Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface Damage Identification for Heritage Site Protection: A Mobile Crowd-sensing Solution Based on Deep Learning

This article addresses the general problem of built heritage protection against both deterioration and loss. To continuously monitor and update the structural health status, a crowd-sensing solution based on powerful and automatic deep learning technique is proposed. The aim of this solution is to get rid of the limitations of manual and visual damage detection methods that are costly and time-consuming. Instead, automatic visual inspection for damage detection on walls is efficiently and effectively performed using an embedded Convolutional Neural Network (CNN). This CNN detects the most frequent types of surface damage on wall photos. The study has been conducted in the Kasbah of Algiers where the four following types of damages have been considered: Efflorescence, spall, crack, and mold. The CNN is designed and trained to be integrated into a mobile application for a participatory crowd-sensing solution. The application should be widely and freely deployed, so any user can take a picture of a suspected damaged wall and get an instant and automatic diagnosis through the embedded CNN. In this context, we have chosen MobileNetV2 with a transfer learning approach. A set of real images have been collected and manually annotated and have been used for training, validation, and test. Extensive experiments have been conducted to assess the efficiency and the effectiveness of the proposed solution, using a 5-fold cross-validation procedure. Obtained results show in particular a mean weighted average precision of 0.868 ± 0.00862 (with a 99% of confidence level) and a mean weighted average recall of 0.84 ± 0.00729 (with a 99% of confidence level). To evaluate the performance of MobileNetV2 as a feature extractor, we conducted a comparative study with other small backbones. Further analysis of CNN activation using Grad-Cam has also been done. Obtained results show that our method remains effective even when using a small network and medium- to low-resolution images. MobileNetV2-based CNN size is smaller, and computational cost better, compared to the other CNNs, with similar performance results. Finally, detected surface damages have also been plotted on a geographic map, giving a global view of their distribution.
Nominal Context-Free Behaviour

This thesis investigates and proposes models for programming and verifying adaptive software at different abstraction levels. First, we design the kernel of a programming language, endowed with primitives for programming the adaptation to different working environments. We provide the language with a type and effect system that allows us to statically prove properties of the behaviour of the program when plugged in different execution environments. Then we extend our language to program the use of the resources currently available in the environment. In this case, the identity and the number of resources is unknown a-priori. The previous analysis technique needs to be extended to capture the behaviour of these programs. We exploit nominal techniques in the literature to propose novel automata models that represent the behaviour and the properties of programs that use an unbound number of unknown resources as (regular and context-free) set of traces. The theoretical properties of these automata are investigated and related with static program verification. We prove that we are able to check regular properties of the usage patterns of the resources when resource reuse is inhibited.
Parallel Patterns for Adaptive Data Stream Processing

In recent years our ability to produce information has been growing steadily, driven by an ever increasing computing power, communication rates, hardware and software sensors diffusion. is data is often available in the form of continuous streams and the ability to gather and analyze it to extract insights and detect patterns is a valuable opportunity for many businesses and scientific applications. e topic of Data Stream Processing (DaSP) is a recent and highly active research area dealing with the processing of this streaming data. e development of DaSP applications poses several challenges, from efficient algorithms for the computation to programming and runtime systems to support their execution. In this thesis two main problems will be tackled: • need for high performance: high throughput and low latency are critical requirements for DaSP problems. Applications necessitate taking advantage of parallel hardware and distributed systems, such as multi/manycores or cluster of multicores, in an effective way; • dynamicity: due to their long running nature (24hr/7d), DaSP applications are affected by highly variable arrival rates and changes in their workload characteristics. Adaptivity is a fundamental feature in this context: applications must be able to autonomously scale the used resources to accommodate dynamic requirements and workload while maintaining the desired Quality of Service (QoS) in a cost-effective manner. In the current approaches to the development of DaSP applications are still missing efficient exploitation of intra-operator parallelism as well as adaptations strategies with well known properties of stability, QoS assurance and cost awareness. ese are the gaps that this research work tries to fill, resorting to well know approaches such as Structured Parallel Programming and Control eoretic models. e dissertation runs along these two directions.
HILO: High-level and Low-level Co-design, Evaluation and Acceleration of Feature Extraction for Visual-SLAM using PYNQ Z1 Board

Image features are widely employed in embedded computer vision applications, from object identification and tracking to motion estimates, 3D reconstruction, and visual simultaneous localization and mapping (VSLAM) applications. Due to the real-time needs of such applications over a continual stream of input data, efficient feature extraction and description is critical. Significant-speed processing is often associated with high power consumption, yet embedded systems are mostly power and resource-limited, making the development of power-aware and compact solutions all the more important. The performance of the low-cost feature detection and description algorithms implemented on particular embedded devices is evaluated in this work (embedded processing units, GPUs, and FPGAs). We implemented the ORB-based feature extraction hardware accelerator using PYNQ overlay in the embedded PYNQ Z1 board. We demonstrated that a speedup of 8.38x was achieved utilizing a hardware-accelerated core compared to the algorithm running on a processor-based software solution.
Accurate Extrinsic Calibration of LiDAR and Camera with Refined Vertex Features

LiDARs and cameras are widely used in many research fields, due to the complementarity of their data. Calibrating the extrinsic parameters of LiDAR frame and camera frame is essential for fusing the two kinds of data. Calibration methods based on a calibration board extract geometry features from point clouds and images, then build geometry constraints to estimate the extrinsic parameters. In this paper, we exhaustively analyze the measurement characteristics of LiDARs that would introduce notable negative effect on the calibration, including the noise of depth measurement and the divergence of laser beams. Therefore, we propose a refining method for vertex features from LiDAR point clouds using the prior information of the board, which can effectively mitigate the effect of systematic measurement errors and improve the accuracy of the calibration results. Meanwhile, our calibration method reduces the minimal number of calibration datasets to one, which promotes the efficiency of calibration processes. Besides, we propose an objective and independent evaluation method for target-based calibration methods. Extensive experiments and comparisons with state-of-the-art methods show that using refined vertex features can notably improve the accuracy and efficiency of extrinsic parameter calibration.
Reply to Comment by Anand et al. on “Groundwater Affects the Geomorphic and Hydrologic Properties of Coevolved Landscapes”

Here we acknowledge an omission and offer a perspective on the importance of domain size for the simple landscape evolution model that appears in our original manuscript. For completeness, the domain size should have been included in the nondimensionalization. However, it can be shown to be of little consequence in the region of the parameter space that we examined. Furthermore, we show that our choice of nondimensionalization (with landscape evolution process rates rather than domain size) allows for a clearer view of controls on intrinsic features of the results, including valley spacing and channel head contributing areas, which were the focus of our original analysis.
Diffuse gamma-ray emission near the young massive cluster NGC 3603

We report the Fermi Large Area Telescope's detection of extended gamma-ray emission towards the direction of the young massive star cluster NGC 3603. The emission shows a hard spectrum with a photon index of 2.3 from 1 GeV to 250 GeV. The large size and high luminosity of this structure make it unlikely a pulsar wind nebular. On the other hand the spatial correlation with the ionised gas indicate a hadronic origin. The total cosmic ray (CR) protons energy are estimated to be of the order $10^{50} ~\rm erg$ assuming the gamma-ray are produced in the interaction of CRs with ambient gas . The environment and spectral features show significant similarity with the Cygnus cocoon. It reveals that the young star clusters may be a gamma-ray source population and they can potentially accelerate a significant fraction of the Galactic cosmic rays.
AW-FLoc: An Adaptive Weighted Fusion Localization Algorithm Based on CSI and Multidirectional Image

Localization algorithms play an important role in various location-based services (LBS), including production, public services, living, entertainment, and social networks. In order to improve the accuracy of the indoor positioning algorithm, this paper proposes an adaptive weight-based channel state information (CSI) and multi-azimuth image fusion positioning method to solve the limitation of single-sensor positioning. In order to reduce the image dimension and weaken the interference of low-texture images on fusion features, an image feature fusion algorithm based on texture degree evaluation (TDE-IFF) is proposed. In TDE-IFF, the texture evaluation algorithm assigns weights to images of different orientations and fuses image features of opposite orientations based on the weights to obtain a vectorized representation of multi-orientation images. On this basis, this paper proposes an adaptive weight-based fusion location framework (AW-FLoc), which measures CSI features and multi-directional image features through a gating network to adaptively enhance the effective signal features of different fingerprint points. This improves the accuracy of the fusion positioning algorithm. Experiments show that the fusion positioning based on AW-FLoc achieves better positioning performance, and the average error in the comprehensive office scene is 0.61m.
Is wifi 802.11mc fine time measurement ready for prime-time localization?

WiFi's fine time measurement (FTM) based ranging protocol has set the stage for mass adoption of location-aware applications and services in WiFi-pervading enterprise and consumer ecosystems. However, the lack of deployment of such commercial-scale localization solutions has motivated us to conduct a comprehensive experimental study that aims to verify whether WiFi's FTM is indeed ready for prime-time localization. With heterogeneity in operation (devices, environments, and spectrum) being the fundamental essence of commercial deployments, our study focuses on FTM's ability to deliver useable localization under such practical conditions. Being a first of its kind, our study reveals several interesting insights for practical operation of FTM, with the most critical of them being its inability to eliminate substantial offsets in estimated ranges between heterogeneous devices and configurations that degrade performance significantly (up to 20 m error). Albeit a negative result for FTM's readiness, we also propose a simple but promising remedy - an over-the-top auto-calibration solution that allows every WiFi device, when it enters an enterprise environment, to self-calibrate its offsets on-demand, thereby salvaging FTM to render it useful (median error of 2 m) for localization.
Smart Continuous Glucose Monitoring Prediction with Grid Long Short-Term Memory

Diabetes is one of the four diseases that are endangering human health, which affects nearly 10% of the world's adults. To solve the continuous glucose monitoring data prediction problem, machine learning methods, including ARIMA, RNN, LSTM, stack LSTM have been applied in CGM prediction. Nevertheless, most of these methods have their deficiency of insufficient accuracy and are sensitive to noise. This paper uses Grid Long Short-Term Memory (LSTM) to predict continuous glucose monitoring data and improve the accuracy obviously. First, the dataset of 30 patients’ glucose is preprocessed and is divided into two groups, training set, and test set. To eliminate the negative effect of model architecture complexity, the paper enhances and formats the data to lower the difficulty of the processing part. Then, the proposed Grid LSTM is constructed. Furthermore, the dropout optimization and batching strategy are used to lower lost during training. Finally, the model is tested as well as the lost are calculated and characterized by RMSE and CG-EGA, which is an index of BG level accuracy. To verify the effectiveness of the proposed method, Grid LSTM is compared with ARIMA method. The experimental results show the ARIMA method has a RMSE level of 42.492 whilst the Grid LSTM method has a RMSE level of 33.858, our analysis demonstrates that the proposed method obviously outperforms related baselines, LSTM, RNN, ARIMA, for predicting continuous glucose monitoring.
Expectation of primordial gravity waves generated during inflation

The inflationary paradigm is extremely successful regarding predictions of temperature anisotropies in the CMB. However, inflation also makes predictions for a CMB B-mode polarization, which has not been detected. Moreover, the standard inflationary paradigm is unable to accommodate the evolution from the initial state, which is assumed to be symmetric, into a nonsymmetric aftermath. In [G. Le\'on, A. Majhi, E. Okon, and D. Sudarsky, Phys. Rev. D 96, 101301(R), (2017)], we show that the incorporation of an element capable of explaining such a transition drastically changes the prediction for the shape and size of the B-mode spectrum. In particular, employing a realistic objective collapse model in a well-defined semiclassical context, we find that, while predictions of temperature anisotropies are nor altered (with respect to standard predictions), the B-mode spectrum gets strongly suppressed---in accordance with observations. Here, we present an in-depth discussion of that analysis, together with the details of the calculation.
Parametric Solutions to the Generalized Discrete Yakubovich‐Transpose Matrix Equation

This paper is concerned with the complete parametric solutions to the generalized discrete Yakubovich‐transpose matrix equation X − AXTB = CY. which is related with several types of matrix equations in control theory. One of the parametric solutions has a neat and elegant form in terms of the Krylov matrix, a block Hankel matrix and an observability matrix. In addition, the special case of the generalized discrete Yakubovich‐transpose matrix equation, which is called the Karm‐Yakubovich‐transpose matrix equation, is considered. The explicit solutions to the Karm‐Yakubovich‐transpose matrix equation are also presented by the so‐called generalized Leverrier algorithm. At the end of the paper, two examples are given to show the efficiency of the proposed algorithm.
Quality Detection Method for Hyperspectral Qukuai Fermentation

Baijiu is deeply loved by people because of its special brewing process, and Qukuai is the soul of the brewing process, the starter of solid-state brewing, and the key factor affecting the quality of Baijiu. In the process of wine making and fermentation, starter fermentation is a dynamic fermentation process coordinated by various mixed substances, microorganisms and fungi. At present, artificial experience is still used to judge the fermentation quality of Qukuai The degree of manual intervention is high, the quality fluctuates greatly, and the cost of manpower and time is high, so it is impossible to carry out quantitative and qualitative detection. At the same time, there are many factors affecting the fermentation quality of Qukuai. In order to meet the intelligent brewing and intelligent production needs of online detection and big data analysis of starter fermentation, it is difficult to simply analyze the specific microbial content and species in different fermentation stages to detect the fermentation status. Therefore, this paper proposes to apply hyperspectral nondestructive testing technology and correlation clustering analysis technology to starter fermentation. On the premise of not affecting the fermentation environment in the starter room and not damaging the starter fermentation substrate, pectral analysis and cluster analysis technology are used to automatically mine the change state of substances in the fermentation process of Qukuai so as to realize the real-time online nondestructive detection of starter fermentation quality.
A Language-based Approach to Distributed Resources

Modern computing paradigms for distributed applications advocate a strong control on shared resources available on demand in order to guarantee their correct usages. An illustrative example of such paradigms is Cloud Computing. In this dissertation, we study formal models for distributed applications, paying particular attention to resource usage analysis. Formal methods for specifying and analysing different aspects of resource management could play an important role for the widespread usages of distributed resources. They provide not only the theoretical framework to understand the stages underlying the design and implementation issues, but also the mathematically-based techniques for the specification and verifications of properties of such systems. In this dissertation, we introduce two models, called λ-calculus and G-Local π-calculus, which are extensions of λ-calculus and π-calculus respectively. The λ-calculus is an extension of concurrent λ-calculus enriched with suitable mechanisms to express and enforce application-level security policies governing usages of resources available on demand in the clouds. We focus on the server side of cloud systems, by adopting a pro-active approach, where explicit security policies, which are expressed as a set of execution traces, regulate server’s behaviour. By providing an abstract cloud semantics, we ensure that enforcing security policies embedded in cloud applications is sound. The G-Local π-calculus is built over the standard π-calculus by introducing new primitives to manage resources. Unlike the previous model, where resources are highly abstract, resources in this approach are modelled as stateful entities with local states and global policies. A high degree of loose coupling among applications and resources is achieved through the publish/subscribe model. Furthermore, we develop two static, language-based techniques, namely Control Flow Analysis (CFA) and Type and Effect Systems, to reason about resource usages and therefore able to predict bad usages of resources. The CFA mainly focuses on reachability properties related to resource usages. It computes an over-approximation of resource usages of applications. As a result, if the approximation does not contain bad usages, then it guarantees that applications correctly use resources. The type and effect system provides a closer view of resource behaviour. Resource behaviour is extracted in the form of side effect of the type system. We exploit side effect to verify regular linear time properties, expressed by Linear Time Logic formulas, of resource usages.
The landscape of non-canonical ‘only’ in German

Abstract This paper is part of a series of experimental studies that try to empirically chart the landscape of non-canonical uses of the particle nur ‘only’ in German, aiming at a comprehensive picture of how those uses can affect the interpretation of utterances in different clausal environments. While previous studies have focused on modal particle uses of German ‘only’ in wh-questions and imperatives, the present paper focuses on a non-canonical use in adverbial contexts, which has not been studied experimentally before.
Semidefinite Optimization and Convex Algebraic Geometry

This book provides a self-contained, accessible introduction to the mathematical advances and challenges resulting from the use of semidefinite programming in polynomial optimization. This quickly evolving research area with contributions from the diverse fields of convex geometry, algebraic geometry, and optimization is known as convex algebraic geometry. Each chapter addresses a fundamental aspect of convex algebraic geometry. The book begins with an introduction to nonnegative polynomials and sums of squares and their connections to semidefinite programming and quickly advances to several areas at the forefront of current research. These include semidefinite representability of convex sets, duality theory from the point of view of algebraic geometry, and nontraditional topics such as sums of squares of complex forms and noncommutative sums of squares polynomials. Suitable for a class or seminar, with exercises aimed at teaching the topics to beginners, Semidefinite Optimization and Convex Algebraic Geometry serves as a point of entry into the subject for readers from multiple communities such as engineering, mathematics, and computer science. A guide to the necessary background material is available in the appendix. Audience This book can serve as a textbook for graduate-level courses presenting the basic mathematics behind convex algebraic geometry and semidefinite optimization. Readers conducting research in these areas will discover open problems and potential research directions. Contents: List of Notation; Chapter 1: What is Convex Algebraic Geometry? ; Chapter 2: Semidefinite Optimization; Chapter 3: Polynomial Optimization, Sums of Squares, and Applications; Chapter 4: Nonnegative Polynomials and Sums of Squares; Chapter 5: Dualities; Chapter 6: Semidefinite Representability; Chapter 7: Convex Hulls of Algebraic Sets; Chapter 8: Free Convexity; Chapter 9: Sums of Hermitian Squares: Old and New; Appendix A: Background Material.
Solid fuel briquette from biomass: Recent trends

Abstract Solid fuel briquette from biomass materials has gained considerable attention as sustainable replacement source of energy. The characteristics of briquette are imperative to ensure its effectiveness as fuel in a long run. This paper is aimed to summarize the key properties of solid briquette made from various natural and waste-based resources, and to highlight the future research directions in the briquette manufacture. The commentary is expected to shed insight towards the greener, cleaner, and efficient briquette materials.