Reviewerly/exHarmony huggingface dataset:
Modified Kalman Filtering for Stochastic Nonlinear Systems Under Non-Gaussian–Lévy Noise and Cyber Attacks

This article studies the filtering problem for a class of stochastic nonlinear system under non-Gaussian–Lévy noise and cyber attacks, where the denial-of-service (DoS) attacks and the false data-injection (FDI) attacks are both considered. Since the covariance of the Lévy noise is unknown and infinite, the standard Kalman filter fails to estimate system states. By exploiting saturation functions, a modified Kalman filter is proposed, where the extremely large values of the measurement outputs caused by the Lévy noises can be clipped. In the presence of Lévy noise and cyber attacks, an upper bound for the error covariance is guaranteed and can be minimized via designing the filter parameter. Besides, a sufficient condition is provided to guarantee the boundedness of the upper bound, and the convergence analysis of the filtering error is presented. Finally, the simulation results are given to verify the algorithm.
A Practical and Efficient Blockchain-Assisted Attribute-Based Encryption Scheme for Access Control and Data Sharing

Attribute-based encryption (ABE) is a powerful encryption scheme with flexible access control over encrypted data that has been widely adopted in cloud computing scenarios to facilitate data sharing. However, despite convenience and efficiency provided by data sharing based on cloud, it is commonly vulnerable to issues like key abuse (namely, illegal key sharing by user or key distribution by authority) and key escrow (namely, illegal decryption by ABE authority). Hence, exploring a more secure ABE scheme that can be key abuse and key escrow resistant is crucial. Furthermore, data modification that happens in cloud storage and outsourced computation is also a challenge for the cloud-based data sharing schemes. To handle the above issues, in this paper, we propose a secure and efficient data sharing scheme based on attribute-based encryption (ABE) and blockchain equipped with InterPlanetary File System (IPFS). In particular, we show that the large-universe ABE with outsourced decryption (LU-ABE-OD) scheme proposed by Ning et al. is vulnerable to key escrow attack, which is not secure enough in the data sharing scenario. Therefore, based on their basic proposal, we construct an improved multi-authority LU-ABE-OD scheme to encrypt personal data, which are stored in the IPFS system while blockchain is applied to store the hash value returned by IPFS and be responsible for the outsourced decryption. As a result, our scheme greatly reduces the decryption overheads of the user while risks of key abuse and key escrow can be settled. Meanwhile, the introduction of IPFS significantly reduces the storage burden on chain without data tampering problem. Through theoretical analysis and experimental simulation, we prove the feasibility, security, and efficiency of our scheme.
Deep Learning-based Eye Gaze Estimation for Military Aviation

Eye gaze estimation and cognitive load estimation of the pilot garnered great attention in the aviation domain due to the numerous possible applications. Earlier works proposed to use eye gaze tracking to interact with multi-function displays (MFDs) and head-up display (HUD) in place of traditional interaction devices. Further, researchers also investigated the accuracy of commercially available gaze trackers during in-flight conditions by conducting studies under various actual flying scenarios like varying g-conditions and different maneuvers. In this paper, we first studied the functioning of a wearable eye gaze tracker using two one-hour long flights. Pilots undertook various challenging maneuvers during the flight. We analyzed the gaze tracking data recorded using the gaze tracker and observed that the &#x223C;42&#x0025; and &#x223C;31&#x0025; of flight duration resulted in loss of gaze data in flight1 and flight 2 respectively. Further, we analyzed unsynchronized raw data and observed that both flights recorded error-prone gaze samples for &#x223C;51&#x0025; of the flight duration. We hypothesized and verified that this loss of data is caused due to the higher levels of illumination on eyes and limited field of view provided by the gaze tracker in the vertical direction. The data from both flights supported our hypothesis and it was evident that the current field of view offered by the eye tracking glasses is not sufficient for the military aviation. We addressed the first limitation by using Machine learning approach. We built an end-to-end gaze estimation system which takes IR-eye images recorded using wearable eye tracking glasses to predict the gaze point. We sampled 10K images with proper ground truth gaze points. Our dataset contained wide variation in illumination and pupil dilation. We observed that the proposed approach using a convolutional neural network resulted in low gaze estimation errors and consistent gaze predictions.
Reviewer reliability and XGboost whale optimized sentiment analysis for online product recommendation

Nowadays, people always use online promotions to know about best shops to buy the best products. This shopping experience and shopper’s opinion about the shop can be observed by the customer-experience shared on social media. A new customer when searching a shop needs information about manufacturing date (MRD) and manufacturing price (MRP), offers, quality, and suggestions which are only provided by the previous customer experience. Several approaches were used previously for predicting the product details, but no one approach provides accurate information. To overcome these issues, Reviewer Reliability and XGboost whale Optimized Sentiment Analysis for Online Product Recommendation is proposed in this manuscript.Initially, Amazon Product recommendation datathe data are preprocessed and given to XGboost Classifier that classifies the product recommendation result as, good, bad and average. Generally the XGboost Classifier does not reveal any adoption of optimization techniques for computing the optimal parameters for assuring accurate classification of product recommendation. Therefore in this work, proposed Whale optimization algorithm utilized to optimize the weight parameters of the XGboost. Then the proposed model is implemented in MATLAB. The proposed method attains 18.31%, 12.81%, 45.75%, 26.97% and 25.55% lower Mean Absolute error, 18.31%, 12.81%, 27.97%, 25.97%, and 25.55% higher Mean absolute percentage error and 15.31%, 10.33%, 25.86%, 22.86% and 15.22% lower Mean Square Error than the existing methods.
System Analysis of the Learning Behavior Recognition System for Students in a Law Classroom: Based on the Improved SSD Behavior Recognition Algorithm

Classroom teaching activities have always been the focus of research in the field of pedagogy. The main body of classroom teaching activities is students, and students’ classroom behavior status can reflect classroom efficiency to a certain extent, making it an important reference index for classroom quality assessment. With the rapid development of artificial intelligence, school education is gradually becoming more intelligent. At present, most of the classrooms are equipped with video equipment. These videos record the real behavior status of the students in the classroom. For example, by analyzing the data, combining artificial intelligence, deep learning, and other related technologies with education to develop behavioral intelligence, the analysis system has a certain positive effect on helping the reform of classroom education. This study proposes an improved SSD behavior recognition model. The network model is optimized and the model convergence speed is accelerated based on the RMSProp optimization algorithm Through a database of 2,500 images of five behaviors, including raising hands, sitting up, writing, sleeping, and playing with mobile phones, and using them as object detection datasets, we use the OpenCV library to extract frames from classroom screen recording videos as image data sources for student behavior recognition and face recognition. Finally, an improved method is proposed to change the virtual network to MobileNet and complete the fusion function. The results show that compared with the traditional SSD method, the improved model has a significantly improved effect in recognizing small objects and the recognition speed is not significantly reduced.
A multi-object tracker using dynamic Bayesian networks and a residual neural network based similarity estimator

In this paper we introduce a novel multi-object tracker based on the tracking-by-detection paradigm. This tracker utilises a Dynamic Bayesian Network for predicting objects' positions through filtering and updating in real-time. The algorithm is trained and then tested using the MOTChallenge1 challenge benchmark of video sequences. After initial testing, a state-of-the-art residual neural network for extracting feature descriptors is used. This ResNet feature extractor is integrated into the tracking algorithm for object similarity estimation to further enhance tracker performance. Finally, we demonstrate the effects of object detection on tracker performance using a custom trained state of the art You Only Look Once (YOLO) V5 object detector. Results are analysed and evaluated using the MOTChallenge Evaluation Kit, followed by a comparison to state-of-the-art methods.
Multi-perspective human robot interaction through an augmented video interface supported by deep learning

As the world surpasses a billion cameras [1] and their coverage of the public and private spaces increases, the possibility of using their visual feed to not just observe, but to command robots through their video becomes an ever more interesting prospect. Our work deals with multi-perspective interaction, where a robot autonomously maps image pixels from reachable cameras to positions on its global coordinate space. This enables an operator to send the robot to specific positions in a camera with no manual calibration. Furthermore, robot information, such as planned paths, can be used to augment all affected camera images with an overlayed projection of their visual information. The robustness of this approach has been validated in both simulated and real world experiments.
Incidental Encounters with Robots

This paper reports on a field study of more than three hundred ‘incidental’ human-robot encounters in a public outdoor space. We have used an ethnomethodologically informed ethnographic method, employing breaching experiments and membership categorization analysis to reveal how Incidentally Co-present Persons (InCoPs) interact with and make sense of robots in an urban environment.
An Online Unsupervised Streaming Features Selection Through Dynamic Feature Clustering

Streaming feature selection (SFS) is emerging as a key research direction that addresses the nonstationary property of feature streams when the sample size is fixed. Most existing SFS techniques are supervised methods and ignore the label scarcity. Real-world datasets are typically unlabeled and the labeling costs are expensive. Although some unsupervised SFS approaches are proposed, these approaches are either limited to the homogeneous feature types or require substantial computational complexity. To address these problems, we propose an online unsupervised feature selection framework using dynamic feature clustering in this article. We derived a recursive density lower bound to estimate the density distribution of feature streams and developed a density-based dynamic clustering method to perform the online feature stream clustering for exploring feature redundancy. An unsupervised online feature relevance maximization and redundancy minimization strategy is introduced to extract a subset of important features with low redundancy from the feature stream. Experimental results on 13 well-known benchmark datasets and comparison studies with 7 state-of-the-art supervised SFS methods demonstrate that the proposed unsupervised method provides statistically comparable performance with the supervised SFS techniques while the label information is unknown.
Research Overview on Edge Detection Algorithms Based on Deep Learning and Image Fusion

Edge detection is a boundary-based segmentation method to extract important information from an image, and it is a research hotspot in the fields of computer vision and image analysis. Especially feature extraction is also the basis of image segmentation, target detection, and recognition. In recent years, in order to solve the problems of edge detection refinement and low detection accuracy, the industry has proposed multiscale fusion wavelet edge, spectral clustering, network reconstruction, and other edge detection algorithms based on deep learning. In order to enable researchers to understand the current research status of edge detection, this paper first introduces the classic algorithm of traditional edge detection, compare with advantages and disadvantages of different edge detection algorithms. Then, it summarizes the main edge detection methods based on deep learning in recent years and classifies and compares them according to the implementation technology. Finally, it shows the development direction of edge detection algorithm research.
Digital Management and Optimization of Tourism Information Resources Based on Machine Learning

With the gradual growth of economy, tourism has become a pillar industry in many countries and plays an important role in promoting national development. The individualization and diversification of tourism resources must be supported by a powerful information resource management system. However, the traditional tourism information resource management system has some problems, such as scattered sources of tourism information, low interactivity, and slow update of information resources. Tourists cannot get detailed information of scenic spots and make detailed plans for tourism which hinder the further development of the tourism industry. In order to solve these problems and promote the development of the tourism industry, this paper carried out digital management of tourism information based on machine learning and digital management of information resources of different tourist attractions and surveyed and tested the number of tourists, expenditure of scenic spots, income of scenic spots, and satisfaction of tourists. The total result showed that the digital management of tourism information resources in scenic spots can increase the passenger flow, increase the income of scenic spots, reduce the expenditure of scenic spots by 6.7%, and increase the satisfaction of tourists by 4.1%. The digital management of tourism information resources based on machine learning can optimize the tourism industry and promote its development.
A Study of Blockchain-Based Federated Learning

Federated Learning (FL) has made an essential step towards enhancing the privacy of traditional model training. However, gaps in the conventional FL framework make it vulnerable. FL is dealing with a double-edged sword by following the data minimization principle. Although FL provides privacy by design, it makes data verification challenging as no one can see others' data. Therefore, participants may act dishonestly, increasing the risk of information leakage or performance degradation. It also lacks an incentive mechanism. Most recent studies leveraged blockchain technology to deal with privacy and security problems and address centralization and fairness issues. This study provides a comprehensive literature review on blockchain-based FL systems. Research and applications are presented, and future research directions are offered.
Monadic second-order incorrectness logic for GP 2

Program logics typically reason about an over-approximation of program behaviour to prove the absence of bugs. Recently, program logics have been proposed that instead prove the presence of bugs by means of under-approximate reasoning, which has the promise of better scalability. In this paper, we present an under-approximate program logic for GP 2, a rule-based programming language for manipulating graphs. We define the proof rules of this program logic extensionally, i.e. independently of fixed assertion languages, then instantiate them with a morphism-based assertion language able to specify monadic second-order properties on graphs (e.g. path properties). We show how these proof rules can be used to reason deductively about the presence of forbidden graph structure or failing executions. Finally, we prove our ‘incorrectness logic’ to be sound, and our extensional proof rules to be relatively complete.
Large-Scale Expensive Optimization with a Switching Strategy

Some optimization problems in scientific research, such as the robustness optimization for the Internet of Things and the neural architecture search, are large-scale in decision space and expensive for objective evaluation.In order to get a good solution in a limited budget for the large-scale expensive optimization, a random grouping strategy is adopted to divide the problem into some low-dimensional subproblems.A surrogate model is then trained for each sub-problem using different strategies to select training data adaptively.After that, a dynamic infill criterion is proposed corresponding to the models currently used in the surrogate-assisted sub-problem optimization.Furthermore, an escape mechanism is proposed to keep the diversity of the population.The performance of the method is evaluated on CEC'2013 benchmark functions.Experimental results show that the algorithm has better performance in solving expensive large-scale optimization problems.
Designing Social Robots' Speech in the Hotel Context - A Series of Online Studies

Social robots found their way into several economic fields of our daily life, e.g., as info points in shopping malls or hotels. However, research on robots in the tourism sector is still in its infancy. Conducting three online studies, we examined the design of robotic speech for a robotic concierge. Providing self-disclosure via speech or using local dialect did not affect robot acceptance and attitudes. In contrast, informal speech led to higher likeability and perceived warmth of the robot concierge. Overall, our results reveal positive evaluations of our robotic concierge regardless of its manipulation.
Trustworthiness assessment in multimodal human-robot interaction based on cognitive load

In this study, we extend our robot trust model into a multimodal setting in which the Nao robot leverages audio-visual data to perform a sequential multimodal pattern recalling task while interacting with a human partner who has different guiding strategies: reliable, unreliable, and random. Here, the humanoid robot is equipped with a multimodal auto-associative memory module to process audio-visual patterns to extract cognitive load (i.e., computational cost) and an internal reward module to perform cost-guided reinforcement learning. After interactive experiments, the robot associates a low cognitive load (i.e., high cumulative reward) yielded during the interaction with high trustworthiness of the guiding strategy of the partner. At the end of the experiment, we provide a free choice to the robot to select a trustworthy instructor. We show that the robot forms trust in a reliable partner. In the second setting of the same experiment, we endow the robot with an additional simple theory of mind module to assess the efficacy of the instructor in helping the robot perform the task. Our results show that the performance of the robot is improved when the robot bases its action decisions on factoring in the instructor assessment.
Characterization of surface texture-measuring optical microscopes using a binary pseudo-random array standard

Accurate topography measurements of engineered surfaces over a wide range of spatial frequencies are required in many applications. The instrument transfer function (ITF) of the microscope used to characterize the surface topography must be taken into consideration to ensure that the height, or depth, of features with higher spatial frequency content is not underestimated. This applies especially when comparing surface topography measurements made by different types of microscopes. We discuss ITF measurements of a confocal microscope and an interferometric microscope using a binary pseudo-random array (BPRA) standard. BPRA standards are surfaces designed to have constant inherent power spectral density (PSD) over the spatial frequency passband of a microscope. The ITF of a microscope can thus be derived from a PSD measurement of a BPRA standard in a straight-forward manner. We further show how BPRA standards can be used as efficient diagnostic tools to characterize aspects of the imaging performance of topography-measuring microscopes.
Video-Based Detection of Combat Positions and Automatic Scoring in Jiu-jitsu

Due to the increasing capabilities of computer vision methods, it is now possible to apply them even to the most difficult scenarios, such as for vision-based analysis of a jiu-jitsu match. One of the biggest challenges of such scenarios are heavily occluded scenes. Jiu-jitsu is a grappling martial art in which athletes are interlocked in complex positions most of the time. This produces significant challenges for computer vision methods. We propose a method to track the athletes' poses even in such scenarios. The advantage of our method is that it combines positional, structural, and visual cues to overcome this problem and is able to cope with severe occlusions. We use this data to automatically predict combat positions at a high accuracy. Finally, we propose a novel approach for automatic scoring of a jiu-jitsu match from video using these predictions.
Digital Accessibility in Intelligent Environments

Intelligent everyday environments are expected to empower their inhabitants, assisting them in carrying out their everyday tasks, but also ensuring their well-being and prosperity. In this regard, the accessibility of an intelligent environment is of utmost importance to ensure that it fulfills user needs, but also that it is usable and useful for everyone, without imposing barriers or excluding individuals with disabilities or older adults. This chapter carries out a review of the state of the art in the field of interaction techniques in intelligent environments, analyzing their accessibility challenges and benefits for different user categories. Furthermore, toward the direction of universally accessible intelligent environments, the issue of multimodal interaction is discussed, summarizing the modalities that can be employed for each user group.
A convergent wavelet-based method for solving linear stochastic differential equations included 1D and 2D noise

In this paper, we present a Collocation method based on scaling function of Daubechies Wavelet(CDW) to solve linear stochastic differential equations with one and two dimensional noise. By applying this method, the problem transforms to a linear system of algebraic equations with coefficients of expansion as unknowns. Due to interesting properties of the Daubechies wavelet such as orthogonality, compactly support and vanishing moments, the coefficients of expansion are obtained fast. The convergence of the proposed method is presented. To verify the accuracy and efficiency of the proposed method some numerical examples are provided.