Reviewerly/exHarmony huggingface dataset:
Effective appearance model update strategy in object tracking

Robust and accurate tracking for fast moving object in correlation filter framework is a challenging research problem. The appearance model update strategy impact on performance is usually very significant and hence is worth studying. Unfortunately, very few works focus on this component. This study proposes an adaptive appearance model update method, which utilises both average peak‐to‐correlation energy (APCE) threshold and gradient APCE threshold to measure tracking reliability. When tracking is unreliable, the update rate is modified and the initial template information is used to assist in correcting the model parameters. Different from conventional methods, both APCE threshold and gradient APCE threshold are enhanced by weighting the coefficients. More importantly, the gradient APCE threshold can capture the rapid decline of APCE which implies that the object is in fast motion. Sufficient evaluations on OTB datasets demonstrate that the proposed method can tackle challenging videos well, especially the authors obtain a gain of 4.6% in precision score on the selected 32 videos with fast motion attribute, and a mean gain of 3.0 and 2.2% in precision and success plots on OTB50, compared with the baseline tracker SAMF.
Decision tree combined with PSO-based feature selection for sentiment analysis

Sentiment analysis can be considered as a classification task in natural language processing as it harnesses classification algorithm to predict a particular class in a text data. In the classification task, feature extraction is a process to extract the features of the data so that it can be used as the input of the classification algorithm. However, not all features are particularly relevant for a classifier. Irrelevant features might significantly decrease the performance of classification algorithm. This paper proposes a PSO-based feature selection, combined with decision tree algorithm (PSO-C4.5) for sentiment analysis. The PSO-C4.5 is validated on a private data set, which is a sentiment data set about online transportation in Indonesia. The proposed method considerably enhances the performance of decision tree in comparison with the baseline.
Effects of Different Activation Functions for Unsupervised Convolutional LSTM Spatiotemporal Learning

Convolutional LSTMs are widely used for spatiotemporal prediction.We study the effect of using different activation functions for two types of units within convolutional LSTM modules, namely gate units and non-gate units.The research provides guidance for choosing the best activation function to use in convolutional LSTMs for video prediction.Moreover, this paper studies the behavior of the gate activation and unit activation functions for spatiotemporal training.Our methodology studies the different non-linear activation functions used deep learning APIs (such as Keras and Tensorflow) using the moving MNIST dataset which is a baseline for video prediction problems.Our new results indicate that: 1) the convolutional LSTM gate activations are responsible for learning the movement trajectory within a video frame sequence; and, 2) the non-gate units are responsible for learning the precise shape and characteristics of each object within the video sequence.
A lightweight privacy and integrity preserving range query scheme for mobile cloud computing

Mobile cloud computing has received a particular attention in recent years thanks to the expansion and easy accessibility of smartphones with enhanced capabilities, pushing for data owners to outsource more data to the cloud in order to serve the increasing number of mobile users. To protect data privacy, data have to be encrypted prior to outsourcing. Nevertheless, mobile users should still be able to query data, get the results corresponding to their requests and verify the integrity of the query results, all within a query privacy-preserving and efficient fashion. We focus in this work on the secure range query allowing to search for data belonging to a specific range. At the difference of previous works where data privacy and query results verification require separate structures and thus more overhead, our work achieves both data privacy and query results completeness verification using the encrypted data index and the vector neighbor chain (VNC). As a response to the intrinsic limitations of mobile devices, we design a lightweight protocol based on linear algebra operations for both 1-dimensional and multi-dimensional data. Practical experiments analysis on a real-world U.S. Census demographic dataset shows that our scheme guarantees the privacy-preserving property and is efficient in terms of computation and communication overhead.
Efficient Convolutional Neural Network Weight Compression for Space Data Classification on Multi-fpga Platforms

Convolutional Neural Networks (CNNs) represent the cutting edge in signal analysis tasks like classification and regression. Realization of such architectures in hardware capable of performing high throughput computations, with minimal energy consumption, is a key enabling factor towards the proliferation of analysis immediately after acquisition. Our driving problem is a satellite-based remote sensing platform in which onboard signal processing and classification tasks must take place, given strict bandwidth and energy limitations. In this work, we exploit the implementation of a CNN on Field Programmable Gate Array (FPGA) platforms and explore different ways to minimize the impact of different hardware restrictions to performance. We compare our results against competing technologies such as Graphics Processing Units (GPU) in terms of throughput, latency and energy consumption. In actual experimental runs we demonstrate competitive latency and throughput of the FPGA platform vs. GPU technology at an order-of-magnitude energy savings, which is especially important for space-borne computing.
Spatial and Channel Attention Based Convolutional Neural Networks for Modeling Noisy Speech

In recent years, Residual Networks (ResNets) have significantly increased the modeling power of convolutional neural networks (CNNs) by introducing residual connections. In this paper, we explore the incorporation of spatial and channel attention into the structure of ResNets for noisy speech recognition tasks. In our experiments, we implemented spatial attention as a bottom-up top-down structure where the input features are first down sampled and then up sampled to generate attention maps. At each block of the ResNet, the generated CNN features are composed with spatial attention maps over the temporal-frequency space, learning to attend to salient acoustic features and suppress noise. Our model also includes channel attention that attends to different channels of feature maps. ResNet blocks with spatial and channel attention modules can be easily stacked to construct deeper networks. We show that the proposed network structure has the ability to suppress noisy signals in speech audio without requiring parallel clean speech for training, and achieve promising WER reductions on CHiME2 and CHiME3.
Kullback-Leibler distance and graph cuts based active contour model for local segmentation

Graph cuts based active contour model can obtain the global optimal solution and run faster. To achieve robust local segmentation of inhomogeneous images and improve operation efficiency, a modified Kullback-Leibler distance and graph cuts based active contour (KLD-GCBAC) model in the iterative narrow band frame is proposed in this paper. The dynamic narrow band is constructed between the morphological erosion of evolving curve and the preset outer boundary. In the boundary term of discrete energy formulation, an adaptive coefficient is designed to adjust the weights of edges connecting adjacent pixels. And the Kullback-Leibler distance between the inner and outer local neighborhoods is introduced to the region term as the weight of n-links connecting pixels with terminal nodes. Experimental results on synthetic and medical images with intensity inhomogeneity show that the proposed model can make full use of local features of the image and achieve local segmentation more efficiently.
Object Detection in Curved Space for 360-Degree Camera

360° camera has recently become popular since it can capture the whole 360° scene. A large number of related applications have been springing up. In this paper, We propose a deep learning based object detector that can be applied directly on 360° images. The proposed detector is based on modifications of the faster RCNN model. Three modification schemes are proposed here, including (1) distortion data augmentation, (2) introducing muilti-kernel layers for improving accuracy for distorted object detection, and (3) adding position information into the model for learning spatial information. Additionally, we create two datasets, 360GoogleStreetView and 360Videos, and perform experiments on these two datasets to demonstrate that our object detector provides superior accuracy for object detection directly on 360° images.
Cloud service composition using minimal unsatisfiability and genetic algorithm

Summary The software‐as‐a‐service layer of cloud computing has evolved much interest among the researchers in the academic and industry worlds. It is based on service‐oriented architectures and web service (WS) technology, which are used to offer a remotely accessible application. When a single WS is unable to satisfy all the customer's requirements, a WS composition (WSC) method is applied to connect together various available WSs for building distributed application, using discovery, compatibility checking, selection, and deployment steps. This paper has two‐fold contributions: The major one is the proposition of a new algorithm based on minimally unsatisfiable subset formal algorithm to compose WSs. This contribution ensures a faster WSC, which is prominent in an online context. The second contribution is the proposition of a new penalty‐based genetic algorithm to identify near‐optimal solutions whenever an exact one is not identified using our formal algorithm. Indeed, in some cases, many available WSs provide overlapping/identical functionality; in this context, the choice of participated services is given using quality of service. This work proposes a WSC ensuring to find out an exact or a near‐optimal solution according to the user need. A set of experimentations is applied to validate our contributions.
Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time

In this paper, we present a real-time American Sign Language (ASL) hand gesture recognizer based on an artificial intelligence execution, instead of the classical and outdated image processing modalities. Our approach uses a Convolutional Neural Network (CNN) to train a dataset of hundreds of instances from the ASL alphabet, extracting the features from each and every pixel and constructing an accurate translator based on predictions. This approach employs an atypical trade-off for a translator, where a superior precision and speed at the inference phase compensates for the computational expense at the early training. Furthermore, and to the best of our knowledge, the accuracy obtained by using the proposed deep learning technique, surpass the accuracy obtained using non-machine learning practices. The performance obtained by the proposed algorithm has also been compared with existing literature, showing that the suggested methodology outperformed the accuracy of its analogous counterparts.
Circle of Trust: A High Volume, Energy Efficient, Stake Blind and High Attack Tolerant Blockchain Consensus Protocol

Popular cryptocurrencies like Bitcoin, NEO and Ripple have their own advantages and disadvantage. Bitcoin causes massive depletion of energy and computational power. NEO rewards rich miners with more blocks to validate. Ripple can only process a mere 1500 transactions per second <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> with a block time of 3.5 seconds <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> . All these major blockchains have major flaws. My Consensus Protocol “Circle of Trust” overcomes the drawbacks of other consensus protocols such as PoW, PoS and DPos. Apart from this, Circle of Trust also has an architecture capable of scaling the transactions per second, as the network of miners grow, which no other blockchain on the market is capable of doing. This consensus protocol will be able to beat VISAs 56,000 transactions per second <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</sup> while maintaining the sequence of blocks. All this can be done because, the concept of trust score is at the heart of this consensus protocol.
On the Apparently Inappropriate Use of Multiple Hypothesis Testing in Earthquake Prediction Studies

Research Article| April 10, 2019 On the Apparently Inappropriate Use of Multiple Hypothesis Testing in Earthquake Prediction Studies Mamoru Kato Mamoru Kato aEarth Science Group, Graduate School of Human and Environmental Studies, Kyoto University, Yoshida‐Nihonmatsu, Sakyo, Kyoto 606‐8501, Japan, mkato@gaia.h.kyoto-u.ac.jp Search for other works by this author on: GSW Google Scholar Author and Article Information Mamoru Kato aEarth Science Group, Graduate School of Human and Environmental Studies, Kyoto University, Yoshida‐Nihonmatsu, Sakyo, Kyoto 606‐8501, Japan, mkato@gaia.h.kyoto-u.ac.jp Publisher: Seismological Society of America First Online: 10 Apr 2019 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (3): 1330–1334. https://doi.org/10.1785/0220180378 Article history First Online: 10 Apr 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Mamoru Kato; On the Apparently Inappropriate Use of Multiple Hypothesis Testing in Earthquake Prediction Studies. Seismological Research Letters 2019;; 90 (3): 1330–1334. doi: https://doi.org/10.1785/0220180378 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT With the goal of mitigating the disasters that are associated with large earthquakes, various geophysical phenomena have been suggested as potential earthquake precursors. The theoretical explanations underlying these proposals are lacking in most cases, and these proposals are often supported by observational evidence in which the precursors are discovered through trial and error. This heuristic approach requires researchers to simultaneously find precursory anomalies and the thresholds used to detect them in a single study. This approach requires the repeated use of statistical hypothesis tests. The theory of multiple hypothesis testing predicts that the repeated application of statistical tests increases the chance that we will obtain false positives. Several measures that can help avoid the identification of false positives have been recommended, but these measures are frequently not applied in studies of earthquake precursors. We argue that the inappropriate use of multiple hypothesis testing may have resulted in apparently successful discoveries of earthquake precursors. We recommend the reevaluation of earthquake precursors that have been identified using heuristic search methods. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Sentiment Analysis for Scraping of Product Reviews from Multiple Web Pages Using Machine Learning Algorithms

Sentiment analysis is the computational task of automatically determining what feelings a writer is expressed in text. Sentiment analysis is gaining much attention in recent years. It is often framed as a binary distinction, i.e. positive vs. negative, but it can also be a more fine-grained, like identifying the specific emotion an author is expressing like fear, joy or anger. Globally, business enterprises can leverage opinion polarity and sentiment, topic recognition to gain deeper understanding of the drivers and the overall scope. Subsequently, these insights can advance competitive intelligence and improve customer service, thereby creating a better brand image and providing a competitive edge. To extract the content from e-commerce website using web scraping technique. It will be looping through then number of pages or so of comments for each of the products. In this work, online product reviews are collected using web scraping technique. The collected online product reviews are analyzed using opinion or sentiment analysis using classification models such as KNN, SVM, Random Forest, CNN (Convolutional Neural Network) and proposed hybrid SVM-CNN. Experiments for the classification models are performed with promising outcomes.
Development and future trends in the application of visualization toolkit (VTK): The case for medical image 3D reconstruction

Three-dimensional reconstruction of medical images plays a vital role in clinical medicine and medical research. It serves as a powerful tool to help doctors analyze multiple 2D medical images in a 3D perspective. The developed 3D model offers a faster and accurate approach in clinical diagnosis and therapy. Before, the development of 3D reconstruction programs it has always been a challenge to visualize in a 3D perspective but with the advent of powerful computing and graphics processing, medical imaging equipment development is on the rise. The advancement of 3D image reconstruction is not only due to the innovative hardware, but also because of the increase in open-source platforms or libraries, such as visualization toolkit (VTK), that assist on its development to provide an information visualization framework and advanced 3D modeling techniques. VTK is an object-oriented 3D graphics software program that is free to obtain and used both for open- and closed-source applications such as 3D computer graphics, image processing, and visualization. In medical imaging, data from ultrasound systems, magnetic resonance imaging (MRI), and computed tomography (CT) scans can be managed in VTK. Different algorithms of image processing such as color mapping, image re-slicing or resampling, iso-contouring, thresholding, and other algorithms can be utilized to make volume images from the data. Subsequently, data presentation is simplified with the data reconstruction through the use of supplemental software for analysis. VTK library contains a large number of filters, which are multi-threaded to facilitate parallel processing and interpretation. In this paper, recent technologies on the application of VTK for 3D reconstruction of biomedical images will be presented. Moreover, current methodologies will be discussed to clearly understand the different integral steps needed in image data conversion.
Query Expansion for Effective Retrieval Results of Hindi–English Cross-Lingual IR

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Fully convolutional multi‐scale dense networks for monocular depth estimation

Monocular depth estimation is of vital importance in understanding the 3D geometry of a scene. However, inferring the underlying depth is ill‐posed and inherently ambiguous. In this study, two improvements to existing approaches are proposed. One is about a clean improved network architecture, for which the authors extend Densely Connected Convolutional Network (DenseNet) to work as end‐to‐end fully convolutional multi‐scale dense networks. The dense upsampling blocks are integrated to improve the output resolution and selected skip connection is incorporated to connect the downsampling and the upsampling paths efficiently. The other is about edge‐preserving loss functions, encompassing the reverse Huber loss, depth gradient loss and feature edge loss, which is particularly suited for estimation of fine details and clear boundaries of objects. Experiments on the NYU‐Depth‐v2 dataset and KITTI dataset show that the proposed model is competitive to the state‐of‐the‐art methods, achieving 0.506 and 4.977 performance in terms of root mean squared error respectively.
A Recommender System Based on Deep Neural Network and Matrix Factorization for Collaborative Filtering

In this paper, a revised recommendation system is constructed that ensembles deep neural network and matrix factorization under its framework and uses the explicit feedback for collaborative filtering. Recent works used deep neural network in recommendation for processing auxiliary attributes, but their interaction function is just an inner product on latent features of users and items. For modelling the recommendation system in this research, multi-layer perceptron was used to learn the interaction function. Experiments show significant decrease in MAE and RMSE to be 0.69 and 0.94 respectively, which is comparatively better than general collaborative filtering methods.
Heterogeneous Information Fusion for Multitarget Tracking Using the Sum-product Algorithm

The sum-product algorithm (SPA) was recently shown to provide a scalable methodology for multitarget tracking (MTT) using multiple sensors. Here, we focus on another advantage of the SPA frame-work, namely, its capacity for Bayesian fusion of heterogeneous data sources and auxiliary information. We develop extensions of the SPA-based multisensor MTT algorithm that integrate data from an auxiliary surveillance system and geographic information about standard target routes. The effectiveness of our approach is demonstrated for a simulated scenario and for a real maritime scenario.
Image Based Phishing Detection Using Transfer Learning

This paper considers the problem of a phishing detection based on the visual appearance of a login page of a website. Two main procedures which are the login page data augmentation and the login page classification are taken into account to achieve the phishing detection task. Convolutional neural networks (CNNs) are applied to the classification task since they justify their effectiveness on the image classification during the current decade but training the whole weights of a CNN model requires a large dataset and spends a lot of time. This leads to the requirement of the data augmentation to produce more data from the original data. This paper proposes a subimage random placement method for the data augmentation task which is the main contribution of this paper. Transfer learning is also applied to reduce a long training time by using a pre-trained model and training only the fully connected layer for a new classification task. This paper will conduct the experiment in two aspects, the binary classification and the multiclass classification, and the accuracy comparisons among various pre-trained CNNs. The result will show that transfer learning works well with the data that are generated in the same way of the proposed data augmentation.