Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictors of the age of autism spectrum disorder diagnosis: A North Carolina Cohort

Background and aims This study investigated timing of diagnosis for African American (n = 50) and European American (n = 118) children with autism spectrum disorder in a North Carolina sample. Methods Using survey methods, a total of 168 North Carolina families were recruited. Results The two racially diverse groups did not differ significantly in the age at diagnosis of autism spectrum disorder (African American: M = 49.72, SD = 25.83; European American: M = 43.78, SD = 20.16; t(75) = 1.45, p = . 15, 95% CI [−14.10, 2.22], d = . 27; BF10 = . 582). Exploratory analyses revealed that within the African American sample, the age of diagnosis was positively correlated with parental ratings of Social Motivation from the Social Responsiveness Scale (r = .30, p < 0.05). This correlation was non-significant and close to zero (r = −0.03, p > 0.05) for the European American sample. Additionally, children who received another initial diagnostic label had a later age of diagnosis for autism spectrum disorder. This finding had a larger effect size in the African-American group. Conclusions The differential findings for the two groups may reflect variable interpretations of autism spectrum disorder symptoms, or a greater impact of later diagnosis on symptom severity in certain populations. Implications Our findings reflect the need for continued exploration of symptom interpretation among various racial/ethnic groups.
An automated hint generation approach for supporting the evolution of requirements specifications

Updating the requirements specification during software evolution is a manual and expensive task. Therefore, software engineers usually choose to apply modifications directly to the code and leave the requirements unchanged. This leads to the loss of the knowledge contained in the requirements documents and thus limits the evolvability of a software system. In this paper, we propose to employ the co-evolution of the code and its test suite to preserve or restore the alignment between implementation and requirements: when a change has been applied to the code, subsequent changes in the test suite as well as failing tests are analyzed and used to automatically generate hints about the affected requirements and how they should be changed. These hints support the engineer in maintaining the requirements specification and thus ease the further evolution of the software system.
Splitting Water: Precision and Anti-Precision to Reduce Pool Bias

For many tasks in evaluation campaigns, especially those modeling narrow domain-specific challenges, lack of participation leads to a potential pooling bias due to the scarce number of pooled runs. It is well known that the reliability of a test collection is proportional to the number of topics and relevance assessments provided for each topic, but also to same extent to the diversity in participation in the challenges. Hence, in this paper we present a new perspective in reducing the pool bias by studying the effect of merging an unpooled run with the pooled runs. We also introduce an indicator used by the bias correction method to decide whether the correction needs to be applied or not. This indicator gives strong clues about the potential of a "good" run tested on an "unfriendly" test collection (i.e. a collection where the pool was contributed to by runs very different from the one at hand). We demonstrate the correctness of our method on a set of fifteen test collections from the Text REtrieval Conference (TREC). We observe a reduction in system ranking error and absolute score difference error.
A New Software Data-Flow Testing Approach via Ant Colony Algorithms

Search-based optimization techniques (e.g., hill climbing, simulated annealing, and genetic algorithms) have been applied to a wide variety of software engineering activities including cost estimation, next release problem, and test generation. Several search based test generation techniques have been developed. These techniques had focused on finding suites of test data to satisfy a number of control-flow or data-flow testing criteria. Genetic algorithms have been the most widely employed search-based optimization technique in software testing issues. Recently, there are many novel search-based optimization techniques have been developed such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Immune System (AIS), and Bees Colony Optimization. ACO and AIS have been employed only in the area of control-flow testing of the programs. This paper aims at employing the ACO algorithms in the issue of software data-flow testing. The paper presents an ant colony optimization based approach for generating set of optimal paths to cover all definition-use associations (du-pairs) in the program under test. Then, this approach uses the ant colony optimization to generate suite of test-data for satisfying the generated set of paths. In addition, the paper introduces a case study to illustrate our approach. Keywordsdata-flow testing; path-cover generation, test-data generation; ant colony optimization algorithms
Small Multiples, Large Singles: A New Approach for Visual Data Exploration

We present a novel visual exploration method based on small multiples and large singles for effective and efficient data analysis. Users are enabled to explore the state space by offering multiple alternatives from the current state. Users can then select the alternative of choice and continue the analysis. Furthermore, the intermediate steps in the exploration process are preserved and can be revisited and adapted using an intuitive navigation mechanism based on the well‐known undo‐redo stack and filmstrip metaphor. As proof of concept the exploration method is implemented in a prototype. The effectiveness of the exploration method is tested using a formal user study comparing four different interaction methods. By using Small Multiples as data exploration method users need fewer steps in answering questions and also explore a significantly larger part of the state space in the same amount of time, providing them with a broader perspective on the data, hence lowering the chance of missing important features. Also, users prefer visual exploration with small multiples over non‐small multiple variants.
IMGT/3Dstructure-DB and IMGT/DomainGapAlign: a database and a tool for immunoglobulins or antibodies, T cell receptors, MHC, IgSF and MhcSF

IMGT/3Dstructure-DB is the three-dimensional (3D) structure database of IMGT®, the international ImMunoGenetics information system® that is acknowledged as the global reference in immunogenetics and immunoinformatics. IMGT/3Dstructure-DB contains 3D structures of immunoglobulins (IG) or antibodies, T cell receptors (TR), major histocompatibility complex (MHC) proteins, antigen receptor/antigen complexes (IG/Ag, TR/peptide/MHC) of vertebrates; 3D structures of related proteins of the immune system (RPI) of vertebrates and invertebrates, belonging to the immunoglobulin and MHC superfamilies (IgSF and MhcSF, respectively) and found in complexes with IG, TR or MHC. IMGT/3Dstructure-DB data are annotated according to the IMGT criteria, using IMGT/DomainGapAlign, and based on the IMGT-ONTOLOGY concepts and axioms. IMGT/3Dstructure-DB provides IMGT gene and allele identification (CLASSIFICATION), region and domain delimitations (DESCRIPTION), amino acid positions according to the IMGT unique numbering (NUMEROTATION) that are used in IMGT/3Dstructure-DB cards, results of contact analysis and renumbered flat files. In its Web version, the IMGT/DomainGapAlign tool analyses amino acid sequences, per domain. Coupled to the IMGT/Collier-de-Perles tool, it provides an invaluable help for antibody engineering and humanization design based on complementarity determining region (CDR) grafting as it precisely defines the standardized framework regions (FR-IMGT) and CDR-IMGT. IMGT/3Dstructure-DB and IMGT/DomainGapAlign are freely available at http://www.imgt. org.
Searching for Control Lyapunov Functions using Sums of Squares Programming

Construction of a Control Lyapunov Function (CLF) for a nonlinear system is generally a difficult problem, but once a CLF is found, stabilization of the system is straight-forward. In this paper, we present an algorithm that searches for CLFs for polynomial systems that are affine in control using sums of squares programming. We also present an algorithm for searching local CLFs for the same class of nonlinear system when global asymptotic stabilization is not possible.
The role of captioned video in developing speech segmentation for learners of English as a second language

Over three decade’s worth of research into the effects of captioned video on second language learning suggests that it may improve L2 listening comprehension (Vanderplank, 2010). However, there are several limitations to the experimental designs of some early studies in this field (Vanderplank, 2013), thus there is a need for more robust research to be conducted. Mitterer and McQueen (2009) designed an innovative experiment to investigate the effects of captioned video on L2 speech perception. The aim of this research is to build upon their work, primarily by investigating whether repeated exposure to captioned video, delivered by several speakers from a broadly similar variety of English, could (a) lead to improved speech segmentation when listening to speakers that the learner has never heard before, and (b) improved L2 listening comprehension. For example, whether watching a number of documentaries with subtitles could lead to enhanced listening comprehension when watching subsequent subtitles-free documentaries delivered by different presenters. The main contribution of this study is that it is the first to specifically investigate the ability of participants to segment the speech of (a) previously encountered utterances; (b) different utterances by the same speaker; and (c) different utterances by different speakers of a similar accent. A pre-test / intervention / post-test experimental design was performed multiple times on international university students in the UK. Participants who watched captioned video during the treatment phase, consistently outperformed control groups, which suggests that L2 learners of English can improve their L2 listening skills (and more specifically, their L2 speech segmentation ability) by simply watching same-language subtitled TV programmes on a regular basis.
Remotely Sensed Change Detection Based on Artificial Neural Networks

A new method for remotely sensed change detection based on artificial neural networks is presented. The algorithm for an automated land-cover change-detection system was developed and implemented based on the current neural network techniques for multispectral image classification. The suitability of application of neural networks in change detection and its related network design considerations unique to change detection were first investigated. A neural-network-based change-detection system using the backpropagation training algorithm was then developed. The trained four-layered neural network was able to provide complete categorical information about the nature of changes and detect land-cover changes with an overall accuracy of 95.6 percent for a four-class (i.e., 16 change classes) classification scheme. Using the same training data, a maximum-likelihood supervised classification produced an accuracy of 86.5 percent. The experimental results using multitemporal Landsat Thematic Mapper imagery of Wilmington, North Carolina are provided. Findings of this study demonstrated the potential and advantages of using neural network in multitemporal change analysis.
Fast and Scalable Pattern Matching for Memory Architecture

Multi-pattern matching is known to require intensive memory accesses and is often a performance bottleneck. Hence specialized hardware-accelerated algorithms are being developed for line-speed packet processing. While several pattern matching algorithms have already been developed for such applications, we find that most of them suffer from scalability issues. We present a hardware-implementable pattern matching algorithm for content filtering applications, which is scalable in terms of speed, the number of patterns and the pattern length. We modify the classic Aho-Corasick algorithm to consider multiple characters at a time for higher throughput. Furthermore, we suppress a large fraction of memory accesses by using Bloom filters implemented with a small amount of on-chip memory. The resulting algorithm can support matching of several thousands of patterns at more than 10 Gbps with the help of a less than 50 KBytes of embedded memory and a few megabytes of external SRAM.
An Ultrastructure Study on the Effect of Adrenalectomy and Cortisone on the Preameloblasts of the Rat Maxillary Incisor

This Thesis is brought to you for free and open access by the Theses and Dissertations at Loyola eCommons. It has been accepted for inclusion in Master's Theses by an authorized administrator of Loyola eCommons. For more information, please contact ecommons@luc.edu. Microbiology. He then proudly served his country in military service and upon receiving his honorable discharge entered Marquette University Dental School. ACKNOWLEDGEMENTS The author wishes to thank and express his sincerest appreciation and gratitude to the following: Michael L. Kiely, Ph.D. my advisor, for his ever present help, direction, guidance and understanding throughout the period of my graduate training. Also my sincere thanks to Dr. Hal McReynolds for his helpfulness, suggestions and advice. I am also grateful to other members of the Anatomy Department for their suggestions, help and encouragement. I am especially grateful to Mrs. Letta Prevas, for her cooperation and perserverance in the typing of this manuscript. A very special appreciation to the technicians of the Pathology Department , especially Mrs. Birute Prapuolenis, for without her help and contributions this study would have been impossible. And finally, to my wife Linda, my sons James and John for their love, devotion and understanding during the past two years.
Categorize and Efficient: Top k keyword Search of Spatial-Textual data on the Road Network

: Spatio-Textual information nowadays becomes so popular and mandatory for each searching application. Many organization have shown their concern regarding importance of using spatial information (“latitude” & “longitude”) along with Textual data (i.e. Keyword Query “Hospital”). In parallel study, of searching trend and getting Top k result out of Spatio-Textual search data by using two spatial-Textual indexing technique IR-Tree and IQuad-Tree. As previously prove that the IQuad-Tree having better performing result in terms of storage and retrieval of spatial query point and the content related to it spatial point. Consequently, Spatio-Textual retrieve data for top k result on road network have different result for the nearest neighbor search as compare to simple Spatio-Textual searching. While the information which is retrieve should be efficiently relevance to the query keyword and spatial constraint. In this paper, we discuss the technique to calculate distance for the nearest neighbor search on road network and efficient algorithm that can dynamically retrieve the relevant data for every keyword search query. Extracting actual data out of user unusual query keyword search many time prove to be bottle neck, so it is better trained data in that environment. In previous approaches, for extracting Top k result out of data on road network is not having efficiently relevance and faster result. In this paper, we are going to propose the method that will give boom to our Spatio-Textual relevance search on road network with quality of top k result as compare to previous methods.
A Kalman filtering based data fusion for object tracking

To solve that single camera has its limitation of field of view, this paper proposed an object tracking method using multiple camera data fusion in image sequences. In this approach, a tracking filter and a multiple-view data fusion algorithm are applied. An estimation structure, called hierarchical estimation, is used to generate local and global estimate and to combine the estimates obtained from each camera views to form a global estimate. The advantage of this approach is the data of one camera view complements that of another camera view in order to obtain better target measurement information and to make more accurate estimates. A set of image sequences from multiple views are applied to evaluate performance. Computer simulation and experimental results indicate that this approach successfully tracks objects and has good estimation.
Stylized facts for mobile game analytics

There are numerous widely disseminated beliefs in the rapidly growing domain of Mobile Game Analytics, notably within the context of the Free-to-Play model. However, the field remains in its infancy, as there is limited conclusive empirical knowledge available across industry and academia, to provide evidence for these beliefs. Additionally, the current knowledge base is highly fragmented. For Mobile Game Analytics to mature, empirical frameworks are needed. In this paper the concept of stylized facts is presented as a means to develop an initial framework for a common understanding of key hypotheses and concepts in the field, as well as organizing the available empirical knowledge. A focus on stylized facts research will not only facilitate communication but also, more importantly, improve the quality and actionability of insights. Unified terminology and a comprehensive collection of stylized facts can be the building blocks for a conceptually well-founded understanding of mobile gaming.
Switched linear systems: stability and the convergence of random products

In this paper we provide conditions for the stability of discrete time switched linear systems. We accomplish this by calculating the mean and covariance of the set of matrices obtained by using all possible switching sequences. The theory of switched linear systems has received con- siderable attention in the systems theory literature in the last two decades. However, for discrete time switched systems the literature is much older going back to at least the early 1960's with the publication of the paper of Furstenberg and Kesten in the area of products of random matrices, or, if you like, the random products of matrices. The way that we have approached this problem is to consider the switched linear system as evolving on a partially ordered network that is, in fact, a tree. This allows us to make use of the developments of 50 years of study on random products that exist in the statistics literature. 1. Introduction. The theory of switched linear systems has received consider- able attention in the systems theory literature in the last two decades and there is a wealth of solid results concerning the stability and stabilizability of such systems. A recent survey by Lin and Antsaklis, (14), gives a rather complete overview of the field. The main theorem cited there on stability for discrete time switched linear sys- tems, (their Theorem 6, (13)), requires that 2 n matrix norms must be calculated. In this paper we give a simpler calculation but one that will only hold for "almost all" (5) switching sequences. An excellent and readable source for the general theory of switched linear systems is the monograph by Liberzon, (12). In his book the primary focus is on continuous dynamics but the concepts are much the same. However, for discrete time switched systems the literature is much older than the systems theory literature, going back to at least the early 1960's with the publication of the papers of Furstenberg and Kesten, (9, 10), in the area of products of random matrices, or, if you like, the random products of matrices. In the statistical literature on random products "almost all" is the usual condition. We approach the problem in the following way. We study the switched linear system as evolving on a partially ordered network that is in fact a tree. This allows us to make use of the developments of 50 years of study on random products that exist in the statistics literature.
Emotional state transfer model based on FSM

In this paper, the mathematical model of emotional state transition is proposed based on finite state machine (FSM) and Gross's process model of emotion regulation strategies. Firstly, Gross emotion regulation theory and FSM are described. And the initial values of emotional state transition probability matrix are calculated by the interaction between emotions. Then emotional intensity attenuation function by external stimuli is constructed, and we analyzed its effect on the emotional state transition probability. Finally, by the simulation and verified of the mathematical model, the results show that the model proposed can effectively describe the influence of emotional intensity attenuation to transition probability.
Recurrent Neural Network for Storytelling

Storytelling is the act of passing on what you want to tell other people as so interesting and true-to-life story. As the study in text mining progresses to express words, sentences and paragraphs as vector, it is possible to classify text and generate text using vectors. However, it has not much progressed to generate a correct flow of context and a correct grammar of context in text mining. In this paper, we propose first neural network model that learn one sentence and one vector is mapped for representing vectors as sentences. And we propose second neural network model that learn the contents of stories for generating sentences. Finally we evaluate the proposed model can generate sentences having stories than other method using ROUGE.
Topology Aware Adaptive Security Conference Item Topology Aware Adaptive Security Conference Item

Adaptive security systems aim to protect valuable assets in the face of changes in their operational environment. They do so by monitoring and analysing this environment, and deploying security functions that satisfy some protection (security, privacy, or forensic) requirements. In this paper, we suggest that a key characteristic for engineering adaptive security is the topology of the operational environment, which represents a physical and/or a digital space including its structural relationships, such as containment, proximity, and reachability. For adaptive security, topology expresses a rich representation of context that can provide a system with both structural and semantic awareness of important contextual characteristics. These include the location of assets being protected or the proximity of potentially threatening agents that might harm them. Security-related actions, such as the physical movement of an actor from a room to another in a building, may be viewed as topological changes. The detection of a possible undesired topological change (such as an actor possessing a safe’s key entering the room where the safe is located) may lead to the decision to deploy a particular security control to protect the relevant asset. This position paper advocates topology awareness for more effective engineering of adaptive security. By monitoring changes in topology at runtime one can identify new or changing threats and attacks, and deploy adequate security controls accordingly. The paper elaborates on the notion of topology and provides a vision and research agenda on its role for systematically engineering adaptive security systems.