Reviewerly/exHarmony huggingface dataset:
Structured Texture Recovery

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.
Stateful to Stateless: Modelling Stateless Ethereum

The concept of 'Stateless Ethereum' was conceived with the primary aim of mitigating Ethereum's unbounded state growth. The key facilitator of Stateless Ethereum is through the introduction of 'witnesses' into the ecosystem. The changes and potential consequences that these additional data packets pose on the network need to be identified and analysed to ensure that the Ethereum ecosystem can continue operating securely and efficiently. In this paper we propose a Bayesian Network model, a probabilistic graphical modelling approach, to capture the key factors and their interactions in Ethereum mainnet, the public Ethereum blockchain, focussing on the changes being introduced by Stateless Ethereum to estimate the health of the resulting Ethereum ecosystem. We use a mixture of empirical data and expert knowledge, where data are unavailable, to quantify the model. Based on the data and expert knowledge available to use at the time of modelling, the Ethereum ecosystem is expected to remain healthy following the introduction of Stateless Ethereum.
Assessment of the variation of soil trace metals using artificial intelligence: A case study from Eastern Province, Saudi Arabia

&amp;lt;p&amp;gt;Soils may preserve metals from different sources that might pollute the environment. Hence, it is very important to assess the concentration of the geochemical elements of areas where intensified agriculture and industrial activities. This study involved the spatial assessment of topsoil contamination with trace metals in selected areas in Eastern Province, Saudi Arabia. To achieve this objective, more than 130 samples of topsoil from residential, industrial, and agricultural areas were collected and analysed. Inductively coupled plasma - optical emission spectroscopy (ICP-OES) was used to analyse the samples for various trace metals. Moreover, different artificial intelligence (AI) models such as artificial neural network (ANN) were applied to estimation the zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental results depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79&amp;amp;#177;37.9 mg/kg), Copper (Cu) (6.76&amp;amp;#177;12.54 mg/kg), Lead (Pb) (6.34&amp;amp;#177;14.55 mg/kg), and Zinc (Zn) (23.44&amp;amp;#177;84.43 mg/kg). The modelling accuracy showed that agricultural and industrial stations performance merit with goodness-of-fit ranges of 51-91% and 80-99%, respectively. This study concluded that AI models might be successfully applied for the quick estimation of soil trace metals and for decision support system.&amp;lt;/p&amp;gt;
Meta-Lidar: the sensor technology that makes AR/VR experiences real

Virtual and augmented reality gadgets need to map surrounding, locate objects and track gestures to create surreal and engaging experiences. And, to be ubiquitous, they need to do so over larger spaces in bright outdoor conditions using as few as possible tiny, low-cost and low-power sensors. Join this talk to see how Lumotive’s Meta-Lidar technology makes it possible with a single sensor — making AR/R experiences real.
MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural Networks

Deep neural network (DNN) based approaches have been intensively studied to improve video quality thanks to their fast advancement in recent years. These approaches are designed mainly for desktop devices due to their high computational cost. However, with the increasing performance of mobile devices in recent years, it became possible to execute DNN based approaches in mobile devices. Despite having the required computational power, utilizing DNNs to improve the video quality for mobile devices is still an active research area. In this paper, we propose an open-source mobile platform, namely MoViDNN, to evaluate DNN based video quality enhancement methods, such as super-resolution, denoising, and deblocking. Our proposed platform can be used to evaluate the DNN based approaches both objectively and subjectively. For objective evaluation, we report common metrics such as execution time, PSNR, and SSIM. For subjective evaluation, Mean Score Opinion (MOS) is reported. The proposed platform is available publicly at https://github.com/cd-athena/MoViDNN
Creative Intent Based Ambient Compensation for HDR10+ Content Using Metadata

The paper proposes a high dynamic range (HDR) tone mastering system which dynamically modified the picture appearance based on creative intent metadata to preserve content providers&#x0027; creative intent in different ambient light levels/ranges. Luminance percentile information based creative intent metadata is used to compensate for imagery degradation caused by ambient light. Multiple sections of tone mapping curves with multiple adjustment points along explicit Bezier curve is modified for better tone mapping curve control. Simulation results show that the proposed method can adapt to both ambient light levels and the scene content to keep the creative intent in different ambient conditions.
Three-dimensional motion analysis of women’s pole sports chair and Straddle technology

The purpose of this study was to analyze biomechanical motion of the basic skills of high level pole sports moves, Chair and Straddle. Six women performed the Chair move and five women performed the Straddle move who have experienced in pole sports for more than a year. To assess biomechanical motion 10 Vicon infrared cameras and X-Pole Stage was used, The camera"s signal was tuned using an analog-type signal control connected to the data station of the Vicon motion system. The Chair group showed the angle of rotation centering around the knee of 776.67±219.24°, knee flexion angle of 85.20±12.92°, the lower back flexion of angle of 3.09±11.99°. The Straddle group showed the holding time of posture of 2.54±0.64sec, the knee extension angle of 0.92±7.44°, the lower ankle flexion of angle of -43.60±4.41°, vertical height from ankle to pelvis of 11.34±8.67cm. In Chair group, the posture maintenance conditions and knee flexion angle were satisfied, and in Straddle group, posture maintenance conditions were satisfied.
A Study on Dementia-Related Factors Affecting Successful Aging of Active Seniors

The purpose of this study was to identify dementia-related factors that affect successful aging through the analysis of the relationship between dementia awareness, empowerment for dementia, dementia prevention behavioral intention and successful aging in the active senior group. In this study, 231 copies of data were used for actual analysis of active seniors(aged 50-69) who exercise regularly at least once a week. The survey was conducted both online and offline from August to November 2021. For data processing in this study, frequency analysis, reliability analysis, and correlation analysis were performed using SPSS(ver. 24.0), and confirmatory factor analysis and structural equation model analysis which were using the LISREL(ver. 8.52) program were performed.<BR> The results of this study are as follows. First, dementia awareness had a statistically significant effect on empowerment for dementia. Second, dementia awareness did not have a significant effect on dementia prevention behavioral intention. Third, dementia awareness did not have a significant effect on successful aging. Fourth, empowerment for dementia had a significant effect on dementia prevention behavioral intention. Fifth, empowerment for dementia had a significant effect on successful aging. Sixth, dementia prevention behavioral intention had a significant effect on successful aging.
Artes Liberales as a Prototype for Academic Education of 21st Century

An increasingly globalised world of the new millennium with its technological, socio-economic, and political challenges requires such type of a university that could guarantee an effective educational space for the academic training of professionals for those disciplines that are and will be crucial for the very existence and progress of the state, the preservation and development of its potential for full participation and competitiveness in global markets. To this end, academic institutions need to take into account, as part of their educational and research process, the latest technological and socio-economic trends in global developments in the industrial and scientific sectors. One possibility to meet institutionally these criteria is a liberal university with its potential to contribute a qualification basis for, e.g. the so-called global managers, top performers, creators of technical and social innovation, and for the enhancement of globalising science. This paper aims to present and highlight, via synthesis, descriptive, analytical and historical methods, how a liberal university can contribute to increasing the political and economic openness of the European Union within its own space, as well as to its openness to the outside world in global educational and socio-economic space.
Plug-and-play interconnects for chip-based quantum communications

A complete chip-based quantum communication system is demonstrated. The core functions of quantum transmitter, quantum receiver and quantum random number generator are implemented onto photonic integrated circuits (PICs) of different materials. For the first time, these PICs are all interfaced in a compact optoelectronic assembly where they operate synchronously to distribute information theoretically secure encryption keys in real-time. After reviewing the challenges of system integration for quantum photonic circuits, we present our development of plug-and-play quantum communication modules that are practical, scalable, power efficient and perform with high stability under real-life conditions.
A Feature Pyramid Based Multi-stage Framework for Object Detection in Low-altitude UAV Images

This paper proposes a high-performance framework for accurate multi-stage object detection in low-altitude based UAV images. The proposed system employs a cascade style architecture with increasing thresholds for achieving accurate detection. The framework makes use of highly efficient Feature Pyramid Networks (FPNs) to detect objects of small sizes, and various scales which are the main challenge in low-altitude aerial images. FPNs aim to resolve scale variation problems in object detection by combining features of multiple levels. The experiments have been performed on a complex low-altitude aerial dataset VisDrone which has multiple categories of classes. The FPN-Cascade detector has been supported by slicing the data horizontally and vertically that resulted in an advancement of 8% mAP when compared with the base detector. The experiments compare the FPN-Cascade performance on the standard as well as augmented VisDrone dataset. A concrete methodology about the training process, hyperparameter tuning, and performance evaluation methods for Cascade RCNN on the VisDrone dataset is highlighted. The proposed framework achieves state of the art 30.04% mAP value on the VisDrone dataset.
An Omnidirectional Cooperative Transportation of a Large Object by Differential Drive Wheeled Mobile Robots with the Active-caster Control

In this paper, we present a coordinated omnidirectional transporting system of a large object by multiple mobile robots. Conventional differential drive wheeled mobile robots are used for the system, but the connection point with the transported object is installed at a position separated from the midpoint of the two drive wheels in the traveling direction. The mobile robot is driven to control the movement of that point. The control method for the differential drive wheeled mobile robot is a kind of active caster control. From this, it is possible to perform independent running of the mobile robot, docking with the transport object, and omnidirectional motion control of the transport object by multiple mobile robots. In this paper, to verify the effectiveness of the proposed system, we present some simulation studies together with the system design and mechanical design of a prototype of mobile robot prototype.
Construction of Complex Lattice Codes via Cyclotomic Fields

Through algebraic number theory and Construction $A$ we extend an algebraic procedure which generates complex lattice codes from the polynomial ring \mathbb{F}_{2}[x]/(x^{n}-1), where \mathbb{F}_{2}=\{0,1\}, by using ideals from the generalized polynomial ring \frac{\mathbb{F}_{2}[x,\frac{1}{2}\mathbb{Z}_{0}]}{((x^{\frac{1}{2}})^{n}-1)} through the ring of integers $\mathcal{O}_{\mathbb{L}}$ of the cyclotomic field \mathbb{L}=\mathbb{Q}(\zeta_{2^{s}}), where \zeta_{2^{s}} is a 2^{s}-th root of the unit, with s2.
Recognition of the MNIST dataset with defective rows

Abstract One of the well-known classification problems in machine learning is the problem of recognizing handwritten numbers. This task is solved, since there are different neural networks that determine with fairly high accuracy which number is shown in the picture. In this paper, we will consider the recognition of handwritten digits subjected to certain deformations.
Preliminary fracture analysis from the frontal most exposure of a major roof thrust in the Eastern Himalaya: Insights from the Ramgarh thrust, Sikkim Himalaya.

&amp;lt;p&amp;gt;Shear zones associated with major thrust faults generally record overprinting of deeper crustal deformation signatures by shallower crustal signatures due to faults climbing up-section along the transport direction. In this study, we investigate the deformation signatures related to the shallow crustal conditions on one such major thrust, the Ramgarh thrust (RT) from Sikkim Himalayan Fold Thrust Belt (FTB). RT is an intermediate crustal thrust that has recorded a translation of ~58-65 km and overprinting of deformation structures. RT acts as the roof thrust of Lesser Himalayan duplex, hence got reactivated several times, and records a long deformation history.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In Sikkim Himalaya, the frontal most exposure of the RT is near Setikhola (N26&amp;amp;#176; 56.178&amp;amp;#8217;, E88&amp;amp;#176; 26.607&amp;amp;#8217;) as ~57m thick shear zone that exposes the lower Lesser Himalayan Daling quartzite and phyllite in the hanging wall over Gondwana sandstone in the footwall. The mean bedding is oriented ~72&amp;amp;#176;, 298&amp;amp;#176;, and the mean dominant cleavage is ~ 70&amp;amp;#176;, 305&amp;amp;#176;. The outcrop forms the overturned forelimb of a fault-bend antiform. The outcrop is strongly fractured. Based on the angular relationship with respect to the bedding, three sets of fractures were identified. Low angle fractures (&amp;lt; 30&amp;amp;#176; to bedding) constitute ~20.23 %, moderate (30&amp;amp;#176; &amp;amp;#8211; 60&amp;amp;#176; to bedding) and high angle fractures (60&amp;amp;#176;- 90&amp;amp;#176; to bedding) constitute ~39.88% of the total fracture population. The fractures are uniformly distributed throughout the stretch of the shear zone. Daling quartzites accommodate more number of fractures than the phyllites. Preliminary investigation indicates that the thicker beds have higher fracture intensity than thinner beds. Few of the fractures were identified as opening mode fractures based on their association with the plumose structures. ~ 17.3% of the total measured fractures records slickenline lineations. These shear fractures reveal two clusters on the stereonet (Set 1: ~90&amp;amp;#176;, 098&amp;amp;#176;; Set 2: ~77&amp;amp;#176;, 331&amp;amp;#176;). They have a dihedral angle of ~54&amp;amp;#8304; and set 1 and set 2 are oriented ~ 27&amp;amp;#8304; and ~ 32&amp;amp;#8304; to the bedding respectively. Based on preliminary analysis, the local maximum principal stress (&amp;amp;#963;1) is oriented sub-horizontally with a SSW trend. Interestingly, this estimate is in agreement with the current global stress orientations from the Eastern Himalaya, where &amp;amp;#963;1 is near horizontal and trends NNE &amp;amp;#8211; SSW (Larson et al., 1999).&amp;lt;/p&amp;gt;
Efficient and Privacy Preserving Clustering Algorithm for Spatiotemporal Data

The efficiency of a spatiotemporal data analysis algorithm decreases as the amount of data increases. Many clustering techniques have been proposed for data analysis applications. However, applying those techniques to spatiotemporal data clustering is still in its infancy. In this paper, we tackle the issue of clustering spatiotemporal data on public Cloud based on the distance between them. To increase the efficiency of spatiotemporal clustering, we have proposed a MapReduce-based framework for clustering. However, as spatiotemporal dataset contains sensitive information, directly outsourcing spatiotemporal data to Cloud servers will raise privacy concerns. To address the problem of privacy, we have proposed a privacy preserving clustering algorithm based on MapReduce for spatiotemporal data that can be efficiently outsourced for data processing on the Cloud servers. The proposed scheme allows the clustering operation to be performed directly on the encrypted spatiotemporal data by Cloud server. Extensive experimental evaluation with trajectory data shows that our scheme efficiently produces higher quality clustering results.
Image Description Using the Relation Between Color and Texture in Retrieval Task

In the past years, significant efforts have been made for new theories and models of descriptors for Content-Based Image Retrieval systems and many effective descriptors, which use color and texture, have been established. This article presents the analysis and modifications of descriptors that use color and texture for the image retrieval task. To provide a complete detailed, and fair analysis, exposing weaknesses in descriptors and ideas to correct them. We evaluated descriptors that use color and texture, with image sets and metrics found in the literature. We compared classical descriptors that only use one low-level characteristic with descriptors that use color and texture. The analysis showed discrepancies between the model and the implementation of one of the descriptors, as well as the descriptors with the best performance, their main weaknesses, and complications when we trying to correct them. likewise, we present variants that improve the image retrieval in some cases.
Distributed Graph Matching Network for Deep Situation Recognition

In order to recognize battlefield situation or envi-ronment, one of the most important issue is to merge semantic in-formation obtained by multiple agents. In this paper, we propose a distributed graph matching network to classify multiple agents based on graph similarity analysis. To this end, we construct a hypergraph to analyze a high-order relationship between agents (i.e., graphs). Then a hypergraph incidence matrix is used to sample a tuple of (anchor, positive, negative) agents, where triplet loss is minimized to train agent embedding vector such that the trained vectors are similar for graphs with similar topological and semantic information. To tackle the over-fitting problem caused by fixed samples of agent, we propose a hypergraph induced sample validation for online update of hyperedges in training step. Experiment results demonstrate the performance of our proposed model.
Tiered trust for useful embedded systems security

Traditional embedded systems rely on custom C code deployed in a monolithic firmware image. In these systems, all code must be trusted completely, as any code can directly modify memory or hardware registers. More recently, some embedded OSes have improved security by separating userspace applications from the kernel, using strong hardware isolation in the form of a memory protection unit (MPU). Unfortunately, this design requires either a large trusted computing base (TCB) containing all OS services, or moving many OS services into userspace. The large TCB approach offers no protection against seemingly-correct backdoored code, discouraging the use of kernel code produced by others and complicating security audits. OS services in userspace come at a cost to usability and efficiency. We posit that a model enabling two tiers of trust for kernel code is better suited to modern embedded software practices. In this paper, we present the threat model of the Tock Operating System, which is based on this idea. We compare this threat model to existing security approaches, and show how it provides useful guarantees to different stakeholders.
A weighted sparse coding model on product Grassmann manifold for video-based human gesture recognition

It is a challenging problem to classify multi-dimensional data with complex intrinsic geometry inherent, such as human gesture recognition based on videos. In particular, manifold structure is a good way to characterize intrinsic geometry of multi-dimensional data. The recently proposed sparse coding on Grassmann manifold shows high discriminative power in many visual classification tasks. It represents videos on Grassmann manifold using Singular Value Decomposition (SVD) of the data matrix by vectorizing each image in videos, while vectorization destroys the spatial structure of videos. To keep the spatial structure of videos, they can be represented as the form of data tensor. In this paper, we firstly represent human gesture videos on product Grassmann manifold (PGM) by Higher Order Singular Value Decomposition (HOSVD) of data tensor. Each factor manifold characterizes features of human gesture video from different perspectives and can be understood as appearance, horizontal motion and vertical motion of human gesture video respectively. We then propose a weighted sparse coding model on PGM, where weights can be understood as modeling the importance of factor manifolds. Furthermore, we propose an optimization algorithm for learning coding coefficients by embedding each factor Grassmann manifold into symmetric matrices space. Finally, we give a classification algorithm, and experimental results on three public datasets show that our method is competitive to some relevant excellent methods.