Reviewerly/exHarmony huggingface dataset:
Data-Independent Feature Learning with Markov Random Fields in Convolutional Neural Networks

In image classification, deriving robust image representations is a key process that determines the performance of vision systems. Numerous image features and descriptors have been developed manually over the years. As an alternative, however, deep neural networks, in particular convolutional neural networks (CNNs), have become popular for learning image features or representations from data and have demonstrated remarkable performance in many real-world applications. But CNNs often require huge amount of labelled data, which may be prohibitive in many applications, as well as long training times. This paper considers an alternative, data-independent means of obtaining features for CNNs. The proposed framework makes use of the Markov random field (MRF) and self-organising map (SOM) to generate basic features and model both intra- and inter-image dependencies. Various MRF textures are synthesized first, and are then clustered by a convolutional translation-invariant SOM, to form generic image features. These features can be directly applied as early convolutional filters of the CNN, leading to a new way of deriving effective features for image classification. The MRF framework also offers a theoretical and transparent way to examine and determine the influence of image features on performance of CNNs. Comprehensive experiments on the MNIST, rotated MNIST, CIFAR-10 and CIFAR-100 datasets were conducted with results outperforming most state-of-the-art models of similar complexity.
Lifelong learning and inductive bias

Effective learning from finite data requires assumptions about the data source, a notion referred to as inductive bias in the Machine Learning literature. A fundamental question pertains to the source of a ‘good’ inductive bias. One natural way to form such a bias is through lifelong learning, where an agent continually interacts with the world through a sequence of tasks, aiming to improve its performance on future tasks based on the tasks it has seen, and solved, so far. We review the problem of lifelong learning within Machine Learning, provide a basic theoretical formulation, and describe some theoretically motivated algorithms that demonstrate the feasibility of inductive bias formation through transfer of knowledge from previous tasks.
Data security and privacy in the cloud

Relying on the cloud for storing data and performing computations has become a popular solution in today’s society, which demands large data collections and/or analysis over them to be readily available, for example, to make knowledge-based decisions. While bringing undeniable benefits to both data owners and end users accessing the outsourced data, moving to the cloud raises a number of issues, ranging from choosing the most suitable cloud provider for outsourcing to effectively protecting data and computation results. In this paper, we discuss the main issues related to data protection arising when data and/or computations over them are moved to the cloud. We also illustrate possible solutions and approaches for addressing such issues.
File Matching Based on Secure Authentication and Proxy Homomorphic Re-encryption

With the rapid development of cloud computing and big data, users often outsource configuration files to the cloud for computing and matching to reduce the burden on the terminals. In order to prevent the leakage of privacy, this paper proposes a new privacy protection scheme based on a dual identity authentication and proxy homomorphic re-encryption private matching, and relies on two non-collusion cloud servers to complete the calculation and matching of attribute configuration files. The plaintext of the configuration file is invisible to both servers from start to finish. Even if the user colludes with one of the servers, they cannot decipher the properties file. The purpose of this is to prevent disguise by attackers and effectively protect personal privacy.
The Challenge of API Management: API Strategies for Decentralized API Landscapes

The Web as the world’s largest information system has largely settled on a solid foundation of HTTP-based connectivity, and the representation of User Interface (UI) information resources through a mix of HTML and scripting. In contrast, the similarly rapidly evolving “Web of Services” is still based on a more diverse and more quickly evolving set of approaches and technologies. This can make architectural decisions harder when it comes to choosing on how to expose information and services through an Application Programming Interface (API). This challenge becomes even more pronounced when organizations are faced with developing strategies for managing constantly growing and evolving API landscapes.
LSIM

Perceptual similarity measurement allows mobile applications to eliminate unnecessary computations without compromising visual experience. Existing pixel-wise measures incur significant overhead with increasing display resolutions and frame rates. This paper presents an ultra lightweight similarity measure called LSIM, which assesses the similarity between frames based on the transformation matrices of graphics objects. To evaluate its efficacy, we integrate LSIM into the Open Graphics Library and conduct experiments on an Android smartphone with various mobile 3D games. The results show that LSIM is highly correlated with the most widely used pixel-wise measure SSIM, yet three to five orders of magnitude faster. We also apply LSIM to a CPU-GPU governor to suppress the rendering of similar frames, thereby further reducing computation energy consumption by up to 27.3% while maintaining satisfactory visual quality.
Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation

While many individual tasks in the domain of human analysis have recently received an accuracy boost from deep learning approaches, multi-task learning has mostly been ignored due to a lack of data. New synthetic datasets are being released, filling this gap with synthetic generated data. In this work, we analyze four related human analysis tasks in still images in a multi-task scenario by leveraging such datasets. Specifically, we study the correlation of 2D/3D pose estimation, body part segmentation and full-body depth estimation. These tasks are learned via the well-known Stacked Hourglass module such that each of the task-specific streams shares information with the others. The main goal is to analyze how training together these four related tasks can benefit each individual task for a better generalization. Results on the newly released SURREAL dataset show that all four tasks benefit from the multi-task approach, but with different combinations of tasks: while combining all four tasks improves 2D pose estimation the most, 2D pose improves neither 3D pose nor full-body depth estimation. On the other hand 2D parts segmentation can benefit from 2D pose but not from 3D pose. In all cases, as expected, the maximum improvement is achieved on those human body parts that show more variability in terms of spatial distribution, appearance and shape, e.g. wrists and ankles.
RGBD temporal resampling for real-time occlusion removal

Occlusions disrupt the visualization of an object of interest, or target, in a real world scene. Video inpainting removes occlusions from a video stream by cutting out occluders and filling in with a plausible visualization of the object, but the approach is too slow for real-time performance. In this paper, we present a method for realtime occlusion removal in the visualization of a real world scene that is captured with an RGBD stream. Our pipeline segments the current RGBD frame to find the target and the occluders, searches for the best matching disoccluded view of the target in an earlier frame, computes a mapping between the target in the current frame and the target in the best matching frame, inpaints the missing pixels of the target in the current frame by resampling from the earlier frame, and visualizes the disoccluded target in the current frame. We demonstrate our method in the case of a walking human occluded by stationary or walking humans. Our method does not rely on a known 2D or 3D model of the target or of the occluders, and therefore it generalizes to other shapes. Our method runs at an interactive frame rate of 30fps.
Inverse Document Frequency-Weighted Word2Vec Model to Recommend Apparels

With the rapid growth of e-commerce markets the need for recommendation engine and efficient algorithms are becoming the need of the hour for business models of the companies to generate a huge amount of profit. This paper proposes a hybrid algorithm to benefit apparel retailing market which gives the benefits of both, semantics based search and frequency based search. Later this paper compares results of the proposed hybrid algorithm with the other known algorithms used to recommend products.
Semantic Recognition of Web Structure to Retrieve Relevant Documents from Google by Formulating Index Term

Nowadays among various search mechanism, Google is one of the best information retrieval mechanisms for retrieving useful result as per user query. Generally, the complete searching process of Google is completed by crawler and indexing process. Actually, Google performs the documents indexing process considering various features of web documents (title, meta tags, keywords, etc.) which helps to fetches the complementary result by exactly matching the given query with the index term with user interest. Though appropriate indexing process is much difficult but it essential as extracting relevant document completely or partially depend on how much relevant the index term with the document is. However, sometimes, this indexing process is influenced by assorted number of feature of web documents which produced variegated result those are either completely or partially irrelevant to the search and seems unexpected. To reduce this problem, we analyze web documents considering its unstructured (web content and link features) data in terms of efficiency, quality, and relevancy with the user search query and present a keyword-based approach to formulate appropriate index term through semantic analysis using NLP concept. This approach helps to understand the current web structure (effectiveness, quality, and relevancy) and mitigate the current inconsistency problem by generating appropriate, efficient, and relevant index term which might improve the search quality; as satisfied search result is the major concern of Google search engine. The analysis helps to generate appropriate Google web documents index term which might useful to retrieve appropriate and relevant web documents more systematically than other existing approaches (lexicon and web structure based approaches). The experimental result demonstrates that, the proposed approach is an effective and efficient methodology to predict about the competence of web documents and finding appropriate and relevant web documents.
An Improved Over-sampling Algorithm based on iForest and SMOTE

Imbalance learning is one of the most challenging problems in supervised learning, so many different strategies are designed to tackle balanced sample distribution. The over-sampling techniques which achieve a relatively balanced class distribution through synthesizing samples receive more and more attention. In this paper, we present an over-sampling approach based on isolation Forest (iForest) and SMOTE, called iForest-SMOTE. Firstly, for minority class samples, iForest-score is employed to assess the importance of each sample based on iForest model. Then, in each SMOTE process, roulette wheel selection based on iForest-score is utilized to select the neighbor sample. Finally, M-dimensional-sphere interpolation approach is employed to generate a new sample. The experiments illustrate that our approach takes into account the spatial distribution of minority class samples and sample synthetic simultaneously. Therefore, iForest-SMOTE can effectively improve the performance of the classification model.
Adaptive Label Propagation for Facial Appearance Transfer

Facial appearance transfer (FAT) is a critical component of various facial editing tasks. It aims to transfer the facial appearance of a reference into a target with good visual consistency. When there are considerable visual differences between a reference and a target, however, it may introduce visual artifacts into the results. To tackle this problem, we propose a facial appearance map with illumination-aware and region-aware properties that allows seamless FAT. We formulate the appearance-map generation as label propagation (LP) on a similarity graph, and propose a new regularization structure to facilitate the adaptive appearance-map diffusion. Solving the original LP model of appearance map in general requires on the order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(kn^2)$</tex-math></inline-formula> time for an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -nodes graph where each node has <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> neighbors. It may be computationally prohibitive for an image with a large spatial resolution. To tackle this problem, we mathematically analyze the graph-based LP model and propose a fast algorithm with smart subset sampling. It selects a subset with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> nodes of the graph with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> nodes ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m\ll n$</tex-math></inline-formula> ) to approximate the solution to the original system, which significantly reduces its computational requirements from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(kn^2)$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(m^2n)$</tex-math></inline-formula> . Based on the adaptive LP-based appearance map, we construct a framework to achieve various editing effects with FAT, including face replacement, face dubbing, face swapping, and transfiguring. Comparisons with related methods show the effectiveness of the adaptive LP model for FAT. Qualitative and quantitative evaluations verify the computational improvements of the approximation algorithm.
Hardware Efficient Convolution Processing Unit for Deep Neural Networks

Convolutional Neural Network (CNN) is a type of deep neural networks that are commonly used for object detection and classification. State-of-the-art hardware for training and inference of CNN architectures require a considerable amount of computation and memory intensive resources. CNN achieves greater accuracy at the cost of high computational complexity and large power consumption. To optimize the memory requirement, processing speed and power, it is crucial to design more efficient accelerator architecture for CNN computation. In this work, an overlap of spatially adjacent data is exploited in order to parallelize the movement of data. A fast, re-configurable hardware accelerator architecture along with optimized kernel design suitable for a variety of CNN models is proposed. Our design achieves 2.1x computational benefits over state-of the-art accelerator architectures.
FPGA-based phase measuring profilometry system

This paper proposes an architecture for a phase measuring profilometry system, that can be efficiently implemented on a Xilinx Zynq-7000 SoC. After a brief system overview, the paper starts at the very beginning point of such a task, that is the camera calibration. A calibration procedure using OpenCV functions is outlined and the calculation of compressed rectification maps is described in more detail. The compressed rectification maps are used for lens undistortion and rectification to reduce the memory load. The hardware accelerated part of the system comprises the image acquisition, the lens undistortion and image rectification, the phase accumulation with following phase unwrapping, the phase matching and the 3D reconstruction. For phase unwrapping a multi-frequency approach is used that can be easily implemented on the given architecture. The interfacing of the hardware modules follows a fully pipelined implementation scheme so that the image processing can be done in real time.
Development Support of User Interfaces Adaptive to Use Environment

Many applications are used across various platforms, including personal computers (PCs) and mobile devices such as tablets and smartphones. User interfaces must consider various use environments to realize high quality and user-friendly applications. However, user interfaces have to be created for each platform which is costly work for developers. To reduce the burden on developers, this research aims to realize support for user interface development adaptive to the use environment. Concretely, we build user interfaces based on a model that references UsiXML, which is a model-driven user interface development method.
Tracking Visualization Of 3 Dimensional Object Natural Science Learning Media In Elementary School With Markerless Augmented Reality Based On Android

Many materials in the Natural Sciences lesson are not understood by students. Teacher-centered learning methods cause students to learn passively. Students barely listen to teachers who giving lectures on natural science subject. There are already many learning media for natural sciences in 2-dimensional forms such as learning videos. Science learning media uses a 3-dimensional animation model, which offers several visualizations of science lessons. Learning media for natural science for elementary schools created to facilitate students to learnn in a fun way. This application uses markerless augmented reality based on Android. In the initial stage, it will be explained how to create an application using the ADDIE method. ADDIE is one method for developing learning media products. This application is created without using a marker but using capture texture so it is necessary to check the tracking and structure from motion(SfM) the camera so that the visualization effect of 3d animation objects will appear. After that, it will be recommended to use the application correctly. This research is still in the early stages of research in the field of computer vision.
Towards insight-driven sampling for big data visualisation

Creating an interactive, accurate, and low-latency big data visualisation is challenging due to the volume, variety, and velocity of the data. Visualisation options range from visualising the entire big dataset, which could take a long time and be taxing to the system, to visualising a small subset of the dataset, which could be fast and less taxing to the system but could also lead to a less-beneficial visualisation as a result of information loss. The main research questions investigated by this work are what effect sampling has on visualisation insight and how to provide guidance to users in navigating this trade-off. To investigate these issues, we study an initial case of simple estimation tasks on histogram visualisations of sampled big data, in hopes that these results may generalise. Leveraging sampling, we generate subsets of large datasets and create visualisations for a crowd-sourced study involving a simple cognitive visualisation task. Using the results of this study, we quantify insight, sampling, visualisation, and perception error in comparison to the full dataset. We use these results to model the relationship between sample size and insight error, and we propose the use of our model to guide big data visualisation sampling.
Handling subjective information through augmented (fuzzy) computation

Since it can result in significant benefits for a company or an individual, the inclusion of information extracted from subjective social media content into a decision making process is becoming a more frequent activity. However, such benefits are usually linked to the usability of the extracted information, which, among other aspects, depends on the reliability of its source. In this regard, people whose understandings of a topic are alike to the understanding possessed by an information seeker can be considered fairly reliable information sources. Hence, we propose a novel technique for detecting social media users with whom an information seeker shares a similar understanding of a given topic. Through this technique, posts on social media are digested to build a kind of database consisting of augmented Atanassov fuzzy sets, or AAIFSs for short, each resembling a collection of experience-based evaluations given by a particular source with respect to a given topic. Since such AAIFSs can be used in comparisons in which not only the extents but also the contexts of those evaluations are taken into account for computation, extracting more reliable (and usable) information is possible. An illustrative example shows how the proposed technique works and how it can help to detect sources having a common understanding of a topic.
InsideNet: A tool for characterizing convolutional neural networks

In this work we present InsideNet, a novel tool built on top of Caffe DL framework aimed to assist researchers in exploring the values generated during the inference procedure of a Convolutional Neural Network (CNN). More precisely, InsideNet allows in-depth analysis of the values of the filters and fmaps within the convolution layers of a trained CNN during an ongoing inference procedure. To do so, InsideNet features three main operation modes. First, the Fmap Visualization Mode (FVM), which allows users to examine in a visual manner the generated fmap channels during the inference procedure of a set of images. Second, the Statistic Collector Mode (SCM), which offers a rich set of statistics for the fmap channels and weights of every convolutional layer. And third, the Histogram Collector Mode (HCM), which allows deeper exploration of value-based patterns by generating the corresponding histograms of the network. In addition, we describe a methodology and a set of standard metrics that can be used to characterize any CNN using InsideNet. We demonstrate the potential of InsideNet by applying it to two real case studies that focus on contemporary lightweight CNNs. The first is a comprehensive characterization of the novel Google’s MobileNets CNN, aimed to find out new patterns that can lead to new hardware optimizations. Specifically, we have found that on average, each fmap could be divided into 8 parts of the same size, each of which with consecutive, identical values. The second case study uses InsideNet for understanding and solving the accuracy drop (around 6%) that appears in SqueezeNet CNN when it is quantized from 32 to 16 bits.