Reviewerly/exHarmony huggingface dataset:
Electrooculography-based Eye Movement Classification using Deep Learning Models

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease (MND), is a specific disease that causes the death of neurons controlling voluntary muscles. Most ALS patients eventually lose the ability to walk, use their hands, speak, swallow, and breathe. In this paper, we use the electrooculogram (EOG) signals captured using four sensors placed on the controlling muscles of the eye movement in horizontal and vertical directions to classify four different eye movements. The classifier output is used to control a wheelchair, or any other device developed to help ALS patients in performing their daily needs. Contrary to the classical classification techniques where features are extracted first from the EOG signals, then used with a trained classifier, in this paper the EOG signals are fed directly into two deep neural networks using, respectively, the long-short term memory (LSTM) and the convolutional neural network (CNN). The results show an accuracy of 88.33% for the LSTM network and 90.3% for the CNN network in eye movement classification.
Quantum Embedded Superstates

Abstract Optical supercavity modes (superstates), i.e., hybrid modes emerging from the strong coupling of two modes of an open cavity, can support ultranarrow lines in their scattering spectra associated with quasi bound states in the continuum (quasi‐BIC). These modes are of great interest for sensing applications as they enable compact systems with unprecedented sensitivity. However, classical quasi‐BIC sensors are fundamentally limited by the shot‐noise limit, which may be overcome in quantum sensors. Here, it is shown that a three‐level quantum system (e.g., atom, quantum dot, superconducting qubit) can be tailored to support the quantum analog of superstates with an unboundedly narrow emission line. Remarkably, it is demonstrated that the coupling of such a system with a cavity (e.g., plasmonic or dielectric nanoparticle, microcavity, microwave resonator) enables sensing properties with excellent statistical features. The results can be applied to a plethora of quantum platforms, from superconducting circuits to cold atoms and quantum dots, opening exciting opportunities for quantum sensing and computing.
Deep Learning for Visual Analytics of the Spread of COVID-19 Infection in Crowded Urban Environments

The novel SARS-CoV-2 coronavirus caused a global pandemic in 2020 with millions of diagnosed cases and a staggering number of deaths. As a preventive measure, many governments issued social distancing and shelter-in-place mandates to limit human contact and slow the rate of infection. The large extent and duration of the crisis is poised to transform the health sector and alter current practices in retail, business, manufacturing, and construction. While medical researchers are working on antidote and vaccine solutions, contact tracing and self-isolation are deemed effective methods to control community spread. This paper presents a visual analysis approach that uses convolutional neural networks (CNNs) to generate quantifiable metrics of contact tracing. In particular, the YOLO-v3 architecture was trained on an annotated video dataset containing pedestrians. Network pruning and non-maximum suppression were applied to optimize model performance, resulting in 69.41% average precision. The fully trained model was then tested on sample crosswalk video data from Xiamen, China, recorded during the COVID-19 pandemic, followed by projecting detected pedestrians onto an orthogonal map for contact tracing by tracking movement trajectories and simulating the spread of droplets among the healthy population. Results demonstrate that the proposed technique is capable of tracing and documenting infection sources, times, and locations.
Tech-Art-Theory

This paper explores the nature and potential of improvisation as a method for learning and teaching in CSCW and HCI. It starts by reviewing concepts of improvisational learning in classic and more recent work in educational theory, art and music, and HCI that emphasize the reconstructive, materially-driven, error-engaged, transgressive, and collaborative nature of human learning processes. It then describes three pedagogical interventions of our own in which improvisational techniques were deployed as methods of teaching and learning. From this integrated study, we report specific pedagogical conditions (socio-material evaluations, multi-sensory practices, and making safe spaces for error) that can support improvisational learning, and three common challenges of HCI pedagogy relevance, assessment, and inclusion that improvisational methods can help to address.
Multi-label graph node classification with label attentive neighborhood convolution

Learning with graph structured data is of great significance for many practical applications. A crucial and fundamental task in graph learning is node classification. In reality, graph nodes are often encoded with various attributes. In addition, the task is usually multi-labeled in nature. In this paper, we tackle the problem of multi-label graph node classification, by leveraging structure, attribute and label information simultaneously. Specifically, to obtain rational node feature representations, we propose an intuitive yet effective graph convolution module to aggregate local attribute information of a given node. Moreover, the homophily hypothesis motivates us to build a label attention module. By exploiting both input and output contextual representations, we utilize the additive attention mechanism and build a label-aware representation learning framework to measure the compatibility between pairs of node embeddings and label embeddings. The proposed novel neural network-based, multi-label classification method has been verified by extensive experiments conducted on five public-available benchmark datasets, including both attributed and non-attributed networks. The results demonstrate the effectiveness of the proposed model with respect to micro-F1, macro-F1 and Hamming loss, comparing with several state-of-the-art methods, including two relational neighbor classifiers and several popular graph neural network models.
Blind microscopy image denoising with a deep residual and multiscale encoder/decoder network

In computer-aided diagnosis (CAD) focused on microscopy, denoising improves the quality of image analysis. In general, the accuracy of this process may depend both on the experience of the microscopist and on the equipment sensitivity and specificity. A medical image could be corrupted by several perturbations during image acquisition. Nowadays, CAD deep learning applications pre-process images with image denoising models to reinforce learning and prediction. In this work, an innovative and lightweight deep multiscale convolutional encoder-decoder neural network is proposed. Specifically, the encoder uses deterministic mapping to map features into a hidden representation. Then, the latent representation is rebuilt to generate the reconstructed denoised image. Residual learning strategies are used to improve and accelerate the training process using skip connections in bridging across convolutional and deconvolutional layers. The proposed model reaches on average 38.38 of PSNR and 0.98 of SSIM on a test set of 57458 images overcoming state-of-the-art models in the same application domain.Clinical relevance - Encoder-decoder based denoiser enables industry experts to provide more accurate and reliable medical interpretation and diagnosis in a variety of fields, from microscopy to surgery, with the benefit of real-time processing.
Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders

Causal discovery from observational data is a fundamental problem in science. Though the linear non-Gaussian acyclic model (LiNGAM) has shown promising results in various applications, it still faces the following challenges in the data with multiple latent confounders: 1) how to detect the latent confounders and 2) how to uncover the causal relations among observed and latent variables. To address these two challenges, we propose a hybrid causal discovery method for the LiNGAM with multiple latent confounders (MLCLiNGAM). First, we utilize the constraint-based method to learn the causal skeleton. Second, we identify the causal directions, by conducting regression and independence tests on the adjacent pairs in the causal skeleton. Third, we detect the latent confounders with the help of the maximal clique patterns raised by the latent confounders and reconstruct the causal structure with latent variables. Theoretical results show the correctness and efficiency of the algorithms. We conduct extensive experiments on synthetic and real data, which illustrates the efficiency and effectiveness of the proposed algorithms.
Impact of Sentiment Analysis in Fake Online Review Detection

Online business is one of the rapidly growing business sectors of current world. Now-a-days people purchase a lot of things from online shopping sites. Sales of online products are most often review driven. Thus, detecting deceptive reviews is getting more importance day by day. Sentiment analysis has great importance in fake review detection system. This paper introduces a sentiment analysis model that can separate positive and negative sentimental reviews efficiently. It shows an analysis of sentiment distribution for fake and truthful reviews. Also, the proposed sentiment model is used to find the impact of probabilistic sentiment score in fake online review detection using a hotel review dataset.
Blockchain Technology: Opportunities and Challenges for Small and Large Banks During COVID-19

New technologies are assuming a prominent role in the transformation of economic and social systems and are capturing the interest of many businesses and international institutions. Blockchain is an emerging technology that is relevant in all sectors, as its applications can be adapted to multiple contexts with possible significant advantages for business. The ability to perform transactions without a central entity acting as a trust guarantor, typical of blockchain, offers the opportunity to rethink the way business processes are managed. Based on the case study methodology, this work aims to understand how blockchain could affect the business model in the banking sector and how the COVID-19 pandemic impacts the implementation of blockchain.
A model validation to establish the relationship between teacher performance and student satisfaction

The work purpose was to validate a teacher performance model, by establishing a relationship with the satisfaction of educational service at the university level, in Peru as in other countries of the world, are currently experiencing social isolation as one of the policies of governments in order to minimize the negative impacts of Covid-19. In Peru as part of an extreme set of measures adopted by the government, is the suspension of educational activities at all levels of education, so that, at the university level, from one day to another, teachers and their students have been conducting online learning sessions, through Learning Manangement System - LMS, this process of adaptation of a face-to-face education to the virtual, is having its impacts, which will surely be evaluated in the future. One of the key factors for the success of university education is undoubtedly the teacher's performance. Now, with the implementation of a virtual educational model, it is necessary to evaluate the models implemented. The research proposed is the validity of an evaluation model about teacher performance, for which the first step was the development of an instrument with 5 factors and 15 politomic items of graduated answers in the Likert scale, the selected factors respond to a theoretical construct related to the evaluation and teacher performance and a decisive question about the satisfaction of the service, adapted from the Net Promoter Score (NPS). For the model creation, we started with an exploratory analysis, which included a factorial analysis, secondly, we performed a confirmatory analysis with the PLS-SEM methodology which is a second generation multivariate method called Structural Equation Modeling with Partial Least Squares. The sample consists of 358 cases in university students of the social sciences area. It is concluded that the model proposed to evaluate the teaching performance establishing a relation with the student satisfaction, is valid.
Quantum maximum mean discrepancy GAN

Generative adversarial network (GAN) has shown profound power in machine learning. It inspires many researchers from other fields to create powerful tools for various tasks, including quantum state preparation, quantum circuit translation, and so on. It is known as classical techniques cannot efficiently simulate the quantum system, and the existing works haven’t investigated the quantum version of maximum mean discrepancy as the metric in learning models and applied it to quantum data. In this paper, we propose a metric named quantum maximum mean discrepancy (qMMD), which can be used to measure the distance between quantum data in Hilbert space. Based on the qMMD, we then design a quantum generative adversarial model, named qMMD-GAN, under the hybrid quantum–classical methods. We also provide the construction of qMMD-GAN that can be easily implemented on a quantum device. We demonstrate the power of our qMMD-GAN by applying it to a crucial real-world application that is generating an unknown quantum state. Our numerical experiments show that qMMD-GAN has a competitive performance compared to existing results. We believe that the hybrid-based models will not only be applied to physics research but provide a new direction for improving classical data processing tasks.
Virtual Numeric Authentication System Using Contour Detection of Color-Banded Fingertips

In this paper, simplistic and much-awaited solution to the enigma of a secure user authentication system is proposed. For the same, we designed a virtually controllable numpad authentication system. To provide an effortless, immersive, and robust virtual experience which is color recognized, we employed a web camera along with the applied algorithm. The proposed modal intends to accomplish particular objectives, i.e., detection, recognition of an object, specialized graphical user interface, and authentication. Proposed methodology makes use of preprocessing tasks to convert a sequence of frames to detect and recognize a specified color. Numeric keys are placed at random positions in a definitive structure to provide a security feature. As a result, using our system, users can control a virtual numeric keypad by the movements of their color-banded fingertips. The webcam captures the consecutive frames and compares them to detect a difference in the contour, perceiving it as a keypress.
NavDog

Guide dogs can vastly improve vision-impaired people's daily-life quality by guiding them to destinations while avoiding obstacles. Animal guide dogs are costly for training. This paper presents a robot guide dog system to take a vision-impaired user to a destination while avoiding obstacles in the environment for both the user and the robot dog. A novel human-robot kinematic model and an MPC-based motion planning and control algorithm are proposed. We implement the method on a wheeled ground robot. All the sensors are mounted on the robot, and the human user does not have to take additional sensor devices. Simulation and real-world experiment results show that the proposed method can tackle challenging navigation tasks in narrow corridors for vision-impaired people.
Just-in-time defect prediction for Android apps via imbalanced deep learning model

Android mobile apps have played important roles in our daily life and work. To meet new requirements from users, the mobile apps encounter frequent updates, which involves in a large quantity of code commits. Previous studies proposed to apply Just-in-Time (JIT) defect prediction for mobile apps to timely identify whether new code commits can introduce defects into apps, aiming to assure the quality of mobile apps. In general, the number of defective commit instances is much fewer than that of clean ones, in other words, the defect data is class imbalanced. In this work, we propose a novel Imbalanced Deep Learning model, called IDL, to conduct JIT defect prediction task for Android mobile apps. More specifically, we introduce a state-of-the-art cost-sensitive cross-entropy loss function into the deep neural network to learn the high-level feature representation, in which the loss function alleviates the class imbalance issue by taking the prior probability of the two types of classes into account. We conduct experiments on a benchmark defect data consisting of 12 Android mobile apps. The results of rigorous experiments show that our proposed IDL model performs significantly better than 23 comparative imbalanced learning methods in terms of Matthews correlation coefficient performance indicator.
Video Anomaly Detection Based on Deep Generative Network

In this paper, we present a framework for the detection of anomalies in video scenes. Both spatial and temporal features extract and learn through the framework. We employ inception modules and residual skip connections inside the framework to make the network learning higher-level features, which we call "multi-scale U-Net". A multi-scale U-Net kept useful features of the image that lost during training caused by the convolution operator. The numbers of training and testing parameters in our framework are reduced while the detection accuracy is still improved. We evaluated the proposed framework on three benchmark datasets: UCSD, CHUK Avenue and ShanghaiTech dataset. Our proposed framework achieved 95.7%, 86.8% and 73.0% in terms of AUC, which surpasses the state-of-art learning-based methods.
Enriching the Multi-Object Detection using Convolutional Neural Network in Macro-Image

An object recognition and localization is a primary issue that is harder than a classification of an image even with precise object location and their annotations available at the time of training. The feature is identified for localizing the objects and classification identifies the classes from recognized object regions. This research work is proposed the two approaches i. Support Vector Machine (SVM) is optimized using the Firefly Algorithm (FA), and Scale Invariant Feature Transform (SIFT) descriptors, ii. Convolutional Neural Network (CNN) with Adam (Adaptive Moment) optimizer. In first method, FA has been used in the searching of optimal parameters through the simulation of the social behavior of the fireflies using the bioluminescent i.e. emission of light intensity. This FA is trained the Lagrangian multiplier and smoothness parameters in the SVM continuously. The second research work Adam based CNN has recognized the objects in multi-object images. The hash directory is proposed to store the highest scored bounding boxes to speed up the process. The experiments have been evaluated with binary as well as multi-object images. The proposed model has been trained and validated using VOC2012 dataset. It consists the kind of general macro photography images for object identification. The Average Precision (AP) is computed, and comparison shows that CNN performs better than FA-SVM method.
Reverse engineering imperceptible backdoor attacks on deep neural networks for detection and training set cleansing

Backdoor data poisoning (a.k.a. Trojan attack) is an emerging form of adversarial attack usually against deep neural network image classifiers. The attacker poisons the training set with a relatively small set of images from one (or several) source class(es), embedded with a backdoor pattern and labeled to a target class. For a successful attack, during operation, the trained classifier will: 1) misclassify a test image from the source class(es) to the target class whenever the backdoor pattern is present; 2) maintain high classification accuracy for backdoor-free test images. In this paper, we make a breakthrough in defending backdoor attacks with imperceptible backdoor patterns (e.g. watermarks) before/during the classifier training phase. This is a challenging problem because it is a priori unknown which subset (if any) of the training set has been poisoned. We propose an optimization-based reverse engineering defense that jointly: 1) detects whether the training set is poisoned; 2) if so, accurately identifies the target class and the training images with the backdoor pattern embedded; and 3) additionally, reverse engineers an estimate of the backdoor pattern used by the attacker. In benchmark experiments on CIFAR-10 (as well as four other data sets), considering a variety of attacks, our defense achieves a new state-of-the-art by reducing the attack success rate to no more than 4.9% after removing detected suspicious training images.
Faster Schrödinger-style simulation of quantum circuits

Recent demonstrations of superconducting quantum computers by Google and IBM and trapped-ion computers from IonQ fueled new research in quantum algorithms, compilation into quantum circuits, and empirical algorithmics. While online access to quantum hardware remains too limited to meet the demand, simulating quantum circuits on conventional computers satisfies many needs. We advance Schrödinger-style simulation of quantum circuits that is useful standalone and as a building block in layered simulation algorithms, both cases are illustrated in our results. Our algorithmic contributions show how to simulate multiple quantum gates at once, how to avoid floating-point multiplies, how to best use data-level and thread-level parallelism as well as CPU cache, and how to leverage these optimizations by reordering circuit gates. While not described previously, these techniques implemented by us supported published high-performance distributed simulations up to 64 qubits. To show additional impact, we benchmark our simulator against Microsoft, IBM and Google simulators on hard circuits from Google.