Reviewerly/exHarmony huggingface dataset:
Learning Spatial Regularization Correlation Filters With the Hilbert-Schmidt Independence Criterion in RKHS for UAV Tracking

Unmanned aerial vehicle (UAV) object tracking have been widely employed in military and civilian tasks and is very challenging due to factors such as aerial view, low resolution, background clutter, and small scale. Currently, many correlation filter (CF) methods for UAV object tracking introduce predefined regularization terms to improve the discriminative power. However, the predefined spatial constraints neither exploit the changing foreground and background information during tracking nor adapt to complex video attributes, such as background clutter, illumination variation, and viewpoint change. In this work, a novel CF tracking model is proposed to learn the spatial regularization correlation filters with the Hilbert-Schmidt independence criterion (HSIC_SRCF) in the reproducing kernel Hilbert space (RKHS) for UAV tracking. First, HSIC_SRCF learns the high confidence spatial weight coefficient for the filters, which encourages the filters to focus on the more reliable region. Second, the alternative direction method of multipliers (ADMM) can be used to optimize the proposed model, and the subproblems of the model have closed-form solutions. Third, the model update scheme uses RKHS and the peak-to-sidelobe ratio to obtain the confidence of the object bounding box and reduce the effect of background noise for the UAV object. The ablation study shows that the proposed approach achieves a precision of 0.699 and a success rate of 0.607, which are 2.8% higher and 2.2% higher, respectively, than the baseline AutoTrack on the UAV123@10fps. Experimental results on four popular UAV datasets, including VisDrone2018-test-dev, UAV123@10fps, UAV123, and UAVDT, show that our approach performs favorably compared with current state-of-the-art methods.
Blockchain-Based Decentralized and Lightweight Anonymous Authentication for Federated Learning

Federated learning (FL) is a promising technology for achieving privacy-preserving edge intelligence and has attracted extensive attention from industry and academia. However, in the FL training process, the server directly aggregates local models from mobile devices, which poses serious privacy and security threats. The identity authentication mechanism can provide FL with local model integrity and source authentication. However, the existing schemes are centralized, and most of them are computationally expensive, resulting in limited performance. To address these issues, this paper proposes a decentralized and lightweight anonymous FL identity authentication scheme, namely DAFL. In our scheme, we first design a decentralized and simplified storage FL authentication framework by combining the directed acyclic graph (DAG) blockchain and accumulator. Then, we propose a lightweight digital signature algorithm that supports batch verification for authentication. Finally, nodes interact through pseudonyms to achieve anonymous communication, and the trusted authority (TA) can track and recover the real identities of nodes when malicious behavior occurs. We theoretically prove the security of the proposed DAFL. The extensive experiments demonstrate that DAFL achieves lower authentication overhead and better convergence performance compared to existing authentication schemes and vanilla FL systems.
Free gs-Monoidal Categories and Free Markov Categories

Categorical probability has recently seen significant advances through the formalism of Markov categories, within which several classical theorems have been proven in entirely abstract categorical terms. Closely related to Markov categories are gs-monoidal categories, also known as CD categories. These omit a condition that implements the normalization of probability. Extending work of Corradini and Gadducci, we construct free gs-monoidal and free Markov categories generated by a collection of morphisms of arbitrary arity and coarity. For free gs-monoidal categories, this comes in the form of an explicit combinatorial description of their morphisms as structured cospans of labeled hypergraphs. These can be thought of as a formalization of gs-monoidal string diagrams ($=$term graphs) as a combinatorial data structure. We formulate the appropriate $2$-categorical universal property based on ideas of Walters and prove that our categories satisfy it. We expect our free categories to be relevant for computer implementations and we also argue that they can be used as statistical causal models generalizing Bayesian networks.
Improving the entrepreneurial ability of rural migrant workers returning home in China: Study based on 5,675 questionnaires

Innovation and entrepreneurship play a key role in the development of a country's economy and society. In recent years, along with the comprehensive implementation of China's rural revitalization strategy, the government has issued many policies to support and benefit farmers, and the investment environment in villages has been greatly improved, attracting more and more migrant workers to return to their hometowns to start businesses. Improving the entrepreneurial ability of China's returning migrant workers is very important. A review of the previous literature showed that both internal personal factors and external environmental factors can affect the entrepreneurial ability of entrepreneurs. Combining the results of the questionnaire survey, this paper incorporates eight internal personal factors and eight external environmental factors into the index system for assessing entrepreneurial ability and uses a combination of SWOT analysis and AHP analysis to prioritize the factors influencing the entrepreneurial ability of returning migrant workers in China. The results are as follows: (1) The factors influencing the entrepreneurial ability of returnee entrepreneurs could be divided into two types: the individual factors of returnee entrepreneurs, and the external environmental factors. (2) Among the internal factors, technical experience and business management ability are the two main factors influencing entrepreneurial activity. (3) Among the external environmental factors, the opportunity for industrial structure adjustment and low technical content would have a higher influence on the entrepreneurial activity of returnee entrepreneurs. Accordingly, this study proposes some policy recommendations on how to improve the entrepreneurial ability of returnee entrepreneurs. Finally, the research methodology of this paper can be used as a reference for other scholars, and the research results can also be used for administrators to develop relevant policies to better improve the entrepreneurial ability of returnee entrepreneurs.
Anomaly detection with correlation laws

Datasets from different domains usually contain data defined over a wide set of attributes among which various degrees of correlation exist. The identification of data objects not complying with these hidden correlations is a formidable task. Moreover, often attributes may play different roles in applications. Specifically, some features can be perceived as independent variables which are responsible for the definition of a context in which a dependent variable exhibits anomalous behaving values. Hence, in this work we focus on the detection of data objects showing an anomalous behavior on a subset of attributes, called behavioral, w.r.t. some other ones, called contextual. As a main contribution, we design a model to describe the correlation laws hidden in data distributions over pairs of behavioral–contextual attributes. We introduce a probability measure aimed at scoring subsequently observed objects based on how much their behavior deviates from the detected correlation laws. We test our method on both synthetic and real dataset to demonstrate its effectiveness and show its ability in outperforming some competitors. Moreover, we discuss a case study in the field of gene expression data analysis to prove that it can provide a valuable contribution when dealing with those scenarios in which the features are much more abundant than the samples available for the analysis.
An INS/DVL integrated navigation filtering method against complex underwater environment

In this paper, a filtering method against complex underwater environment is proposed for the inertial navigation system/doppler velocity log (INS/DVL) of the unmanned underwater vehicle (UUV). The method has two stages. The first stage is the pre-cruise. In this stage, the martingale distance is constructed. And according to chi-square test, the two thresholds can be obtained, which are used to determine the complexity of the environment. Then, these thresholds are used in cruise of UUV. During cruise, the martingale distance is similarly constructed. The environment is judged in real time according to chi-square test. When the martingale distance exceeds the first threshold, the environment is considered to be of high complexity. The robust method based on covariance matching is used. When the martingale distance lies between the first and second thresholds, the UUV is considered to be in a low complex environment, and the adaptive method is used. Otherwise, the conventional nonlinear filter is performed. Finally, the results of field experiment show that the method can avoid the over compensation of parameters in stable case from conventional methods, and comprehensively solve the problem of degradation of accuracy due to different environment.
Knowledge-aware sequence modelling with deep learning for online course recommendation

The recent boom in online courses has necessitated personalized online course recommendation. Modelling the learning sequences of users is key for course recommendation because the sequences contain the dynamic learning interests of the users. However, current course recommendation methods ignore heterogeneous course information and collective sequential dependency between courses when modelling the learning sequences. We thus propose a novel online course recommendation method based on knowledge graph and deep learning which models course information via a course knowledge graph and represents courses using TransD. It then develops a bidirectional long short-term memory network, convolutional neural network, and multi-layer perceptron for learning sequence modelling and course recommendation. A public dataset called MOOCCube was used to evaluate the proposed method. Experimental results show that: (1) employing the course knowledge graph in learning sequence modelling improves averagely the performance of our method by 13.658%, 16.42%, and 15.39% in terms of HR@K, MRR@K, and NDCG@K; (2) modelling the collective sequential dependency improves averagely the performance by 4.11%, 6.37%, and 5.47% in terms of the above metrics; and (3) our method outperforms popular methods with the course knowledge graph in most cases.
Securing the Internet of Things-enabled smart city infrastructure using a hybrid framework

Since the Internet of Things (IoT) employs a diverse range of new technologies, it is impossible to build a single recommended design adopted as a master plan for all possible requests. Some possible IoT application areas have not been looked into yet or do not have enough information on how to approach them. This shows that more research needs to be done in this difficult area to find new and potentially big benefits for society. Although smart cities offer residents and providers of capital several advantages, there are numerous ways that breaches could compromise the safety and security of individuals. As a result, several different recommendation designs can coexist in the IoT. This research examines the effects of ethics and technology on the security of IoT-enabled systems in smart city infrastructure. Hence provides a secure IoT network architecture for smart cities combining blockchain and deep learning to safeguard privacy and credibility. This research presents a Secure Smart City Infrastructure using Blockchain and Deep Learning (SSCI-BDL) framework to ensure privacy protection and trustworthiness among IoT communication in smart cities. This framework involves the blockchain network for security management in the smart city infrastructure. This framework integrates the deep learning model with an optimization algorithm that maintains efficient resource utilization in the smart city infrastructure. The simulation results show that the system has high security of 99.5% and the lowest latency rate of 4.1% compared to existing models. Overall, the proposed framework's efficiency gives the highest rate of 99.8%.
An Overview of Quantum-Safe Approaches: Quantum Key Distribution and Post-Quantum Cryptography

Common cryptographic algorithms may no longer be considered secure under future quantum computers, which will cause a serious threat to network security. Hence, investigating quantum-safe cryptography and evaluating the safety of traditional cryptographic algorithms are essential and have become urgent demands. In this paper, we study the quantum-safe cryptography approaches and conduct a survey of the various quantum key distribution protocols, simulation tools, and commercial applications. In addition, we provide a comparison of the first four post-quantum cryptographic algorithms recently announced by the National Institute of Standards and Technology. The challenges of quantum-safe approaches will be discussed, which aim to find future research directions in quantum cryptography.
Technical debt in systems engineering—A systematic literature review

Abstract The metaphor of “technical debt” is used in software engineering to describe technical solutions that may be pragmatic in the near‐term but may have a negative long‐term impact. Similar decisions and similar dynamics are present in the field of systems engineering. This work investigates the current body of knowledge to identify if, and how, the technical debt metaphor is used within the systems engineering field and which systems engineering lifecycle stages are most susceptible to technical debt. A systematic literature review was conducted on 354 papers in February 2022, of which 18 were deemed relevant for inclusion in the study. The results of the systematic literature review show that the technical debt metaphor is not prevalent within systems engineering research and that existing research is limited to specific fields and theoretical discussions. This paper concludes with recommendations for future work to establish a research agenda on the identification and management of technical debt within systems engineering.
A systematic review of global research on natural user interface for smart home system

The smart home system (SHS) equipped with intelligent sensors and actuators aims at providing seamless services for users. The natural user interface (NUI) enables intuitive interaction between users and the SHS, significantly lowering the barrier to entry and enhancing user experience. However, a comprehensive evaluation of research on NUI for the SHS to provide a valuable synthesis of existing research and informing future research directions remains unavailable. This study attempts to address this gap by systematically evaluating 1979 relevant journal and conference articles from the Web of Science Core Collection and Scopus, and those articles with a weak connection to the topic and poor data integrity were excluded. Finally, 1330 articles were chosen to for analysis in terms of contributions, critical papers, co-occurrence, and clustering using scientometric techniques. It was revealed that, with the help of advancing technologies, the research field of NUI aims to enrich the variety of interactive device types, improve the accuracy of interpreting and responding to user behavior, and optimize the compatibility with diverse user demographics. Besides, a conceptual framework summarizing typical types of NUI for the SHS was proposed, and strengths and weaknesses of each NUI were discussed for investigating specific future directions. Furthermore, four general research directions (strategies for promoting effective multimodal NUI, adoption of artificial intelligence for better performance, exploration of mixed reality applications in the SHS, and standardization of protocols in the smart home ecosystem) were also identified for academics and practitioners in their future research and practice.
Context-driven ontology-based risk identification for onshore wind farm projects: A domain-specific approach

Risk identification is a knowledge-based process that requires the time-consuming and laborious identification of project-specific risk factors. Current practices for risk identification in construction rely heavily on an expert’s subjective knowledge of the current project and of similar historical projects to determine if a risk may affect the project under study. When quantitative risk-related data are available, they are often stored across multiple sources and in different types of documents complicating data sharing and reuse. The present study introduces an ontology-based approach for construction risk identification that maps and automates the representation of project context and risk information, thereby enhancing the storage, sharing, and reuse of knowledge for the purpose of risk identification. The study also presents a novel wind farm construction project risk ontology that has been validated by a group of industry experts. The resulting ontology-based risk identification approach is able to accommodate project context in the risk identification process and, through implementation of the proposed approach, has identified risk factors that affect the construction of onshore wind farm projects.
Single-Shot Three-Dimensional Measurement by Fringe Analysis Network

Fringe projection profilometry (FPP) has been broadly applied in three-dimensional (3D) measurements, but the existing multi-shot methods, which mostly utilize phase-shifting techniques, are heavily affected by the disturbance of vibration and cannot be used in dynamic scenes. In this work, a single-shot 3D measurement method using a deep neural network named the Fringe Analysis Network (FrANet) is proposed. The FrANet is composed of a phase retrieval subnetwork, phase unwrapping subnetwork, and refinement subnetwork. The combination of multiple subnetworks can help to recover long-range information that is missing for a single U-Net. A two-stage training strategy in which the FrANet network is pre-trained using fringe pattern reprojection and fine-tuned using ground truth phase maps is designed. Such a training strategy lowers the number of ground truth phase maps in the data set, saves time during data collection, and maintains the accuracy of supervised methods in real-world setups. Experimental studies were carried out on a setup FPP system. In the test set, the mean absolute error (MAE) of the refined absolute phase maps was 0.0114 rad, and the root mean square error (RMSE) of the 3D reconstruction results was 0.67 mm. The accuracy of the proposed method in dynamic scenes was evaluated by measuring moving standard spheres. The measurement of the sphere diameter maintained a high accuracy of 84 μm at a speed of 0.759 m/s. Two-stage training only requires 8800 fringe images in data acquisition, while supervised methods require 96,000 fringe images for the same number of iterations. Ablation studies verified the effectiveness of two training stages and three subnetworks. The proposed method achieved accurate single-shot 3D measurements comparable to those obtained using supervised methods and has a high data efficiency. This enables the accurate 3D shape measurement of moving or vibrating objects in industrial manufacturing and allows for further exploration of network architecture and training strategy with few training samples for single-shot 3D measurement.
A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure

The unauthorized usage of various services and resources in cloud computing is something that must be protected against. Authentication and access control are the most significant concerns in cloud computing. Several researchers in this field suggest numerous approaches to enhance cloud authentication towards robustness. User names and associated passwords have been a common practice for long as Single Factor Authentication. However, advancements in the speed of computing and the usage of simple methods, starting from the Brute Force technique to the implementation of advanced and efficient crytographic algorithms, have posed several threats and vulnerabilities for authentication systems, leading to the degradation of their effectiveness. Multi-factor authentication has emerged as a robust means of securing the cloud using simultaneous and multiple means of authentication factors. This employs multiple levels of cascaded authentication checks. This paper covers an extensive and systematic survey of various factors towards their adoption and suitability for authentication for multi-factor authentication mechanisms. The inference drawn from the survey is in terms of arriving at a unique authentication factor that does not require any additional, specialized hardware or software for multi-factor authentication. Such authentication also uses the distinct biometric characteristics of the concerned user in the process. This arrangement augments the secured and robust user authentication process. The mechanism is also assessed as an effective means against impersonation attacks.
Protecting Digital Images Using Keys Enhanced by 2D Chaotic Logistic Maps

This research paper presents a novel digital color image encryption approach that ensures high-level security while remaining simple and efficient. The proposed method utilizes a composite key r and x of 128-bits to create a small in-dimension private key (a chaotic map), which is then resized to match the color matrix dimension. The proposed method is uncomplicated and can be applied to any image without any modification. Image quality, sensitivity analysis, security analysis, correlation analysis, quality analysis, speed analysis, and attack robustness analysis are conducted to prove the efficiency and security aspects of the proposed method. The speed analysis shows that the proposed method improves the performance of image cryptography by minimizing encryption–decryption time and maximizing the throughput of the process of color cryptography. The results demonstrate that the proposed method provides better throughput than existing methods. Overall, this research paper provides a new approach to digital color image encryption that is highly secure, efficient, and applicable to various images.
Exploration of deep learning models for localizing bare-hand in the practical environment

Detection and tracking of the bare-hand are vital for gesture-based human–computer interaction systems. To detect and track the bare-hand efficiently in successive frames is challenging when the motion blur occurs due to the speed or acquisition device. In addition, the variations like pose, position, scale, rotation, illumination, occlusion, speed, and impostors make detection more complex. The traditional bare-hand detectors utilize rectangular windows to train the detector, which results in the background feature domination effect. We present a holistic approach to DAWT for bare-hand localization by combining detection and tracking to overcome this. This combination reduces the system's computational time and provides near-real-time performance. In the detection module, HandSNet, and YOLO-H are implemented by customizing the SqueezeNet network. This network contains only 0.35 million parameters. Followed by that, the Kalman filter and point tracker are utilized in the tracking module. For the depth analysis of the deep architecture models on the bare-hand, we have also implemented DCNN, RCNNs, and YOLO models. This comparative analysis gives a brief overview of the various deep learning models' performance for bare-hand detection. To evaluate the ability and robustness of the proposed models, the NITS hand gesture database, OUHands, EgoHands, Oxford hand, and EgoGesture database are considered.