Reviewerly/exHarmony huggingface dataset:
Vehicle Detection and Classification using Deep Learning for Hazy Images

Haze is created as a result of air light and attenuation which reduces image contrast and air lighting makes images whiter. An image can be made less white and have more contrast to dehaze it. For advanced driver assistance systems to work, vehicle detection is a fundamental component. For which the removal of haze or dehazing of the image is the most efficient solution to the problem of vehicle recognition. One of the important dehazing techniques is called Dark Channel Prior (DCP), which relies on observing the haze-free image’s distinguishing features. This paper discusses a detailed examination of the single picture haze reduction with the dark channel prior, proceeded by vehicle detection using the GoogleNet deep learning method.
Empirical Analysis on Crime Prediction using Machine Learning

Criminal offence or Crime is an detrimental act that not only affects an individuals but affects an community. In the current scenario crimes sequences happens every second in various places, in various crime patterns, as the results the crime rate is increasing each growing places. It is very much essential to determine the different factors for the possible occurrence of crimes and find the optimized ways to reduce the crime. Crimes can be compartmented into multiple factors like Rape, Murder, Assault and Theft and so on. Data were gathered from National Crime Records Bureau. This proposed work focuses to apply various machine learning algorithms such as Naïve bayes, Random forest, Decision tree and Support vector machine and Logistic regression for reducing of crime rates in India. Performance of the proposed models compared with various measures like precision, recall, accuracy, and fl score along with the confusing matrix. Also considers the error value for various models with the help of mean absolute error technique. As a result, the Naïve bayes model gives the best results of 98.94% comparing with other.
Securing Cloud Identity Information Under Key Introduction

A fine-grained question authorization scheme with integrity verification is proposed primarily based totally on Density primarily based totally Space-filling Curve (DSC). The proposed scheme extricates the appropriation of the re- appropriated POIs, and makes use of the non-uniform parcel withinside the spatial area. Each divided district includes curve obstacles for spatial extrade in that locale, and the parceled districts with a comparable length scale are consolidated. Then, the curve obstacles of those mixed locales are refreshed primarily based totally at the obstacles in their sub districts. These curve obstacles are indicated because the extrade keys evaluating to those consolidated areas. The extrade keys are coordinated in a quad tree shape, and tree hubs at diverse ranges examine to distinct spatial locales, which empowers the fine-grained question cap potential authorization. Moreover, the proposed scheme creates digests facts for every parceled vicinity, which can be then coordinated in a quadtree shape for question end result verification. Tree hubs at diverse ranges examine to diverse spatial districts, and the pertaining to digests are applied for question end result verification. Subsequently, the fine-grained verification potential authorization is upheld, which suggests simply the customers with the verification shape referring to the authorised vicinity can verify the integrity of the question end result.
Implementation and Classification of Breast Cancer Histopathological Image Processing using Support Vector Machine

Deep learning (ML) is commonly used in the categorization of breast cancer (BC) pattern due to its advantages in modelling important image features from complex BC information. In our proposed system for autonomous prediction and diagnosis of BC using an ensemble of classifiers, different image areas are defined by different tissue types that have incredibly variable looks. Tumor prognosis, image categorization, and performance improvisation make up the three main contributions. Our approach is evaluated on a publicly available dataset. The performance of the application is then evaluated using performance improvisation and predictions. Lastly, the procedure’s efficacy is evaluated using accuracy, sensitivities, and precision.
Design and Implementation of Fuzzy Logic Controller for Boiler Temperature Control using LabView

This paperwork aims design, implementation of fuzzy logic controller for controlling of water in the tank using LABVIEW software. This system mainly contains the single tank, Resistance Temperature Detector sensor and myrio. The signal from the temperature sensor is transmitted to LABVIEW software via myrio interface, connected to the system. Fuzzy logic controller (FLC) algorithm is designed using knowledge-based inference engine, which can be used to replace the conventional controllers tuning parameters using analytical equations. The performing of fuzzy logic controller for controlling the temperature has been explored in detail. It provides a improved response, and quickly tracks the set point. FLC functions well with the system involved uncertainties, noise at sensory signal. This developed FLC could also be used to measure several variables like temperature at industrial applications.
Motion Planning of Mobile Manipulator for Navigation Including Door Traversal

Door traversal is an essential task for a mobile manipulator to achieve autonomous navigation in indoor environment. However, it remains challenging since it requires the coupled motion of the robot and door and is composed of several sub-problems such as approaching, opening, passing through, and closing the door. This letter proposes a planning framework that formulates these sub-problems as a single problem and computes a path for the mobile manipulator to navigate from the initial position, traverse through the door, and arrive at the target position. The proposed framework obtains the path of the whole-body configuration in two steps. First, the path for the pose of the mobile robot and the path for the door angle are computed by using the graph search algorithm. In graph search, an integer variable called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">area indicator</i> is defined as an element of state, which indicates where the robot is located relative to the door. Especially, the area indicator expresses a process of door traversal. In the second step, the configuration of the manipulator is computed by the Inverse Kinematics (IK) solver from the path of the mobile robot and door angle. The proposed framework has a distinct advantage over the existing methods that manually determine several parameters such as which direction to approach the door and the angle of the door required for passage. The effectiveness of the proposed framework was validated through experiments with a mobile manipulator.
Semi-Supervised MIMO Detection Using Cycle-Consistent Generative Adversarial Network

In this paper, a new semi-supervised deep multiple-input multiple-output (MIMO) detection approach using a cycle-consistent generative adversarial network (CycleGAN) is proposed for communication systems without any prior knowledge of underlying channel distributions. Specifically, we propose the CycleGAN detector by constructing a bidirectional loop of two modified least squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to model the transmission process, while the backward LS-GAN learns to detect the received signals. By optimizing the cycle-consistency of the transmitted and received signals through this loop, the proposed method is trained online and semi-supervisedly using both the pilots and the received payload data. As such, the demand on labelled training dataset is considerably controlled, and thus the overhead is effectively reduced. Numerical results show that the proposed CycleGAN detector achieves better performance in terms of both bit error-rate (BER) and achievable rate than existing semi-blind deep learning (DL) detection methods as well as conventional linear detectors, especially when considering signal distortion due to the nonlinearity of power amplifiers (PA) at the transmitter.
Handling Ill-Conditioned Omics Data With Deep Probabilistic Models

The advent of high-throughput technologies has produced an increase in the dimensionality of omics datasets, which limits the application of machine learning methods due to the great unbalance between the number of observations and features. In this scenario, dimensionality reduction is essential to extract the relevant information within these datasets and project it in a low-dimensional space, and probabilistic latent space models are becoming popular given their capability to capture the underlying structure of the data as well as the uncertainty in the information. This article aims to provide a general classification and dimensionality reduction method based on deep latent space models that tackles two of the main problems that arise in omics datasets: the presence of missing data and the limited number of observations against the number of features. We propose a semi-supervised Bayesian latent space model that infers a low-dimensional embedding driven by the target label: the Deep Bayesian Logistic Regression (DBLR) model. During inference, the model also learns a global vector of weights that allows it to make predictions given the low-dimensional embedding of the observations. Since this kind of dataset is prone to overfitting, we introduce an additional probabilistic regularization method based on the semi-supervised nature of the model. We compared the performance of the DBLR against several state-of-the-art methods for dimensionality reduction, both in synthetic and real datasets with different data types. The proposed model provides more informative low-dimensional representations, outperforms the baseline methods in classification, and can naturally handle missing entries.
Learning Product Graphs From Spectral Templates

Graph Learning (GL) is at the core of leveraging connections in machine learning (ML). By observing a dataset of graph signals and considering specific assumptions, Graph Signal Processing (GSP) provides practical constraints in GL. Inferring a graph with desired frequency signatures, i.e., spectral templates, from stationary graph signals has gained great attention. However, a severe computational burden is a challenging barrier, especially for inference from high-dimensional product graph signals, i.e., graph signals live on the product of smaller factor graphs. Few product GL methods have been proposed for mostly inference with smoothness assumption, while they are limited to learning only two factor graphs, handle only the Cartesian products, and have not addressed GL with desired spectral templates. To bridge the mentioned gaps, we propose a method for learning product graphs from product graph signals in which the product GL problem can be broken into separated optimizations associated with each (significantly smaller) factor graph. Besides, unlike the current approaches, our method can learn from any type of product graph (possibly with more than two factor graphs) without needing to know the type of graph products beforehand, and with significantly reduced complexity than learning directly from product graph signals. In addition to devising theoretical sufficient recovery conditions and validating them numerically, experimental results on synthetic and real-world data, i.e., multi-view image and brain signal analysis, illustrate meaningful factor graphs supported by expert-related research and also superiority over the current methods for learning from spectral templates. The implementation codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ArefEinizade2/ProdSpecTemp</uri> .
Traffic Police 3D Gesture Recognition Based on Spatial–Temporal Fully Adaptive Graph Convolutional Network

It is critical for autonomous vehicles to recognize traffic police gestures timely and accurately. During the movement of the vehicle, the collected traffic police scales change all the time, in addition, the frequency and amplitude of actions of different traffic police are different. First, we use gesture normalization to fix the traffic police actions at a unified scale and remove the influence of scale changes on traffic police gesture recognition. Meanwhile, a fully adaptive spatial-temporal graph convolution network (FA-STGCN) is proposed to recognize the actions with different amplitude and frequencies. The adaptive spatial graph network can dig the latent joints connection relation of the traffic police under different gestures, which weakens the amplitude impact on the action recognition. The adaptive temporal graph network is composed of the global temporal module and the local temporal module. The global temporal module can obtain the coarse-grained features of the traffic police gestures’ speed and then naturally use the coarse-grained features to guide the local temporal module to adaptively learn the fine-grained temporal features of the traffic police action. The adaptive spatial graph network and the temporal graph network are alternately stacked to finally output accurate traffic police gestures. We thoroughly evaluated our method through intensive experiments, the result shows that our method achieved the best results on public datasets. What’s more, we proofed the effectiveness of each module and verified our methods for moving vehicles for the first time, the performance present meets the vehicle’s practical requirements.
Graph-Based Active Learning With Uncertainty and Representativeness for Industrial Anomaly Detection

Anomaly detection is essential for automated industrial production. Sufficiently labeled samples play an important role in improving the detection capability of the model. However, existing anomaly detection methods cannot balance the detection performance against the labeling cost in practice owing to the lack of an effective and efficient sample selection strategy. To address this issue, a graph-based active anomaly detection method called GAAD is proposed in this article. In the proposed method, a graph structure is adopted to rapidly spread the labeling information, and a heuristic strategy is designed to select samples combined with uncertainty and representativeness, which can rapidly and adequately explore the sample distribution with limited labeled samples. Moreover, a simple but effective autoencoder is proposed to confuse low-level features and preserve the locality by a pre-trained model, which achieves a better image encoding and anomaly detection performance. We created a new through-hole technology solder joint dataset (THT) and conducted extensive comparative experiments with mainstream active and semi-supervised anomaly detection methods on both THT and the publicly available NEU datasets. In the experiments, GAAD achieved the best detection performance and had an extremely low update time of 0.0071 s during active learning, demonstrating its superiority for industrial applications.
Differential Privacy Data Release Scheme Using Microaggregation With Conditional Feature Selection

Differential privacy (DP) has achieved great progress in addressing the user privacy preservation issues related to data analysis in the Internet of Things (IoT) services and applications. However, the existing DP models tend to overlook the effect of the correlation of data features on the utility of the smart IoT data. To mitigate this gap, we propose a Microaggregation-based DP method using Conditional Mutual Information (M-DPCMI) for prerelease data processing and feature selection. With the new method, we leverage the anonymous microaggregation approach to improve data utility while preventing potential sensitive IoT user information leakage. In addition, M-DPCMI is theoretically proved to satisfy the definition of DP and experimentally validated over real data sets. It is shown that the new model achieves better data utility than the state-of-the-art DP methods.
Content-Aware Adaptive Device–Cloud Collaborative Inference for Object Detection

Many intelligent applications based on deep neural networks (DNNs) are increasingly running on Internet of Things (IoT) devices. Unfortunately, the computing resources of these IoT devices are limited, which will seriously hinder the widespread deployment of various smart applications. A popular solution is to offload part of computation tasks from the IoT device to cloud by way of device–cloud collaboration. However, existing collaboration approaches may suffer from long network transmission delay or degraded accuracy due to the large amount of intermediate results, bring enormous challenges to the tasks, such as object detection, that require massive computing resources. In this article, we propose an efficient device–cloud collaborative inference (DCCI) object detection framework, which dynamically adjusts the amount of transferred data according to the content of input images. Specifically, a content-aware hard-case discriminator is proposed to automatically classify the input images as hard-cases or simple-cases, the hard-cases are uploaded to the cloud to be processed by a deployed heavyweight model, and the simple cases are processed by a lightweight model deployed to the IoT device, where the lightweight model is automatically compressed based on reinforcement learning according to the resource constraints of the IoT device. Furthermore, a collaborative scheduler based on the runtime load and network transmission capability of IoT devices is proposed to optimize the collaborative computation between IoT devices and the cloud. Extensive experimental evaluations show that compared to the Device-only approach, DCCI can reduce the memory footprint and compute resources of IoT devices by more than 90.0% and 30.87%, respectively. Compared to Cloud-centric, DCCI can save <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.0\times $ </tex-math></inline-formula> of network bandwidth. In addition, compared with the state-of-the-art DNN partitioning method, DCCI can save <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times $ </tex-math></inline-formula> of inference latency, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.3\times $ </tex-math></inline-formula> of IoT device energy consumption with the same accuracy constraint.
FedVAE: Communication-Efficient Federated Learning With Non-IID Private Data

Federated learning (FL), collaboratively training a shared global model without exchanging and centralizing local data, provides a promising solution for privacy preserving. On the other hand, it is faced with two main challenges: First, high communication cost, and second, low model quality due to imbalanced or nonindependent and identically distributed (non-IID) data. In this article, we propose FedVAE, an FL framework based on variational autoencoder (VAE) for remote patient monitoring. FedVAE contains two lightweight VAEs: one for projecting data onto a lower dimensional space with similar distribution so as to alleviate the issues of excessive communication overhead and slow convergence rate caused by non-IID data, and the other for shunning training bias due to imbalanced data distribution through generating minority class samples. In general, the proposed FedVAE can improve the overall performance of FL models while consuming only a small amount of communication bandwidth. The experimental results show that the area under the curve (AUC) value of FedVAE can reach 0.9937, which is even higher than that of the traditional centralized model (0.9931). Besides, fine-tuning the global model with personalization can raise the average AUC to 0.9947. Moreover, compared with vanilla FL, FedVAE shows 0.87% improvement in AUC while reducing communication traffic by at least 95%.
Deep Atomic Norm Denoising Network for Jointly Range-Doppler Estimation With FRI Sampling

Finite rate of innovation (FRI) sampling is widely used in modern high-resolution range-Doppler (RD) detection systems to reduce the sampling rate. However, as the sampling rate decreases, the method becomes more noise-sensitive. This study proposes a deep atomic norm denoising network (DAND-Net) to denoise FRI samples of RD signals and estimate the noise levels (NLs). Based on the sparse common support property of the FRI samples, the denoising problem of the RD signals was modeled as an atomic norm soft thresholding problem, which jointly used multiple measurement vectors and could be unfolded into a model-driven deep network. The trainability and generalization ability of the network were improved by introducing piecewise linear functions and new trainable variables. By introducing an NL estimation operator, the NL could be better estimated during denoising, and the network training and convergence better supported. Additionally, the plug-and-play method could be used to complete denoising prior to many existing FRI methods. The simulation experiments showed that the proposed denoising network can achieve better denoising and NL estimation results with fewer iterations.
Reanalyzing Cyber Insider Threats

Insider threat programs have continually encountered difficulties within their programs, with security technicians and incident response members struggling for dominance. According to research completed by cybersecurity organizations, the trend of insider threats will only increase into 2023 and possibly beyond. While many governments and organizations have threat programs dedicated to insiders and have set a “zero-trust” environment designed to negate insiders, the problem only continues. The research on insider threats and their commonalities throughout history, as well as the current issues that organizations face, indicates that these attacks have a long-standing precedent associated with socioeconomic and political factors. This essay acknowledges that workers within the industry who are ensuring that companies thrive, depending on circumstances, could be the most susceptible to becoming insider threats should their needs not be met.
Data Collection, Presentation and Analysis

This chapter covers the topics of data collection, data presentation and data analysis. It gives attention to data collection for studies based on experiments, on data derived from existing published or unpublished data sets, on observation, on simulation and digital twins, on surveys, on interviews and on focus group discussions. One of the interesting features of this chapter is the section dealing with using measurement scales in quantitative research, including nominal scales, ordinal scales, interval scales and ratio scales. It explains key facets of qualitative research including ethical clearance requirements. The chapter discusses the importance of data visualization as key to effective presentation of data, including tabular forms, graphical forms and visual charts such as those generated by Atlas.ti analytical software.