Miracle-dz/UniFAR-FTUs huggingface dataset:
Steering of pedal wave of a snake-like robot by superposition of curvatures

A snake-like robot has an advantage in moving into a narrow space. Particularly “pedal wave”, which is one of its locomotion styles, is suitable for entering a thin path. However, pedal wave have been used only for going straight in most of the prior studies. In particular horizontal steering of pedal wave by a real robot has not been investigated sufficiently. Therefore in this paper we studied a kinematically appropriate steering method of pedal wave using a continuous snake-like robot model. We proposed steering by superposition of a steering curvatures and a pedal wave's curvature, and investigated the condition of suitable steering curvature using kinematic simulations. The proposed steering method was verified by experiments with a real snake-like robot.
Ultrastructure of the liver microcirculation influences hepatic and systemic insulin activity and provides a mechanism for age‐related insulin resistance

While age‐related insulin resistance and hyperinsulinemia are usually considered to be secondary to changes in muscle, the liver also plays a key role in whole‐body insulin handling and its role in age‐related changes in insulin homeostasis is largely unknown. Here, we show that patent pores called ‘fenestrations’ are essential for insulin transfer across the liver sinusoidal endothelium and that age‐related loss of fenestrations causes an impaired insulin clearance and hyperinsulinemia, induces hepatic insulin resistance, impairs hepatic insulin signaling, and deranges glucose homeostasis. To further define the role of fenestrations in hepatic insulin signaling without any of the long‐term adaptive responses that occur with aging, we induced acute defenestration using poloxamer 407 (P407), and this replicated many of the age‐related changes in hepatic glucose and insulin handling. Loss of fenestrations in the liver sinusoidal endothelium is a hallmark of aging that has previously been shown to cause deficits in hepatic drug and lipoprotein metabolism and now insulin. Liver defenestration thus provides a new mechanism that potentially contributes to age‐related insulin resistance.
Exploring Motivations For Engineers Without Borders- USA

With its rapid growth and near gender balance, Engineers Without Borders (EWB-USA) represents a model that the engineering field is striving towards. Understanding the motivations for members’ involvement and how it impacts their involvement with engineering can offer insight for how project-based organizations can influence recruitment and retention in the larger field. Using qualitative data, this research aimed to explore the motivations for EWB-USA involvement and for engineering involvement, and it compared how these motivations differed by organizational membership and gender. One hundred sixty-five engineers took part in the interviews and focus groups, representing a mix of males, females, professionals, and students from across the United States. Transcriptions were coded for motivations based on emergent themes and relative frequencies and patterns were reported. Trends showed that EWB-USA members were uniquely motivated by less-traditional engineering motivations than engineers not involved with the organization, and that the EWB-USA females, in particular, showed less traditional engineering motivations than the males. The findings also highlighted four ways in which EWB-USA impacts engineering motivations, which suggest that other project-based engineering organizations can leverage their unique position in the engineering field to help aid recruitment and retention of atypical engineers.
Scalable Analytics Model Calibration with Online Aggregation

Model calibration is a major challenge faced by the plethora of statistical analytics packages that are increasingly used in Big Data applications. Identifying the optimal model parameters is a time-consuming process that has to be executed from scratch for every dataset/model combination even by experienced data scientists. We argue that the lack of support to quickly identify sub-optimal configurations is the principal cause. In this paper, we apply parallel online aggregation to identify sub-optimal configurations early in the processing by incrementally sampling the training dataset and estimating the objective function corresponding to each configuration. We design concurrent online aggregation estimators and define halting conditions to accurately and timely stop the execution. The end-result is online approximate gradient descent—a novel optimization method for scalable model calibration. We show how online approximate gradient descent can be represented as generic database aggregation and implement the resulting solution in GLADE—a state-of-the-art Big Data analytics system.
Analysis on compact data formats for the performance of handwritten signature biometrics

This paper deals with the Signature Data Formats proposed by ISO 19794 project: 19794 part 7 Full Format and Compact Format (published in 2007) and the new 19794 part 11, which is under development. It will be shown how these formats handle the raw data coming from a Signature Input Device, and what the size of a Biometric Information Record is for each one. Another compression method, using LZ77 compression algorithm, is proposed and tested. The paper will also show the impact of using these compact formats on the performance of two different algorithms: Dynamic Time Warping and Gaussian Mixture Models. MCyT and SVC2004 signature databases have been used to carry out all tests.
An Improved Classification of Network Traffic Using Adaptive Nearest Cluster Based Classifier

In modern network security and management architecture, Classification of Traffic plays a major role in recent years. In particular, the process of intrusion detection and QOS control is considered as a essential components in traffic classification. Recent method uses statistical feature related classification approach with machine learning techniques for Traffic classification. Earlier method used several machine learning classifiers for classification purpose. Due to the lack of classifier performance in each aspect, the overall classification of traffic affected while least size of training data are used. To deal with this process, this paper proposes an efficient classifier called adaptive nearest cluster based classifier (ANCC-classifier). The proposed classifier classifies the traffic by collecting the statistical feature based correlated information. Such information is obtained by analysing the normal and abnormal flow of network. The present system is analysed in theoretical and experiential perspectives. Experimental result provides improved performance when compared with the other state of art methods.
Structural Characterization of the N Terminus of IpaC from Shigella flexneri

ABSTRACT The primary effector for Shigella invasion of epithelial cells is IpaC, which is secreted via a type III secretion system. We recently reported that the IpaC N terminus is required for type III secretion and possibly other functions. In this study, mutagenesis was used to identify an N-terminal secretion signal and to determine the functional importance of the rest of the IpaC N terminus. The 15 N-terminal amino acids target IpaC for secretion by Shigella flexneri, and placing additional amino acids at the N terminus does not interfere with IpaC secretion. Furthermore, amino acid sequences with no relationship to the native IpaC secretion signal can also direct its secretion. Deletions introduced beyond amino acid 20 have no effect on secretion and do not adversely affect IpaC function in vivo until they extend beyond residue 50, at which point invasion function is completely eliminated. Deletions introduced at amino acid 100 and extending toward the N terminus reduce IpaC's invasion function but do not eliminate it until they extend to the N-terminal side of residue 80, indicating that a region from amino acid 50 to 80 is critical for IpaC invasion function. To explore this further, the ability of an IpaC N-terminal peptide to associate in vitro with its translocon partner IpaB and its chaperone IpgC was studied. The N-terminal peptide binds tightly to IpaB, but the IpaC central hydrophobic region also appears to participate in this binding. The N-terminal peptide also associates with the chaperone IpgC and IpaB is competitive for this interaction. Based on additional biophysical data, we propose that a region between amino acids 50 and 80 is required for chaperone binding, and that the IpaB binding domain is located downstream from, and possibly overlapping, this region. From these data, we propose that the secretion signal, chaperone binding region, and IpaB binding domain are located at the IpaC N terminus and are essential for presentation of IpaC to host cells during bacterial entry; however, IpaC effector activity may be located elsewhere.
Towards an argument interchange format

The theory of argumentation is a rich, interdisciplinary area of research straddling the fields of artificial intelligence, philosophy, communication studies, linguistics and psychology. In the last few years, significant progress has been made in understanding the theoretical properties of different argumentation logics. However, one major barrier to the development and practical deployment of argumentation systems is the lack of a shared, agreed notation or ‘interchange format’ for argumentation and arguments. In this paper, we describe a draft specification for an argument interchange format (AIF) intended for representation and exchange of data between various argumentation tools and agent-based applications. It represents a consensus ‘abstract model’ established by researchers across fields of argumentation, artificial intelligence and multi-agent systems. In its current form, this specification is intended as a starting point for further discussion and elaboration by the community, rather than an attempt at a definitive, all-encompassing model. However, to demonstrate proof of concept, a use case scenario is briefly described. Moreover, three concrete realizations or ‘reifications’ of the abstract model are illustrated.
∈-entropy of piecewise polynomial functions and tree partitioning compression

Most of the signals in nature are piecewise smooth. One of the simple and yet efficient models for representing smooth signals is the class of piecewise polynomials. In this paper compression of this class of functions is considered. Some bounds are derived for the epsiv-entropy of this class of functions. These bounds show us the best performance the optimum compression scheme can have. By comparing it with the performance of traditional binary trees, it is demonstrated that the rate- distortion behavior of binary tree is far from optimum. We will then show that a simple modification of binary trees results in much better performance binary tree algorithms. This modification will retain all the advantages of binary trees.
Adolescent patients’ perspectives on rapid-refeeding: a prospective qualitative study of an inpatient population

ABSTRACT In this study, we aimed to examine the psychological experience over the course of an inpatient treatment implementing rapid-refeeding protocol for adolescent patients with anorexia nervosa (AN). Ten female participants were included in the study and interviews were conducted on a weekly basis over the course of their admissions. Interviews were audio-recorded and analysed according to the principles of thematic analysis. Results revealed a multidimensional process broadly implicating three phases of treatment; reconciling with the ‘AN’ diagnosis, adjustment to treatment, and reflection and integration. Overall, these phases demonstrated a complex and often ambivalent psychological process whereby acceptance for physical and medical interventions was often in conflict with individuals’ affective experiences. Clinical implications of these findings are discussed and avenues for future research highlighted.
The limits of automatic OS fingerprint generation

Remote operating system fingerprinting relies on implementation differences between OSs to identify the specific variant executing on a remote host. Because these differences can be subtle and difficult to find, most fingerprinting tools require expert manual effort to construct discriminative fingerprints and classification models. In prior work, Caballero et al. proposed a promising technique to eliminate manual intervention: the automatic generation of fingerprints using an approach similar to fuzz testing [6]. Their work evaluated the technique in a small-scale, carefully controlled test environment. In this paper, we re-examine automatic OS fingerprinting in a more challenging large-scale scenario to better understand the viability of the technique. In contrast to the prior work, we find that automatic fingerprint generation suffers from several limitations and technical hurdles that can limit its effectiveness, particularly in more demanding, realistic environments. We use machine learning algorithms from the well-known Weka [11] data mining toolkit to automatically generate fingerprints over 329 different machine instances, and we compare the accuracy of our automatically generated fingerprints to Nmap. Our results suggest that overfitting to non-OS-specific behavioral differences, the indistinguishability of different OS variants, the biasing of an automatic tool to the makeup of the training data, and the lack of ability of an automatic tool to exploit protocol and software semantics significantly limit the usefulness of this technique in practice. Automatic techniques can help identify candidate signatures, but our results suggest that manual expertise will remain an integral part of fingerprint generation.
A Probabilistic Model for Estimating Real-valued Truth from Conflicting Sources

One important task in data integration is to identify truth from noisy and conflicting data records collected from multiple sources, i.e., the truth finding problem. Previously, several methods have been proposed to solve this problem by simultaneously learning the quality of sources and the truth. However, all those methods are mainly designed for handling categorical data but not numerical data. While in practice, numerical data is not only ubiquitous but also of high value, e.g. price, weather, census, polls, economic statistics, etc. Quality issues on numerical data can also be even more common and severe than categorical data due to its characteristics. Therefore, in this work we propose a new truth-finding method specially designed for handling numerical data. Based on Bayesian probabilistic models, our method can leverage the characteristics of numerical data in a principled way, when modeling the dependencies among source quality, truth, and claimed values. Experiments on two real world datasets show that our new method outperforms existing state-of-the-art approaches.
The Rotating Molecular Core and Precessing Outflow of the Young Stellar Object Barnard 1c

We investigate the structure of the core surrounding the recently identified deeply embedded young stellar object Barnard 1c. B1c lies within the Perseus molecular cloud at a distance of 250 pc. It is a deeply embedded core of 2.4 M☉ (Kirk et al.) and a luminosity of 4 ± 2 L☉. Observations (and resolutions) of 12CO J = 1-0 (9.″2 × 5.″9), 13CO J = 1-0, C18O J = 1-0 (14.″3 × 6.″7), HCO+ J = 1-0 (7.″6 × 5.″8), and N2H+ J = 1-0 (5.″9 × 4.″6) were obtained with the BIMA array, together with the continuum at 3.3 mm (6.″4 × 4.″9) and 2.7 mm (9.″5 × 6.″3). Single-dish measurements of N2H+ J = 1-0 and HCO+ J = 1-0 with FCRAO reveal the larger scale emission in these lines with ~60 resolution. The 12CO and HCO+ emission traces the outflow extending over the full field of view (2.′1), which coincides in detail with the S-shaped jet recently found in Spitzer IRAC imaging. The N2H+ emission, which anticorrelates spatially with the C18O emission, originates from a rotating envelope with effective radius ~2400 AU and mass 2.1-2.9 M☉, as derived from the 3.3 mm continuum emission. N2H+ emission is absent from a 600 AU diameter region around the young star, offset from the continuum peak. The remaining N2H+ emission may lie in a coherent torus of dense material. With its outflow and rotating envelope, B1c closely resembles the previously studied object L483 mm, and we conclude that it is a protostar in an early stage of evolution, i.e., Class 0 or in transition between Class 0 and Class I. We hypothesize that heating by the outflow and star has desorbed CO from grains, which has destroyed N2H+ in the inner region, and surmise that the presence of grains without ice mantles in this warm inner region can explain the unusual polarization signature observed from B1c.
Visual-VM: A Social Network Visualization Tool for Viral Marketing

The paper presents Visual-VM, a social network visualization tool, where the main focus is to provide utilities for viral marketing (e.g., Influence maximization). Besides, Visual-VM utilizes the location information of each user (which could be estimated from the user's profile) for social network visualization, which is not used in existing social network visualization tools. Visual-VM also supports common utilities for social network exploration.
FAQ Mining via List Detection

This paper presents an approach to FAQ mining via a list detection algorithm. List detection is very important for data collection since list has been widely used for representing data and information on the Web. By analyzing the rendering of FAQs on the Web, we found a fact that all FAQs are always fully/partially represented in a list-like form. There are two ways to author a list on the Web. One is to use some specific tags, e.g. tag for HTML. The lists authored in this way can be easily detected by parsing those special tags. Another way uses other tags instead of the special tags. Unfortunately, many lists are authored in the second way. To detect lists, therefore, we present an algorithm, which is independent of Web languages. By combining the algorithm with some domain knowledge, we detect and collect FAQs from the Web. The mining task achieved a performance of 72.54% recall and 80.16% precision rates.
Automatic Document Classification - A thorough Evaluation of various Methods

(Automatic) document classification is generally defined as content-based assignment of one or more predefined categories to documents. Usually, machine learning, statistical pattern recognition, or neural network approaches are used to construct classifiers automatically. In this paper we thoroughly evaluate a wide variety of these methods on a document classification task for German text. We evaluate different feature construction and selection methods and various classifiers. Our main results are: (1) feature selection is necessary not only to reduce learning and classification time, but also to avoid overfitting (even for Support Vector Machines); (2) surprisingly, our morphological analysis does not improve classification quality compared to a letter 5-gram approach; (3) Support Vector Machines are significantly better than all other classification methods.
LAMINAR FLOW IN CONCENTRIC ANNULUS WITH A MOVING CORE

An analytical solution and a numerical analysis are presented to study the flow behavior in concentric annulus with moving core in pipe for laminar flow condition. The analytical analysis is presented as exact solution for steady, fully developed and one dimensional flow. The numerical model is presented to study two-dimensional, steady, developing and fully developed flows. The numerical model established a staggered grid for axial and radial velocities (Vz and Vr) and using the pressure correction technique. The analyses were used to predict not only the fully developed velocity profile for negative, zero, and adverse pressure gradient but also the developing velocity profiles until the flow becomes fully developed. The developing length (entrance length), the boundary layer thickness and the pressure distribution along the moving core at different Reynolds numbers are also obtained by the presented model. The drag coefficient relative to both the moving core wall and the pipe wall can be calculated at different Reynolds numbers and diameter ratios. The results of the laminar model were shown to be in good agreement with the analytical (exact) solution for the fully developed flow. This also gives confidence to the obtained results for the developing flow through the entrance length using the numerical model.
Risk modeling and simulation of airport passenger departures process

Airport security is a key element of Homeland Security strategy. This paper presents a process based approach to modeling and simulation of airport security. A quantitative measure of process security, called permeability, has been defined. We use the Simplifying Passenger Travel (SPT) model of passenger departures process to illustrate the methodology. Monte Carlo simulation has been used for the departure process to determine passenger process permeability of large airport hubs in a system of interconnected airports. We show the relative impact of the Registered Traveler scheme on improving the security of the passenger departure process. The proposed approach would allow decision makers (a) to identify weakest links in a security chain; (b) to assess quantitatively the impact of deploying specific security technologies on the overall process security; and (c) to help choose the optimum technical solutions to achieve the security goals for a given process in an operational environment.
Update: the Hoosier Audiovisual Multi-talker Database Research on Spoken Language Processing Update: the Hoosier Audiovisual Multi-talker Database Update: the Hoosier Audiovisual Multi-talker Database

This report describes continued work on the intelligibility of the stimuli contained in the Hoosier Audiovisual Multi-Talker Database (Sheffert, Lachs, & Hernandez, 1997). All the stimuli in the database have now been tested using three different presentation conditions: audio-visual, audio-only, and visual only. A re-analysis of the audio-visual data, together with new analyses of the data obtained in the unimodal conditions, revealed significant effects of presentation modality. The data are presented as summary statistics for use in the selection of stimuli from the database.
Differences in Ventilatory Threshold for Exercise Prescription in Outpatient Diabetic and Sarcopenic Obese Subjects

Aim of the study was to examine cardiorespiratory parameters at individual ventilatory threshold (IVT) and peak exercise capacity (V˙O2peak) in outpatient diabetic and sarcopenic obese subjects. Seventeen obese subjects (BMI: 36.6 ± 4.1 kg·m−1) and sixteen SO subjects (BMI: 37.0 ± 7.3 kg·m−1) were compared with sixteen T2DM subjects (BMI: 37.7 ± 5.6 kg·m−1). All groups performed an incremental exercise test on a treadmill according to their physical ability. V˙O2peak, %HRmax, and maximal metabolic equivalent (METmax) were evaluated at maximal effort. Moreover, V˙O2ivt, %V˙O2peak, %HRmax, %HRR, ΔHR, and METivt were assessed at IVT. No significant differences were found in any physiological parameters at maximal effort (V˙O2peak, %HRmax, and METmax) in all groups. On the contrary, V˙O2ivt, %V˙O2peak, %HRmax, %HRR, ΔHR, and METivt were significantly lower in T2DM subjects as compared to OB and SO subjects at IVT (p < 0.05). Our results show that while at maximal effort there are no differences among groups, at IVT the physiological parameters are lower in T2DM subjects than in OB and SO subjects. Therefore, due to the differences observed in the groups, we suggest usng the IVT as a useful parameter to prescribe aerobic exercise in obese with sarcopenia or diabetes mellitus conditions.