Reviewerly/exHarmony huggingface dataset:
TIE: Time-Informed Exploration for Robot Motion Planning

Anytime sampling-based methods are an attractive technique for solving kino-dynamic motion planning problems. These algorithms scale well to higher dimensions and can efficiently handle state and control constraints. However, an intelligent exploration strategy is required to accelerate their convergence and avoid redundant computations. Using ideas from reachability analysis, this work defines a “Time-Informed Set (TIS),” that focuses the search for time-optimal kino-dynamic planning after an initial solution is found. Such a Time-Informed Set includes all trajectories that can potentially improve the current best solution and hence exploration outside this set is redundant. Benchmarking experiments show that an exploration strategy based on the TIS can accelerate the convergence of sampling-based kino-dynamic motion planners.
Virtual Indian Sign Language Interpreter

We present a complete system to automatically translate English content to Indian Sign Language, using animated avatars to perform the output. This is the first work of this scale in ISL, including our own animation and translation system. Our work is further intended to be completely open sourced to foster collaboration from the general public and interested HI members, and also serve as a starting point for future research in the field.
Railway Train Device Fault Causality Model Based on Knowledge Graph

The safe operation of railway train is very important to the safety of the railway system. Therefore, it is necessary to master the failure rules and causes of the railway train devices. However, the current common fault analysis methods have some shortcomings, such as complicated compilation process and insufficient expression. Therefore, we choose to use the knowledge graph technology. Firstly, the common faults of railway train devices are obtained through the statistics of real fault data; secondly, the causes of faults are analyzed and the fault tree is established; thirdly, the ontology of railway train system is modeled according to the idea of fault tree construction; finally, the logical relationship between ontology is established according to the ontology model, and the railway train device fault causality model based on knowledge graph is established.
Designing a Sensor Glove Using Deep Learning

When designing a smart glove for gesture recognition, the set of sensors available and their layout on the glove are crucial. However, once a computational model reaches acceptable recognition accuracy, it is often not clear which sensors are more important for the task. Nor whether some sensors can be strategically removed while retaining similar performance in order to save cost. Furthermore, when aiming for a personalized setup, there can be minor deviation in how gestures are performed by each participant, and so the importance of a sensor may vary between participants. In this paper, we use feature selection to explore whether a personalised glove can be produced, and whether the set of significant sensors persist between users. We present a deep learning algorithm which utilises a layer of weights to estimate the importance of each sensor in relation to each other. Besides estimating importance in relation to recognition accuracy, it is demonstrated how the importance estimates can be extended to take into account factors external to the computational model, such as costs. This allows for a cost effective elimination of sensors to reduce hardware redundancy whilst having a controlled impact on performance. We provide 2 methods: generic or specific. The generic method exploits the importance estimate from all participants to select a set of sensors for removal. Whereas the specific method estimates importance, and removes sensors based on individuals to provide a personalised setup.
Fast kernel<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si10.svg"><mml:mi>k</mml:mi></mml:math>-means clustering using incomplete Cholesky factorization

Kernel-based clustering algorithms can identify and capture the nonlinear structure in datasets, thereby achieving better performance than linear clustering. However, the large amount of memory involved in computing and storing the entire kernel matrix makes it difficult for kernel-based clustering to handle large-scale datasets. In this study, we prove that an incomplete Cholesky factorization (InCF) can generate an approximate matrix with rank s for a kernel matrix after s iterations. We also show that the approximation error decreases exponentially as the number of iterations increases when the exponential decay of the kernel matrix eigenvalues is sufficiently fast. We therefore employ InCF to accelerate kernel clustering and save memory space. The key idea of the proposed kernel k-means clustering using InCF is that the entire kernel matrix is approximated as the product of a low-rank matrix and its transpose. Then, linear k-means clustering is applied to the columns of the transpose of the low-rank matrix. We prove that the clustering error achieved by this method decreases exponentially as the rank of the approximate matrix increases. Experimental results show that the performance of the proposed algorithm is similar to that of the kernel k-means clustering algorithm, but the method can be applied to large-scale datasets.
Analyzing COVID-19 Dataset through Data Mining Tool “Orange”

Data mining is comprehended as a procedure used to get important data from any more extensive cluster of raw data. It infers assessment of data designs in huge data sets utilizing at least one or more applications. Data mining has applications in various fields, for instance science and research. Associations can improve client connections as a use of data mining and execute systems identified with different business capacities and thus upgrade business in an increasingly ideal and compelling way. This encourages organizations draw nearer to arriving at their objectives and to settle on smarter decisions as well. Orange is segment based visual programming for data mining, Artificial Intelligence, and data examination. Work forms are made by interfacing predefined or customer organized parts called widgets. They read the data, process it, envision it, and do gathering, collect prescient models, and so on. Towards the start of December 2019, an erupt of coronavirus contamination 2019 (COVID-19), brought by a new critical intense respiratory issue coronavirus 2 transpired in Wuhan City, Hubei Province, China. On January 30, 2020 the World Health Organization announced the scene as a Public Health Emergency of International Concern. Several episodes forecast models for COVID-19 are being utilized by authorities internationally to settle on instructed decisions and authorize applicable precaution measures. Amidst the standard models for COVID-19 worldwide pandemic expectation, basic epidemiological and authentic models have gotten more consideration by specialists, and they are notable in the media. In this paper, we have taken COVID-19 dataset (downloaded June 2020) from Kaggle, related to India, and with the help of ORANGE tool we have visually represented to understand how certain relations can be depicted.
Tokamak visible image sequence recognition using nonlocal spatio-temporal CNN for attention needed area localization

In this paper, we report a study that was conducted to explore the feasibility of developing a system that classifies the image sequence as ‘disruptive’ shot images or ‘non-disruptive’ shot images. The classifier identifies an image sequence as ‘non-disruptive’ shots or ‘disruptive’ shots using a non-local spatio-temporal 3D convolution neural network (CNN) from image sequences from the Tokamak visible imaging diagnostic system. To analyze the classification result, we localize an area that has contributed to the classification of an image sequence. We use class activation mapping (CAM) for CNN with global average pooling to localize the area. To train this classifier, we created a plasma disruption image sequence dataset using the data acquired from the KSTAR experiment. This classifier recognized disruption image sequences on the test dataset with 91.11% accuracy. Analysis of the CAMs of these image sequences revealed that this classifier recognizes the disruption of the plasma with a relative change in brightness over time in areas other than the plasma area of the image. Through this work, we will be able to develop a system that automatically classifies plasma disruption image sequence after experiments.
Trustworthiness study of HDFS data storage based on trustworthiness metrics and KMS encryption

Since its introduction in 2006, Hadoop technology has evolved dramatically and the Hadoop ecosystem has flourished. The Hadoop ecosystem is now composed of more than 60 components, ranging from HDFS and MapReduce. Hadoop's architecture makes it far superior to other products in large-scale data processing and analysis, making it the best choice for all industries. This makes Hadoop the preferred framework for data analysis and processing in a variety of industries. With the widespread application of Hadoop, people are increasingly concerned about the data trustworthiness of HDFS such as the explicit storage of data and over-reliance on authentication mechanisms. In order to ensure the trustworthiness of Hadoop data and to take into account the performance factors of the big data framework, this study focuses on the HDFS-based data trustworthiness problem and the use of node classification, data encryption, trustworthiness measures and other measures to comprehensively enhance the trustworthiness of HDFS file system, improve the existing HDFS system, and ensure the data storage communication in a trustworthy environment.
Optimized phase-coding method for absolute phase retrieval based on K-means algorithm

The phase-coding method for absolute phase measurement possesses a strong robustness because its codewords are encoded into phase domain rather than intensity. However, a misalignment problem arises between fringe order and wrapped phase in this method owing to random noises and image blurring, especially in the high-frequency fringes situation. Therefore, the measurement result is unavailable. To overcome this problem, an optimized phase-coding algorithm is proposed in the study. We use the sinusoidal patterns to generate two opposite binary masks, which can locate half of each codeword. Then, the K-means algorithm is used to correct the codewords errors. The accurate fringe order is obtained by merging two halves of codewords and the absolute phase is recovered afterward. The used method is experimentally verified by an established measurement system, and the results show the proposed method is robustness and efficiency.
Exploring IS security themes: a literature analysis

This study presents a literature review of the Information Systems (IS) security field. The purpose of this review is to identify IS 'security themes'. Articulating IS 'security themes' can assist in making effective decisions and reducing risks faced by organisations. This review analyses 87 journal articles from the AIS Senior Scholars' Basket of Journals, and journals recommended by the AIS Special Interest Group in Information Security and Privacy (SIGSEC). An inductive approach to open coding was followed which led to the emergence of twelve IS 'security themes'. Based on our analysis, we found significant research focus on certain 'security themes' (e.g. IS Security Policy, IS Security Behaviour, IS Security Management, and IS Security Awareness) over others (e.g. IS Security Knowledge, IS Security Ethics, and IS Security Investment). We also examine the role of Security Education, Training, and Awareness (SETA) programmes across the IS 'security themes' and provide some initial observations.
Unsupervised Discriminative Deep Hashing With Locality and Globality Preservation

Deep hashing has greatly improved retrieval performance with the powerful learning capability of deep neural network. However, deep unsupervised hashing can hardly achieve impressive performance due to the lack of the semantic supervision. This letter proposes Unsupervised Discriminative Deep Hashing (UD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> H) to fulfill this gap. UD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> H is formulated to jointly perform hash code learning and clustering, and trained in an asymmetric manner to improve the efficiency. The cluster labels supervise the training of deep model to enable hash code discriminative. Based on the outputs of the deep model, UD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> H adaptively constructs a similarity graph that considers the local and global structures. Experiments on three benchmark datasets show that the proposed UD$^2$H outperforms the state-of-the-art unsupervised deep hashing methods.
Memetic Strategy of Particle Swarm Optimization for One-Dimensional Magnetotelluric Inversions

The heuristic algorithm represented by particle swarm optimization (PSO) is an effective tool for addressing serious nonlinearity in one-dimensional magnetotelluric (MT) inversions. PSO has the shortcomings of insufficient population diversity and a lack of coordination between individual cognition and social cognition in the process of optimization. Based on PSO, we propose a new memetic strategy, which firstly selectively enhances the diversity of the population in evolutionary iterations through reverse learning and gene mutation mechanisms. Then, dynamic inertia weights and cognitive attraction coefficients are designed through sine-cosine mapping to balance individual cognition and social cognition in the optimization process and to integrate previous experience into the evolutionary process. This improves convergence and the ability to escape from local extremes in the optimization process. The memetic strategy passes the noise resistance test and an actual MT data test. The results show that the memetic strategy increases the convergence speed in the PSO optimization process, and the inversion accuracy is also greatly improved.
Gaussian Pseudorandom Number Generator Using Linear Feedback Shift Registers in Extended Fields

A new proposal to generate pseudorandom numbers with Gaussian distribution is presented. The generator is a generalization to the extended field GF(2n) of the one using cyclic rotations of linear feedback shift registers (LFSRs) originally defined in GF(2). The rotations applied to LFSRs in the binary case are no longer needed in the extended field due to the implicit rotations found in the binary equivalent model of LFSRs in GF(2n). The new proposal is aligned with the current trend in cryptography of using extended fields as a way to speed up the bitrate of the pseudorandom generators. This proposal allows the use of LFSRs in cryptography to be taken further, from the generation of the classical uniformly distributed sequences to other areas, such as quantum key distribution schemes, in which sequences with Gaussian distribution are needed. The paper contains the statistical analysis of the numbers produced and a comparison with other Gaussian generators.
WSDM 2021 Tutorial on Conversational Recommendation Systems

Recent years have witnessed the emerging of conversational systems, including both physical devices and mobile-based applications. Both the research community and industry believe that conversational systems will have a major impact on human-computer interaction, and specifically, the IR/DM/RecSys communities have begun to explore Conversational Recommendation Systems. Conversational recommendation aims at finding or recommending the most relevant information (e.g., web pages, answers, movies, products) for users based on textual- or spoken-dialogs, through which users can communicate with the system more efficiently using natural language conversations. Due to users' constant need to look for information to support both work and daily life, conversational recommendation system will be one of the key techniques towards an intelligent web. The tutorial focuses on the foundations and algorithms for conversational recommendation, as well as their applications in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating conversational recommendation methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
Factors Affecting Synchronization Time of Tree Parity Machines in Cryptography

This article presents experimental results of evaluating factors affecting synchronization time of tree parity machines. Tree parity machines are proposed as a modification of the symmetric encryption algorithm. One of the advantages of the method consists in using the phenomenon of mutual synchronization of neural networks to generate an identical encryption key for users without the need to transfer it. As a result, the factors influencing the synchronization time of neural networks and the level of key cryptographic strength were determined. The degree of influence factors was found out experimentally. The influence of the learning rule on timing and stability of synchronization of neural networks was also determined. As a result, it was determined that the best rule for mutual learning of neural networks is Hebb's rule, and when the architecture of neural networks becomes more complex, the number of hidden neurons should be increased first. The tasks of further research are defined.
Applying and Comparison of Chaotic-Based Permutation Algorithms for Audio Encryption

This research presents, and clari es the application of two permutation algorithms, based on chaotic map systems, and applied to a le of speech signals.They are the Arnold cat map-based permutation algorithm, and the Baker's chaotic map-based permutation algorithm.Both algorithms are implemented on the same speech signal sample.Then, both the premier and the encrypted le histograms are documented and plotted.The speech signal amplitude values with time signals of the original le are recorded and plotted against the encrypted and decrypted les.Furthermore, the original le is plotted against the encrypted le, using the spectrogram frequencies of speech signals with the signal duration.These permutation algorithms are used to shuf e the positions of the speech les signals' values without any changes, to produce an encrypted speech le.A comparative analysis is introduced by using some of sundry statistical and experimental analyses for the procedures of encryption and decryption, e.g., the time of both procedures, the encrypted audio signals histogram, the correlation coef cient between specimens in the premier and encrypted signals, a test of the Spectral Distortion (SD), and the Log-Likelihood Ratio (LLR) measures.The outcomes of the different experimental and comparative studies demonstrate that the two permutation algorithms (Baker and Arnold) are suf cient for providing an ef cient and reliable voice signal encryption solution.However, the Arnold's algorithm gives better results in most cases as compared to the results of Baker's algorithm.
Deep Visual Domain Adaptation

Domain adaptation (DA) aims at improving the performance of a model on target domains by transferring the knowledge contained in different but related source domains. With recent advances in deep learning models which are extremely data hungry, the interest for visual DA has significantly increased in the last decade and the number of related work in the field exploded. The aim of this paper, therefore, is to give a comprehensive overview of deep domain adaptation methods for computer vision applications. First, we detail and compared different possible ways of exploiting deep architectures for domain adaptation. Then, we propose an overview of recent trends in deep visual DA. Finally, we mention a few improvement strategies, orthogonal to these methods, that can be applied to these models. While we mainly focus on image classification, we give pointers to papers that extend these ideas for other applications such as semantic segmentation, object detection, person re-identifications, and others.
WITHDRAWN: Majority vote ensemble classifier for accurate detection of credit card frauds

This paper proposes to design a majority vote ensemble classifier for accurate detection of credit card frauds. In this technique, the behaviour, operational and transactional features of users are combined into a single feature. The user behaviours over a banking website are collected and so that normal and abnormal behaviours of users are classified using Web Markov Skeleton Process (WMSP) model. The operational and transaction features of users are collected and classified using the Random Forest (RF) classifier and Support Vector Machine (SVM), respectively. Finally, the classification results of WMSP, RF and SVM are passed on to the majority voting based ensemble (MVE) classifier, which accurately predicts fraud users. By experimental results, it was shown that the MVE classifier achieve higher detection rate with good accuracy.
Critical Factors Influencing the Adoption of Cloud Computing in Indian Private Sector Organizations

Cloud computing is to gain proficiency with the distributed computing contemplate and distinguishes the challenges for future research. It has numerous advantages over the interferences and difficulties that emerge in the private sector organizations. Distributed computing is a promising innovation and proposed to defeat the difficulties in the private sectors.
A model to estimate the Self-Organizing Maps grid dimension for Prototype Generation

Due to the high accuracy of the K nearest neighbor algorithm in different problems, KNN is one of the most important classifiers used in data mining applications and is recognized in the literature as a benchmark algorithm. Despite its high accuracy, KNN has some weaknesses, such as the time taken by the classification process, which is a disadvantage in many problems, particularly in those that involve a large dataset. The literature presents some approaches to reduce the classification time of KNN by selecting only the most important dataset examples. One of these methods is called Prototype Generation (PG) and the idea is to represent the dataset examples in prototypes. Thus, the classification process occurs in two steps; the first is based on prototypes and the second on the examples represented by the nearest prototypes. The main problem of this approach is a lack of definition about the ideal number of prototypes. This study proposes a model that allows the best grid dimension of Self-Organizing Maps and the ideal number of prototypes to be estimated using the number of dataset examples as a parameter. The approach is contrasted with other PG methods from the literature based on artificial intelligence that propose to automatically define the number of prototypes. The main advantage of the proposed method tested here using eighteen public datasets is that it allows a better relationship between a reduced number of prototypes and accuracy, providing a sufficient number that does not degrade KNN classification performance.