Miracle-dz/UniFAR-FTUs huggingface dataset:
Syllable Timing in Repetition Disfluency

This paper investigates rhythmic patterns in 3-cycle repetition disfluency (e.g. We went to the park and1 and2 and3 watched the birds.). Results show a harmonic timing effect: in a slow-speech set of corpus data, the distribution of and2 phases relative to the and1 to and3 interval is trimodal (χ 2 = 20.1, p < .005), with modes approximating low-order harmonic ratios (1/3, 1/2, 2/3). A task-dynamic coupled-oscillators model can represent the structure of the observed distribution as attractor-structure in a potential function, accounting for why harmonic modes are observed. Furthermore, the model can treat the ratio of oscillator amplitudes as a control parameter representing speech-rate; as this parameter is varied, higher-order modes become unstable, explaining why trimodality is not observed in a fast-speech subset of the data. In contrast to previous experimental findings of harmonic timing, the effects observed here operate on a smaller time scale and cannot be attributed to an external stimulus or self-entrainment.
Extending Object-Oriented Middleware for Location-Aware Services

Mobile ad hoc networks (manets) are dynamically reconfigurable multi-hop wireless networks with no fixed infrastructure, consisting of radio-equipped mobile hosts. Each host acts as a router and moves in an arbitrary manner. Under such a network environment routing becomes a challenging task that can be significantly supported and facilitated by the exploitation of location information, as this paper argues. More specifically, after a brief introduction to routing in manets, a location discovery algorithm is proposed. Then, the paper focuses on location-aware routing and after presenting briefly the most important related protocols attempts to compare them based on a number of qualitative properties. Finally, the emergence of location aware services through the support of object-oriented middleware is discussed, a service discovery scenario based on Jini technology is proposed and important related deployment challenges are highlighted
An unsupervised audio segmentation method using Bayesian information criterion

Audio segmentation is a well-known problem which can be considered from various angles. In the context of this paper, audio segmentation problem is to extract small “homogeneous” pieces of audio in which the content does not change in terms of the present audio events. The proposed method is compared with the well-known segmentation method; Bayesian Information Criterion (BIC) based Divide-and-Conquer, in terms of average segment duration and computational complexity.
Shifts in sensory neuron identity parallel differences in pheromone preference in the European corn borer

Pheromone communication relies on highly specific signals sent and received between members of the same species. However, how pheromone specificity is determined in moth olfactory circuits remains unknown. Here we provide the first glimpse into the mechanism that generates this specificity in Ostrinia nubilalis. In Ostrinia nubilalis it was found that a single locus causes strain-specific, diametrically opposed preferences for a 2-component pheromone blend. Previously we found pheromone preference to be correlated with the strain and hybrid-specific relative antennal response to both pheromone components. This led to the current study, in which we detail the underlying mechanism of this differential response, through chemotopically mapping of the pheromone detection circuit in the antenna. We determined that both strains and their hybrids have swapped the neuronal identity of the pheromone-sensitive neurons co-housed within a single sensillum. Furthermore, neurons that mediate behavioral antagonism surprisingly co-express up to five pheromone receptors, mirroring the concordantly broad tuning to heterospecific pheromones. This appears as possible evolutionary adaptation that could prevent cross attraction to a range of heterospecific signals, while keeping the pheromone detection system to its simplest tripartite setup.
IFN/ENIT: database of handwritten arabic words

Dans cet article on presente une nouvelle base de donnees, qui contient des noms manuscrits de villes/villages arabes. Pour chaque nom les informations de base, par exemple l'ordre des formes de caractere, les informations sur le style de l'ecriture et la ligne de base, sont codees. 411 auteurs ont rempli des formulaires avec plus de 26400 noms contenant plus de 210 000 caracteres. La base de donnees est decrite en detail, et elle est concue pour la formation et l'essai des systemes d'identification pour les mots arabes manuscrits. La base de donnees IFA/ENIT est disponible pour la recherche.
Phosphoproteomics technologies and applications in plant biology research

Protein phosphorylation has long been recognized as an essential mechanism to regulate many important processes of plant life. However, studies on phosphorylation mediated signaling events in plants are challenged with low stoichiometry and dynamic nature of phosphorylated proteins. Significant advances in mass spectrometry based phosphoproteomics have taken place in recent decade, including phosphoprotein/phosphopeptide enrichment, detection and quantification, and phosphorylation site localization. This review describes a variety of separation and enrichment methods for phosphoproteins and phosphopeptides, the applications of technological innovations in plant phosphoproteomics, and highlights significant achievement of phosphoproteomics in the areas of plant signal transduction, growth and development.
LEARNING ALGORITHM FOR CONNECTIONIST SYSTEMS

The main goal of this paper is to present a new learning algorithm which has been applied to feedforward neural networks. It was used not only for learning of a given network, but also to optimize the number of hidden layer neurons. Besides, this learning algorithm is inspired on the traditional backpropagation algorithm. Nevertheless it owns some variations due to kind of network used. This algorithm was applied to a particular network which has AND/OR fuzzy neurons.
Two-stage system for robust neutral/lombard speech recognition

Performance of current speech recognition systems is significantly deteriorated when exposed to strongly noisy environment. It can be attributed to background noise and Lombard effect (LE). Attempts for LE-robust systems often display a tradeoff between LE-specific improvements and the portability to neutral speech. Therefore, towards LE-robust recognition, it seems effective to use a set of conditionsdedicated subsystems driven by a condition classifier, rather than attempting for one universal recognizer. Presented paper focuses on a design of a two-stage recognition system (TSR) comprising talking style classifier (neutral/LE) followed by two style-dedicated recognizers differing in input features. First, the binary neutral/LE classifier is built, with a particular interest in developing suitable features for the classification. Second, performance of common speech features (MFCC, PLP), LE-robust features (Expolog) and newly proposed features is compared in neutral/LE digit recognition tasks. In addition, robustness to the changes of average speech pitch and various noise backgrounds is evaluated. Third, the TSR is built, employing two recognizers, each using style-specific features. Comparison of the proposed system with either neutralspecific or LE-specific recognizer on a joint neutral/LE speech shows an improvement 6.5o4. 2 % WER on neutral and 48.1o28. 4 % WER on LE Czech utterances.
A Fault Tolerance Scheduling Algorithm for Parallel Terrain Analysis

In recent years, due to the increasing calculation demands for the massive spatial data analysis, the parallel computing based on high-performance computers has become an inevitable trend of Digital Terrain Analysis (DTA in short). A key problem is to design fault-tolerant software to enhance the stability and robustness of scientific application. This paper presents an approach of failure recovery solution in distributed parallel computing environment. This approach to schedule the failing task is built by dividing all the failing data into several partitions according the calculating scale of failure. By means of the Fault-Tolerant Granularity Model, the scheduling algorithm can assign the failing task dynamically. Finally, two experiments focusing data size and failure size are discussed.
Hepatitis a molecular epidemiology in the United States, 1996-1997: sources of infection and implications of vaccination policy.

BACKGROUND The genetic relatedness of hepatitis A virus (HAV) isolates was determined to identify possible infection sources for case patients in the Sentinel Counties Study of Acute Viral Hepatitis. METHODS A 315-nucleotide segment of the VP1-P2 region of the HAV genome was amplified and sequenced from serum of case patients and analyzed together with risk-factor data. RESULTS Of 508 HAV-RNA-positive case patients, 449 (88.4%) were interviewed, and 255 (50.1%) reported >/=1 risk factor. Some 123 unique nucleotide sequence patterns (UNSPs) were identified--77 (62.6%) from only 1 case patient and the rest in 2-99 persons. Among international travelers, a single person was more often infected with a single type of UNSP (17/54 [31.5%]), compared with other case patients (48/393 [12.2%]; P<.001). UNSPs from travelers to Mexico (33/37 [89.2%]) clustered with those from Hispanic children (47/49 [95.9%]). Of 119 men who had sex with men, 96 (80.7%) had the same or similar UNSPs, which were also found in 37 men and 10 women with no identified infection source. CONCLUSION HAV is often transmitted within networks of persons with similar risk factors, which may be the infection source for others in the community.
Elastic stream cloud (ESC): A stream-oriented cloud computing platform for Rich Internet Application

Recently, a number of cloud computing paradigms have been proposed. The new term of cloud computing is not a new concept, is a long-held dream of computing as a utility [1]. From the view of datacenters, the common understanding of the cloud computing concept is Software as a Service (SaaS), utility computing and application virtualization. In the domain of Rich Internet Application (RIA) domain, the view is different. Hence, in this paper, we define stream-oriented cloud computing and provide the architecture for creating Clouds with steam-oriented objects allocation by leveraging technologies such as flash stream media. We also provide insights on stream-based object management strategies that encompass both user centered service management and stream computational distribution and large scale video slides allocation. In addition, we reveal our early thoughts on interconnecting Clouds for dynamically creating global stream cloud exchanges and management. Then, we present a sample deployment of the elastic stream cloud, especially those developed in internet TV broadcast industries along with our current work towards realizing stream-oriented stream cloud technologies.
Searching in the presence of linearly bounded errors

We consider the problem of determining an unknown quantity x by asking “yes-no” questions, where some of the answers may be erroneous. In particular, we focus on a linearly bounded model of errors where for some known constant r, 0 < r < 1 2 , each initial sequence of i answers is guaranteed to have no more than ri errors. This model allows the errors to occur in a somewhat malicious way: for example, we must deal with a scenario where all errors occur in the last few answers. The problem is examined under the following variations: kinds of questions allowed (comparison or membership), nature of the domain of the searched quantity (bounded [x ∈ {1, . . . , n}, for some fixed n] or unbounded [x is any positive integer]). In the bounded domain, the only previous bound on the number of questions which works for the entire range 0 < r < 1 2 (with either comparison or membership questions) is O(n2 1 1−2r ). We improve this significantly by showing that O(log n) membership questions are enough. The upper bound on number of comparison questions needed is improved to O(n2 1 1−r ), which is o(n) even when r gets arbitrarily close to 12 . In the unbounded domain, where now n is the number being searched, we show that O(log n) membership questions or O([n log n]2 1 1−r ) comparison questions are enough. The problem is solved using the framework of chip games. Author was supported by DARPA Contract N00014-87-K-825 and National Science Foundation Grant CCR-8914428. Author’s net address: jaa@theory.lcs. mit.edu Author was supported by DARPA Contract N00014-87-K825, National Science Foundation Grant CCR-8912586, and Air Force Contract AFOSR-89-0271. Author’s net address: aditi@theory.lcs. mit.edu
Predictors of maternal stress in pre-school and school-aged children with autism

ABSTRACT Background: Mothers of children with autism experience high levels of stress. However, little is known about whether maternal stressors differ across children of different ages. Method: This study investigated stressors in mothers of 29 pre-school-aged and 27 school-aged children with autism. Correlation and regression analyses were used to determine if maternal stressors differed between the two age groups. Results: Findings revealed no difference in maternal stress levels across the two age groups; however, stressors for each age group were different. Stressors in the school-aged group were related to repetitive behaviours, sensory sensitivities, and problematic behaviours, while stressors in the pre-school-aged group were mainly related to adaptive behaviours. Conclusions: These findings are informative for practitioners and highlight the need for tailored supports and therapies for mothers of children with autism of different ages.
An Initial Analytical Exploration of Retrievability

We approach the problem of retrievability from an analytical perspective, starting with modeling conjunctive and disjunctive queries in a boolean model. We show that this represents an upper bound on retrievability for all other best match algorithms. We follow this with an observation of imbalance in the distribution of retrievability, using the Gini coefficient. Simulation-based experiments show the behavior of the Gini coefficient for retrievability under different types and lengths of queries, as well as different assumptions about the document length distribution in a collection.
Chapter 4 : Structured Language Model Parsing

The Structured Language Model (SLM) was introduced by Ciprian Chelba and Frederick Jelinek as a language model that uses a statistical parser in a left to right manner in order to exploit syntactic information for use in a language model as part of a speech recognition system (Chelba, Jelinek, 1998; Chelba, 1997). In traversing a sentence, the Structured Language Model produces a series of partial lexical parses whose exposed headwords are used instead of the previous two words in a trigram-like prediction process. It is conjectured that this language model could be easily modified to produce good complete parses for Czech. One of the attractive features of the parsing method used in this model is that it can be easily modified to handle "crossing" or non-projective dependencies, a feature of the Prage Dependency Treebank that our other parsers currently ignore. Chelba, who implementated his Structured Language Model in C++, was gracious enough to allow us access to his code in order to modify it for our purposes. During the workshop, with time constraints and the usual range of difficulties encountered, we had time only to modify this Structured Language Model to work properly with the Czech data, to experiment with unknown word statistics, and to use part of speech tags generated by a version of Jan Hajic’s exponential model statistical tagger. The results obtained during the workshop are encouraging as they were obtained with a version of the SLM which, though modified, is still not optimized for parsing.
Encrypted Internet Traffic Classification Method based on Host Behavior

Accurate network traffic classification plays important roles in many areas such as traffic engineering, QoS and intrusion detection etc. Encrypted Peer-to-Peer (P2P) applications have dramatically grown in popularity over the past few years, and now constitute a significant share of the total traffic in many networks. To solve the drawback of the previous classification scheme for encrypted network traffic, we propose an encrypted Internet traffic classification method based on host behavior. Experiment results illustrate this method can classify encrypted traffic using sourcedestination IP pairs and connection characteristics with high accuracy and faster computational time.
Identification of Polyadenylation Sites within Arabidopsis Thaliana

2 The thesis of Manal Kalkatawi is approved by the examination committee. ABSTRACT Identification of Polyadenylation Sites within Arabidopsis Thaliana Manal Kalkatawi Machine Learning (ML) is a field of artificial intelligence focused on the design and implementation of algorithms that enable creation of models for clustering, classification, prediction, ranking and similar inference tasks based on information contained in data. Many ML algorithms have been successfully utilized in a variety of applications. The problem addressed in this thesis is from the field of bioinformatics and deals with the recognition of polyadenylation (poly(A)) sites in the genomic sequence of the plant Arabidopsis thaliana. During the RNA processing, a tail consisting of a number of consecutive adenine (A) nucleotides is added to the terminal nucleotide of the 3'-untranslated region (3'UTR) of the primary RNA. The process in which these A nucleotides are added is called polyadenylation. The location in the genomic DNA sequence that corresponds to the start of terminal A nucleotides (i.e. to the end of 3'UTR) is known as a poly(A) site. Recognition of the poly(A) sites in DNA sequence is important for better gene annotation and understanding of gene regulation. In this study, we built an artificial neural network (ANN) for the recognition of poly(A) sites in the Arabidopsis thaliana genome. Our study demonstrates that this model achieves improved accuracy compared to the existing predictive models for this purpose. The key factor contributing to the enhanced predictive performance of our ANN model is a distinguishing set of features used in creation of the model. These features include a number of physico-chemical characteristics of relevance, such as dinucleotide thermodynamic characteristics, electron-ion interaction potential, etc., but also many of 5 the statistical properties of the DNA sequences from the region surrounding poly(A) site, such as nucleotide and polynucleotide properties, common motifs, etc. Our ANN model was compared in performance with several other ML models, as well as with the PAC tool that is specifically developed for poly(A) site recognition in Arabidopsis thaliana and rice. The comparison analysis shows that our model performs better compared to the others available, and achieves on average 93% accuracy. 6 ACKNOWLEDGEMENTS I am vey fortunate to have been surrounded and supported by many great individuals. My sincerest thank goes to my adviser, Prof. Vladimir Bajic, for giving me the opportunity to work under his supervision. His constant support and attention motivated me beyond my limits. I do …
The Impact of Imitation on Vaccination Behavior in Social Contact Networks

Previous game-theoretic studies of vaccination behavior typically have often assumed that populations are homogeneously mixed and that individuals are fully rational. In reality, there is heterogeneity in the number of contacts per individual, and individuals tend to imitate others who appear to have adopted successful strategies. Here, we use network-based mathematical models to study the effects of both imitation behavior and contact heterogeneity on vaccination coverage and disease dynamics. We integrate contact network epidemiological models with a framework for decision-making, within which individuals make their decisions either based purely on payoff maximization or by imitating the vaccination behavior of a social contact. Simulations suggest that when the cost of vaccination is high imitation behavior may decrease vaccination coverage. However, when the cost of vaccination is small relative to that of infection, imitation behavior increases vaccination coverage, but, surprisingly, also increases the magnitude of epidemics through the clustering of non-vaccinators within the network. Thus, imitation behavior may impede the eradication of infectious diseases. Calculations that ignore behavioral clustering caused by imitation may significantly underestimate the levels of vaccination coverage required to attain herd immunity.
Whither Tightness of Packing? The Case for Stable VM Placement

To date, virtual machine (VM) placement has traditionally been viewed as a bin packing problem where a number of virtual machines need to be placed on a given number of physical machines. The goal of bin packing based approach is to minimize the number of physical machines (PMs) used. However, the resource utilization of VMs is dynamic unlike static artifacts in the bin packing problem and varies with the workload being handled by applications on the VM. This means that tight packing may result in a situation where the applications run out of resources needed to handle the workload since there is no room to expand. This will trigger a VM migration to another PM having sufficient resources to allow the VM to expand. Migration is an expensive process both in terms of the resources needed for migration as well as in terms of the degradation of application performance during migration. A simple solution to this is to simply provision every VM for its peak usage-however this results in wasted resources since peaks occur infrequently and only for short durations of time. This peak usage based approach is one of very loose packing which is inefficient but provides stability of the VM in the context of migration. What we need is a balance between the tightness of packing (to optimize the number of physical machines used) and VM stability (to minimize the number of the migrations resulting from a given placement). In this paper we propose a metric to quantify this notion of balance and an algorithm to place VMs so as to minimize “imbalance”. We show through extensive experiments that balanced placement is not so loose as to be inefficient while giving us good stability as measured from the number of resource shortfalls that would occur with a given placement. In our experiments we found that balanced placement results in 15 to 80 percent lesser resource shortfalls and upto 90 percent less severe resource shortfalls when compared to other placement schemes.
Towards natural non-invasive hand neuroprostheses for daily living

In this paper we show how healthy subjects can operate a non-invasive asynchronous BCI for controlling a FES neuroprosthesis and manipulate objects to carry out daily tasks in ecological conditions. Both, experienced and novel subjects proved to be able to deliver mental commands with high accuracy and speed. Our neuroprosthetic approach relies on a natural interaction paradigm, where subjects delivers congruent MI commands (i.e., they imagining a movement of the same hand they control through FES). Furthermore, we have tested our approach in a common daily task such as handwriting, which requires the user to split his/her attention to multitask between BCI control, reaching, and the primary handwriting task itself. Interestingly, the very low number of erroneous trials illustrates how during the experiments subjects were able to deliver commands just when they intended to do so. Similarly, the subjects could perform actions while delivering, or preparing to deliver, mental commands.