Reviewerly/exHarmony huggingface dataset:
Pose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior

The 3D reconstruction of objects is a prerequisite for many highly relevant applications of computer vision such as mobile robotics or autonomous driving. To deal with the inverse problem of reconstructing 3D objects from their 2D projections, a common strategy is to incorporate prior object knowledge into the reconstruction approach by establishing a 3D model and aligning it to the 2D image plane. However, current approaches are limited due to inadequate shape priors and the insufficiency of the derived image observations for a reliable alignment with the 3D model. The goal of this paper is to show how 3D object reconstruction can profit from a more sophisticated shape prior and from a combined incorporation of different observation types inferred from the images. We introduce a subcategory-aware deformable vehicle model that makes use of a prediction of the vehicle type for a more appropriate regularisation of the vehicle shape. A multi-branch CNN is presented to derive predictions of the vehicle type and orientation. This information is also introduced as prior information for model fitting. Furthermore, the CNN extracts vehicle keypoints and wireframes, which are well-suited for model-to-image association and model fitting. The task of pose estimation and reconstruction is addressed by a versatile probabilistic model. Extensive experiments are conducted using two challenging real-world data sets on both of which the benefit of the developed shape prior can be shown. A comparison to state-of-the-art methods for vehicle pose estimation shows that the proposed approach performs on par or better, confirming the suitability of the developed shape prior and probabilistic model for vehicle reconstruction.
The Cost of a True Random Bit—On the Electronic Cost Gain of ASIC Time-Domain-Based TRNGs

Random number generators are of paramount importance in numerous fields. Under certain well-defined adversarial settings, True Random Number Generators (TRNGs) are more secure than their computational (pseudo) random number generator counterparts. TRNGs are also known to be more efficiently implemented on hardware platforms where, for various applications, efficiency in terms of electronic cost factors is critical. In this manuscript, we first provide an evaluation of robustness and reliability of efficient time-domain-based TRNG implementation over FPGA platform. In particular, we demonstrate sensitivities which imply a TRNG construction which is not agnostic to electronic-design-automation tools and to the level of designers’ know-how. This entails a large amount of effort and validation to make the designs robust, as well as requires a high degree of complexity from non-trivial FPGAs flows. This motivates the second part of the manuscript, where we propose an ASIC-based implementation of the TRNG, along with the optimization steps to enhance its characteristics. The optimized design improves the randomness-throughput by 42× for the same entropy level described in previous works, and it can provide maximal entropy level of 0.985 with 7× improvement in randomness throughput over the raw samples (no pre-processing). The proposed design simultaneously provides a reduced energy of 0.1 (mW/bit) for the same entropy level as previous works, and 1.06 (mW/bit) for the higher entropy flavor, and a lower area utilization of 0.000252 (mm2) on a 65 nm technology evaluation, situating it in the top-class of the discuss ratings. This leads to the quantitative question of the gain in electronic cost factors over ASIC TRNGs, and the minimum Cost Per Bit/Source possible to date. Finally, we exemplify a TRNG versus PRNG cost-extrapolation for security architects and designers, targeting an ASIC scenario feeding a lightweight encryption core.
Thermometry of Gaussian quantum systems using Gaussian measurements

We study the problem of estimating the temperature of Gaussian systems with feasible measurements, namely Gaussian and photo-detection-like measurements. For Gaussian measurements, we develop a general method to identify the optimal measurement numerically, and derive the analytical solutions in some relevant cases. For a class of single-mode states that includes thermal ones, the optimal Gaussian measurement is either Heterodyne or Homodyne, depending on the temperature regime. This is in contrast to the general setting, in which a projective measurement in the eigenbasis of the Hamiltonian is optimal regardless of temperature. In the general multi-mode case, and unlike the general unrestricted scenario where joint measurements are not helpful for thermometry (nor for any parameter estimation task), it is open whether joint Gaussian measurements provide an advantage over local ones. We conjecture that they are not useful for thermal systems, supported by partial analytical and numerical evidence. We further show that Gaussian measurements become optimal in the limit of large temperatures, while {on/off} photo-detection-like measurements do it for when the temperature tends to zero. Our results therefore pave the way for effective thermometry of Gaussian quantum systems using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">experimentally realizable measurements</mml:mtext></mml:mrow></mml:math>.
Students’ opinions toward interactive apps used for teaching chemistry

The solution of the problem of effective use of apps is not only in the analysis of learning outcomes, but also in consideration of the students’ opinions toward learning chemistry with the help of these applications. Good results can undoubtedly be achieved in teaching chemistry if traditional and interactive teaching methods are intelligently combined. Mobile interactive apps allow educators to teach regardless of place and time, they provide the opportunity to learn both in the classroom and outside, and this is their big advantage. It also gives the teacher opportunity to interact with students on a more personal level with the help of mobile digital devices that the learners use regularly. This article, presents the results of a study of students' opinions about the use of mobile interactive applications in chemistry lessons. The approbation was carried out at a specialized school of information technologies in Karaganda (Kazakhstan), at the school Chýně, and at the first private language gymnasium Hradec Králové (Czech Republic). The results showed that more than 60 % of the students enjoy interactive apps, which positively affects their opinions towards the subject.
Security and Privacy Protection in the e-Health System: Remote Monitoring of COVID-19 Patients as a Use Case

The Internet of Things (IoT) is characterized by heterogeneous technologies, which contribute to the provision of innovative services in various fields of application. Among these applications, we find the field of e-Health which provides a huge amount of data that served the health of patients remotely and in real time but also medical records, health monitoring and emergency response. e-Health systems require low latency and delay which is not guaranteed since data are transferred to the cloud and then back to the application, which can seriously affect performance. Also, COVID-19 pandemic has accelerated the need of remote monitoring of patients to reduce chances of infection among physicians and healthcare workers. To this end, Fog computing has emerged, where cloud computing is extended to the edge of the network to reduce latency and network congestion. This large amount of data is downloaded and stored on remote public cloud servers to which users cannot be fully trusted, especially when we are dealing with sensitive data like health data. In this scenario, meeting the confidentiality and patient privacy requirements becomes urgent for a large deployment of cloud systems. In this context, we offer a solution to secure the personal data of the e-Health system and protect the privacy of patients in an IoT–Fog–cloud environment while being based on cryptographic techniques especially CP-ABE and the blockchain paradigm. The results obtained are satisfactory, which allowed us to deduce that the solutions are protected against the most known attacks in IoT–Fog–cloud systems.Sassi, Mounira Abid, Mohamed
Explorasi Limbah Kaca pada Badan Keramik Stoneware

The objective of glass waste exploration in clay for ceramic products has a mean to look for novelties and innovations for ceramic products which are now under threat due to mass production ceramics with “handmade” characteristics. The existence of “handmade” mass-produced ceramic products will have an impact on the value and price of ceramic products that are actually crafted by hand with more complex techniques, and high craftsmanship. From Kyomi Ceramic Studio's case studies, the author uses design-by-doing methods and experimental research to develop ceramic products from available and affordable raw materials for the industry. By looking for the possibility of a new identity through the exploration of mixing stoneware clay with glass waste, it is hoped that it will generate new aesthetic potential and can raise the integrity and value of handmade ceramic products that are not easily imitated or mass-produced.
One-Shot Yield-Cost Relations in General Quantum Resource Theories

Although it is well known that the amount of resources that can be asymptotically distilled from a quantum state or channel does not exceed the resource cost needed to produce it, the corresponding relation in the nonasymptotic regime hitherto has not been well understood. Here, we establish a quantitative relation between the one-shot distillable resource yield and dilution cost in terms of transformation errors involved in these processes. Notably, our bound is applicable to quantum state and channel manipulation with respect to any type of quantum resource and any class of free transformations thereof, encompassing broad types of settings, including entanglement, quantum thermodynamics, and quantum communication. We also show that our techniques provide strong converse bounds relating the distillable resource and the resource dilution cost in the asymptotic regime. Moreover, we introduce a class of channels that generalize the twirling maps encountered in many resource theories, and by directly connecting it with resource quantification, we compute analytically several smoothed resource measures and improve our one-shot yield-cost bound in relevant theories. We use these operational insights to exactly evaluate important measures for various resource states in the resource theory of magic states.Received 10 October 2021Accepted 26 January 2022Corrected 12 April 2022DOI:https://doi.org/10.1103/PRXQuantum.3.010348Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasNonlocalityQuantum channelsQuantum communicationQuantum computationQuantum entanglementResource theoriesQuantum Information
Detection and Tracking Meet Drones Challenge

Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the developments of object detection and tracking algorithms, we have organized three challenge workshops in conjunction with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first present a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms for the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from https://github.com/VisDrone/VisDrone-Dataset.
Where2Act: From Pixels to Actions for Articulated 3D Objects

One of the fundamental goals of visual perception is to allow agents to meaningfully interact with their environment. In this paper, we take a step towards that long-term goal -- we extract highly localized actionable information related to elementary actions such as pushing or pulling for articulated objects with movable parts. For example, given a drawer, our network predicts that applying a pulling force on the handle opens the drawer. We propose, discuss, and evaluate novel network architectures that given image and depth data, predict the set of actions possible at each pixel, and the regions over articulated parts that are likely to move under the force. We propose a learning-from-interaction framework with an online data sampling strategy that allows us to train the network in simulation (SAPIEN) and generalizes across categories. Check the website for code and data release: https://cs.stanford.edu/~kaichun/where2act/
Underwater image enhancement method with non-uniform illumination based on Retinex and ADMM

In order to solve the image blurring and distortion problem caused by underwater non-uniform and low illumination, this paper proposes an underwater image enhancement algorithm based on the Retinex theory and the Alternating Direction Method of Multipliers (ADMM). Firstly, the L component of the original image in the Lab space is extracted as the initial illumination map, and an Augmented Lagrange Multiplier (ALM) framework is constructed based on the ADMM to optimize the initial illumination map in order to obtain an accurate illumination image. In addition, the illumination map is further corrected in the luminance region with the Gamma Correction. Secondly, combined with the color constancy characteristics in the Retinex theory, the reflected image of the object is obtained. Finally, the bilateral filter is picked to suppress the underwater noise and obtain a more detailed enhanced image. The experimental results show that the underwater image enhancement algorithm can effectively solve the non-uniform illumination problem caused by natural light or artificial light source and improve the underwater image quality, thus having a better performance than other algorithms.
AI-Aided Individual Emergency Detection System in Edge-Internet of Things Environments

Recently, many disasters have occurred in indoor places. In order to rescue or detect victims within disaster scenes, vital information regarding their existence and location is needed. To provide such information, some studies simply employ indoor positioning systems to identify each mobile device of victims. However, their schemes may be unreliable, since people sometimes drop their mobile devices or put them on a desk. In other words, their methods may find a mobile device, not a victim. To solve this problem, this paper proposes a novel individual monitoring system based on edge intelligence. The proposed system monitors coexisting states with a user and a smart mobile device through a user state detection mechanism, which could allow tracking through the monitoring of continuous user state switching. Then, a fine-grained localization scheme is employed to perceive the precise location of a user who is with a mobile device. Hence, the proposed system is developed as a proof-of-concept relying on off-the-shelf WiFi devices and reusing pervasive signals. The smart mobile devices of users interact with hierarchical edge computing resources to quickly and safely collect and manage sensing data of user behaviors with encryption by cipher-block chaining, and user behaviors are analyzed via the ensemble paradigm of three machine learning technologies. The proposed system shows 98.82% prevision for user activity recognition, and 96.5% accuracy for user localization accuracy is achieved.
On Actively Secure Fine-Grained Access Structures from Isogeny Assumptions

We present an actively secure threshold scheme in the setting of Hard Homogeneous Spaces (HHS) which allows fine-grained access structures. More precisely, we elevate a passively secure isogeny-based threshold scheme to an actively secure setting. We prove the active security and simulatability of our advanced schemes. By characterising the necessary properties, we open our schemes to a significantly wider field of applicable secret sharing schemes. Furthermore, we show that Shamir's scheme has our generalised properties, and thereby our approach truly represents a less restrictive generalisation.
Vehicle Detection and Tracking from Surveillance Cameras in Urban Scenes

Detecting and tracking vehicles in urban scenes is a crucial step in many traffic-related applications as it helps to improve road user safety among other benefits. Various challenges remain unresolved in multi-object tracking (MOT) including target information description, long-term occlusions and fast motion. We propose a multi-vehicle detection and tracking system following the tracking-by-detection paradigm that tackles the previously mentioned challenges. Our MOT method extends an Intersection-over-Union (IOU)-based tracker with vehicle re-identification features. This allows us to utilize appearance information to better match objects after long occlusion phases and/or when object location is significantly shifted due to fast motion. We outperform our baseline MOT method on the UA-DETRAC benchmark while maintaining a total processing speed suitable for online use cases.
A random forest-based approach for survival curves comparing: principles, computational aspects and asymptotic time complexity analysis

The log-rank test and Cox's proportional hazard model can be used to compare survival curves but are limited by strict statistical assumptions.In this study, we introduce a novel, assumption-free method based on a random forest algorithm able to compare two or more survival curves.A proportion of the random forest's trees with sufficient complexity is close to the test's p-value estimate.The pruning of trees in the model modifies trees' complexity and, thus, both the method's robustness and statistical power.The discussed results are confirmed using a simulation study, varying the survival curves and the tree pruning level.
Single Image Dehazing Algorithm Based on Modified Dark Channel Prior

Single image dehazing algorithm based on Dark Channel Prior (DCP) is widely known. More and more image dehazing algorithms based on DCP have been proposed. However, we found that it is more effective to use DCP in the RAW images before the ISP pipeline. In addition, for the problem of DCP failure in the sky area, we propose an algorithm to segment the sky region and compensate the transmission. Extensive experimental results on both subjective and objective evaluation demonstrate that the performance of the modified DCP (MDCP) has been greatly improved, and it is competitive with the state-of-the-art methods.
Cluster Analysis of a Symbolic Regression Search Space

In this chapter we take a closer look at the distribution of symbolic regression models generated by genetic programming in the search space. The motivation for this work is to improve the search for well-fitting symbolic regression models by using information about the similarity of models that can be precomputed independently from the target function. For our analysis, we use a restricted grammar for uni-variate symbolic regression models and generate all possible models up to a fixed length limit. We identify unique models and cluster them based on phenotypic as well as genotypic similarity. We find that phenotypic similarity leads to well-defined clusters while genotypic similarity does not produce a clear clustering. By mapping solution candidates visited by GP to the enumerated search space we find that GP initially explores the whole search space and later converges to the subspace of highest quality expressions in a run for a simple benchmark problem.
Improved Genetic Algorithm (VNS-GA) using polar coordinate classification for workload balanced multiple Traveling Salesman Problem (mTSP)

The multiple traveling salesman problem (mTSP) is an extension of the traveling salesman problem (TSP), which has wider applications in real life than the traveling salesman problem such as transportation and delivery, task allocation, etc. In this paper, an improved genetic algorithm (VNS-GA) that uses polar coordinate classification to generate the initial solutions is proposed. It integrates the variable neighbourhood algorithm to solve the multiple objective optimization of the mTSP with workload balance. Aiming to workload balance, the first design of this paper is about generating initial solutions based on the polar coordinate classification. Then a distance comparison insertion operator is designed as a neighbourhood action for allocating paths in a targeted manner. Finally, the neighbourhood descent process in the variable neighbourhood algorithm is fused into the genetic algorithm for the expansion of search space. The improved algorithm is tested on the TSPLIB standard data set and compared with other genetic algorithms. The results show that the improved genetic algorithm can increase computational efficiency and obtain a better solution for workload balance and this algorithm has wild applications in real life such as multiple robots task allocation, school bus routing problem and other optimization problems.
Deep learning approach to obtain collaborative filtering neighborhoods

Abstract In the context of recommender systems based on collaborative filtering (CF), obtaining accurate neighborhoods of the items of the datasets is relevant. Beyond particular individual recommendations, knowing these neighbors is fundamental for adding differentiating factors to recommendations, such as explainability, detecting shilling attacks, visualizing item relations, clustering, and providing reliabilities. This paper proposes a deep learning architecture to efficiently and accurately obtain CF neighborhoods. The proposed design makes use of a classification neural network to encode the dataset patterns of the items, followed by a generative process that obtains the neighborhood of each item by means of an iterative gradient localization algorithm. Experiments have been conducted using five popular open datasets and five representative baselines. The results show that the proposed method improves the quality of the neighborhoods compared to the K -Nearest Neighbors (KNN) algorithm for the five selected similarity measure baselines. The efficiency of the proposed method is also shown by comparing its computational requirements with that of KNN.