Reviewerly/exHarmony huggingface dataset:
Competitive-blockchain-based parking system with fairness constraints

Parking slot detection is one of the most popular applications of Vehicular ad Hoc Network research field. Proposing smart algorithms for fast parking is crucial not only to facilitate drivers, but also to reduce traffic congestion, pollution, and vehicle energy consumption. Typically, an urban area has several competitive car parks and, in order to make the parking process automatic, a mechanism to ensure a fair competition among them is needed. Among all the methods able to guarantee transparency and equity in a system, blockchain is a robust technology. It has been successfully applied in many different research fields, from financial to health. In this work, we provide an automatic parking system in which vehicles are allocated among several competitive parking areas (called competitors), through a blockchain-based approach, by applying a consensus mechanism to manage the system modifications. To this aim, two classes of fairness constraints are defined, according to which any new operation on the parking consortium must be approved by the members. Such an approach brings benefits for different reasons, starting from traffic condition improvement, up to driver stress and pollution decrease.
Deep domain adaptation via joint transfer networks

Domain adaptation aims to transfer the enrich label knowledge from a large-scale labeled tasks to new ones with no labeled data. In the real-world scenario, the domain discrepancy of feature distributions between different tasks (domains) is usually uncontrollable, which is dramatically motivated to match the feature distributions in the face of the domain discrepancy is completely divergence. Under the condition that target task (domain) annotations are unknown, how to successfully adapt the trained classified from source to the target of interest still remains an open issue. In this paper, a unified domain adaptation method is proposed, Joint Transfer Networks (JTN), which jointly generates domain transferable features across two different domains and a double weighting scheme is able to learn more meaningful and transferable features. Additionally, we also exploit the pseudo-labeled target data to generate discriminative features in the target domain, which further boosts the adaptation performance. Finally, after a thorough evaluation of proposed method utilizing several benchmark datasets of varying difficulty. JTN yielded the state-of-the-art performance and outperformed baseline approaches for various domain adaptation tasks.
Designing real-time 3D image security with CA-based random mode decomposition

3D integral images have drawn significant interest as an important part of digital entertainment. Thus, there is an urgent need to protect the 3D imaging sources away from malicious attack and manipulation. This paper proposes a real-time 3D integral imaging (InIm) encryption method by employing a novel remapping algorithm and a random mode decomposition (RMD) encryption based on cellular automata (CA). This method has simultaneous advantages of improving the quality of 3D images and enhancing the security by combining the asymmetric RMD and highly random sequence from CA in both time and Fourier domain. Furthermore, compared with the previous 3D image encryption method for InIm, the proposed approach mainly reduces the computing complexity, achieving a real-time encrypting performance by importing remapping algorithm. In experiment, we present the results of time-cost of the InIm security system and the facility-cost of 3D decrypting reconstruction. The results also validate the feasibility and robustness of our encryption method.
Comprehensive Analysis of Privacy Leakage in Vertical Federated Learning During Prediction

Abstract Vertical federated learning (VFL), a variant of federated learning, has recently attracted increasing attention. An active party having the true labels jointly trains a model with other parties (referred to as passive parties ) in order to use more features to achieve higher model accuracy. During the prediction phase, all the parties collaboratively compute the predicted confidence scores of each target record and the results will be finally returned to the active party. However, a recent study by Luo et al . [28] pointed out that the active party can use these confidence scores to reconstruct passive-party features and cause severe privacy leakage. In this paper, we conduct a comprehensive analysis of privacy leakage in VFL frameworks during the prediction phase. Our study improves on previous work [28] regarding two aspects. We first design a general gradient-based reconstruction attack framework that can be flexibly applied to simple logistic regression models as well as multi-layer neural networks. Moreover, besides performing the attack under the white-box setting, we give the first attempt to conduct the attack under the black-box setting. Extensive experiments on a number of real-world datasets show that our proposed attack is effective under different settings and can achieve at best twice or thrice of a reduction of attack error compared to previous work [28]. We further analyze a list of potential mitigation approaches and compare their privacy-utility performances. Experimental results demonstrate that privacy leakage from the confidence scores is a substantial privacy risk in VFL frameworks during the prediction phase, which cannot be simply solved by crypto-based confidentiality approaches. On the other hand, processing the confidence scores with information compression and randomization approaches can provide strengthened privacy protection.
Industrial network-based behavioral anomaly detection in AI-enabled smart manufacturing

Existing manufacturing systems are isolated from the outside world to protect their sites and systems. However, following the trend of the 4th Industrial Revolution, manufacturing systems have also increased the connectivity of various domains and the convergence of numerous technologies. These systems are referred to as smart manufacturing systems. However, this trend has increased the challenge of network anomaly detection methods, which are a major approach to network security in smart manufacturing. Existing methods define normality under the premise that network components are static, and network operation is periodic compared to the information technology environment. Therefore, comprehensive and volatile network environments require significant time, cost, and labor to define normality. Consequently, artificial intelligence (AI)-based anomaly detection studies have been actively conducted to solve this problem. However, such studies require manual analysis based on expert knowledge of each site during the preprocessing stage to extract the learning features from the collected network data. To solve the above problems, this study proposes a protocol reverse engineering method corresponding to the preprocessing stage of exiting AI studies. Through this method, existing AI-based anomaly detection studies can directly use the collected network data to learn normality without expert knowledge of the site. Furthermore, non-polling or reporting network operating environments that are rarely studied in the manufacturing security domain are targeted. Finally, we propose an anomaly detection method that uses an external signature, time information, the pattern of time intervals, and classified messages. Thus, the proposed method can detect anomalies in the encrypted contents of the manufacturing protocols.
A Robust Generalized $t$ Distribution-Based Kalman Filter

Since the Gaussian-inverse Wishart hierarchical form has similar properties to Student’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> distribution, we name it generalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> distribution in this article. Based on this, a robust generalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> distribution-based Kalman filter (GTKF) is proposed for state-space models that are eroded by state and measurement outliers. Different from the existing algorithms, the state transition and measurement likelihood densities are directly modeled as generalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> distributions by employing the one-step smoothing strategy.An analytical closed-form solution can be obtained through the variational inference approach. Moreover, two variants of the proposed GTKF are also presented to apply to different engineering scenarios. Simulation and experimental examples demonstrate that the proposed GTKFs yield improved robustness over the existing algorithms.
Exploration indicators of the Jiama porphyry–skarn deposit, southern Tibet, China

Whole-rock geochemical data and compositional balance analysis (CoBA), combined with a magnetic susceptibility model obtained from a high-precision magnetic survey, are useful exploration tools in the Jiama porphyry–skarn deposit, southern Tibet, China. In this paper, three innovative aspects of the model are described that have assisted exploration of the Jiama deposit: an improved geological understanding of a thrust–nappe structure, hydrothermal fluid pathways, and magma mixing; data- and knowledge-driven compositional balance analysis of whole-rock geochemistry; and a high-precision magnetic survey of the deposit. The local strata provided the materials and natural trap for skarn formation, and a site for fluid circulation and porphyry ore precipitation. An interlayer detachment zone, gliding nappe structure, and cylindrical fissure system provided channels for fluid migration, and the Niumatang Anticline provided the accommodation space for the porphyry system. Miocene magma mixing and crustal contamination led to the development of complex mineralization in the Jiama area. The geochemical signatures obtained from CoBA, and the spatial distribution of the strata, structures, igneous rocks, alteration, mineralization, and degree of erosion enable integration of the geochemical and geological data. The “demagnetization skylight” identified by magnetic anomalies and a magnetic susceptibility model obtained with UBCmag3D software revealed the location of a concealed porphyry system. A magnetic susceptibility of 0.014–0.016 was found to identify the location of an interlayer skarn. Our exploration indicators of the Jiama porphyry–skarn deposit can be used to guide ore exploration throughout the region.
End-to-End Speaker Verification via Curriculum Bipartite Ranking Weighted Binary Cross-Entropy

End-to-end speaker verification achieves the verification through estimating directly the similarity score between a pair of utterances, which is formulated as a binary (i.e., target versus non-target) classification problem. Unlike the stage-wise method, an end-to-end verification approach optimizes the evaluation metrics directly and its output layer is parameter-free, which can save great computing and memory resources. However, there are two important issues that need to be meticulously handled in training an end-to-end speaker verification model. The first one is how to deal with severely imbalanced trials, i.e., the number of target trials is much smaller than that of nontarget trials, and the other is about how to handle easy trials that do not help improve the model in training. To circumvent these two issues, we propose in this paper a binary cross-entropy (BCE) type of loss function and present a method to train the deep neural network (DNN) models based on the proposed loss function for end-to-end speaker verification. The training process employs a bipartite ranking method to deal with the trial imbalance problem and a curriculum learning method to help improve both the training stability and performance of the model by selecting non-target trials from easy to hard ones gradually along the convergence process. Since the training process employs bipartite ranking and curriculum learning and the loss function is of the generalized BCE form, we name the new approach \textit{curriculum bipartite ranking weighted binary cross-entropy} (CBRW-BCE). Experimental results show that the model trained with CBRW-BCE not only achieves the state-of-the-art performance but is also well calibrated.
Towards Trustworthy AutoGrading of Short, Multi-lingual, Multi-type Answers

Abstract Autograding short textual answers has become much more feasible due to the rise of NLP and the increased availability of question-answer pairs brought about by a shift to online education. Autograding performance is still inferior to human grading. The statistical and black-box nature of state-of-the-art machine learning models makes them untrustworthy, raising ethical concerns and limiting their practical utility. Furthermore, the evaluation of autograding is typically confined to small, monolingual datasets for a specific question type. This study uses a large dataset consisting of about 10 million question-answer pairs from multiple languages covering diverse fields such as math and language, and strong variation in question and answer syntax. We demonstrate the effectiveness of fine-tuning transformer models for autograding for such complex datasets. Our best hyperparameter-tuned model yields an accuracy of about 86.5%, comparable to the state-of-the-art models that are less general and more tuned to a specific type of question, subject, and language. More importantly, we address trust and ethical concerns. By involving humans in the autograding process, we show how to improve the accuracy of automatically graded answers, achieving accuracy equivalent to that of teaching assistants. We also show how teachers can effectively control the type of errors made by the system and how they can validate efficiently that the autograder’s performance on individual exams is close to the expected performance.
Flight Trajectories Optimization of Fixed-Wing UAV by Bank-Turn Mechanism

This paper addresses an optimization of Unmanned Aerial Vehicle (UAV) flight trajectories by bank-turn mechanism for a fixed-wing UAV at a constant altitude. The flight trajectories should be optimal and stay in the UAV flight operational area. The maneuver planning is conducted in two steps, which are UAV path planning and UAV flight trajectory planning. For the first step, the Bezier curve is employed as a maneuvering path. The path planning optimization objective is to minimize the path length while satisfying maximum curvature and collision avoidance constraints. The flight trajectories optimization objective is to minimize maneuvering time and load factor considering, minimum/maximum speed, minimum/maximum acceleration, maximum roll angle, maximum turn rate, and aerodynamics constraints. The variable speed trajectory generation is developed within allowable speed zone considering these UAV flight constraints by employing meta-heuristic optimizations. Results show that the PSO have outperformed the GA and the GWO for both steps of path planning and trajectory planning. The variable speed has succeeded in reducing the load factor during the bank-turn mechanism using the Bezier curve. The variable speed is recommended to be conducted when the result of the maneuvering path involve the lower turning radius. A simultaneous on arrival target mission has also succeeded to be conducted using the combination of the variable speed and constant speed strategies.
Augmented Reality (AR) in Language Learning: A Principled Review of 2017-2021

With the changing and developing technology, numerous new systems that cannot be given up and have a great impact are becoming a part of our lives. Augmented reality (AR) is one of these systems which can be claimed as magic. AR has been gaining considerable interest in the field of education, and language learning since Pokémon Go, the most well-known AR location-based game, was launched. There has been much evidence to support the effectiveness of AR in education. The present study set out to review in detail the current research studies published in SSCI/ESCI/ERIC indexed privileged journals in the field of language education and technology within the last five years, on the topic of augmented reality in language learning. The selection of articles was made on the basis of empirical studies on the use of AR in language education. Surprisingly, only seventeen studies seem to fall into the said category in these selected, SSCI/ESCI/ERIC journals. On the grounds that there emerges a lack of such studies and complying with the nature of the present study, all these articles have been reviewed in detail, in terms of three aspects: (1) research features, such as research focus, design, data collection tools, study, and educational context; (2) the use of AR, such as theory behind AR use, and AR applications used in the studies; (3) results of the studies. The results showed that much emphasis has been on vocabulary learning, the learning effectiveness, attitudes, and motivations of language learners with scant attention given to language skills. There is a current paucity of empirical research focusing specifically on different language skills. Hence, it is desirable to examine the effects of AR on different language skills of language learners.
Efficient Data Integrity Auditing Supporting Provable Data Update for Secure Cloud Storage

Cloud storage, an economically attractive service offered by cloud service providers (CSPs), has attracted a large number of tenants. However, because the ownership and management of outsourced data are separated, outsourced data faces a lot of security challenges, for instance, data security, data integrity, data update, and so on. In this article, we primarily investigate the problem of efficient data integrity auditing supporting provable data update in cloud computing environment. Subsequently, on the basis of the Merkel sum hash tree (MSHT), we introduce an efficient outsourced data integrity auditing scheme. Our designed scheme could synchronously meet the requirements of provable data update and data confidentiality without dependency on a third authority. At the same time, the numerical analysis shows that the computing complexity logarithmically grows with the number of outsourced subfiles. Finally, a prototype implementation is developed to simulate our designed scheme and measure its performance. The consequences of experiments present that compared with some previous solutions, our designed scheme has much more attractive practicality and higher efficiency in practical applications.
R-Net: A novel fully convolutional network–based infrared image segmentation method for intelligent human behavior analysis

Infrared weak-illumination image segmentation is a complex task in computer vision and intelligence education. In this work, we proposed a novel convolutional neural network for infrared image segmentation, which can overcome the problems of motion blur, low resolution, and random noise. In particular, we proposed a new loss function that considers the shape, area, and centroid during learning and integrates them into a simple deep learning model. We evaluated our method on our annotated dataset (Night Human in the Wild Scene dataset), comprising approximately 22,338 natural low-quality images corrupted by mixed noises, and several public datasets with high-resolution images, such as ADE20K and PASCAL VOC. Experimental results show that the proposed R-Net outperforms the existing infrared image segmentation models.
AGCNNs: Attention-guided convolutional neural networks for infrared head pose estimation in assisted driving system

Infrared head pose estimation (HPE) is considered as the important step in understanding drivers' intention. Although significant progress has been made in the HPE tasks, previous literature rarely pays attention to the driving environment. In this study, we focus on the similarities of head pose image information and construct the head pose label. The infrared camera is commonly located in the center of the rearview mirror of the car, which determines the angle range for collecting the head pose. According to the angle range values, the head pose distribution label is learned as the two-dimensional Gamma distribution. Given that the camera cannot be mounted directly in front of the line of sight, traditional HPE methods are not applicable. The contributions of adjacent pose images for the central image pose estimation vary. The constructed Gamma distributions are considered as the groundtruth labels and guide the learning process. Experimental results show that the developed model outperforms state-of-the-art approaches and obtains robust performance on several public HPE datasets.
Active Exploration for Neural Global Illumination of Variable Scenes

Neural rendering algorithms introduce a fundamentally new approach for photorealistic rendering, typically by learning a neural representation of illumination on large numbers of ground truth images. When training for a given variable scene, such as changing objects, materials, lights, and viewpoint, the space \( \mathcal {D} \) of possible training data instances quickly becomes unmanageable as the dimensions of variable parameters increase. We introduce a novel Active Exploration method using Markov Chain Monte Carlo, which explores \( \mathcal {D} \) , generating samples (i.e., ground truth renderings) that best help training and interleaves training and on-the-fly sample data generation. We introduce a self-tuning sample reuse strategy to minimize the expensive step of rendering training samples. We apply our approach on a neural generator that learns to render novel scene instances given an explicit parameterization of the scene configuration. Our results show that Active Exploration trains our network much more efficiently than uniformly sampling and, together with our resolution enhancement approach, achieves better quality than uniform sampling at convergence. Our method allows interactive rendering of hard light transport paths (e.g., complex caustics), which require very high samples counts to be captured, and provides dynamic scene navigation and manipulation, after training for 5 to 18 hours depending on required quality and variations.
Maintaining Scalability in Blockchain

The history of cryptocurrencies like Bitcoin and Litecoin and even the meme coins like Dogecoin have developed fast. Blockchain, the technology that underpins these digital currencies, has drawn considerable interest from the academic community and the business community, including on Twitter. Aside from security and privacy, the blockchain has several other advantages. The average time it takes for a transaction to be validated and stored in each peer node so that it cannot be reversed or revoked is used to measure the performance of blockchain networks. Even though this is referred to as "throughput," it should not be confused with the number of transactions processed at any given time. To put it another way, the ability of a blockchain network to handle more transactions and more nodes is called scalability. Yet, scalability is problematic to reach a more robust platform of users and transaction load. We will focus on scalability issues and ways to maintain them efficiently. We will also go through the research challenges and future work for blockchain. Hence, researchers working on blockchain have aimed for a lower level of scalability to let the network's throughput grow sub-linearly because the size of the network increases. The resulting schemes are mostly mentioned as scale-out blockchains. We have heard of Sharding, Lightning Network or Ethereum Plasma and Matic; they all will be considered as scale-out solutions to the matter of blockchain scalability.