Reviewerly/exHarmony huggingface dataset:
SensiX: A System for Best-effort Inference of Machine Learning Models in Multi-device Environments

Multiple sensory devices on and around us are on the rise and require us to redesign a system to make an inference of ML models accurate, robust, and efficient at the deployment time. While this multiplicity opens up an exciting opportunity to leverage sensor redundancy, it is still extremely challenging to benefit from such multiplicity and boost the runtime performance of deployed ML models without model retraining and engineering. From our experience, we uncovered two prime caveats, device and data variabilities, that affect the runtime performance of ML models. We develop an ML system that addresses these variabilities without modifying deployed models by building on prior algorithmic work. It decouples model execution from sensor data and employs two essential operations between them: a) device-to-device data translation for principled mapping of training and inference data and b) quality-aware dynamic selection of the execution pipeline as a function of runtime accuracy. We evaluate the system on wearable devices with motion and audio-based models. The results show that ML models achieve a 7-13% increase in runtime accuracy solely by running on our system, and the increase goes up to 30% in dynamic environments, at the expense of 3 mW on the host device.
Target Capacity Filter Pruning Method for Optimized Inference Time Based on YOLOv5 in Embedded Systems

Recently, convolutional neural networks (CNNs), which exhibit excellent performance in the field of computer vision, have been in the spotlight. However, as the networks become wider for higher accuracy, the number of parameters and the computational costs increase exponentially. Therefore, it is challenging to use deep learning networks in embedded environments with limited resources, computational performance, and power. Moreover, CNNs consume a great deal of time for inference. To solve this problem, we propose a practical method for filter pruning to provide an optimal network architecture for target capacity and inference acceleration. After revealing the correlation between the inference time and the FLOPs, we proposed a method to generate a network with the desired inference time. Various object detection datasets were used to evaluate the performance of the proposed filter pruning method. The inference time of the pruned network was measured and analyzed using the NVIDIA Jetson Xavier NX platform. As a result of pruning the number of parameters and FLOPs of the YOLOv5 network in the PASCAL VOC dataset by 30%, 40%, and 50%, the mAP decreased by 0.6%, 2.3%, and 2.9%, respectively, while the inference time was improved by 14.3%, 26.4%, and 34.5%, respectively.
IncepX-Ensemble: Performance Enhancement Based on Data Augmentation and Hybrid Learning for Recycling Transparent PET Bottles

Recycling used plastic bottles is a significant step towards environmental protection and land pollution.Lifestyle changes in developing countries such as South Korea have substantially impacted the increase in the use rate of the plastic waste year by year.Plastic bottles of various types usually have varied recycling values.In most countries, human labor is used to manually categorize and handle recyclable waste.This study aims to provide an automated recyclable transparent plastic bottle classification system that can be used to replace existing trash disposal methods.Studies on the usefulness of Transfer Learning (TL) and Ensemble Learning (EL) techniques in image categorization have been conducted recently.At first developed InceptonV3, Xception, ResNet152, and DenseNet169 based TL structure.Then to enhance the level, we have proposed an ensemble model with InceptionV3 and Xception, named IncepX-Ensemble, to classify images in well-manner and poorly transparent plastic bottle images.After that, to evaluate the proposed algorithm, we have applied data augmentation to overcome the imbalanced problem.In our research, the accuracy for predicting transparent plastic bottles value reached 99.76% accuracy.The proposed ensemble model's potential use and limitations have also has examined.This method provides the image classification of transparent plastic bottles and has essential potential value for environmental protection and pollution control.
Real-Time Adaptive Dynamics Based State Estimation Scheme for Unmanned Aircrafts

In this paper we present a state estimation scheme for Unmanned Aircrafts (UAs) utilizing dynamics based models and multi-sensor data fusion. Employing the UA dynamics in estimation can substantially enhance the estimator performance, but obtaining accurate dynamics parameters for each UA is computationally costly and complex. To eliminate these issues, we propose two decoupled Extended Kalman Filters (EKFs), namely the Rotational Decoupled Extended Kalman Filter (RDEKF) and the Translational Decoupled Extended Kalman Filter (TDEKF). The dynamics parameters in these filters are identified in real-time using the Deep Neural Network and the Modified Relay Feedback Test (DNN-MRFT) approach. This approach doesn’t demand prior knowledge of the UA physical parameters, requiring only an Inertial Measurement Unit (IMU) and a positioning system for model classification. Our estimation scheme provides position, velocity and attitude estimates, in addition to smooth lag-free inertial acceleration estimates. We show experimentally the advantages of our approach on trajectory tracking problems that uses low rate position sensors. We also demonstrate how utilizing the estimated acceleration in feedback control can reduce the tracking error of an optimally tuned system by 43%. Moreover, the proposed estimator produces smooth estimates that leads to a reduction of controller action by 6.6%, when compared to kinematic based estimators. We compare the achieved results against other methods that require full prior knowledge of the UA parameters or the noise models, and show advantages in performance and real-time capability.
Person Tracking by Detection Using Dual Visible-Infrared Cameras

We study the problem of cross-modality person reidentification (ReID) and tracking with dual visible-infrared (VI) cameras, while most existing efforts on tracking-by-detection have been paid on single-modality visible ReID which is inapplicable for poor-light environments. The major difficulties for cross-modality (e.g., VI) ReID stem from the large modality gap between three-channel visible images and one-channel infrared images and such unknown environmental factors as background clutter, occlusions, etc. To tackle these issues, we propose to enrich the diversities of visible and infrared images for intra- and cross-modality matching by using both the channel-aware data augmentation (DA) techniques (e.g., channel exchanged augmentation and random occlusions) and standard DA techniques. On top of these DA techniques, we incorporate ResNet50 and vision transformer (ViT) into the feature extraction backbone network and apply the dynamic weight average (DWA) strategy for learning loss weights by regarding the minimization of identity loss and triplet loss as a multitask learning problem. We then apply the proposed ReID approach for person tracking in the field of interests. The experiments on two public data sets, i.e., RegDB and SYSU-MM01, show that our approach can improve the performance of state-of-the-art rank-1, mAP, and mINP for cross-modality matching. In addition, the experiments on our data set show that tracking by VI-ReID using dual VI cameras can achieve an accuracy of around 0.24 m.
Implementation and Optimization of Image Processing Algorithm using Machine Learning and Image Compression

This research paper deals with the implementation of an image captioning algorithm using Tensorflow, Keras, and Python, as well as a way proposed for optimization, using image compression techniques. The objective is to use image compression techniques to minimize data size, execution time, and computer resources since machine learning applications often have numerous constraints concerning energy consumption, processing power, and dataset sizes, thus making them less efficient for real-time, applied use cases. We can find new ways to apply machine learning in more simple real-life applications by attempting to reduce such obstacles.
Developing Cooperative Policies for Multi-Stage Reinforcement Learning Tasks

Many hierarchical reinforcement learning algorithms utilise a series of independent skills as a basis to solve tasks at a higher level of reasoning. These algorithms don’t consider the value of using skills that are cooperative instead of independent. This paper proposes the Cooperative Consecutive Policies (CCP) method of enabling consecutive agents to cooperatively solve long time horizon multi-stage tasks. This method is achieved by modifying the policy of each agent to maximise both the current and next agent’s critic. Cooperatively maximising critics allows each agent to take actions that are beneficial for its task as well as subsequent tasks. Using this method in a multi-room maze domain and a peg in hole manipulation domain, the cooperative policies were able to outperform a set of naive policies, a single agent trained across the entire domain, as well as another sequential HRL algorithm.
A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation

This paper proposes an improved brain magnetic resonance imaging (MRI) segmentation model by integrating U-SegNet with fire modules and residual convolutions to segment brain tissues in MRI. In the proposed encoder-decoder method, the residual connections and squeeze-expand convolutional layers from the fire module lead to a lighter and more efficient architecture for brain MRI segmentation. The residual unit helps in the smooth training of the deep architecture, and features obtained from residual convolutions exhibit a superior representation of the features in the segmentation network. In addition, the method provides a design with more efficient architecture, fewer network parameters, and better segmentation accuracy for brain MRI. The proposed architecture was evaluated on publicly available open access series of imaging studies (OASIS) and internet brain segmentation repository (IBSR) datasets for brain tissue segmentation. The experimental results showed superior performance compared to other state-of-the-art methods on brain MRI segmentation with a dice similarity coefficient (DSC) score of 0.96 and Jaccard index (JI) of 0.92.
Firefly Optimization Technique for Software Quality Prediction

The stakeholder satisfaction is associated with software quality assurance. If the producer and the consumer is pleased with a certain product, it is a quality product, either from the mechanical, electrical, or software industries. The present landscape for today’s software development market is dependent on commercial-off-the-shelf components (COTS) and open-source software (OSS). As a result, predicting the quality of component-based software (CBS) is critical. With the use of the firefly optimization methodology of computational intelligence, this study suggested an objective function for predicting software quality that offers superior results on MATLAB with actual data.
Flexible Traceable Generic Genetic Algorithm

This document elaborates on the generic implementation one of the main heuristics algorithms verified through its quick application to a biology problem requiring to find out an optimal sequences tree topology.In order to solve this problem, categorized as Non-Polynomial Hard (NP-Hard), "to minimize differences between given (leaf) and/or derived (parent) sequences", many popular methods are used."The higher the number of given sequences is, the more advisable and efficient it would be to go towards heuristics as they would provide a close-enough solution faster, as for instance genetic algorithms amongst others do.Thus, as part of a larger research in Heuristics and phylogenies, this paper aims to suggest a generic advanced flexible implementation of the Genetic Algorithm verified by a "general way to encode the problem into instances of different heuristic algorithms" as mentioned in our first reference below.The proposed algorithm will also present a chronology traceability feature for further analysis and potential improvements.
A Novel Deep Convolutional Neural Network Architecture for Customer Counting in the Retail Environment

Machine-learning and feature-based approaches have been developed in recent years to count shoppers in retail stores utilizing RGB-D sensors without occlusion in a top-view configuration. Since entering the era of large-scale media, deep learning approaches have become very popular and are used for a various variety of applications like the detection and identification of people in crowded scenes. Detecting and counting people is a difficult task especially in cluttered and crowded environments like malls, airports, and retail stores. Understanding the behavior of humans in a retail store is crucial for the efficient functioning of the business. We present an approach to segment and count people heads in a heavy occlusion environment by using a convolutional neural network. We present a novel semantic segmentation approach to detect people heads using top-view depth image data. The goal of our approach is to segment and count the human heads where the datasets are acquired by depth sensors (ASUS Xtion pro). For semantic segmentation, RGB images are used, but here in this case we are going to use depth images to segment human heads. The proposed architecture begins with ResNet50 as the pre-trained encoder and is then followed by the decoder network. The framework is assessed using the publicly available TVHeads Dataset, which contains depth images of people collected using an RGB-D sensor positioned in a top-view configuration. The results show good accuracy and prove that our approach is efficient and appropriate.
Image-Bot: Generating Synthetic Object Detection Datasets for Small and Medium-Sized Manufacturing Companies

Training datasets for image recognition are poorly available for small and medium-sized manufacturing companies, due to the specialized products they work with, and the disproportionate investment to generate their own ones. Thus, we investigate a new approach: The Image-Bot consists of a physical apparatus and a processing pipeline to generate training datasets from real-world objects easily. It takes pictures of the objects in front of a green screen and blends them with random backgrounds. The approach was tested with 23 objects and a YOLOv5 algorithm. It creates a state-of-the-art training dataset with about 2,000 images per object in under 45 min.
Federated Learning for Anomaly Detection in Industrial IoT-enabled Production Environment Supported by Autonomous Guided Vehicles

Intelligent production requires maximum downtime avoidance since downtimes lead to economic loss. Thus, Industry 4.0 (today's IoT-driven industrial revolution) is aimed at automated production with real-time decision-making and maximal uptime. To achieve this, new technologies such as Machine Learning (ML), Artificial Intelligence (AI), and Autonomous Guided Vehicles (AGVs) are integrated into production to optimize and automate many production processes. The increasing use of AGVs in production has far-reaching consequences for industrial communication systems. To make AGVs in production even more effective, we propose to use Federated Learning (FL) which provides a secure exchange of experience between intelligent manufacturing devices to improve prediction accuracy. We conducted research in which we exchanged experiences between the three virtual devices, and the results confirm the effectiveness of this approach in production environments.
NttpFL: Privacy-Preserving Oriented No Trusted Third Party Federated Learning System Based on Blockchain

In federated learning, multiple parties may use their data to cooperatively train a model without exchanging raw data. Federated learning protects the privacy of users to a certain extent. However, model parameters may still expose private information. Moreover, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsuitable for federated learning and vulnerable to security risks. To mitigate these issues, we propose a privacy-preserving oriented no trusted third party federated learning system based on blockchain (NttpFL). The initiator of the federated learning task and the partners negotiate keys through the conference key agreement and do not need to distribute keys through a trusted third party. We design a double-layer encryption mechanism to ensure privacy. Partners cannot obtain any private information other than their information. The decentralized nature of blockchain suits our system. In addition, blockchain makes the entire process transparent and traceable and avoids the single node failure problem. Experimental results confirm that the proposed method significantly reduces the communication costs and computational complexity compared to existing encrypted federated learning without compromising the performance and security.
Sandbox Computing: A Data Privacy Trusted Sharing Paradigm via Blockchain and Federated Learning

As a new trusted data sharing pattern with privacy protection, the integration mechanism of blockchain and Federated Learning has attracted extensive attention. Generally, this mechanism uses blockchain technology to supervise the original data and calculation results, which ignores the supervision of the Federated Learning model and computing process. Therefore, we introduce the concepts of the sandbox and state channel to construct a new data privacy sharing paradigm via Blockchain and Federated Learning. Under this paradigm, we use state channel to connect Blockchain and Federated Learning. And state channel is used to create a &#x201C;trusted sandbox&#x201D; to instantiate Federated Learning tasks in the trustless edge computing environment. Meanwhile, we also mainly solve problems about data privacy sharing in Federated Learning and system performance degradation caused by data quality. The simulation results show that the proposed method has better performance and efficiency than the traditional data sharing method.
Analyzing the Scientific Evolution of Face Recognition Research and Its Prominent Subfields

This paper presents a science mapping approach to analyze thematic evolution of face recognition research.For this reason, different bibliometric tools are combined (performance analysis, science mapping and Co-word analysis) in order to identify the most important, productive and the highestimpact subfields.Moreover, different visualization tools are used to display a graphical vision of face recognition field to determine the thematic domains and their evolutionary behavior.Finally, this study proposes the most relevant lines of research for the face recognition field.Findings indicate a huge increase in face recognition research since 2014.Mixed approaches revealed a great interest compared to local and global approaches.In terms of algorithms, the use of deep learning methods is the new trend.On the other hand, the illumination variation impact on face recognition algorithms performances is nowadays, the most important and impacting challenge for the face recognition field.
Performance Evaluation of 2D CNN Optimizers for Lung and Colon Cancer Image Classification

The paper presents a convolutional neural network (CNN) based approach to classify lung cancer and colon cancer image dataset using two popular optimization algorithms: Adam and RMSprop. The lung cancer and colon cancer are very common among cancer-related diseases as around 25% of cancer patients suffer from either one. Survival rate may be increased if we identify the cancer in early stages. Deep learning, a field of machine learning has revolutionized the field of medical science. In the present study, separate models are built for the lung cancer and colon cancer using CNN to predict the class of the disease more accurately. The image dataset taken was available in the form of histopathological images. Paper consists of four models lung cancer using Adam, lung cancer using RMSprop, colon cancer using Adam, colon cancer using RMSprop, and performance (accuracy) of Adam and RMSprop is compared. The results are particularly useful for cancer prediction from images and can be generalized to other image classification problems as well.
User Interactions in Virtual Data Explorer

Cybersecurity practitioners face the challenge of monitoring complex and large datasets. These could be visualized as time-varying node-link graphs, but would still have complex topologies and very high rates of change in the attributes of their links (representing network activity). It is natural, then, that the needs of the cybersecurity domain have driven many innovations in 2D visualization and related computer-assisted decision making. Here, we discuss the lessons learned while implementing user interactions for Virtual Data Explorer (VDE), a novel system for immersive visualization (both in Mixed and Virtual Reality) of complex time-varying graphs. VDE can be used with any dataset to render its topological layout and overlay that with time-varying graph; VDE was inspired by the needs of cybersecurity professionals engaged in computer network defense (CND). Immersive data visualization using VDE enables intuitive semantic zooming, where the semantic zoom levels are determined by the spatial position of the headset, the spatial position of handheld controllers, and user interactions (UIa) with those controllers. This spatially driven semantic zooming is quite different from most other network visualizations which have been attempted with time-varying graphs of the sort needed for CND, presenting a broad design space to be evaluated for overall user experience (UX) optimization. In this paper, we discuss these design choices, as informed by CND experts, with a particular focus on network topology abstraction with graph visualization, semantic zooming on increasing levels of network detail, and semantic zooming to show increasing levels of detail with textual labels.
Analysis of Efficient Security Using Machine Learning Methods

Billions of brilliant gadgets that can speak with each other with insignificant human intercession. It is one of the quickest creating fields throughout the entire existence of processing, with an expected 50 billion gadgets before the finish of 2020. From one viewpoint, IoT advancements assume a critical function in upgrading a few genuine keen applications that can improve life quality. However, the cross-cutting concept of frameworks, as well as the multidisciplinary components associated with their organization, have described security challenges. Actualizing safety efforts, for example, encryption, validation, access control, network security and application security, for the gadgets and their characteristic weaknesses is incapable. In this manner, existing security techniques ought to be improved to make sure about the ML environment adequately. Machine learning (ML) has progressed significantly in the course of the most recent couple of years, and machine insight has changed from research centre interest to commonsense apparatus in a few significant applications.
A Maturity Assessment Model for Cyber Security Education in Europe

Maturity assessment models have repeatedly been applied to education and to cyber security with the aim of improving the performance and management of private and public institutions. However, no attempts have been made so far to create a framework for the evaluation of cyber security education, which is an increasingly pressing matter due to the demand for cyber security professionals in the European Union. This paper contributes with a proposal for a maturity assessment model of cyber security education, including a discussion of one of the main issues in the field: the definition of knowledge units for the standardization of cyber security education in Europe.