Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Block Chain based E-Procurement System –A Novel Approach

In recent years everything is digitalized in the world. E-procurement is one of the services used by the government in business sectors. Security is the main aspect of this e-procurement method. In existing systems, various cryptographic mechanisms are used. Recently block chain made a tremendous change in business and economic activities. blockchain is a distributed ledger that stores the transactions in a secure and immutable way. In this paper Blockchain mechanism is introduced in the proposed system for a secure E-Procurement system. Hyper ledger composer supporting e-procurement framework is designed. Few consensus algorithms in blockchainare discussed to support the blockchain method.
Two-Dimensional DICOM Feature Points and Their Mapping Extraction for Identifying Brain Shifts

In order to model organ deformation precisely, we extract numerous feature points and also their mapping correspondences from two layered two-dimensional Digital Imaging and Communications in Medicine (DICOM) images. In this study, we first selected the same image twice (the 68th image) from 124 layered two-dimensional DICOM images, and then two consecutive images (the 68th and 69th) and two that were far apart (the 55th and 80th). Next, twodimensional feature points were extracted from these images, and their mapping was searched. We utilized the two-dimensional image feature point extraction/correspondence algorithms scale-invariant feature transform (SIFT), KAZE, Accelerated KAZE (AKAZE), and oriented FAST and rotated BRIEF (ORB) from OpenCV with real DICOM files to confirm that the aforementioned extraction and mapping was possible. According to our results, although the method for searching for matches by only looking for similar feature points in the vicinity of a certain feature point required slightly more calculation time than the method of looking for matches across the entire DICOM area, in the end it did decrease the number of mistaken matching correspondences.
Design of controllers for omnidirectional robot based on the ystem identification technique for trajectory tracking

Omnidirectional wheeled mobile robots are gaining a lot of importance owing to its widespread applications in healthcare, industries and various other service sectors. Most of these applications demand the mobile base to track the desired trajectory with the desired speed, optimal time and energy consumption. This work focusses on the trajectory tracking of the four-wheeled mobile robot with omnidirectional wheels. Most of the existing works make use of the models developed using first principles but in this work, system modeling is carried out by the system identification technique using the data obtained from the fabricated mobile base. Trajectory tracking is then carried out using PID and Linear Quadratic Tracking (LQT) based controllers. The simulation results are compared for tracking errors and energy consumption and the results obtained in real time are presented.
Recent Progress of Direct Ink Writing of Electronic Components for Advanced Wearable Devices

Three-dimensional (3D) printing technology has been recognized as an emerging advanced fabrication technology in both industry and academia. Direct ink writing (DIW), a type of 3D printing technology, can build 3D structures through the deposition of custom-made inks, printing devices with complex architectures, excellent mechanical properties, and enhanced functionalities. DIW can greatly facilitate the fabrication of miniaturized or flexible electronic components. These components are potentially useful for their applications in advanced wearable devices. This article highlights recent advancements in 3D direct ink written electronic components with an emphasis on their potential applications for wearable devices. The relationship among ink formulations, DIW techniques, and printed devices is highlighted. In particular, the DIW-assisted fabrication of key components in wearable electronics, including power generation (nanogenerators), energy storage (e.g., lithium ion batteries), and energy consuming products (e.g., strain sensors) are reviewed in terms of performance metrics and fabrication strategies. Optimized ink preparations, evolving DIW techniques, and device designs can work synergistically to enhance the development of printed advanced wearable devices.
Application of Classifier Integration Model with Confusion Table to Audio Data Classification

—A novel and efficient method for the Classifier Integration Model(CIM) by adopting the concept of confusion table is extensively studied and reported in this paper. The method considers not only the diagonal elements of each confusion table but also the non-diagonal elements obtained from the existing confusion tables for local classifiers in CIM for more accurate classification performance. The CIM with Confusion Table (CIM-CT) method is applied to two different data sets, Iris data set and an audio signal data set for evaluation of the CNN-CT scheme. The experimental results show that the CIM-CT method outperforms a conventional classifier and the classifier with utilizing only the diagonal elements of confusion tables in CIM in terms of classification accuracy.
HisRect: Features from Historical Visits and Recent Tweet for Co-Location Judgement

Enabled by smartphones, social media users are increasingly going mobile. This trend fosters various location based services on social media platforms (e.g., Twitter). Many services like friends notification and community detection benefit from co-location judgement, i.e., to decide whether two Twitter users are co-located in some point-of-interest (POI). This problem is challenging due to the limited information in tweets and the lack of explicit geo-tags in tweets that can be used as labeled data. Our approach to this problem is based on a novel concept of HisRect features extracted from users’ historical visits and recent tweets: The former has impacts on where a user visits in general, whereas the latter gives more hints about where a user is currently. In practice, labeled data is scarce. Therefore, we design a semi-supervised learning (SSL) framework that leverages unlabeled data to extract HisRect features. Moreover, we employ an embedding neural network layer to process HisRect features of two users, which decides co-location based on the embedding difference between the two features. Our model is extensively evaluated on two large sets of real Twitter data from more than one million users. The experimental results demonstrate that our HisRect features and SSL framework are highly effective at deciding co-locations. In terms of multiple metrics, our approach clearly outperforms alternative approaches using state-of-the-art techniques.
Speech Communication from an Information Theoretical Perspective

Throughout the last century, models of human speech communication have been proposed by linguists, psychologists, and engineers. Advancements have been made, but a theory of human speech communication that is both comprehensive and quantitative is yet to emerge. This thesis hypothesises that a branch of mathematics known as information theory holds the answer to a more complete theory. Information theory has made fundamental contributions to wireless communications, computer science, statistical inference, cryptography, thermodynamics, and biology. There is no reason that information theory cannot be applied to human speech communication, but thus far, a relatively small effort has been made to do so. The goal of this research was to develop a quantitative model of speech communication that is consistent with our knowledge of linguistics and that is accurate enough to predict the intelligibility of speech signals. Specifically, this thesis focuses on the following research questions: 1) how does the acoustic information rate of speech compare to the lexical information rate of speech? 2) How can information theory be used to predict the intelligibility of speech-based communication systems? 3) How well do competing models of speech communication predict intelligibility? To answer the first research question, novel approaches for estimating the information rate of speech communication are proposed. Unlike existing approaches, the methods proposed in this thesis rely on having a chorus of speech signals where each signal in the chorus contains the same linguistic message, but is spoken by a different talker. The advantage of this approach is that variability inherent in the production of speech can be accounted for. The approach gives an estimate of about 180 b/s. This is three times larger than estimates based on lexical models, but it is an order of magnitude smaller than previous estimates that rely on acoustic signals. To answer the second research question, a novel instrumental intelligibility metric called speech intelligibility in bits (SIIB) and a variant called SIIB are proposed. SIIB is an estimate of the amount of information shared between a talker and a listener in bits per second. Unlike existing intelligibility metrics that are based on information theory, SIIB accounts for talker variability and statistical dependencies between time-frequency units. Finally, to answer the third research question, a comprehensive evaluation of intrusive intelligibility metrics is provided. The results show that SIIB and SIIB have state-of-the-art performance, that intelligibility metrics tend to perform poorly on data sets that were not used during their development, and show the advantage of reducing statistical dependencies between input features.
Joint Container Placement and Task Provisioning in Dynamic Fog Computing

Fog computing has emerged as a promising technology that can bring cloud applications closer to the devices at the network edge. The fog infrastructure contains mainly distributed and heterogeneous fog devices such as in the context of the Internet of Things. Unlike traditional data centers, those devices are characterized by sporadic resources availability, mobility, and increased flexibility. However, resource allocation mechanisms proposed currently for fog computing still lack the support of dynamic behavior. In this article, we propose novel resource management algorithms capable of flexible service provisioning in a dynamic fog computing environment. Specifically, the joint problem of container placement and task provisioning is formulated with integer linear programming. Due to its NP-hardness, we propose a low-complex particle-swarm-optimization-based metaheuristic and a greedy heuristic. Our solutions aim to optimize the number of served end-users with a predefined delay-threshold while considering dynamic fog nodes behavior/mobility and resources availability of fog nodes. Using real-world mobility data sets and different resources’ availability models, conducted simulations demonstrate that the PSO-based algorithm achieves near-optimal results. Whereas, the greedy algorithm realizes only 10%–30% less success ratio than the optimal solution with negligible execution time.
Performance improvement for robotic mechanisms by: synthesis design, dynamic balancing and adaptive control techniques

ii ACKNOWLEDGEMENT . iv TABLE OF CONTENTS . v LIST of FIGURES . ix LIST of TABLES . xiii NOMENCLATURE . xiv LIST OF ABBREVIATIONS . xviii CHAPTER
Efficient Tensor Operations via Compression and Parallel Computation

Author(s): SHI, YANG | Advisor(s): Singh, Sameer | Abstract: Linear algebra is the foundation of machine learning, especially for handling big data. We want to extract useful information that can represent the behavior of the data. For data with underlying known structures, it is straightforward to apply algorithms that maintain that structure. For instance, the generalized singular value decomposition, tensor decomposition, is the crux of model estimation for tensors. However, not all data has a trivial structure. Multi-modality data that contains information from different sources can be complex and hard to extract the structure. A data-independent randomized algorithm, such as sketching, is the solution for this case. Under both scenarios, the information extraction process may be statistically challenging as the problems are non-convex optimization problems. More importantly, the large size and the high-dimensionality of the data have been significant obstacles in discovering hidden variables and summarizing them. Thus, how to improve high-dimensional data computation efficiency is vitally important. This thesis contains the theoretical analysis for learning the underlying information from high-dimensional structured or non-structured data via tensor operations such as tensor decomposition and tensor sketching. It is easy to consider tensors as multi-dimensional vectors or matrices and apply vector/matrix-based algorithms to find the solution. However, these methods omit multi-dimensionality of the data and can be computational inefficient than considering the tensor as a whole. We show the superiority of our approximation algorithms over these methods from computation and memory efficiency point of views. This thesis also discusses optimizing tensor operation computations from the high-performance computing aspect. Conventional methods treat tensors as flattened matrices or vectors. Operations between tensors may require lots of permutations and reshapes. We propose new tensor algebra computation routines that avoid the prepossessing as much as possible.
Recommendations for sports games to bet on

The outcome of sports games, such as football, is non-deterministic since it is subject to many human actions of players and referees, but also injuries, accidents, etc. Betting on the outcome is becoming increasingly popular which is reflected by the growing sports betting market. This research tries to maximize profit from sports betting on football outcomes. Predicting the outcome can be considered as a classification problem (Home team/Draw/Away team). To decide on which games to bet (betting strategy) and the size of the stake of the bet (money management), recommendations can be provided based on personal characteristics (risk taking/risk averse). Profitable ternary classifiers were found for each of the five major European football leagues. Using these classifiers, a personal assistant for bettors was engineered as a recommendation tool. It recommends the betting strategies and money management systems that were the most profitable in recent history and outputs the game outcome probabilities generated by the classifier.
Steganography Method for Advanced Audio Coding

An information hiding method on little data region of quantized MDCT coefficients which can embed a great deal of secret information into AAC files is proposed based on the research of AAC coding standard.The proposed algorithm first partly decodes the cover AAC file to search for the little data region under code books,and then gets a set of quantized coefficients by a code word,and modifies the last quantized coefficient according to rules,and finally partly encodes to get the embedded AAC file.The secret information can be extracted blindly,and the computational complexity is low.Experimental results reveal that the proposed algorithm can obtain higher hidden data capacity,furthermore,its imperceptibility is good and it can resist the common steganalysis methods of LSB and the steganalysis method of additive noise proposed by Harmsen.
The design of remote-control system for photographic equipment

In demand of remote control of photographic equipment, we design a remote-control system for photographic equipment. The system uses the stabilizer as the platform to realize the transmission of the state parameters of photographic equipment and stabilizer to the mobile device by the self-designed wireless communication interaction protocol and control protocol. It can also control the camera equipment and stabilizer according their states. The test results show that the remote-control system of the photographic equipment designed in this paper meets the design requirements, facilitates the user’s surveillance filming work and harmonizes the user experience.
The cardiovascular system: Mathematical modelling, numerical algorithms and clinical applications *

Mathematical and numerical modelling of the cardiovascular system is a research topic that has attracted remarkable interest from the mathematical community because of its intrinsic mathematical difficulty and the increasing impact of cardiovascular diseases worldwide. In this review article we will address the two principal components of the cardiovascular system: arterial circulation and heart function. We will systematically describe all aspects of the problem, ranging from data imaging acquisition, stating the basic physical principles, analysing the associated mathematical models that comprise PDE and ODE systems, proposing sound and efficient numerical methods for their approximation, and simulating both benchmark problems and clinically inspired problems. Mathematical modelling itself imposes tremendous challenges, due to the amazing complexity of the cardiocirculatory system, the multiscale nature of the physiological processes involved, and the need to devise computational methods that are stable, reliable and efficient. Critical issues involve filtering the data, identifying the parameters of mathematical models, devising optimal treatments and accounting for uncertainties. For this reason, we will devote the last part of the paper to control and inverse problems, including parameter estimation, uncertainty quantification and the development of reduced-order models that are of paramount importance when solving problems with high complexity, which would otherwise be out of reach.
Towards meticulous data plane monitoring

Software-defined networks (SDNs) are built on the promise of consistency between control and data plane. Data plane consists of software or hardware and is subject to failures e.g., hardware or software bugs. Sometimes, these failures are outside the knowledge of the control plane consisting of network operating system or the SDN controller. The current state-of-the art tools either check the network statically from the control plane or use tagging or active probe generation to check the data plane behaviour. There is a spectrum of control plane mechanisms [1], [2], [3], [4] which check for some or all of the network-wide invariants like reachability, forwarding loops, waypoint enforcement and slice isolation. While most of the control plane mechanisms cannot model ECMP or NAT, all of them, however, are unaware of the actual behaviour on the data plane and thus, assume that control plane has a consistent view. There is a panoply of data plane mechanisms involving tagging [5], [6] or active probe generation [7], [8] which either check the path or the installed flow rules to monitor the data plane. Moreover, ping and traceroute continue to be the only tools available for network debugging to the network administrators in practice. Bugs in flow table match-action logic or switch hardware can manifest in various ways which are hard to detect and thus, to localize by any of the existing approaches. Moreover, tagging comes with its own limitations. There cannot be available and sufficient space for placing tags for a big scale scenario. Some of the existing mechanisms end up placing a lot of flow rules on the scarce TCAM to take action on tagged packets [5], [9]. The tagging mechanisms which end up tagging a packet on each switch may not detect faults where the output port is same but packet matched a different rule. Considering all of the above limitations, there is a need for a meticulous data plane monitoring tool. In software debugging, we design test cases which check the “code coverage” for finding possible faults, similarly, the monitoring tool should check all of the flow rules and links for possible faults and localize them.
How to Boost Customer Relationship Management via Web Mining Benefiting from the Glass Customer's Openness

Customer Relationship Management refers to the consistent orientation of a company towards its customers. Since this requires customer-specific data sets, techniques such as web mining are used to acquire information about customers and their behavior. In this case study, we show how web mining can be used to automatically collect information from clients’ websites for Customer Relationship Management systems in Business-to-Business environments. Here, we use tailored local grammars to extract relevant information in order to build up a data set that meets the required high quality standards. The evaluation shows that local grammars produce substantial high-quality results, but turn out to be too rigid in some cases. In summary, our case study demonstrates that web mining in combination with local grammars is suitable for Business-to-Business CRM as long as the information demand can be defined precisely and the requested information is available online.
Improved Measurement of the Low-Frequency Complex Permeability of Ferrite Annulus for Low-Noise Magnetic Shielding

The ferrite magnetic shield can provide a low-noise magnetic environment for various ultrahigh-sensitivity measurements. The low-frequency complex permeability, which determines the magnetic hysteresis noise and the shielding factor, is a key parameter of the ferrite shield and thus should be measured accurately. In this paper, the measurement error of conventional coil method is analyzed at low frequency, and an improved method is proposed which could eliminate the measurement error introduced by the wire resistance. Using this method, four ferrite samples made of PC40 and PC47 materials are measured. The measurement accuracy of the low-frequency complex permeability has improved by two orders of magnitude. On the basis of the measurement results, magnetic hysteresis noise and the shielding factor of ferrite shields are calculated. The calculated results of the noise analysis indicate that the PC40 material has lower magnetic hysteresis noise. This study can help select the ferrite materials and estimate the performance for low-noise magnetic shield design.
IDENTIFICATION AND ANALYSIS OF SOURCE CONTRIBUTIONS TO THE AIR QUALITY IN THE AMSTERDAM REGION

Air pollution has become a growing concern in the past few years, with an increasing number of acute air pollution episodes in many cities worldwide. Ozone (O3) is a powerful oxidising agent and one of the air pollutants of most concern in Europe. Source apportionment modelling provides valuable information on the contributions of different source sectors and source regions to ozone concentrations. This information can be useful in designing air quality management strategies and in understanding the potential benefits of reducing emissions from a particular source category. In the present work, the Comprehensive Air Quality Model with Extensions (CAMx), with its Ozone Source Apportionment Technology (OSAT), was applied over Amsterdam Region, Netherlands, for the period of 8th to 12th July 2010, when high concentration of ozone were measured in several air quality monitoring stations in the region of Amsterdam. The contributions of different source categories and the NOx/VOC limitation to the ozone formation were quantified. Results indicated a great influence of transboundary transport on the O3 simulated concentrations. The OSAT tool revealed that this is the main factor affecting O3 levels in the area of study, followed by VOC emissions from solvent use and NOx emissions from industry and other sources not individually analysed. O3 formation is slightly more NOx limited than VOC limited.