Reviewerly/exHarmony huggingface dataset:
Optical Extreme Learning Machines with Atomic Vapors

Extreme learning machines explore nonlinear random projections to perform computing tasks on high-dimensional output spaces. Since training only occurs at the output layer, the approach has the potential to speed up the training process and the capacity to turn any physical system into a computing platform. Yet, requiring strong nonlinear dynamics, optical solutions operating at fast processing rates and low power can be hard to achieve with conventional nonlinear optical materials. In this context, this manuscript explores the possibility of using atomic gases in near-resonant conditions to implement an optical extreme learning machine leveraging their enhanced nonlinear optical properties. Our results suggest that these systems have the potential not only to work as an optical extreme learning machine but also to perform these computations at the few-photon level, paving opportunities for energy-efficient computing solutions.
On monitorability of AI

Abstract Artificially intelligent (AI) systems have ushered in a transformative era across various domains, yet their inherent traits of unpredictability, unexplainability, and uncontrollability have given rise to concerns surrounding AI safety. This paper aims to demonstrate the infeasibility of accurately monitoring advanced AI systems to predict the emergence of certain capabilities prior to their manifestation. Through an analysis of the intricacies of AI systems, the boundaries of human comprehension, and the elusive nature of emergent behaviors, we argue for the impossibility of reliably foreseeing some capabilities. By investigating these impossibility results, we shed light on their potential implications for AI safety research and propose potential strategies to overcome these limitations.
Analysis of Visual and Vestibular Information on Motion Sickness in Flight Simulation

Virtual reality (VR) is in its nascent technological advancement and market diffusion stages. Interestingly, the scientific exploration concerning the impact of non-isometric mapping disparities within visual–vestibular stimuli on motion sickness remains deficient. This investigation focuses on scrutinizing the visual–vestibular implications for motion sickness within the context of flight simulation. The developed motion platform, offering specific pitch and roll ranges of ±16 and ±17 degrees, respectively, was employed to induce varying ratios of simulated visual–vestibular cues. Involving a cohort of five participants, the study exposed them to two prevalent simulated mission profiles, subsequently assessing their motion sickness symptoms. Sixty responses were analyzed using the subjective assessment of the Simulator Sickness Questionnaire (SSQ). The findings reveal a reduction in cybersickness severity with congruent visual–vestibular stimuli in proportion to the variance observed among visual–vestibular coupling ratios. A comparative analysis of SSQ sub-categories demonstrates that disorientation holds the most significance in the hierarchy of motion sickness contributors, followed by oculomotor discomfort, with nausea manifesting as the least influential. This study can lead to situation awareness analysis by integrating VR-based flight-simulation setups in the formal training of pilots and UAV operators.
A Survey on Video Streaming for Next-Generation Vehicular Networks

As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In addition to conventional 2D videos, 360° videos are gaining popularity, and volumetric videos, which can offer users a better immersive experience, have been discussed. However, these applications place high demands on network capabilities, leading to a dependence on next-generation wireless communication technology to address network bottlenecks. Therefore, this study provides an exhaustive overview of the latest advancements in video streaming over vehicular networks. First, we introduce related work and background knowledge, and provide an overview of recent developments in vehicular networking and video types. Next, we detail various video processing technologies, including the latest released standards. Detailed explanations are provided for network strategies and wireless communication technologies that can optimize video transmission in vehicular networks, paying special attention to the relevant literature regarding the current development of 6G technology that is applied to vehicle communication. Finally, we proposed future research directions and challenges. Building upon the technologies introduced in this paper and considering diverse applications, we suggest a suitable vehicular network architecture for next-generation video transmission.
High-Performance Hardware Implementation of the Saber Key Encapsulation Protocol

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

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

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

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

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

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

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

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

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

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.
What are virtual items, and are they real?

A central debate in the philosophy of virtual reality (VR) focuses on the reality of virtual items. Broadly, there are two main disagreements. Some views accept a metaphysical orientation to VR, and disagree on the reality of virtual items. For instance, David Chalmers (Disputatio 9(46):309-352, 2017, Disputatio 11(55):453-486, 2019, 2022) defends digitalism, the view that virtual items are real digital items. Neil McDonnell & Nathan Wildman (Disputatio 11(55):371-397, 2019), by contrast, defend fictionalism, which maintains that virtual items are unreal fictions. Other views, like Grant Tavinor's (2021), reject the metaphysical orientation, arguing that focusing on the reality of virtual items is a mistake. In what follows I evaluate these two disagreements. I argue that experiences of virtual items depend on episodes of picture perception. Recognizing this reveals that the current debates are either easily resolved in favor of one view, or are not disagreeing about the reality of virtual items, but about what virtual items are.
Dynamic Knowledge Management in an Agent-Based Extended Green Cloud Simulator

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.
Prediction of Global Horizontal Irradiance Using an Explainable Data Driven Machine Learning Algorithms

Estimating global horizontal irradiance (GHI) with a high level of accuracy and precision is very challenging due to the volatile climate parameters and location constraints. To overcome this challenge, several machine learning (ML)-based techniques such as Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), and Extra Trees (ET) are implemented to forecast the GHI. The first stage of model development is to select the optimal subset of features by using the variance inflation factor feature selection method. In the second stage, the selected features are fed into the ML models and trained. The predictive performance of the ML models is improved the result of removal of insignificant input features. The predictive accuracy of the ML models is compared and evaluated by performance metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). Conclusively, after feature selection it is seen that the ET algorithm outperforms the others because of its lowest MAE and RMSE value of 3.01 and 1.748, respectively, as compared to the other models, indicating its relevancy, legitimacy, and viability for the estimation of GHI. The higher R2 value of 0.99 obtained by the ET model indicates that it is best fitted with the dataset. Additionally, optimal shapely additive explanation values have been used as feature attributions for determining the magnitude and direction of the impact of each feature on the outcome.