Miracle-dz/UniFAR-FTUs huggingface dataset:
Features extraction for low-power face verification

Mobile communication devices now available on the market, such as so-called smartphones, are far more advanced than the first cellular phones that became very popular one decade ago. In addition to their historical purpose, namely enabling wireless vocal communications to be established nearly everywhere, they now provide most of the functionalities offered by computers. As such, they hold an ever-increasing amount of personal information and confidential data. However, the authentication method employed to prevent unauthorized access to the device is still based on the same PIN code mechanism, which is often set to an easy-to-guess combination of digits, or even altogether disabled. Stronger security can be achieved by resorting to biometrics, which verifies the identity of a person based on intrinsic physical or behavioral characteristics. Since most mobile phones are now equipped with an image sensor to provide digital camera functionality, biometric authentication based on the face modality is very interesting as it does not require a dedicated sensor, unlike e.g. fingerprint verification. Its perceived intrusiveness is furthermore very low, and it is generally well accepted by users. The deployment of face verification on mobile devices however requires overcoming two major challenges, which are the main issues addressed in this PhD thesis. Firstly, images acquired by a handheld device in an uncontrolled environment exhibit strong variations in illumination conditions. The extracted features on which biometric identification is based must therefore be robust to such perturbations. Secondly, the amount of energy available on battery-powered mobile devices is tightly constrained, calling for algorithms with low computational complexity, and for highly optimized implementations. So as to reduce the dependency on the illumination conditions, a low-complexity normalization technique for features extraction based on mathematical morphology is introduced in this thesis, and evaluated in conjunction with the Elastic Graph Matching (EGM) algorithm. Robustness to other perturbations, such as occlusions or geometric transformations, is also assessed and several improvements are proposed. In order to minimize the power consumption, the hardware architecture of a coprocessor dedicated to features extraction is proposed and described in VHDL. This component is designed to be integrated into a System-on-Chip (SoC) implementing the complete face verification process, including image acquisition, thereby enabling biometric face authentication to be performed entirely on the mobile device. Comparison of the proposed solution with state-of-the-art academic results and recently disclosed commercial products shows that the chosen approach is indeed much more efficient energy-wise.
Detecting Local Channels in Distributed Poly/ML

Distributed Poly/ML is a variation on Standard ML that includes primitives for creating threads and for inter-thread communication. Threads may be spawned on remote machines. Values are sent from one thread to another over dynamically-created channels. A channel is considered local iff all its uses take place on the processor on which it was created. We present a constraint-based static analysis that detects local channels. Using a tree replacement technique, we show that constraint solutions may be maintained as invariants at each transition step in a concurrent operational semantics. Relying on such invariants, we prove the soundness of the analysis with respect to the operational semantics.
Learning Kendo UI Web Development

An easy-to-follow practical tutorial to add exciting features to your web pages without being a JavaScript expert with this book and ebook Overview Learn from clear and specific examples on how to utilize the full range of the Kendo UI tool set for the web Add powerful tools to your website supported by a familiar and trusted name in innovative technology Learn how to add amazing features with clear examples and make your website more interactive without being a JavaScript expert In Detail Creating useful and attractive web sites for todays audiences requires more JavaScript programming than ever before. JavaScript, however, isnt easy and staying up to date with recent trends makes it even harder. You need a way to integrate the latest technology into your web site without having to start from the beginning. "Learning Kendo UI Web Development" shows you how to add the latest web features to your site without doing all of the work yourself. The experts at Telerik have created a fully supported code library that lets you focus on what makes your sites special while letting them focus on how that translates into cutting edge JavaScript code. This book will take you on a survey through Kendo UI for the web from the basic widgets that wrap input fields to the full page scaffolding widgets for setting up your site. Kendo UI for the web is a rich framework of JavaScript widgets and tools that will add immediate value to your web sites. Learn the full spectrum of what Kendo UI has to offer in specialized widgets that gather input from users by displaying calendars, sliders, or date pickers all the way up to widgets that structure your web pages through data-driven models in accordions, tabs, list views, and tree views. "Learning Kendo UI Web Development" is the perfect companion for navigating the broad features offered by Telerik in JavaScript web development. What you will learn from this book Leverage data source objects and JavaScript templates with the TabStrip and Grid widgets Guide users in date selection with the Calendar widget Create fast and fluid word wheels with the AutoComplete widget and date selection with the Calendar widget Take advantage of the powerful MVVM JavaScript architectural pattern Take full HTML input from users with a graphical editor Structure your site with the Menu and ListView widgets Build interactive accordions with the PanelBar widget and a fun way to select numbers with the Slider widget Organize your data with the Splitter and TreeView widgets and Customize pop-up windows and file uploads with the Window and Upload widgets Approach A practical tutorial with step-by-step example based approach. Who this book is written for This book is for web developers who want to take advantage of cutting edge JavaScript and HTML 5 web site features, but who dont have the time or the knowledge to write all of that code by hand. The reader should be familiar with basic HTML 5 and JavaScript but does not need to be an expert.
Enabling efficiency in data center systems

Internet-scale computer systems have established themselves as the dominant platform for the majority of popular computing today. These systems handle workloads that span Web processing to scalable storage to batch processing. The massive quantity of servers that handle these workloads are housed in data centers and require large quantities of power and cooling. To extract the most benefit from these resources, we must ensure that they are utilized efficiently. In this dissertation, we address three important challenges in efficient data-center computing. First, we present the storage configuration compiler that combines an application model and workload characterization to identify low-cost hardware configurations. Next, we present Stout, a software layer that enables more efficient use of shared storage. Finally, we consider the accuracy and limitations of several lightweight power modeling techniques and the potential impact on dynamic power management. Together, they can significantly lower hardware costs and ensure effective consideration of energy management techniques.
Hiding Jitter in an Audio Stream

There is currently great interest in transmitting continuous media by packet-switched networks. In particular, we are interested in audio data. Variation in packet arrival times (called delay jitter) can interrupt the steady delivery of the data stream. We can hide delay jitter by buffering the data before playing it. How long should we buffer? If the buffer time is short, we do not effectively hide delay jitter. If it is large, we introduce undesirable latency into the stream. This project explores methods of choosing this buffer time. We define a model of a stream and choose a measure of sound quality. We present probability theory based on our model that allows us to choose the buffer size for exponentially distributed packet delivery times. If the delivery times are not exponential, we define a new heuristic solution to choosing the buffer size called History (based on an ordered list of past interarrival times) and compare it to two statistical heuristics: StDev (based on standard deviation), and AbsDev (based on absolute deviation). History appears to have some benefits over StDev and AbsDev: a user-level knob for hiding delay jitter, and less sensitivity to single late packet arrivals.
On Algorithms for Computing the Diameter of a t-Spanner

A t-spanner of a point set V is a undirected weighted graph G such that for each pair of points p, q ∈ V , there exist a path between p and q in G of length at most t times the distance between p and q (t-path). The diameter (t-diameter) of a t-spanner is the smallest integer d such that for any pair of vertices, there is a t-path in the graph between them containing at most d edges. As far as we know there is no known algorithm on how to compute the diameter of a t-spanner. In this paper we will give some algorithms for computing the diameter of a t-spanner. The time complexity of the most efficient algorithm is O(dmn), where n is the number of vertices, m is the number of edges and d is the diameter of the input graph, and it requires O(n) space.
Filters for RSSI-based measurements in a device-free passive localisation scenario

There are a number of techniques used in modernLocation aware systems such as Received Signal Strength Indicator(RSSI), Time of Arrival (TOA), Time Difference of Arrival(TDOA) and Angle of Arrival (AOA). However the benefit ofRSSI-based location positioning technologies, is the possibilityto develop location estimation systems without the need forspecialised hardware.The human body contains more than 70% water which iscausing changes in the RSSI measurements. It is known thatthe resonance frequency of the water is 2.4 GHz. Thus a humanpresence in an indoor environment attenuates the wireless signal.Device-free Passive (DfP) localisation is a technique to detect aperson without the need for any physical devices i.e. tags orsensors. A DfP Localisation system uses the Received SignalStrength Indicator (RSSI) for monitoring and tracking changesin a Wireless Network infrastructure. The changes in the signalalong with prior fingerprinting of a physical location allowidentification of a person’s location.This research is focused on implementing DfP Localisationbuilt using a Wireless Sensor Network (WSN). The aim of thispaper is the evaluation of various smoothing algorithms forthe RSSI recorded in a Device-free Passive (DfP) Localisationscenario in order to find an algorithm that generates the bestoutput. The best output is referred to here as results that canhelp us decide if a person entered the monitored environment.The DfP scenario considered in this paper is based on monitoringthe changes in the wireless communications due to the presenceof a human body in the environment. Thus to have a clear imageof the changes caused by human presence indoors, the wirelessrecordings need to be smoothed. The following algorithms aredemonstrated with results: five-point Triangular Smoothing Algorithm,Moving Average filter, Lowess filter, Loess filter, Rlowessfilter, Rloess filter, 1-D median filter, Savittzky-Golay filter, andKalman filter.
Automotive path following using model predictive control

The field of autonomous driving is rapidly expanding and extensive research is conducted in this field. Many of the autonomous functions may be simplified to the desire to follow a path, among them steer assist, self-parking and collision avoidance. The general idea is that an autonomous vehicle would be able to do the maneuver faster, safer and more accurate than a human driver. Model Predictive Control (MPC) utilizes a model of the system and therefore has detailed knowledge of the system’s future behavior. Based on this knowledge, an optimization problem can be solved to obtain an optimal input corresponding to a desired behavior. MPC can handle multiple variables and natively offers possibilities for performance trade-offs and actuator limitations. It can operate closer to constraints than conventional controllers. Since path following is a complex problem with multiple variables MPC is considered an attractive approach. Throughout this thesis an MPC framework for automotive path following is developed, which is suitable for both simulation and vehicle. The project considered two applications with different dynamics used; a parking maneuver requiring high precision at low speed and an evasive maneuver with high dynamics at high speed. The framework consists of functions for pretreating the path and packaging it for the MPC controller, and the MPC controller itself. To validate the framework the stability of the discretization and the accuracy of the internal models are evaluated. The MPC framework is compared to a conventional controller (P-controller) using certain performance criteria such as positioning and comfort. It is concluded that it is possible to develop an MPC framework suitable for both low and high speed applications with the internal model as the only difference. With the methodology used in this project the MPC performance is clearly more satisfactory for a low speed case. The P-controller has better precision in high speed simulations, however, the MPC manages trade-offs for varying speeds better. There is a lack of coverage between the speeds 10 and 40 km/h. The MPC framework was successfully implemented in a Volvo V40.
New tools and specification languages for biophysically detailed neuronal network modelling

Increasingly detailed data are being gathered on the molecular, electrical and anatomical properties of neuronal systems both in vitro and in vivo. These range from the kinetic properties and distribution of ion channels, synaptic plasticity mechanisms, electrical activity in neurons, and detailed anatomical connectivity within neuronal microcircuits from connectomics data. Publications describing these experimental results often set them in the context of higher level network behaviour. Biophysically detailed computational modelling provides a framework for consolidating these data, for quantifying the assumptions about underlying biological mechanisms, and for ensuring consistency in the explanation of the phenomena across scales. Such multiscale biophysically detailed models are not currently in wide- spread use by the experimental neuroscience community however. Reasons for this include the relative inaccessibility of software for creating these models, the range of specialised scripting languages used by the available simulators, and the difficulty in creating and managing large scale network simulations. This thesis describes new solutions to facilitate the creation, simulation, analysis and reuse of biophysically detailed neuronal models. The graphical application neuroConstruct allows detailed cell and network models to be built in 3D, and run on multiple simulation platforms without detailed programming knowledge. NeuroML is a simulator independent language for describing models containing detailed neuronal morphologies, ion channels, synapses, and 3D network connectivity. New solutions have also been developed for creating and analysing network models at much closer to biological scale on high performance computing platforms. A number of detailed neocortical, cerebellar and hippocampal models have been converted for use with these tools. The tools and models I have developed have already started to be used for original scientific research. It is hoped that this work will lead to a more solid foundation for creating, validating, simulating and sharing ever more realistic models of neurons and networks.
Hierarchical gestures: ggestural shortcuts for touchscreen devices

The aim of this project was to evaluate whether hierarchical gestures are a usable interaction technique. Hierarchical gestures are a technique to construct a gesture set for touchscreen devices that I propose in this report. The gestures of the vocabulary are constructed by chaining primitives, for instance Call and Frieder, to the command Call Frieder. These primitives are assigned with gestures. A command is invoked by a concatenation of the gestures that are assigned to the primitives that form the command. I expected hierarchical gestures to be a usable technique to control a smartphone. The vocabulary is motivated by the benefits that are gained from its structure, which are better learnability and higher expressivity. Furthermore, using hierarchical gestures is expected to be more efficient and to be more satisfying than using the standard interface of the smartphone. Besides, gesture-based interfaces are suitable for mobile interaction because they allow for one-handed use and require low visual attention. To validate the hypotheses, an application for Android smartphones that uses hierarchical gestures was developed. The design and the implementation of this application is described. The application supports basic functionality of a smartphone operating system: opening contacts, sending SMS, and call. This application was evaluated in a controlled study in a laboratory (n = 14). The underlying motivations of learnability, improved efficiency, and improved satisfaction were supported by that study. Improvements of the application were developed based on the results of the evaluation and implemented in the application. An additional field study was carried out with an improved application to find out whether it would be used in practice. The field study proceeded successfully and indicated the validity of the hypothesis. Furthermore, the critical parts of the evaluated application turned out to be robust. However, bugs hampered the users’ interaction with the system and the population of the study was small (n = 3). I propose revising the application again and putting it in another field study with a bigger number of participants.
Data Dependencies in the Quantitation of Affymetrix Gene Expression Data

The decision of which quantitation algorithm to use for the quantitation of Affymetrix gene expression data is often made on the class of data rather than the data itself. Despite the fact that Affymetrix gene expression data can be highly variable, it is common practice in the quantitation of microarrays for researchers to pick one quantitation algorithm and use it in all cases, believing that the choice of quantitation method doesn’t make a large impact on the outcome. However, in this paper we use correlation to show that methods for the quantitation of Affymetrix gene expression data can have very different behavior on seemingly similar data, demonstrating strong data dependencies. The existence of strong data dependencies in the quantitation of Affymetrix gene expression data suggests further work is needed to characterize the sensitivities of quantitation algorithms to various properties in raw GeneChip CEL files.
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way

Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford UniversityThe new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today's signal processing. The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications.Features:* Balances presentation of the mathematics with applications to signal processing* Algorithms and numerical examples are implemented in WaveLab, a MATLAB toolbox* Companion website for instructors and selected solutions and code available for studentsNew in this edition* Sparse signal representations in dictionaries* Compressive sensing, super-resolution and source separation* Geometric image processing with curvelets and bandlets* Wavelets for computer graphics with lifting on surfaces* Time-frequency audio processing and denoising* Image compression with JPEG-2000* New and updated exercisesA Wavelet Tour of Signal Processing: The Sparse Way, third edition, is an invaluable resource for researchers and R&D engineers wishing to apply the theory in fields such as image processing, video processing and compression, bio-sensing, medical imaging, machine vision and communications engineering.Stephane Mallat is Professor in Applied Mathematics at cole Polytechnique, Paris, France. From 1986 to 1996 he was a Professor at the Courant Institute of Mathematical Sciences at New York University, and between 2001 and 2007, he co-founded and became CEO of an image processing semiconductor company.Companion website: A Numerical Tour of Signal Processing Includes all the latest developments since the book was published in 1999, including itsapplication to JPEG 2000 and MPEG-4Algorithms and numerical examples are implemented in Wavelab, a MATLAB toolboxBalances presentation of the mathematics with applications to signal processing
Large scale networked dynamical systems: Distributed inference

The thesis develops methodology and algorithms to study distributed inference problems in large scale networked systems. Typical examples that fall under the scope of this study include distributed detection, distributed field reconstruction (estimation) arising in wireless sensor network (WSN) applications, and filtering in networked dynamical (cyberphysical) systems. The systems in question operate in random environments and are constrained in terms of resources, like communication bandwidth or power. Due to the inherent randomness in sensor deployment or field sampling, often there is no center coordinating the network activity. The nodes (sensors or dynamical agents) need to collaborate with each other through local information exchange to achieve desired global network behavior. One aspect of our work involves the development of robust distributed algorithms for collaborative information processing in these networks. We study the performance of these distributed schemes in terms of their robustness to communication failures, external stochastic perturbations, and convergence to the corresponding centralized counterparts. The other aspect of the work addresses fundamental system theoretic questions in such networked settings. For example, we formulate notions of observability in distributed estimation and stability and robustness of distributed filters in the face of non-classical and uncertain information patterns. The technical tools used in the study include mixed time scale stochastic approximation, random dynamical systems, and large deviation theory. Our methods are generic and often contribute to the general theory of these classical disciplines.