Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional simulations applied to recoverable reserves

A very common and long-standing problem in operating mines is the achievement of predicted grades and tonnages at the mill head as recovered from the mining operation. The discrepancies found between what is expected according to the Long-term block model and mine production are frequently significant. For evaluation and planning purposes, it is desirable to minimize these discrepancies. There are several factors affecting these differences, which include the smoothing effect of a moving average estimator such as kriging, the SMU being variable in volume from blast to blast in the sequence mined, sometimes varying due to geological factors, sometimes due to the geometry of the pit, or sometimes due to the local distribution of the mineralization, and other aspects of the volume-variance problem. Thus, typically the predicted grade-tonnage curves do not match the grades and tonnages of the material recovered. Conditional simulations can be used to assess the uncertainty of the predicted grade in terms of minable reserves. For Open Pit mines, this is done by interpreting the optimization process as a Transfer Function between the Long-term block model and the material predicted to be selected within the pit. The usefulness of using any popular pit optimizer in conjunction with several simulated grade models of the deposit, yielding corresponding possible minable reserves, is discussed. The more refined assessment of uncertainty and sensitivity analyses using conditional simulations performed for deposit evaluation or development planning comes only with at a higher labor and time cost. This paper will discuss the practical situations where the extra effort is deemed to pay for the extra costs, as well as practical issues regarding the successful implementation of the procedures involved. The work presented here has been developed for open pit mines, but the concepts are also applicable to underground operations.
Convolution Neural Network Application in Kidney Tumor Segmentation on CT Images

In this paper, we propose an novel network model which is similar to V-net and prove its superiority and efficiency in tumor segmentation. And The model of segmentation of Kidney is Dense V-Network [1]. Then we ensemble the results of two networks together to get a final predict result for kidney and tumor. In particularly, we apply a series of method to image preprocessing, which is proved to be effective in improving dice.
Design and synthesis of mechanical systems with coupled units

Ce memoire traite de nouveaux principes de conception qui sont inspires par le couplage de deux unites representant les differentes structures mecaniques. Les criteres de conception optimale et les types d'unites combinees sont differents. Cependant, toutes les tâches sont considerees dans le couplage de ces unites. L'examen critique presente dans le premier chapitre est divise en trois sections en raison de la nature des problemes traites: les robots marcheurs, les compensateurs de gravite et les robots collaboratifs. Le deuxieme chapitre traite du developpement de robots marcheurs a actionneur unique, concus par couplage de deux mecanismes ayant les fonctionnants de jambe. Basee sur l'algorithme genetique, la synthese proposee permet d'assurer la reproduction de la trajectoire obtenue a partir de la marche humaine. Par l'ajustement des parametres geometriques des unites concues, il devient possible non seulement d'assurer une marche du robot a des pas variables, mais egalement de monter les escaliers. Ensuite la conception et la synthese des equilibreurs pour les robots sont consideres. Un costume robotise type exosquelette permettant d'aider aux personnes transportant des charges lourdes est examine dans le chapitre suivant La conception proposee presente une symbiose d'un support rigide et leger et d'un systeme de câbles monte sur ce support. L'etude et l'optimisation statique et dynamique ont conduit aux tests sur un mannequin. Le dernier chapitre propose l'etude et 'optimisation d'un systeme couple, comprenant un manipulateur equilibre a commande manuelle et un robot collaboratif. Le but d'une telle cooperation est de manipuler de lourdes charges avec un cobot.
No-Reference Quality Assessment of Stereoscopic Videos With Inter-Frame Cross on a Content-Rich Database

With the wide application of stereoscopic video technology, the quality of stereoscopic video has attracted people’s attention. Objective stereoscopic video quality assessment (SVQA) is highly challenging, but essential, particularly the no-reference (NR) SVQA method, where reference information is not needed and a large number of samples are required for training and testing sets. However, as far as we know, there are only a few samples in the established stereo video database, which is unsuitable for NR quality assessment and seriously hampers the development of NR-SVQA method. For these difficulties that we encountered, we carry out a comprehensive subjective evaluation of stereoscopic video quality in our newly established TJU-SVQA databases that contain various contents, mixed resolution coding and symmetrically/asymmetrically distorted stereoscopic videos. Furthermore, we propose a new inter-frame cross map to predict the objective quality scores. We compare and analyze the performance of several state-of-the-art 2D and 3D quality evaluation methods on our new databases. The experimental results on our established databases and a public database demonstrate that the proposed method can robustly predict the quality of stereoscopic videos.
SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)

Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi.
Capture, representation, and rendering of 3D audio for virtual and augmented reality

Devices for augmented and virtual reality (AR/VR) find applications in gaming, entertainment, education, design and science. An AR/VR headset consists of a head-mounted display, headphones or loud-speakers, and a computing platform. The spatial audio system plays integral party of achieving the realism. In this article we present an overview of our work on technologies for the 3D audio targeting AR/VR scenarios.
The Socratic method reloaded: How to make it work in large classes?

The Socratic method with its constant questioning poses a challenge to students, more so than any other teaching method. By the same token, it teaches law students what they need to know: legal analysis, critical thinking and verbal skills. In court, in the negotiation room, in the lecture theatre, a lawyer needs to be able to react to new allegations or novel arguments on the spot. Practice makes perfect. Where, if not at law school, is this practice supposed to take place? On the job would be too late as employers expect fully operational recruits from day one. Having said that, the Socratic method is not a panacea. The myriad of skills a good lawyer should be able to master can be best acquired by a mix of teaching styles.
A Survey of the Use of Test Report in Crowdsourced Testing

With the rise of crowdsourced software testing in recent years, the issuers of crowd test tasks can usually collect a large number of test reports after the end of the task. These reports have insufficient validity and completeness, and manual review often takes a lot of time and effort. The crowdsourced test task publisher hopes that after the crowdsourced platform collects the test report, it can analyze the validity and completeness of the report to determine the severity of the report and improve the efficiency of crowdsourced software testing. In the past ten years, researchers have used various technologies (such as natural language processing, information retrieval, machine learning, deep learning) to assist in analyzing reports to improve the efficiency of report review. We have summarized the relevant literature of report analysis in the past ten years, and then classified from report classification, duplicate report detection, report prioritization, report refactoring, and summarized the most important research work in each area. Finally, we propose research trends in these areas and analyze the challenges and opportunities facing crowdsourced test report analysis.
Vernier: Accurate and Fast Acoustic Motion Tracking Using Mobile Devices

Acoustic motion tracking has been viewed as a promising user interaction technique in many scenarios such as Virtual Reality (VR), Smart Appliance, video gaming, etc. Existing acoustic motion tracking approaches, however, suffer from long window of accumulated signal and time-consuming signal processing. They are inherently difficult to achieve both high accuracy and low delay. In this paper, we present Vernier, an efficient and accurate acoustic tracking method based on commodity mobile devices. We design a new approach to efficiently and accurately derive phase change and thus moving distance. Vernier significantly reduces the tracking delay/overhead by removing the complicated frequency analysis and long window of signal accumulation, while keeping a high tracking accuracy. We implement Vernier on Android, and evaluate its performance with COTS mobile devices including Samsung Galaxy S7 and Sony L50t. Experimental results show that Vernier outperforms previous approaches with a tracking error less than 4 mm. The tracking speed achieves 3× improvement to the previous phase based approaches and 10× to Doppler Effect based approaches. Vernier is also validated in applications like controlling and drawing, and we believe it is generally applicable in many real applications.
Theoretical and Experimental Perceived Depths in Arc 3D Displays: On/Off Switching Using Liquid-Crystal Active Devices

In this article, we obtain precise theoretical expressions and various experimental dependences of the perceived depth in an arc 3D display and find that they coincide. We also propose two liquid-crystal (LC) active devices that can switch arc 3D images on and off by changing either the amplitude or frequency of a high applied voltage and clarify their main characteristics. The perceived depth in an arc 3D display can be increased by increasing the arc radius and/or adjusting the illumination angle so that it approaches the viewing angle vertically. The perceived depth remains unchanged even when the observer position changes in the horizontal and/or depth directions, resulting in a continuous motion parallax.
Deep Mixture Model-Based Land Surface Temperature Retrieval for Hyperspectral Thermal IASI Sensor

A deep mixture model was developed to retrieve land surface temperatures (LSTs) from infrared atmospheric sounding interferometer (IASI) observations. The IASI brightness temperature (Tb) data and the Advanced Very High Resolution Radiometer onboard MetOp (AVHRR/MetOp) LST data were randomly divided into training and test datasets, and a deep mixture model was constructed to simulate radiation transmission in order to invert the LST. The constructed model could evaluate dataset characteristics that included global features, local features, and time-domain predictions, covering most of the features of the satellite dataset. For the test datasets, the root mean square error (RMSE) indicated that the LST in Algeria and South Africa could be retrieved with an error of less than 2 K and 2.5 K, respectively. Compared with the AVHRR/MetOp LST product in March and December 2019 for Algeria and South Africa, the LST could be retrieved with the maximum RMSE of 2.5 K. The LST retrievals at nighttime had an RMSE of less than 2.0 K, which was superior to those retrieved during daytime for Algeria. This deep mixture model can be applied to time-series temperature prediction.
Energy-efficient Design of MTJ-based Neural Networks with Stochastic Computing

Hardware implementations of Artificial Neural Networks (ANNs) using conventional binary arithmetic units are computationally expensive, energy-intensive, and have large area overheads. Stochastic Computing (SC) is an emerging paradigm that replaces these conventional units with simple logic circuits and is particularly suitable for fault-tolerant applications. We propose an energy-efficient use of Magnetic Tunnel Junctions (MTJs), a spintronic device that exhibits probabilistic switching behavior, as Stochastic Number Generators (SNGs), which forms the basis of our NN implementation in the SC domain. Further, the error resilience of target applications of NNs allows approximating the synaptic weights in our MTJ-based NN implementation, in ways brought about by properties of the MTJ-SNG, to achieve energy-efficiency. An algorithm is designed that, given an error tolerance, can perform such approximations in a single-layer NN in an optimal way owing to the convexity of the problem formulation. We then use this algorithm and develop a heuristic approach for approximating multi-layer NNs. Classification problems were evaluated on the optimized NNs and results showed substantial savings in energy for little loss in accuracy.
External Joint Calibration of A Novel Multi-Modal Perception System

We design a new multi-modal perception fusion system for the whole scene, which can be divided into two parts. The first part is lidar, rgbameral, rgbamera2. The second part is RGB camera 3, RGB camera 4, depth camera. The combination of these three modes provides a close range (0.3-10 m) of environmental perception, and provides the texture and distance information of the environment in the cone-shaped blind area of lidar. Our main work is to design the experimental equipment of the proposed multi-modal perceptual fusion system, and to push forward the optimization theory with the re projection error as the objective function and analyze the multi-modal fusion sensing system. The graph structure is established, and the relative pose transformation between various modes is analyzed. In the back-end optimization part, MCPE (Minimally connected pose estimation) and PSE(Pose and structure estimation)are used to analyze and compare the system. In the experimental part, we use matlab to calibrate the binocular camera by minimizing the error of re projection. The lidar, RGB camera and depth camera are calibrated with the help of the multi-modal calibration tool (binding on ROS) proposed. It provides a solid foundation for the future experiments in perception of self-driving, UAV perception, medical field and military field.
The development of an efficient tyre testing procedure to gather data for the parameterisation of Magic Formula 6.1 tyre models

3 Acknowledgements . 4 Author’s Publications . 4 Author’s Patents . 4 Lists of Figures, Tables and Equations . 10 List of Figures . 10 List of Tables . 20 List of Equations . 22 Nomenclature . 24 TYDEX Standard . 24 Deviations from TYDEX Standard . 25 Axis System . 26 Calculated Channels . 27 Slip Ratio . 28 Sliding Power and Energy . 29 Glossary of Terms . 30 1.0 Introduction . 32 1.1 Background . 32 1.1.1 CAE Driven Vehicle Design Process . 32 1.1.2 The Role of the Tyre Model . 33 1.2 Aim and Objectives . 35 Aim . 35 Objectives . 35 1.3 Structure of Thesis . 35 2.0 Literature Review . 37 2.1 Pure Tyre Behaviour . 37 2.1.1 Tyre components . 37 2.1.2 Contact Patch Pressure Distribution . 39 2.1.3 Tyre Radii Definitions . 40 2.2 Tyre Testing Facilities . 43 2.2.1 Outdoor Testing . 43
Evaluating the Real-World Performance of 5G Fixed Wireless Broadband in Rural Areas

Rural communities are often neglected in the rollout of communication infrastructure, delaying or even preventing the delivery of common digital services that are now ubiquitous in modern society. Addressing this technological gap is of paramount importance. In this paper, we analyze the performance of two new and emerging 5G networking technologies, each capable of overcoming some of the challenges present in rural environments. Our analysis is based around a series of deployments of TV White Space (TVWS) and millimetre wave (mm Wave) technologies across rural parts of the UK. These deployments are enhanced with a custom designed monitoring platform that includes adaptive streaming video clients that enable the accurate assessment of end-user quality of experience. Our findings demonstrate that TVWS solutions, which operate on an unlicensed TV band, have clear potential for providing stable broadband services in those areas. Our results also show that although mmWave-based solutions can provide sub-gigabit-speed, they are vulnerable to excessive rainfall and are limited to short line-of-sight usage, making them less applicable to deployment in irregular terrain.
Passenger Response to Driving Style in an Autonomous Vehicle

Despite rapid advancements in automated driving systems (ADS), current HMI research tends to focus more on the safety driver in lower level vehicles. That said, the future of automated driving lies in higher level systems that do not always require a safety driver to be present. However, passengers might not fully trust the capability of the ADS in the absence of a safety driver. Furthermore, while an ADS might have a specific set of parameters for its driving profile, passengers might have different driving preferences, some more defensive than others. Taking these preferences into consideration is, therefore, an important issue which can only be accomplished by understanding what makes a passenger uncomfortable or anxious. In order to tackle this issue, we ran a human study in a real-world autonomous vehicle. Various driving profile parameters were manipulated and tested in a scenario consisting of four different events. Physiological measurements were also collected along with selfreport scores, and the combined data was analyzed using Linear Mixed-Effects Models. The magnitude of a response was found to be situation dependent: the presence and proximity of a lead vehicle significantly moderated the effect of other parameters. Stopping events also generated a higher level of response than non-stopping events. Finally, a statistically significant association between physiological responses and self-reported scores showed that such responses could potentially be used to indicate comfort or anxiety in future adaptive systems.
LiDAR cone detection as part of a perception system in a Formula student car

This project seeks to design and implement algorithms to detect the 3D position and color of cones in a track with a LiDAR sensor, within the framework of the construction of the first electric and autonomous car for Formula Student in Spain. The project includes the sensor distribution in the vehicle, the computational and communications system used, as well as the integration with ROS (Robot Operating System) to work in real time. An introduction to the LiDAR sensor that is used (Velodyne VLP-32C) is realized and a robust detection system is proposed, with different alternatives that can work in parallel: a system with only LiDAR and another with fusion of LiDAR and cameras.
Application of remote sensing for detecting plant disease using color and morphological features

Remote sensing provides the ability for relatively rapid and early detection of the spatial distribution of a plant disease using methods based on thermography and canopy reflectance in visual and near-infrared wavebands. By remote mapping using color imaging and subsequent data analysis routines, it is possible to realize early detection, identification, and quantification of different relevant plant diseases. In the present work, an applicability of remote sensing with digital color imaging for detecting plant disease is demonstrated. The color and morphological features are used for analysis and classification purposes. As a case study, the image processing is applied to the color photos of banana fields obtained by a manned aerial vehicle equipped with a high resolution camera. Major banana diseases (e.g. panama etc.) exhibit symptoms on leaf area in their earlier stage of infection. Change of color and morphology features act as criteria used to identify and classify the disease. The results of plant disease recognition and identification are demonstrated.
A drug association based inventory control system for ambulatory care

Abstract The purpose of the research is to explore an innovated drug inventory control system for medical organization to improve the system performance in terms of daily patients’ prescriptions and drugs flows. It is not easy to well control drugs inventory due to large numbers of stock keeping units and huge quantities of them in a medical organization. In order to maintain both the daily jobs of ambulatory care and the rights of the patients, the availability of all drugs keeping at a reasonable stock level for medical staffs and patients is required. Also, to avoid increase of cost due to over stocking to overcome the prediction of possible shortage of drugs is a must. In traditional control system, cost is always be considered as the decision variable. Based on the total cost of each item in an inventory management within a planning cycle, and to calculate out the quantity for that particular item within the total inventory, the total amount of inventory ratio for that item will also be derived and be classified to make different inventory management strategies for different catalogues. There are hundreds of variable pharmaceutical goods in medical organization, hence, large number of prescription data and critical information will be ignored if the inventory classification is determined by the traditional cost approach. This study used association rules analysis to mining the patient treatments database, in order to derive prescription-oriented association rules for improving cost-oriented categories inventory management. In this research, data mining technology is utilized to detect hiddenly issues, which might cause drug shortages. Around 480,000 data, ranges from January to March, 2010, were extracted from Taiwan’s National Health Insurance files, and applied with multi-level association rule analysis, 19 association rules were obtained. In conclusion, the potential relationship in a huge database, found by data mining technique, can be used to support managing staff to adjust inventory policies accordingly.
IoT Based Landmine Detection Robot With GPS System

Now a day in places like Afghanistan and Iraq we know that land mines are causing serious threat to the lives of civilians. A land mine is an explosive device that designed to destroy or disable enemy and hided under or on the surface of ground, especially in mine-affected countries like Afghanistan and Iraq. Most land mines are laid on just below the surface of ground and are activated by pressure or trip-wire. Usually most of the land mines will contain many metallic parts, which can be made use of in their detection. The mines which are embedded amid the war time may stay undetected. As the name suggests detection is done using Metal Detector and hence can be done further away from the mine carefully. Mine detection robot will go from this path where solders passing the roads in mine areas. This is the method in which special purpose of robot are use to find landmines without setting foot into the ground offering safe and efficient way of landmine detection. There are some methods for detection of land mines, such radar bullets, Biological method and mechanical method. These methods are dangerous and risk is involved to life of solider. A safe method for detecting land mines is “Landmine detection robot”, where we are dealing with IoT as well as GPS technique.