Reviewerly/exHarmony huggingface dataset:
Continuous-Time Trajectory Optimization for Decentralized Multi-Robot Navigation

Multi-robot systems have begun to permeate into a variety of different fields, but collision-free navigation in a decentralized manner is still an arduous task. Typically, the navigation of high speed multi-robot systems demands replanning of trajectories to avoid collisions with one another. This paper presents an online replanning algorithm for trajectory optimization in labeled multi-robot scenarios. With reliable communication of states among robots, each robot predicts a smooth continuous-time trajectory for every other remaining robots. Based on the knowledge of these predicted trajectories, each robot then plans a collision-free trajectory for itself. The collision-free trajectory optimization problem is cast as a non linear program (NLP) by exploiting polynomial based trajectory generation. The algorithm was tested in simulations on Gazebo with aerial robots.
Revocable Certificateless Public Key Encryption With Outsourced Semi-Trusted Cloud Revocation Agent

Certificateless public key cryptography (CL-PKC) not only eliminates the need for certificates in traditional certificate-based PKC but also solves the inherent key escrow problem in identity-based PKC.However, an unsolved but critical issue in CL-PKC is how to revoke a misbehaving user.Some revocable certificateless public key encryption (RCL-PKE) schemes have been proposed, but these schemes have two main drawbacks: 1) public key uniqueness is not guaranteed, thus allowing the existence of multiple copies of each initial secret key.2) The existing outsourced RCL-PKE schemes place excessive trust in the cloud server, which may continue to update decryption keys stealthily for misbehaving users.In this paper, we address these issues by proposing a novel RCL-PKE with semi-trusted cloud revocation agents (s-CRAs).We describe the framework and the security model for the RCL-PKE with s-CRA and prove that the proposed scheme is semantically secure against adaptive chosen-ciphertext attacks under the bilinear Diffie-Hellman assumption in the random oracle model.Furthermore, we compare the proposed scheme with previous RCL-PKE schemes in terms of performance and robustness.The evaluation results show that the proposed scheme achieves public key uniqueness and reliable revocation flexibility at low computational and communication costs.
Traceability of Agricultural Product Quality and Safety Based on Blockchain – Taking Fresh E-commerce as an Example

At present, there are many problems in the traceability system of agricultural products. It is difficult to integrate the centralized traditional management and operation mode and data information maintenance. At the same time, there are trust problems in different links of agricultural products, especially in the field of fresh e-commerce. In view of these problems, this paper constructs a traceability system framework of fresh e-commerce agricultural product quality and safety based on blockchain technology. Using the characteristics of decentralization, non tamperability and security encryption of blockchain technology, it can realize the traceability of information in the production, processing, storage, transportation, retail and other links of fresh e-commerce agricultural products, ensure the safety and reliability of data, and solve the problem of fresh e-commerce agricultural products Data trust in product traceability system.
Recursive Block LU Decomposition based ELM in Apache Spark

Due to the massive memory and computational resources required to build complex machine learning models on large datasets, many researchers are employing distributed environments for training the models on large datasets. The parallel implementations of Extreme Learning Machine (ELM) with many variants have been developed using MapReduce and Spark frameworks in the recent years. However, these approaches have severe limitations in terms of Input-Output (I/O) cost, memory, etc. From the literature, it is known that the complexity of ELM is directly propositional to the computation of Moore-Penrose pseudo inverse of hidden layer matrix in ELM. Most of the ELM variants developed on Spark framework have employed Singular Value Decomposition (SVD) to compute the Moore-Penrose pseudo inverse. But, SVD has severe memory limitations when experimenting with large datasets. In this paper, a method that uses Recursive Block LU Decomposition to compute the Moore-Penrose generalized inverse over the Spark cluster has been proposed to reduce the computational complexity. This method enhances the ELM algorithm to be efficient in handling the scalability and also having faster execution of the model. The experimental results have shown that the proposed method is efficient than the existing algorithms available in the literature.
U-ASG: A Universal Method to Perform Adversarial Attack on Autoencoder based Network Anomaly Detection Systems

Semi-supervised machine learning models, especially deep neural networks, have been widely used in network anomaly detection for their capability of capturing patterns in normal data. However, the models face security challenges when an attacker has obtained their full details. In this paper, we propose a universal adversarial sample generator (U-ASG), to perform white-box adversarial attacks on autoencoder-based semi-supervised network anomaly detection (SSNAD) systems. The purpose of adversarial attacks is to generate small adversarial perturbations and add them to targeted anomalous samples to fly under the radar. We model the generation process of adversarial perturbations as an optimization problem, in which we minimize the reconstruction errors of the adversarial samples through the trained autoencoder and approximate it to solve. Furthermore, to improve the attack performance against the variational autoencoder (VAE), which is robust to tiny perturbations through uncertainty modeling, we design a mechanism to weaken its robustness by introducing a variance regularizer to the optimization. Simulation results show that the adversarial attacks generated by our U-ASG can effectively degrade the performance of the autoencoder-based SSNAD systems.
Data Augmentation Methods for Low-Resource Orthographic Syllabification

An n-gram syllabification model generally produces a high error rate for a low-resource language, such as Indonesian, because of the high rate of out-of-vocabulary (OOV) n-grams. In this paper, a combination of three methods of data augmentations is proposed to solve the problem, namely swapping consonant-graphemes, flipping onsets, and transposing nuclei. An investigation on 50k Indonesian words shows that the combination of three data augmentation methods drastically increases the amount of both unigrams and bigrams. A previous procedure of flipping onsets has been proven to enhance the standard bigram-syllabification by relatively decreasing the syllable error rate (SER) by up to 18.02%. Meanwhile, the previous swapping consonant-graphemes has been proven to give a relative decrement of SER up to 31.39%. In this research, a new transposing nuclei-based augmentation method is proposed and combined with both flipping and swapping procedures to tackle the drawback of bigram syllabification in handling the OOV bigrams. An evaluation based on k-fold cross-validation (k-FCV), using k = 5, for 50 thousand Indonesian formal words concludes that the proposed combination of the three procedures relatively decreases the mean SER produced by the standard bigram model by up to 37.63%. The proposed model is comparable to the fuzzy k-nearest neighbor in every class (FkNNC)-based model. It is worse than the state-of-the-art model, which is developed using a combination of bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNN), and conditional random fields (CRF), but it offers a low complexity.
A hybrid <scp>MCDM</scp> model combining <scp>DANP</scp> with <scp>TODIM</scp> to evaluate the information quality of community question answering in a two‐dimensional linguistic environment

Abstract Evaluating the information quality of cQA (community question‐answering) websites helps users select a cQA website with high‐quality information and improves the information quality. In this paper, an approach to evaluating the information quality of cQA based on a novel hybrid multicriteria decision‐making (MCDM) model is proposed. First, the source, content, expression and usefulness criteria for evaluating the information quality of the cQA are established. Then, considering the ambiguity and inner correlations of the criteria, DANP ([DEMATEL]‐based Analytic Network Process) and TODIM (interactive and multiple attribute decision making, in Portuguese) methods are combined in a two‐dimensional linguistic environment to process linguistic evaluation information. Lastly, the proposed approach is applied to evaluate the quality of five popular cQA websites. The key criteria are identified, and the evaluation results are derived comprehensively. The key factors consist of reputation, coverage, politeness, usability, helpfulness, clarity, readability and conciseness. The websites, Know almost and Baidu knows, perform better in terms of information quality. The application, along with the sensitivity analysis and comparative analysis, shows the effectiveness of the proposed model. The proposed approach has both practical and research implications. It provides an approach for users to choose websites, and, for operators, to improve the information quality of their website. The evaluation criteria and their relations provide a reference for research on the information quality. The evaluation method considers the user's psychological information to provide a more accurate MCDM approach. The comprehensive aspects of the experiment can be used to verify the other MCDM methods.
Metric Temporal Description Logics with Interval-Rigid Names

In contrast to qualitative linear temporal logics, which can be used to state that some property will eventually be satisfied, metric temporal logics allow us to formulate constraints on how long it may take until the property is satisfied. While most of the work on combining description logics (DLs) with temporal logics has concentrated on qualitative temporal logics, there is a growing interest in extending this work to the quantitative case. In this article, we complement existing results on the combination of DLs with metric temporal logics by introducing interval-rigid concept and role names. Elements included in an interval-rigid concept or role name are required to stay in it for some specified amount of time. We investigate several combinations of (metric) temporal logics with A ℒ C by either allowing temporal operators only on the level of axioms or also applying them to concepts. In contrast to most existing work on the topic, we consider a timeline based on the integers and also allow assertional axioms. We show that the worst-case complexity does not increase beyond the previously known bound of 2-E xp S pace and investigate in detail how this complexity can be reduced by restricting the temporal logic and the occurrences of interval-rigid names.
Simulation and real-time implementation of a nonlinear fuzzy PI/PD controller

The purpose of this manuscript is to show the applicability of a recently developed simplest fuzzy PI/PD controller in simulation and real-time. An unstable plant with large time-delay is controlled in simulation using the simplest fuzzy PI controller, and a nonlinear and unstable magnetic levitation system is controlled in real-time with the help of the simplest fuzzy PD controller. The simulation results are compared with the existing results to show the usefulness of the newly designed controller.
Context-Aware Learning for Generative Models

This work studies the class of algorithms for learning with side-information that emerge by extending generative models with embedded context-related variables. Using finite mixture models (FMM) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maximization (EM) improves over the regular unsupervised case and can approach the performances of supervised learning, despite the absence of any explicit ground truth data labeling. By direct application of the missing information principle (MIP), the algorithms' performances are proven to range between the conventional supervised and unsupervised MLE extremities proportionally to the information content of the contextual assistance provided. The acquired benefits regard higher estimation precision, smaller standard errors, faster convergence rates and improved classification accuracy or regression fitness shown in various scenarios, while also highlighting important properties and differences among the outlined situations. Applicability is showcased with three real-world unsupervised classification scenarios employing Gaussian Mixture Models. Importantly, we exemplify the natural extension of this methodology to any type of generative model by deriving an equivalent context-aware algorithm for variational autoencoders (VAs), thus broadening the spectrum of applicability to unsupervised deep learning with artificial neural networks. The latter is contrasted with a neural-symbolic algorithm exploiting side-information.
A Monolithically Integrated Silicon Photonics 8×8 Switch in 90nm SOI CMOS

This work presents the first fully packaged silicon photonics 8×8 switch with monolithically integrated electrical control circuits in 90nm SOI CMOS. The switch is a strictly non-blocking network built from 2×1 and 2×2 Mach-Zehnder switches (MZSs). 180 DACs tune the MZSs and 112 ADCs measure optical power across the network for feedback control. Digital interfaces provide low-speed tuning/monitoring and high-speed switching controls. 5.6ns optical switching is achieved with thermo-optically tuned crosstalk <; -33.5dB.
Statistical Aspects of the Quantum Supremacy Demonstration

In quantum computing, a demonstration of quantum supremacy (or quantum advantage) consists of presenting a task, possibly of no practical value, whose computation is feasible on a quantum device, but cannot be performed by classical computers in any feasible amount of time. The notable claim of quantum supremacy presented by Google's team in 2019 consists of demonstrating the ability of a quantum circuit to generate, albeit with considerable noise, bitstrings from a distribution that is considered hard to simulate on classical computers. Very recently, in 2020, a quantum supremacy claim was presented by a group from the University of Science and Technology of China, using a different technology and generating a different distribution, but sharing some statistical principles with Google's demonstration. Verifying that the generated data is indeed from the claimed distribution and assessing the circuit's noise level and its fidelity is a statistical undertaking. The objective of this paper is to explain the relations between quantum computing and some of the statistical aspects involved in demonstrating quantum supremacy in terms that are accessible to statisticians, computer scientists, and mathematicians. Starting with the statistical modeling and analysis in Google's demonstration, which we explain, we study various estimators of the fidelity, and different approaches to testing the distributions generated by the quantum computer. We propose different noise models, and discuss their implications. A preliminary study of the Google data, focusing mostly on circuits of 12 and 14 qubits is given in different parts of the paper.
Intelligent privacy preservation electronic health record framework using soft computing

With the recent advancements in the technology, the biggest challenges are to protect the important sensitive data of electronic health records that are available over web. The real issues on EHRs to hide the sensitive huge data especially stored in distributed environment, and shared between numbers of stakeholders. It is an essential condition to eliminate the superfluous data and maintains the privacy of individual record stored in EHRs. An efficient mechanism is required to apply for privacy preservation on EHRs before it is disclosed to the public domain. The perturbation techniques are used to hide the data from unauthorized person for the security and privacy of a patient’s personal and sensitive information in electronic health records system. In this paper we propose a privacy preservation of electronic health records (PPEHR) framework to hide sensitive information using combination of fuzzy logic and perturbation association rules. In this framework, the privacy preservation is being tested using soft computing. Experimental results are demonstrated and verified with the help of machine learning algorithms.
Deep multi query image retrieval

There exist few studies investigating the multi-query image retrieval problem. Existing methods are not based on hash codes. As a result, they are not efficient and fast. In this study, we develop an efficient and fast multi-query image retrieval method when the queries are related to more than one semantic. Image hash codes are generated by a deep hashing method. Consequently, the method requires lower storage space, and it is faster compared to the existing methods. The retrieval is based on the Pareto front method. Reranking performed on the retrieved images by using non-binary deep-convolutional features increase retrieval accuracy considerably. Unlike previous studies, the method supports an arbitrary number of queries. It outperforms similar multi-query image retrieval studies in terms of retrieval time and retrieval accuracy.
BS-IoT: Blockchain Based Software Defined Network Framework for Internet of Things

Software Defined Networking (SDN) is a promising platform to secure and manage large-scale Internet of Things (IoT) due to its separated control and data plane functionality as well as programmability in the network. The original design of SDN faces single point of failure, and therefore several decentralized SDN architectures for IoT were proposed. However, practical SDN may be deployed by various networking operators, which incurs the conflict between data security of different networking operators and cooperative network management among them. Existing schemes cannot well support the cooperative network management among multiple controllers and meanwhile guarantee data security. To tackle this problem, we propose a blockchain based SDN framework for IoT called BS-IoT. BS-IoT introduces blockchain into SDN to support secure and cooperative network management. Besides, we leverage blockchain sharding for better efficiency and improve it with secure multi-party computation (SMPC) to make it suitable for decentralized SDN management with data security. The security analysis illustrate the security and effectiveness of our scheme.
Sensor-Based Human Activity Recognition: Challenges Ahead

Human Activity Recognition (HAR) has explored a lot recently in the academia and industries for numerous applications. There are lots of progress in the domain of vision-based action or activity recognition due to the advent of deep learning models and due to the availability of very large datasets in the last several years. However, there are still a number of genuine challenges in vision-based HAR. On the other hand, sensor-based HAR has more constraints to decipher, and is still far from maturity due to various challenges. These core challenging issues are addressed in this chapter. The challenges regarding data collection issues have been discussed in detail. Prospective research works and challenges in the field of sensor-based activity recognition have been discussed in terms of new researchers perspective, possible developments in industries for experts, and smart IoT solutions in the medical sector.
Mechanism of organisational excellence as a mediator on the relationship between human resource management and organisational performance: empirical evidence from public sector

Purpose The main purpose of this paper aims to evaluate the mediating effect of organisational excellence between human resource management (HRM) and organisational performance through the perspective of the resource-based view theory of the firm. Design/methodology/approach The four hypotheses presented in this study were tested using data collected from employees of the Dubai Police Force in the United Arab Emirates. A questionnaire survey was performed and the SmartPLS analysis software was used to examine the relationship between the proposed variables by testing the measurement and structural models. Findings The results obtained in this study indicated that all four of the proposed hypotheses were supported. Practical implications The findings of this research can be used by decision-makers, managers and practitioners to improve the performance of their organisations through the implementation of innovative strategies such as organisational excellence. Originality/value This study is considered one of the very few empirical studies that examine the effect of HRM, excellence and performance.
Linear Recursive Non-Local Edge-Aware Filter

It is challenging to extend the support region of state-of-the-art local edge-preserving filtering approaches to the entire input image on account of huge memory cost and heavy computational burden. In this paper, we propose an O(N) time recursive non-local edge-aware filter. A novel graph and a linear time algorithm are presented to effectively propagate information across the entire image. In this graph, information is propagated along all directions to avoid visual artifacts. Both the intensity similarity and the spatial affinity are utilized to estimate the similarity of neighboring pixels, and the distance of any two pixels is the length of the path between these two pixels on a binary tree. Specifically, the input image is filtered at four directions, namely left-to-right, right-to-left, top-to-bottom and bottom-to-top. In each pass, the un-normalized output and the normalization constant of the root node are computed recursively from leaf nodes to the root node on a binary tree in linear time. The filtering output is the average of the outputs for these four directions. A comparison with other non-local edge-aware filters is presented to show the advantages of our approach. Extensive experiments demonstrate that our recursive non-local edge-preserving filter is effective in a variety of computer vision and image processing applications, including edge-aware filtering, detail enhancement, stylization, and stereo matching.