Reviewerly/exHarmony huggingface dataset:
Self-deStaining Textiles: Designing Interactive Systems with Fabric, Stains and Light

This work introduces “destaining” as an interactive component for the HCI community. While staining happens unintentionally (e.g., spilling coffee), destaining can be used as an intentional design tool that selectively degrades stains on textiles. We explore the design space using silver doped titanium dioxide (TiO2/Ag), stains and light as a set of design primitives for interactive systems. We then developed replicable and accessible fabrication and testing methods that enable HCI researchers and designers to upgrade various fabrics to self-destaining textiles. Next, we demonstrate a Self-deStaining textile interface with embedded Light Emitting Diodes (LEDs) and moisture sensors that activate cleaning. Lastly, we showcase how the textile can be used in everyday objects such as self-cleaning clothes, a patterning station for phone cases, and accessories that change patterns and colors based on the user’s experiences.
Comparison of Different Types of Augmented Reality Visualizations for Instructions

Augmented Reality (AR) is increasingly being used for providing guidance and supporting troubleshooting in industrial settings. While the general application of AR has been shown to provide clear benefits regarding physical tasks, it is important to understand how different visualization types influence user's performance during the execution of the tasks. Previous studies evaluating AR and user's performance compared different media types or types of AR hardware as opposed to different types of visualization for the same hardware type. This paper provides details of our comparative study in which we identified the influence of visualization types on the performance of complex machine set-up processes. Although our results show clear advantages to using concrete rather than abstract visualizations, we also find abstract visualizations coupled with videos leads to similar user performance as with concrete visualizations.
Using Siamese Neural Networks to Perform Cross-System Behavioral Authentication in Virtual Reality

In this paper, we provide an approach on using behavioral biometrics to perform cross-system high-assurance authentication of users in virtual reality (VR) environments. VR is currently being explored as a critical tool to ensure seamless delivery of essential services, such as education, healthcare, and personal finance, while enabling users to work from home environments. Due to the sensitive nature of personal data generated, VR applications for essential services need to provide secure access. Traditional PIN or password-based credentials can be breached by malicious impostors, or be handed over by an intended user of a VR system to a confederate to assist the intended user in completing a task, e.g., an exam or a physical therapy routine. Existing approaches that use the behavior of the user in VR as a biometric signature fail when users provide enrollment and use-time data on different VR systems. We use Siamese neural networks to learn a distance function that characterizes the systematic differences between data provided across pairs of dissimilar VR systems. Our approach provides average equal error rates (EERs) ranging from 1.38% to 3.86% for authentication using a benchmark dataset that consists of 41 users performing a ball-throwing task with 3 VR systems-an Oculus Quest, an HTC Vive, and an HTC Vive Cosmos. To compare to prior approaches in VR biometrics, we also obtain average accuracies for the task of identification, where given an input user's trajectory in a use-time VR system, we use Siamese networks to return the user with the top matching trajectory in an enrollment VR system as the label. We report identification results ranging from 87.82% to 98.53% with average improvements of 29.78%±8.58% and 30.78%±3.68% over existing approaches that use generic distance matching and fully convolutional networks on the enrollment dataset respectively.
A Feature Fusion Method to Improve the Driving Obstacle Detection Under Foggy Weather

Driving obstacle detection has great importance in automatic driving safety. However, the deterioration of image quality in the foggy environment brings many difficulties to the detection and even endangers driving safety. Therefore, in this article, a feature fusion method is designed to improve the performance of the camera sensor in driving obstacle detection under foggy weather. The difference in image features of the same traffic scene on sunny and foggy days is compared, and the reason for the decrease in detection performance of the camera sensor in foggy environments is found. The foggy image dataset is constructed, and the feature fusion of the detection object in foggy and sunny environments is realized through YOLOv3. Compared with the regular trained model, the method in this article improves 30% in mean average precision (mAP), 40% in recall, and 22% in F1-Score of the object detection performance in foggy images. Unlike the image defogging method, foggy images can be directly used for detection and have higher detection accuracy and time-efficient. This is of great significance for improving driving obstacle detection in adverse weather, which is important to ensure driving safety.
Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs

This work proposes a new unsupervised (or self-supervised) node representation learning method that aims to leverage the coarse-grain information that is available in most graphs. This extends previous attempts that only leverage fine-grain information (similarities within local neighborhoods) or global graph information (similarities across all nodes). Intuitively, the proposed method identifies nodes that belong to the same clusters and maximizes their mutual information. Thus, coarse-grain (cluster-level) similarities that are shared between nodes are preserved in their representations. The core components of the proposed method are (i) a jointly optimized clustering of nodes during learning and (ii) an Infomax objective term that preserves the mutual information among nodes of the same clusters. Our method is able to outperform competing state-of-art methods in various downstream tasks, such as node classification, link prediction, and node clustering. Experiments show that the average gain is between 0.2% and 6.1%, over the best competing approach, over all tasks. Our code is publicly available at: https://github.com/cmavro/Graph-InfoClust-GIC.
Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice

Machine learning sustains successful application to many diagnostic and prognostic problems in computational histopathology. Yet, few efforts have been made to model gene expression from histopathology. This study proposes a methodology which predicts selected gene expression values (microarray) from haematoxylin and eosin whole-slide images as an intermediate data modality to identify fulminant-like pulmonary tuberculosis ('supersusceptible') in an experimentally infected cohort of Diversity Outbred mice (n=77).Gradient-boosted trees were utilized as a novel feature selector to identify gene transcripts predictive of fulminant-like pulmonary tuberculosis. A novel attention-based multiple instance learning model for regression was used to predict selected genes' expression from whole-slide images. Gene expression predictions were shown to be sufficiently replicated to identify supersusceptible mice using gradient-boosted trees trained on ground truth gene expression data.The model was accurate, showing high positive correlations with ground truth gene expression on both cross-validation (n = 77, 0.63 ≤ ρ ≤ 0.84) and external testing sets (n = 33, 0.65 ≤ ρ ≤ 0.84). The sensitivity and specificity for gene expression predictions to identify supersusceptible mice (n=77) were 0.88 and 0.95, respectively, and for an external set of mice (n=33) 0.88 and 0.93, respectively.Our methodology maps histopathology to gene expression with sufficient accuracy to predict a clinical outcome. The proposed methodology exemplifies a computational template for gene expression panels, in which relatively inexpensive and widely available tissue histopathology may be mapped to specific genes' expression to serve as a diagnostic or prognostic tool.National Institutes of Health and American Lung Association.
Joint Source-Channel Coding Over Additive Noise Analog Channels Using Mixture of Variational Autoencoders

In this paper, we present a learning scheme for Joint Source-Channel Coding (JSCC) over analog independent additive noise channels. We formulate the learning problem by showing that the minimization loss function from rate-distortion theory, is upper bounded by the loss function of the Variational Autoencoder (VAE). We show that when the source dimension is greater than the channel dimension, the encoding of two source samples in the neighborhood of each other need not be near each other. Such discontinuous projection needs to be accounted for by using multiple encoders and selecting an encoder to encode samples on a particular side of the discontinuity. We explore two selection methodologies, one based on an intuitive rule and the other where it is posed as a learning task in a Mixture-of-Experts (MoE) setup. We analyze the gradients of these methods and reason why the latter is better at avoiding local optima. We show the efficacy of the proposed methodology by simulating the performance of the system for JSCC of Gaussian sources over AWGN channels and showing that the learned solutions are close to or better than the ones proposed earlier. The proposed methodology is also naturally capable of generalizing to other source distributions which we showcase by simulating for Laplace sources. The learned systems are also robust to changes in channel conditions. Further, a single system can be trained to generalize over a range of channel conditions provided the channel conditions are known at both the transmitter and the receiver. Finally, we evaluate our proposed methodology on three different image datasets and showcase consistent improvement over existing methods due to the VAE formulation.
A financial statement fraud model based on synthesized attribute selection and a dataset with missing values and imbalanced classes

Many financial fraud events have occurred over the past few decades. These events have led to massive losses for investors. Hence, government officials have begun to focus on the problem and have issued several decrees (acts) on financial fraud. Many scholars have explored the factors of financial fraud, and although their results performed well, most studies have not generated a set of useful rules to support auditors. Furthermore, data on financial statement fraud usually constitute an imbalanced class problem, and previous work minimally addresses this problem. Therefore, this study, based on the handling of missing values and imbalanced classes, builds a detecting model of financial statement fraud. First, it utilizes listwise and pairwise deletion to remove missing values. Second, it proposes three merged attribute selection methods and applies a nonlinear distance correlation to select important attributes. Third, it applies undersampling and oversampling to address the imbalanced classes. Finally, it uses rule-based classifiers to generate a set of useful rules. In practice, this study employs a list of fraudulent companies to collect data on financial statement fraud. We summarize the results as follows: (1) the pairwise deletion removes fewer records than does listwise removal in handling missing values; (2) the merged attribute selection (Com_I4) has the best performance on the four evaluation criteria; (3) the oversampling can enhance accuracy, and has the lowest type 1 and type 2 errors; (4) the random forest of Com_I4 can build the optimal model of financial statement fraud in the pairwise deletion and random oversampling; and (5) the results show that the ensemble learning (random forest) is a robust model in this study. Finally, these results in this study can be provided to practitioners, investors, and auditing personnel as references. • Builds a financial fraud model based on handling of missing values and imbalanced classes. • Propose three merged attribute selection methods and a distance correlation to select attributes. • Utilize rule-based classifiers to generate a set of effective rules. • The generated rules and key attributes can be provided to stakeholders as reference.
Edge-Intelligence-Empowered, Unified Authentication and Trust Evaluation for Heterogeneous Beyond 5G Systems

Integrating artificial intelligence (AI) with mobile edge computing, edge intelligence (EI) has emerged as a new paradigm for 5G and beyond 5G (B5G) systems. The integration of EI and heterogeneous networks (e.g., mobile and wireless local area networks) also raises new concerns about security and privacy. This article examines two important security aspects of EI-empowered, heterogeneous, B5G networks, that is, authentication and trust-evaluation-based compromised user equipment (UE) detection. Technical challenges are discussed. A new edge-computing-enabled, unified authentication framework is developed, which authenticates UEs consistently via heterogeneous networks with UEs' privacy preserved. A new trust-evaluation-based compromised UE detection method is developed based on reinforcement learning to prevent compromised UEs from launching internal attacks. Case studies show that the new frameworks improve the authentication efficiency and detection rate of compromised UEs, as compared to technologies specified by current 5G standards. The frameworks have great potential to secure B5G in future heterogeneous network environments.
PAASH: A privacy-preserving authentication and fine-grained access control of outsourced data for secure smart health in smart cities

Very recently, a number of intelligent applications have evolved in smart cities such as smart grid, smart parking, smart waste management, smart manufacturing, smart home, and smart health, making our cities more smarter. In the context of smart health, smart devices are employed to collect and transmit biomedical information to a medical server, allowing medical practitioners to give better and improved healthcare services. However, considering the sensitive nature of the medical data, any illegal access may have devastating effects on the patient's health condition. A lightweight privacy-preserving authentication and fine-grained access control scheme for smart health known as PAASH is proposed. In PAASH, a new ultraefficient certificateless signature scheme with compact aggregation is developed to achieve source authentication and data integrity. To achieve efficient user authorization and data confidentiality, a privacy-preserving access control technique using the developed aggregate signature and ciphertext-policy attribute-based encryption (CP-ABE) is implemented. An analysis of PAASH reveals that it can provide desirable privacy and security measures in smart health. Moreover, the formal security demonstrated in the random oracle model (ROM) shows that PAASH is semantically secure under the intractability of the Discrete Logarithm Problem (DLP). The performance evaluation shows that PAASH is par excellence practically suitable for smart health.
Learning Word-Level Confidence for Subword End-To-End ASR

We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR). Although prior works have proposed training auxiliary confidence models for ASR systems, they do not extend naturally to systems that operate on word-pieces (WP) as their vocabulary. In particular, ground truth WP correctness labels are needed for training confidence models, but the non-unique tokenization from word to WP causes inaccurate labels to be generated. This paper proposes and studies two confidence models of increasing complexity to solve this problem. The final model uses self-attention to directly learn word-level confidence without needing subword tokenization, and exploits full context features from multiple hypotheses to improve confidence accuracy. Experiments on Voice Search and long-tail test sets show standard metrics (e.g., NCE, AUC, RMSE) improving substantially. The proposed confidence module also enables a model selection approach to combine an on-device E2E model with a hybrid model on the server to address the rare word recognition problem for the E2E model.
A generalized multipath delayed-choice experiment on a large-scale quantum nanophotonic chip

Abstract Bohr’s complementarity is one central tenet of quantum physics. The paradoxical wave-particle duality of quantum matters and photons has been tested in Young’s double-slit (double-path) interferometers. The object exclusively exhibits wave and particle nature, depending measurement apparatus that can be delayed chosen to rule out too-naive interpretations of quantum complementarity. All experiments to date have been implemented in the double-path framework, while it is of fundamental interest to study complementarity in multipath interferometric systems. Here, we demonstrate generalized multipath wave-particle duality in a quantum delayed-choice experiment, implemented by large-scale silicon-integrated multipath interferometers. Single-photon displays sophisticated transitions between wave and particle characters, determined by the choice of quantum-controlled generalized Hadamard operations. We characterise particle-nature by multimode which-path information and wave-nature by multipath coherence of interference, and demonstrate the generalisation of Bohr’s multipath duality relation. Our work provides deep insights into multidimensional quantum physics and benchmarks controllability of integrated photonic quantum technology.
Deep learning approach for one-hour ahead forecasting of energy production in a solar-PV plant

Solar power production (SPP) using photovoltaics is one of the most effective ways of solar energy utilization. Prediction of SPP is of great importance due to mitigating the effect of random fluctuations in the incoming solar energy and enabling the operator to access solar power output data in advance. Accurate prediction for SPP is also important for providing high-quality electricity to end-consumers. In the present study, a deep learning approach established on Long Short-Term Memory (LSTM) neural network was introduced. The network aimed to forecast one hour-ahead electrical energy production from the solar-PV power plant with 1.15 MW capacity. In addition to the LSTM neural network, two different data-driven methods, namely, adaptive neuro-fuzzy inference system (ANFIS) accompanied by fuzzy c-means (FCM) and ANFIS with grid partition (GP) were applied. The data obtained from the models were also validated using measured data. The results from the comparison revealed that the LSTM model gives the best results with RMSE, MAE, and R equal to 60.66 kWh, 30.47 kWh, and 0.9777, respectively.
On the Value of Oversampling for Deep Learning in Software Defect Prediction

One truism of deep learning is that the automatic feature engineering (seen in the first layers of those networks) excuses data scientists from performing tedious manual feature engineering prior to running DL. For the specific case of deep learning for defect prediction, we show that that truism is false. Specifically, when we pre-process data with a novel oversampling technique called fuzzy sampling, as part of a larger pipeline called GHOST (Goal-oriented Hyper-parameter Optimization for Scalable Training), then we can do significantly better than the prior DL state of the art in 14/20 defect data sets. Our approach yields state-of-the-art results significantly faster deep learners. These results present a cogent case for the use of oversampling prior to applying deep learning on software defect prediction datasets.
Effective Prediction of Bug-Fixing Priority via Weighted Graph Convolutional Networks

With the increasing number of software bugs, bug fixing plays an important role in software development and maintenance. To improve the efficiency of bug resolution, developers utilize bug reports to resolve given bugs. Especially, bug triagers usually depend on bugs' descriptions to suggest priority levels for reported bugs. However, manual priority assignment is a time-consuming and cumbersome task. To resolve this problem, recent studies have proposed many approaches to automatically predict the priority levels for the reported bugs. Unfortunately, these approaches still face two challenges that include words' nonconsecutive semantics in bug reports and the imbalanced data. In this article, we propose a novel approach that graph convolutional networks (GCN) based on weighted loss function to perform the priority prediction for bug reports. For the first challenge, we build a heterogeneous text graph for bug reports and apply GCN to extract words' semantics in bug reports. For the second challenge, we construct a weighted loss function in the training phase. We conduct the priority prediction on four open-source projects, including Mozilla, Eclipse, Netbeans, and GNU compiler collection. Experimental results show that our method outperforms two baseline approaches in terms of the F-measure by weighted average of 13.22%.
Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research

Abstract Objective The rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production and incremental updates of data feeds to the networks from raw EHR data. However, the demands of COVID-19 research for timely data are far higher, and the requirements for updating faster than previous collaborative research using national data networks have increased. New approaches need to be developed to address these demands. Methods In this article, we describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical data model and the automated transformation of clinical data to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs for data sharing and research collaboration on COVID-19. Results FHIR data resources could be transformed to operational PCORnet and OMOP CDMs with minimal production delays through a combination of real-time and postprocessing steps, leveraging the FHIR data subscription feature. Conclusions The approach leverages evolving standards for the availability of EHR data developed to facilitate data exchange under the 21st Century Cures Act and could greatly enhance the availability of standardized datasets for research.
Quantum computation: Algorithms and Applications

As the prospect of commercial quantum computers turns ever more real in recent times, research in quantum algorithms becomes the center of attention. Due to the strong parallelism of quantum computing in Hilbert space, ordinarily intractable calculation problems could now be solved very efficiently with non-classical means. To exploit parallelism, creative quantum algorithms are required so that efficient quantum oracles can be tailor-designed to specific computation needs. Therefore, in the quest for quantum supremacy, quantum algorithms and their related applications are as important as the quantum computer hardware. This article covers the basic concepts of quantum computation and reviews some important quantum algorithms and their applications.