Reviewerly/exHarmony huggingface dataset:
Smart contract‐based resource allocation service mechanism for internet of vehicles

Abstract As an essential part of the intelligent transportation system, vehicular ad hoc network plays a vital role in the application of the Internet of Vehicles and security services. However, as the scale of the network continues to increase, trust‐based access and data sharing between vehicles have also become essential security issues. Given how to allocate vehicle resources safely, efficiently and reliably, this paper proposes a blockchain‐based key data sharing mechanism for the Internet of Vehicles. We first utilize certificateless public key cryptography to alleviate redundancy overhead and key escrow issues. Second, with the help of the security, decentralization, and non‐tampering characteristics of the blockchain, two smart contracts are designed and used to realize vehicle identity management and data access control. The identity chain smart contract solves the security problem of vehicle identity management, and the data chain smart contract ensures reliable transaction verification and efficient resource management. At the same time, an auction method is proposed to facilitate vehicle resource transactions. The experimental results show that our solution can realize secure data sharing of the Internet of Vehicles and ensure the operating efficiency of the system. Regarding system overhead, the proposed scheme has 30.6% and 33% lower private key generation overhead and 3.3% and 20.1% lower resource transaction time than AFSN and BSM under the same conditions. In addition, using our bidding method in data chain smart contracts can have a regulatory effect on market data transactions. In the aspect of resource transaction efficiency, for the change of the number of buyers, the transaction completion rate and transaction time of the proposed scheme are better than those of the contrast scheme. When the number of buyers is 100, the transaction completion rate was 9.2% higher than that of AFSN and 17.3% higher than that of BAM. The transaction time of the scheme is 32.2% and 48.1% lower than that of AFSN and BSM, respectively.
Physics Students’ Innovation Readiness for Digital Learning within the University 4.0 Model: Essential Scientific and Pedagogical Elements That Cause the Educational Format to Evolve in the Context of Advanced Technology Trends

Students’ readiness for innovative educational formats is a determining factor that can affect the effectiveness of digital transformations in a higher education institution. Evaluation of students’ innovative readiness for the transition to the University 4.0 model can provide guidance on what actions and policies should be taken to achieve an educational institution’s digital transformation goals. This article aims to study the essential scientific and pedagogical elements that cause the educational format to evolve in the context of advanced technology trends and determine physics students’ innovation readiness for digital learning within the University 4.0 model. The survey was extended to the online forum University 4.0: Prospects for Modernising Approaches to Teaching Physics. The survey results showed the degree of readiness of physics students to implement digital approaches in teaching in four dimensions. Students had a high level of creativity and a positive attitude toward innovative educational activities (M = 3.89); they also had a high level of motivation for digital innovative training and sufficient knowledge of how the training will help them achieve their educational goals and professional and personal competence (M = 3.85); and they had a high level of digital literacy (M = 3.96). The scientific value of this article lies in demonstrating the developed methodology for determining and evaluating the degree of innovative readiness of students in the process of transition to a new educational format University 4.0. This methodology can be used in the process of developing an innovative strategy for organizing the educational process in the conditions of Industry 4.0 taking into account its challenges and opportunities.
CCALK: (When) CVA6 Cache Associativity Leaks the Key

In this work, we study an end-to-end implementation of a Prime + Probe covert channel on the CVA6 RISC-V processor implemented on a FPGA target and running a Linux OS. We develop the building blocks of the covert channel and provide a detailed view of its behavior and effectiveness. We propose a realistic scenario for extracting information of an AES-128 encryption algorithm implementation. Throughout this work, we discuss the challenges brought by the presence of a running OS while carrying out a micro architectural covert channel. This includes the effects of having other running processes, unwanted cache evictions and the OS’ timing behavior. We also propose an analysis of the relationship between the data cache’s characteristics and the developed covert channel’s capacity to extract information. According to the results of our experimentations, we present guidelines on how to build and configure a micro architectural covert channel resilient cache in a mono-core mono-thread scenario.
Batch Gradient Learning Algorithm with Smoothing L1 Regularization for Feedforward Neural Networks

Regularization techniques are critical in the development of machine learning models. Complex models, such as neural networks, are particularly prone to overfitting and to performing poorly on the training data. L1 regularization is the most extreme way to enforce sparsity, but, regrettably, it does not result in an NP-hard problem due to the non-differentiability of the 1-norm. However, the L1 regularization term achieved convergence speed and efficiency optimization solution through a proximal method. In this paper, we propose a batch gradient learning algorithm with smoothing L1 regularization (BGSL1) for learning and pruning a feedforward neural network with hidden nodes. To achieve our study purpose, we propose a smoothing (differentiable) function in order to address the non-differentiability of L1 regularization at the origin, make the convergence speed faster, improve the network structure ability, and build stronger mapping. Under this condition, the strong and weak convergence theorems are provided. We used N-dimensional parity problems and function approximation problems in our experiments. Preliminary findings indicate that the BGSL1 has convergence faster and good generalization abilities when compared with BGL1/2, BGL1, BGL2, and BGSL1/2. As a result, we demonstrate that the error function decreases monotonically and that the norm of the gradient of the error function approaches zero, thereby validating the theoretical finding and the supremacy of the suggested technique.
A Noise-Aware Multiple Imputation Algorithm for Missing Data

Missing data is a common and inevitable phenomenon. In practical applications, the datasets usually contain noises for various reasons. Most of the existing missing data imputing algorithms are affected by noises which reduce the accuracy of the imputation. This paper proposes a noise-aware missing data multiple imputation algorithm NPMI in static data. Different multiple imputation models are proposed according to the missing mechanism of data. Secondly, the method to determine the imputation order of multivariablesmissing is given. A random sampling consistency algorithm is proposed to estimate the initial values of the parameters of the multiple imputation model to reduce the influence of noise data and improve the algorithm’s robustness. Experiments on two real datasets and two synthetic datasets verify the accuracy and efficiency of the proposed NPMI algorithm, and the results are analyzed.
Low-Complexity One-Dimensional Parallel Semi-Systolic Structure for Field Montgomery Multiplication Algorithm Perfect for Small IoT Edge Nodes

The use of IoT technology in several applications is hampered by security and privacy concerns with IoT edge nodes. Security flaws can only be resolved by implementing cryptographic protocols on these nodes. The resource constraints of the edge nodes make it extremely difficult to implement these protocols. The majority of cryptographic protocols’ fundamental operation is finite-field multiplication, and their performance is significantly impacted by their effective implementation. Therefore, this work mainly focuses on implementing low-area with low-energy and high-speed one-dimensional bit-parallel semi-systolic multiplier for the Montgomery multiplication algorithm. The space and delay complexity analysis of the proposed multiplier structure reveals that the proposed design has a significant reduction in delay and a marginal reduction in the area when compared to the competitive one-dimensional multipliers. The obtained ASIC synthesis report demonstrates that the suggested multiplier architecture saves a marginal amount of space as well as a significant amount of time, area–delay product (ADP), and power–delay product (PDP) when compared to the competitive ones. The obtained results indicate that the proposed multiplier layout is very appropriate for use in devices with limited resources such as IoT edge nodes and tiny embedded devices.
VGGNet-Cnn based classification of white blood cell leukemia with efficient salp swarm optimization algorithm

White blood cell (WBC) leukemia is caused by an excess of leukocytes in the bone marrow, and image-based identification of malignant WBCs is important for its detection. This research describes a new hybrid technique for accurate classification of WBC leukemia. To increase the image quality, the preprocessing is done using Contrast Limited Adaptive Histogram Equalization (CLAHE). The images are then segmented using Hidden Markov Random Fields (HMRF). To extract features from WBC images, Visual Geometry Group Network (VGGNet), a powerful Convolutional Neural Network (CNN) architecture, is used After that, an Efficient Salp Swarm Algorithm (ESSA) is used to optimize the extracted features. The proposed method is tested on two Acute Lymphoblastic Leukemia Image Databases, yielding good accuracy of 98.1% for dataset 1 and 98.8% for dataset 2. While enhancing accuracy, the ESSA optimization picked just 1K out of 25K features retrieved with VGGNet. The combination of CNN feature extraction with ESSA feature optimization could be effective for a variety of additional image classification tasks.
A New Monocular Pose Estimation Method for the Coplanar P4P Problem

The Perspective of N Points (PNP) problem in optical measurement and machine vision is to estimate the pose parameters of an object by a monocular image. For traditional coplanar P4P methods, the four points must have known coordinates or be a rectangle’s four vertices. In this paper, a new method of the P4P problem for parallelogram objects without shape parameters is proposed to solve the objects’ pose and shape parameters based on the parallelogram vertices’ relative coordinate constraints. The correctness and effectiveness of the method have been verified through experiments. In addition to being used to estimate a parallelogram object’s pose and shape, the new method extends the conclusions of the coplanar P4P problem when the four points are a parallelogram’s four vertices.
DAMNet: A Dual Adjacent Indexing and Multi-Deraining Network for Real-Time Image Deraining

Image deraining is increasingly critical in the domain of computer vision. However, there is a lack of fast deraining algorithms for multiple images without temporal and spatial features. To fill this gap, an efficient image-deraining algorithm based on dual adjacent indexing and multi-deraining layers is proposed to increase deraining efficiency. The deraining operation is based on two proposals: the dual adjacent method and the joint training method based on multi-deraining layers. The dual adjacent structure indexes pixels from adjacent features of the previous layer to merge with features produced by deraining layers, and the merged features are reshaped to prepare for the loss computation. Joint training method is based on multi-deraining layers, which utilise the pixelshuffle operation to prepare various deraining features for the multi-loss functions. Multi-loss functions jointly compute the structural similarity by loss calculation based on reshaped and deraining features. The features produced by the four deraining layers are concatenated in the channel dimension to obtain the total structural similarity and mean square error. During the experiments, the proposed deraining model is relatively efficient in primary rain datasets, reaching more than 200 fps, and maintains relatively impressive results in single and crossing datasets, demonstrating that our deraining model reaches one of the most advanced ranks in the domain of rain-removing.
Enhanced Signed Graph Neural Network with Node Polarity

Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and thus limit the ability to learn the underlying structure of real signed graphs. To address this limitation, a deep graph neural network framework SiNP to learn Signed network embedding with Node Polarity is proposed. To be more explicit, a node-signed property metric mechanism is developed to encode the individual characteristics of the nodes. In addition, a graph convolution layer is added so that both positive and negative information from neighboring nodes can be combined. The final embedding of nodes is produced by concatenating the outcomes of these two portions. Finally, extensive experiments have been conducted on four significant real-world signed network datasets to demonstrate the efficiency and superiority of the proposed method in comparison to the state-of-the-art.
Improving Modulation Recognition Using Time Series Data Augmentation via a Spatiotemporal Multi-Channel Framework

Automatic modulation recognition technology with deep learning has a broad prospective owing to big data and computing power. However, the accuracy of modulation recognition largely depends on the massive volume of data and the applicability of the model. Here, to eliminate the difficulties of manual feature extraction, a low accuracy, and a small sample dataset, we propose an effective recognition method that combines time series data augmentation with a spatiotemporal multi-channel learning framework. Compared with other advanced network models, the results showed that the method gave a positive index in the order of 93.5% for ten modulation signal types, which was increased by at least 15%. Especially for QAM16 and QAM64 signals, the average recognition accuracy was improved by nearly 50% at SNRs as low as −2 dB, showing a significant recognition performance. The proposed method provides an attractive method for signal modulation recognition in wireless or wired communication fields.
Knowledge Graph Completion Based on Entity Descriptions in Hyperbolic Space

Hyperbolic space has received extensive attention because it can accurately and concisely represent hierarchical data. Currently, for knowledge graph completion tasks, the introduction of exogenous information of entities can enrich the knowledge representation of entities, but there is a problem that entities have different levels under different relations, and the embeddings of different entities in Euclidean space often requires high dimensional space to distinguish. Therefore, in order to solve the above problem, we propose a method that use entity descriptions to complete the knowledge graph in the Poincaré ball model of hyperbolic space. In this method, the text representation of entity descriptions is in Euclidean space and mapped to hyperbolic space through exponential map. Next, the triple embeddings are initialized in hyperbolic space, and the structured representation of the triple is trained by the hyperbolic model. Finally, the text representation and the structured representation of the entity are cross-fused in hyperbolic space, and then the balance factors are used to adjust the unbalanced energy function. Experimental results show that, compared with baseline models, the proposed method can improve the performance of knowledge graphs completion.
ATSUKF-Based Actuator Health Assessment Method for Quad-Copter Unmanned Aerial Vehicles

The actuator, which generally consists of motors, electrical regulations, and propellers, is the key component of the quadrotor Unmanned Aerial Vehicle. During the operation of the UAV, actuators are prone to degrade performance and even cause serious failure, which affects the service quality and flight safety of Unmanned Aerial Vehicles. Therefore, timely and accurate monitoring and evaluation of the health condition of actuators is of great significance to ensure the mission reliability of UAVs. This paper proposes an Adaptive Two-stage Unscented Kalman Filter-based actuator health assessment method for Quadcopter Unmanned Aerial Vehicles. Firstly, a state space equation is established based on dynamic analysis to characterize the degradation mechanism of the actuator. Then, by modifying the Two-stage Unscented Kalman Filter algorithm, the Adaptive Two-stage Unscented Kalman Filter algorithm is constructed by combining the filter divergence criterion and the covariance matching technique to implement the health assessment of actuators. Finally, experiments are carried out for different degradation scenarios to verify the effectiveness of the proposed method.
How to model contrary-to-duty with GCP-nets

Preferences are ubiquitous in our everyday life. They are essential in the decision making process of individuals. Recently, they have also been employed to represent ethical principles, normative systems or guidelines. In this work we focus on a ceteris paribus semantics for deontic logic: a state of affairs where a larger set of respected prescriptions is preferable to a state of affairs where some are violated. Conditional preference networks (CP-nets) are a compact formalism to express and analyse ceteris paribus preferences, with some desirable computational properties. In this paper, we show how deontic concepts (such as contrary-to-duty obligations) can be modeled with generalized CP-nets (GCP-nets) and how to capture the distinction between strong and weak permission in this formalism. To do that, we leverage on an existing restricted deontic logic that will be mapped into conditional preference nets.
Learning Domain and Pose Invariance for Thermal-to-Visible Face Recognition

Interest in thermal to visible face recognition has grown significantly over the last decade due to advancements in thermal infrared cameras and analytics beyond the visible spectrum. Despite large discrepancies between thermal and visible spectra, existing approaches bridge domain gaps by either synthesizing visible faces from thermal faces or by learning the cross-spectrum image representations. These approaches typically work well with frontal facial imagery collected at varying ranges and expressions, but exhibit significantly reduced performance when matching thermal faces with varying poses to frontal visible faces. We propose a novel Domain and Pose Invariant Framework that simultaneously learns domain and pose invariant representations. Our proposed framework is composed of modified networks for extracting the most correlated intermediate representations from off-pose thermal and frontal visible face imagery, a sub-network to jointly bridge domain and pose gaps, and a joint-loss function comprised of cross-spectrum and pose-correction losses. We demonstrate efficacy and advantages of the proposed method by evaluating on three thermal-visible datasets: ARL Visible-to-Thermal Face, ARL Multimodal Face, and Tufts Face. Although DPIF focuses on learning to match off-pose thermal to frontal visible faces, we also show that DPIF enhances performance when matching frontal thermal face images to frontal visible face images.
BitXpro: Regularity-Aware Hardware Runtime Pruning for Deep Neural Networks

Classic deep neural network (DNN) pruning mostly leverages software-based methodologies to tackle the accuracy/speed tradeoff, which involves complicated procedures such as critical parameter searching, fine-tuning, and sparse training to find the best plan. In this article, we explore the opportunities of hardware runtime pruning and propose a regularity-aware hardware runtime pruning methodology, termed “BitXpro” to empower versatile DNN inference. The method targets the bit-level sparsity and the sparsity irregularity in the parameters and pinpoints and prunes the useless bits on-the-fly in the proposed BitXpro accelerator. The versatility of BitXpro lies in: 1) software effortless; 2) orthogonal to the software-based pruning; and 3) multiprecision support (including both floating point and fixed point). Empirical studies on various domain-specific artificial intelligence (AI) tasks highlight the following results: 1) up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.27\times $ </tex-math></inline-formula> speedup over the original nonpruned DNN and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10.81\times $ </tex-math></inline-formula> speedup collaborated with the software-pruned DNN; 2) up to 0.3% and 0.04% higher accuracy for the floating- and fixed-point DNNs, respectively; and 3) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.01\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.20\times $ </tex-math></inline-formula> performance improvement over the state-of-the-art accelerators, with 0.068 mm2 and 74.82 mW (floating point 32) and 40.44 mW (16-bit fixed point) power consumption under the TSMC 28-nm technology library.
Customers’ perceptions toward service robots at restaurants: The moderating role of consumer innovativeness and the mediating role of perceived hedonic benefits

ABSTRACTThe number of restaurants that have added service robots to their operation has increased recently; however, research on their impact on customers’ perceptions and intentions is scarce. The purpose of this study was to investigate, while considering consumer innovativeness, the effects of customers’ perceived service newness and hedonic benefits on their perceived service quality and value and, consequently, their willingness to select a restaurant that uses robot services. Data were collected from 675 respondents who watched a video about service robots at restaurants. Structural equation modeling and hierarchical regression were used to analyze data. The results indicated that participants’ perceived service newness influenced their perceived hedonic benefits directly and their perceived service quality indirectly. Perceived service newness negatively influenced customers’ perceived value, and perceived value enhanced their willingness to select a restaurant with service robots. It was also found that consumer innovativeness moderated the relationship between perceived hedonic benefits and perceived service quality.KEYWORDS: Service robotsperceived service newnessperceived hedonic benefitsperceived valueperceived service qualityconsumer innovativeness Disclosure statementNo potential conflict of interest was reported by the author(s).