Miracle-dz/UniFAR-FTUs huggingface dataset:
Shape prior based object extraction using a log-polar domian representation

In this paper, we address the problem of extracting objects in an image that conform to prior object shape knowledge. Major challenges include the judgment of scale and rotation parameters as well as tolerating occlusions and noise. We propose the use of a novel log polar domain mapping of the Cartesian domain image to efficiently and effectively overcome these. This method greatly simplifies rotation, scaling and provides an opportunity to incorporate a decision threshold. Whilst, the initial theoretical framework is developed on binary images, placement of this into the gray scale domain is achieved by incorporating a pre-processing binary segmentation step. A post processing Cartesian domain energy optimization is done to counteract discripancies caused at the initial stage. We demonstrate our results using several examples.
Agile development in large and distributed environments: A systematic literature review on organizational, managerial and cultural aspects

Agile Software Development has been around for more than a decade. Over this period, the focus of applications, problems and consequently research themes has been changed dramatically. However, the main efforts have been arguably yielded to employ agile development in large and distributed environments. This imposes sophisticated needs to deal with e.g. project definition and management in such environments, as well as techniques and methods for communication, coordination, collaboration, etc., and even, the resulting cultural and organizational problems. This paper provides a systematic literature review in which main problems and themes of agile software development in large organizations, systems or projects and/or in distributed teams and environments have been sought and classified. The source of data is limited to IEEE publishing whether conferences or journals, and from 2005 onward. The results show that the major problems chronologically began with issues of cultural readiness, method implementation/adoption and project management, and then for the most recent years, the emphasis has been mainly switched to quality management as well as communication and collaboration mechanisms. Meanwhile, the results also reveal that indeed large organizations are even more eager and pioneer to transition to agile development due to their high degrees of cost as well as competitiveness. This fact paves the way to use their valuable, early experiences for other large as well as medium and small teams and organizations particularly those who have not switched yet. The study ends up with a few predictions for the future of agile development research themes in large and distributed environments.
Empirical Studies of Sentence Processing

My PhD thesis concerns a recurrent neural network model which will be trained to comprehend and produce sentences drawn from a fairly large subset of English grammar. In order to validate and better understand the model, its performance will be compared to that of humans on a wide range of sentence processing tests. The purpose of this chapter is to summarize a variety of reliable empirical findings regarding sentence processing and to describe how the relevant syntactic structures and related constraints will be incorporated in the language on which the network will be trained.
A Comparative Intelligibility Study of Speech Enhancement Algorithms

In this paper, we report on the evaluation of intelligibility of speech enhancement algorithms. IEEE sentences were corrupted by four types of noise including babble, car, street and train at two SNR levels (0 dB and 5 dB), and then processed by eight speech enhancement methods encompassing four classes of algorithms: spectral subtractive, subspace, statistical model based and Wiener-type algorithms. The processed speech files were presented to normal hearing listeners for identification in formal listening tests. Intelligibility was assessed as the percentage of words identified correctly. This paper reports the results of the intelligibility tests.
Robust visual tracking by coupling 2D motion and 3D pose estimation

We present an original method for tracking, in an image sequence, complex objects which can be modeled approximately by a polyhedral shape. The approach relies on the estimation of the object image motion as well as the computation of the object pose. The proposed method fulfills real-time constraints along with reliability and robustness requirements.
South Africa: In Transition to What?

Foreword by Helen Kitchen South Africa: What Kind of Change? by William J. Foltz The Process of Decision Making in Contemporary South Africa by Robert I. Rotberg A Guide to Black Politics in South Africa Steven McDonald Black Education in South Africa: Key or Chimera? by John A. Marcum Destabilization and Dialogue: South Africa's Emergence as a Regional Superpower by John de St. Jorre Eight New Realities in Southern Africa Heribert Adam and Stanley Uys Seven Scenarios for South Africa by Robert I. Rotberg A Fundamental Shift in South African Politics? by Ken Owen People's Power: A Beginning, Not an End by Zelakhe Sisulu South Africa: A Reporter's Notebook John de St. Jorre Sanctions: Another Way by Michael Spicer The 1987 Elections by Heribert Adam Index
An Estimation-Theoretic Framework for the Presentation of Multiple Stimuli

A framework is introduced for assessing the encoding accuracy and the discriminational ability of a population of neurons upon simultaneous presentation of multiple stimuli. Minimal square estimation errors are obtained from a Fisher information analysis in an abstract compound space comprising the features of all stimuli. Even for the simplest case of linear superposition of responses and Gaussian tuning, the symmetries in the compound space are very different from those in the case of a single stimulus. The analysis allows for a quantitative description of attentional effects and can be extended to include neural nonlinearities such as nonclassical receptive fields.
The Human Sulfotransferase SULT1A1 Gene Is Regulated in a Synergistic Manner by Sp1 and GA Binding Protein

Human sulfotransferase SULT1A1 is an important phase II xenobiotic metabolizing enzyme that is highly expressed in the liver and mediates the sulfonation of drugs, carcinogens, and steroids. Until this study, the transcriptional regulation of the SULT1A subfamily had been largely unexplored. Preliminary experiments in primary human hepatocytes showed that SULT1A mRNA levels were not changed in response to nuclear receptor activators, such as dexamethasone and 3-methylcolanthrene, unlike other metabolizing enzymes. Using HepG2 cells, the high activity of the TATA-less SULT1A1 promoter was shown to be dependent on the presence of Sp1 and Ets transcription factor binding sites (EBS), located within-112 nucleotides from the transcriptional start site. The homologous promoter of the closely related SULT1A3 catecholamine sulfotransferase, which is expressed at negligible levels in the adult liver, displayed 70% less activity than SULT1A1. This was shown to be caused by a two-base pair difference in the EBS. The Ets transcription factor GA binding protein (GABP) was shown to bind the SULT1A1 EBS and could transactivate the SULT1A1 promoter in Drosophila melanogaster S2 cells. Cotransfection of Sp1 could synergistically enhance GABP-mediated activation by 10-fold. Although Sp1 and GABP alone could induce SULT1A3 promoter activity, the lack of the EBS on this promoter prevented a synergistic interaction between the two factors. This study reports the first insight into the transcriptional regulation of the SULT1A1 gene and identifies a crucial difference in regulation of the closely related SULT1A3 gene, which accounts for the two enzymes' differential expression patterns observed in the adult liver.
Structural and functional analysis of the solute-binding protein UspC from Mycobacterium tuberculosis that is specific for amino sugars

Mycobacterium tuberculosis (Mtb), the aetiological agent of tuberculosis, has evolved to scavenge nutrients from the confined environment of host macrophages with mycobacterial ATP-binding cassette (ABC) transporters playing a key role in nutrient acquisition. Mtb-UspC (Rv2318) is the solute-binding protein of the essential transporter UspABC, one of four Mtb ABC transporters implicated by homology in sugar acquisition. Herein, we report the structural and functional characterization of Mtb-UspC. The 1.5 Å resolution structure of UspC reveals a two subdomain architecture that forms a highly acidic carbohydrate-substrate binding cleft. This has allowed a distinct preference of Mtb-UspC for amino sugars as determined by thermal shift analysis and solution saturation transfer difference-NMR. Taken together our data support the functional assignment of UspABC as an amino-sugar transporter. Given the limited availability of carbohydrates within the phagosomal environmental niche during Mtb intracellular infection, our studies suggest that UspABC enables Mtb to optimize the use of scarce nutrients during intracellular infection, linking essentiality of this protein to a potential role in recycling components of cell-wall peptidoglycan.
Reinventing Flashcards to Increase Student Learning

Two studies examined the effectiveness of a flashcard-based study strategy, Flashcards-Plus, in an ecologically valid context. The strategy requires students to create flashcards designed to increase their ability to retain, comprehend, and apply textbook material to exams. In Studies 1a (n = 73) and 1b (n = 62), we introduced all students to the Flashcards-Plus method and compared their exam scores. Students who used this strategy scored significantly higher than those who did not. In Study 2 (n = 434), we randomly assigned six introductory psychology courses to either receive a classroom lecture with the Flashcards-Plus strategy (i.e., three experimental courses) or no lecture (i.e., three control courses). Students in the experimental courses scored significantly higher than those in the control courses after the lecture. The results from all three studies demonstrate that students who were introduced to the Flashcards-Plus study strategy scored significantly higher on exams following the lecture than students who were not. These findings suggest that this easily implemented teaching strategy can help students achieve deeper levels of processing (i.e., comprehension and application) in a self-directed manner, which benefit students’ performance.
On the Up: Voluntary Sector Wages in the UK 1998 - 2007

Since 1997 the UK Government has sought to expand the provision of public services by the independent nonprofit sector. With policies to build the capacity of the sector, public spending on voluntary organisations has grown from £2 billion in 1996/97 to £6.88 billion in 2005/06. Theory suggests that the comparative advantage of nonprofits lies in the mission-motivation of those who work in them, and predicts that motivated workers will accept lower wages. We examine sector wage differentials in time-series to show that growth in voluntary sector wages has outpaced the private and public sectors. This state intervention in the market has had big consequences for the make-up of the voluntary sector workforce.
Screening for Diabetic Retinopathy Using Computer Vision and Physiological Markers

Background: Hyperglycemia and diabetes result in vascular complications, most notably diabetic retinopathy (DR). The prevalence of DR is growing and is a leading cause of blindness and/or visual impairment in developed countries. Current methods of detecting, screening, and monitoring DR are based on subjective human evaluation, which is also slow and time-consuming. As a result, initiation and progress monitoring of DR is clinically hard. Methods: Computer vision methods are developed to isolate and detect two of the most common DR dysfunctions—dot hemorrhages (DH) and exudates. The algorithms use specific color channels and segmentation methods to separate these DR manifestations from physiological features in digital fundus images. The algorithms are tested on the first 100 images from a published database. The diagnostic outcome and the resulting positive and negative prediction values (PPV and NPV) are reported. The first 50 images are marked with specialist determined ground truth for each individual exudate and/or DH, which are also compared to algorithm identification. Results: Exudate identification had 96.7% sensitivity and 94.9% specificity for diagnosis (PPV = 97%, NPV = 95%). Dot hemorrhage identification had 98.7% sensitivity and 100% specificity (PPV = 100%, NPV = 96%). Greater than 95% of ground truth identified exudates, and DHs were found by the algorithm in the marked first 50 images, with less than 0.5% false positives. Conclusions: A direct computer vision approach enabled high-quality identification of exudates and DHs in an independent data set of fundus images. The methods are readily generalizable to other clinical manifestations of DR. The results justify a blinded clinical trial of the system to prove its capability to detect, diagnose, and, over the long term, monitor the state of DR in individuals with diabetes.
Extracting 3D objects from photographs using 3-sweep

We introduce an interactive technique to extract and manipulate simple 3D shapes in a single photograph. Such extraction requires an understanding of the shape's components, their projections, and their relationships. These cognitive tasks are simple for humans, but particularly difficult for automatic algorithms. Thus, our approach combines the cognitive abilities of humans with the computational accuracy of the machine to create a simple modeling tool. In our interface, the human draws three strokes over the photograph to generate a 3D component that snaps to the outline of the shape. Each stroke defines one dimension of the component. Such human assistance implicitly segments a complex object into its components, and positions them in space. The computer reshapes the component to fit the image of the object in the photograph as well as to satisfy various inferred geometric constraints between components imposed by a global 3D structure. We show that this intelligent interactive modeling tool provides the means to create editable 3D parts quickly. Once the 3D object has been extracted, it can be quickly edited and placed back into photos or 3D scenes, permitting object-driven photo editing tasks which are impossible to perform in image-space.
The response of soil organic carbon of a rich fen peatland in interior Alaska to projected climate change

It is important to understand the fate of carbon in boreal peatland soils in response to climate change because a substantial change in release of this carbon as CO2 and CH4 could influence the climate system. The goal of this research was to synthesize the results of a field water table manipulation experiment conducted in a boreal rich fen into a process‐based model to understand how soil organic carbon (SOC) of the rich fen might respond to projected climate change. This model, the peatland version of the dynamic organic soil Terrestrial Ecosystem Model (peatland DOS‐TEM), was calibrated with data collected during 2005–2011 from the control treatment of a boreal rich fen in the Alaska Peatland Experiment (APEX). The performance of the model was validated with the experimental data measured from the raised and lowered water‐table treatments of APEX during the same period. The model was then applied to simulate future SOC dynamics of the rich fen control site under various CO2 emission scenarios. The results across these emissions scenarios suggest that the rate of SOC sequestration in the rich fen will increase between year 2012 and 2061 because the effects of warming increase heterotrophic respiration less than they increase carbon inputs via production. However, after 2061, the rate of SOC sequestration will be weakened and, as a result, the rich fen will likely become a carbon source to the atmosphere between 2062 and 2099. During this period, the effects of projected warming increase respiration so that it is greater than carbon inputs via production. Although changes in precipitation alone had relatively little effect on the dynamics of SOC, changes in precipitation did interact with warming to influence SOC dynamics for some climate scenarios.
Hierarchical fusion in particle filtering track-before-detect

Track fusion is the problem of combining tracks based on different sensor observations. In the sequential Monte Carlo framework, track fusion is solved by either imposing linear or Gaussian assumptions, or relying on kernel density estimation (KDE). In this paper, we introduce a novel track fusion algorithm suited to the hierarchical multi-sensor architecture. The algorithm can be incorporated in the particle filtering framework without restricting the densities by imposing assumptions, or requiring the non-trivial selection of additional parameters, as e.g., is needed in KDE. Furthermore, the proposed method is equivalent to the optimal centralised fusion architecture, in which all sensor measurements are communicated to the fusion node. Numerical results show that the newly proposed method outperforms the existing methods either by reducing estimation errors or by reducing the computation time significantly.
1 On Analyzing Web Log Data: A Parallel Sequence Mining Algorithm

Activities at enterprise-class web sites, as well as other w eb sites, are usually recorded via web logs. Collected logs consist of records fro m many click streams, which are defined as collections of hits (requests) from a spe cific user during a specific session. Using web logs is the most common way of collecting click stream data at this time. Thus data warehouses are built based on the crucial data extracted from web logs. This article proposes a parallel sequence mining algorithm, webSPADE, to analyze the click streams found in site web logs. In this pr ocess, raw web logs are first cleaned and inserted into a data warehouse. The click st reams are then mined by webSPADE, the proposed algorithm, which uses one full scan and several partial scans of the data. An innovative web-based front-end is used for visualizing and querying the sequence mining results. By utilizing relatio nal database technology, this analysis technique enables the analysis of very large a mounts of data in a short amount of time.
Putative Epimutagens in Maternal Peripheral and Cord Blood Samples Identified Using Human Induced Pluripotent Stem Cells

The regulation of transcription and genome stability by epigenetic systems are crucial for the proper development of mammalian embryos. Chemicals that disturb epigenetic systems are termed epimutagens. We previously performed chemical screening that focused on heterochromatin formation and DNA methylation status in mouse embryonic stem cells and identified five epimutagens: diethyl phosphate (DEP), mercury (Hg), cotinine, selenium (Se), and octachlorodipropyl ether (S-421). Here, we used human induced pluripotent stem cells (hiPSCs) to confirm the effects of 20 chemicals, including the five epimutagens, detected at low concentrations in maternal peripheral and cord blood samples. Of note, these individual chemicals did not exhibit epimutagenic activity in hiPSCs. However, because the fetal environment contains various chemicals, we evaluated the effects of combined exposure to chemicals (DEP, Hg, cotinine, Se, and S-421) on hiPSCs. The combined exposure caused a decrease in the number of heterochromatin signals and aberrant DNA methylation status at multiple gene loci in hiPSCs. The combined exposure also affected embryoid body formation and neural differentiation from hiPSCs. Therefore, DEP, Hg, cotinine, Se, and S-421 were defined as an “epimutagen combination” that is effective at low concentrations as detected in maternal peripheral and cord blood.
Semantic Concept Annotation For User Generated Videos Using Soundtracks

With the increasing use of audio sensors in user generated content (UGC) collections, semantic concept annotation from video soundtracks has become an important research problem. In this paper, we investigate reducing the semantic gap of the traditional data-driven bag-of-audio-words based audio annotation approach by utilizing the large-amount of wild audio data and their rich user tags, from which we propose a new feature representation based on semantic class model distance. We conduct experiments on the data collection from HUAWEI Accurate and Fast Mobile Video Annotation Grand Challenge 2014. We also fuse the audio-only annotation system with a visual-only system. The experimental results show that our audio-only concept annotation system can detect semantic concepts significantly better than does random guessing. The new feature representation achieves comparable annotation performance with the bag-of-audio-words feature. In addition, it can provide more semantic interpretation in the output. The experimental results also prove that the audio-only system can provide significant complementary information to the visual-only concept annotation system for performance boost and for better interpretation of semantic concepts both visually and acoustically.
A cloud-based framework for Thai Large Vocabulary Speech Recognition

This paper presents an improvement of a distributed Thai speech recognizer (SR). Two main objectives of the improvement are investigated; 1) the response time in terms of a real-time factor (RTF), 2) the cloud computing deployment. The proposed framework adapts and migrates the baseline collaborative DSR system to the Docker platform. Multiple containers are shared system resources such as CPU, memory, storage and I/O. We separate the services from the baseline framework into Docker containers; a front-end service container and SR engine containers. Message Queue service is initiated by an event-based trigger for controlling and communicating among containers. We also modify the recognition workflow to improve the overall run-time operation by using multiple SR engines to perform the parallel recognition processes by mean of utterance decoding. To support the parallel decoding process, an endpoint detection and voice activity detection are implemented in the client side for defining a chunk of a speech utterance. Meanwhile, a SR engine load balancer is also developed to find an available SR engine container, and assign to the queue respectively. The proposed system is compared with a baseline system regarding their computational efficiency and cost of resources. The results show that the implemented cloud-based framework with the parallel of multiple SR engines can substantially reduce the computational cost concerning the RTF and can handle the higher number of concurrent input on average. Moreover, in the same hardware setting, our proposed framework can support more SR engine than the baseline one. The system infrastructure can be flexible, scalable, and reliable with no significant difference in WER between the proposed and baseline systems.
Failure recovery in XOR'ed networks

The advantage of network coding has recently been used to several diverse problems of broadcast (single and multi-source), network efficiency, algorithmic complexities and in removal of faulty nodes. In our previous work we have shown the significance of network coding in parallel network communication. The fault tolerance and data loss problems in parallel computation are always a bottleneck in implementing the parallel topologies. Subsequently, the key goal is to minimize the amount of bandwidth required during such failures. In this paper, we exploit the distributed nature of network coding to overcome the issues of data loss due to communication failures. This paper also elucidates the procedure of failure recovery by buffering the communicating data at alternative network levels. With our approach, we are able to minimize the chance of data loss and utilize coding opportunities.