Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Virtual Human Bodies with Clothing and Hair from Images to Animation

Virtual human bodies, clothing, and hair are widely used in a number of scenarios such as 3D animated movies, gaming, and online fashion. Machine learning can be used to construct data-driven 3D human bodies, clothing, and hair. In this thesis, we provide a solution to 3D shape and pose estimation under the most challenging situation where only a single image is available and the image is captured in a natural environment with unknown camera calibration. We also demonstrate that a simplified 2D clothing model helps to increase the accuracy of 2D body shape estimation significantly.
An Optimality-Theoretic Analysis of Lachmann's Law

Lachmann's Law is a peculiarity of the grammar of Latin whereby the past participles (and some other morphologically complex forms) of certain verbal roots show an etymologically unexpected long vowel. So, for example, we find u¯ ısus alongside u˘ ıdeo, ¯ esus alongside ˘ edo, ¯ actus alongside ˘ ago and t¯ usus alongside t˘ undo The most important fact from the point of view of any phonological analysis of Lachmann's Law is that, as a lengthening process, it is not surface-true, which is to say that there is no featural statement of a surface environment which will suffice to capture every instance where lengthening is attested, while including none in which it is not. If, for example, we take as our environment Cto (or subsequent reflex thereof, as in e.g. c¯ asus from *kad-tos), then we have successfully accounted for the fourteen forms that appear to show lengthening by Lachmann's Law, but we have also included a greater number of forms that show no such lengthening, viz: 2 (1.1)
Dealing with Space and Place in Standard Survey Data

Heterogeneity of local conditions and spatial dependencies are typical aspects of sociological phenomena. However, large-scale empirical data is often rather limited with regard to the spatial references that are (publicly) available to researchers. We describe several aspects of the problem and assess possibilities and potential errors associated with limited information. Our examples are returns to education and gender-based and migration-related wage gaps as popular research topics. We base our analyses upon widely used survey data from Germany, the GSOEP, which contains geographical information on various levels of aggregation. Our particular interest is in the decisions that have to be made with regard to problems of space and place in standard surveys, available options and consequences. We conclude with a number of practical suggestions for data users.
The Hobgoblin of Little Minds

The above quote appears in the chapter on Data Consistency and Concurrency in older editions of the Oracle Concepts Manual. Tom Kyte, Vice President of Core Technologies at Oracle, characterizes differences in approaches to Data Consistency and Concurrency as the fundamental difference between Oracle and other database vendors, saying that it can be Oracle's best feature or it's worst feature (if you don’t understand it) (Reference [8]). He also says that, if you don’t understand it, you are probably doing some transactions wrong in your system and that “Do-ItYourself” Referential Integrity is almost always wrong!
Punctuation at Work: Simple Principles for Achieving Clarity and Good Style

In the workplace, good punctuation is much more than a matter of correctness. It’s a matter of efficiency. Professionals who aren’t sure how to punctuate take more time than necessary to write, as they fret about the many inconsistent and contradictory rules they’ve picked up over the years. Good punctuation is also a matter of courtesy: In workplace writing, a sentence should yield its meaning instantly, but when punctuation is haphazard, readers need to work to understand – or guess at – the writer’s intent. Weak punctuation results in time-wasting confusion, questions about professionalism, and some times even serious and costly miscommunication. Without using the jargon of grammar — and providing 18 common sense principles to live by — Punctuation at Work shows busy professionals exactly how the marks can be used to make meaning clear and emphasize ideas. All the marks are covered, with hundreds of examples taken from today’s workplace. From hyphens and semicolons to brackets and quotation marks.all the way to ellipses (and the eternal struggle between “that” and “which”), this book explains the many ways punctuation makes things plain.
Digital spectra of non-uniformly sampled signals: theories and applications. III. A robust sampling time offset estimation algorithm for ultra high speed waveform digitizers using interleaving

For pt.II see IEEE Transactions on Instrumentation and Measurement, vol.37, no.3 (Sept. 1988). The author presents an algorithm for estimating the sampling time offsets of the parallel sampling paths of an ultra-high-speed waveform digitizing system that is realized by interleaving many sample-and-hold and A/D (analog/digital) converter modules. One obvious application of this algorithm is to feed these estimates back to adjustable delay units in all the sampling paths to compensate the sampling time offsets. Simulation results indicate that with an 8-bit quantizer the residual timing error can be reduced to just about 0.05% of the sampling period. <<ETX>>
The Time-Budget Perspective of the Role of Time Dimension in Modular Network Dynamics during Functions of the Brain

Information processing plays a key role in the daily activities of human and nonhuman primates. Information processing in the brain, underlying behavior, is constrained by the four-dimensional nature of external physical surroundings. In contrast to three geometric dimensions, there are no known peripheral sensory organs for the perception of time dimension. However, the representation of time dimension in modular neural networks is critical for the brain functions that require interval timing or the temporal coupling of action with perception. Recent experimental and theoretical studies are shedding light on how the representation of time dimension in neural circuits plays a key role in the diverse functions of the brain, which also includes motor interactions with environment as well as social interactions, such as verbal and nonverbal communication. Although different lines of evidence strongly suggest that rhythmic neural activities represent time dimen- sion in the brain, how the information represented by rhythmic activities is processed to time behavioral responses by the brain remains unclear. Theoretical considerations sug- gest that the rhythmic activities represent a physical aspect of the time dimension rather than the source of simple additive temporal units for coding time intervals in neural circuits.
Electromagnetic Channel Modeling and Measurement using MIMO FMCW radar

Multiple input multiple output (MIMO) systems have the potential to dramatically increase data speed of wireless systems. Implementation and evaluation of MIMO systems need a comprehensive understanding of MIMO channels. This dissertation deals with electromagnetic channel modeling and measurement for MIMO systems, important issues for the practical deployment of multiple antenna systems. One of the focuses of this research has been on modeling MIMO channels with the use of polarized antennas in three dimensional (3D) propagation environments. In this regard, the proposed model can be used to evaluate any kind MIMO array antennas including 3D array or polarized array in a given propagation environment. The channel measurement in a practical environment is important as it provides statistic results, such as delay profiles or cross-polarization discrimination (XPD) for realistic channel modeling. Channel measurements can be carried out by using a pseudorandom noise (PN) code radar system or by sweeping a vector network analyzer (VNA). However, this system is expensive, especially for high frequency bands like X band or Ku band. Much of the work in this thesis is to develop a Ku-band frequency modulated continuous wave (FMCW) radar system, worked as a channel sounder, to capture channel coefficients. High performance of FMCW signals in terms of linearity and phase noise is achieved through the proposed signal generation scheme based on a two sweeping voltage-controlled oscillator (VCO) source, eventuating to an accurate ranging ability, 0.2% range error with a fast sweeping rate of 100μ . The proposed FMCW radar system is then used as a channel sounder to investigate the delay profile and XPD in reverberation chamber. The measurement results can be used as references for transmission in Ku band.
Architectural design of IoT-cloud computing integration platform

An integration between the Internet of Things (IoT) and cloud computing can potentially leverage the utilization of both sides. As the IoT based system is mostly composed by the interconnection of pervasive and constrained devices, it can take a benefit of virtually unlimited resources of cloud entity i.e storage and computation services to store and process its sensed data. On the other hand, the cloud computing system may get benefit from IoT by broadening its reach to real world environment applications. In order to incarnate this idea, a cloud software platform is needed to provide an integration layer between the IoT and cloud computing taking into account the heterogenity of network communication protocols as well as the security and data management issues. In this study, an architectural design of IoT-cloud platform for IoT and cloud computing integration is presented. The proposed software platform can be decomposed into five main components namely cloud-to-device interface, authentication, data management, and cloud-to-user interface component. In general, the cloud-to-device interface acts as a data transmission endpoint between the whole cloud platform system and its IoT devices counterpart. Before a session of data transmission established, the communication interface contact the authentication component to make sure that the corresponding IoT device is legitimate before it allowed for sending the sensor data to cloud environment. Notice that a valid IoT device can be registered to the cloud system through web console component. The received sensor data are then collected in data storage component. Any stored data can be further analyzed by data processing component. User or any developed applications can then retrieve collected data, either raw or processed data, through API data access and web console.
Autistic Spectrum Disorder: EEG Analysis and Classification

Autistic spectrum disorder (ASD) which also known as autism is a syndrome shows neurological disorder found in brain development. Autistic patients suffer from communication disorder and lack of social interaction. This study is aimed to integrate the Electroencephalogram (EEG) signal processing and classification into a graphical user interface (GUI). In this study, severity of the autistic children is classified into three stages, namely, mild, moderate and severe which determined from their sensory response. An electrical signal is obtained by attaching the electrode onto the scalp by following the rules of the system. Then, sensory response test is carried out. The targeted channels on the scalp of the subject are C3, Cz and C4. The signal obtained from these three channels processed for artefact and noise removal suing band pass filter. Features extracted from the preprocessed signal is analysed using Short Time Fourier Transform (STFT.) These extracted features will undergo multilayer perceptron neural network and genetic algorithm for the classification process. The task is performed by implementing the algorithms of signal analysis and classification in the simplest form into GUI. The pattern of the signal and the result of the autism severity are shown in the window from GUI. The GUI also allows the user to insert the profile of the patient as a record to prevent mixing of data and for reference purpose. The GUI designed has to successfully classify the sensory data to identify the level of severity of the autistic child.
Energy-Aware Design of Stochastic Applications With Statistical Deadline and Reliability Guarantees

Energy efficiency, reliability, and real-time are three key requirements of mission-critical embedded systems. Existing approaches over emphasize the worst case design of real-time embedded systems, which will lead to serious waste of resources. In this paper, we aim at the energy-efficient design of soft real-time and reliable applications on uniprocessor embedded systems. We consider soft real-time tasks with stochastic execution durations regarding certain distributions. Thereby, we provide real-time guarantee with probability consideration. We utilize dynamic voltage and frequency scaling (DVFS) for saving energy, and also take into account the impact of DVFS on reliability. Our objective is to minimize the expected energy consumption of the system subject to statistical reliability and deadline constraints. The design optimization problem is a typical multidimensional multiple-choice knapsack problem, which is NP-hard. We first propose a dynamic programming-based optimal algorithm to solve the problem. To reduce the time complexity, we then develop a (<inline-formula> <tex-math notation="LaTeX">$1+{\beta }$ </tex-math></inline-formula>)-approximation algorithm based on a binary search approach, where <inline-formula> <tex-math notation="LaTeX">${\beta }$ </tex-math></inline-formula> is the approximating factor. The approximation algorithm can obtain the near-optimal solution with at most (<inline-formula> <tex-math notation="LaTeX">$1{+\beta }$ </tex-math></inline-formula>) times of optimal energy cost under given real-time and reliability constraints and has fully polynomial time complexity. Extensive experiments and a real-life synthetic application are conducted to evaluate the performance of the proposed techniques. Compared with existing approaches, the approximation approach can save much energy with low time overhead while guaranteeing the statistical deadline and reliability constraints.
An FPGA-based hardware emulator for fast fault emulation

An FPGA-based hardware emulation system is shown to boost the speed of fault simulation for sequential circuits. The circuit is downloaded into the emulation system which emulates the faulty circuit's behavior by synthesizing from the good circuit and the given fault list in a novel way. Fault injection is made easy by shifting the content of a fault injection chain, with which we get rid of the highly time-consuming bit-stream regeneration process. Experimental results for ISCAS-89 benchmark circuits show that the fault emulator is about twenty times faster than HOPE (parallel fault simulator). A parallel fault emulation approach is also proposed, in which faults that are not activated or with short propagation distance are screened off before fault emulation, and non-stem faults are collapsed into their equivalent stem faults, further reducing the number of faults actually emulated.
Bias Comparison on Memory Parameter of Skip Sampled Long Memory and Exponentially Smooth Transition Autoregressive Process

This research investigates the bias of the memory parameter of two aggregated processes i.e. long memory and Exponentially Smooth Transition Autoregressive (ESTAR), estimated by GPH estimator. Intensive simulation study has been carried out in order to identify the patterns generated by the simulation processes. It turns out that the bias of memory parameter of the skip sampled long memory process consistently increases by increasing the sampling interval only for short sampling intervals, while memory of the ESTAR process changes irregularly with increasing the sampling interval. Moreover, the memory parameters lies in the region of long memory process for true long memory, while memory parameter of ESTAR frequently biased towards short memory process. These facts thus can be considered as a simple procedure for discriminating long memory with ESTAR.
SLIMSVM : a simple implementation of support vector machine for analysis of microarray data

SLIMSVM: A SIMPLE IMPLEMENTATION OF SUPPORT VECTOR MACHINE FOR ANALYSIS OF MICROARRAY DATA by Avik Karmaker Support Vector Machine (SVM) is a supervised machine learning technique being widely used in multiple areas of biological analysis including microarray data analysis. SlimSVM has been developed with the intention of replacing OSU SVM as the classification component of GenolterSVM in order to make it independent of other SVM packages. GenolterSVM, developed by Dr. Marc Ma, is a SVM implementation with an iterative refinement algorithm for improved accuracy of classification of genotype microarray data. SlimSVM is an object-oriented, modular, and easy-to-use implementation written in C++. It supports dot (linear) and polynomial (non-linear) kernels. The program has been tested with artificial non-biological and microarray data. Testing with microarray data was performed to observe how SlimSVM handles medium-sized data files (containing thousands of data points) since it would ultimately be used to analyze them. The results were compared to those of LIBSVM, a leading SVM software, and the comparison demonstrates that implementation of SlimSVM was carried out accurately. SLIMSVM: A SIMPLE IMPLEMENTATION OF SUPPORT VECTOR MACHINE FOR ANALYSIS OF MICROARRAY DATA
Library Attendance For Students With Face Recognition

In every college there will be a huge number of students who uses the facility of library to access or to refer different varieties of books to gain knowledge about subjects. Every Library maintains a register to maintain the data about members who entered library. Some colleges still use the traditional approaches of maintaining register, which is difficult to maintain, to check and to analyze data. So, we propose a new system in which students are passed through camera at entry point of the library where the students facial patterns are recognized and create the entry record into the database on clicking the button.
Adaptive Hypermedia in Education

The aims of this chapter are (1) to give the definition of adaptive hypermedia (AH) and state what AH means and (2) to explore the role of AH in education at this digital age, in which the majority of learners are generally digitally fluent and competitive although some claim otherwise. Pedagogically and epistemologically, educational institutions (i.e., nursery, primary, secondary and high schools, colleges, vocational schools and colleges, life-long learning centers, adult education centers, and universities) should respond to such learning demands and differences to accommodate the digital-literate, wise, and efficient learning style preferences of today’s learners by providing AH learning materials for them. More frankly, educational institutions have to use and provide AH learning materials for their learners in order to be competitive in this digital age.
Gérer l'incertitude lors de l'extraction de relations et lors de l'inférence de nouvelles connaissances

Malgre leur volume important et leur accessibilite, de nombreuses donnees numeriques ne peuvent etre correc-tement exploitees car elles sont contenues dans des textes sous des formes peu ou pas structurees. L'extraction de relations est un processus qui rassemble des techniques pour extraire des entites et des relations a partir de textes, nous donnant la possibilite d'enrichir des bases de connais-sances de facon automatique. Cependant le langage na-turel est de facon intrinseque porteur d'ambiguite, ce qui constitue un premier niveau d'incertitude auquel on peut rajouter l'imprecision due aux formulations telles que "je crois que", "il semble que", etc. La base de connaissances doit donc tenir compte de cette incertitude par exemple en associant a chaque nouvelle connaissance extraite un score de confiance dependant du degre de certitude. Cet article est une communication de synthese qui detaille les diffe-rentes problematiques liees a l'incertitude et a l'impreci-sion au cours de la chaine de traitement allant de l'extrac-tion d'information dans les textes a l'inference de connais-sances. Il y sera notamment question de strategie d'agre-gation des differentes sources d'incertitude et d'impreci-sion et de leur prise en compte dans les traitements ulte-rieurs (par exemple la recherche d'information ou l'aide a la decision). Mots Clef TALN, extraction d'information, incertitude, inference de regles Abstract Among the increasing volume of electronic resources available , non-structured texts expressed through natural language are difficult to process automatically. In this context, relation extraction techniques propose to combine various approaches to extract entities and their relations from texts, e.g. to automatically enrich a knowledge base. Nevertheless , the natural language is per se ambiguous, which makes extraction results uncertain. It can also be used to express imprecise or uncertain statements, "It seem to me", "I believe", etc. Therefore, any knowledge base enriched through text analyses must consider these uncertainties , for instance by combining a confidence score to each knowledge extraction according to its associated level of uncertainty. This information will be of major importance to infer additional knowledge from these extractions. However , how to characterize, capture and integrate the uncertainty and imprecision of natural language ? In addition , how to take into account this uncertainty to infer new knowledge ? This paper is a synthesis communication related to the consideration of uncertainty and imprecision in the context of Information Extraction from texts and knowledge inference from these extractions. We propose in particular to define the terminology, to characterize the several sources of uncertainty and to discuss strategies that can be used to capture and consider the uncertainty in knowledge extraction and knowledge inference treatments.
Towards efficient search methods in object tracking: An evaluation and application to precise tracking

Object tracking is a much researched subject in the computer vision community. With more and more tracking algorithms reported every year, standard benchmarking and evaluation methods are reported for long term tracking systems. In this thesis we present a public dataset to evaluate trackers used for human and robot manipulation tasks. For these tasks high degrees of freedom (DOF) motion of the object is to be tracked with high accuracy. We describe in detail, both the process of recording the sequences and how ground truth data was generated for the videos. As an initial example, we evaluate the performance of seven published trackers [42, 67, 57, 23, 6, 9, 82] and analyze the results. We describe a new evaluation metric to test sensitivity of trackers to speed. A total of 100 annotated and tagged sequences are reported. All the videos, ground truth data, tagged image frames, original implementation of trackers and evaluation scripts are made publicly available. We also introduce a new search method in tracking. Sequential Graph based Approximate Nearest Neighbour Search algorithm [70, 33] or SGANNS. It uses overlapping image features in videos to build a connected graph, offline. This graph is then searched efficiently during tracking to predict the best warp parameters. We test this algorithm on the dataset reported and further analyze the results. Finally we show that using a detection module, registration based trackers can be made more robust. We address tracking challenges of occlussion and varying appearance where a regular registration based tracker fails to track.