Reviewerly/exHarmony huggingface dataset:
A new particle filter based on smooth variable structure filter

Summary In this paper, a new particle filter (PF) based on the smooth variable structure filter (SVSF) is presented. Here, PF is a popular estimation method that makes use of distributed particles to form an approximation of the probability distribution function. The SVSF is a sliding mode estimator, which is based on predictor‐corrector methods and sliding mode concepts. Moreover, the gain of the SVSF is calculated based on a switching surface. The new filter called SVSF‐PF utilizes a smoothing boundary layer of SVSF to control the particles in a range, which can improve the overall estimation accuracy and stability. Finally, a simulation example of nonlinear target tracking is used to demonstrate effectiveness.
Enhancing Automatic Reasoning of human errors in an operating system using fuzzy logic

In this paper a novel fuzzy mechanism for automatic reasoning of human errors in operating systems is presented. The presented mechanism combines Human Plausible Reasoning (HPR) theory with fuzzy logic. HPR is used for inferring the commands the user of an operating system should have type and fuzzy logic is used to handle the uncertainty that characterizes the complex reasoning process by modelling the errors' types in a more realistic way. Particularly, the output of HPR theory guesses all the possible command that the user may wants to type. These guesses can include many types of errors varying from typographic to wrong use of a legal command. In the presented mechanism, these guesses are input in a fuzzy reasoner, which takes into account the needs, characteristics and misconceptions of each individual user and decides about the most appropriate explanation of user's error and gives personalized advice that fits better in each context and situation. The mechanism has been applied on the sub-domain of file manipulation of UNIX. The potential of the presented mechanism to reason about operating system's users' slips and misconceptions are discussed.
WITHDRAWN: Feature extraction algorithm of audio and video based on clustering in sports video analysis

Abstract In order to extract audio/visual mid-level features of sport videos, this paper uses supervised audio classification and unsupervised scene clustering to satisfy versatility requirements. The sound in the sports video is more robust, and the supervised audio classification can be used for the versatility of a competition. When extending this method to other competitions, such as diving, baseball, supervised methods also require only a small number of annotations. For video, due to heavy scene differences, unsupervised scene clustering is used to achieve versatility. This paper proposes a new and effective scene clustering algorithm, which automatically determines the stopping point of the algorithm without prior knowledge. The experimental results show that although the number of clusters of different videos is different, but the final scene classification results are consistent with people's expectations.
Effect of Feature Selection in Software Fault Detection

The quality of software is enormously affected by the faults associated with it. Detection of faults at a proper stage in software development is a challenging task and plays a vital role in the quality of the software. Machine learning is, now a days, a commonly used technique for fault detection and prediction. However, the effectiveness of the fault detection mechanism is impacted by the number of attributes in the publicly available datasets. Feature selection is the process of selecting a subset of all the features that are most influential to the classification and it is a challenging task. This paper thoroughly investigates the effect of various feature selection techniques on software fault classification by using NASA's some benchmark publicly available datasets. Various metrics are used to analyze the performance of the feature selection techniques. The experiment discovers that the most important and relevant features can be selected by the adopted feature selection techniques without sacrificing the performance of fault detection.
Kernel-Based Generative Adversarial Networks for Weakly Supervised Learning

In recent years, Deep Learning methods have become very popular in NLP classification tasks, due to their ability to reach high performances by relying on very simple input representations. One of the drawbacks in training deep architectures is the large amount of annotated data required for effective training. One recent promising method to enable semi-supervised learning in deep architectures has been formalized within Semi-Supervised Generative Adversarial Networks (SS-GANs). In this paper, an SS-GAN is shown to be effective in semantic processing tasks operating in low-dimensional embeddings derived by the unsupervised approximation of rich Reproducing Kernel Hilbert Spaces. Preliminary analyses over a sentence classification task show that the proposed Kernel-based GAN achieves promising results when only 1% of labeled examples are used.
Detecting Spam Images with Embedded Arabic Text in Twitter

Detecting image-based spam in Online Social Networks (OSNs), such as Facebook and Twitter, is an ongoing problem. Spam is prevalent in all forms of online communication (such as email and the web) However, researchers' and practitioners' attention has increasingly shifted to spam in OSNs, due to the growing number of spammers and the possible negative effects on users. There are different types of spam messages that can be found in OSNs. Spam images, which are images embedded with malicious text, are one of the most difficult types of spam to tackle. Processing images overwhelms classifiers and affects detection performance. Consequently, spammers take advantage of this issue to launch more sophisticated attacks, such as evasion attacks. After observing some Arabic trending hashtags and topics in Twitter, a substantial amount of image-based spam was found. Thus, this paper proposes an approach for detecting image-based spam with Arabic text in Twitter through using Deep Learning (DL) techniques. In this paper, an Efficient and Accurate Scene Text Detector (EAST) and Convolutional Recurrent Neural Network (CRNN) models were used for text detection and text recognition. After the text extraction step, a blacklist and whitelist approach was applied for classifying text as either spam or non-spam. The proposed text classification technique is adaptable and robust against some text classification attacks.
Three-dimensional Interaction Technique Using an Acoustically Manipulated Balloon

We propose a system that uses an acoustically manipulated balloon as a visual and tangible interface for the representation of a mid-air virtual object in a full-body augmented reality environment. In this system, airborne ultrasound phased-array transducers on the ceiling actuate a spherical balloon inflated with a mixture of helium and air. This configuration permits (1) a full-body workspace with lateral scalability, (2) a long flight time, (3) good visibility, and (4) easily tangible access to the balloon. A projector-camera system projects a 2D or 3D perspective-correct image onto the balloon. The user can manipulate the corresponding virtual object by physically manipulating the balloon.
Review on Text Sequence Processing with use of different Deep Neural Network Model

This paper underlines the importance of Deep Neural Network model in different learning tasks for Natural language processing.Deep learning techniques carry a lot of support to handle a large amount of trained or labeled data sets.Deep Neural Network model involves multiple processing layers to learn Sequential representations of Text data to achieve excellent performance in many domains.In this paper, we review significant deep learning models with end to end approach for Natural language processing that makes a more accurate assumption on the word sequence as well as represents a different experimental analysis for NLP tasks in terms of text and words with the help of CNN, RNN, LSTM, and GRU bidirectional Encode-Decoder.Numerous NLP tasks are involved in semantic execution to understand and generate a complete sentence where different Deep learning models represent awareness about numerous NLP tasks including sequential information processing.That's a reason why Deep Neural Network Model gradually became more famous for different NLP applications.Even we have endorsed reinforcement learning as an extension to the Deep neural model which is frequently used in Natural Language Processing.This paper differentiates the Deep Neural Model in various language processing tasks and directs the selection of Deep Neural Model in different NLP tasks.
A Comparison of Machine Learning Techniques for the Prediction of the Student’s Academic Performance

The aim of this paper to predict a student performance using traditional and machine learning techniques: Bayes algorithm, linear regression, logistic Regression, k-nn algorithm, decision tree. Naive based algorithm is the emerging field which compose the procedure of verified students details like semester marks, assignment, attendance, lab work which are used to improve students' performance. This paper shows a model of students data prediction based on Bayes algorithm, linear regression, logistic Regression, k-nearest neighbor, decision tree and suggest the best algorithm among these algorithms based on performance details. Classification is an important area to predict and application in a variety of fields. In the view of full knowledge of the algorithm underlying probabilities, Bayes decision theory shows the optimal error rate. Decision tree algorithm is been used successfully in expert systems in capturing prediction. Mainly the decision tree classifiers are used to design and classify the student's data with Boolean class labels. Linear regression is a linear approach to modeling the relationship between the details of students in scalar response.
Edge Computing for Terminal Query Based on IoT

With the fast development of the IoT, more and more attention has been paid to the acquisition of valuable information from real-time data streams. Nowadays, sampling-based approximate data analytics are properly the most widely used, which trade the output quality for efficiency. What's more, large bandwidth is need for transmitting large amounts of data is these systems. In fact, for some simple queries, we can use the computing ablility of edge nodes to get accurate and lossless results. This idea promotes the design of CalculIot, a real-time data flow system for edge computing and analysis. We construct edge computing nodes and transmission nodes to transfer the computing process originally in the cloud data center to the edge end, making full use of the computing resources of edge nodes, and at the same time, reducing the network transmission pressure and improving efficiency. To showcase the effectiveness of this algorithm, we implemented CalculIot and evaluated its effectiveness using a set of microbenchmarks and real-world case studies. The results illustrated that for terminal query, our algorithm has higher accuracy and efficiency.
Where to Place the Camera

This paper describes aspects which are important for camera positioning in cinematic virtual reality. For our findings, we took a closer look at proxemics, the study on how humans behave in regard to space and distances. We explored well-known shot sizes used in traditional filmmaking and put them in relation to proxemics distances. The results were adapted to camera distances in cinematic virtual reality.
Quality-aware Service Selection Approach for Adaptive Context Recognition in IoT

While developing context-aware applications, there may be uncertainty with respect to the available data sources. Applications that are developed to a fixed set of data sources may not be flexible enough, to adapt to change in the sensing environment such as sensor disappearance or degradation. Opportunistic sensing tackles this problem by enabling the automatic detection and selection of data sources. However, existing approaches rely on a limited number of quality metrics and do not take into account the influence of data processing on the quality of context recognition. In this paper, we present an extension of the opportunistic sensing approach that is able to take into account quality metrics like execution time affecting the overall quality. Our approach consists of modelling of available data sources and data processing methods that can be used to assemble context recognition chains and estimate their quality. We present a prototypical implementation of the models and mechanisms in an autonomous driving test environment and provided testing results on a use case for finding traffic congestions in a specified route.
Mapping the "How" of Collaborative Action

Process has been a concern since the beginning of CSCW. Developments in sociotechnical landscapes raise new challenges for studying processes (e.g., massive online communities bringing together vast crowds; Big Data technologies connecting many through the flow of data). This re-opens questions about how we study, document, conceptualize, and design to support processes in complex, contemporary sociotechnical systems. This one-day workshop will bring together researchers to discuss the CSCW community's unique focus and methodological toolkit for studying process and workflow; provide a collaborative space for the improvement and extension of research projects within this space; and catalyze a network of scholars with expertise and interest in addressing challenging methodological questions around studying process in contemporary, sociotechnical systems.
Augmentation Invariant Training

Data augmentation is acknowledged to help deep neural networks generalize better, while their augmentation transfer ability is far from satisfactory. The networks perform worse when tested with augmentations not used during training, which is also a manifestation of insufficient generalization ability. To address this problem, we carefully design a novel Augmentation invariant Loss (AiLoss) to assist networks to learn augmentation invariant by minimizing intra-augmentation variation. Based on AiLoss, we propose a simple yet efficient training strategy, Augmentation Invariant Training (AIT), to enhance the generalization ability of networks. Extensive experiments show that AIT can be applied to a variety of network architectures, and consistently improve their performance on CIFAR-10, CIFAR-100 and ImageNet without increasing computational cost. Further extending AIT to multiple networks, we propose multi-AIT to learn inter-network augmentation invariant, which achieves better performance in enhancing generalization ability. Moreover, further experiments present that networks trained with our strategy do obtain better augmentation transfer ability and learn features that are invariant to augmentations. Our source code is available at Github.
Shoot Less and Sketch More: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-Shot Learning

With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training, which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of sketches, showing promising results.
A Mobile Application Classification Method with Enhanced Topic Attention Mechanism

Faced with the explosive growth of mobile applications, how to classify mobile applications correctly and efficiently is more helpful for users to choose their own mobile applications, which has become a challenging issue. For this reason, we propose a mobile application classification method with enhanced topic attention mechanism. Firstly, our approach uses LSA to obtain the global topic of mobile application description text. Then, the local hidden representations of mobile application are trained by BiLSTM model. Secondly, for mobile application content representation text rich in global topic information and local semantic information, attention mechanism is introduced to distinguish the contribution degree of different words and calculate their weight values. Thirdly, the classification and prediction of mobile application can be completed by using the softmax activation function through a full connection layer. Finally, we evaluate our method on a real and open dataset Mobile App Store. On the whole, the experimental results illustrate that the performance of our approach is better than other comparison methods, and the classification accuracy of mobile applications is indeed improved. Particularly, compared with the standard LSTM model, the method proposed in this paper increased more than 12.7% in F1 score.
I-Ulysses: A technical report

The I-Ulysses: Poetry in Motion project is as a virtual reality experience, guiding the user through the unfolding events of James Joyce's Ulysses in real-time. Currently there is a lack of research looking at adapting literature into virtual reality, for the purposes of cultural heritage, or for serious learning. I-Ulysses is aimed at addressing this gap, by providing an educational tool, intended to help the user understand key aspects of the book. What follows is a technical report, outlining the objectives, aims and results of the I-Ulysses: Poetry in Motion project.
Integrating Multi-Network Topology via Deep Semi-supervised Node Embedding

Node Embedding, which uses low-dimensional non-linear feature vectors to represent nodes in the network, has shown a great promise, not only because it is easy-to-use for downstream tasks, but also because it has achieved great success on many network analysis tasks. One of the challenges has been how to develop a node embedding method for integrating topological information from multiple networks. To address this critical problem, we propose a novel node embedding, called DeepMNE, for multi-network integration using a deep semi-supervised autoencoder. The key point of DeepMNE is that it captures complex topological structures of multiple networks and utilizes correlation among multiple networks as constraints. We evaluate DeepMNE in node classification task and link prediction task on four real-world datasets. The experimental results demonstrate that DeepMNE shows superior performance over seven state-of-the-art single-network and multi-network embedding algorithms.
Discriminatively Learned Convex Models for Set Based Face Recognition

Majority of the image set based face recognition methods use a generatively learned model for each person that is learned independently by ignoring the other persons in the gallery set. In contrast to these methods, this paper introduces a novel method that searches for discriminative convex models that best fit to an individual's face images but at the same time are as far as possible from the images of other persons in the gallery. We learn discriminative convex models for both affine and convex hulls of image sets. During testing, distances from the query set images to these models are computed efficiently by using simple matrix multiplications, and the query set is assigned to the person in the gallery whose image set is closest to the query images. The proposed method significantly outperforms other methods using generative convex models in terms of both accuracy and testing time, and achieves the state-of-the-art results on four of the five tested datasets. Especially, the accuracy improvement is significant on the challenging PaSC, COX and ESOGU video datasets.
The Novel Applications of Deep Reservoir Computing in Cyber-Security and Wireless Communication

This chapter introduces the novel applications of deep reservoir computing (RC) systems in cyber-security and wireless communication.The RC systems are a new class of recurrent neural networks (RNNs).Traditional RNNs are very challenging to train due to vanishing/exploding gradients.However, the RC systems are easier to train and have shown similar or even better performances compared with traditional RNNs.It is very essential to study the spatio-temporal correlations in cyber-security and wireless communication domains.Therefore, RC models are good choices to explore the spatio-temporal correlations.In this chapter, we explore the applications and performance of delayed feedback reservoirs (DFRs), and echo state networks (ESNs) in the cyber-security of smart grids and symbol detection in MIMO-OFDM systems, respectively.DFRs and ESNs are two different types of RC models.We also introduce the spiking structure of DFRs as spiking artificial neural networks are more energy efficient and biologically plausible as well.
A Futuristic Vision of Forensic Science

Journal of Forensic SciencesVolume 65, Issue 1 p. 8-10 Editorial A Futuristic Vision of Forensic Science Itiel E. Dror Ph.D., Corresponding Author Itiel E. Dror Ph.D. i.dror@ucl.ac.uk UCL Centre for the Forensic Sciences, University College London, London, U.K UCL Department of Security and Crime Science, University College London, London, U.K Corresponding author: Itiel E. Dror, Ph.D. E-mail: i.dror@ucl.ac.ukSearch for more papers by this authorRuth M. Morgan D.Phil., Ruth M. Morgan D.Phil. orcid.org/0000-0002-4146-654X UCL Centre for the Forensic Sciences, University College London, London, U.K UCL Department of Security and Crime Science, University College London, London, U.KSearch for more papers by this author Itiel E. Dror Ph.D., Corresponding Author Itiel E. Dror Ph.D. i.dror@ucl.ac.uk UCL Centre for the Forensic Sciences, University College London, London, U.K UCL Department of Security and Crime Science, University College London, London, U.K Corresponding author: Itiel E. Dror, Ph.D. E-mail: i.dror@ucl.ac.ukSearch for more papers by this authorRuth M. Morgan D.Phil., Ruth M. Morgan D.Phil. orcid.org/0000-0002-4146-654X UCL Centre for the Forensic Sciences, University College London, London, U.K UCL Department of Security and Crime Science, University College London, London, U.KSearch for more papers by this author First published: 11 November 2019 https://doi.org/10.1111/1556-4029.14240Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume65, Issue1January 2020Pages 8-10 RelatedInformation