Miracle-dz/UniFAR-FTUs huggingface dataset:
Low dimensional vs point-wise complex object detection, refinement and tracking

In this dissertation, I focus on algorithms to detect, refine, and track complex visual objects, as determined by their shape, appearance, or range of motion. Two different classes of representations, namely low-dimensional and point-wise (nonparametric), are explored, compared, and analyzed by combining multiple features from both imaging and depth sensors. Such perception modules may provide crucial decision-making and planning information for intelligent systems, and we show links to several robotic application areas. On one side, a multimodal human detection algorithm which utilizes multiple sensor data sources is detailed. The human is represented in a low-dimensional space, and a ladar-camera architecture is constructed which with visual and geometric cues improves on the detection rate and speed of conventional human classifiers. Unlike existing approaches, the proposed human classifier does not make any restrictive assumptions on the range scan positions, and thus is applicable to a wide range of real-life detection tasks. On another side, a modified graph cut-based method is proposed to refine complex objects–e.g. human hands, hiking trails for navigation, and household objects– from a rough estimate of the object pose given by a low-dimensional shape detector or tracker. The standard graph cut method is modified to incorporate color and shape distance terms, adaptively weighting them at run time to favor the most informative cue in different visual conditions. Also, this method is extended for point-wise tracking of the objects through iterative refinement without estimation of the object displacement over time. Moreover, a novel algorithm which combines lowand high-level observations in a graph cut framework is developed for the purpose of refining low-dimensional
Information Seeking Behavior & Information Resources Management:Mental Process Selecting Subjects & Identifying Information Needs ) Case study: Graduate Students in Women seminaries of Shiraz of Academic year 1393- 1394(

The aim of this research is information resources management: mental process of selecting subjects and identifying information needs. The research method used in this study is a quantitative method. Sampling is purposeful. This means that it includes graduate students of Women Seminaries of Shiraz who have information-seeking experience and are able to express their views and information needs. The sample was selected according to random sampling method with Cochran formula from 710 students. According to this sampling method, there is 241 Graduate Students in seminaries year 1392-1393 in Women Seminaries of Shiraz. This is a survey research which has been carried out by employing a questionnaire and SPSS for windows to analyze data. The results showed that students for selecting subjects and identifying information needs used methods and media such as prying mind, reviewing of information resources, consulting with subject specialists. Iranian Research Institute for Science and Technology ISSN 2251-8223
Analytics4Action Evaluation Framework: A Review of Evidence-Based Learning Analytics Interventions at the Open University UK

There is an urgent need to develop an evidence-based framework for learning analytics whereby stakeholders can manage, evaluate, and make decisions about which types of interventions work well and under which conditions. In this article, we will work towards developing a foundation of an Analytics4Action Evaluation Framework (A4AEF) that is currently being tested and validated at the Open University UK. By working with 18 introductory large-scale modules for a period of two years across the five faculties and disciplines within the OU, Analytics4Action provides a bottom-up-approach for working together with key stakeholders within their respective contexts. A holistic A4AEF has been developed to unpack, understand and map the six key steps in the evidence-based intervention process. By means of an exemplar in health and social science, a practical illustration of A4AEF is provided. In the next 3–5 years, we hope that a rich, robust evidence-base will be presented to show how learning analytics can help teachers to make informed, timely and successful interventions that will help each learner to achieve the module’s learning outcomes.
Interference alignment: capacity bounds and practical algorithms for time-varying channels

Wireless communication systems are becoming essential to everyday life. Modern network deployments and protocols are struggling to keep up with these growing demands, due to interference between devices. The recent discovery of interference alignment has shown that, in principle, it may be possible to overcome this interference bottleneck in dense networks. However, most theoretical results are limited to very high signal-to-noise ratios (SNRs) and practical algorithms have only developed for interference alignment via multiple antennas. In this thesis, we develop new capacity bounds for the finite SNR regime by taking advantage of time-varying channel gains. We also explore practical algorithms for parallel single-antenna interference channels, which could arise due to orthogonal frequency-division multiplexing (OFDM). From the theoretical side, we study the phase-fading Gaussian interference channel. We approximate the capacity region in the very strong interference regime to
Exploration and exploitation of multilingual data for statistical machine translation

Shortly after the birth of computer science, researchers realised the importance of machine translation as a task worth of concentrated effort, but it is only recently that algorithms are able to provide automatic translations usable by the masses. Modern translation systems are dependent on bilingual corpora, a modern Rosetta Stone, from which the learn cross-lingual relationships that can be used to translate sentences which are not in the training corpus. This data is crucial. If it is insufficient, or out-of-domain, then translation quality degrades. To improve quality, we need to both perfect methods that extract usable translation from additional multilingual resources, and improve the constituent models of a translation system to better exploit existing multilingual data sets. In this thesis, we focus on these dual problems. Our approach is two-fold, and the thesis is structures accordingly. In part I we study the problem of extracting translations from the web, with a focus on exploiting the growing predominance of microblog platforms. We present novel methods for the language identification of microblog posts, and conduct a thorough analysis of existing methods that explore these microblog posts for new translations. In part II we study the orthogonal problem of improving language models for the tasks of reranking and source side morphological analysis. We begin by analysing a plethora of syntactic features for reranking n-best lists output from an automatic translation system. We then present a novel algorithm that allows for exact inference from high-order hidden Markov models, which we use to segment source text input. In this way, the thesis gives insight into the retrieval of relevant training data, and introduces novel methods that better utilise existing multilingual corpora.
A Novel Multi-Detector Fusion Framework for Multi-Object Tracking

In order to track all persons in a scene, the tracking-by-detection paradigm has proven to be a very effective approach. Yet, relying solely on a single detector is also a major limitation, as useful image information might be ignored. This work demonstrates how to incorporate several detectors into a tracking system, using a novel multi-object tracking formulation. We cast tracking as a weighted graph labeling problem, resulting in a binary quadratic program. As such problems are NP-hard, the solution can only be approximated. Based on the Frank-Wolfe algorithm, we present a new solver that is crucial to handle such difficult problems. As a result, the tracker can take information from many frames and different detectors holistically into account. When applied with head and full-body detections, the fusion helps to recover heavily occluded persons and to reduce false positives. Evaluation on pedestrian tracking is provided for multiple scenarios, showing superior results over single detector tracking and standard QP-solvers. Finally, our tracker performs state-of-the-art on the MOT16 benchmark and is the winner of the MOT17 challenge.
Using Wiki to Teach Part-Time Adult Learners in a Blended Learning Environment.

This exploratory study investigated the perceptions of 31 part-time adult learners who participated in an online collaborative writing experience. Situated in the context of a blended learning environment of an advanced English learning course, this study looked into learners’ perceptions with respect to the benefits of collaborative writing using Wiki in five aspects: motivation, group interaction, knowledge sharing, confidence in writing, and improvement in language skills. The relationships between each of these five aspects and learners’ participation in Wiki were also investigated. An online questionnaire was used to obtain feedback from respondents. The findings showed that, in general, learners positively perceived Wiki as beneficial for online collaborative writing in all areas measured. The results also indicated a positive relationship between learners’ contributions in Wiki and their perceptions regarding the benefits of online collaborative writing in terms of motivation, confidence in writing, and improvement in language skills. However, there was no significant relationship between learners’ contributions in Wiki and their perception regarding the benefits of online collaborative writing in terms of group interaction and knowledge sharing. Implications of the findings on adult learning were also discussed.
A Review of String Matching Algorithms and Recent Implementations using GPU

String matching algorithms are an important element used in several computer science bb fields. These algorithms process strings of characters to find coincidences. The amount of processing required for modern applications that use string matching algorithms has increased over the years and new algorithms and solutions have been developed. In this paper we present a state of the art for the various types of string matching algorithms; and also review recent implementations done in GPU.
Space-time MMSE reception for multisatellite environment

This paper deals with the introduction of two new schemes of space-time MMSE reception implemented for the forward link of a WCDMA multisatellite UMTS environment and the comparison with other two similar space-time MMSE receiver already presented in the literature. In all this works the so called space-time transmit diversity technique (STTD) has been coupled with the minimum mean square error (MMSE) interference suppression technique. The idea has come from the realization that the diversity gain is limited by a medium-high level of multiple access interference (L. Mucchi et al., 2002, R. Tesi et al., 2001). The proposed schemes can be distinguished by the order in which the space-time processing and the MMSE interference suppression are performed. We named ST-MMSE pre STTD combining if the MMSE filtering is performed before the space-time combining and ST-MMSE post STTD combining if the interference suppression and the space-time combining are jointly processed. A realistic multisatellite UMTS environment has been simulated in order to compare the proposed detectors. Bit error rates have been calculated by mean of Monte Carlo simulations assuming a time-varying satellite channel model. Simulations show that the post-combining scheme gives the best results, but it implies more complexity to the mobile terminal. Anyway, both the proposed ST-MMSE schemes yield significantly better performance than the standard STTD receiver or the conventional RAKE receiver.
Reachability Analysis and its Application to the Safety Assessment of Autonomous Cars

This thesis is about the safety verification of dynamical systems using reachability analysis. Novel solutions have been developed for classical reachability analysis, stochastic reachability analysis, and their application to the safety assessment of autonomous cars. Classical reachability analysis computes the set of states that can be reached by a system. If the reachable set does not intersect any set of unsafe states, the safety of the system is guaranteed. Algorithms for this problem have been developed for linear, nonlinear, and hybrid systems. Stochastic reachability analysis measures the probability of reaching an unsafe set. One pursued approach computes over-approximative solutions for linear systems; another one generates a Markov chain which approximately computes the stochastic reachable set of arbitrary dynamics.
Action Recognition Using Particle Flow Fields

In recent years, research in human action recognition has advanced on multiple fronts to address various types of actions including simple, isolated actions in staged data (e.g., KTH dataset), complex actions (e.g., Hollywood dataset), and naturally occurring actions in surveillance videos (e.g, VIRAT dataset). Several techniques including those based on gradient, flow, and interest-points, have been developed for their recognition. Most perform very well in standard action recognition datasets, but fail to produce similar results in more complex, large-scale datasets. Action recognition on large categories of unconstrained videos taken from the web is a very challenging problem compared to datasets like KTH (six actions), IXMAS (thirteen actions), and Weizmann (ten actions). Challenges such as camera motion, different viewpoints, huge interclass variations, cluttered background, occlusions, bad illumination conditions, and poor quality of web videos cause the majority of the state-of-the-art action recognition approaches to fail. An increasing number of categories and the inclusion of actions with high confusion also increase the difficulty of the problem. The approach taken to solve this action recognition problem depends primarily on the dataset and the possibility of detecting and tracking the object of interest. In this dissertation, a new method for video representation is proposed and three new approaches to perform action recognition in different scenarios using varying prerequisites are presented. The prerequisites have decreasing levels of difficulty to obtain: 1) Scenario requires human detection and track-
On the significance of different facial parts for automatic age estimation

In this paper we describe an experimental investigation into the significance of different facial parts in the task of age estimation. For this purpose we use an automatic age estimation algorithm capable of producing an estimate of the age of a subject based on his/her face image. We perform automatic age estimation experiments using different facial parts of varying resolution so that the significance of facial parts in this application can be assessed. According to the results obtained, the internal part of the face and in particular the area around the eyes proved to be the most significant for the task of automatic age estimation.
A coupled electromagnetic transformer model for the analysis of winding inter-turn short-circuits

This paper presents the development and implementation of a coupled electromagnetic transformer model for the simulation of three-phase power and distribution transformers affected by the presence of winding inter-turn short-circuits. Generically, the coupled electromagnetic transformer model is based on the simultaneous consideration of both magnetic and electric equivalent circuits. To incorporate the internal faults in this model a suitable equivalent circuit of the faulty winding is proposed. Both simulation and laboratory tests results are presented in the paper, which demonstrate the adequacy of the proposed model. Index Terms — Transformers; modelling and simulation; coupled electromagnetic model; winding inter-turn short-circuit faults; Park's Vector Approach.
Enabling and Measuring Complexity in Evolving Designs using Generative Representations for Artificial Architecture

As the complexity of evolutionary design problems grow, so too must the quality of solutions scale to that complexity. In this research, we develop a genetic programming system with individuals encoded as tree-based generative representations to address scalability. This system is capable of multi-objective evaluation using a ranked sum scoring strategy. We examine Hornby’s features and measures of modularity, reuse and hierarchy in evolutionary design problems. Experiments are carried out, using the system to generate three-dimensional forms, and analyses of feature characteristics such as modularity, reuse and hierarchy were performed. This work expands on that of Hornby’s, by examining a new and more difficult problem domain. The results from these experiments show that individuals encoded with those three features performed best overall. It is also seen, that the measures of complexity conform to the results of Hornby. Moving forward with only this best performing encoding, the system was applied to the generation of three-dimensional external building architecture. One objective considered was passive solar performance, in which the system was challenged with generating forms that optimize exposure to the Sun. The results from these and other experiments satisfied the requirements. The system was shown to scale well to the architectural problems studied.
The Name and Nature of Translator Studies

A number of recent research tendencies in Translation Studies focus explicitly on the translator in some way, rather than on translations as texts. These trends might be grouped under the term “Translator Studies”. The article argues that this new focus is inadequately represented in Holmes’ classic map. Evidence of the recent trends is found especially in translation sociology, but also in translation history and in research into the translator’s decision-making processes. A broad outline of Translator Studies would cover sociology, culture and cognition, all looking at the translator’s agency, in different ways.
A New Dynamic Synchrophasor Estimator for Digital Relaying

ABSTRACT One of the most important challenges in modern protective requirements is in security and accuracy in the presence of different fluctuations in the network. Therefore, techniques with faster and more accurate estimations have to be developed to measure waveform phasors under steady-state and transient conditions. This paper proposes a new least error squares algorithm based on Taylor series to improve the performances of the phasor estimators. The accuracy is improved by using the fundamental phasor estimations and their estimated derivatives of Taylor series for the next step calculation. Then the proposed algorithm is evaluated through several simulations that specified in the Standard IEEE C37.118. 1-2011 for different conditions. By using the proposed algorithm, a remarkable result is achieved in the presence of decaying DC component and harmonics. In addition, to demonstrate the performances of the proposed algorithm, a comparison with the conventional algorithms has also been presented.
Appendix S 1 : Evolution of assortative mating in a population expressing dominance

Here, we derive invasion and fixation conditions for modifiers inducing stronger or weaker assortment. We label the genotypic values and the genotype frequencies according to Table S1. In all cases, we assume that fitness is given by equation (8) in the main text and that population size is constant and close to demographic equilibrium. To derive invasion conditions we can neglect matings between individuals carrying a modifier allele, i.e., O(pipj) terms (i, j ∈ {4, . . . , 10}). Moreover, the genetic composition of the population is adequately described by the vector (p1, p2, p3, p4, p5, p6, p7) T . It is easily verified that the linearized recursion matrix of the vector (p4, p5, p6, p7) T is
Assessing the Effects of Animation in Online Banner Advertising

Abstract The present study attempts to examine the effects of animated banner ads, as well as the moderating effects of involvement, on each stage of the hierarchy of effects model, and to explore the applicability of the hierarchy of effects model to the banner advertising environment through an online experiment. The results provide support for the notion that animated banner ads prompt better advertising effects than do static ads. Animated banner advertising has better attention-grabbing capabilities, and generates higher recall, more favorable Aad, and higher click-through intention than static ads. Furthermore, an individual’s product involvement moderates the effects of animated banner advertising on recall, Aad, and click-through intention. However, the study does not provide solid evidence of the feasibility of the traditional hierarchical model (Cognition -> Affect -> Behavior) in the online banner advertising environment. Several implications and limitations of these results are discussed, and future research is suggested.
Predicting Day-Zero Review Ratings: A Social Web Mining Approach

Social Web Mining: is a term closely associated with modern day use of the Internet; with large Internet companies Google, Apple, IBM moving towards integration of intelligence into their product eco-system, a large number of different applications have popped up in the Social sphere. With the aid of machine learning techniques there is no dearth of learning that is possible from endless streams of user-generated content. One of the tasks in this domain that has seen relatively less research is the task of predicting review scores prospectively i.e. prior to the release of the entity a movie, electronic product, game or book in question. It is easy to locate this chatter on social streams such as Twitter; what’s difficulty is extracting relevant information and facts about these entities and even more the task of predicting these Day-Zero review rating scores which provide insightful information about these products, prior to their release. In this thesis, we propose just such a framework a setup capable of extracting facts about reviewable entities. Populating a list of potential objects for a year, we follow an approach similar to boot-strapping in order to learn relevant facts about these prospective entities, all geared towards the task of learning to predict scores in a machine learning setting. Towards the end-goal of predicting review scores for potential products our system supports alternative strategies which perform competitively on the task problem. All the predictions from the learning framework, within a certain allowable error margin output scores comparable to human judgment. The results bode well for potential large-scale predictive tasks on real-time data streams; in addition this framework proposes alternative feature spaces which in aggregation go on to describe a multi-method approach to achieving higher accuracy on tasks which have previously seen lack-luster results.