Reviewerly/exHarmony huggingface dataset:
Noise-Aware Framework for Robust Visual Tracking

Both siamese network and correlation filter (CF)-based trackers have exhibited superior performance by formulating tracking as a similarity measure problem, where a similarity map is learned by the correlation between a target template and a region of interest (ROI) with a cosine window. Nevertheless, this window function is usually fixed for various targets and not changed, undergoing significant noise variations during tracking, which easily makes model drift. In this article, we focus on the study of a noise-aware (NA) framework for robust visual tracking. To this end, the impact of various window functions is first investigated in visual tracking. We identify that the low signal-to-noise ratio (SNR) of windowed ROIs makes the above trackers degenerate. At the prediction phase, a novel NA window customized for visual tracking is introduced to improve the SNR of windowed ROIs by adaptively suppressing the variable noise according to the observation of similarity maps. In addition, to further optimize the SNR of windowed pyramid ROIs for scale estimation, we propose to use the particle filter to dynamically sample several windowed ROIs with more favorable signals in temporal domains instead of this pyramid ROIs extracted in spatial domains. Extensive experiments on the popular OTB-2013, OTB-50, OTB-2015, VOT2017, TC128, UAV123, UAV123@10fps, UAV20L, and LaSOT datasets show that our NA framework can be extended to many siamese and CF trackers and our variants obtain superior performance than baseline trackers with a modest impact on efficiency.
A Multi-Objective Enhancement Technique for Poor Contrast Magnetic Resonance Images of Brain Glioblastomas

Magnetic Resonance Imaging (MRI) is primary imaging technique for detection of Brain tumors called Glioblastomas. The quality of the MRI images deteriorate due to the patient condition, imaging procedures, resolution of MRI machines and skills of the technician. Image Enhancement is performed to improve the image quality after acquisition. Most of the enhancement techniques deal with varying the intensities of the pixels; however, the image entropy or edge information is never enhanced. Contrast Limited Adaptive Histogram Equalization (CLAHE) is widely used contrast enhancement technique that modifies only the intensity of the pixels based on the intensity of the neighbouring pixels. In addition, it involves selection of the operational parameters empirically. Hence, there is a need to develop a multi-objective enhancement technique to enhance the image quality in terms of entropy and edge information and not only intensity of pixels. As well as to select the parameters for CLAHE adaptively, without any user intervention. The proposed technique shows improved results for contrast, entropy, peak signal to noise ratio, structural similarity, unique image quality index and mean square error.
Simplifying the Search of npm Packages

Code reuse, generally done through software packages, allows developers to reduce time-to-market and improve code quality. The npm ecosystem is a Node.js package management system which contains more than 700 K Node.js packages and to help developers find high-quality packages that meet their needs, npms developed a search engine to rank Node.js packages in terms of quality, popularity, and maintenance. However, the current ranking mechanism for npms tends to be arbitrary and contains many different equations, which increases complexity and computation. The goal of this paper is to empirically improve the efficiency of npms by simplifying the used components without impacting the current npms package ranks. We use feature selection methods with the aim of simplifying npms' equations. We remove the features that do not have a significant effect on the package's rank. Then, we study the impact of the simplified npms on the packages' rank, the amount of resources saved compared to the original npms, and the performance of the simplified npms as npm evolves. Our findings indicate that (1) 31% of the unique variables of npms' equation can be removed without breaking the original packages' ranks; (2) The simplified npms, on average, preserves the overlapping of the packages by 98% and the ranking of those packages by 97%; (3) Using the simplified npms saves 10% of packages scoring time and more than 1.47 million network requests on each scoring run; (4) As the npm evolve through a period of 12 months, the simplified-npms was able to achieve results similar to the original npms. Our results show that the simplified npms preserves the original ranks of packages and is more efficient than the original npms. We believe that using our approach, helps the npms community speed up the scoring process by saving computational resources and time.
Altering the Speed of Reality?

Many events happen so fast that we cannot observe them well with our naked eye. The temporal and spatial limitations of visual perception are well known and determine what we can actually see. Over the last years, sensors and camera systems became available that have surpassed the limitations of human perception. In this paper, we investigate how we can use augmented reality to create a system that allows altering the speed in which we perceive the world around us. We contribute an experimental exploration of how we can implement visual slow-motion to amplify human perception. We outline the research challenges and describe a conceptual architecture for manipulating the temporal perception. Using augmented reality glasses, we created a proof-of-concept implementation and conducted a study of which we report qualitative and quantitative results. We show how providing visual information from the environment at different speeds has benefits for the user. We also highlight the required new approaches to design interfaces that deal with decoupling the perception of the real would.
Grouped Heterogeneous Mixture Modeling for Clustered Data

Clustered data are ubiquitous in a variety of scientific fields. In this article, we propose a flexible and interpretable modeling approach, called grouped heterogeneous mixture modeling, for clustered data, which models cluster-wise conditional distributions by mixtures of latent conditional distributions common to all the clusters. In the model, we assume that clusters are divided into a finite number of groups and mixing proportions are the same within the same group. We provide a simple generalized EM algorithm for computing the maximum likelihood estimator, and an information criterion to select the numbers of groups and latent distributions. We also propose structured grouping strategies by introducing penalties on grouping parameters in the likelihood function. Under the settings where both the number of clusters and cluster sizes tend to infinity, we present asymptotic properties of the maximum likelihood estimator and the information criterion. We demonstrate the proposed method through simulation studies and an application to crime risk modeling in Tokyo.
Proving data-poisoning robustness in decision trees

Machine learning models are brittle, and small changes in the training data can result in different predictions. We study the problem of proving that a prediction is robust to data poisoning, where an attacker can inject a number of malicious elements into the training set to influence the learned model. We target decision-tree models, a popular and simple class of machine learning models that underlies many complex learning techniques. We present a sound verification technique based on abstract interpretation and implement it in a tool called Antidote. Antidote abstractly trains decision trees for an intractably large space of possible poisoned datasets. Due to the soundness of our abstraction, Antidote can produce proofs that, for a given input, the corresponding prediction would not have changed had the training set been tampered with or not. We demonstrate the effectiveness of Antidote on a number of popular datasets.
Service-Less Video Multicast in 5G: Enablers and Challenges

The massive upsurge in production and demand of crowdsourced live streaming necessitates feasible and cost effective solutions in future cellular networks. Video multicast is one of the viable options to deliver live video using standardized multicast technologies, such as evolved Multimedia Broadcast Multicast Services (eMBMS) architecture. However, eMBMS being a service-oriented architecture, it does not support crowdsourced live video streaming from popular providers like Twitch, YouTube Live, and Facebook Live, where millions of viewers watch live streams. This article explores the possibilities for the crowdsourced service-less video multicast in fifth generation (5G) Radio Access Network (RAN). We highlight major limitations in the standard eMBMS architecture and propose a viable solution using Network Function Virtualization (NFV) and Mobile Edge Computing (MEC). The main challenges related to the initiation of an eMBMS service are presented, followed by our proposed network architecture, which augments the eMBMS architecture to initiate a service-less multicast video delivery for crowdsourced live videos. We evaluate and validate the proposed architecture using a real-world live video dataset. Our results depict the feasibility of the proposed architecture to multicast crowdsourced live streams and significant savings in cost and network consumption.
NITCAD - Developing an object detection, classification and stereo vision dataset for autonomous navigation in Indian roads

Autonomous vehicles with various levels of autonomy are becoming popular in developed countries due to their effectiveness in reducing the fatalities caused by road accidents. A developing country like India with the second largest population in the world, creates unique road scenarios for an autonomous car which requires a lot of testing and fine tuning before implementation. This leads to the importance of datasets providing information about various traffic situations in India. For planning its path ahead, autonomous vehicles have to detect, classify and estimate the depth of obstacles that they encounter on roads. The purpose of this paper is to provide a dataset for object classification, detection and stereo vision corresponding to Indian roads which can serve as a platform for developing effective algorithms for autonomous cars in Indian roads. In this work, we benchmarked the object classification by using confusion matrix obtained from various deep learning models, evaluated detection using Faster R-CNN and compared depth estimation processed by Realsense stereo camera by applying convolutional neural network based algorithms.
Towards the cognitive and psychological perspectives of crowd behaviour: a vision-based analysis

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Object Recognition App for Visually Impaired

Vision is one of the most important senses that help people interact with the real world. There are nearly 200 million blind people all over the world, and being visually impaired hinders a lot of day to day activities. Thus it is very necessary for blind people to understand their surroundings, and to know what objects they interact with. This project proposes an android application to help blind people see through handheld device like mobile phone. It integrates various techniques to build a rich android application that will not only recognize objects around visually impaired people in real time but also give an audio output to assist them as quickly as possible. SSD (Single Shot Detector) Algorithm is used for the object recognition as well as detection. Also this algorithm gives nearly accurate results for real time object detection and is proven to be faster than other relative algorithms. The application further uses android tensorflow APIs and android TextToSpeech API to give audio output.
Total organic carbon quantification from wireline logging techniques: A case study in the Vaca Muerta Formation, Argentina

The Vaca Muerta Formation, one of the most prolific unconventional play in South America, exhibits concentrations of total organic carbon (TOC) of up to 16% with variations in TOC paralleling the general facies evolution of the prograding system. This study examines the applicability and accuracy of predicting TOC from two methods of quantifying organic-rich zones from wireline logs: the relationship of uranium from spectral gamma-ray to TOC, and the δlogR technique for calculating TOC. A database from extensive outcrop geochemical sampling (n = ~1900) was correlated to the uranium signal from a handheld spectral gamma-ray logging tool. The U/TOC relationship derived from the outcrop samples shows high variability between outcrop sections measured across the formation. The U/TOC ratio increases from proximal to distal positions within the basin. A second dataset, consisting of core samples, was also analyzed and compared to the TOC calculated using a combination of the sonic and resistivity data obtained from downhole logging. This second method of TOC prediction reliably and accurately reproduces laboratory measurements, and is the far superior choice when estimating organic richness from wireline log signatures. To further test this method of TOC prediction, a subset of sidewall plugs were analyzed for petrophysical and geochemical measurements and using the δlogR technique, was able to produce TOC values that compared to discrete laboratory measurements with a correlation coefficient of R2 = 0.83. The time-tested method of determining organic richness from wireline logs can be successfully applied in the Neuquén Basin and could be attempted in other basins once all available data has been carefully considered.
Sentiment Analysis on Customer Reviews using Convolutional Neural Network

21st century is touted as a data revolution where tons of data silos being created every second. These data silos are stored across various servers and creating authentic data mines across multiple locations. Business leaders across the world realized the significance of the information residing in these data silos and constantly trying to extract relevant information which can be used to strategize and make savvy decisions. Prodigious salient information out there concealed in customer reviews, tweets, social media comments, and the challenge is to extract hidden insights from it. It is an arduous process to extract meaningful information from these data silos manually. One of the imperative aspects in Natural Language Processing is sentiment Analysis - Extracting emotions, opinions, sentiments, fervor from the corpus of data and showcasing general opinion of the reviewers about the product. All previous works in NLP rely on traditional classification techniques like Support Vector Machines, Naïve Bayes, Maximum Entropy, Random forest, etc. With the advent of deep learning models, there is a reliable improvement in accuracy in Natural Language Processing. This paper introduces a deep learning technique namely Deep Convolutional Neural Network which captures a sentiment in the text corpus. The performance of this model will be evaluated on the Amazon product reviews and IMDB movie reviews.
Web Usage Mining: Data Pre-processing Impact on Found Knowledge in Predictive Modelling

We describe the importance of pre-processing data in the web usage mining and the impact of mistakes to the analysis of the data during this phase. We analyse data of commercial bank and accesses of stakeholders to the selected part of the website according to the rules of Basel 2 Pillar 3. These rules are focused on the Market discipline and were created after the financial crisis in 2008. We model the time dependent behaviour of the web user. Modelling the probabilities of the accesses to web categories depending on time was done using the multinomial logit model, which is a part of generalised linear models. We found non-human access to the web portal during the modelling phase, which significantly influenced the obtained knowledge. We had to repeat the pre-processing phase and repeat the modelling. After the identification and removing of the problem accesses to web portal pages, we identified a new model parameter from which we calculated the logit estimates and subsequently estimated the probabilities of accesses to the web parts of the web portal. Our estimates of theoretical logits fit (model) empirical logits. It is essential to not underestimate the data pre-processing phase in the process of web usage mining, as the pre-processing phase directly affects the quality of the acquired knowledge.
Weighted <i>k</i>‐nearest neighbor based data complexity metrics for imbalanced datasets

Abstract Empirical behavior of a classifier depends strongly on the characteristics of the underlying imbalanced dataset; therefore, an analysis of intrinsic data complexity would appear to be vital in order to choose classifiers suitable for particular problems. Data complexity metrics (CMs), a fairly recent proposal, identify dataset features which imply some difficulty for the classification task and identify relationships with classifier accuracy. In this paper, we introduce two CMs for imbalanced datasets, which help in explaining the factors responsible for the deterioration in classifier performance. These metrics are based on the weighted k ‐nearest neighbors approach. The experiments are performed in MATLAB software using 48 simulated datasets and 22 real‐world datasets for different choices of neighborhood size k considered as 3, 5, 7, 9, 11. The results help to illustrate the usefulness of the proposed metrics.
Single-Stage End-to-End Temporal Activity Detection in Untrimmed Videos

In this paper, we address the problem of human activity detection in temporally untrimmed long video sequences, where the goal is to classify and temporally localize each activity instance in the input video. Inspired by the recent success of the single-stage object detection methods (e.g., SSD [1]), we propose an end-to-end trainable framework that learns task-specific spatio-temporal feature representations of a video sequence using a 3D convolutional neural network followed by a multi-scale detection architecture for direct classification and localization of the activities at varying temporal scales. Our proposed approach sets new state-of-the-art on the highly challenging THUMOS’14 temporal activity detection bench-mark – up from 44.2% mAP to 49.0% mAP (an absolute 4.8% improvement) when the tIoU threshold is set to 0.5 during evaluation.
Learning Translation-Based Knowledge Graph Embeddings by N-Pair Translation Loss

Translation-based knowledge graph embeddings learn vector representations of entities and relations by treating relations as translation operators over the entities in an embedding space. Since the translation is represented through a score function, translation-based embeddings are trained in general by minimizing a margin-based ranking loss, which assigns a low score to positive triples and a high score to negative triples. However, this type of embedding suffers from slow convergence and poor local optima because the loss adopts only one pair of a positive and a negative triple at a single update of learning parameters. Therefore, this paper proposes the N-pair translation loss that considers multiple negative triples at one update. The N-pair translation loss employs multiple negative triples as well as one positive triple and allows the positive triple to be compared against the multiple negative triples at each parameter update. As a result, it becomes possible to obtain better vector representations rapidly. The experimental results on link prediction prove that the proposed loss helps to quickly converge toward good optima at the early stage of training.
Fast linear motion deblurring for 2D barcode

Barcode image deblurring has been an important task in image processing domain. On production lines, the demand of using 2D barcode to identify moving products requires scanners to decode the blurred images efficiently. Therefore, a fast and robust deblurring method is needed. However, most of state-of-the-art methods are not suitable for the fast decoding of 2D barcode images due to their being time-consuming. In this paper, we propose an efficient deblurring method for 2D barcode image focusing on linear motion blur in manufacturing situations. We first deconvolute image with Wiener Filter. Then we form a metrics which is based on the binary property of 2D barcode image to evaluate the quality of each deblurred image. With the guide of this metrics, we adopt a line-search method to reduce the time spent on searching the best blur kernel. We compare our method with the existing methods for 2D barcode regarding efficiency and effectiveness. The experiment results show that our method is much faster on recovering information from motion-blurred 2D barcode.
Guided neural style transfer for shape stylization

Designing logos, typefaces, and other decorated shapes can require professional skills. In this paper, we aim to produce new and unique decorated shapes by stylizing ordinary shapes with machine learning. Specifically, we combined parametric and non-parametric neural style transfer algorithms to transfer both local and global features. Furthermore, we introduced a distance-based guiding to the neural style transfer process, so that only the foreground shape will be decorated. Lastly, qualitative evaluation and ablation studies are provided to demonstrate the usefulness of the proposed method.
CompareCFG: Providing Visual Feedback on Code Quality Using Control Flow Graphs

The quality of the code impacts the cost of its maintenance, yet "code quality" is often not given attention in introductory programming courses, perhaps due to the difficulty of providing automated code quality feedback. We have been exploring how to provide automated feedback on complexity, one aspect of code quality. We have developed CompareCFG that provides feedback based on control flow graphs (CFGs). It generates visualisations of students' submissions and provides the means for a student to compare the CFG of their own code with CFGs of less complex submissions, helping to support their understanding of code complexity. CompareCFG also provides actionable feedback by indicating specific issues in a submission that can reduce its complexity. We evaluated CompareCFG in a pilot study. We found it provides useful feedback to participants that helped them reduce the complexity of their code. CompareCFG offers a convenient way to provide programming students with automated visual feedback on code quality.
Efficient Semantic Interrogation Scheme over Cryptographic Data in Cloud

Cloud based computing is a skill which provide communications for large scale storage of data, along with its refine and assigning. The security and retreat become main concern for uploading sensitive data without any information leak. This can be achieved by the cloud servers by integrating the encryption techniques into it, which becomes a major objection to guide the efficient keyword based questions and the encrypted data of the matching results. The searching can be an interrogation technique which is either based on a single keyword or multi-keyword as input. In this paper, a fresh algorithm known as Multi-Keyword Interrogation Scheme (MKIS) algorithm is recommended. It lessens the maintenance overhead to a greater scope when the keyword dictionary expansion occurs dynamically. The experimental results show that MKIS presents excellent performance when compared with the existing schemes. The proposed technique is presented in 2 aspects: many-keyword graded the look to attain exact look for output and meaning-based explore to maintain synonym questions. Search Terms— Cloud based computing, Multi-keyword interrogation, Data encryption, Security preserving, semantic grilling.