Miracle-dz/UniFAR-FTUs huggingface dataset:
A neuroscientific and cognitive examination of individual differences in face recognition ability

There are large individual differences in face processing ability, with Super-Recognisers (SRs) being exceptionally superior to individuals with average face recognition ability. This thesis describes seven experiments examining SRs’ cognitive performance as well as neural/electrical activity in order to explore potential quantitative and qualitative contributions to their face processing superiority. Chapter 4 examined whether SRs rely on holistic and parts-based processing to the same extent as controls, and whether their face recognition superiority can be observed at the face perception stage as well. SRs outperformed controls at face recognition and face matching, inverted face recognition, object recognition and feature matching. SRs also demonstrated normal (Part-Whole Effect), greater (Inversion Effect), or reduced (Composite Face Effect) holistic processing, implying a more effective use of holistic and parts-based processing. Chapter 5 explored whether SRs’ face processing superiority transcends to faces they have less experience with (infant faces), and whether this Other Age Effect could be observed on a neural/electrical level. SRs outperformed controls on adult and infant faces despite limited experience with the latter. Furthermore, EEG analysis indicated enhanced P1 (pictorial processing) and P600 (explicit recognition) in SRs during face recognition, suggesting they may benefit from a more effective pictorial processing of faces. Chapter 6 employed the Remember/Know paradigm with EEG recording to explore SRs’ recollection and familiarity of faces and objects. SRs’ recognition was often accompanied by contextual information, suggesting they remembered more than just the stimuli’s identity. Furthermore, SRs’ visual recognition was reflected in neural/electrical activity in central and right brain sites, while controls only demonstrated central site activation. Applicability of this thesis’ findings, as well as the design’s limitations and new potential directions for future research are discussed in the final chapter.
Computer-Assisted Morphological Analysis of Ancient Greek

This system for automated morphological analysis of ancient Greek had its origin in a practical need rather than a theoretical concern for natural language parsing. Our immediate goal was to develop a new textbook and curriculum for teaching ancient Greek to American university students. Most traditional methods assume that a student is willing to spend at least one year in the study of grammar before reading any significant quantity of literature. Our conviction is that students can begin reading literature very early in the first year if the initial grammatical instruction is focused on those features of the language which actually occur in the texts first read. To test this theory, we have used the computer to help produce a complete lexical and grammatical analysis of 40,000 words of ancient Greek selected from texts which students might wish to read in their first year. We have concentrated our attention initially on morphological analysis since the complexity of Greek morphology is the major obstacle to learning the language. We have prepared statistical summaries of the morphology of each text, as well as complete concordances organized both according to dictionary lemma and morphological category. Although our first goal is to collect information for a textbook, it is obvious that an automated system for morphological analysis will have uses far beyond the teaching of elementary Greek.
Shallow-Syntax Phrase-Based Translation: Joint versus Factored String-to-Chunk Models

This work extends phrase-based statistical MT (SMT) with shallow syntax dependencies. Two string-to-chunks translation models are proposed: a factored model, which augments phrase-based SMT with layered dependencies, and a joint model, that extends the phrase translation table with microtags, i.e. per-word projections of chunk labels. Both rely on n-gram models of target sequences with different granularity: single words, micro-tags, chunks. In particular, n-grams defined over syntactic chunks should model syntactic constraints coping with word-group movements. Experimental analysis and evaluation conducted on two popular Chinese-English tasks suggest that the shallow-syntax joint-translation model has potential to outperform state-of-the-art phrase-based translation, with a reasonable computational overhead.
The impact of agile methods on the development of an agile culture: research proposal: [the agile evolution]

Today, establishing agile methods and an agile organisational culture in parallel is a commonly accepted prerequisite for the success of agile in organisations. Latest research show a correlation between agile methods and agile culture. Less discussed in Software Engineering is how the realisation of agile methods affects the development of an agile culture. The aim of this PhD research is to investigate how the impact of agile methods on the development of an agile culture can be identified, described and measured. The research follows qualitative research methods, e.g. grounded theory. In the first phase, semi-structured interviews have been conducted among experienced agile coaches, structured analysis techniques have been applied to develop well-grounded theories, and the systems thinking approach will be used to create a system model. The second phase will use the DELPHI-method to validate the theories and the model. This paper presents my motivation, basic assumptions and describes my research methodology with the status of my research. At the end I propose a roadmap for the second phase of my PhD research and conclude with preliminary findings.
Investigating the Relationship between Active Learning Task Characteristics and Student Success

Leanne Doughty,1 Laurel Hartley,2 Paul Le,2 Mary Nyaema,3 Jeffrey Boyer,4 and Robert M. Talbot III1 School of Education and Human Development, University of Colorado Denver, 1380 Lawrence St, Denver, Colorado, 80204 Department of Integrative Biology, University of Colorado Denver, 1151 Arapahoe St, Denver, Colorado, 80204 STEM Transformation Institute, Florida International University, 11200 SW 8th St, Miami, Florida, 33199 College of Science and Mathematics, North Dakota State University, 1340 Administration Ave, Fargo, North Dakota, 58105
Superpixel-based automatic segmentation of villi in confocal endomicroscopy

Confocal Laser Endomicroscopy (CLE) is a technique permitting on-site microscopy of the gastrointestinal mucosa after the application of a fluorescent agent, allowing the evaluation of mucosa alterations. These are used as features by skilled technicians to stage the severity of multiple diseases, celiac disease or irritable bowel syndrome among the others. We present an automatic method for villi detection from confocal endoscopy images, whose appearance changes with mucosal alterations. Superpixel segmentation, a well-known technique originating from computer vision, is used to identify and cluster together pixels belonging to uniform regions. Each image in the dataset is analyzed in a multiscale fashion (scale 1, 0.5 and 0.25). From each superpixel, 37 features are extracted at multiple image scales. Each superpixel is classified using a random forest, and a post-processing step is performed to refine the final output. Results in the test set (70 images, 30870 superpixels) show 85.87% accuracy, 92.88% sensitivity, 76.99% specificity in the superpixel space, and 86.36% of accuracy and 87.44% Dice score in the pixel domain.
Taxes, Quotas and Externalities in Monopolistic Competition

In this paper we investigate environmental regulation by taxes and quotas in the context of a monopolistically competitive industry. Firstly, we find the combination of a quota and a tax supporting the first best solution. Secondly, we explain why the allocative equivalence between the two instruments vanish in monopolistically competitive industries. Thirdly, we show that if the regulator is to choose between the two instruments then quotas are unambiguously preferable to taxes if income effects can be ignored.
Estimation of voice source and vocal tract parameters using combined subspace-based and amplitude spectrum-based algorithm

In this paper, a high quality pole-zero speech analysis technique is proposed. The speech production process is represented by a source-filter model. A Rosenberg-Klatt model is used to approximate a voicing source waveform for voiced speech, whereas a white noise is assumed for unvoiced. The vocal tract transfer function is represented by a pole-zero filter. For voiced speech, parameters of the source model are jointly estimated with those of the vocal tract filter. A combined algorithm is developed to estimate the vocal tract parameters, i.e., formants and anti-formants which are calculated from the poles and zeros of the filter. By the algorithm, poles are estimated based on a subspace algorithm, while zeros are estimated from the amplitude spectrum. For unvoiced speech, an AR model is assumed, which can be solved by LPC analysis. An experiment using synthesized nasal sounds shows that the poles and zeros are estimated quite accurately.
Vector Watermarking Robust to Both Global and Local Geometrical Distortions

A blind watermarking algorithm for vector graphic images is presented. The algorithm is resilient to both global and local geometrical distortions. The polygonal line is represented by the wavelet descriptor. An additive watermarking scheme is used to embed the watermark by slightly modifying the wavelet descriptor, and that causes invisible distortions to the coordinates of the vertices. The invariant properties of the wavelet descriptor ensure that the presented algorithm is resilient against both global and local geometrical distortions. Using vector graphic images from contour maps, we demonstrate that the presented algorithm outperforms the algorithm based on the Fourier descriptor.
A Qualitative Model for Hybrid Control

Complex artifacts that exhibit a mixture of continuous and discrete behaviors are difficult to control since one has to calculate a continuous control actuation that is interleaved with commanded discrete mode changes. Our paper presents a novel scheme for such a hybrid controlproblem that uses a qualitative description of the system’s hybrid dynamics to pre-select feasible mode sequences prior pursuing the computationally expensive task of numerical control-law synthesis. We present a qualitative modeling framework that captures the hybrid model in terms of qualitative abstractions of a system’s continuous behavior together with the description of the discrete dynamics (mode changes) and other control-induced constraints. The model’s representation builds upon a structural analysis of the artifact’s hybrid model and compactly encodes the hybrid dynamics as an Ordered Binary Decision Diagram like representation that supports fast on-line reasoning.
Efficient Tests for Realistic Faults in Dual-Port SRAMs

This paper begins with an overview of realistic fault models for dual-port memories, divided into single-port faults and faults unique for dual-port memories. The latter faults cannot be detected with conventional single-port memory tests; they require special tests. A precise notation for all faults, such that ambiguities and misunderstandings are prevented, has been emphasized. Next, the paper presents a methodology to design tests for realistic unique dual-port memory faults, resulting in a set of three linear single-addressing tests which are merged into a single march test (March s2PF), and one linear double-addressing test (March d2PF). March s2PF and March d2PF have been implemented at Intel. The results show that they detect unique faults, i.e., faults that cannot be detected with conventional single-port memory tests. This make them very attractive industrially.
Classification and Verification of Law School Outlines

The ability to automatically verify and classify course related documents is of interest to websites such as CourseHero.com and OutlineDepot.com. In particular, OutlineDepot.com manages more than 55,000 law school outlines that must be manually verified and classified which cause delays and inaccuracies throughout the system. An algorithm that can automatically verify and classify law school outlines will allow for fast information access, increased verification/classification accuracy and reduced costs. Therefore, we have developed a machine learning algorithm that can quickly and accurately verify and classify law school outlines into six major first year law courses. We investigate both supervised (naı̈ve Bayes) and unsupervised (K-means) learning algorithms to validate uploaded law school outlines. To address the classification problem, we have developed and tested three outline classification algorithms that encompass both supervised and unsupervised learning methodologies; multinomial naı̈ve Bayes, Support Vector Machine (SVM), and K-means. We find that our naı̈ve Bayes based algorithms achieve perfect (100%) classification and verification accuracy. However, we find that an unsupervised approach using k-means for both classification and verification provides a dramatic improvement in computational efficiency while only sacrificing less than 1% in classification/verification accuracy.
Gait Detection in Children with and without Hemiplegia Using Single-Axis Wearable Gyroscopes

In this work, we develop a novel gait phase detection algorithm based on a hidden Markov model, which uses data from foot-mounted single-axis gyroscopes as input. We explore whether the proposed gait detection algorithm can generate equivalent results as a reference signal provided by force sensitive resistors (FSRs) for typically developing children (TD) and children with hemiplegia (HC). We find that the algorithm faithfully reproduces reference results in terms of high values of sensitivity and specificity with respect to FSR signals. In addition, the algorithm distinguishes between TD and HC and is able to assess the level of gait ability in patients. Finally, we show that the algorithm can be adapted to enable real-time processing with high accuracy. Due to the small, inexpensive nature of gyroscopes utilized in this study and the ease of implementation of the developed algorithm, this work finds application in the on-going development of active orthoses designed for therapy and locomotion in children with gait pathologies.
Solving sudoku puzzles by using hopfield neural networks

In this paper two different approaches to solve Sudoku puzzles with neural networks are presented. The first approach is proposed by J.J. Hopfield. He tries to solve the Sudoku puzzle with help of a Hopfield network and treated the problem as an integer optimization problem that is also used for the solution of the well known Traveling Salesmen Problem (TSP). Second solution uses the Hopfield network with an extension, called co-processor. Since neural networks can exactly solve linear programming problems, such a network can be used as co-processor to improve the performance of the Hopfield network. Combination of both networks, where the Hopfield network was used first, was able to solve a lot of puzzles.
Use of suppression subtractive hybridization to identify genes regulated by ciliary neurotrophic factor in postnatal retinal explants

Purpose The retinal progenitors are multipotential, and the decision taken by a progenitor to differentiate along a particular path depends on both cell-intrinsic and cell-extrinsic factors. Ciliary neurotrophic factor (CNTF), a member of the interleukin-6 (IL-6) family, added to rat postnatal retinal progenitors inhibits rod photoreceptor cell differentiation, promotes Müller glia genesis and enhances the expression of bipolar neuron markers. We hypothesized that those transcripts regulated during CNTF-influenced retinal differentiation may be involved in the choice of progenitor cell fate. Our aim was to isolate these genes, characterize their expression in the retina, and to subsequently focus on candidates that may promote photoreceptor cell differentiation. Methods Retinas were cultured in vitro as explants at postnatal day 0 (P0) in the absence or presence of CNTF for six days. Transcripts regulated by CNTF after six days in vitro (DIV) were selected by subtraction suppressive hybridization (SSH) and cloned as two libraries. The UC6 and DC6 libraries contained those genes upregulated and downregulated, respectively, in the presence of CNTF at 6DIV. Results In the first library, UC6, eight clones representing seven different genes were isolated as up-regulated by CNTF. In the DC6 library, 21 clones, representing 17 different genes appeared as down-regulated by CNTF. Genes were classified in six categories, such as protein modification, signal transduction, and regulation of transcription according to the Gene Ontology Annotation. Conclusions Among the 24 selected genes, our study revealed 11 genes (two upregulated and nine downregulated) potentially involved in CNTF biological effects.
A Study on the Using Behavior of Depot-Logistic Information System in Taiwan: An Integration of Satisfaction Theory and Technology Acceptance Theory

The Depot-Logistic Information Management System (DLIMS) is aimed to provide the Air Force logistics staff of all sectors with an information technology platform for convenience of management, supervision, aircraft maintenance, along with supply operations. The DLIMS in Taiwan has been established for more than 10 years. However, it has not been evaluated for its effectiveness. This study integrates service quality into Wixom and Todd (2005) model to evaluate the effectiveness of the DLIMS. A survey of 273 users of DLIMS was conducted to validate the proposed model. The research results show that the overall goodness-of-fit indices indicate a reasonable fit of the model and data. This study emphasizes the importance of integration of satisfaction theory and technology acceptance theory to comprehensively illustrate the effectiveness of DLIMS. The integrative viewpoint implies that DLIMS is not only a logistic information system but also a service provider to the Air Force. Accordingly, the professional staff officers IS department, logistics sectors and headquarters should value simultaneously object-based functions and service quality, in order to improve users' satisfaction, which in turn can promote users' behavioral beliefs and usage intention. The findings can be a reference for the Air Force logistics sectors to make policies as well as be a basis for evaluation of success and acceptance of military-related information system.
An MBO Scheme on Graphs for Classification and Image Processing

In this paper we present a computationally efficient algorithm utilizing a fully or seminonlocal graph Laplacian for solving a wide range of learning problems in binary data classification and image processing. In their recent work [Multiscale Model. Simul. , 10 (2012), pp. 1090--1118], Bertozzi and Flenner introduced a graph-based diffuse interface model utilizing the Ginzburg--Landau functional for solving problems in data classification. Here, we propose an adaptation of the classic numerical Merriman--Bence--Osher (MBO) scheme for minimizing graph-based diffuse interface functionals, like those originally proposed by Bertozzi and Flenner. We also make use of fast numerical solvers for finding eigenvalues and eigenvectors of the graph Laplacian. Various computational examples are presented to demonstrate the performance of our algorithm, which is successful on images with texture and repetitive structure due to its nonlocal nature. The results show that our method is multiple times more efficient than o.
Antibodies against Immature Virions Are Not a Discriminating Factor for Dengue Disease Severity

Humoral immunity plays an important role in controlling dengue virus (DENV) infection. Antibodies (Abs) developed during primary infection protect against subsequent infection with the same dengue serotype, but can enhance disease following secondary infection with a heterologous serotype. A DENV virion has two surface proteins, envelope protein E and (pre)-membrane protein (pr)M, and inefficient cleavage of the prM protein during maturation of progeny virions leads to the secretion of immature and partially immature particles. Interestingly, we and others found that historically regarded non-infectious prM-containing DENV particles can become highly infectious in the presence of E- and prM-Abs. Accordingly, we hypothesized that these virions contribute to the exacerbation of disease during secondary infection. Here, we tested this hypothesis and investigated the ability of acute sera of 30 DENV2-infected patients with different grades of disease severity, to bind, neutralize and/or enhance immature DENV2. We found that a significant fraction of serum Abs bind to the prM protein and to immature virions, but we observed no significant difference between the disease severity groups. Furthermore, functional analysis of the Abs did not underscore any specific correlation between the neutralizing/enhancing activity towards immature DENV2 and the development of more severe disease. Based on our analysis of acute sera, we conclude that Abs binding to immature virions are not a discriminating factor in dengue pathogenesis.
Independent Component Analysis of Natural and Urban Image Ensembles

The Neural-JADE(nJADE), various other non-adaptive, semi-adaptive, adaptive ICA algorithms and a non-linear PCA algorithm are applied to natural and urban image ensembles to learn appropriate filter structures. The latter are shown to be represented quantitatively by Gabor and Haar wavelets in case of natural and urban image stimuli, respectively. A quantitative comparison concerning various filter characteristics demonstrates the influence of various score functions upon the resulting filter structures.
Layered Approach Using Conditional Random Fields for Intrusion Detection

Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, we address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach. We demonstrate that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered Approach. Experimental results on the benchmark KDD '99 intrusion data set show that our proposed system based on Layered Conditional Random Fields outperforms other well-known methods such as the decision trees and the naive Bayes. The improvement in attack detection accuracy is very high, particularly, for the U2R attacks (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance.