Reviewerly/exHarmony huggingface dataset:
Drone Surveillance Using Detection, Tracking and Classification Techniques

In this work, we explore the process of designing a long-term drone surveillance system by fusing object detection, tracking and classification methods. Given a video stream from an RGB-camera, a detection module based on YOLOV5 is trained for finding drones within its field of view. Although in drone detection, high accuracy and robustness is achieved with the underlying complex architecture, the detection speed is hindered on ultra HD-streams. To solve this problem, we integrate a high efficient object tracker to update target status while avoiding running the detection at each frame. Benefited from lightweight backbone networks with powerful Transformer design, the object tracker achieves real-time speed on standalone CPU devices. Moreover, a drone classification model is applied on the output of the detection and tracking mechanisms to further distinguish drones from other background distractors (birds, balloons). By leveraging inference optimization with TensorRT and ONNX, our system achieves extremely high inference speed on NVIDIA GPUs. A ROS package is designed to integrate the aforementioned components together and provide a flexible, end-to-end drone surveillance tool for real-time applications. Comprehensive experiments on both standard benchmarks and field tests demonstrate the effectiveness and stability of proposed system.
Event-Based Object Detection and Tracking - A Traffic Monitoring Use Case -

Traffic monitoring is an important task in many scenarios, in urban roads to identify dangerous behavior and on-highway to check for vehicles moving in the wrong direction. This task is usually performed using conventional cameras but these sensors suffer from fast illumination changes, particularly at night, and extreme weather conditions. This paper proposes a solution for object detection and tracking using event-based cameras. This new technology presents many advantages to address traditional cameras limitations; the most evident are the high dynamic range and temporal resolution. However, due to the different nature of the provided data, solutions need to be implemented to process them in an efficient way. In this work, we propose two solutions for object detection, one based on standard geometrical approaches and one using a deep learning framework. We also release a novel dataset for this task, and present a complete application for road monitoring using event cameras (Dataset available at: https://airlab.deib.polimi.it/datasets-and-tools/ ).
A Survey on Few-Shot Techniques in the Context of Computer Vision Applications Based on Deep Learning

This review article about Few-Shot Learning techniques is focused on Computer Vision Applications based on Deep Convolutional Neural Networks. A general discussion about Few-Shot Learning is given, featuring a context-constrained description, a short list of applications, a description of a couple of commonly used techniques and a discussion of the most used benchmarks for FSL computer vision applications. In addition, the paper features a few examples of recent publications in which FSL techniques are used for training models in the context of Human Behaviour Analysis and Smart City Environment Safety. These examples give some insight about the performance of state-of-the-art FSL algorithms, what metrics do they achieve, and how many samples are needed for accomplishing that.
The Morra Game: Developing an Automatic Gesture Recognition System to Interface Human and Artificial Players

Morra is an ancient hand game still played nowadays. In its more popular variant, two players simultaneously extend one hand in front of the opponent to show a number of fingers, while uttering a number from 2 to 10. The player who successfully guesses the total number of fingers scores a point. Morra can be defined as a serious game, as it has the potential to positively affect cognition and to improve cognitive and perceptual skills. Moreover, with its involvement of many perceptual, cognitive and motor skills, morra is ideal to test several cognitive processes. This paper describes aspects of Gavina 2121, an artificial Morra player that successfully predicts the numbers of human opponents taking advantage of the limited ability of humans in random sequence generation. This study focuses on automatic gesture recognition. We developed and tested a system to allow Gavina 2121 to detect and count in real time the number of extended fingers in a human hand. The system is based on the open source MediaPipe Hand framework developed by Google. Our tests indicate that the system is able to accurately recognize the number of fingers extended by a human hand in real time, both in prone and supine positions. The system is still imprecise in semi-naturalistic conditions of an actual morra game, where the fingers of two hands need to be computed simultaneously. Our test, still in its pilot phase, shows promising results towards a flexible implementation of an artificial morra player that can sensibly expand the educational, rehabilitation and research applications of Morra.
Gender Recognition from 3D Shape Parameters

Gender recognition from images is generally approached by extracting the salient visual features of the observed subject, either focusing on the facial appearance or by analyzing the full body. In real-world scenarios, image-based gender recognition approaches tend to fail, providing unreliable results. Face-based methods are compromised by environmental conditions, occlusions (presence of glasses, masks, hair), and poor resolution. Using a full-body perspective leads to other downsides: clothing and hairstyle may not be discriminative enough for classification, and background cluttering could be problematic. We propose a novel approach for body-shape-based gender classification. Our contribution consists in introducing the so-called Skinned Multi-Person Linear model (SMPL) as 3D human mesh. The proposed solution is robust to poor image resolution and the number of features for the classification is limited, making the recognition task computationally affordable, especially in the classification stage, where less complex learning architectures can be easily trained. The obtained information is fed to an SVM classifier, trained and tested using three different datasets, namely (i) FVG, containing videos of walking subjects (ii) AMASS, collected by converting MOCAP data of people performing different activities into realistic 3D human meshes, and (iii) SURREAL, characterized by synthetic human body models. Additionally, we demonstrate that our approach leads to reliable results even when the parametric 3D mesh is extracted from a single image. Considering the lack of benchmarks in this area, we trained and tested the FVG dataset with a pre-trained Resnet50, for comparing our model-based method with an image-based approach.
Effect of Gender, Pose and Camera Distance on Human Body Dimensions Estimation

Human Body Dimensions Estimation (HBDE) is a task that an intelligent agent can perform to attempt to determine human body information from images (2D) or point clouds or meshes (3D). More specifically, if we define the HBDE problem as inferring human body measurements from images, then HBDE is a difficult, inverse, multi-task regression problem that can be tackled with machine learning techniques, particularly convolutional neural networks (CNN). Despite the community’s tremendous effort to advance human shape analysis, there is a lack of systematic experiments to assess CNNs estimation of human body dimensions from images. Our contribution lies in assessing a CNN estimation performance in a series of controlled experiments. To that end, we augment our recently published neural anthropometer dataset by rendering images with different camera distance. We evaluate the network inference absolute and relative mean error between the estimated and actual HBDs. We train and evaluate the CNN in four scenarios: (1) training with subjects of a specific gender, (2) in a specific pose, (3) sparse camera distance and (4) dense camera distance. Not only our experiments demonstrate that the network can perform the task successfully, but also reveal a number of relevant facts that contribute to better understand the task of HBDE .
A Simple yet Effective Image Repairing Algorithm

A 2D binary image is well-composed if it does not contain $$2\times 2$$ blocks of two diagonal black and two diagonal white pixels, called critical configurations. Some image processing algorithms are simpler on well-composed images. The process of transforming an image into a well-composed one is called repairing. We propose a new topology-preserving approach, which produces two well-composed images starting from an image I depending on the chosen adjacency (vertex or edge adjacency), in the same original square grid space as I. The size of the repaired images depends on the number and distribution of the critical configurations. A well-composed image I is not changed, while in the worst case the size increases at most two times (or four times if we want to preserve the aspect ratio). The advantage of our approach is in the small size of the repaired images, with a positive impact on the execution time of processing tasks. We demonstrate this experimentally by considering two classical image processing tasks: contour extraction and shrinking.
An Image-Based Classification Module for Data Fusion Anti-drone System

Means of air attack are pervasive in all modern armed conflict or terrorist action. We present the results of a NATO-SPS project that aims to fuse data from a network of optical sensors and low-probability-of-intercept mini radars. The requirements of the image-based module aim to differentiate between birds and drones, then between different kind of drones: copters, fixed wings, and finally the presence or not of payload. In this paper, we outline the experimental results of the deep learning model for differentiating drones from birds. Based on the trade-off between speed and accuracy, the YOLO v4 was chosen. A dataset refine process for YOLO-based approaches is proposed. The experimental results verify that such an approach provide a reliable source for situational awareness in a data fusion platform. However, the analysis indicates the necessity of enriching the dataset with more images with complex backgrounds as well as different target sizes.
A Novel Method for Improving the Voxel-Pattern-Based Euler Number Computing Algorithm of 3D Binary Images

As an important topological property of a 3D binary image, the Euler number can be calculated by counting certain 2 × 2 × 2 voxel patterns in the image. This paper presents a novel method for improving the voxel-pattern-based Euler number computing algorithm of 3D binary images. In the proposed method, by changing the accessing order of voxels in 2 × 2 × 2 voxel patterns and combining the voxel patterns which provide the same Euler number increments for the given image, the average numbers of voxels to be accessed for processing a 2 × 2 × 2 voxel pattern can be decreased from 8 to 4.25, which will lead to an efficient processing. Experimental results demonstrated that the proposed method is much more efficient than the conventional voxel-pattern-based Euler number computing algorithm.
Review of Mathematical Concepts

With classical physics, or Newtonian physics, it is not possible to explain phenomena that occur on an atomic scale. The reason is the incommensurability of microscopic particles, which cannot be described mathematically using classical mechanics.
Color Codes

As seen in previous chapters, in 1996 a new class of codes, now known as CSS codes, was proposed by Robert Calderbank, Peter Shor, and Andrew Steane. These codes were generalized into a rich code structure, which is the stabilizer quantum codes.
Introduction

This book is intended to be an introductory material on topological quantum codes, with the necessary elements from a mathematical and engineering point of view, aimed at postgraduate or undergraduate students at more advanced level, in the courses of Mathematics, Engineering, or Computing.
Topological Quantum Codes

As is already known, the computer-based on quantum properties is very promising. However, for this to become a reality, it is necessary to overcome great challenges for the computation process, such as the loss of information due to system interactions with the surrounding environment and systematic errors in unitary transformations. To protect the information until a computational calculation is completed, the quantum correction of errors through codes arises and the correction of errors on a physical level to make the fault-tolerant computation.
Awareness of Executors and Implementing Factors of Continuous Processes of Sustainable Forestry Education

Education for Sustainable Development (ESD) is being promoted in Japan as one of the roles to develop human resources who contribute to a sustainable society. Forest education provides a diverse opportunity in ESD through contact with forest resources and the natural environment. For this reason, forest education has been attracting attention in recent years. However, it is not easy to implement forest education continuously. This study focuses on the Kumano Forestry Education Project that is being carried out on a continuous basis in Kumano to elucidate (1) the awareness of practitioners who can continuously implement forest education, and (2) the process leading to continuous implementation through an interview survey. As a result of analyzing the interview using the modified grounded theory approach (M-GTA): 1) Implementation of the Kumano Forestry Education Project and awareness of the practitioners are closely related to the relationship between the community and themselves. 2) In the course of implementing the project, the practitioners feel a sense of ethical affirmation, accompanied by a sense that the project they are implementing is recognized and needed in their hometown. 3) There is an ethic of implementing forest education as a method of community development, with the practitioner recognizing that the program will have a universal positive impact on the target population.
Hierarchical DDPG for Manipulator Motion Planning in Dynamic Environments

In this paper, a hierarchical reinforcement learning (HRL) architecture, namely a “Hierarchical Deep Deterministic Policy Gradient (HDDPG)” has been proposed and studied. A HDDPG utilizes manager and worker formation similar to other HRL structures. However, unlike others, the HDDPG enables sharing an identical environment and state among workers and managers, while a unique reward system is required for each Deep Deterministic Policy Gradient (DDPG) agent. Therefore, the HDDPG allows easy structural expansion with probabilistic action selection of a worker by the manager. Due to its innate structural advantage, the HDDPG has a merit in building a general AI to deal with a complex time-horizon tasks with various conflicting sub-goals. The experimental results demonstrated its usefulness with a manipulator motion planning problem in a dynamic environment, where path planning and collision avoidance conflict each other. The proposed HDDPG is compared with an HAM and a single DDPG for performance evaluation. The result shows that the HDDPG demonstrated more than 40% of reward gain and more than two times the reward improvement rate. Another important feature of the proposed HDDPG is the biased manager training capability. By adding a preference factor to each worker, the manager can be trained to prefer a certain worker to achieve better success rate for a specific objective if needed.
Looking for ethnography in design research through three decades

Ethnographic methods are widely used in design research and provide valuable data for gaining in-depth understanding of any particular situation. To broaden the use of ethnography in design research, first, we investigated how ethnography and design research are related in the last 36 years by focusing on which ethnographic methods are utilized; why and in which domains of design research these methods are applied and, how these three aspects relate to each other altogether. Through content analyses we found meaningful associations between methods, goals and domains of application. Our research identifies gaps on theoretical, methodological and contextual implications. Based on these gaps we propose particular opportunities for future research.
Investigating the Modal Behavior of a Violin Top and a Back Plate

Regarding structural dynamic behavior, the violin is a very versatile entity to study, since it combines many different effects in one single structure. Investigating this instrument means dealing with the uncertainty in natural materials, geometric nonlinearities, many interfaces, and sound radiation. Hence, this multifaceted structure serves as research subject for present work. The study starts with modal analyses of the raw spruce and maple woods, with the intention to show that an a priori material parameter identification is possible. The identified values serve as input for the orthotropic violin plates in the associated finite element model. Using 2D laser scanning vibrometry, the modal parameters are obtained for each plate. The components are supported on soft foams and on cotton wool in order to show the significant deviations in the measurement results with varying foam compliances.
Dynamic permission access control model based on privacy protection

Access control technology is one of the key technologies to ensure safe resource sharing. Identity authentication and authority distribution are two key technologies for access control technology to restrict unauthorized users from accessing resources, and only authorised legal users can access resources. However, user privacy protection and frequent permission changes are two thorny issues that need to be solved urgently by access control technology. In this paper, a dynamic access control model based on privacy protection is proposed to deal with these problems. Compared with existing access control technologies, the main advantages of this paper are as follows: (1) Encrypt and hide the attributes of entities, and use attribute-based identity authentication technology for identity authentication, which not only achieves the purpose of traditional identity authentication, but also ensures the attributes and privacy of entities are not leaked; (2) Binding resource access permissions with entity attributes, dynamically assigning and adjusting resource access control permissions through changes in entity attributes, making resource access control more fine-grained and more flexible. Security proof and performance analysis show that the proposed protocol is secure under the hardness assumption of the discrete logarithm problem and the decision bilinear Diffie–Hellman problem. Compared with the cited references, this model has the advantages of low computational complexity, short computational time, and low communication overhead.
Learner Profiles and Attitudes Towards Accent in the Foreign Language The Role of Language Backgrounds

This paper is part of collaborative research investigating the attitudes of first-year university students in Italy towards foreign accents. It examines the first part of the survey which aims to define the students’ identity and language profile. The analysis focuses on the characteristics of the group which are hypothesised to correlate with students' attitudes towards foreign accents examined in the other sections of the questionnaire. Factors that could have an impact are gender, motivation for studying languages, bi-/plurilingualism, the nature of the languages studied, the language repertoire and the background of its acquisition.
Controllable Nonclassical Correlation by an Incoherent Pumping Field in a Λ‐Type Atom‐Cavity System

Abstract It is important to generate stable nonclassical correlation and wide‐band quantum field in quantum optical communication. In this paper, a scheme is proposed to manipulate the nonclassical intensity correlation of two output fields by a coherent coupling laser and an incoherent pumping field. The core of the scheme is that the coherent field is applied to manipulate the intracavity polaritons, and the incoherent pumping field is used to control the linear absorption and the atomic coherence. With the incoherent field, atoms are repopulated on the excited state . It induces a wide‐band zero absorption of a probe field and the negative dispersion that can be used to realize a white‐light cavity. In addition, with the increasement of the incoherent pumping rate, the wide‐band maximum stable intensity correlation and the nonclassical field with sub‐Poissonian distribution are attained, which is verified by the evolution of the second‐order correlation of two output channels.
Roadside Geology of Northern and Central California, Second Edition

Book Review| August 03, 2022 Roadside Geology of Northern and Central California, Second Edition Robert Anderson Robert Anderson 2552 Humphrey Road, Loomis, CA 9565 Search for other works by this author on: GSW Google Scholar Environmental & Engineering Geoscience (2022) 28 (3): 331. https://doi.org/10.2113/gseegeosci.28.3.331 Article history first online: 03 Aug 2022 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Robert Anderson; Roadside Geology of Northern and Central California, Second Edition. Environmental & Engineering Geoscience 2022;; 28 (3): 331. doi: https://doi.org/10.2113/gseegeosci.28.3.331 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyEnvironmental & Engineering Geoscience Search Advanced Search Reading Roadside Geology of Northern and Central California, Second Edition (Alt and Hyndman, 2016), was like getting reacquainted with an old friend. A few things were different but nothing of consequence. For those looking for a short overview of local and regional geology along the alignments described, it is an easy and quick read supported by color images, easy-to-read cross sections, and simplified local geologic maps. At $26.00, the book is small enough to carry in a day pack or suitcase and travels well. The book is quite a bargain and would make a great gift for anyone... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Smart grid: energy storage and transaction

Energy storage units (ESUs) and transactions are becoming effective features for improved grid resilience, for effective demand response, and to lower bills of modern smart grids. This chapter gives an insight about smart grids and ESUs employed. The method could aid in the resolution of a number of complex issues relating to the integrity and reliability of fast, dispersed, and complicated energy transactions and data transfers. Employment of blockchain could lower transactive energy prices while also improving the security and long-term viability of distributed energy resource integration, removing hurdles to a more decentralized and resilient power system. This chapter explores more on the basic issues regarding this.