Reviewerly/exHarmony huggingface dataset:
Using Optimization and Auction Approach: Security provided to Vehicle network through Blockchain Technology

The internet of vehicles (IoV) a distributed network to allow the vehicles to communicate and data exchanged in real-time with other vehicles., humans., roadside infrastructures., etc. To communicate vehicles within non-trusted environments is difficult to evaluate the trustworthiness. By integrated blockchain with IoV., we improved the data sharing among vehicles., driving safety., security., and traceability. In this paper., miners are selected through the integration of multi attribute and two-stage auction to increase the trusted security between nodes. Further., to avoid the internal collision between miners., the blocks are selected through the Discrete Particle Swarm Optimization algorithm for miner-centric block validation selection. In every timeslot., the blocks are verified by miners and their positions updated dynamically. The proposed incentive mechanism performance is shown in the experiment results to improve the trustworthiness and security for data sharing in Blockchain-based IoV.
Uncertainty Estimation in Autoregressive Structured Prediction

Uncertainty estimation is important for ensuring safety and robustness of AI systems. While most research in the area has focused on un-structured prediction tasks, limited work has investigated general uncertainty estimation approaches for structured prediction. Thus, this work aims to investigate uncertainty estimation for structured prediction tasks within a single unified and interpretable probabilistic ensemble-based framework. We consider: uncertainty estimation for sequence data at the token-level and complete sequence-level; interpretations for, and applications of, various measures of uncertainty; and discuss both the theoretical and practical challenges associated with obtaining them. This work also provides baselines for token-level and sequence-level error detection, and sequence-level out-of-domain input detection on the WMT’14 English-French and WMT’17 English-German translation and LibriSpeech speech recognition datasets.
Item-based Collaborative Filtering Algorithm Based on Group Weighted Rating

Item-based collaborative filtering algorithm is one of the main collaborative filtering algorithms. However, its recommendation quality is seriously influenced by the sparsity of user ratings. To solve the problem, an improved item-based collaborative filtering algorithm based on group weighted rating is proposed. The union of user rating items is used as the basis of similarity computing between items, moreover a group weighted rating method has been proposed to compute and complete the missing values in the union of user rating items for decreasing the sparsity. The experimental results show that the new algorithm can efficiently improve recommendation quality.
Benchmarking of Computer Vision Algorithms for Driver Monitoring on Automotive-grade Devices

The continuing evolution of technologies in the automotive industry has led to the development of the so-called Advanced Driver Assistance Systems (ADAS). ADAS is the term used to describe vehicle-based intelligent safety systems designed to support the driver, with the aim to significantly improve his safety, and the driving safety in general. In terms of development, current ADAS technologies are based on control functions about the vehicle movements with respect to the objects and entities detected in the same environment (e.g., other vehicles, pedestrian, roads, etc.). However, there is an ever growing interest on the use of internal cameras to infer additional information regarding the driver status (e.g., weakness, level of attention). The purpose of such technologies is to provide accurate details about the environment in order to increase safety and smart driving. In the last few years, Computer Vision technology has achieved impressive results on several tasks related to recognition and detection of customized objects/entities on images and videos. However, automotive-grade devices' hardware resources are limited, with respect to the once usually required for the implementation of modern Computer Vision algorithms. In this work, we present a benchmarking evaluation of a standard Computer Vision algorithm for the driver behaviour monitoring through face detection and analysis, comparing the performances obtained on a common laptop with the same experiments on an existing commercial automotive-grade device based on the Accordo5 processor by STMicroelectronics.
Automated Readability Assessment for Spanish e-Government Information

This paper automatically evaluates the readability of Spanish e-government websites. Specifically, the websites collected explain e-government administrative procedures. The evaluation is carried out through the analysis of different linguistic characteristics that are presumably associated with a better understanding of these resources. To this end, texts from websites outside the government websites have been collected. These texts clarify the procedures published on the Spanish Government’s websites. These websites constitute the part of the corpus considered as the set of easy documents. The rest of the corpus has been completed with counterpart documents from government websites. The text of the documents has been processed, and the difficulty is evaluated through different classic readability metrics. At a later stage, automatic learning methods are used to apply algorithms to predict the difficulty of the text. The results of the study show that government web pages show high values for comprehension difficulty. This work proposes a new Spanish-language corpus of official e-government websites. In addition, a large number of combined linguistic attributes are applied, which improve the identification of the level of comprehensibility of a text with respect to classic metrics.
On Elastic Incentives for Blockchain Oracles

A fundamental open question for oracles in blockchain environments is a determination of the amount of trust to be placed in the oracle. Oracles serve as intermediaries between a trusted blockchain environment and the untrusted external environment from where the oracles fetch data. As such, it is important to understand the uncertainty introduced by the oracle in the trusted blockchain environment and the implications of this uncertainty on blockchain performance. This paper develops a model for commoditization of trust. The model provides for dynamic trust environments that incorporates oracle selfishness. The work also considers the equilibrium behavior for the demand and supply for trust and introduces elastic incentives for increasing the trust. These results are used to determine optimum size of the network that can be served by an oracle with varying degrees of selfishness. Key consequences and challenges of incorporating oracles in trusted distributed ledger environments are presented.
Audio Augmented Reality Using Unity for Marine Tourism

The development of marine tourism content is a much-needed challenge at this point. Audio augmented reality (AAR) is a new and fast-growing technology. We assume that AAR will improve the quality of services provided by marine tourism. In this research, the actual potential of AAR in marine tourism was studied by developing an AAR prototype using the Unity engine. We have used whale images with 3D sounds. The prototype can localize several 3D sounds in real-world locations. We conducted a listening test comparing the normal whale audios with the 3D whale audios generated by AAR. 81% of the participants were positive for 3D sounds and whale images. Interviewees felt better engagement in the service. They considered that the technology can be useful for receiving more information easily and making them more physically active. We conclude that the main contributions of AAR may be giving insightful knowledge about marine life in an interesting way and stimulating people to engage in nature closer.
Reference-free Detection of LSB Steganography Using Histogram Analysis

Due to the difficulty of obtaining a database of original images that are required in the classification process to detect tampering, this paper presents a technique for detecting image tampering such as image steganography in the spatial domain. The system depends on deriving the auto-correlation function of the image histogram, then applying a high-pass filter with a threshold. This technique can be used to decide which image is cover or a stego image, without adopting the original image. The results have eventually revealed the validity of this system. Although this study has focused on least-significant-bit (LSB) steganography, we expect that it could be extended to other types of image tapering.
UAV Trajectory Generation Based on Integration of RRT and Minimum Snap Algorithms

Aiming at the problems that carrying out forest fire monitoring and fighting missions by using the Rapidly-exploring Randomized Tree (RRT) algorithm to plan paths cannot adapt to the autonomous movement of an Unmanned Aerial Vehicle (UAV) and that high-order dynamic characteristics may mutate during the mission, this paper investigates a trajectory generation algorithm based on the integration of the RRT algorithm and the minimum snap algorithm. First, the RRT algorithm is used to generate the initial path, then the minimum snap algorithm is used to smooth the initial path and obtain a trajectory suitable for the actual flight of the UAV. However, because the UAV is considered as a particle in the simulation, during the actual flight, this trajectory may not guarantee the safe flight of the UAV and may cause the UAV to collide with an obstacle or other nearby UAVs in the cases of formation flight. To solve this problem, flight corridor concept is used to limit the UAV's flight trajectory for ensuring the safe flight of the UAV. Simulation results show that the algorithm can effectively ensure the safety, smoothness, feasibility, and trajectory of unmanned aerial vehicles.
Recognition of Hyperparathyroidism based on Transfer Learning

Hyperparathyroidism (HPT) is a disorder in which the parathyroid glands produce too much parathyroid hormone (PTH), which may lead to hypocalcemic convulsions, cardiomyopathy, hypertension and other diseases, even threaten the lives of patients under certain severe conditions. Since HPT is usually multiple and ectopic with variable symptoms, the diagnosis and location of HPT is a difficult task even for senior radiologists. A transfer learning-based computer-aided diagnosis (CAD) approach is proposed for automated recognition of HPT in this paper. A dataset of the brightness-mode ultrasound images is developed for the HPT recognition, which is usually annotated by senior radiologists. We addressed the HPT recognition using the various computer vision algorithms on the HPT dataset and obtained good performances for all the algorithms. The experimental results demonstrated that the dataset is effective in aiding the diagnosis of HPT.
Study of Algorithm for Aerial Target Detection Based on Lightweight Neural Network

Real-time detection of targets by video satellites is widely applied for civil and military purposes, but spaceborne platforms are generally limited in memory and computing capacity, with tougher demands on detection algorithms, which can hardly be met by traditional target detection algorithms. Therefore, this paper proposed a lightweight target detection algorithm based on YOLO v3 framework and lightweight neural network MobileNet v3. Compared with YOLO v3, the size of the improved network is reduced by 2.9 times at the same level of detection precision. Experimental results showed that the detection speed of the improved lightweight network could reach up to 40.35FPS, with the mean average precision (mAP) of 87.8%.
Ensuring the Quality of Training Engineers in a Virtual Environment

The introduction of information technology in the educational process provides many opportunities for the development and implementation of modern methods of training future engineering professionals with the use of electronic educational content. Paper considers the peculiarities of the introduction of blended and distance learning technologies in Sumy State University into the educational process system. The stages of formation and application of virtual laboratory work in the educational process are considered, taking into account the formation of the professional competences of the engineer, the shortcomings and advantages of their use in training are revealed. It should be noted that the paper examines the introduction of a virtual simulator in the learning process to improve the efficiency of the learning process, improve the quality of acquisition of skills and knowledge, development of professional engineering competencies. The analysis of the research results suggests that the virtual objects of distance learning are somewhat imperfect in the question of forming the communicative competence of the future engineer. However, at the same time, they allow to expand the student’s ability to prepare and analyze complex engineering problems, to evaluate and select the necessary information, apply the required theoretical and practical methods for the analysis of complex engineering developments. This study provides recommendations for improving the effectiveness of learning through the introduction of virtual objects in the study of engineering disciplines.
Knowledge Reasoning Method for Military Decision Support Knowledge Graph Mixing Rule and Graph Neural Networks Learning together

The military decision support knowledge graphs (KGs) have been widely used in helping the commanders to understand the complicated, changefully battlefield situation. Not only combat action entities and their relations are embraced in the KGs, but also the military rules with numbers which are established by the army are encompassed. Proposed in the paer is the knowledge reasoning method mixing rule and graph neural networks learning together, called context-surrounding graph neural networks with numbers (CS-GNN-N). The rule learning, rule injection and graph neural networks learning are iteratively done in the CS-GNN-N. The two graph smoothness metrics, feature smoothness and label smoothness, are applied to measure the quantity and quality of neighborhood information of nodes respectively. Finally, the effectiveness of the CS-GNN-N on link prediction tasks is compared with four datasets and competitive reasoning methods. The results show that not only the military rules and relative numbers can be learned in the CS-GNN-N, but also the reasoning ability of the CS-GNN-N can be enhanced.
A Dynamic Group Equivariant Convolutional Networks for Medical Image Analysis

Group equivariant Convolutional Neural Networks (G-CNNs) has led to big empirical success in the medical domain, one fundamental assumption is that equivariance provides a powerful inductive bias for medical images. By leveraging concepts from group representation theory, we can generalize vanilla Convolutional Neural Networks (CNNs) to G-CNN. Currently, although embedding an arbitrary equivariance to CNNs can learn powerful disentangled representations in a higher dimensional domain, they lack explicit means to learn meaningful relationships among the equivariant convolutional kernels. In this paper, we propose a generalization of the dynamic convolutional method, named as dynamic group equivariant convolution, to strengthen the relationships and increase model capability by aggregating multiple group convolutional kernels via attention. Meanwhile, we generalize attention to an equivariant one to preserve equivariant of dynamic group convolution. In our approach, this leads to a flexible framework that enables a dynamic convolutional in G-CNNs by means of a dynamic routing layer expansions. We demonstrate that breast tumor classification is substantial improvements when compared to a recent baseline architecture.
On Using zk-SNARKs and zk-STARKs in Blockchain-Based Identity Management

One possible applicability of blockchain technology is in identity management. Especially for public blockchains, the need to reduce (ideally to zero) the exposure of sensitive identification data is clear. Under these settings, zero-knowledge proofs, in particular in the advanced forms of Zero-Knowledge Succinct Non-Interactive ARguments of Knowledge (zk-SNARK) and Zero-Knowledge Scalable Transparent ARguments of Knowledge (zk-STARK), can be used as a potential privacy-preserving technique. The current work looks at the existing libraries that implement zk-SNARKs and zk-STARKs and exemplifies and discusses the use of zk-SNARKs in blockchain-based identity management solutions.
Incremental few-shot learning via vector quantization in deep embedded space

The capability of incrementally learning new tasks without forgetting old ones is a challenging problem due to catastrophic forgetting. This challenge becomes greater when novel tasks contain very few labelled training samples. Currently, most methods are dedicated to class-incremental learning and rely on sufficient training data to learn additional weights for newly added classes. Those methods cannot be easily extended to incremental regression tasks and could suffer from severe overfitting when learning few-shot novel tasks. In this study, we propose a nonparametric method in deep embedded space to tackle incremental few-shot learning problems. The knowledge about the learned tasks are compressed into a small number of quantized reference vectors. The proposed method learns new tasks sequentially by adding more reference vectors to the model using few-shot samples in each novel task. For classification problems, we employ the nearest neighbor scheme to make classification on sparsely available data and incorporate intra-class variation, less forgetting regularization and calibration of reference vectors to mitigate catastrophic forgetting. In addition, the proposed learning vector quantization (LVQ) in deep embedded space can be customized as a kernel smoother to handle incremental few-shot regression tasks. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods in incremental learning.
Certifying dimension of quantum systems by sequential projective measurements

This work analyzes correlations arising from quantum systems subject to sequential projective measurements to certify that the system in question has a quantum dimension greater than some<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math>. We refine previous known methods and show that dimension greater than two can be certified in scenarios which are considerably simpler than the ones presented before and, for the first time in this sequential projective scenario, we certify quantum systems with dimension strictly greater than three. We also perform a systematic numerical analysis in terms of robustness and conclude that performing random projective measurements on random pure qutrit states allows a robust certification of quantum dimensions with very high probability.
Transferable Feature Learning on Graphs Across Visual Domains

Unsupervised domain adaptation adapts classifiers to an un-labeled target domain by exploiting a labeled source domain. To reduce discrepancy between source and target domains, adversarial learning methods are typically selected to seek domain-invariant representations by confusing the domain discriminator. However, classifiers may not be well adapted to such a domain-invariant representation space, as the sample-level and class-level data structures could be distorted during adversarial learning. In this paper, we propose a novel Transferable Feature Learning approach on Graphs (TFLG) for unsupervised adversarial domain adaptation, which jointly incorporates sample-level and class-level structure information across two domains. TFLG first constructs graphs and identifies the class-wise correspondence across domains. A novel cross-domain graph convolutional operation is designed to jointly align the sample-level and class-level structures in two domains. Extensive experiments on benchmark datasets demonstrate the effectiveness of our approach, compared to the representative unsupervised domain adaptation methods.
Cryptanalysis and improvement of a new certificateless signature scheme in the standard model

Digital signature, as an important cryptographic primitive, has been widely used in many application scenarios, such as e-commerce, authentication, cloud computing, and so on. Certificateless Public Key Cryptography (PKC) can get rid of the certificate management problem in the traditional Public Key Infrastructure (PKI) and eliminate the key-escrow problem in the identity-based PKC. Lately, a new Certificateless Signature (CLS) scheme has been proposed by Kyung-Ah Shim (IEEE SYSTEMS JOURNAL, 2018, 13(2)), which claimed to achieve provable security in the standard model. Unfortunately, we present a concrete attack to demonstrate that the scheme cannot defend against the Type I adversary. In this type of attack, the adversary can replace the public key of the signer, and then he plays the role of the signer to forge a legal certificateless signature on any message. Furthermore, we give an improved CLS scheme to resist such an attack. In terms of the efficiency and the signature length, the improved CLS is preferable to the original scheme and some recently proposed CLS schemes in the case of precomputation.
Micro-architectural Cache Side-Channel Attacks and Countermeasures

Central Processing Unit (CPU) is considered as the brain of a computer. If the CPU has vulnerabilities, the security of software running on it is difficult to be guaranteed. In recent years, various micro-architectural cache side-channel attacks on the CPU such as Spectre and Meltdown have appeared. They exploit contention on internal components of the processor to leak secret information between processes. This newly evolving research area has aroused significant interest due to the broad application range and harmfulness of these attacks. This article reviews recent research progress on micro-architectural cache side-channel attacks and defenses. First, the various micro-architectural cache side-channel attacks are classified and discussed. Then, the corresponding countermeasures are summarized. Finally, the limitations and future development trends are prospected.
Research on the Application of K-Means Clustering Algorithm in Student Achievement

This paper uses the K-Means algorithm in data mining to perform cluster analysis based on the final grade data of students majoring in software and information services in a certain university to effectively divide the sample data set. Through the analysis of the cluster analysis results, the characteristics of the distribution of student performance in each cluster category are refined, which provides a reference for teachers in project grouping and personalized teaching in the “project-driven” mode of teaching. At the same time, according to the visual analysis of the clustering effect in different clustering categories based on the performance of a single subject, the importance of courses can be predicted. Important courses can appropriately increase teachers or class hours. This provides scientific basis for better implementation of teaching reforms and revision of talent training programs.