Reviewerly/exHarmony huggingface dataset:
A Study on 4D Light Field Compression Using Multi-focus Images and Reference Views

We propose a novel method of light field compression using multi-focus images and reference views. Light fields enable us to observe scenes from various viewpoints. However, it generally consists of 4D enormous data, that are not suitable for storing or transmitting without effective compression at relatively low bit-rates. On the other hand, 4D light fields are essentially redundant because it includes just 3D scene information. While robust 3D scene estimation such as depth recovery from light fields is not so easy, a method of reconstructing light fields directly from 3D information composed of multi-focus images without any scene estimation is successfully derived. Based on the method, we previously proposed light field compression via multi-focus images as effective representation of 3D scenes. Actually, its high performance can be seen only at very low bit-rates, because there exists some degradation of low frequency components and occluded regions on light fields predicted from multi-focus images. In this paper, we study higher quality light field compression by using reference views to improve quality of the prediction from multi-focus images. Our contribution is twofold: first, our improved method can keep good performance of 4D light field compression at a wider range of low bit-rates than the previous one working effectively only for very low bit-rates; second, we clarify how the proposed method can improve its performance continuously by introducing recent video codec such as HEVC and VVC into our compression framework, that does not depend on 3D-SPIHT previously adopted for the corresponding component. We show experimental results by using synthetic and real images, where quality of reconstructed light fields is evaluated by PSNR and SSIM for analyzing characteristics of our novel method well. We notice that it is much superior to light field compression using HEVC directly at low bit-rates regardless of its light field scan order.
Local linear convergence of stochastic higher-order methods for convex optimization

We propose a stochastic higher-order algorithm for solving finite sum convex optimization problems. Our algorithmic framework is based on the notion of stochastic higher-order upper bound approximations of the finite sum objective function. For building such a framework we only require that this bound approximate the objective function up to an error that is p times differentiable and has a Lipschitz continuous p derivative. This leads to a stochastic higher-order majorization-minimization algorithm, which we call SHOM. We show that the algorithm SHOM achieves local linear convergence rate for the function values provided that the finite sum objective function is uniformly convex. Numerical simulations confirm the efficiency of our method.
Neural Network for Nonlinear Dimension Reduction Through Manifold Recovery

The curse of dimensionality is a classic problem in machine learning which states that the number of data points required to achieve a desirable accuracy increases as the square of the dimensionality of the data points. Many dimension reduction techniques have been developed to combat this. Maximum Variance Unfolding (MVU) is one such state-of-the-art nonlinear dimension reduction algorithm that recovers a lower dimensional manifold which most of the data lies near. The algorithm's inability to handle large data sets or those that fail to meet certain distribution criteria are severe detriments to its efficiency and robustness. In this report we have introduced a variant of MVU which utilizes a novel Feed Forward Neural Network to recover the lower dimensional manifold representation. We demonstrate that our method succeeds in recovering lower dimensional manifolds and outperforms MVU in several ways including increased efficiency and flexibility.
Wearable-User Authentication via Cross-Technology Interference in Heterogeneous Environments

The increasing deployment of wireless sensors enables a broad spectrum of health-related wearable applications. Due to the sensitivity of collected personal health information, these wearables should be authenticated together with their users as “wearable-user pairs” to ensure that they are attached to legitimate users. However, various devices are equipped with dedicated sensing abilities and wireless protocols corresponding to data characteristics in practice. Traditional authentication methodologies may not work in this heterogeneous environment because of protocol incompatibility. For example, how to verify a new ZigBee-enabled monitor when the existing trusted device is Wi-Fi-enabled? Therefore, to achieve authentication across protocols, in this article, we leverage the unique cross-technology interference (CTI), triggered by heterogeneous wireless transmissions, along with human physiological activity measurements (e.g., respiration patterns) to design an authentication scheme between wearables and users. Specifically, the authentication from an unknown ZigBee wearable to a trusted Wi-Fi device is achieved by monitoring the channel state information (CSI) changes according to human respiration. Our approach not only successfully recognizes a legitimate wearable-user pair but also blocks illegal access from adversaries. Extensive experiments have been conducted to demonstrate both the security and feasibility of the proposed scheme. The designed mechanism can achieve over 92% authentication accuracy with human subjects.
Recognizing Characters and Relationships from Videos via Spatial-Temporal and Multimodal Cues

Video contains rich semantic knowledge of multiple modalities related to a person. Mining deep or potential semantic knowledge in the video could help artificial intelligence better understand the behavior and emotion of humans in the video. The researches for deep and context semantic knowledge in the video are few at present. Many researches on the knowledge mining of characters and visual relationships between humans still remain on static picture, lacking attention to the temporal visual features and other important modalities. In order to better mine the semantic knowledge in the video, we propose the novel Global-local VLAD (GL-VLAD) module, using the convolution of different scales to enlarge different receptive fields and extract the global and local information of features in the video. In addition, we propose a Multimodal Fusion Graph(MFG) to focus on the knowledge of different modalities, which can represent the general features in multi-modal video scenes. We use this method to conduct a large number of experiments of social relation extraction and person recognition on the dataset MovieGraphs and IQIYI- VID-2019. The accuracy and mAP respectively reach 90.23% and 89.87% on IQIYI-VID-2019. The accuracy achieves 56.13 % on the fine-grained dataset MovieGraphs for relation extraction task, while the person recognition of which has values 89.31 % and 85.24% on accuracy and mAP. The experimental results show that our proposed method has better performance than the state-of-the-art methods.
Behaviour of Sample Selection Techniques Under Explicit Regularization

There is a multitude of sample selection-based learning strategies that have been developed for learning with noisy labels. However, It has also been indicated in the literature that perhaps early stopping is better than fully training the model for getting better performance. It leads us to wonder about the behavior of the sample selection strategies under explicit regularization. To this end, we considered four of the most fundamental sample selection-based models MentorNet, Coteaching, Coteaching-plus and JoCor. We provide empirical results of applying explicit L2 regularization to the above-mentioned approaches. We also compared the results with a baseline - a vanilla CNN model trained with just regularization. We show that under explicit regularization, the pre-conceived ranking of the approaches might change. We also show several instances where the baseline was able to outperform some or all of the existing approaches. Moreover, we show that under explicit regularization, the performance gap between the approaches can also reduce.
Mechanics, Heat, and General Physics

Abstract The chapter covers the topics from mechanics, general physics, heat, and elementary mathematics in the physics context to simple but, at times surprising, phenomena in the day-to-day setting, for instance the physics of hot-dog bending and rupture. Some problems (e.g., Lightbulb and Coal) will teach students how to make back-of-the-envelope estimates, while others (e.g., Ostrich Egg, Joker’s Pendulum, and Slinky Magic) will show methods (such as scaling arguments) to easily solve problems that are difficult to solve via “brute force.”
Machine Learning model for Numeral Recognition

Numeral recognition from images of documents has drawn much more attention due to its vast applications involving processing bank cheque leaf, automatic number-plate recognition, identification of ID cards and zip codes. In a computer vision system, numeral recognition is a complex task since the numbers are not written or scripted accurately, as they differ in shape or size, due to which, feature extraction and segmentation of handwritten numerical script is very hard. The aim of the proposed methodology is to build and develop an application such that the model is trained using our own dataset. This ML model can recognize handwritten digits (0 to 9) either through camera or predict the digits drawn on the screen in real time. In this proposed work, optimized python codes are scripted using technologies such as Machine learning, python and its libraries like Scikit-learn, Open CV as well as SVM to develop a training and testing model to produce optimum results.
A Novel Electronic Medical Record Design Using Cryptography and Steganography Techniques

Health data is classified as a top priority data to be protected and maintained. Management of paper medical records is difficult as they are prone to theft and misplacement. Therefore, there is a need to provide precise, up-to-date, and complete data about the patients at the time of care. This leads to the era of Electronic Medical Records (EMR) which is a digital solution for management of health records. Current EMR have a lot of challenges like - security, interoperability, lack of Data Analytics, etc. The proposed solution addresses these challenges and provides the functions like - data capture, data storage, data retrieval, data provisioning and data analytics. Double security algorithm which is a combination of both cryptography and steganography has been used to enhance the security. Moreover, the information directly obtained from the EMR provides an insight into methods to enhance delivery of healthcare facilities relying on individual approach towards every patient.
Delayed Asynchronous Iterative Graph Algorithms

Iterative graph algorithms often compute intermediate values and update them as computation progresses. Updated output values are used as inputs for computations in current or subsequent iterations; hence the number of iterations required for values to converge can potentially reduce if the newest values are asynchronously made available to other updates computed in the same iteration. In a multi-threaded shared memory system, the immediate propagation of updated values can cause memory contention that may offset the benefit of propagating updates sooner. In some cases, the benefit of a smaller number of iterations may be diminished by each iteration taking longer. Our key idea is to combine the low memory contention that synchronous approaches have with the faster information sharing of asynchronous approaches. Our hybrid approach buffers updates from threads locally before committing them to the global store to control how often threads may cause conflicts for others while still sharing data within one iteration and hence speeding convergence. On a 112-thread CPU system, our hybrid approach attains up to 4.5% - 19.4% speedup over an asynchronous approach for Pagerank and up to 1.9% - 17% speedup over asynchronous Bellman Ford SSSP. Further, our hybrid approach attains 2.56x better performance than the synchronous approach. Finally, we provide insights as to why delaying updates is not helpful on certain graphs where connectivity is clustered on the main diagonal of the adjacency matrix.
The Impact of Synchronization in Parallel Stochastic Gradient Descent

In this paper, we discuss our and related work in the domain of efficient parallel optimization, using Stochastic Gradient Descent, for fast and stable convergence in prominent machine learning applications. We outline the results in the context of aspects and challenges regarding synchronization, consistency, staleness and parallel-aware adaptiveness, focusing on the impact on the overall convergence.
A discourse on metaheuristics techniques for solving clustering and semisupervised learning models

This study presents a succinct view on metaheuristics techniques for solving problems pertinent to the field of data mining and deep learning, especially those cognate to unsupervised and semisupervised learning models. As is implied by one of the four Vs of data, our learning models must comply with the proliferating volume of data available in contemporary world. By undertaking the erstwhile study, we endeavor to do away with the computational time related to such high volumes of data at the cost of optimality, accuracy, and precision of our learning algorithms. We delve upon certain prevalent metaheuristic techniques ranging from evolutionary algorithms like genetic algorithm to particle swarm optimization, ant colony optimization, or an ensemble of different learning models to name a few, so as to instill an understanding of the methods used and thereafter apply those to our learning algorithms.
Face Recognition and Facial Expression Recognition Algorithm Based on Novel Features Extraction Descriptors HDG and HDGG

Emotional expressions exchanged through visual communication require a very particular interest particularly to ensure the robust identification of any kind of facial expressions. This problem is becoming harder in unstable environments such as laying, lighting, etc. In this paper, we present novel face and facial expression recognition descriptors called HDG (Histogram of Directional Gradient) and HDGG (Histogram of Directional Gradient Generalized). These descriptors are based on the extraction of local directional gradients. Our main goal is to reach a high face identification accuracy and fast execution time. To reach this goal, we choose to work on two standard databases, JAFFE and YALE. The most recent face descriptors are used to evaluate the efficiency of the presented descriptors. This approach has less complexity since it describe facial expressions by reduced features vectors, which minimizes the computation time. We detail the obtained results by using the well-known metrics to evaluate the recognition rate and execution time of the proposed descriptors.
An Efficient Level Set Method Based on a Novel Pixel-Region Dissimilarity Function in Image Segmentation

We focus on region-based image segmentation methods with the level set formulation to propose our method based on pixel-region dissimilarity within the energy functional. The dissimilarity function contains a term called smoother, which is based on the gradient of image intensity. The role of the smoother is to improve robustness against different types of noise. In addition to the single level set formulation, we extend the proposed method to multiple level sets to deal with the multiregion segmentation not only in gray scale images but also color images. Finally, we performed numerical experiments with respect to medical images and some real world images including color images, and demonstrated the advantages of our proposed method in comparison to other region-based methods.
Virtual Inductive Loop: Real time video analytics for vehicular access control

Automated access control entails automatically detecting incoming vehicles in real-time and allowing access only to authorized vehicles. Access control systems typically adopt one or more sensors such as inductive loops, light array sensors, wireless magnetometers in detecting vehicles at access points. This paper 1 provides a detailed account on a real-time video analytics system named the “ Virtual Inductive Loop ” (VIL), that we developed as an alternative cost-efficient solution for access control. The VIL system poses precision and recall rates over 98%, performs on par with current systems in latency towards detecting event onset and further adds a suite of additional capabilities to access control systems such as vehicle classification, tailgate detection and unusual event detection. The system was tested in live conditions in different site at a Naval Facility in the United States over a two year period. The project was funded by the Office of Naval Research (#N000l4-l7-C-7030).
Chapter 14. Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning

Deep learning has proven effective for various application tasks, but its applicability is limited by the reliance on annotated examples. Self-supervised learning has emerged as a promising direction to alleviate the supervision bottleneck, but existing work focuses on leveraging co-occurrences in unlabeled data for task-agnostic representation learning, as exemplified by masked language model pretraining. In this chapter, we explore task-specific self-supervision, which leverages domain knowledge to automatically annotate noisy training examples for end applications, either by introducing labeling functions for annotating individual instances, or by imposing constraints over interdependent label decisions. We first present deep probabilistic logic (DPL), which offers a unifying framework for task-specific self-supervision by composing probabilistic logic with deep learning. DPL represents unknown labels as latent variables and incorporates diverse self-supervision using probabilistic logic to train a deep neural network end-to-end using variational EM. Next, we present self-supervised self-supervision (S4), which adds to DPL the capability to learn new self-supervision automatically. Starting from an initial seed self-supervision, S4 iteratively uses the deep neural network to propose new self supervision. These are either added directly (a form of structured self-training) or verified by a human expert (as in feature-based active learning). Experiments on real-world applications such as biomedical machine reading and various text classification tasks show that task-specific self-supervision can effectively leverage domain expertise and often match the accuracy of supervised methods with a tiny fraction of human effort.
Image Classification in the Era of Deep Learning

The use of deep learning has taken immense step in today’s technology, with profound use of Artificial Neural Network. The notable part of Deep learning is the formulation and popularity of a specific Neural Network (NN) known as the Convolutional Neural Networks (CNN or ConvNet). CNN is classified as a feed-forward ANN, used mainly to examine visual images. The problem discussed here is how an image classifier can be implemented using CNN. This proposed work is carried out to give a complete insight on CNN and not regarding it as a black box. This paper mainly concentrates on CNN concepts and implements a simple Multi Image Classification using CNN with the help of Tensor flow, Keras and Theano deep learning libraries. A CNN model was built from scratch and was trained with a dataset. Different Loss functions for Multi Image classification were also implemented.
Road Boundary Modeling for Autonomous Bus Docking Subject to Rectangular Geometry Constraints

This paper studies the optimization problem of autonomous bus parallel parking subjected to rectangular geometry constraints. The parking space is a non-smooth and non-convex irregular polygon. We propose a novel method for modeling geometry constraints, which allows using the exact non-smooth parking space, while still being able to formulate the problem as a smooth nonlinear program. The focus of this paper is to compare our method to two other approaches of modeling rectangular geometry constraints, where one uses mixed-integers while the other approximates the rectangle as a smooth function. We show that our novel method prevents collisions and requires shorter computation time, where collision constraints are imposed on a limited number of points on the vehicle contour.
Delivering Cybersecurity Education Effectively

This chapter draws on current research and best practice into teaching in cybersecurity in higher education. The chapter provides a theoretical and pedagogical foundation for helping tutors make decisions about what topics to include and approaches to teaching and assessing the cybersecurity curriculum. There are of course a range of potential stakeholders in cybersecurity education ranging from government, policy, and law makers to all members of society. However, for the purposes of brevity, this chapter will focus on learners and those creating and delivering cybersecurity education in the higher education (HE) sector.
Identifying Salient Entities of News Articles Using Binary Salient Classifier

Entities of an article play a vital role in understanding the essence of the article. In recent years there have been many advancements done in the field of Natural Language Processing to identify entities of a document. However, only a few entities contribute to the central topic of the document. These entities are termed the salient entities of the document. Salient entities are the most noticeable or important topics that an article is fundamentally about. In this paper, we proposed a novel supervised Binary Entity Salience Classifier (BESC) that effectively identifies the salient Wikipedia entities occurring in a document using entity and document features. Our experiments using three different manually annotated datasets show our classifier's effectiveness in determining the salient entities of the document over the baseline method. We further validate the effectiveness of BESC by running A/B test on Yahoo media properties that used our classifier to surface salient article topics. Online A/B results show a huge improvement in user engagement. BESC has not only improved the entity relevance of our news article but also has helped in mitigating risks involved in misidentifying entities.
Security Vulnerability Expressions: A Technology for Empowering Novice Practitioners Around the World with Security Maturity Capabilities

The evolution of security over several decades by advanced countries has generated vast amounts of valuable security knowledge (“Knowledge”) contained in standards, regulations and guidance (“Frameworks”) published in the form of documents and spreadsheets. Knowledge captured in this form is very difficult to consume and adapt, especially by novice practitioners. If this barrier could be removed, it would release its enormous locked-in value to the rest of the world who desperately need it. A model and enabling software application (“Technology”) is proposed for novice practitioners to quickly ingest existing, readily available knowledge contained in frameworks; thereby enabling easy access, search, visualization, navigation, and consumption of the frameworks and their maps. This technology will be made available as open source to the world.