Miracle-dz/UniFAR-FTUs huggingface dataset:
Control strategies for cleaning robots in domestic applications: A comprehensive review

Service robots are built and developed for various applications to support humans as companion, caretaker, or domestic support. As the number of elderly people grows, service robots will be in increasing demand. Particularly, one of the main tasks performed by elderly people, and others, is the complex task of cleaning. Therefore, cleaning tasks, such as sweeping floors, washing dishes, and wiping windows, have been developed for the domestic environment using service robots or robot manipulators with several control approaches. This article is primarily focused on control methodology used for cleaning tasks. Specifically, this work mainly discusses classical control and learning-based controlled methods. The classical control approaches, which consist of position control, force control, and impedance control , are commonly used for cleaning purposes in a highly controlled environment. However, classical control methods cannot be generalized for cluttered environment so that learning-based control methods could be an alternative solution. Learning-based control methods for cleaning tasks can encompass three approaches: learning from demonstration (LfD), supervised learning (SL), and reinforcement learning (RL). These control approaches have their own capabilities to generalize the cleaning tasks in the new environment. For example, LfD, which many research groups have used for cleaning tasks, can generate complex cleaning trajectories based on human demonstration. Also, SL can support the prediction of dirt areas and cleaning motion using large number of data set. Finally, RL can learn cleaning actions and interact with the new environment by the robot itself. In this context, this article aims to provide a general overview of robotic cleaning tasks based on different types of control methods using manipulator. It also suggest a description of the future directions of cleaning tasks based on the evaluation of the control approaches.
Understanding the Linux Kernel, Third Edition

From the Publisher: To thoroughly understand what makes Linux tick and why it's so efficient, you need to delve deep into the heart of the operating system -- into the Linux kernel itself. The kernel is Linux -- in the case of the Linux operating system, it's the only bit of software to which the term "Linux" applies. The kernel handles all the requests or completed I/O operations and determines which programs will share its processing time, and in what order. Responsible for the sophisticated memory management of the whole system, the Linux kernel is the force behind the legendary Linux efficiency. The new edition of Understanding the Linux Kernel takes you on a guided tour through the most significant data structures, many algorithms, and programming tricks used in the kernel. Probing beyond the superficial features, the authors offer valuable insights to people who want to know how things really work inside their machine. Relevant segments of code are dissected and discussed line by line. The book covers more than just the functioning of the code, it explains the theoretical underpinnings for why Linux does things the way it does. The new edition of the book has been updated to cover version 2.4 of the kernel, which is quite different from version 2.2: the virtual memory system is entirely new, support for multiprocessor systems is improved, and whole new classes of hardware devices have been added. The authors explore each new feature in detail. Other topics in the book include: Memory management including file buffering, process swapping, and Direct memory Access (DMA) The Virtual Filesystem and the Second Extended Filesystem Process creation and scheduling Signals, interrupts, and the essential interfaces to device drivers Timing Synchronization in the kernel Interprocess Communication (IPC) Program execution Understanding the Linux Kernel, Second Edition will acquaint you with all the inner workings of Linux, but is more than just an academic exercise. You'll learn what conditions bring out Linux's best performance, and you'll see how it meets the challenge of providing good system response during process scheduling, file access, and memory management in a wide variety of environments. If knowledge is power, then this book will help you make the most of your Linux system.
A method for virtual machine migration in cloud computing using a collective behavior‐based metaheuristics algorithm

Due to the growth of applications and the integration of new customers into the world of computing systems, computing needs to be changed and to become more powerful and flexible than before. Meanwhile, cloud computing is presented as a model beyond a system that is currently capable of answering most request needs. Flexible infrastructure for cloud computing and virtualization technology provide new features to support business activities. Clouds are a very important topic that used secure management tools for storage, security and securing data centers in a flexible manner. One of the important matters in cloud technologies is virtual machine migration (VMM). There are different ways to implement the VMM, but because of the limitation of resources' energy, energy management is also very important and challenging. Due to the NP‐hard nature of this problem, this paper presents an energy‐aware VMM engine for cloud computing using the discrete bacterial foraging algorithm as a new collective behavior‐based metaheuristics algorithm. The CloudSim simulator is employed to investigate the efficiency of this method. The obtained results have shown that the proposed method improves the energy consumption and the migration count.
Event Structure without naïve Physics

What is the real nature of the aspectual division between perfective and imperfective as revealed by the well-known in/for-test? The answer is founded on the idea that this division between completion and incompletion mirrors our cognitive capacity to shift between discreteness and continuity as expressed in the number systems N and R. To get at the real contribution of a verb to aspectual information, the first step is to determine the basic atemporal building block making a tenseless verb stative or non-stative. For this, verbhood is to be understood aspectually in a very strict way abstracting from the contribution of arguments. It follows that one has to get ‘below’ event structure in order to see why the in/for-test works as it turns out to do (or in some cases not).
Toward Making Unsupervised Graph Hashing Discriminative

Recently, hashing has attracted much attention in visual information retrieval due to its low storage cost and fast query speed. The goal of hashing is to map original high-dimensional data into a low-dimensional binary-code space where the similar data points are assigned similar hash codes and dissimilar points are far away from each other. Existing unsupervised hashing methods mainly focus on recovering the pairwise similarity of the original data in hash space, but do not take specific measures to make the generated binary codes to be discriminative. To address this problem, this paper proposes a novel unsupervised hashing method, named “Discriminative Unsupervised Graph Hashing” (DUGH), which takes both similarity and dissimilarity of original data into consideration to learn discriminative binary codes. In particular, a probabilistic model is utilized to learn the encoding of original data in low-dimensional space, which models the original neighbor structure through both positive and negative edges in the KNN graph and then maximizes the likelihood of observing these edges. To efficiently and accurately measure the neighbor structure for large-scale datasets, we propose an effective KNN graph construction algorithm based on the random projection tree and neighbor exploring techniques. The experimental results on one synthetic dataset and four typical real-world image datasets demonstrate that the proposed method significantly outperforms the state-of-the-art unsupervised hashing methods.
Collaborative Edge Computing in Mobile Internet of Things

The proliferation of Internet-of-Things (IoT) devices has opened a plethora of opportunities for smart networking, connected applications and data driven intelligence. The large distribution of IoT devices within a finite geographical area and the pervasiveness of wireless networking present an opportunity for such devices to collaborate. Centralized decision systems have so far dominated the field, but they are starting to lose relevance in the wake of heterogeneity of the device pool. This thesis is driven by three key hypothesis: (i) In solving complex problems, it is possible to harness unused compute capabilities of the device pool instead of always relying on centralized infrastructures; (ii) When possible, collaborating with neighbors to identify security threats scales well in large environments; (iii) Given the abundance of data from a large pool of devices with possible privacy constraints, collaborative learning drives scalable intelligence. This dissertation defines three frameworks for these hypotheses; collaborative computing, collaborative security and collaborative privacy intelligence. The first framework, Opportunistic collaboration among IoT devices for workload execution, profiles applications and matches resource grants to requests using blockchain to put excess capacity at the edge to good use. The evaluation results show app execution latency comparable to the centralized edge and an outstanding resource utilization at the edge. The second framework, Integrity Threat Identification for Distributed IoT, uses a new spatio-temporal algorithm, based on Local Outlier Factor (LOF) uniquely using mean and variance collaboratively across spatial and temporal dimensions to identify potential threats. Evaluation results on real world underground sensor dataset (Thoreau) show good accuracy and efficiency. The third framework, Collaborative Privacy Intelligence, aims to understand privacy invasion by reverse engineering a user’s privacy model using sensors data, and score the level of intrusion for various dimensions of privacy. By having sensors track activities, and learning rule books from the collective insights, we are able to predict ones privacy attributes and states, with reasonable accuracy. As the Edge gains more prominence with computation moving closer to the data source, the above frameworks will drive key solutions and research in areas of Edge federation and collaboration. INDEX WORDS: Collaborative, Blockchain, Opportunistic, Distributed, Mobile, Security, Threat, Integrity, Privacy, Intrusion, Smarthome, Dimensions, IoT, Sensing TITLE: COLLABORATIVE EDGE COMPUTING IN MOBILE INTERNET OF THINGS
Low Complexity Multi-User MIMO Detection for Uplink SCMA System Using Expectation Propagation Algorithm

Sparse code multiple access (SCMA), which combines the advantages of low density signature (LDS) and code-division multiple access (CDMA), is regarded as one of the promising modulation technique candidate for the next generation of wireless systems. Conventionally, the message passing algorithm (MPA) is used for data detector at the receiver side. However, the MPA-SCMA cannot be implemented in the next generation wireless systems, because of its unacceptable complexity cost. Specifically, the complexity of MPA-SCMA grows exponentially with the number of antennas. Considering the use of high dimensional systems in the next generation of wireless systems, such as massive multi-user MIMO systems, the conventional MPA-SCMA is prohibitive. In this paper, we propose a low complexity detector algorithm named the expectation propagation algorithm (EPA) for SCMA. Mainly, the EPA-SCMA solves the complexity problem of MPA-SCMA and enables the implementation of SCMA in massive MU-MIMO systems. For instance, the EPA-SCMA also enables the implemantation of SCMA in the next generation wireless systems. We further show that the EPA can achieve the optimal detection performance as the numbers of transmit and receive antennas grow. We also demonstrate that a rotation design in SCMA codebook is unnecessary, which is quite rather different from the general assumption
Characterising semantically coherent classes of text through feature discovery

There is a growing need to provide support for social scientists and humanities scholars to gather and “engage” with very large datasets of free text, to perform very bespoke analyses. method52 is a text analysis platform built for this purpose (Wibberley et al., 2014), and forms a foundation that this thesis builds upon. A central part of method52 and its methodologies is a classifier training component based on dualist (Settles, 2011), and the general process of data engagement with method52 is determined to constitute a continuous cycle of characterising semantically coherent sub-collections, classes, of the text. Two broad methodologies exist for supporting this type of engagement process: (1) a top-down approach wherein concepts and their relationships are explicitly modelled for reasoning, and (2) a more surface-level, bottom-up approach, which entails the use of key terms (surface features) to characterise data. Following the second of these approaches, this thesis examines ways of better supporting this type of data engagement to more effectively support the needs of social scientists and humanities scholars in engaging with text data. The classifier component provides an active learning training environment emphasising the labelling of individual features. However, it can be difficult to interpret and incorporate prior knowledge of features. The process of feature discovery based on the current classifier model does not always produce useful results. And understanding the data well enough to produce successful classifiers is timeconsuming. A new method for discovering features in a corpus is introduced, and feature discovery methods are explored to resolve these issues. When collecting social media data, documents are often obtained by querying an API with a set of key phrases. Therefore, the set of possible classes characterising the data is defined by these basic surface features. It is difficult to know exactly which terms must searched for, and the usefulness of terms can change over time as new discussions and vocabulary emerge. Building on the feature discovery techniques, a framework is presented in this thesis for streaming data with an automatically adapting query to deal with these issues.
Plug-and-play inertial forward–backward algorithm for Poisson image deconvolution

Abstract. Poisson image deconvolution remains an ill-posed research problem consisting of a nonquadratic data-fidelity term and an implicit regularization function. Recently, the plug-and-play (PnP) framework has provided a new method to reformulate the regularizer model to incorporate efficient denoisers. We develop a PnP inertial forward–backward approach (PnP_IFB) for Poisson noise reconstruction. The key advantages over the conventional alternating direction method of multipliers (ADMM)-based scheme are circumventing the matrix inversion operation and requiring less parameter tuning. The scheme can achieve a numerical convergence rate that is comparable to other acceleration strategies such as the fast iterative shrinkage-thresholding algorithm, but it does not depress the reconstruction quality. Moreover, this characteristic acceleration remains efficient when dealing with some nonconvex regularizers. Then, we demonstrate its effectiveness against other state-of-the-art approaches with respect to their deconvolution performance using synthetic images, and the experimental results prove the superiority of the method when using appropriate denoisers.
Heterogeneity in Prediction Research: methods and applications

markdownabstractWilliam Osler noted in 1893 that “If it were not for the great variability between individuals, medicine might as well be a science, not an art”. In contrast, this thesis is based on the scientific paradigm that prediction models have the potential to guide medical decisions by exploiting identifiable heterogeneity across individual patients. Prediction research focuses on the development of well performing prediction models and on the assessment of their generalizability and applicability. Several methods to measure prediction model performance across clusters of patients are proposed in PART I of this thesis. PART II contains novel methods for development and validation of models that incorporate heterogeneity of treatment effect across patients. In PART III, methods for development and validation of prediction models are applied to several case studies in cardiovascular medicine, oncology, and public health.
Acoustic cue weighting strategy and the impact of training for cochlear implant users and normal hearing listeners with acoustic simulation

This project investigates the impact and plasticity of perceptual cue weighting strategy for normal hearing (NH) listeners with cochlear implant (CI) acoustic simulation and CI users. It is hypothesised that how listeners allocate perceptual attention on different speech cues is related to how accurately and effectively they can restore the phonemic structures from the acoustic inputs. Therefore, it can be beneficial to use auditory training to guide listeners’ attention to the more reliable and informative cues for their own specific language, in order to improve speech recognition and ease listening effort. The first part of this project investigated the impact of perceptual weighting strategy for both groups of listeners. Listeners’ sentence recognition and pupillary response (taken as a measure of listening effort) were measured. They were then taken into the same model with listeners’ acoustic cue weighting ratio and auditory sensitivity to explore their relation. The second aim of this project was to examine the possibility of using auditory training to change listeners’ acoustic cue weighting strategy towards an optimal one. A distributional training method was used here, with the sampling of spectral and temporal cues in the training word stimuli manipulated in a way that only the spectral cue followed a bimodal distribution that resembled the natural speech. This was to increase the saliency of the spectral cue, in order to direct listeners’ attention to the spectral cue. Sentence recognition performance in quiet, acoustic cue weighting strategy and pupillary responses were measured before and after the training to examine the effectiveness of the training. Findings from this project will extend the current understanding on CI users’ perceptual cue weighting strategy and provide inspiration for a more comprehensive rehabilitation scheme for CI users.
A dynamic model for human thermal comfort for smart building applications

Thermal comfort is closely related to the evaluation of heating, ventilation, and air conditioning systems. It can be seen as the result of the perception of the occupants of a given environment, and it is the product of the interaction of a number of personal and environmental factors. Otherwise, comfort issues still do not play an important role in the daily operation of commercial buildings. However, in the workplace, local quality effects, in addition to the health, the productivity that has a significant impact on the performance of the activities. In this regard, researchers have conducted, for decades, investigations related to thermal comfort and indoor environments, which includes developing models and indices through experimentations to establish standards to evaluate comfort and factors and set-up parameters for heating, ventilation, and air conditioning systems. However, to our best knowledge, most of the research work reported in the literature deals only with parameters that are not dynamically tracked. This work aims to propose a prototype for comfort measuring through a wireless sensor network and then presenting a model for thermal comfort prediction. The developed model can be used to set up a heating, ventilation, and air conditioning system to meet the expected comfort level. In particular, the obtained results show that there is a strong correlation between users’ comfort and variables such as age, gender, and body mass index as a function of height and weight.
Feature Selection With GenSVM Using Group Lasso Regularization

An extension is proposed to the GenSVM classification algorithm by replacing the square penalty term by a group lasso regularization. A majorization for this new penalty term is derived and the extended model is implemented in Python. This new technique is then tested on two data sets and compared with the regular GenSVM. The group lasso extension is shown to behave as a feature selector, setting row vectors, corresponding to a specific attribute, of the SVM coefficient matrix collectively to zero. In terms of performance, the group lasso GenSVM is shown to be competitive to the base model.
Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation.
Improvement of illumination‐insensitive features for face recognition under complex illumination conditions

Complex illumination condition is one of the most critical challenging problems for practical face recognition. In this study, the authors propose a novel method to improve the illumination invariants for solving this challenge. Firstly, a new method based on the Lambert reflectance model is proposed to extract illumination invariant, which is insensitive to complex illumination variations. Secondly, in order to repair the defects caused by the process of illumination invariants extraction, the fast mean filter is utilised to smooth and remove noise. Lastly, for raising the richness of information in the output image, a nonlinear normalisation transformation is proposed. Compared with the state-of-the-arts, experimental results show that the proposed method can extract more robust illumination invariants. Apart from it, the richness of information in the processed image is greater and the performance of the face recognition rate is superior.