Reviewerly/exHarmony huggingface dataset:
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes

Monocular depth reconstruction of complex and dynamic scenes is a highly challenging problem. While for rigid scenes learning-based methods have been offering promising results even in unsupervised cases, there exists little to no literature addressing the same for dynamic and deformable scenes. In this work, we present an unsupervised monocular framework for dense depth estimation of dynamic scenes, which jointly reconstructs rigid and nonrigid parts without explicitly modelling the camera motion. Using dense correspondences, we derive a training objective that aims to opportunistically preserve pairwise distances between reconstructed 3D points. In this process, the dense depth map is learned implicitly using the as-rigid-as-possible hypothesis. Our method provides promising results, demonstrating its capability of reconstructing 3D from challenging videos of non-rigid scenes. Furthermore, the proposed method also provides unsupervised motion segmentation results as an auxiliary output.
Semi-supervised Viewpoint Estimation with Geometry-Aware Conditional Generation

There is a growing interest in developing computer vision methods that can learn from limited supervision. In this paper, we consider the problem of learning to predict camera viewpoints, where obtaining ground-truth annotations are expensive and require special equipment, from a limited number of labeled images. We propose a semi-supervised viewpoint estimation method that can learn to infer viewpoint information from unlabeled image pairs, where two images differ by a viewpoint change. In particular our method learns to synthesize the second image by combining the appearance from the first one and viewpoint from the second one. We demonstrate that our method significantly improves the supervised techniques, especially in the low-label regime and outperforms the state-of-the-art semi-supervised methods.
Product Recommendation Platform Based on Natural Language Processing

Online Videos command aJain, Vanita majoritySingh, Mankirat portionBharti, Arpit of the worlds internet traffic. In 2019, consumer Internet video traffic had accounted for 80% of all consumer Internet traffic. At the forefront of this are platforms like YouTube. As the consumption of the online videos increases, it is imperative for various platforms to be able to monetize these videos using relevant advertisements. To maximize revenue from ads, we propose a method of ranking and sorting products based on relevancy to a given video. Using the metadata and captions, the system aims to generate product recommendations that a user may find useful. The system uses Rapid Automatic Keyword Extraction to find the words that best summarize the video and proposes recommendations using similarity measures. The system is then evaluated on a database. Finally we proceeded to conduct an extensive User Study to judge the accuracy of our system. The results conclude that the proposed model is capable of recommending contextually relevant products for a wide variety of Online videos.
Inpainting Electrical Logging Images Based on Deep CNN with Attention Mechanisms

Microresistivity imaging logging is a crucial technique for evaluating the glutenite, carbonate and other complex reservoirs in oil exploration field. Because the size of the electrode pads in the electric imaging logging instrument can not match the borehole size well, it is hard to get full borehole covering images, which leads to many blank strips in the logging images. Thus it is necessary to fill the blank strips to ensure the accuracy of the subsequent processing and interpretation. The existing filling methods work well only in simple formation structures, but for images whose structure is very complex, for example, glutenite formation, the filled regions appear blurred and indistinct. To remedy the drawbacks, in this paper, we propose a machine learning method to inpaint the blank strips in electrical logging images based on deep convolutional neural network with the attention mechanisms embedded, which can capture a large number of low-level image statistical prior information. The attention mechanisms here refer to both the channel attention and the spatial attention. The channel attention focuses on the information to be attended in the intermediate feature maps while the spatial attention serves determining where to attend. Extensive experiments validate and confirm the effectiveness of the proposed filling method, which produce more compelling results than the existing filling approaches.
Development and Evaluation of Stereoscopic Wavelet-based Optical Flow Velocimetry (swOFV)

A first-generation stereoscopic extension of wavelet-based optical flow velocimetry (swOFV) is introduced and an initial evaluation of performance is demonstrated using synthetic tracer particle images from a DNS of homogeneous isotropic turbulence. Specifically, comparisons are made to the DNS result (“truth”) and analysis using stereoscopic particle imaging velocimetry (sPIV) results. First, an initial analysis of the optimal off-axis lens angle is performed for swOFV, yielding a recommended angle near 30°for a two-camera setup in an angular-displacement stereoscopic configuration. The optimal angle is primarily dictated by the accuracy of the out-of-plane velocity component (w), as the in-plane components (u and v) are only slightly sensitive to the off-axis observation angle. Near the optimal angle, the accuracy of the individual velocity components, total velocity vector, and gradient quantities increased by approximately 70% when using swOFV compared to sPIV. swOFV also was able to resolve a larger fraction of the energy and dissipation spectra (by more than an order-of-magnitude) when compared to sPIV, although it was unable to resolve the spectra down to the smallest dissipative scales as is possible with planar (2D) wOFV. The apparent under resolution was observed across all obtainable components of the energy and dissipation spectra, which is attributed to an implicit low-pass filtering involved in the current simple linear interpolation scheme involved in the stereoscopic reconstruction. Overall, the initial implementation of swOFV is shown to significantly improve accuracy and spatial resolution when compared to sPIV
Tuser3: A Profile Matching Based Algorithm Across Three Heterogeneous Social Networks

Matching profiles can be defined as the process of determining whether two profiles in the same or different social networks are instances of the same user in real life or not. Because of the considerable increase in the number of created accounts in social networks, matching profiles across social networks has become a popular focus in a myriad of research works. Current methods in this field require accurate profile analysis to obtain a high user identification quality. However, such studies are restrictive since they do not consider the profile in its globality. In this work, we target the problem of user identification task based on their created profiles in social networks. Specifically, we conduct two main steps. First, we introduce a supervised model which employs the similarity of features for predicting matched profiles. Second, we propose Tuser3 algorithm to search for the correct matched target profile of a source user on three heterogeneous social networks based on several user profiles features. Our experiments show the effectiveness of our method in matching profiles across the three target social networks.
A Study for Accelerating of Convolution Operations Based on Multiple GPUs with MPI

Advances in hardware and software can handle large-scale data in the field of deep learning. It generally uses a parallel GPU to perform large-scale operations, but there is a limit to the number of GPUs available on a single node. Using multiple nodes can overcome this constraint. In this paper, we designed and evaluated a convolution operation method using the Message Passing Interface (MPI) to accelerate the performance using constant memory which is a kind of the GPU’s memory, and multiple nodes with the multiple GPUs environment. According to the performance evaluation results, the use of constant memory of GPU reduces the convolution operation execution time and the convolution operation execution time decreases as the number of GPUs increases in the multiple computing nodes.
Hidden in Plain Sight: Building a Global Sustainable Development Data Catalogue

Modern scientific research for Sustainable Development depends on the availability of large amounts of relevant real-world data. However, there are currently no extensive global databases that associate existing data sets with the research domains they cover. We present the SDG Data Catalogue, an open, extensible, global database of data sets, metadata, and research networks built automatically by mining millions of published open access academic works. Our system leverages advances in Artificial Intelligence and Natural Language Processing Technologies to extract and organise deep knowledge of data sets available that is otherwise hidden in plain sight in the continuous stream of research generated by the scientific community.
Quantile regression for compositional covariates

Quantile regression is a very important tool to explore the relationship between the response variable and its covariates. Motivated by mean regression with LASSO for compositional covariates proposed by Lin et al. (Biometrika 101 (4):785–97, 2014), we consider quantile regression with no-penalty and penalty function. We develop the computational algorithms based on linear programming. Numerical studies indicate that our methods provide the better alternative than mean regression under many settings, particularly for heavy-tailed or skewed distribution of the error term. Finally, we study the fat data using the proposed method.
Mining the online infosphere: A survey

Abstract The evolution of Artificial Intelligence (AI)‐based systems and applications have pervaded everyday life to make decisions that have a momentous impact on individuals and society. With the staggering growth of online data, often termed as the online infosphere , it has become paramount to monitor the infosphere to ensure social good as AI‐based decisions are severely dependent. This survey aims to provide a comprehensive review of some of the most important research areas related to the infosphere, focusing on the technical challenges and potential solutions. The survey also outlines some of the important future directions. We begin by focussing on the collaborative systems that have emerged within the infosphere with a special thrust on Wikipedia. In the follow‐up, we demonstrate how the infosphere has been instrumental in the growth of scientific citations and collaborations, thus fuelling interdisciplinary research. Finally, we illustrate the issues related to the governance of the infosphere, such as the tackling of the (a) rising hateful and abusive behavior and (b) bias and discrimination in different online platforms and news reporting. This article is categorized under: Application Areas &gt; Internet
RuThes Thesaurus for Natural Language Processing

Abstract This chapter describes the Russian RuThes thesaurus created as a linguistic and terminological resource for automatic document processing. Its structure utilizes two popular paradigms for computer thesauri: concept-based units, a small set of relation types, rules for including multiword expression as in information retrieval thesauri; and language-motivated units, detailed sets of synonyms, description of ambiguous words as in WordNet-like thesauri. The development of the RuThes thesaurus is supported for many years: new concepts, new senses, and multiword expressions found in contemporary texts are introduced regularly. The chapter shows some examples of representing newly appeared concepts related to important internal and international events.
Research on Visualization Algorithm of Association Rules Based on Concept Lattice

The traditional association rule representation method has no way to show the essential relationship between concepts and lacks understanding of the concept level. Concept lattice is a kind of data structure which can show the relationship between concepts, concept lattice also has unique advantages in the visualization and discovery of potential knowledge of association rules. In this paper, a visualization algorithm of association rules based on concept lattice is proposed. The algorithm visually displays the association rules extracted from concept lattice. Finally, this article combines the chronic disease record data of a certain hospital to analyze and visualize the association rules. The implementation results show that the visualization method has good effects in knowledge analysis and data display.
Self-Supervised Map-Segmentation by Mining Minimal-Map-Segments

In visual place recognition (VPR), map segmentation (MS) is a preprocessing technique used to partition a given view-sequence map into place classes (i.e., map segments) so that each class has good place-specific training images for a visual place classifier (VPC). Existing approaches to MS implicitly/explicitly suppose that map segments have a certain size, or individual map segments are balanced in size. However, recent VPR systems showed that very small important map segments (minimal map segments) often suffice for VPC, and the remaining large unimportant portion of the map should be discarded to minimize map maintenance cost. Here, a new MS algorithm that can mine minimal map segments from a large view-sequence map is presented. To solve the inherently NP hard problem, MS is formulated as a video-segmentation problem and the recently-developed efficient point-trajectory based paradigm of video segmentation is used. The proposed map representation was implemented with three types of VPC: deep convolutional neural network, bag-of-words, and object class detector, and each was integrated into a Monte Carlo localization algorithm (MCL) within a topometric VPR framework. Experiments using the publicly available NCLT dataset thoroughly investigate the efficacy of MS in terms of VPR performance.
Privacy-Oriented Analysis of Ubiquitous Computing Systems: A 5-D Approach

Abstract Ubiquitous computing systems are commonplace. They have opened the door to great benefits for society as a whole. However, they have to be used with care, otherwise they can cause serious risks for their users. In this chapter, we analyze the privacy risks of ubiquitous computing systems from a new individual-centred perspective based on five privacy dimensions, namely identity, location, footprint, query and intelligence. We describe each dimension and provide an introductory view of the main privacy risks of these systems. Also, we discuss some of the challenges that derive from new trends in the field.
Mutual Information Based Kernel for Image Classification

Significant research has been done to derive kernel function for the linear learning machines like Support Vector Machines (SVM). Kernel methods provides a way to add generative model advantages into a discriminative model. Kullback - Leibler (KL) divergence-based kernel is an excellent example of this combination. In this paper, we proposed new Mutual Information kernel that is an alternative kernel of the KL divergence kernel. We compared our proposed kernel with KL-divergence based kernel and some other standard kernels like Poly, RBF, and Sigmoid kernels. We have done comparison of these kernels with the image classification task. We have also described two different image representations to obtain a generative model for the use of KL-divergence or MI-based kernels.
Crystal-Ball and Magic Wand Combined: Predicting Situations and Making Them Happen

The Internet of Things (IoT) envisions an ecosystem in which everyday objects are enhanced with sensing, computation, and communication capabilities. These ‘smart’ devices (i.e., IoT devices) can sense and collect considerable amounts of data and share it with each other via the Internet. This paper proposes an IoT middleware platform enhanced with context- and situation-prediction capability, called Context-Prediction-as-a-Service (CPaaS). CPaaS offers real-time context prediction capabilities to a variety of IoT applications as a service and enables more effective decision support using relevant validated dependable information. A number of use cases where CPaaS can be deployed are also discussed.
Comparative Analysis on Different Degrees of JPEG Compression Used in CBIR Systems

Content-based image retrieval systems (CBIR) use computer vision methods for organizing, analyzing, processing, and searching of million graphic files uploaded on the Internet daily. For that reason, the retrieval process is a key element of CBIR applications. The best system performance is based on accurate and complete search which must be finished in a reasonable amount of time. It is directly related to extracting, storing, and indexing all necessary metadata from the examined image set. This paper gives information about JPEG compression and how it is used in some of the most widespread CBIR systems. It reveals how different degrees of that compression influence the generated results from custom developed CBIR application. Furthermore, it shows various similarities between the same image set but examined in two different ways - with non-compressed and compressed data retrieved by the system. In the end, conclusions are made on the basis of the results obtained from developed and tested CBIR application which was made for these purposes.
RETRACTED: Data monitoring of athlete physical training based on FPGA processing system and machine vision

Things get a consistent smart gadget consistent with manufacturing, increase alertness, and consistent response is beneficial. An incredible model has an edge and rebounding vibration sensor. This will allow players to continually change their shots and input an error on their specific point and direction. This is not a chance, which provides the necessary makeup / Miss Capture clear images. Another innovative use of an accelerometer equivalent, which is a Field Programmable Gate Array (FPGA) wrist strap. This allows you to measure the ball's speed, leaving it to the second and moving hand. It works with shooting motion minor contacts to help players by shooting. A competitor source of information is usually different sensors (e.g., accelerometer or gyrator). One-step preparation of submitted start information from other sources is the combination information. The report provides a new method for measuring the combined preparation movement to ensure relevant information and prepare and investigate the game. To confirm the communication method's effectiveness, the goal plan is to frame education courses for tennis players in the investigation. The frame's primary function is found, and the travel depends on a tennis wear sensor assembly information packet from the wrist. Location and characteristics of tennis strokes can reduce the amount of spending time tutor students to find information. Self-entertainment athletes can take advantage of these functional advantages and the proposed method of determining and using the vector elements to create a Mel-frequency coefficient from Cestrum acceleration information.
Relational Bayesian Model Averaging for Sentiment Analysis in Social Networks

Nowadays, the exponential diffusion of information forces Machine Learning algorithms to take relations into account in addition to data, which are no longer independent. We propose a Bayesian ensemble learning methodology named Relational Bayesian Model Averaging (RBMA) which, in addition to a probabilistic ensemble voting, takes relations into account. We tested the RBMA on a benchmark dataset for Sentiment Analysis in social networks and we compared it with its previous non-relational variant and we show that the introduction of relations significantly improves the performance of classification. Moreover, we propose a model for making predictions when new data becomes available modifying and increasing the underneath graph of relations on which the RBMA was trained.
Enhancing Game-Based Learning Through Infographics in the Context of Smart Home Security

Constantly evolving advances of smart home devices features require users to persistently keep up with safety concerns. While update reports and news articles are common ways to keep them informed, many users struggle in thoroughly understanding and applying available security recommendations. Educational games have proven to be an intuitive way to increase the incentive for awareness but many of them come short to convey the needed supporting knowledge. In an attempt to raise security awareness on smart home devices, we designed an educational game to demonstrate the latest security challenges and solutions. To ascertain users’ attention and motivation, we have developed two versions of the game to contrast the integration of text and infographics as supporting knowledge which are the hints in this case. Our evaluations give evidence that viewing security-related content with a higher deployment of infographics improves users’ performance significantly, increases users’ interest in the topic, and creates higher levels of confidence solving security problems and complexities.
Cognitive Computation and Systems

This chapter aims to explain how to construct a machine learning system and summarize the current level of machine intelligence as cognitive computation. One-dimensional deep neural network is utilized for illustration and detection of recorded epileptic seizure activity in Electroencephalogram (EEG) segments is given as a practical application. Manual recognition is a time-consuming and laborious process so that it places a heavy burden on neurologists, and hence, the automatic identification of epilepsy has become an important issue. Such intelligent detection is crucial for the classification of seizures. Traditional EEG recognition models depend on artificial experience and are of weak generalization ability. To break these limitations, we propose a novel one-dimensional deep neural network for robust detection of seizures, which consists of three convolutional blocks and three fully connected layers. Thereinto, each convolutional block consists of five layers: convolutional layer, Batch normalization layer, non-linear activation layer, dropout layer, and max-pooling layer. Machine cognition performance is evaluated on the University of Bonn dataset, which achieves an accuracy of 97.6–99.5% in the two-class classification problem, 93.7–98.1% in the three-class EEG classification problem, and 93.6% in classifying the complicated five-class problem.
Machine Learning-Based Information Retrieval System

The machine learning-based information retrieval model would encourage the user(s) to register on a user platform by authentication of identity information by assigning them a unique membership number. The platform would register the user(s) for a paid membership. The user would be able to search for da keyword(s) or phrase(s) on which the platform would apply auto-correction and clustering of the keyword into the databases would be done. The user would be alerted on his search, and the information would be displayed to the logged-in user, using a plagiarism detection algorithm. This system would come on handy as a tool for efficient search, the results displayed are more to the point and of significant relevance of the keyword or phrase entered by the user. The platform would integrate machine learning-based search giving benefits to students, teachers and scholars as a way of efficient searching protocol.