tuned fading factors produced from least-squares optimization are introduced together with regionally optimum attenuation factors. The results of Monte Carlo experiments indicate that the average velocity inaccuracy is reduced by at least 38% in comparison to existing counterparts. The results validate the efficacy of this methodology in observing vehicular movements in metropolitan regions, satisfying the prerequisites of millimeter-wave radar technology for traffic monitoring.
Research on an Enhanced Multimodal Network for Specific Emitter Identification

Authors:
raw_author_name: Kai Xie
raw_affiliation_string: Department of Electronics and Communication Engineering, Sun Yat-Sen University, Shenzhen 518107, China
raw_author_name: Wenxu Zou
raw_affiliation_string: Department of Electronics and Communication Engineering, Sun Yat-Sen University, Shenzhen 518107, China

Abstract: Specific emitter identification (SEI) refers to the task of distinguishing similar emitters, especially those of the same type and transmission parameters, which is one of the most critical tasks of electronic warfare. However, SEI is still a challenging task when a feature has low physical representation. Feature representation largely determines the recognition results. Therefore, this article expects to move toward robust feature representation for SEI. Efficient multimodal strategies have great potential for applications using multimodal data and can further improve the performance of SEI. In this research, we introduce a multimodal emitter identification method that explores the application of multimodal data, time-series radar signals, and feature vector data to an enhanced transformer, which employs a conformer block to embed the raw data and integrates an efficient multimodal feature representation module. Moreover, we employ self-knowledge distillation to mitigate overconfident predictions and reduce intra-class variations. Our study reveals that multimodal data provide sufficient information for specific emitter identification. Simultaneously, we propose the CV-CutMixOut method to augment the time-domain signal. Extensive experiments on real radar datasets indicate that the proposed method achieves more accurate identification results and higher feature discriminability.
Collision avoidance for autonomous vehicles using reachability-based trajectory planning in highway driving

Authors:
raw_author_name: Hadi Raeesi
raw_affiliation_string: Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Mazandaran, Iran
raw_author_name: Pouria Sarhadi
raw_affiliation_string: Faculty of Engineering, Department of Engineering and technology, University of Hertfordshire, Hatfield, Hertfordshire, UK

Abstract: As vehicle applications have evolved to a more intelligent and self-driving stage, autonomous vehicles have attracted more attention in recent years. This paper proposes a trajectory planner that considers feasibility, safety and passenger acceptance. This will ensure autonomous vehicles satisfy the constraints of the traffic environment, driving ability, and comfort drivers experience during collision avoidance. This paper deals with planning collision-free trajectories for autonomous vehicles on highways. The problem is formulated using reachability-based planning via zonotope. According to the vehicle dynamics model, the trajectory feasibility is determined by the vehicle motion feasibility set. The next step is to apply safety constraints to the base planner by collision avoidance checking. Given that this planner uses a receding horizon strategy, it selects a safe parameter in each planning iteration. At each stage of planning, the set of reachable vehicles should not intersect with any obstacles. Since braking cannot prevent a collision, this approach consists of lane changing and overtaking maneuvers to avoid collisions. Finally, knowledge from the safety of the intended functionality (SOTIF) standard is utilized to verify the algorithm performance. The efficiency and performance of different driving styles of trajectory planners are verified by vehicle tests under different vehicle velocities and different obstacle disturbances. Satisfactory results are obtained from the set of simulated scenarios.
Conversational AI Chatbots in library research: An integrative review and future research agenda

Authors:
raw_author_name: Shaker Bani-Melhem
raw_affiliation_string: University of Sharjah, UAE
raw_author_name: Mohd Ahmad Al-Hawari
raw_affiliation_string: University of Sharjah, UAE

Abstract: The growing role of conversational AI Chatbots continues to change the library and information service landscape. Chatbots are replacing some of the library services that humans conventionally perform. In the era of instant evolution of artificial intelligence (AI), the role of Chatbots in libraries keeps expanding and acquiring more experience. This paper aims to examine the extant research on library Chatbots using an integrative literature review (ILR) approach. Empirical and non-empirical papers from the Scopus database to ascertain what is already known about the topic. Forty papers (articles and conference papers) were scrutinized for further analysis. The leading emergent themes in the literature were (1) The evolution of Chatbots technology in libraries, (2) Antecedents for Chatbot use in libraries, (3) User experience with Chatbot use in libraries, (4) Chatbot use in libraries amidst COVID-19, and (5) Challenges facing Chatbot use in libraries. Research on Chatbots in library services is still embryonic and has only begun to flourish. Nevertheless, there is still a significant research gap despite its surging curve. The findings of this integrative review contribute to the body of knowledge on the nexus between artificial intelligence and library operations. It also furnishes academics and practitioners with six potential directions for future research opportunities.
A Hybrid Cryptographic Mechanism for Secure Data Transmission in Edge AI Networks

Authors:
raw_author_name: Abdulmohsen Almalawi
raw_affiliation_string: Computer Science Department, King Abdulaziz University, 21589, Jeddah, Saudi Arabia
raw_author_name: Shabbir Hassan
raw_affiliation_string: Department of Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
raw_author_name: Adil Fahad
raw_affiliation_string: Department of Computer Science, College of Computer Science and Information Technology, Al Baha University, 65527, Al Baha, Saudi Arabia
raw_author_name: Asif Irshad Khan
raw_affiliation_string: Computer Science Department, King Abdulaziz University, 21589, Jeddah, Saudi Arabia

Abstract: Abstract As Edge AI systems become more prevalent, ensuring data privacy and security in these decentralized networks is essential. In this work, a novel hybrid cryptographic mechanism was presented by combining Ant Lion Optimization (ALO) and Diffie–Hellman-based Twofish cryptography (DHT) for secure data transmission. The developed work collects the data from the created edge AI system and processes it using the Autoencoder. The Autoencoder learns the data patterns and identifies the malicious data entry. The Diffie–Hellman (DH) key exchange generates a shared secret key for encryption, while the ALO optimizes the key exchange and improves security performance. Further, the Twofish algorithm performs the data encryption using a generated secret key, preventing security threats during transmission. The implementation results of the study show that it achieved a higher accuracy of 99.45%, lower time consumption of 2 s, minimum delay of 0.8 s, and reduced energy consumption of 3.2 mJ.
Dynamic Knowledge Management in an Agent-Based Extended Green Cloud Simulator

Authors:
raw_author_name: Zofia Wrona
raw_affiliation_string: Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland
raw_author_name: Maria Ganzha
raw_affiliation_string: Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland; Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland
raw_author_name: Marcin Paprzycki
raw_affiliation_string: Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland
raw_author_name: Stanisław Krzyżanowski
raw_affiliation_string: CloudFerro Sp. z o. o., 00-511 Warsaw, Poland

Abstract: Cloud infrastructures operate in highly dynamic environments, and today, energy-focused optimization become crucial. Moreover, the concept of extended cloud infrastructure, which, among others, uses green energy, started to gain traction. This introduces a new level of dynamicity to the ecosystem, as “processing components” may “disappear” and “come back”, specifically in scenarios where the lack/return of green energy leads to shutting down/booting back servers at a given location. Considered use cases may involve introducing new types of resources (e.g., adding containers with server racks with “next-generation processors”). All such situations require the dynamic adaptation of “system knowledge”, i.e., runtime system adaptation. In this context, an agent-based digital twin of the extended green cloud infrastructure is proposed. Here, knowledge management is facilitated with an explainable Rule-Based Expert System, combined with Expression Languages. The tests were run using Extended Green Cloud Simulator, which allows the modelling of cloud infrastructures powered (partially) by renewable energy sources. Specifically, the work describes scenarios in which: (1) a new hardware resource is introduced in the system; (2) the system component changes its resource; and (3) system user changes energy-related preferences. The case study demonstrates how rules can facilitate control of energy efficiency with an example of an adaptable compromise between pricing and energy consumption.