Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Machine Vision for Service Robotics

In this thesis the vision architecture ROVIS (RObust machine VIsion for Service robotics ) is suggested. The purpose of the architecture is to improve the robustness and accuracy of visual perceptual capabilities of service robotic systems. In comparison to traditional industrial robot vision where the working environment is predefined, service robots have to cope with variable illumination conditions and cluttered scenes. The key concept for robustness in this thesis is the inclusion of feedback structures within the image processing operations and between the components of ROVIS. Using this approach a consistent processing of visual data is achieved.Specific for the suggested vision system are the novel methods used in two important areas of ROVIS: definition of an image ROI, on which further image processing algorithms are to be applied, and robust object recognition for reliable 3D object reconstruction. The ROI definition process, build around the well known "bottom-up top-down" framework, uses either pixel level information to construct a ROI bounding the object to be manipulated, or contextual knowledge from the working scene for bounding certain areas in the imaged environment. The object recognition and 3D reconstruction chain is developed for two cases: region and boundary based segmented objects. Since vision in ROVIS relies on image segmentation on each processing stage, that is image ROI definition and object recognition, robust segmentation methods had to be developed. As said before, the robustness of the proposed algorithms, and consequently of ROVIS, is represented by the inclusion of feedback mechanisms at image processing levels. The validation of the ROVIS system is performed through its integration in the overall control architecture of the service robot FRIEND. The performance of the proposed closed-loop vision methods is evaluated against their open-loop counterparts.
Time Series Clustering Based on Singularity

With relevant theories on time series clustering, the thesis makes research into similarity clustering process of time series from the perspective of singularity and proposes the time series clustering based on singularity applying K-means and DBScan clustering algorithms according to the shortage of traditional clustering algorithm. In accordance with the general clustering process of time series, time series clustering based on singularity and K-means are made respectively to get different clustering results and make a comparison, thus proving that similarity clustering research of time series from the perspective of singularity can better find out people's concern on time series.
Education, social class and social exclusion

This paper begins by noting the centrality of the issue of working-class school failure within the sociology of education in Britain. It argues that recent government policies have taken insufficient account of sociological work on the impact of social class on educational success and failure. It also suggests that sociologists should pay more attention to middle-class education. The importance of this is illustrated through reference to research on the trajectories of pupils receiving different forms of secondary education. The paper then argues that social inclusion policies need to address a variety of forms of middle-class self-exclusion from mainstream public provision as well as working-class social exclusion. It concludes that education policy needs to be located within a broader social policy framework.
EDoS-ADS: An Enhanced Mitigation Technique Against Economic Denial of Sustainability (EDoS) Attacks

Cloud computing is an essential technology for the future of the Information Technology (IT) industry. However, the cloud security level is identified as the biggest challenge facing cloud providers and a major concern for cloud adopters. Economic Denial of Sustainability (EDoS) attack is one of the major threats targeting the cloud. The EDoS attack exploits the cloud elasticity and auto scaling features to charge a cloud adopter bill an excessive amount of cost leading to large-scale service withdrawal or bankruptcy. A novel reactive approach referred to as the EDoS Attack Defense Shell (EDoS-ADS) is proposed to mitigate EDoS attacks while taking into account most of the existing mitigation techniques drawbacks. Specifically, the EDoS-ADS has the ability to identify the legitimacy of clients even if they belong to a Network Address Translation (NAT) based network. Thus, EDoS-ADS is the first known technique that effectively prevents an EDoS attack from blocking an entire NAT-based network from accessing the cloud. The EDoS-ADS effectiveness in terms of response time, CPU utilization, throughput, and cost is evaluated using a CloudSim simulator. The simulation results show that EDoS-ADS outperforms other mitigation techniques, and successfully differentiates between legitimate and attacker clients even when they belong to the same NAT-based network.
Interactive QR code beautification with full background image embedding

QR (Quick Response) code is a kind of two dimensional barcode that was first developed in automotive industry. Nowadays, QR code has been widely used in commercial applications like product promotion, mobile payment, product information management, etc. Traditional QR codes in accordance with the international standard are reliable and fast to decode, but are lack of aesthetic appearance to demonstrate visual information to customers. In this work, we present a novel interactive method to generate aesthetic QR code. By given information to be encoded and an image to be decorated as full QR code background, our method accepts interactive user's strokes as hints to remove undesired parts of QR code modules based on the support of QR code error correction mechanism and background color thresholds. Compared to previous approaches, our method follows the intention of the QR code designer, thus can achieve more user pleasant result, while keeping high machine readability.
Applying Machine Learning to the Choice of Size Modifiers

People use different size modiﬁers to refer to different object sizes; “the long table” is likely to be a different table from “the small table”. However, the details inﬂuencing the selection of size modiﬁer have not yet been uncovered. When is something “long”, and when is something “small”? We introduce a connection between the visible dimensions of objects and the kinds of language people use to refer to them. First, we conduct an experiment to elicit size-denoting modi-ﬁers from images of real world objects. We ﬁnd that we are able to effectively model the relationship between dimensional features and modiﬁer choice using decision trees. The images are then used as input to an object segmentation algorithm, and we compare how well we can predict speakers’ behavior using the real world measurements of the pictured objects and the image pixel-based measurements. We ﬁnd that real world measurements are the best predictors of modiﬁer choice, suggesting that people infer real world size features from images. However, automatically extracted pixel measurements do perform relatively well at predicting modiﬁer choice, offering a potential connection between computer vision and natural language. When speaker identity is taken into account, modiﬁer choice can be predicted with even greater accuracy (around 75%), and the difference between automatically extracted and real world measurements is no longer signiﬁcant.
On the fragmentation of filaments in a molecular cloud simulation

The fragmentation of filaments in molecular clouds has attracted a lot of attention as there seems to be a relation between the evolution of filaments and star formation. The study of the fragmentation process has been motivated by simple analytical models. However, only a few comprehensive studies have analysed the evolution of filaments using numerical simulations where the filaments form self-consistently as part of molecular clouds. We address the early evolution of pc-scale filaments that form within individual clouds. We focus on three questions: How do the line masses of filaments evolve? How and when do the filaments fragment? How does the fragmentation relate to the line masses of the filaments? We examine three simulated molecular clouds formed in kpc-scale numerical simulations performed with the FLASH code. We compare the properties of the identified filaments with the predictions of analytic filament stability models. The line masses and mass fraction enclosed in the identified filaments increase continuously after the onset of self-gravity. The first fragments appear early when the line masses lie well below the critical line mass of Ostriker's hydrostatic equilibrium solution. The average line masses of filaments identified in 3D density cubes increases far more quickly than those identified in 2D column density maps. Our results suggest that hydrostatic or dynamic compression from the surrounding cloud has a significant impact on the early dynamical evolution of filaments. A simple model of an isolated, isothermal cylinder may not provide a good approach for fragmentation analysis. Caution must be exercised in interpreting distributions of properties of filaments identified in column density maps, especially in the case of low-mass filaments. Comparing or combining results from studies that use different filament finding techniques is strongly discouraged.
Intelligent Reflecting Surface for Multicell MIMO Communications

Intelligent reﬂecting surface (IRS) is envisioned to be a disruptive and revolutionizing technology that can achieve both spectral and energy efﬁcient wireless communications and provide a radio-controllable environment. In speciﬁc, IRS is a planar that consists of a large number of passive reﬂection elements that that induce phase shifts on the impinging electromagnetic waves so that the reﬂected signal can be constructively added at the desired user or destructively at the unintended users. In this paper, we propose to deploy an IRS at cell boundary of multiple cells to assist the transmission for cell-edge users, and also help alleviate the inter-cell interference, which is a crucial issue in multicell communication systems. We aim to maximize the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the BSs and the phase shifts at the IRS subject to each BS’s power constraint and unit modulus constraint. Both BSs and users are equipped with multiple antennas, which can enhance the spectral efﬁciency. Due to the non-convexity of the problem, we ﬁrst reformulate it into an equivalent one, which is solved by using the block coordinate descent algorithm where precoding matrices and phase shifts are alternatively optimized. The optimal precoding matrices can be obtained in closed form when ﬁxing the phase shifts. Two efﬁcient algorithms are proposed to solve the phase shift optimization problem, i.e., Majorization-Minimization (MM) Algorithm and Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solution. Finally, simulation results conﬁrm the advantages of introducing IRS in enhancing the cell-edge user performance.
TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfer

As researchers teach robots to perform more and more complex tasks, the need for realistic simulation environments is growing. Existing techniques for closing the reality gap by approximating real-world physics often require extensive real world data and/or thousands of simulation samples. This paper presents TuneNet, a new machine learning-based method to directly tune the parameters of one model to match another using an *iterative residual tuning* technique. TuneNet estimates the parameter difference between two models using a single observation from the target and minimal simulation, allowing rapid, accurate and sample-efficient parameter estimation. The system can be trained via supervised learning over an auto-generated simulated dataset. We show that TuneNet can perform system identification, even when the true parameter values lie well outside the distribution seen during training, and demonstrate that simulators tuned with TuneNet outperform existing techniques for predicting rigid body motion. Finally, we show that our method can estimate real-world parameter values, allowing a robot to perform sim-to-real task transfer on a dynamic manipulation task unseen during training. Code and videos are available online at this http URL.
Renoprotective impact of angiotensin 1-7: Is it certain?

The two important arms of renin angiotensin system (RAS) are angiotensin II (Ang II) and angiotensin1-7 (Ang1-7). Both of these peptides are present in the kidney, while the renal hemodynamic responses to these peptides act differently in kidney circulation. For this short-review, we used a variety of sources including PubMed, Google Scholar, and Scopus. Although in normal physiological condition, Ang1-7 has been known as an inactive agent in the renal system, however in past years many experimental and clinical reports indicated the protective role of Ang1-7 in renal hemodynamics and functions under different circumstances. In the current article, the possible renoprotective role of Ang1-7 was briefly reviewed.
Identification of the Main Elements of Single Payer System; A Comparative Study

Background: Progress towards universal coverage requires adequate capital in health sector. Investing and optimal allocation of resources in this sector will contribute to the development and reduction of poverty in countries in order to achieve the goals of health system. Therefore, the more people contribute to risk sharing, we have lower financial risks in facing the issue. The single payer system as a public health coverage model seeks to expand the insurance coverage scope at community level. The present study aimed to identify the main elements of S-PS to conduct a comparative study. Methods: A comparative study was conducted to describe the fundamental of financing and the provision of services in selected countries - Germany, Thailand, Turkey, and Colombia, as well as to achieve the main elements of S-PS. In addition, the health system of Iran has been studied. The basis for selection of countries was health system Garden typology. The main criteria for selection or rejection of studies were the separation of health services provider from financial functions; has allowed a single department to purchasing process. Results: single payer system in two functions of health system, namely, financing and providing health care; consolidation resources (reducing fragmentation by creating a single pooled fund and achieve massive purchase of health care through the insurance agent as single purchaser) and ensuring community health (delivery of services by the network of providers represented by Health Promotion Organization) represents 12 main organizational elements. Conclusion: the multiple insurers and payers of health care in Iran are both inequity and ineffective. And its integration is not a simple task. Iranian financing policies should aimed to achieving universal health coverage by creating greater risk pooling and becoming aware of the important tasks of insurance system; take advantage of the strength in numbers, setting the principles of cross-subsidy and preventing adverse reaction. It is important not to put together a long-term, coherent plan to reach the S-PS.
Gesture based control system

Virtual environments have always been considered as a means for more efficient human computer interaction by a diversified range of applications. Evolution of ubiquitous computing, current user interaction approaches with keyboard, mouse and pen are not sufficient for the still widening spectrum of Human computer interaction. Gloves and sensor based trackers are unwieldy, constraining and uncomfortable to use. Due to the limitation of these devices the useable command set based diligences is also limited. This paper centralizes on the efforts of implementing an application that employs computer vision algorithms and gesture recognition techniques which in turn results in developing a low cost interface device for interacting with objects in virtual environment using hand gestures.
Performance enhancement of multi-human tracking based on K-Shortest Paths by data reduction

Object tracking is a challenging problem and it has been improving dramatically in recent years. In this paper, we perform parallelized multi-object tracking system. Object tracking problem has 2 difficulties; one is to detect objects collect, and the other is to track collect using the collect object detection. Jerome et al. performed a multi-object tracking system using K-Shortest Paths to avoid these problems efficiently. However, it is difficult to calculate in parallel because of the iterations calculation of shortest paths on the graph while changing the weight of graph. In our method, we divided time intervals to apply KSP method from Probability Occupancy Map(POM), which is also obtained via using KSP method. Performance evaluation shows our algorithm is 5.4 times faster than the original KSP with 87% accuracy.
A research on information technology applied to improving performance for tourism enterprises.

The information technology has great influence on both the structure of the tourism industry and the strategy of the tourism enterprises in this age of big data. This paper focuses on the problem how information technology can be used to improve the performance of tourism enterprises. Considering the specialty of tourism industry, this paper develops a data-sharing model including internal and external data-sharing center to promote performance by reducing cost, improving service quality and extending market volume etc. Data-sharing is a complicated process including several steps, such as collection, evaluation, selection, classification and integration. These steps form a data mining cycle to provide more resourceful information for the tourism enterprises and its stakeholders, thus the business performance of the enterprises can be improved steadily and continuously. It can be expected that data-sharing model will be extensively applied to many tourism conglomerates in the near future.
carx: an R Package to Estimate Censored Autoregressive Time Series with Exogenous Covariates

Abstract We implement in the R package carx a novel and computationally efficient quasi-likelihood method for estimating a censored autoregressive model with exogenous covariates. The proposed quasi-likelihood method reduces to maximum likelihood estimation in absence of censoring. The carx package contains many useful functions for practical data analysis with censored stochastic regression, including functions for outlier detection, model diagnostics, and prediction with censored time series data. We illustrate the capabilities of the carx package with simulations and an elaborate real data analysis.
Scalable Machine Learning Methods for Massive Biomedical Data Analysis.

Scalable Machine Learning Methods for Massive Biomedical Data Analysis by Takanori Watanabe Chair: Clayton D. Scott Co-chair: Chandra S. Sripada Modern data acquisition techniques have enabled biomedical researchers to collect and analyze datasets of substantial size and complexity. The massive size of these datasets allows us to comprehensively study the biological system of interest at an unprecedented level of detail, which may lead to the discovery of clinically relevant biomarkers. Nonetheless, the dimensionality of these datasets presents critical computational and statistical challenges, as traditional statistical methods break down when the number of predictors dominates the number of observations, a setting frequently encountered in biomedical data analysis. This difficulty is compounded by the fact that biological data tend to be noisy and often possess complex correlation patterns among the predictors. The central goal of this dissertation is to develop a computationally tractable machine learning framework that allows us to extract scientifically meaningful information from these massive and highly complex biomedical datasets. We motivate the scope of our study by considering two important problems with clinical relevance: (1) uncertainty analysis for biomedical image registration, and (2) psychiatric disease prediction based on functional
Learning how to segment flows in the dark

To optimize routing of flows in datacenters, SDN controllers receive a packet-in message whenever a new flow appears in the network. Unfortunately, flow arrival rates can peak to millions per second [1], impairing the ability of controllers to treat them on time. Flow scheduling, e.g. [2], copes with such sheer numbers by segmenting the traffic between elephant and mice flows and by treating elephant flows in priority, as they disrupt short lived TCP flows and create bottlenecks. We formulate a flow segmentation problem that segment elephant from mice flows; the aim is to schedule a maximum amount of traffic under a constraint on the maximum rate of packet-in events. We propose a learning algorithm able to perform optimal online flow segmentation. Our solution, based on stochastic approximation techniques, is implemented at the switch level and updated by the controller, with minimal signaling over the control channel. Our approach is blind, i.e., it is agnostic to the flow size distribution. It is also adaptive, since it can track traffic variations over time. We prove its convergence properties and its message complexity. Moreover, we specialize our solution to be robust to traffic classification errors and we provide conditions under which our algorithm still converges to the optimal solution. Extensive numerical experiments characterize the performance of our approach in vitro. Finally, results of the implementation in a real OpenFlow controller demonstrate the viability of our method as a solution in production environments.
Filter Bank Regularization of Convolutional Neural Networks

Regularization techniques are widely used to improve the generality, robustness, and efficiency of deep convolutional neural networks (DCNNs). In this paper, we propose a novel approach of regulating DCNN convolutional kernels by a structured filter bank. Comparing with the existing regularization methods, such as $\ell_1$ or $\ell_2$ minimization of DCNN kernel weights and the kernel orthogonality, which ignore sample correlations within a kernel, the use of filter bank in regularization of DCNNs can mold the DCNN kernels to common spatial structures and features (e.g., edges or textures of various orientations and frequencies) of natural images. On the other hand, unlike directly making DCNN kernels fixed filters, the filter bank regularization still allows the freedom of optimizing DCNN weights via deep learning. This new DCNN design strategy aims to combine the best of two worlds: the inclusion of structural image priors of traditional filter banks to improve the robustness and generality of DCNN solutions and the capability of modern deep learning to model complex non-linear functions hidden in training data. Experimental results on object recognition tasks show that the proposed regularization approach guides DCNNs to faster convergence and better generalization than existing regularization methods of weight decay and kernel orthogonality.