Reviewerly/exHarmony huggingface dataset:
Learning Architectures for Binary Networks

Backbone architectures of most binary networks are well-known floating point (FP) architectures such as the ResNet family. Questioning that the architectures designed for FP networks might not be the best for binary networks, we propose to search architectures for binary networks (BNAS) by defining a new search space for binary architectures and a novel search objective. Specifically, based on the cell based search method, we define the new search space of binary layer types, design a new cell template, and rediscover the utility of and propose to use the Zeroise layer instead of using it as a placeholder. The novel search objective diversifies early search to learn better performing binary architectures. We show that our method searches architectures with stable training curves despite the quantization error inherent in binary networks. Quantitative analyses demonstrate that our searched architectures outperform the architectures used in state-of-the-art binary networks and outperform or perform on par with state-of-the-art binary networks that employ various techniques other than architectural changes.
A Deep Learning Instance Segmentation Approach for Global Glomerulosclerosis Assessment in Donor Kidney Biopsies

The histological assessment of glomeruli is fundamental for determining if a kidney is suitable for transplantation. The Karpinski score is essential to evaluate the need for a single or dual kidney transplant and includes the ratio between the number of sclerotic glomeruli and the overall number of glomeruli in a kidney section. The manual evaluation of kidney biopsies performed by pathologists is time-consuming and error-prone, so an automatic framework to delineate all the glomeruli present in a kidney section can be very useful. Our experiments have been conducted on a dataset provided by the Department of Emergency and Organ Transplantations (DETO) of Bari University Hospital. This dataset is composed of 26 kidney biopsies coming from 19 donors. The rise of Convolutional Neural Networks (CNNs) has led to a realm of methods which are widely applied in Medical Imaging. Deep learning techniques are also very promising for the segmentation of glomeruli, with a variety of existing approaches. Many methods only focus on semantic segmentation—which consists in segmentation of individual pixels—or ignore the problem of discriminating between non-sclerotic and sclerotic glomeruli, so these approaches are not optimal or inadequate for transplantation assessment. In this work, we employed an end-to-end fully automatic approach based on Mask R-CNN for instance segmentation and classification of glomeruli. We also compared the results obtained with a baseline based on Faster R-CNN, which only allows detection at bounding boxes level. With respect to the existing literature, we improved the Mask R-CNN approach in sliding window contexts, by employing a variant of the Non-Maximum Suppression (NMS) algorithm, which we called Non-Maximum-Area Suppression (NMAS). The obtained results are very promising, leading to improvements over existing literature. The baseline Faster R-CNN-based approach obtained an F-Measure of 0.904 and 0.667 for non-sclerotic and sclerotic glomeruli, respectively. The Mask R-CNN approach has a significant improvement over the baseline, obtaining an F-Measure of 0.925 and 0.777 for non-sclerotic and sclerotic glomeruli, respectively. The proposed method is very promising for the instance segmentation and classification of glomeruli, and allows to make a robust evaluation of global glomerulosclerosis. We also compared Karpinski score obtained with our algorithm to that obtained with pathologists’ annotations to show the soundness of the proposed workflow from a clinical point of view.
Just Noticeable Distortion Profile Inference: A Patch-Level Structural Visibility Learning Approach

In this paper, we propose an effective approach to infer the just noticeable distortion (JND) profile based on patch-level structural visibility learning. Instead of pixel-level JND profile estimation, the image patch, which is regarded as the basic processing unit to better correlate with the human perception, can be further decomposed into three conceptually independent components for visibility estimation. In particular, to incorporate the structural degradation into the patch-level JND model, a deep learning-based structural degradation estimation model is trained to approximate the masking of structural visibility. In order to facilitate the learning process, a JND dataset is further established, including 202 pristine images and 7878 distorted images generated by advanced compression algorithms based on the upcoming Versatile Video Coding (VVC) standard. Extensive experimental results further show the superiority of the proposed approach over the state-of-the-art. Our dataset is available at: https://github.com/ShenXuelin-CityU/PWJNDInfer.
Distributed recursive filtering for multi-sensor networked systems with multi-step sensor delays, missing measurements and correlated noise

This paper is concerned with the distributed recursive filtering for the discrete-time nonlinear multi-sensor networked system with multi-step sensor delays, missing measurements and correlated noise. Based on the innovation statistical distance, an adaptive time delay estimation method, which belongs to the online methods, is derived to determine whether the measurement is acquired or not along with the time delay step. Then, a nonlinear system model is founded based on a set of selected Bernoulli distributed random variables to describe the multi-step sensor delays, missing measurements and correlated noise. The obtained time delay step can be used to update parameters of the proposed measurement model. Next, a distributed recursive filtering is designed based on linear fitting (LF) and weighted average consensus (WAC) to solve the nonlinear state estimation in the multi-sensor networked system. Meanwhile, a selection strategy is designed based on the innovation statistical distance for the weighted factors to improve the distributed fusion accuracy. Further, filtering errors of the distributed recursive filtering are proved to be exponentially bounded in mean square. Numerical simulations are conducted to evaluate the performance of the proposed algorithm.
A kernel-free double well potential support vector machine with applications

As a well-known machine learning technique, support vector machine (SVM) with a kernel function achieves much success in nonlinear binary classification tasks. Recently, some quadratic surface SVM models are proposed and studied by utilizing quadratic surfaces for nonlinear binary separations. In this paper, a kernel-free soft quartic surface SVM model is proposed by utilizing the double well potential function for highly nonlinear binary classification. Mathematical analysis on the theoretical properties of the proposed model, including the existence, uniqueness and support vector representation of optimal solutions, is shown. The sequential minimal optimization algorithm is adopted to implement the proposed model for computational efficiency. Numerical results on some artificial and public benchmark data sets demonstrate its effectiveness over well-known SVM models with or without kernel functions. The proposed model is extended to successfully handle some real-life corporate and personal credit data sets for applications.
A Binary Equilibrium Optimization Algorithm for 0–1 Knapsack Problems

In this paper, a binary version of equilibrium optimization (BEO) is proposed for the tackling 0–1 knapsack problem characterized as a discrete problem. Because the standard equilibrium optimizer (EO) has been proposed for solving continuous optimization problems, a discrete variant is required to solve binary problems. Hence, eight transfer functions including V-Shaped and S-Shaped are employed to convert continuous EO to Binary EO (BEO). Among those transfer functions, this study demonstrates that V-Shaped V3 is the best one. It is also observed that the sigmoid S3 transfer function can be more beneficial than V3 for improving the performance of other algorithms employed in this paper. We conclude that the performance of any binary algorithm relies on the good choice of the transfer function. In addition, we use the penalty function to sift the infeasible solution from the solutions of the problem and apply a repair algorithm (RA) for converting them to feasible solutions. The performance of the proposed algorithm is evaluated on three benchmark datasets with 63 instances of small-, medium-, and large-scale and compared with a number of the other algorithm proposed for solving 0–1 knapsack under different statistical analyses. The experimental results demonstrate that the BEOV3 algorithm is superior on all the small-, medium-scale case studies. Regarding the large-scale test cases, the proposed method achieves the optimal value for 13 out of 18 instances.2
Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting

Traditional methods for image-based 3D face reconstruction and facial motion retargeting fit a 3D morphable model (3DMM) to the face, which has limited modeling capacity and fail to generalize well to in-the-wild data. Use of deformation transfer or multilinear tensor as a personalized 3DMM for blendshape interpolation does not address the fact that facial expressions result in different local and global skin deformations in different persons. Moreover, existing methods learn a single albedo per user which is not enough to capture the expression-specific skin reflectance variations. We propose an end-to-end framework that jointly learns a personalized face model per user and per-frame facial motion parameters from a large corpus of in-the-wild videos of user expressions. Specifically, we learn user-specific expression blendshapes and dynamic (expression-specific) albedo maps by predicting personalized corrections on top of a 3DMM prior. We introduce novel training constraints to ensure that the corrected blendshapes retain their semantic meanings and the reconstructed geometry is disentangled from the albedo. Experimental results show that our personalization accurately captures fine-grained facial dynamics in a wide range of conditions and efficiently decouples the learned face model from facial motion, resulting in more accurate face reconstruction and facial motion retargeting compared to state-of-the-art methods.
Achieving Reliable Sentiment Analysis in the Software Engineering Domain using BERT

Researchers have shown that sentiment analysis of software artifacts can potentially improve various software engineering tools, including API and library recommendation systems, code suggestion tools, and tools for improving communication among software developers. However, sentiment analysis techniques applied to software artifacts still have not yet yielded very high accuracy. Recent adaptations of sentiment analysis tools to the software domain have reported some improvements, but the f-measures for the positive and negative sentences still remain in the 0.4-0.64 range, which deters their practical usefulness for software engineering tools.In this paper, we explore the potential effectiveness of customizing BERT, a language representation model, which has recently achieved very good results on various Natural Language Processing tasks on English texts, for the task of sentiment analysis of software artifacts. We describe our application of BERT to analyzing sentiments of sentences in Stack Overflow posts and compare the impact of a BERT sentiment classifier to state-of-the-art sentiment analysis techniques when used on a domain-specific data set created from Stack Overflow posts. We also investigate how the performance of sentiment analysis changes when using a much (3 times) larger data set than previous studies. Our results show that the BERT classifier achieves reliable performance for sentiment analysis of software engineering texts. BERT combined with the larger data set achieves an overall f-measure of 0.87, with the f-measures for the negative and positive sentences reaching 0.91 and 0.78 respectively, a significant improvement over the state-of-the-art.
Automated Keyword Filtering in Latent Dirichlet Allocation for Identifying Product Attributes From Online Reviews

Abstract Identifying product attributes from the perspective of a customer is essential to measure the satisfaction, importance, and Kano category of each product attribute for product design. This article proposes automated keyword filtering to identify product attributes from online customer reviews based on latent Dirichlet allocation. The preprocessing for latent Dirichlet allocation is important because it affects the results of topic modeling; however, previous research performed latent Dirichlet allocation either without removing noise keywords or by manually eliminating them. The proposed method improves the preprocessing for latent Dirichlet allocation by conducting automated filtering to remove the noise keywords that are not related to the product. A case study of Android smartphones is performed to validate the proposed method. The performance of the latent Dirichlet allocation by the proposed method is compared to that of a previous method, and according to the latent Dirichlet allocation results, the former exhibits a higher performance than the latter.
Pathways for Entanglement-Based Quantum Communication in the Face of High Noise

Entanglement based quantum communication offers an increased level of security in practical secret shared key distribution. One of the fundamental principles enabling this security -- the fact that interfering with one photon will destroy entanglement and thus be detectable -- is also the greatest obstacle. Random encounters of traveling photons, losses and technical imperfections make noise an inevitable part of any quantum communication scheme, severely limiting distance, key rate and environmental conditions in which QKD can be employed. Using photons entangled in their spatial degree of freedom, we show that the increased noise resistance of high-dimensional entanglement, can indeed be harnessed for practical key distribution schemes. We perform quantum key distribution in eight entangled paths at various levels of environmental noise and show key rates that, even after error correction and privacy amplification, still exceed $1$ bit per photon pair and furthermore certify a secure key at noise levels that would prohibit comparable qubit based schemes from working.
HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification

Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue. Recent approaches acknowledging the structure-function relationship, have linked the structural and spatial patterns of cell organization in tissue via cell-graphs to tumor grades. Though cell organization is imperative, it is insufficient to entirely represent the histopathological structure. We propose a novel hierarchical cell-to-tissue-graph (HACT) representation to improve the structural depiction of the tissue. It consists of a low-level cell-graph, capturing cell morphology and interactions, a high-level tissue-graph, capturing morphology and spatial distribution of tissue parts, and cells-to-tissue hierarchies, encoding the relative spatial distribution of the cells with respect to the tissue distribution. Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently map the HACT representations to histopathological breast cancer subtypes. We assess the methodology on a large set of annotated tissue regions of interest from H&E stained breast carcinoma whole-slides. Upon evaluation, the proposed method outperformed recent convolutional neural network and graph neural network approaches for breast cancer multi-class subtyping. The proposed entity-based topological analysis is more in line with the pathological diagnostic procedure of the tissue. It provides more command over the tissue modeling, therefore encourages the further inclusion of pathological priors into task-specific tissue representation.
Grounded Situation Recognition

We introduce Grounded Situation Recognition (GSR), a task that requires producing structured semantic summaries of images describing: the primary activity, entities engaged in the activity with their roles (e.g. agent, tool), and bounding-box groundings of entities. GSR presents important technical challenges: identifying semantic saliency, categorizing and localizing a large and diverse set of entities, overcoming semantic sparsity, and disambiguating roles. Moreover, unlike in captioning, GSR is straightforward to evaluate. To study this new task we create the Situations With Groundings (SWiG) dataset which adds 278,336 bounding-box groundings to the 11,538 entity classes in the imSitu dataset. We propose a Joint Situation Localizer and find that jointly predicting situations and groundings with end-to-end training handily outperforms independent training on the entire grounding metric suite with relative gains between 8% and 32%. Finally, we show initial findings on three exciting future directions enabled by our models: conditional querying, visual chaining, and grounded semantic aware image retrieval. Code and data available at https://prior.allenai.org/projects/gsr .
Locality-Sensitive IoT Network Traffic Fingerprinting for Device Identification

Engineered systems get smarter with computing capabilities, particularly through a multitude of Internet-of-Things (IoT) devices. IoT devices, however, are prone to be compromised as they are often resource limited and optimized for a certain task. They lack computing power for security software hence, they have become a major target of malicious activities. In order to secure a network, administrators may isolate vulnerable devices and limit traffic to a device based on its communication needs. In this article, we introduce a novel approach to identify an IoT device based on the locality-sensitive hash of its traffic flow. Different from previous studies that employ machine learning, the proposed approach does not require feature extraction from the data, operates in all states of the device (e.g., setup, idle, and active), and does not require to retrain the model when a new device type/version is introduced. The evaluation results on different data sets show that our approach achieves precision and recall above 90% on average and performs equally well compared to the state-of-the-art machine learning-based methods.
Implementation of a Computer Vision Framework for Tracking and Visualizing Face Mask Usage in Urban Environments

The COVID-19 pandemic is an evolving situation in the United States and is spreading at alarming rates. The adoption of public health-informed hygienic practices can have a large impact on community transmission of COVID-19 including the wearing of face masks in public settings. Convolutional Neural Networks (CNN) can be trained to classify people wearing face masks with impressive accuracy. However, current face mask datasets contain clear, high-resolution close-up images of individuals with face masks which is unrepresentative of the lower fidelity images of distant faces more prominent in urban camera images. This paper proposes a practical deep learning computer vision framework for detection and tracking of people in public spaces and the use of face masks. A custom 6,000 image face mask dataset curated from over 50 hours of urban surveillance camera footage is created in this work. CNN-based detectors trained using the dataset are used to perform person detection and face mask classification. Then, a multi-target tracking module extracts individual trajectories from frame-by-frame detection. By associating detected face masks with tracked individuals, overall face mask usage can be estimated. The framework is implemented on several surveillance cameras along the Detroit RiverWalk, a 5-kilometer pedestrian park connecting various greenways, plazas, pavilions, and open green spaces along the Detroit River in Detroit, Michigan. The detection of park user types is shown to have an average precision of 89% and higher for most person classes with the mask detector having an accuracy of 96%. An interactive web application visualizes the data and is used by park managers to inform management decisions and assess strategies used to increase face mask usage rates.
A self structuring artificial intelligence framework for deep emotions modeling and analysis on the social web

The social web has enabled individuals from all walks of life to openly express their emotions and sentiment in relation to current affairs, local issues and personal circumstances. Within the social web, social media encompasses deep emotional expressions that reflect a multitude of personalities and behaviors. Existing research in this space is heavily focused on supervised sentiment analysis and emotion detection, with limited work on modeling these deep emotions, mixed emotions and variations of emotional behaviors from unlabeled and unstructured social media conversations. In this study, we propose a comprehensive framework based on the principles of self-structuring artificial intelligence for emotion modeling and analysis that systematically integrates the modeling capabilities at a granular level on unstructured, unlabeled social media data. The research contributions of this framework are the detection, analysis and synthesis of deep emotion intensity, emotion transitions, emotion latent representations, and profile-based emotion classification. The self-structuring artificial intelligence framework amalgamates an ensemble of novel algorithms to eventuate these contributions. These algorithms extend the current state-of-the-art of natural language processing techniques, word embedding, Markov chains and growing self-organizing maps, specifically for deep emotions modeling and analysis. The framework is empirically evaluated on anonymized conversations from online mental health support forums. The outcomes identify profile-based emotion characteristics, emotion intensities, transitions and an overall latent representation across three distinct mental health groups in these forums. These outcomes are comprehensive in comparison to existing work which singularly focuses on sentiment analysis or emotion detection. The validity and effectiveness of its application on a real-world social media setting further establish the methodological novelty of this ensemble of self-structuring artificial intelligence for deep emotions.
A Virtual Space for All: Exploring Children's Experience in Social Virtual Reality

Social virtual reality (VR) is increasingly becoming an emerging online social ecosystem where multiple users can interact with one another through VR head-mounted displays in 3Dvirtual spaces. The co-existence of minors and adults in social VR presents unique challenges and opportunities regarding how these two groups interact with each other in these shared virtual social spaces, which is a timely and understudied topic in HCI and CHI PLAY. In this paper, we report our findings of a participatory observation study to explore the interaction dynamics between minors and between minors and adults in social VR. Our findings contribute to a better understanding of young people's engagement with technology and point to future directions for designing safer and more socially satisfying social VR experiences for minors.