Reviewerly/exHarmony huggingface dataset:
Image Database Generation and Management System for the Small-pixelized Airborne Target Recognition

Recently, the number of cases of unauthorized intrusion into flight prohibited areas within airport control is increasing rapidly. Most hobby drones are small objects of less than 2kg, and image-based object tracking systems are greatly affected by detection accuracy depending on the small size and image resolution of the drone. if an object is more than 200m away, it is difficult to identify existing object characteristics with small objects of 32x32 pixels or less. In this study, it was confirmed that micro-objects of 32x32 pixels or less can be effectively extracted by histogram binaries based on objects in images using the classical image processing method of HOG (Histogram of oriented gradients) and LBP (Local Binary Pattern) and to identify micro-objects of airborne target datasets collected using handmade tools, pre-processed image dataset is constructed by applying classical image processing techniques, and YOLOv5-based learning is conducted to improve object detection accuracy of 32x32 pixels or less. In addition, there are not many public image datasets (Datasets) for drones and airborne target in flight, we collected images including airplanes, drones, and birds. Qualitative data augmentation was performed using classical image processing methods using the collected datasets, classified into four datasets, and then the YOLOv5s model-based inference performance was analyzed. As a result of the analysis, it was confirmed that the inference performance of the dataset that performed qualitative augmentation using the classical image processing method was improved.
Bibliometric analysis of peer-reviewed literature on augmented reality with an emphasis on education vs. Physics education

Analyzing peer-reviewed literature on augmented reality (AR) is vital for future research and development. The objective of the current study was to assess research activity on AR with an emphasis on education and physics education. A bibliometric method was applied using SciVerse Scopus. The study period was from 1978 to 2021. The search query found 3393 documents in the AR-related education literature and 97 in the AR-related physics education literature. The bibliometric analysis result shows that the similarity of the results of visualization of AR in education networks with AR in physics education is: that research focuses on students, and the majority is integrated with virtual reality. This research has implications for providing insight to the next researcher in finding gap research and novelty in the field of AR in education and physics education.
A critical approach to Machine Learning forecast capabilities: creating a predictive biography in the age of the Internet of Behaviour (IoB)

Based on the notion of the Datacene, understood as the time when data directly affects the social, cultural, economic, political, and even affective structures of the present, in this article we propose how Big Data and Artificial Intelligence give rise to the Internet of Behaviour: a new technological paradigm that has incredible potential to forecast and induce human behaviour. Since ancient times, humans have wanted to predict and alter the future, but in the last ten years, this wish has begun to become a reality due to great advances in the field of social engineering, raising serious doubts regarding social control and the loss of freedom. In this context of analysis, we present two projects developed within the framework of Art, Science, Technology and Society. Data Biography shows the enormous number of digital traces that we generate daily and uses them to compose a person’s biography, composed of 365 printed books. Machine Biography, for its part, investigates how current artificial intelligence techniques can predict and induce future human behaviour, for which we have used various forecast and generative models trained with data from our own digital activity, in order to generate another set of books with our foreseeable activity for the year 2050. Both projects invite us to consider from a critical perspective the present and future of the social transformations produced by Big Data and AI.
HistoTrust: tracing AI behavior with secure hardware and blockchain technology

Abstract In areas of activity where the notion of accountability is strong, the adoption of artificial intelligence (AI) is limited by the opacity and lack of understanding of its behavior, all the more so in the embedded domain where neural networks are compressed and executed on microcontrollers. While the NIST introduced in 2021 several principles allowing the AI explainability, this paper introduces a novel scheme, HistoTrust, combining secure hardware and blockchain technology to bring trust in the traceability of AI behavior and allow its explainability. HistoTrust attests in an Ethereum ledger all the relevant data produced by a physical device, especially the heuristics inferred by AI. Thus, the audition of the ledger allows security verifications and AI behavior analysis.
Speckle Reduction in SAR Images

High-resolution synthetic aperture radar (SAR) systems with various imaging modalities, including polarimetric, interferometric, multi-frequency, and multi-temporal information, are widely used in Earth observation and remote sensing. The occurrence of speckle in SAR images, however, affects the performance of all image processing techniques and may thus prevent full exploitation for various applications. Over the past 40 years, speckle reduction methods have been developed in the literature, highlighting the importance of this topic. Despite this extensive knowledge, speckle removal is still an open problem that is far from being fully solved. In this chapter, a comprehensive overview of despeckling methods for single and multi-channel SAR images is given and specifications of the different methods are pointed out. It provides an overview of nonlocal filtering techniques, focusing on the definition of the similarity measures between two noisy patches in an image. Different filtering performance evaluation methods are presented to evaluate denoised images. These methods focus on the use of reference and referenceless metrics, where the former are commonly used to evaluate results on synthetic datasets for which a reference is available, while the latter define denoising indicators that are useful for evaluating denoising performance on real datasets. The chapter further includes several experiments that are conducted with real SAR data, while it discusses the effectiveness of denoising techniques. Finally, upcoming speckle reduction methods based upon machine learning, such as deep learning, are discussed as a new generation of despeckling techniques.
Analyzing students' attention by gaze tracking and object detection in classroom teaching

Purpose Attention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection. In particular, the proposed attention analysis model does not depend on any smart equipment. Design/methodology/approach Given a first-person view video of students' learning, the authors first estimate the gazing point by using the deep space–time neural network. Second, single shot multi-box detector and fast segmentation convolutional neural network are comparatively adopted to accurately detect the objects in the video. Third, they predict the gazing objects by combining the results of gazing point estimation and object detection. Finally, the personalized attention of students is analyzed based on the predicted gazing objects and the measurable eye movement criteria. Findings A large number of experiments are carried out on a public database and a new dataset that is built in a real classroom. The experimental results show that the proposed model not only can accurately track the students' gazing trajectory and effectively analyze the fluctuation of attention of the individual student and all students but also provide a valuable reference to evaluate the process of learning of students. Originality/value The contributions of this paper can be summarized as follows. The analysis of students' attention plays an important role in improving teaching quality and student achievement. However, there is little research on how to automatically and intelligently analyze students' attention. To alleviate this problem, this paper focuses on analyzing students' attention by gaze tracking and object detection in classroom teaching, which is significant for practical application in the field of education. The authors proposed an effectively intelligent fusion model based on the deep neural network, which mainly includes the gazing point module and the object detection module, to analyze students' attention in classroom teaching instead of relying on any smart wearable device. They introduce the attention mechanism into the gazing point module to improve the performance of gazing point detection and perform some comparison experiments on the public dataset to prove that the gazing point module can achieve better performance. They associate the eye movement criteria with visual gaze to get quantifiable objective data for students' attention analysis, which can provide a valuable basis to evaluate the learning process of students, provide useful learning information of students for both parents and teachers and support the development of individualized teaching. They built a new database that contains the first-person view videos of 11 subjects in a real classroom and employ it to evaluate the effectiveness and feasibility of the proposed model.
The Soft Skills Business Demands of the Chief Information Security Officer

While many researchers have investigated soft skills for different roles related to business, engineering, healthcare and others, the soft skills needed by the chief information security officer (CISO) in a leadership position are not studied indepth.This paper describes a first study aimed at filling this gap.In this multimethod research, both the business leaders perspective as well as an analysis of CISO job ads is studied.The methodology used to capture the business leaders perspective is via a Delphi study and the jobs adds are studied using a quantitative content analysis.With an increasing threat to information security for companies, the CISO role is moving from a technical role to an executive role.This executive function is responsible for information security across all layers of an organisation.To ensure compliance with the security policy among different groups within the company, such as employees, the board, and the IT department, the CISO must be able to adopt different postures.Soft skills are thus required to be able to assume this leadership role in the organisation.We found that when business leaders were asked about the most important soft skills the top three consisted out of 'communication', 'leadership' and 'interpersonal' skills while 'courtesy' was last on the list for a CISO leadership role.
The role of data science in environmental digital twins: In praise of the arrows

Abstract Digital twins are increasingly important in many domains, including for understanding and managing the natural environment. Digital twins of the natural environment are fueled by the unprecedented amounts of environmental data now available from a variety of sources from remote sensing to potentially dense deployment of earth‐based sensors. Because of this, data science techniques inevitably have a crucial role to play in making sense of this complex, highly heterogeneous data. This short article reflects on the role of data science in digital twins of the natural environment, with particular attention on how resultant data models can work alongside the rich legacy of process models that exist in this domain. We seek to unpick the complex two‐way relationship between data and process understanding. By focusing on the interactions, we end up with a template for digital twins that incorporates a rich, highly dynamic learning process with the potential to handle the complexities and emergent behaviors of this important area.
Towards Supporting Semiring in MLIR-Based COMET Compiler

Semirings are widely used in large-scale scientific applications of high-dimensional data and graph analytics for linear algebra computations. In this work, we propose a semiring compiler for today's high-performance computing (HPC) systems, often armed with heterogeneous devices, as an alternative to library-based approaches. In particular, we extend a domain-specific language (DSL) and compiler framework to automatically generate kernel code for semiring operations within the COMpiler for Extreme Targets (COMET) based on the Multi-Level Intermediate Representation (MLIR) framework. We provide a high-level programming abstraction representing various semiring operations with the familiar Einstein notation. We also build a semiring dialect and efficient code generation based on MLIR's extensible framework that can process a variety of semiring operators. By leveraging high-level semantics information and progressive lowering in code generation, we achieved better performance with up to 3.8x speedup compared with operations in the LAGraph library.
Effective Performance Modeling and Domain-Specific Compiler Optimization of CNNs for GPUs

The Convolutional Neural Network (CNN) kernel is a fundamental building block for deep learning, which dominates the computational cost of deep learning pipelines for image analysis. The synthesis of high-performance GPU kernels for CNNs is thus of considerable interest. The current state-of-the-art in optimizing CNN kernels is auto-tuning search using AutoTVM/Ansor, which has been shown to achieve higher performance than vendor libraries as well as polyhedral compilers. A primary reason for the failure of general-purpose optimizing compilers to deliver high-performance code for key kernels like CNN is the challenge of accurate performance modeling to enable effective choice among alternative transformations and/or parameter values such as tile sizes. In this paper we ask if a domain-specific compiler that is customized for the important CNN kernel can be more effective. Our results show that it can be very effective, enabling even higher performance of the generated GPU code for CNNs than auto-tuning with TVM/Ansor. Further, we demonstrate the effectiveness of a performance modeling approach that integrates analytical modeling of data movement volume with machine learning for offline training, enabling much more rapid code optimization than the approach of TVM/Ansor that is based on online construction of a machine learning model to guide auto-tuning search.
LDRNet: Enabling Real-Time Document Localization on Mobile Devices

Modern online services often require mobile devices to convert paper-based information into its digital counterpart, e.g., passport, ownership documents, etc. This process relies on Document Localization (DL) technology to detect the outline of a document within a photograph. In recent years, increased demand for real-time DL in live video has emerged, especially in financial services. However, existing machine-learning approaches to DL cannot be easily applied due to the large size of the underlying models and the associated long inference time. In this paper, we propose a lightweight DL model, LDRNet, to localize documents in real-time video captured on mobile devices. On the basis of a lightweight backbone neural network, we design three prediction branches for LDRNet: (1) corner points prediction; (2) line borders prediction and (3) document classification. To improve the accuracy, we design novel supplementary targets, the equal-division points, and use a new loss function named Line Loss. We compare the performance of LDRNet with other popular approaches on localization for general documents in a number of datasets. The experimental results show that LDRNet takes significantly less inference time, while still achieving comparable accuracy.
Generalized Hough transform for 3D object recognition and visualization in integral imaging

Object recognition is an automated image processing application of great interest in areas ranging from defect inspection to robot vision. In this regard, the generalized Hough transform is a well-established technique for the recognition of geometrical features even when they are partially occluded or corrupted by noise. To extend the original algorithm—aimed at detecting 2D geometrical features out of single images—we propose the robust integral generalized Hough transform, which corresponds to transformation under the generalized Hough transform of an elemental image array obtained from a 3D scene under integral imaging capture. The proposed algorithm constitutes a robust approach to pattern recognition in 3D scenes that takes into account information obtained not only from the individual processing of each image of the array but also from the spatial restrictions arising from perspective shifts between images. The problem of global detection of a 3D object of given size, position, and orientation is then exchanged under the robust integral generalized Hough transform for a more easily solved maximum detection in an accumulation (Hough) space dual to the elemental image array of the scene. Detected objects can then be visualized following refocusing schemes of integral imaging. Validation experiments for the detection and visualization of partially occluded 3D objects are presented. To the best of our knowledge, this is the first implementation of the generalized Hough transform for 3D object detection in integral imaging.
Navigating the Dynamic Noise Landscape of Variational Quantum Algorithms with QISMET

In the Noisy Intermediate Scale Quantum (NISQ) era, the dynamic nature of quantum systems causes noise sources to constantly vary over time. Transient errors from the dynamic NISQ noise landscape are challenging to comprehend and are especially detrimental to classes of applications that are iterative and/or long-running, and therefore their timely mitigation is important for quantum advantage in real-world applications.
A Recognition Algorithm for Complex Spatial Electromagnetic Signal Perception Based on KNN

This paper proposes a real-time processing and recognition algorithm of electromagnetic signals based on KNN, which uses characteristic parameters matching processing and KNN classifier to recognize the modulation type of signal. Finally, the results show that the recognition algorithm can recognize the modulation type of signal from 99 signal samples with an accuracy reaching to 97.73% and a computation time of 0.13s.
Towards understanding the influence of personality and team behaviors on requirements engineering activities

&lt;span lang="EN-US"&gt;Requirements engineers play an important role in the development of software products and services. The nature of requirements engineering (RE) is multifaceted and influences the quality, success, or failure of software products. In gathering software requirements, engineers commonly work in a team, particularly when dealing with the customers or modeling the requirements, hence the team behavior may influence the RE activities. The investigation of requirements engineers’ personality and their team behavior associated with RE activities is still an open area in which research is still developing. This study aims to investigate the personality and team behavior of requirements engineers involved in RE activities using a systematic literature review approach. We included 64 primary studies that addressed the association between personality and team behavior of requirements engineers on the effectiveness of RE activities. The result shows that among personality dimensions, extraversion and conscientiousness were found to be the predominant personality traits that positively affect RE activities. Furthermore, team behavior of requirements engineers such as flexibility, collaboration, creativity, innovation, and norms were discovered as factors that influence the RE process, performance, and success. The findings of this study contribute to the body of knowledge and practice of RE by providing empirical evidence on the influence of requirements engineers’ personality and team behavior on the effectiveness of RE activities.&lt;/span&gt;
Feature Fusion Pyramid Network for End-to-end Scene Text Detection

How to properly involve text characteristics like multi-scale, arbitrary direction, length aspect ratio, into detection network design has become a hot topic in computer vision. Feature Pyramid Network (FPN) is a typical method to achieve robust text detection, where its low-level and high-level feature map retains spatial structure and global semantic information, respectively. However, its strict hierarchical structure fails to fuse low-level and high-level information to improve distinguish ability of feature map. To address this problem, we propose a novel feature fusion pyramid network for end-to-end scene text detection by fusing multi-modal information. By diving pyramid structure into high-level and low-level layers, channel and spatial attention modules are adopted to enhance high-level and low-level feature representation by encoding channel and spatial -wise context information, respectively. In order to reduce information loss by layer transmission, a special residual network is designed to achieve short-cut between high-level and low-level features, so as to realize multi-modal feature fusion. Experiments show the precision and recall of the propose method on ICDAR2015, ICDAR2017-MLT and MSRA-TD500 datasets reach 88.7%/82.1%, 77.0%/60.3% and 85.3%/74.8%, respectively.
Trajectory Planning and Collision Control of a Mobile Robot: A Penalty-Based PSO Approach

In this paper, trajectory planning and navigation control problems have been addressed for a mobile robot. To achieve the objective of the research, an adaptive PSO (Particle Swarm Optimization) motion algorithm is developed using a penalty-based methodology. To deliver the best or collision-free position to the robot, fitness values of the all-random-positioned particles are compared at the same time during the target search action. By comparing the fitness values, the robot occupies the best position in the search space till it reaches the target. The new work integrated with conventional PSO is varying a velocity event that plays a vital role during the position acquisition (continuous change in position during the obstacle negotiation with the communication through random-positioned particles). The obstacle-negotiating angle and positional velocity of the robot are considered as input parameters of the controller whereas the robot's best position according to the target position is considered as the output of the controller. Simulation results are presented through the MATLAB environment. To validate simulation results, real-time experiments have been conducted in a similar workspace. The results of the adaptive PSO technique are also compared with the results of the existing navigational techniques. Improvements in results between the proposed navigation technique and existing navigation techniques are found to be 4.66% and 11.30%, respectively.