Reviewerly/exHarmony huggingface dataset:
Advanced Adaptive Spatial Augmented Reality utilizing Dynamic in-situ Projection in Industrial Site Assembly

An Adaptive Spatial Augmented Reality system with dynamic in-situ projection of work instructions and information guidance already shows benefits in the field of industrial site assembly of aircraft components. Furthermore, the user-adaptive gesture control offers advantages for a human-centered and natural interaction directly in the assembly process. This paper presents results of enhancing dynamic in-situ projection, information guidance and user-adaptive gesture interaction in an advanced assembly process of large-sized toy building bricks. For this purpose, the dynamic projection system displays the location of the bricks in the material provision area as well as the accurate mounting position and dynamically guides the operator through the assembly process. Furthermore, the optimized gesture interaction with YOLO and an optimized synthetic dataset creation process for site assembly, indicate strong intentions for user-adaptive interaction with the projection system. An evaluation in the TU Wien pilot factory demonstrates benefits in terms of time savings in locating parts, usability and cognitive workload.
Adaptive Memory-Enhanced Time Delay Reservoir and its Memristive Implementation

Time Delay Reservoir (TDR) is a hardware-friendly machine learning approach from two perspectives.First, it can prevent the connection overhead of neural networks with increasing neurons.Second, through its dynamic system representation, TDR can also be implemented in hardware by different systems.However, it performs poorly on tasks that involve long-term dependency.In this work, we first introduce a higher-order delay unit, which is capable of accumulating and transferring the long history states in an adaptive manner to further enhance the reservoir memory.Particle Swarm Optimisation is applied to optimize the enhanced degree of memory adaptivity.Our experiments demonstrate its superiority both for short-and long-term memory datasets over seven existing approaches.In light of the hardware-friendly feature of TDR, we further propose a memristive implementation of our adaptive memory-enhanced TDR, where a dynamic memristor and the memristor-based delay element are applied to construct the reservoir.Through circuit simulation, the feasibility of our proposed memristive implementation is verified.The comparisons with different hardware reservoirs show that our proposed memristive implementation is effective both for short-and long-term memory datasets, while exhibiting benefits in terms of smaller circuit area and lower power consumption compared with traditional hardware reservoirs.
Safety Assurance of Machine Learning for Perception Functions

Abstract The latest generation of safety standards applicable to automated driving systems require both qualitative and quantitative safety acceptance criteria to be defined and argued. At the same time, the use of machine learning (ML) functions is increasingly seen as a prerequisite to achieving the necessary levels of perception performance in the complex operating environments of these functions. This inevitably leads to the question of which supporting evidence must be presented to demonstrate the safety of ML-based automated driving systems. This chapter discusses the challenge of deriving suitable acceptance criteria for the ML function and describes how such evidence can be structured in order to support a convincing safety assurance case for the system. In particular, we show how a combination of methods can be used to estimate the overall machine learning performance, as well as to evaluate and reduce the impact of ML-specific insufficiencies, both during design and operation.
BCmaster: A Compatible Framework for Comprehensively Analyzing and Monitoring Blockchain Systems in IoT

With the ever-increasing applications of the Internet of Things (IoT), e.g., smart homes, smart cities, smart factories, etc., data security and device trustworthiness become the major concerns. Although blockchain contributes to achieve the data traceability and fault tolerance, the huge resource consumption and limited performance severely restrict its deployments in IoT. Moreover, the unique features of IoT, such as mobility, resource constraints, and security vulnerabilities, create even greater difficulties for blockchain running. Observing the lack of blockchain analyzing tools for IoT, we intend to provide a fair means with standard metrics for better understanding IoT-oriented blockchain. In this article, we present BCmaster, a blockchain analyzing and monitoring framework focusing on IoT scenarios. Based on the detailed modeling of blockchain-assisted IoT, we propose a novel metric set named 5-D quantitative metric framework, which can conduct the comprehensive blockchain analysis from five dimensions. Moreover, we design a modular architecture for BCmaster, wherein the interaction requests (IRs)-based data parser ensures a high system compatibility and the synchronous metric visualizer facilitates the real-time blockchain monitoring in IoT. Extensive evaluations in a real IoT environment demonstrate the validity of BCmaster and explore the performance of four IoT-oriented blockchain systems. Last but not least, we discuss the ways to customize IoT-oriented blockchain with the help of BCmaster.
Power of Redundancy: Surplus Client Scheduling for Federated Learning against User Uncertainties

Federated learning (FL) has reshaped the learning paradigm by overcoming privacy concerns and siloed data. In FL, an aggregator schedules a set of mobile users (MUs) to collectively train a global model with their local datasets and subsequently aggregates their model updates. However, the users have many uncertainties like unstable network connections and volatile availability, which leads to the straggler problem and deteriorates the efficiency of the FL system. Besides, the issue of non-IID datasets hinders the convergence performance of the global model. To hurdle the user uncertainties, we associate a deadline with the decision in each round and partially collect MUs' updates after the deadline, which can be achieved by considering surplus budget constraints. Moreover, we introduce fairness constraints for the non-IID issue. We propose a deadline-aware task replication for surplus client scheduling policy, called FEDDATE-CS. FEDDATE-CS is developed based on a novel contextual-combinatorial multi-armed bandit (CCMAB) learning framework with fairness guarantee. We extend the hypercube-based CCMAB framework by integrating the Lyapunov queuing technique and rigorously prove that FEDDATE-CS achieves a sublinear regret bound and provides an <formula><tex>$[\mathcal{O}(1/V),\mathcal{O}(V)]$</tex></formula> regret-fairness tradeoff for any fairness control factor <formula><tex>$V&gt;0$</tex></formula>. We conduct extensive evaluations to verify the significant superiority of FEDDATE-CS over benchmarks.
Human Activity Recognition Using Wearable Sensors Based on Image Classification

Two-Dimensional Fast Fourier Transform (2-D FFT) and Wigner-Ville Transform (WVT) are two popular transforms applied to find frequency and time-frequency representations, respectively. By using them, signals acquired from different axes or sensors are mapped to these representations, considered 2D images. Based on this opinion, three novel methods are presented in this paper. The first two methods are called basic methods. The 2D images based on the magnitude of the 2-D FFT are constructed in method 1 and a fine-tuned CNN is also applied for classifying. WVT is used for constructing 2D compressed images in method 2, and classifying is done like method 1. Fusing two basic methods is presented as the proposed method. It attains the accuracies of 93.45%, 96.47%, 99.00%, and 98.20% for UCI HAR, MOTIONSENSE, MHEALTH, and WISDM datasets, respectively. Achieved results show the superiority of the proposed method compared with the state-of-the-art approaches.
Quantile Autoencoder With Abnormality Accumulation for Anomaly Detection of Multivariate Sensor Data

Anomaly detection (AD) is a crucial task in various industrial sectors where large amounts of data are generated from multiple sensors.Deep learning-based methods have made significant progress in AD, owing to big data and deep neural networks (DNN).Most methods for deep anomaly detection (DAD) utilize reconstruction error (i.e., the difference between the original and reconstructed values) as a measure of abnormality.However, AD performance can be improved by diversifying the source of anomaly score.To support this, we introduce the concept of anomaly source diversification and provide mathematical proofs to support this idea.In this regard, we propose a quantile autoencoder (QAE) with abnormality accumulation (AA) as a novel DAD approach that leverages data uncertainty and iteratively obtains reconstruction errors as additional sources.The anomaly score with QAE is derived from both the reconstruction error and the uncertainty term which is the range between the two quantiles.In addition, AA aggregates the errors obtained from the recursive reconstruction of the input, after which calculates the anomaly score based on the Mahalanobis distance.This process induces the score distributions of both the normal and abnormal samples farther apart by narrowing the width of the distributions, which contributes to the improvement of AD performance.The performance of the proposed QAE-AA was verified through the experiments on multi-variate sensor datasets in various domains; QAE-AA achieves 4-23% higher AUROC score on average compared to the other AD methodologies.
CORPORATE STANDARD FOR UNIVERSITY LECTURER’S DIGITAL COMPETENCE

The Corporate Standard for University Lecturer’s Digital Competence was created to increase the level of digital competence, which is recognized as one of the key competencies of a successful person of the XXI century, to improve the quality of the learning and teaching, the educational process. The areas of digital competences’ application at the Borys Grinchenko Kyiv University are the main activities of lecturers, such as: educational activities, scientific research, professional communication and cooperation; digital self-management. The corporate standard defines five levels of digital competence: Analyst-Researcher (A), which is mandatory; Integrator (B1); Expert (B2) - sufficient level; Leader (C1); Innovator (C2) - high level. The descriptors of the Corporate Standard presented in the article detail the skills of university lecturers in accordance with the levels of digital competence and areas of its application. The level of digital competence of the lecturer is determined in the university e-learning system as a result of passing an adaptive practice-oriented test, they study multilevel mini-courses step by step with the ability to build a personal trajectory of professional development and marking in the personal office to achieve the appropriate level. Increasing the level of digital competence will contribute to professional, expert growth in general. Due to the implementation of the Corporate Standard for University Lecturer’s Digital Competence, lecturers will be more effective in using the opportunities and resources of the information and educational environment of the university, educational services will be provided to higher education students at a higher level, including in conditions of limited access to classrooms and when using blended and distance learning.
An Incentive Auction for Heterogeneous Client Selection in Federated Learning

Federated Learning (FL) is a new distributed machine learning (ML) approach which enables thousands of mobile devices to collaboratively train artificial intelligence (AI) models using local data without compromising user privacy. Although FL represents a promising computing paradigm, such training process can not be fully realized without an appropriate economic mechanism that incentivizes the participation of heterogeneous clients. This work targets social cost minimization, and studies the incentive mechanism design in FL through a procurement auction. Different from existing literature, we consider a practical scenario of FL where clients are selected and scheduled at different global iterations to guarantee the completion of the FL job, and capture the distinct feature of FL that the number of global iterations is determined by the local accuracy of all participants to balance between computation and communication. Our auction framework <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> first decomposes the social cost minimization problem into a series of winner determination problems (WDPs) based on the number of global iterations. To solve each WDP, <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> invokes a greedy algorithm to determine the winners, and a payment algorithm for computing remuneration to winners. Finally, <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> returns the best solution among all WDPs. We carried out theoretical analysis to prove that <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> is truthful, individual rational, computationally efficient, and achieves a near-optimal social cost. We further extend our model to consider multiple FL jobs with corresponding budgets and propose another efficient algorithm <inline-formula><tex-math notation="LaTeX">$A_{FL-M}$</tex-math></inline-formula> to solve the extended problem. We conduct large-scale simulations based on the real-world data and testbed experiments by adopting FL frameworks FAVOR and CoCoA. Simulation and experiment results show that both <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$A_{FL-M}$</tex-math></inline-formula> can reduce the social cost by up to 55% compared with state-of-the-art algorithms.
Artificial Intelligence in E-commerce: A Literature Review

With the development of information and communication technologies, artificial intelligence is becoming increasingly popular. The main aim of companies in today's e-commerce world is to influence customer behavior in favor of certain products and brands. The application of artificial intelligence as an innovative tool in the field of e-commerce may seem as a positive step forward. The paper focuses on the description of the essence of e-commerce and artificial intelligence and their benefits. The aim is also to evaluate the importance of artificial intelligence and its use in the context of e-commerce based on available studies on this issue.
Smart City Traffic Intersection: Impact of Video Quality and Scene Complexity on Precision and Inference

Traffic intersections are prime locations for deployment of infrastructure sensors and edge computing nodes to realize the vision of a smart city. It is expected that the needs of a smart city, in regards to traffic and pedestrian traffic systems monitored by cameras/video, can be met by using state-of-the-art artificial-intelligence (AI) based object detectors and trackers. A critical component in designing an effective real-time object detection/tracking pipeline is the understanding of how object density, i.e., the number of objects in a scene, and image-resolution and frame rate influence the performance metrics. This study explores the accuracy and speed metrics with the goal of supporting pipelines that meet the precision and latency needs of a real-time environment. We examine the impact of varying image-resolution, frame rate and object-density on the object detection performance metrics. The experiments on the COSMOS testbed dataset show that varying the frame width from 416 pixels to 832 pixels, and cropping the images to a square resolution, result in the increase in average precision for all object classes. Decreasing the frame rate from 15 fps to 5 fps preserves more than 90&#x0025; of the highest F1 score achieved for all object classes. The results inform the choice of video preprocessing stages, modifications to established AI-based object detection/tracking methods, and suggest optimal hyper-parameter values.
Converting Android Native Apps to Flutter Cross-Platform Apps

Flutter is a development framework for building applications for mobile, web, and desktop platforms from a single codebase. Since its first official release by Google less than a few years ago, it is gaining so much popularity among mobile application developers, even being regarded as a game-changer. There are, however, millions of existing native apps in use that meet the requirements of a particular operating system by using its SDK. Thus, one natural question to ask is about rewriting an existing native app in Flutter. In this paper, we look at the technical side of this question by considering Android apps written in Java. In a small case study, we create a Flutter version of our existing Android app written in Java to support both Android and iOS by rewriting the entire app in Flutter. We share our development experience by discussing technical issues, problems, and challenges associated with such a rewriting effort. We describe our approach as well as the lessons that we learned.
SAFE

Kernel density visualization (KDV) has been the de facto method in many spatial analysis tasks, including ecological modeling, crime hotspot detection, traffic accident hotspot detection, and disease outbreak detection. In these tasks, domain experts usually generate multiple KDVs with different bandwidth values. However, generating a single KDV, let alone multiple KDVs, is time-consuming. In this paper, we develop a share-and-aggregate framework, namely SAFE, to reduce the time complexity of generating multiple KDVs given a set of bandwidth values. On the other hand, domain experts can specify bandwidth values on the fly. To tackle this issue, we further extend SAFE and develop the exact method SAFE all and the 2-approximation method SAFE exp which reduce the time complexity under this setting. Experimental results on four large-scale datasets (up to 4.33M data points) show that these three methods achieve at least one-order-of-magnitude speedup for generating multiple KDVs in most of the cases without degrading the visualization quality.
The IntelliJ Platform: A Framework for Building Plugins and Mining Software Data

In software engineering, a great number of new approaches are being actively researched, and a lot of tools are being developed based on them. These tools require a framework for their creation and an opportunity to be used by potential developers. Modern IDEs provide both. In this paper, we describe the main capabilities of the IntelliJ Platform that could be useful for researchers that are developing code analysis tools. To illustrate the benefits of using the platform, we describe several use cases that researchers might be interested in: mining software data, running machine learning models on code, recommending refactorings, and visualizing data in the IDE. We provide several examples of existing plugins that implement these cases. Finally, to make it easier to start working with the platform, we develop and provide simple plugins for each use case that could serve as a template for a new project.
Feature Fusion based Deep Learning method for Leukemia cell classification

Pathologists typically diagnose leukemia by looking at a blood smear under a microscope. Leukemia may cause due to leukocytes abnormal proliferation in blood and bone marrow. It may also indicated by decrease in numbers of other cells combined with immature leukocytes. The types of leukemia is confirmed by examination of bone marrow by the pathologist. The traditional method for identification of types of leukemia cells is time consuming and accuracy depends upon the expertise of the professional. Hence, an automated method require which can reduce the time and identify cells with high accuracy. In this regards, several machine learning, deep learning techniques are available which take image as an input and predict the types of leukemia cells. In the proposed work a feature fusion, based deep learning method has been utilize to classify blood smears. The fusion-based model can classify with an accuracy of 99.3%.
Accurate Real-time Ship Target detection Using Yolov4

With the development of artificial intelligence, surface ships have been developing toward intelligence and autonomy. Fast and accurate ship target detection is an important source of visual perception information of intelligent ship. In this paper, a fast and accurate ship target detection algorithm based on YOLOv4 is proposed for the problems of low accuracy and poor real-time performance of existing ship detection algorithms. YOLOv4 is currently one of the fastest and most accurate target detection algorithms with a wide range of applications in various fields. For ship targets, in order to achieve better detection results, some methods are used to improve the algorithm, such as K-means clustering, model structure modification and Mixup. In order to verify the effectiveness of the method, a ship target detection dataset is constructed by collecting various ship targets. And the training and tests of the algorithm model are completed in a GPU cloud server. The performance of the algorithm is compared to that of Faster R-CNN, SSD and YOLOv3 through experiments. The results show that this method has satisfactory accuracy and real-time detection of ship targets.
HVS

Approximate nearest neighbor search (ANNS) is a fundamental problem that has a wide range of applications in information retrieval and data mining. Among state-of-the-art in-memory ANNS methods, graph-based methods have attracted particular interest owing to their superior efficiency and query accuracy. Most of these methods focus on the selection of edges to shorten the search path, but do not pay much attention to the computational cost at each hop. To reduce the cost, we propose a novel graph structure called HVS. HVS has a hierarchical structure of multiple layers that corresponds to a series of subspace divisions in a coarse-to-fine manner. In addition, we utilize a virtual Voronoi diagram in each layer to accelerate the search. By traversing Voronoi cells, HVS can reach the nearest neighbors of a given query efficiently, resulting in a reduction in the total search cost. Experiments confirm that HVS is superior to other state-of-the-art graph-based methods.