Reviewerly/exHarmony huggingface dataset:
Multi-modal biometric fusion based continuous user authentication for E-proctoring using hybrid LCNN-Salp swarm optimization

In Covid 19, pandemic remote proctoring of the employee or human being is evolved as a big challenge for the information retrieval process. On the other side, memory-based system access authentication is becoming outdated and less preferred for live applications, especially where data security and customer privacy are crucial. Multi-modal authentication has outperformed the unimodal process with high accuracy and improved security in the user authentication field. Multi-modal biometric verification includes user attributes such as keystrokes, iris, speech, face, etc. For real-time execution of multi-modal biometric fusion-based live tracking for compatible applications. The study proposes an efficient continuous biometric user authentication system for a new challenge of pandemic time, a live online authentication of the evaluation process (CBUA-OE). The proposed CBUA-OE system can address the challenges associated with live proctoring and is also compatible with real-time implementation, deployment of authentication systems. The modified wolf optimization algorithm and CUBA-OE's optimal feature fusion algorithm give an edge over the other contemporary methods and make it more robust. In modern forms of authentication, the classification stage affects the overall outcome of the system, and the model's performance is also a factor of varying quality of datasets. In contrast, a hybrid LCNN-Salp swarm optimization-based classifier is more efficient and consistent in continuous user authentication. Here the performance of the proposed hybrid LCNN-Salp swarm optimization classifier is analyzed with different standard datasets. The results are compared with the existing state-of-art classifiers regarding the accuracy, precision, recall, and F-measure. This projected work is novel in terms of usability factors and scalability to live tracking systems.
MP-RRT#: a Model Predictive Sampling-based Motion Planning Algorithm for Unmanned Aircraft Systems

Abstract This paper introduces a kinodynamic motion planning algorithm for Unmanned Aircraft Systems (UAS), called MP-RRT # . MP-RRT # joins the potentialities of RRT # with a strategy based on Model Predictive Control to efficiently solve motion planning problems under differential constraints. Similar to other RRT-based algorithms, MP-RRT # explores the map constructing an asymptotically optimal graph. In each iteration the graph is extended with a new vertex in the reference state of the UAS. Then, a forward simulation is performed using a Model Predictive Control strategy to evaluate the motion between two adjacent vertices, and a trajectory in the state space is computed. As a result, the MP-RRT # algorithm eventually generates a feasible trajectory for the UAS satisfying dynamic constraints. Simulation results obtained with a simulated drone controlled with the PX4 autopilot corroborate the validity of the MP-RRT # approach.
Publishing Triangle Counting Histogram in Social Networks Based on Differential Privacy

The continuous expansion of the number and scale of social networking sites has led to an explosive growth of social network data. Mining and analyzing social network data can bring huge economic value and social benefits, but it will result in privacy leakage and other issues. The research focus of social network data publishing is to publish available data while ensuring privacy. Aiming at the problem of low data availability of social network node triangle counting publishing under differential privacy, this paper proposes a privacy protection method of edge triangle counting. First, an edge-removal projection algorithm TSER based on edge triangle count sorting is proposed to obtain the upper bound of sensitivity. Then, two edge triangle count histogram publishing methods satisfying edge difference privacy are given based on the TSER algorithm. Finally, experimental results show that compared with the existing algorithms, the TSER algorithm can retain more triangles in the original graph, reduce the error between the published data and the original data, and improve the published data availability.
IoT and Blockchain Technology-Based Healthcare Monitoring

An Internet of Things (IoT)-based healthcare monitoring system is an amalgamation of smart healthcare devices and applications which connect to the healthcare information system through the internet. It has opened up a world of possibilities in the medical field. Connected smart healthcare devices can collect important healthcare data and provide extra details about the symptoms. That further enables remote care as the patient can consult the appropriate healthcare expert. It generally provides patients more control over their lives and treatment. Moreover, a blockchain-enabled IoT-based healthcare monitoring system (BIoT-HMS) is one step ahead as it can reduce unnecessary hospital visits and the burden on healthcare systems by connecting patients to their healthcare experts and allowing the secure transfer and storage of healthcare data by making use of the blockchain mechanism. Some of the possible applications of BIoT-HMS are “remote health monitoring,” “management of healthcare operations,” “detection and prevention of diseases” and many more. However, IoT-based healthcare monitoring systems have issues related to security breaches and privacy of healthcare data. Therefore, it is essential to design some security protocols for IoT-based healthcare monitoring systems. From time to time researchers propose different protocols to secure the data of IoT-based healthcare monitoring systems. However, most of them are vulnerable to various types of attack like, replay, MiTM, impersonation, secret credentials leakage, unauthorized data update, etc., Therefore, researchers came up with the idea of inclusion of blockchain-enabled security in the IoT-based healthcare monitoring system, which further can be considered as a blockchain-enabled IoT-based healthcare monitoring system (BIoT-HMS). In this chapter, we provide the details of various possible architectures of BIoT-HMS. We also provide the details of various threats and attacks along with the threat model in BIoT-HMS. The security requirements and applications of BIoT-HMS are also discussed. We then provide the summary of various existing security protocols related to BIoT-HMS. The comparison of “security and functionality features” of security protocols related to BIoT-HMS is also provided.
PolyChain: A Generic Blockchain as a Service Platform

In recent years, blockchain technology has been attracting intensive attention from both the industries and academia because of its capability of rebuilding trust in trustless environments. There are increasing demands for developing and delivering blockchain applications and services in an agile and continuous way. To this end, Blockchain as a Service (BaaS) emerges which refers to cloud-based blockchain infrastructure developed by a vendor allowing users to develop, host, and use their own blockchain components, functions, and applications. There are many BaaS platforms developed by industries and academia, e.g., Bitcoin, Ethereum, and Hyperledger Fabric. However, they are either limited in scalability or difficult for configuration and customization. In this paper, we propose and develop PolyChain, a generic BaaS platform with high modularity, flexibility, scalability, reliability, and security, which are achieved with the following three design principles. First, each blockchain node is designed as four modularized components, e.g., network, storage, consensus, and application, based on the functionalities. Second, the components in a logic blockchain node interact via communication interfaces and can be deployed on different physical nodes. Finally, the component deployment is optimized based on the capabilities of the physical nodes. We believe PolyChain may benefit the industries and academia in agile development and continuous delivery of blockchain prototypes and applications.
An Adaptive Framework for Anomaly Detection in Time-Series Audio-Visual Data

Anomaly detection is an integral part of a number of surveillance applications. However, most of the existing anomaly detection models are statically trained on pre-recorded data from a single source, thus making multiple assumptions about the surrounding environment. As a result, their usefulness is limited to controlled scenarios. In this paper, we fuse information from live streams of audio and video data to detect anomalies in the captured environment. We train a deep learning-based teacher-student network using video, image, and audio information. The pre-trained visual network in the teacher model distills its information to the image and audio networks in the student model. Features from image and audio networks are combined and compressed using principal component analysis. Thus, the teacher-student network produces an image-audio-based light-weight joint representation of the data. The data dynamics are learned in a multivariate adaptive Gaussian mixture model. Empirical results from two audio-visual datasets demonstrate the effectiveness of joint representation over single modalities in the adaptive anomaly detection framework. The proposed framework outperforms the state-of-the-art methods by an average of 15.00 % and 14.52 % in AUC values for dataset 1 and dataset 2, respectively.
Immersive Journalism: Advantages, Disadvantages and Challenges from the Perspective of Experts

The aim of this text is to analyze the future of immersive journalism from the perspective of experts now, in a moment when the initial experimental stage seems to have passed given the smaller number of pieces being today produced by leading media. To do this, we conducted two focus groups with 15 international leaders in the field, including both professional and academic points of view. We asked them about the main strengths, weaknesses, challenges and how to overcome them so that immersive journalism has continuity in the future. The results reveal few differences between the views of academics and practitioners, especially regarding the strengths and weaknesses of this new narrative, which is as versatile as it is complex. Most of them agree that immersive journalism is still in its experimentation stage regarding its production, although there is a positive perspective regarding the sustainability of this format in the near future. Both groups agree on the need to continue testing to understand this new narrative language and insist on the need to introduce this knowledge into journalism schools, both theoretically and practically.
Catch-22s of reservoir computing

Reservoir computing (RC) is a simple and efficient model-free framework for forecasting the behavior of nonlinear dynamical systems from data. Here, we show that there exist commonly-studied systems for which leading RC frameworks struggle to learn the dynamics unless key information about the underlying system is already known. We focus on the important problem of basin prediction---determining which attractor a system will converge to from its initial conditions. First, we show that the predictions of standard RC models (echo state networks) depend critically on warm-up time, requiring a warm-up trajectory containing almost the entire transient in order to identify the correct attractor. Accordingly, we turn to next-generation reservoir computing (NGRC), an attractive variant of RC that requires negligible warm-up time. By incorporating the exact nonlinearities in the original equations, we show that NGRC can accurately reconstruct intricate and high-dimensional basins of attraction, even with sparse training data (e.g., a single transient trajectory). Yet, a tiny uncertainty in the exact nonlinearity can render prediction accuracy no better than chance. Our results highlight the challenges faced by data-driven methods in learning the dynamics of multistable systems and suggest potential avenues to make these approaches more robust.
Learning Disentangled Representation with Pairwise Independence

Unsupervised disentangled representation learning is one of the foundational methods to learn interpretable factors in the data. Existing learning methods are based on the assumption that disentangled factors are mutually independent and incorporate this assumption with the evidence lower bound. However, our experiment reveals that factors in real-world data tend to be pairwise independent. Accordingly, we propose a new method based on a pairwise independence assumption to learn the disentangled representation. The evidence lower bound implicitly encourages mutual independence of latent codes so it is too strong for our assumption. Therefore, we introduce another lower bound in our method. Extensive experiments show that our proposed method gives competitive performances as compared with other state-of-the-art methods.
A Self-Organized Task Distribution Framework for Module-Based Event Stream Processing

Tackling bottleneck and privacy issues of cloud computing, we attempt to push event stream processing down to devices which are currently empowered to compute and communicate at the edge of the networks. To accomplish that, we propose a self-organized task distribution framework that is composed of multiple brokers collaborating through our module-based event stream processing engine called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EdgeCEP</i> . Our system request is event-dependent specified in a brand-new event specification language; still, the event is stored and processed by the relational database. We newly formulate the problem of self-organized task distribution subjective to preferable constraints of computation and communication. The solution for each broker to find individual optimal decision is to apply tabu search with flow-based greedy move regarding pre-ranking flow table. Many experiments are conducted to study and evaluate the performance of the proposed system. The simulation shows that the proposed flow optimization outperforms the naïve algorithm, concretely, 2-times more tasks getting processed and successfully delivered within the same fixed period. The proposed edge-centric method achieves data traffic 7-times less than the cloud-centric approach. The prototype engines have been deployed and evaluated in the real environment.
Rewriting Abstract Structures: Materialization Explained Categorically

Abstract The paper develops an abstract (over-approximating) semantics for double-pushout rewriting of graphs and graph-like objects. The focus is on the so-called materialization of left-hand sides from abstract graphs, a central concept in previous work. The first contribution is an accessible, general explanation of how materializations arise from universal properties and categorical constructions, in particular partial map classifiers, in a topos. Second, we introduce an extension by enriching objects with annotations and give a precise characterization of strongest post-conditions, which are effectively computable under certain assumptions.
Image denoising in impulsive noise via weighted Schatten p-norm regularization

Low-rank methods have been widely exploited in image denoising and have shown admirable denoising performance, of which weighted Schatten p-norm minimization (WSNM) is particularly effective. However, the WSNM method which applies Frobenius-norm loss model cannot obtain a satisfactory denoising performance when images corrupted by impulse noise. An optimization strategy based on the alternating direction method of multipliers framework is used to solve the proposed model efficiently. Experimental results show that the proposed method outperforms some state-of-the-art denoising methods both quantitatively and qualitatively under various impulsive noise models.
A semantic web methodological framework to evaluate the support of integrity in thesaurus tools

With the Semantic Web, thesauri regain a relevant role supporting semantic searches and other added-value services. Thesaurus standards define the constructs a thesaurus can have and the integrity rules it must comply with. Thesaurus editors can be helped in their work if thesaurus tools offer them support for integrity, warning when integrity rules are violated and/or helping them to correct these mistakes. The most recent thesaurus standard is ISO 25964, which supersedes ISO 2788, evolving towards concept-based thesauri and better aligned with the Semantic Web approach than the term-based thesauri of ISO 2788. However, the W3C recommendation for Knowledge Organisation System (KOS) representation in the semantic web context is Simple Knowledge Organisation Systems (SKOS), which is in fact prior to ISO 25964. This article focuses on thesaurus integrity and the evolution from ISO 2788 to ISO 25964. Its effect on integrity issues is analysed. A methodological proposal for evaluating integrity support in thesaurus tools, arising from the results of this work, is presented. Its target audience is professionals in charge of thesaurus editing. Besides being adapted to the most recent thesaurus standard, ISO 25964, it also includes the comparison of ISO standards with SKOS. The article concludes with the presentation of the results obtained by applying the framework to three thesaurus tools.
Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm

Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportional hazard (PH) regression on the basis of the BBO algorithm. The dataset is prospectively maintained by the Division of Breast Surgery at Kaohsiung Medical University Hospital. A total of 1896 patients with breast cancer were included and tracked from 2005 to 2017. Fifteen general breast cancer clinicopathologic variables were collected. We used the BBO algorithm to select the clinicopathologic variables that could potentially contribute to predicting breast cancer prognosis. Subsequently, Cox PH regression analysis was used to demonstrate the association between overall survival and the selected clinicopathologic variables. C-statistics were used to test predictive accuracy and the concordance of various survival models. The BBO-selected clinicopathologic variables model obtained the highest C-statistic value (80%) for predicting the overall survival of patients with breast cancer. The selected clinicopathologic variables included tumor size (hazard ratio [HR] 2.372, p = 0.006), lymph node metastasis (HR 1.301, p = 0.038), lymphovascular invasion (HR 1.606, p = 0.096), perineural invasion (HR 1.546, p = 0.168), dermal invasion (HR 1.548, p = 0.028), total mastectomy (HR 1.633, p = 0.092), without hormone therapy (HR 2.178, p = 0.003), and without chemotherapy (HR 1.234, p = 0.491). This number was the minimum number of discriminators required for optimal discrimination in the breast cancer overall survival model with acceptable prediction ability. Therefore, on the basis of the clinicopathologic variables, the survival prediction model in this study could contribute to breast cancer follow-up and management.
A study and analysis of a discrete quantum walk-based hybrid clustering approach using d-regular bipartite graph and 1D lattice

Traditional machine learning shares several benefits with quantum information processing field. The study of machine learning with quantum mechanics is called quantum machine learning. Data clustering is an important tool for machine learning where quantum computing plays a vital role in its inherent speed up capability. In this paper, a hybrid quantum algorithm for data clustering (quantum walk-based hybrid clustering (QWBHC)) is introduced where one-dimensional discrete time quantum walks (DTQW) play the central role to update the positions of data points according to their probability distributions. A quantum oracle is also designed and it is mainly implemented on a finite [Formula: see text]-regular bipartite graph where data points are initially distributed as a predefined set of clusters. An overview of a quantum walk (QW) based clustering algorithm on 1D lattice structure is also introduced and described in this paper. In order to search the nearest neighbors, a unitary and reversible DTQW gives a quadratic speed up over the traditional classical random walk. This paper also demonstrates the comparisons of our proposed hybrid quantum clustering algorithm with some state-of-the-art clustering algorithms in terms of clustering accuracy and time complexity analysis. The proposed quantum oracle needs [Formula: see text] queries to mark the nearest data points among clusters and modify the existing clusters. Finally, the proposed QWBHC algorithm achieves [Formula: see text] performance.
Tag Recommendation by Word-Level Tag Sequence Modeling

In this paper, we transform tag recommendation into a word-based text generation problem and introduce a sequence-to-sequence model. The model inherits the advantages of LSTM-based encoder for sequential modeling and attention-based decoder with local positional encodings for learning relations globally. Experimental results on Zhihu datasets illustrate the proposed model outperforms other state-of-the-art text classification based methods.
Visualizing event sequence game data to understand player’s skill growth through behavior complexity

Analysis of game data is used to study player behavior. For puzzle-based games where solutions are usually defined by their action sequences, player behavior can also be studied by their solution complexity. In this paper, we present a visualization system to help learning expert to understand how actions, timing and the resulting strategy change with regard to the solution complexity. To establish a novel perspective into the patterns not only in action choices but also in behavior complexity, we designed an interactive, customized line chart to track how complexity and performance change at each stage of skill acquisition. Specialized glyph system (Strategy Signature) is implemented to find strategy differences easily with simple visual cues. Contextual information can be explored by switching the view modes to see potential links between complexity and raw attributes. Evaluation with expert users shows that the system effectively reduced their time and effort in finding interesting subgroups and gave them unexplored angles of behavior complexity to contemplate player’s skill growth. In summary, this paper illustrates a visualization approach to enable analysis into the subtleties of behavior complexity in video games.
SmartWriting: Pen-Holding Gesture Recognition with Smartwatch

Performing the correct pen-holding gesture plays an important role in handwriting efficiency and quality, especially for early education. In this paper, we present the design, implementation and evaluation of SmartWriting, which utilizes the smartwatch for pen-holding gesture recognition for both Chinese and English writing. SmartWriting can automatically identify whether the user is writing Chinese or English, based on which two classifiers are built to detect pen-holding gestures by strokes in Chinese characters or English letters. In particular, we propose to leverage the combined signal of one vertical stroke and one horizontal stroke for efficient pen-holding gesture identification in Chinese writing, and we are able to infer pen-holding gesture according to any letter in English writing. We build a fully functional prototype of SmartWriting, and extensive experiments confirm its effectiveness and robustness. The detection accuracy is 9. &#x0025; and 9 &#x0025; for Chinese and English respectively. SmartWriting provides a natural, convenient and inexpensive way to improve users' writing habits.