Miracle-dz/UniFAR-FTUs huggingface dataset:
Opinion Mining Using The Prosodic Information In The Spoken Utterance's For Efficient Dialogue Management In A Spoken Dialogue System

Utterances used by a human while framing a response during the interaction with a software agent like Spoken Dialogue system(SDS) carries a lot of valuable information with regards to the internal mental state of the user. Opinion mining is an analysis of the mental state of a person, his opinion, appraisal or emotion towards an event, entities or their attributes. The users level of certainty about a topic could be determined by the analysis not only of the text used in the utterance but by studying the prosody information structure. Prosody reveals Information about the context by highlighting information structure and aspects of the speaker hearer relationship. Most often it is observed that the speaker's internal state is not depicted by the words he uses but by the tone of his utterance or facial expression of the user. In this paper we had analyzed a sample of student's after a lecture on operating system's subject and used the prosodic features of each dialogue with the student based on few questions. It was determined whether the students were certain, uncertain or neutral about their understanding of the lecture contents. This paper uses PRAAT a software tool for speech analysis which uses 15 acoustic features to determine the certainty of the responses of the user through classification by RAPIDMINER based on the prosody information which will actually aid the dialogue management component of the SDS in framing a better dialogue strategy.
Mapping land subsidence over the eastern Beijing city using satellite radar interferometry*

ABSTRACT Beijing City has suffered from groundwater-induced subsidence since the late 1930s and the over-exploration of groundwater could lead to subsidence as much as −12.0 cm yr−1. Previous studies on the ground deformation at Beijing City mainly focused on the period before the year of 2014 when a mega-engineering project was launched to reduce water shortage in Beijing. To study the most recent ground deformation, 19 L-band ALOS-1 PALSAR images (June 2007–January 2011), 24 C-band Sentinel-1 SAR images (June 2015–November 2016) together with 9 ALOS-2 PALSAR acquisitions (September 2014–February 2017) were analysed in this work. Levelling measurements were exploited to verify the ALOS-1-based time series InSAR (TS-InSAR) result while Sentinel-1 and ALOS-2 result were cross-verified with each other. Furthermore, the whole study area was divided into four sub-zones, and the result indicated that the subsidence rates over five townships, Cuigezhuan, Jinzhan, Liyuan, Songzhuang and Yanjiao were accelerating and more attentions should be paid. On the contrary, the town centre of Douge Zhuang township experienced a decreasing trend between these two temporal-periods. Additionally, the time series measurements with respect to five selected measurement points and the profile line along the subsidence hot spots were analysed.
Verification of Hypermedia Characteristic of RESTful Finite-State Machines

Being an architectural style rather than a specification or a standard, the proper design of REpresentational State Transfer (REST) APIs is not trivial, since developers have to deal with a flood of recommendations and best practices, especially the proper application of the hypermedia constraint requires some decent experience. Furthermore, testing RESTful APIs is a missing topic within literature and especially, hypermedia testing is not mentioned at all. To deal with this state of affairs, we have elaborated a Model-Driven Software Development (MDSD) approach for creating RESTful APIs. As this project matured, we also explored the possibility of Model-Driven Testing (MDT). This work addresses the challenges of hypermedia testing and proposes approaches to overcome them with MDT techniques. We present the results of hypermedia testing for RESTful APIs using a model verification approach that were discovered within our research. MDT enables the verification of the underlying model of a RESTful API and ensuring its correctness before initiating any code generation. Therefore, we can prevent a poorly designed model from being transformed into a poorly designed RESTful API.
A compressed framework for monitoring and anomaly detection in cloud networks

The frequent and increased number of attacks on large-scale cloud networks have necessitated a need for scalable network analyzing techniques for monitoring and anomaly/threat detection. This paper presents a lightweight Discrete Cosine Transform (DCT) based network analysis and measurement approach that can identify, visualize along with mid level categorization of attacks and anomalies in cloud traffic. We propose a network data traffic flow in terms of frames or images. With such an approach, a series of samples can represent a sequence of frames or video, thus, allows categorization/identification of various kinds of attacks. This enables various image processing techniques that can be applied to the network traffic to analyze interesting features of cloud platform traffic. We use two real world datasets and show that 2 dimensional DCT may be used to obtain reduction in storage of network traffic traces up to 98 percent in lossy compressed format. Similarly, 1 dimensional DCT provides efficient anomaly detection in the network revealing hidden anomalous trends. We also compare the efficacy of 1 dimensional Discrete Cosine Transform (DCT) based approach with classical 1 dimensional Fast Fourier Transform (FFT) based approach for anomaly detection.
From Infanticide to Parental Care: Why Spatial Structure Can Help Adults Be Good Parents

We investigate the evolution of parental care and cannibalism in a spatially structured population where adults can either help or kill juveniles in their neighborhood. We show that spatial structure can reverse the selective pressures on adult behavior, leading to the evolution of parental care, whereas the nonspatial model predicts that cannibalism is the sole evolutionary outcome. Our analysis emphasizes that evolution of such spatially structured populations is best understood at the level of the cluster of invading mutants, and we define invasion fitness as the growth rate of that cluster. We derive an analytical expression for the selective pressures on the trait and show that relatedness and Hamilton’s rule are recovered as emergent properties of the spatial ecological dynamics. When adults can also help other adults, the benefits to each class of recipients are weighted by the class reproductive value, a result consistent with that of other models of kin selection. Finally, we advocate a different approach to moment equations and argue that even though the development of moment closure approximations is a necessary line of research, much‐needed ecological and evolutionary insight can be gained by studying the unclosed moment equations.
Synthetic spatially graded Rac activation drives cell polarization and movement

Migrating cells possess intracellular gradients of active Rho GTPases, which serve as central hubs in transducing signals from extracellular receptors to cytoskeletal and adhesive machinery. However, it is unknown whether shallow exogenously induced intracellular gradients of Rho GTPases are sufficient to drive cell polarity and motility. Here, we use microfluidic control to generate gradients of a small molecule and thereby directly induce linear gradients of active, endogenous Rac without activation of chemotactic receptors. Gradients as low as 15% were sufficient not only to trigger cell migration up the chemical gradient but to induce both cell polarization and repolarization. Cellular response times were inversely proportional to the steepness of Rac inducer gradient in agreement with a mathematical model, suggesting a function for chemoattractant gradient amplification upstream of Rac. Increases in activated Rac levels beyond a well-defined threshold augmented polarization and decreased sensitivity to the imposed gradient. The threshold was governed by initial cell polarity and PI3K activity, supporting a role for both in defining responsiveness to Rac activation. Our results reveal that Rac can serve as a starting point in defining cell polarity. Furthermore, our methodology may serve as a template to investigate processes regulated by intracellular signaling gradients.
Dielectric electrorheological fluids: Theory and experiment

Electrorheological (ER) fluids are a class of materials whose rheological properties are controllable by the application of an electric field. A dielectric electrorheological (DER) fluid is the simplest type of ER fluid, in which the material components follow a linear electrostatic response. We review and discuss the progress of the studies on physics of this type of material. A first-principles theory of DER fluids, along with relevant experimental verifications, are presented in some detail. In particular, the properties presented include static equilibrium structure, shear modulus, static yield stress and its variation with applied electric field frequency, and structure-induced dielectric nonlinearity.
Online Handwriting Recognition of Tamil Script Using Fractal Geometry

We present a fractal coding method to recognize online handwritten Tamil characters and propose a novel technique to increase the efficiency in terms of time while coding and decoding. This technique exploits the redundancy in data, thereby achieving better compression and usage of lesser memory. It also reduces the encoding time and causes little distortion during reconstruction. Experiments have been conducted to use these fractal codes to classify the online handwritten Tamil characters from the IWFHR 2006 competition dataset. In one approach, we use fractal coding and decoding process. A recognition accuracy of 90% has been achieved by using DTW for distortion evaluation during classification and encoding processes as compared to 78% using nearest neighbor classifier. In other experiments, we use the fractal code, fractal dimensions and features derived from fractal codes as features in separate classifiers. While the fractal code is successful as a feature, the other two features are not able to capture the wide within-class variations.
On interleaver design for BICM system with low error-floors

In this paper, we propose a design rule of a modified quasi-cyclic structure for bit-interleaved coded modulation with iterative decoding (BICM-ID). The proposed structure is especially favorable for low-complexity hardware implementation. In addition, it will be shown that the proposed structure effectively removes low-weight codewords, which in turn greatly improves error-floor behaviors as compared to quasi-cyclic interleaver.
Preparation of Mesoporous Sb‐, F‐, and In‐Doped SnO2 Bulk Powder with High Surface Area for Use as Catalyst Supports in Electrolytic Cells

The M‐doped tin oxides (M = Sb, F, and In) to be used as catalyst support are synthesized by using templating process with tetradecylamine (TDA) as the template, combined with a hydrothermal (HT) method to improve its thermal stability. The obtained materials are characterized by XRD, SAXS, TEM, EDX, SEM, and BET to study microstructure and physical properties, which have a mesoporous structure, small particle size, and high surface area (125–263 m2 g–1). The materials show an overall conductivity of 0.102–0.295 S cm–1. Repetitive potential cycling is employed to characterize the electrochemical properties and stability. The M‐doped tin oxides are highly electrochemical stable compared to carbon black. From the observed results, it can be concluded that the combination of TDA and HT treatment are an effective synthetic method for designing mesoporous M‐doped tin oxide as catalyst supports.
Robust energy demodulation based on continuous models with application to speech recognition

In this paper, we develop improved schemes for simultaneous speech interpolation and demodulation based on continuous-time models. This leads to robust algorithms to estimate the instantaneous amplitudes and frequencies of the speech resonances and extract novel acoustic features for ASR. The continous-time models retain the excellent time resolution of the ESAs based on discrete energy operators and perform better in the presence of noise. We also introduce a robust algorithm based on the ESAs for amplitude compensation of the filtered signals. Furthermore, we use robust nonlinear modulation features to enhance the classic cepstrum-based features and use the augmented feature set for ASR applications. ASR experiments show promising evidence that the robust modulation features improve recognition.
Understanding the link between exposure to violence and aggression in justice-involved adolescents

Abstract The current study advanced research on the link between community violence exposure and aggression by comparing the effects of violence exposure on different functions of aggression and by testing four potential (i.e., callous–unemotional traits, consideration of others, impulse control, and anxiety) mediators of this relationship. Analyses were conducted in an ethnically/racially diverse sample of 1,216 male first-time juvenile offenders (M = 15.30 years, SD = 1.29). Our results indicated that violence exposure had direct effects on both proactive and reactive aggression 18 months later. The predictive link of violence exposure to proactive aggression was no longer significant after controlling for proactive aggression at baseline and the overlap with reactive aggression. In contrast, violence exposure predicted later reactive aggression even after controlling for baseline reactive aggression and the overlap with proactive aggression. Mediation analyses of the association between violence exposure and reactive aggression indicated indirect effects through all potential mediators, but the strongest indirect effect was through impulse control. The findings help to advance knowledge on the consequences of community violence exposure on justice-involved youth.
Do Poor Countries Really Need More IT?

Productivity differences across countries are often attributed to differences in technological capabilities. This paper asks whether there are systematic cross-country differences in the adoption of information technologies (IT). We document a positive correlation between IT use and income, which weakens over time. However, given that IT use is an endogenous outcome of both technological capabilities and the abundance of complementary factors of production, it tends to over-state the degree of cross-country differences in technology. We propose two novel calibration approaches to address this problem. After accounting for endogenous differences in industrial composition, we find that there is no systematic relationship between income and IT capabilities.
Deriving Validity Time in Knowledge Graph

Knowledge Graphs (KGs) are a popular means to represent knowledge on the Web, typically in the form of node/edge labelled directed graphs. We consider temporal KGs, in which edges are further annotated with time intervals, reflecting when the relationship between entities held in time. In this paper, we focus on the task of predicting time validity for unannotated edges. We introduce the problem as a variation of relational embedding. We adapt existing approaches, and explore the importance example selection and the incorporation of side information in the learning process. We present our experimental evaluation in details.
An LTB-entrapped protein in PLGA nanoparticles preserves against enterotoxin of enterotoxigenic Escherichia coli

Objective(s): Enterotoxigenic Escherichia coli (ETEC) is known as the most common bacterial causes of diarrheal diseases related to morbidity and mortality. Heat-labile enterotoxin (LT) is a part of major virulence factors in ETEC pathogenesis. Antigen entrapment into nanoparticles (NPs) can protect them and enhance their immunogenicity. Materials and Methods: In the present study, recombinant LTB protein was expressed in E. coli BL21 (DE3) and purified by an Ni-NTA agarose column. The protein was entrapped in PLGA polymer by the double emulsion method. NPs were characterized physicochemically and the protein release from the NPs was evaluated. ELISA assay was performed for investigation of raised antibody against the recombinant protein in mice. The anti-toxicity and anti-adherence attributes of the immune sera against ETEC were also evaluated. Results: It showed the successful cloning of a 313 bp DNA fragment encoding LTB protein in the pET28a vector. Over-expression in BL21 (DE3) led to the formation of corresponding 15.5 kDa protein bands in the SDS-PAGE gel. Western blotting by using anti-CTX confirmed the purified LTB. Protein-entrapped NPs had a spherical shape with the size of 238 nm mean diameter and 85% entrapment efficiency. Immunological analyses showed the production of a high titer of specific IgG antibody in immunized animals. The neutralizing antibody in the sera of immunized animals was approved by GM1 binding and Ileal loop assays. Conclusion: The results indicate the efficacy of the entrapped LTB protein as an effective immunogen which induces the humoral responses.
Understanding Real and Fictional Persons: Narrative Negotiations Seen Through Cognitive Poetics

Abstract Narrative theories of personal identity have traditionally taken literary characters as models to better understand how our identities are constituted through the narratives of our lives. However, there have been several recent criticisms of these comparisons, showing that philosophers of personal identity paid no attention to the nature of literary characters, and consequently, these philosopher’s comparisons were under-motivated. In the present article, I rely on a cognitive framework to define literary characters. From that point of view, I assert that it is fruitful to compare ourselves with literary characters to understand the role that the narratives told by others about ourselves play in the constitution of our own personal identities.
A Story Coherence based Neural Network Model for Predicting Story Ending

Predicting the ending of a story is an interesting issue that has attracted considerable attention, as in case of the ROC Story Cloze Task (SCT). Although several studies have addressed this issue, the performance remains unsatisfactory due to ineffectiveness of story comprehension. In this paper, we propose to construct a story coherence based neural network model (SCNN) with well-designed optimizations. The preliminary evaluation demonstrates the effectiveness of our model which is superior to that of state-of-the-art approaches.
Interaction and non-neutral effects of factors in Chinese wheat production

In this paper we examine the role of the interaction between labour productivity and the use of factors in explaining the recent (1998-2007) 11% decline in wheat production in China. We employ a non-neutral stochastic production frontier approach that enables us to identify the interaction and non-neutral effects of factors that are used in wheat production. For regional level wheat production in China we find that identifying the technical inefficiency effects and the non-neutral effects of factors assist big time in explaining the recent decline in wheat production. A higher level of labour productivity can stimulate efficiency gains in production, but adding more labour to the workforce or adding to the stock of machinery power can depress this potential marginal efficiency gain. We also find significant marginal efficiency gain of land reforms that add to the stock of cultivable land. Our results indicate that future agricultural reforms in China should address the incentive scheme for labour.
Related Securities, Allocation of Attention and Price Discovery: Evidence from NYSE-Listed Non-U.S. Stocks

In this paper we explore how the composition of a market maker's portfolio and allocation of attention across securities in the portfolio affect pricing. We analyze whether more attention devoted to similar securities enables a market maker to extract information relevant to a stock from order flow to related securities and consequently whether it leads to improved price discovery of the stock. We base on the recent literature on allocation of attention in share trading (Corwin and Coughenour, 2008; Boulatov et al., 2009) and define the prominence of a security as the proportion of its dollar volume in the total volume of the specialist portfolio it belongs to. Our empirical tests are focused on New York Stock Exchange specialists and the U.S. share in price discovery of 64 British and French companies cross-listed on the NYSE. We define related securities as stocks from the same country, the same region or other foreign stocks. We find strong evidence that an increase in the prominence of related stocks in the specialist portfolio leads to a higher U.S. share in price discovery of our sample stocks. We interpret our findings as evidence that concentrating market makers in similar stocks reduces information asymmetries and improves the information environment. To support our argument, we show that an increase in the prominence of other foreign stocks in the specialist portfolio significantly reduces the adverse selection component of the bid-ask spread.
Scattering properties of dense media from Monte Carlo simulations with application to active remote sensing of snow

Monte Carlo simulations are used to derive the phase matrix, effective permittivity, and scattering coefficient for a random medium consisting of densely packed spheres up to 5000 in number. The results include correlated scattering and coherent wave interaction among the scatterers. The Monte Carlo simulations are based on a multiple-scattering formulation of the Foldy-Lax equations. It is shown that the derived phase matrix is in good agreement with dense media radiative transfer theory for copolarized scattering. The depolarization, however, can be substantially larger than conventional theory. Two methods are used to analyze the behavior of the coherent wave to obtain the real part of the effective permittivity. For the small particle case both methods yield values of permittivity that agree with the results of mixing formulas such as the Clausius-Mossoti mixing formula. The phase matrix and scattering coefficient obtained by simulation are used in a second-order radiative transfer model to predict the amount of backscatter from a layer of snow. It is also shown that sticky spheres, which can be used to model metamorphosed snow, produce high levels of copolarized and depolarized backscatter that can exceed the independent scattering model.
Extending the software tool TimeNET by power consumption estimation of UML MARTE models

This paper presents an extension of the software tool TimeNET, which supports modeling and analysis of stochastic Petri nets. The new extension implements a previously proposed method for power consumption modeling and evaluation based on extended UML models. Two new net classes have been developed to support the necessary operational and application models and edit them in the graphical user interface. Several stereotypes of the UML Profile for Modeling and Analysis of Real-Time and Embedded Systems (MARTE) have been added to extend the information about power consumption of system and application states. The two UML models are then automatically transformed into a stochastic Petri net. Power consumption of the system can be predicted by standard Petri net analysis modules of TimeNET. An example of an industrial control system is provided.