Reviewerly/exHarmony huggingface dataset:
Edge-based Video Analytic for Smart Cities

Video analytic is the important tool for smart city development.The video analytic application requires more memories and high processing devices.The problems of cloudbased approach for video analytic are high latency and more network bandwidth to transfer data into the cloud.To overcome these problems, we propose a model based on dividing the jobs into smaller sub-tasks with less processing requirements in a typical video analytics application for the development of smart city.The object detection, tracking and pattern recognition method to reduce the size of videos based on edge network will be proposed.We will design a video analytic model, and simulation is performed using iFogSim simulator.We will also propose Convolutional Neural Network (CNN) based object tracking model.The experimental verification shows that our tracking model is more than 96% accurate, and the proposed edge and cloud-based model is more than 80% effective than only cloudbased approach for video analytic applications.
An Improvised Facial Emotion Recognition System using the Optimized Convolutional Neural Network Model with Dropout

Facial expression detection has long been regarded as both verbal and nonverbal communication.The muscular expression on a person's face reflects their physical and mental state.Using computer programming to integrate all face curves into a categorization class is significantly more important than doing so manually.Convolutional Neural Networks, an Artificial Intelligence approach, was recently developed to improve the task with more acceptance.Due to overfit during the learning step, the model performance may be lowered and regarded underperforming.There is a method dropout uses to reduce testing error.The influence of dropout is applied at convolutional layers and dense layers to classify face emotions into a distinct category of Happy, Angry, Sad, Surprise, Neutral, Disgust, and Fear and is represented as an improved convolutional neural network model.The experimental setup used the datasets namely JAFEE, CK48, FER2013, RVDSR, CREMAD and a selfprepared dataset of 36,153 facial images for observing train and test accuracy in presence and absence of dropout.Test accuracies of 92.33, 96.50, 97.78, 99.44, and 98.68 are obtained on Fer2013, RVDSR, CREMA-D, CK48, and JAFFE datasets are obtained in presence of dropout.The used features are countably large in the computation as a result the higher computation support of NVDIA with the capacity of GPU 16GB, CPU 13GB and memory 73.1 GB are used for the experimental purposes.
Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following Tasks

There is a growing interest in the community in making an embodied AI agent perform a complicated task while interacting with an environment following natural language directives. Recent studies have tackled the problem using ALFRED, a well-designed dataset for the task, but achieved only very low accuracy. This paper proposes a new method, which outperforms the previous methods by a large margin. It is based on a combination of several new ideas. One is a two-stage interpretation of the provided instructions. The method first selects and interprets an instruction without using visual information, yielding a tentative action sequence prediction. It then integrates the prediction with the visual information etc., yielding the final prediction of an action and an object. As the object's class to interact is identified in the first stage, it can accurately select the correct object from the input image. Moreover, our method considers multiple egocentric views of the environment and extracts essential information by applying hierarchical attention conditioned on the current instruction. This contributes to the accurate prediction of actions for navigation. A preliminary version of the method won the ALFRED Challenge 2020. The current version achieves the unseen environment's success rate of 4.45% with a single view, which is further improved to 8.37% with multiple views.
Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning

This article focuses on applying a deep learning approach to predict daily total solar energy for the next day by a neural network. Predicting future solar irradiance is an important topic in the renewable energy generation field to improve the performance and stability of the system. The forecast is used as a support parameter to control the operation duty-cycle, data collection or communication activities at energy-independent energy harvesting embedded devices. The prediction is based on previous hourly-measured atmospheric pressure values. For prediction, a back-propagation algorithm in combination with deep learning methods is used for multilayer network training. The ability of the proposed system to estimate the daily solar energy is compared to the support vector regression model and to the evolutionary-fuzzy prediction scheme presented in previous research studies. It is concluded that the presented neural network approach gave satisfying predictions in early spring, autumn, and winter. In a particular setting, the proposed solution provides better results than a model using the support vector regression method (e.g., the MAPE value of the proposed algorithm is 0.032 less than the MAPE value of support vector regression method). The time and computational complexity for neural network training is considerable, and therefore it was assumed to train the network on an external computer or a cloud, where only the network parameters have been obtained and transferred to the embedded devices.
Automatic Information Exchange in the Early Rescue Chain Using the International Standard Accident Number (ISAN)

Thus far, emergency calls are answered by human operators who interview the calling person in order to obtain all relevant information. In the near future-based on the Internet of (Medical) Things (IoT, IoMT)-accidents, emergencies, or adverse health events will be reported automatically by smart homes, smart vehicles, or smart wearables, without any human in the loop. Several parties are involved in this communication: the alerting system, the rescue service (responding system), and the emergency department in the hospital (curing system). In many countries, these parties use isolated information and communication technology (ICT) systems. Previously, the International Standard Accident Number (ISAN) has been proposed to securely link the data in these systems. In this work, we propose an ISAN-based communication platform that allows semantically interoperable information exchange. Our aims are threefold: (i) to enable data exchange between the isolated systems, (ii) to avoid data misinterpretation, and (iii) to integrate additional data sources. The suggested platform is composed of an alerting, responding, and curing system manager, a workflow manager, and a communication manager. First, the ICT systems of all parties in the early rescue chain register with their according system manager, which tracks the keep-alive. In case of emergency, the alerting system sends an ISAN to the platform. The responsible rescue services and hospitals are determined and interconnected for platform-based communication. Next to the conceptual design of the platform, we evaluate a proof-of-concept implementation according to (1) the registration, (2) channel establishment, (3) data encryption, (4) event alert, and (5) information exchange. Our concept meets the requirements for scalability, error handling, and information security. In the future, it will be used to implement a virtual accident registry.
Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network

Detection of anomalies from the medical image dataset improves prognosis by discovering new facts hidden in the data.The present study aims to discuss anomaly detection using autoencoders and convolutional neural networks.The autoencoder identifies the imbalance between normal and abnormal samples.They create learning models flexible and accurate on training data.The problem is addressed in four stages: 1) training: an autoencoder is initialized with the hyperparameters and trained on the lung cancer CT scan images, 2) test: the autoencoder reconstructs the input from the latent space representation with a slight variation from the original data, indicated by a reconstruction error as Mean Squared Error (MSE), 3) evaluate: the MSE value of the training and test dataset are compared.The MSE values of anomalous data are higher than a base threshold, detecting those as anomalies, 4) validate: the efficiency metrics such as accuracy and MSE scores are used at both training and validation phases.The dataset was further classified as benign and malignant.The accuracy reported for outlier detection and the classification task is 98% and 97.2%.Thus, the proposed autoencoder-based anomaly detection could positively isolate anomalies from the CT scan images of lung cancer.
Interpretable Minority Synthesis for Imbalanced Classification

This paper proposes a novel oversampling approach that strives to balance the class priors with a considerably imbalanced data distribution of high dimensionality. The crux of our approach lies in learning interpretable latent representations that can model the synthetic mechanism of the minority samples by using a generative adversarial network(GAN). A Bayesian regularizer is imposed to guide the GAN to extract a set of salient features that are either disentangled or intensionally entangled, with their interplay controlled by a prescribed structure, defined with human-in-the-loop. As such, our GAN enjoys an improved sample complexity, being able to synthesize high-quality minority samples even if the sizes of minority classes are extremely small during training. Empirical studies substantiate that our approach can empower simple classifiers to achieve superior imbalanced classification performance over the state-of-the-art competitors and is robust across various imbalance settings. Code is released in github.com/fudonglin/IMSIC.
On Machine-Learning Morphological Image Operators

Morphological operators are nonlinear transformations commonly used in image processing. Their theoretical foundation is based on lattice theory, and it is a well-known result that a large class of image operators can be expressed in terms of two basic ones, the erosions and the dilations. In practice, useful operators can be built by combining these two operators, and the new operators can be further combined to implement more complex transformations. The possibility of implementing a compact combination that performs a complex transformation of images is particularly appealing in resource-constrained hardware scenarios. However, finding a proper combination may require a considerable trial-and-error effort. This difficulty has motivated the development of machine-learning-based approaches for designing morphological image operators. In this work, we present an overview of this topic, divided in three parts. First, we review and discuss the representation structure of morphological image operators. Then we address the problem of learning morphological image operators from data, and how representation manifests in the formulation of this problem as well as in the learned operators. In the last part we focus on recent morphological image operator learning methods that take advantage of deep-learning frameworks. We close with discussions and a list of prospective future research directions.
Disentangled Face Attribute Editing via Instance-Aware Latent Space Search

Recent works have shown that a rich set of semantic directions exist in the latent space of Generative Adversarial Networks (GANs), which enables various facial attribute editing applications. However, existing methods may suffer poor attribute variation disentanglement, leading to unwanted change of other attributes when altering the desired one. The semantic directions used by existing methods are at attribute level, which are difficult to model complex attribute correlations, especially in the presence of attribute distribution bias in GAN's training set. In this paper, we propose a novel framework (IALS) that performs Instance-Aware Latent-Space Search to find semantic directions for disentangled attribute editing. The instance information is injected by leveraging the supervision from a set of attribute classifiers evaluated on the input images. We further propose a Disentanglement-Transformation (DT) metric to quantify the attribute transformation and disentanglement efficacy and find the optimal control factor between attribute-level and instance-specific directions based on it. Experimental results on both GAN-generated and real-world images collectively show that our method outperforms state-of-the-art methods proposed recently by a wide margin. Code is available at https://github.com/yxuhan/IALS.
Multi-Channel Pooling Graph Neural Networks

Graph pooling is a critical operation to downsample a graph in graph neural networks. Existing coarsening pooling methods (e.g. DiffPool) mostly focus on capturing the global topology structure by assigning the nodes into several coarse clusters, while dropping pooling methods (e.g. SAGPool) try to preserve the local topology structure by selecting the top-k representative nodes. However, there lacks an effective method to integrate the two types of methods so that both the local and the global topology structure of a graph can be well captured. To address this issue, we propose a Multi-channel Graph Pooling method named MuchPool, which captures the local structure, the global structure, and node feature simultaneously in graph pooling. Specifically, we use two channels to conduct dropping pooling based on the local topology and node features respectively, and one channel to conduct coarsening pooling. Then a cross-channel convolution operation is designed to refine the graph representations of different channels. Finally, the pooling results are aggregated as the final pooled graph. Extensive experiments on six benchmark datasets present the superior performance of MuchPool. The code of this work is publicly available at Github.
Operationalizing Human Values in Software Engineering: A Survey

Human values (e.g., pleasure, privacy, and social justice) are what a person or a society considers important.Inability to address them in software-intensive systems can result in numerous undesired consequences (e.g., financial losses) for individuals and communities.Various solutions (e.g., methodologies, techniques) are developed to help "operationalize values in software".The ultimate goal is to ensure building software (better) reflects and respects human values.In this survey, "operationalizing values" is referred to as the process of identifying human values and translating them to accessible and concrete concepts so that they can be implemented, validated, verified, and measured in software.This paper provides a deep understanding of the research landscape on operationalizing values in software engineering, covering 51 primary studies.It also presents an analysis and taxonomy of 51 solutions for operationalizing values in software engineering.Our survey reveals that most solutions attempt to help operationalize values in the early phases (requirements and design) of the software development life cycle.However, the later phases (implementation and testing) and other aspects of software development (e.g., "team organization") still need adequate consideration.We outline implications for research and practice and identify open issues and future research directions to advance this area.
Analysis of Missing Value Imputation Application with K-Nearest Neighbor (K-NN) Algorithm in Dataset

Missing value is a problem that is still often found in many studies. Missing value is where data or data features are not available completely and intact. This still happens a lot in datasets that will be used in research. The missing value is caused by many factors such as human error, unavailable data or even from a virus in the database. Data is important for research, incomplete data will affect the results obtained. Data mining is a process that is very influential on data, including the classification process. Classification in data mining can be done if the data is complete. These problems can be overcome by the Imputation process by combining it with the K-Nearest Neighbor process or the process can be called K-Nearest Neighbor Imputation (K-NNI). In the research that has been done the K-Nearest Neighbor Imputation algorithm can overcome the problem of missing values in the dataset. This can be seen from the level of accuracy obtained where the accuracy of the classification process before handling the missing value is 77.01% while after the imputation process the accuracy is 78.31%
Object Detection in Densely Packed Scenes via Semi-Supervised Learning with Dual Consistency

Deep neural networks have been shown to be very powerful tools for object detection in various scenes. Their remarkable performance, however, heavily depends on the availability of a large number of high quality labeled data, which are time-consuming and costly to acquire for scenes with densely packed objects. We present a novel semi-supervised approach to addressing this problem, which is designed based on a common teacher-student model, integrated with a novel intersection-over-union (IoU) aware consistency loss and a new proposal consistency loss. The IoU-aware consistency loss evaluates the IoU over the prediction pairs of the teacher model and the student model, which enforces the prediction of the student model to approach closely to that of the teacher model. The IoU-aware consistency loss also reweights the importance of different prediction pairs to suppress the low-confident pairs. The proposal consistency loss ensures proposal consistency between the two models, making it possible to involve the region proposal network in the training process with unlabeled data. We also construct a new dataset, namely RebarDSC, containing 2,125 rebar images annotated with 350,348 bounding boxes in total (164.9 annotations per image average), to evaluate the proposed method. Extensive experiments are conducted over both the RebarDSC dataset and the famous large public dataset SKU-110K. Experimental results corroborate that the proposed method is able to improve the object detection performance in densely packed scenes, consistently outperforming state-of-the-art approaches. Dataset is available in https://github.com/Armin1337/RebarDSC.
Quantum Computing in High Frequency Trading and Fraud Detection

Quantum Computing in high-frequency trading and fraud detection is an analysis of quantum computing and how it can be used by the different industries especially finance. It is an evolution of computing from the traditional computing method. Quantum computing is a process that is concentrated on creating systems and technology based on quantum theory rules. Quantum theory describes the energy on atomic and subatomic levels. Quantum computing uses quantum bits (qubits) which are more advanced than the traditional bits used by traditional computers. This article focuses on deploying quantum computers in solving problems that cannot be efficiently solved using traditional computers. In the finance sector, such as banking, insurance, and high-frequency trading, quantum computers can help optimize service by providing targeting and predictive analytics to reduce risk, provide personalized customer service, and provide the needed security framework against fraud.
A New Pseudolinear Filter for Bearings-Only Tracking without Requirement of Bias Compensation

In bearings-only tracking systems, the pseudolinear Kalman filter (PLKF) has advantages in stability and computational complexity, but suffers from correlation problems. Existing solutions require bias compensation to reduce the correlation between the pseudomeasurement matrix and pseudolinear noise, but incomplete compensation may cause a loss of estimation accuracy. In this paper, a new pseudolinear filter is proposed under the minimum mean square error (MMSE) framework without requirement of bias compensation. The pseudolinear state-space model of bearings-only tracking is first developed. The correlation between the pseudomeasurement matrix and pseudolinear noise is thoroughly analyzed. By splitting the bearing noise term from the pseudomeasurement matrix and performing some algebraic manipulations, their cross-covariance can be calculated and incorporated into the filtering process to account for their effects on estimation. The target state estimation and its associated covariance can then be updated according to the MMSE update equation. The new pseudolinear filter has a stable performance and low computational complexity and handles the correlation problem implicitly under a unified MMSE framework, thus avoiding the severe bias problem of the PLKF. The posterior Cramer–Rao Lower Bound (PCRLB) for target state estimation is presented. Simulations are conducted to demonstrate the effectiveness of the proposed method.
Entanglement-enhanced estimation of a parameter embedded in multiple phases

Quantum-enhanced sensing promises to improve the performance of sensing tasks using non-classical probes and measurements that require far fewer scene-modulated photons than the best classical schemes, thereby granting previously-inaccessible information about a wide range of physical systems. We propose a generalized distributed sensing framework that uses an entangled quantum probe to estimate a scene-parameter encoded within an array of phases, with a functional dependence on that parameter determined by the physics of the actual system. The receiver uses a laser light source enhanced by quantum-entangled multi-partite squeezed-vacuum light to probe the phases and thereby estimate the desired scene-parameter. The entanglement suppresses the collective quantum vacuum noise across the phase array. We report simple analytical expressions for the Cram\'er Rao bound that depend only on the optical probes and the physical model of the measured system, and we show that our structured receiver asymptotically saturates the quantum Cram\'er-Rao bound in the lossless case. Our approach enables Heisenberg limited precision in estimating a scene-parameter with respect to total probe energy, as well as with respect to the number of modulated phases. Furthermore, we study the impact of uniform loss in our system and examine the behavior of both the quantum and the classical Cram\'er-Rao bounds. We apply our framework to examples as diverse as radio-frequency phased-array directional radar, beam-displacement tracking for atomic-force microscopy, and fiber-based temperature gradiometry.
Determining Decomposition Levels for Wavelet Denoising Using Sparsity Plot

We present a method to select decomposition levels for noise thresholding in wavelet denoising.It is essential to determine the accurate decomposition levels to avoid inadequate noise reduction and/or signal distortion by noise thresholding.We introduce the concept of sparsity plot that captures the abrupt transition from noisy to noise-free Detail component, readily revealing the cut-off for the maximum decomposition levels.The method uses the sparsity parameter to determine the noise presence in each detail component and measures the magnitude change in the sparsity values to distinguish between noisy and noise-free Detail components.The method is tested on both model and experimental signals, and proves effective for various signal lengths and types, as well as different Signal-to-Noise Ratios (SNRs).The method can be embedded with any wavelet denoising method to improve its performance.The code is available via GitHub and denoising.cornell.edu,as well as the corresponding author's group website (signalsciencelab.com).