Reviewerly/exHarmony huggingface dataset:
Attentive Hybrid Feature with Two-Step Fusion for Facial Expression Recognition

Facial expression recognition is inherently a challenging task, especially for the in-the-wild images with various occlusions and large pose variations, which may lead to the loss of some crucial information. To address it, in this paper, we propose an attentive hybrid architecture (AHA) which learns global, local and integrated features based on different face regions. Compared with one type of feature, our extracted features own complementary information and can reduce the loss of crucial information. Specifically, AHA contains three branches, where all sub-networks in those branches employ the attention mechanism to further localize the interested pixels/regions. Moreover, we propose a two-step fusion strategy based on LSTM to deeply explore the hidden correlations among different face regions. Extensive experiments on four popular expression databases (i.e., CK+, FER-2013, SFEW 2.0, RAF-DB) show the effectiveness of the proposed method.
An Alternative Definition for Digital Convexity

This paper proposes full convexity as an alternative definition of digital convexity, which is valid in arbitrary dimension. It solves many problems related to its usual definitions, like possible non connectedness or non simple connectedness, while encompassing its desirable features. Fully convex sets are digitally convex, but are connected and simply connected. They have a morphological characterisation, which induces a simple convexity test algorithm. Arithmetic planes are fully convex too. We obtain a natural definition of tangent subsets to a digital surface, which gives rise to the tangential cover in 2D, and to its extensions in arbitrary dimension. Finally it leads to a simple algorithm for building a polygonal mesh from a set of digital points.
Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.
S&amp;I Reader: Multi-Granularity Gated Multi-Hop Skimming and Intensive Reading Model for Machine Reading Comprehension

Machine reading comprehension is a very challenging task, which aims to determine the answer span based on the given context and question. The newly developed pre-training language model has achieved a series of successes in various natural language understanding tasks with its powerful contextual representation ability. However, these pre-training language models generally lack the downstream processing structure for specific tasks, which limits further performance improvement. In order to solve this problem and deepen the model’s understanding of the question and context, this paper proposes S&I Reader. On the basis of the pre-training model, skimming, intensive reading, and gated mechanism modules are added to simulate the behavior of humans reading text and filtering information. Based on the idea of granular computing, a multi-granularity module for computing context granularity and sequence granularity is added to the model to simulate the behavior of human beings to understand the text from words to sentences, from parts to the whole. Compared with the previous machine reading comprehension model, our model structure is novel. The skimming module and multi-granularity module proposed in this paper are used to solve the problem that the previous model ignores the key information of the text and cannot understand the text with multi granularity. Experiments show that the model proposed in this paper is effective for both Chinese and English datasets. It can better understand the question and context and give a more accurate answer. The performance has made new progress on the basis of the baseline model.
CQNN: Convolutional Quadratic Neural Networks

Image classification is a fundamental task in computer vision. A variety of deep learning models based on the Convolutional Neural Network (CNN) architecture have proven to be an efficient solution. Numerous improvements have been proposed over the years, where broader, deeper, and denser networks have been constructed. However, the atomic operation for these models has remained a linear unit (single neuron). In this work, we pursue an alternative dimension by hypothesizing the atomic operation to be performed by a quadratic unit. We construct convolutional layers using quadratic neurons for feature extraction and subsequently use dense layers for classification. We perform analysis to quantify the implication of replacing linear neurons with quadratic units. Results show a keen improvement in classification accuracy with quadratic neurons over linear neurons.
New Directions for NewHope: Improving Performance of Post-Quantum Cryptography through Algorithm-level Pipelining

With continuing research into quantum computing, current public key cryptographic algorithms such as RSA and ECC will become insecure. These algorithms are based on the difficulty of integer factorization or discrete logarithm problems, which are difficult to solve on classical computers but become easy with quantum computers. Because of this threat, government and industry are investigating new public key standards, based on mathematical assumptions that remain secure under quantum computing. This paper investigates methods of improving the area and performance of one of the proposed algorithms for key exchanges, “NewHope.” We describe a pipelined implementation of NewHope512cpa which dramatically increases the throughput for a similar design area. Our pipelined encryption implementation achieves 652.2 Mbps and 0.088 Mbps/LUT throughput-to-area (TPA) ratio, which are the best known results to date, and achieves an energy efficiency of 1.35 nJ/bit. This represents TPA and energy efficiency improvements of 10.05× and 8.58×, respectively, over a non-pipelined approach. Additionally, we propose replacing the large SHAKE XOF function with a lightweight Trivium based PRNG, which reduces the area by 32% and improves energy efficiency by 30%, and which could be considered for future cipher specifications.
Efficiency and scalability of multi-lane capsule networks (MLCN)

Some Deep Neural Networks (DNN) have what we call lanes, or they can be reorganized as such. Lanes are paths in the network which are data-independent and typically learn different features or add resilience to the network. Given their data-independence, lanes are amenable for parallel processing. The Multi-lane CapsNet (MLCN) is a proposed reorganization of the Capsule Network which is shown to achieve better accuracy while bringing highly-parallel lanes. However, the efficiency and scalability of MLCN had not been systematically examined. In this work, we study the MLCN network with multiple GPUs finding that it is 2x more efficient than the original CapsNet when using model-parallelism. We introduce the load balancing problem of distributing heterogeneous lanes in homogeneous or heterogeneous accelerators and show that a simple greedy heuristic can be almost 50% faster than a naïve random approach. Further, we show that we can generate MLCN models with heterogeneous lanes with a balanced fit for a given set of devices. We describe a Neural Architectural Search generating MLCN models matching the device's memory that are load balanced. This search discovered models with 18.6% better accuracy for CIFAR-10 .
Low-Complexity Pupil Tracking for Sunglasses-Wearing Faces for Glasses-Free 3D HUDs

This study proposes a pupil-tracking method applicable to drivers both with and without sunglasses on, which has greater compatibility with augmented reality (AR) three-dimensional (3D) head-up displays (HUDs). Performing real-time pupil localization and tracking is complicated by drivers wearing facial accessories such as masks, caps, or sunglasses. The proposed method fulfills two key requirements: low complexity and algorithm performance. Our system assesses both bare and sunglasses-wearing faces by first classifying images according to these modes and then assigning the appropriate eye tracker. For bare faces with unobstructed eyes, we applied our previous regression-algorithm-based method that uses scale-invariant feature transform features. For eyes occluded by sunglasses, we propose an eye position estimation method: our eye tracker uses nonoccluded face area tracking and a supervised regression-based pupil position estimation method to locate pupil centers. Experiments showed that the proposed method achieved high accuracy and speed, with a precision error of &lt;10 mm in &lt;5 ms for bare and sunglasses-wearing faces for both a 2.5 GHz CPU and a commercial 2.0 GHz CPU vehicle-embedded system. Coupled with its performance, the low CPU consumption (10%) demonstrated by the proposed algorithm highlights its promise for implementation in AR 3D HUD systems.
Using High Fidelity Avatars to Enhance Learning Experience in Virtual Learning Environments

Virtual Learning Environments (VLEs) are the quintessence of the integration of several Information and Communication Technologies (ICTs) in the learning process. In this study, we investigate the performance of a multi-user Virtual Reality Learning Environment (VRLE), incorporating high-fidelity avatars, as a tool for enhancing learning experience in VLEs. This work presents the system architecture along with preliminary evaluation results provided by 19 post-graduate students.
A parameterized perspective on protecting elections

We study the parameterized complexity of the Optimal Defense and Optimal Attack problems in voting. In both the problems, the input is a set of voter groups (every voter group is a district consisting of a set of votes) and two integers ka and kd corresponding to respectively the number of voter groups the attacker can attack and the number of voter groups the defender can defend. A voter group gets removed from the election if it is attacked but not defended. In the Optimal Defense problem, we want to know if it is possible for the defender to commit to a strategy of defending at most kd voter groups such that, no matter which ka voter groups the attacker attacks, the outcome of the election does not change. In the Optimal Attack problem, we want to know if it is possible for the attacker to commit to a strategy of attacking ka voter groups such that, no matter which kd voter groups the defender defends, the outcome of the election is always different from the original one (without any attack). We show that both the Optimal Defense problem and the Optimal Attack problem are computationally intractable for every scoring rule and the Condorcet voting rule even when we have only 3 candidates. We also show that the Optimal Defense problem for every scoring rule and the Condorcet voting rule is W[2]-hard for both the parameters ka and kd, while it admits a fixed parameter tractable algorithm parameterized by the combined parameter (ka,kd). The Optimal Attack problem for every scoring rule and the Condorcet voting rule turns out to be much harder – it is W[1]-hard even for the combined parameter (ka,kd). We propose two greedy algorithms for the Optimal Defense problem and empirically show that they perform effectively on many voting profiles.
An Optimal Feature Selection Approach Using IBBO for Histopathological Image Classification

The automated methods for the categorization of the histopathological images is very useful in disease diagnosis and prognosis. However, due to complex image background and morphological variations these images generate very large feature vectors which make the automated classification task difficult. Therefore, in this paper, a new feature selection method based on improved biogeography-based optimization algorithm is proposed to select the prominent features. These feature are further used for the classification process. The elimination rate of the proposed method is 74.25% and 71.28% on BreaKHis and BACH histopathological image datasets respectively. A comparative analysis has been performed using different classifiers on the the selected features and simulation analysis depict that the IBBO-based feature selection and classification method outperforms.
A Systematic Review of Blogging : Opportunities and Challenges

By this research, we want to display the key factors of how effective blogging as technology is. We collected information curated by several researchers on the same for understanding their point of view as well. By doing this we found out what makes blogging the best way of sharing information online. We also analyzed the technological aspect of modern-day blogging which includes modern technologies being used, privacy rules and control as well as data analytics. By these approaches, we showed how blogging is not only the best medium for sharing information but can also help in other fields as well like marketing, education, data analysis, community development. With this research, we also wanted to highlight the downsides of using blogging or micro-blogging.
Context checklist for industrial software engineering research and practice

The relevance of context is particularly stressed in case studies, where it is said that "case study is an empirical method aimed at investigating contemporary phenomena in their context". In this research, we classify context information and provide a context checklist for industrial software engineering. The checklist serves the purpose of (a) supporting researchers and practitioners in characterizing the context in which they are working; (b) supporting researchers with a checklist to identify relevant contextual information to elicit and report during primary and secondary studies. We utilized a systematic approach for constructing the classification of context information and provided a detailed definition for each item. We collected feedback from researchers as well as practitioners. The usefulness of the checklist was perceived more positively by researchers than practitioners, though they highlighted benefits (raising awareness of the importance of context and usefulness for management). The understandability was perceived positively by both practitioners and researchers. The checklist may serve as a "meta-model", forming the basis for specific adaptations for different research areas, and as input for researchers deciding which context information to extract in systematic reviews. The checklist may also help researchers in reporting context in research papers.
Using Meta Labels for the Training of Weighting Models in a Sample-Specific Late Fusion Classification Architecture

The performance of multiple classifier systems can be significantly improved by the use of intelligent classifier combination approaches. In this study, we introduce a novel late fusion architecture, which can be interpreted as a combination of the well-known mixture of experts and stacked generalization methods. Our proposed method aggregates the outputs of classification models and corresponding sample-specific weighting models. A special feature of our proposed architecture is that each weighting model is trained on an individual set of meta labels. Using individual sets of meta labels allows each weighting model to separate regions, on which the predictions of the corresponding classification model can be associated to an estimated confidence value. We test our proposed architecture on a set of publicly available databases, including different benchmark data sets. The experimental evaluation shows the effectiveness and potential of our proposed method. Moreover, we discuss different approaches for further improvement of our proposed architecture.
Tilting at windmills: Data augmentation for deep pose estimation does not help with occlusions

Occlusion degrades the performance of human pose estimation. In this paper, we introduce targeted keypoint and body part occlusion attacks. The effects of the attacks are systematically analyzed on the best performing methods. In addition, we propose occlusion specific data augmentation techniques against keypoint and part attacks. Our extensive experiments show that human pose estimation methods are not robust to occlusion and data augmentation does not solve the occlusion problems.
Gambling on Innovation

The purpose of this article is to provide a method for choosing an innovation portfolio, which is based on a simple and fundamental result from information theory. Given a set of innovation projects, estimated payoffs and probabilities of success for each, and an established capital fund, the question is “How much should one invest in each project in order to maximize the growth and reduce the risk of going bust?” That is, how should innovation leaders gamble on their innovation projects? The question is part of the process design for innovation processes. The concept in this article is especially important for early-stage innovation projects, where the probability of success is often <50%. As a heuristic, for p = 50%, one needs a payoff of b = 4 to achieve a 25% median compound annual growth rate. This article provides an algorithm to guide investment decisions in innovation, plus some heuristics that can be used for guidance. It also emphasizes the critical importance of estimating both the probabilities of success and the payoffs for projects.
Rapid Prototyping of XR Experiences

This course introduces participants to rapid prototyping for augmented, virtual, and mixed reality. Participants will learn about physical prototyping with paper and Play-Doh and digital prototyping via visual authoring tools. After an overview of the XR prototyping process and tools, participants will complete two hands-on sessions. A combination of paper-based XR design templates and easy-to-use digital authoring tools will be used to create working interactive prototypes that can be run on XR devices. The course is targeted at non-technical audiences including HCI practitioners, user experience researchers, and interaction design professionals and students interested in XR design.
Stability of Algebraic Neural Networks to Small Perturbations

Algebraic neural networks (AlgNNs) are composed of a cascade of layers each one associated to and algebraic signal model, and information is mapped between layers by means of a nonlinearity function. AlgNNs provide a generalization of neural network architectures where formal convolution operators are used, like for instance traditional neural networks (CNNs) and graph neural networks (GNNs). In this paper we study stability of AlgNNs on the framework of algebraic signal processing. We show how any architecture that uses a formal notion of convolution can be stable beyond particular choices of the shift operator, and this stability depends on the structure of subsets of the algebra involved in the model. We focus our attention on the case of algebras with a single generator.
CubeSense: Wireless, Battery-Free Interactivity through Low-Cost Corner Reflector Mechanisms

Ubiquitous computing systems rely on ubiquitous methods to sense user interactions, which have manifested in our daily environments as physical buttons, switches, sliders, and beyond. These conventional controllers are either wired, which eliminates flexible deployments, or powered by batteries that require user maintenance. Additionally, built-in wireless communications such as Wi-Fi, Bluetooth, and RFID add up to the total cost. All aforementioned constraints prevent truly ubiquitous interactions from intelligent environments such as smart homes, industry 4.0, precision farming, and a wider range of Internet-of-Things (IoT) applications. We present CubeSense, a wireless and battery-free interactive sensing system which encodes user interactions into radar cross section (RCS) of corner reflectors. Through careful designs of corner reflector mechanisms, CubeSense achieves robust accuracies with controllers made of ultra-low-cost plastics and metal films, resulting in a total cost of around only 20 cents per unit.
Efficient Rendering of Ocular Wavefront Aberrations using Tiled Point‐Spread Function Splatting

Abstract Visual aberrations are the imperfections in human vision, which play an important role in our everyday lives. Existing algorithms to simulate such conditions are either not suited for low‐latency workloads or limit the kinds of supported aberrations. In this paper, we present a new simulation method that supports arbitrary visual aberrations and runs at interactive, near real‐time performance on commodity hardware. Furthermore, our method only requires a single set of on‐axis phase aberration coefficients as input and handles the dynamic change of pupil size and focus distance at runtime. We first describe a custom parametric eye model and parameter estimation method to find the physical properties of the simulated eye. Next, we talk about our parameter sampling strategy which we use with the estimated eye model to establish a coarse point‐spread function (PSF) grid. We also propose a GPU‐based interpolation scheme for the kernel grid which we use at runtime to obtain the final vision simulation by extending an existing tile‐based convolution approach. We showcase the capabilities of our eye estimation and rendering processes using several different eye conditions and provide the corresponding performance metrics to demonstrate the applicability of our method for interactive environments.
DSNet: Deep Shadow Network for Illumination Estimation

Illumination consistency has applications to modeling and rendering in virtual reality. In 3D reconstruction and Mixed Reality(MR) fusion, the appearance of a large-scale outdoor scene may change in response to lighting and seasons, for example. Since 3D reconstruction from scratch is costly, it is helpful to be able to update existing models with recently captured photographs. However, the illumination conditions of the captured photograph can be arbitrary, making it challenging to fit to the existing model. To tackle this problem, this paper proposes a novel approach that can precisely estimate the illumination of the input image. Our Deep Shadow Network (DSNet) collaboratively utilizes illumination-based data augmentation for sun position estimation, along with a dataset of illumination-based augmented renderings. Our run-time rendering and optimization strategy is also discussed. We show that accurate simulation of illumination can improve the performance of visual applications including place recognition and long-term localization. Experimental results validate the effectiveness of the proposed approach, and show its superiority over the state-of-the-art.