Miracle-dz/UniFAR-FTUs huggingface dataset:
First Insight into Quality-Adaptive Dialogue

While Spoken Dialogue Systems have gained in importance in recent years, most systems applied in the real world are still static and error-prone. To overcome this, the user is put into the focus of dialogue management. Hence, an approach for adapting the course of the dialogue to Interaction Quality, an objective variant of user satisfaction, is presented in this work. In general, rendering the dialogue adaptive to user satisfaction enables the dialogue system to improve the course of the dialogue and to handle problematic situations better. In this contribution, we present a pilot study of quality-adaptive dialogue. By selecting the confirmation strategy based on the current IQ value, the course of the dialogue is adapted in order to improve the overall user experience. In a user experiment comparing three different confirmation strategies in a train booking domain, the adaptive strategy performs successful and is among the two best rated strategies based on the overall user experience.
Comparison of keyword spotting approaches for informal continuous speech

This paper describes several approaches to keyword spotting (KWS) for informal continuous speech. We compare acoustic keyword spotting, spotting in word lattices generated by large vocabulary continuous speech recognition and a hybrid approach making use of phoneme lattices generated by a phoneme recognizer. The systems are compared on carefully deﬁned test data extracted from ICSI meeting database. The acoustic and phoneme-lattice based KWS are based on a phoneme recognizer making use of temporal-pattern (TRAP) feature extraction and posterior estimation using neural nets. We show its superiority over traditional HMM/GMM systems. The advantages and drawbacks of different approaches are discussed
Designing and Validating an Agent-Based Commodity Trading Simulation

In this paper, an agent-based commodity trading simulation platform is presented, with focus on simulation design and validation. We propose a novel combination of event-based approach and event study method for market dynamics generation and validation. In our event-based approach, the simulation is progressed by announcing news events that affect various aspects of the commodity supply chain. Upon receiving these events, market agents that play different roles, e.g., producers, consumers, and speculators, would adjust their views on the market and act accordingly. Their actions would be based on their roles and also their private information, and collectively the market dynamics will be shaped. The generated market dynamics can then be validated by a variant of the event study method. We demonstrate how the methodology works with several numerical experiments and conclude by highlighting the practical significance of such simulation platform.
A far-infrared view of the Lockman hole from ISO 95-μm observations - II. Optical identifications and insights into the nature of the far-infrared sources

We present the optical identifications of a 95-μm ISOPHOT sample in the Lockman hole over an area of approximately half a deg 2 . The Rodighiero et al. catalogue includes 36 sources, making up a complete flux-limited sample for S 95μm ≥ 100 mJy. Reliable sources were detected, with decreasing but well-controlled completeness, down to S 95μm ≃ 20 mJy. We have combined mid-infrared (IR) and radio catalogues in this area to identify the potential optical counterparts of the far-IR sources. We found 14 radio and 13 15-μm associations, 10 of which have both associations. For the 11 sources with spectroscopic redshift, we have performed a spectrophotometric analysis of the observed spectral energy distributions (SEDs). Four of these 95-μm sources have been classified as faint IR (FIR) galaxies (L FIR < 1.e11 L), six as luminous IR galaxies (LIRGs) and only one as an ultraluminous IR galaxy (ULIRG). We have discussed the redshift distribution of these objects, comparing our results with evolutionary model predictions 95 and 175 μm. Given their moderate distances (the bulk of the closest spectroscopically identified objects lying at z < 0.2), their luminosities and star formation rates (SFR; median value ∼ 10 M yr -1 ), the sources unveiled by ISOPHOT at 95 μm seem to correspond to the low redshift (z < 0.3) FIRBACK 175-μm population, composed of dusty, star-forming galaxies with moderate SFRs. We computed and compared different SFR estimators, and found that the SF derived from the bolometric IR luminosity is well correlated with that computed from the radio and mid-IR fluxes.
Flexibility and Fragility: Use of Chaining Strategies in the Presence of Disruption Risks

Michael Lim• Achal Bassamboo• Sunil Chopra• Mark S. Daskin Department of Business Administration, University of Illinois, Urbana-Champaign, IL 61820, USA • mlim@illinois.edu Department of Managerial Economics and Decision Sciences, Northwestern University, Evanston, IL 60208, USA • a-bassamboo@kellogg.northwestern. edu; s-chopra@kellogg.northwestern. edu Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA • msdaskin@umich.edu
Effects on hepatic microsomal N- and C-oxygenation of aromatic amines by in vivo corticosteroid or aminofluorene treatment, diet, or stress.

Various environmental, endocrine, and inherent conditions, which enhance the tumor-inducing capacity of carcinogenic aromatic amines, are associated with an increased N -oxygenation of these compounds in vivo . The impairment of protein-synthesizing functions of liver ribosomes by reactive metabolites of the carcinogenic amines is, to a certain extent, correlated with these functions. Here the effect of similar conditions on microsomal N -oxygenation and C -oxygenation in vitro was studied, using N -oxide formation and N -demethylation of N,N -dimethylaniline as test reactions. Increased microsomal N -oxygenation, and to a considerably smaller extent N -demethylation was obtained by in vivo treatment with corticosteroid or 2-aminofluorene. Vitamin E depletion, protein deficiency, or stress caused an increase in N -oxygenation accompanied by decreased N -demethylation. These conditions thus gave rise to an increase in the N -oxygenation/ C -oxygenation ratio. Guinea pig liver microsomes showed a higher N -demethylating capacity and a lower N -oxygenation than did rat liver microsomes. The N -oxygenation of N, N -dimethylaniline by liver microsomes thus seems to be well correlated with the tumor-inducing properties and ribosome-impairing capacity of carcinogenic aromatic amines under various in vivo conditions.
Measurement–based traffic modeling: capturing important statistics

Measurement–based traffic characterization has come to acquire a great deal of importance in high–speed networks. In this paper, we segregate the traffic behaviour into the macrodynamics and microdynamics depending on the time scales at which the process is observed. Our study compares the queueing solutions obtained using traffic models with very different underlying structure viz. Markovian vs non–Markovian which are identical only in their second–order and steady–state statistics. Our study suggests that higher–order traffic statistics are generally unimportant to queueing solutions. We also investigate the effect of the microdynamics of the traffic on the queueing performance. The microdynamics are associated with the cell interarrival times and bulk arrivals. We show that the second–order statistics of the microdynamics are well captured by white noise in the arrival process power spectrum, which can have a significant impact on the queueing performance. A new modeling technique is introduced in whic.
PEERING: An AS for Us

Internet routing suffers from persistent and transient failures, circuitous routes, oscillations, and prefix hijacks. A major impediment to progress is the lack of ways to conduct impactful interdomain research. Most research is based either on passive observation of existing routes, keeping researchers from assessing how the Internet will respond to route or policy changes; or simulations, which are restricted by limitations in our understanding of topology and policy. We propose a new class of interdomain research: researchers can instantiate an AS of their choice, including its intradomain topology and interdomain interconnectivity, and connect it with the "live" Internet to exchange routes and traffic with real interdomain neighbors. Instead of being observers of the Internet ecosystem, researchers become members. Towards this end, we present the Peering testbed. In its nascent stage, the testbed has proven extremely useful, resulting in a series of studies that were nearly impossible for researchers to conduct in the past. In this paper, we present a vision of what the testbed can provide. We sketch how to extend the testbed to enable future innovation, taking advantage of the rise of IXPs to expand our testbed.
Semantic segmentation with heterogeneous sensor coverages

We propose a new approach to semantic parsing, which can seamlessly integrate evidence from multiple sensors with overlapping but possibly different fields of view (FOV), account for missing data and predict semantic labels over the spatial union of sensors coverages. The existing approaches typically carry out semantic segmentation using only one modality, incorrectly interpolate measurements of other modalities or at best assign semantic labels only to the spatial intersection of coverages of different sensors. In this work we remedy these problems by proposing an effective and efficient strategy for inducing the graph structure of Conditional Random Field used for inference and a novel method for computing the sensor domain dependent potentials. We focus on RGB cameras and 3D data from lasers or depth sensors. The proposed approach achieves superior performance, compared to state of the art and obtains labels for the union of spatial coverages of both sensors, while effectively using appearance or 3D cues when they are available. The efficiency of the approach is amenable to realtime implementation. We quantitatively validate our proposal in two publicly available datasets from indoors and outdoors real environments. The obtained semantic understanding of the acquired sensory information can enable higher level tasks for autonomous mobile robots and facilitate semantic mapping of the environments.
A comparative analysis of routing architecture for information dissemination in Vehicular Ad Hoc Networks

Recently there has been considerable attention for developing information dissemination architecture for Vehicular Ad Hoc Networks (VANETs). Information dissemination is considered an indispensable requirement for various VANETs applications. Packet Delivery Ratio (PDR), End to End Delay and Congestion phenomena are the factors which are directly affected by information dissemination architecture in VANETs. A conceptual architecture for information dissemination in VANETs is presented in this paper. The main goal is to optimize routing performance by categorical division of information being processed namely safety to life, safety and non safety information. The calculations for most stable links, near optimal suitable weights for link selection parameters, link stability and life time of under considered link will help improving performance factors for the hybrid architecture. The simulations performed confirm our architecture batter in terms of packet delivery ratio, end to end delay and routing overhead as compared to the other approaches under consideration.
Model-based learning with Bayesian and MAXQ value function decomposition for hierarchical task

How to improve efficiency of learning is always the key issue for implementation of reinforcement learning. This paper makes use of advantages of both hierarchical learning and model-based learning, so that an improved algorithm, named Bayesian-MAXQ learning, is introduced, in which several modifications are developed to solve the value update of hierarchy, while the possible performance damages brought by prioritized sweeping is reduced to trivial. The simulation results show that, Bayesian-MAXQ learning performs with high efficiency, and it can serve as a good framework for further study on hierarchical model-based learning.
Efficient Semiparametric Detection of Changes in Trend

This paper proposes a test for the correct specification of a dynamic time-series model that is taken to be stationary about a deterministic linear trend function with no more than a finite number of discontinuities in the vector of trend coefficients. The test avoids the consideration of explicit alternatives to the null of trend stability. The proposal also does not involve the detailed modelling of the data-generating process of the stochastic component, which is simply assumed to satisfy a certain strong invariance principle for stationary causal processes taking a general form. As such, the resulting inference procedure is effectively an omnibus specification test for segmented linear trend stationarity. The test is of Wald-type, and is based on an asymptotically linear estimator of the vector of total-variation norms of the trend parameters whose influence function coincides with the efficient influence function. Simulations illustrate the utility of this procedure to detect discrete breaks or continuous variation in the trend parameter as well as alternatives where the trend coefficients change randomly each period. This paper also includes an application examining the adequacy of a linear trend-stationary specification with infrequent trend breaks for the historical evolution of U.S. real output.
Probing the Regulation of M (Kv7) Potassium Channels in Intact Neurons with Membrane-Targeted Peptides

M-type (Kv7) potassium channels are closed by Gq/11 G-protein-coupled receptors. Several membrane- or channel-associated molecules have been suggested to contribute to this effect, including depletion of phosphatidylinositol-4,5-bisphosphate (PIP2) and activation of Ca2+/calmodulin and protein kinase C. To facilitate further study of these pathways in intact neurons, we have devised novel membrane-targeted probes that can be applied from the outside of the neuron, by attaching a palmitoyl group to site-directed peptides (“palpeptides”) (cf. Covic et al., 2002a,b). A palpeptide incorporating the 10-residue C terminus of Gαq/11 reduced Gq/11-mediated M-current inhibition in sympathetic neurons by the muscarinic acetylcholine receptor (mAChR) agonist oxotremorine-M but not Go-mediated inhibition of the N-type Ca2+ current by norepinephrine. Instead, the latter was inhibited by the corresponding Go palpeptide. A PIP2 palpeptide, based on the putative PIP2 binding domain of the Kv7.2 channel, inhibited M current (IC50 = ∼1.5 μm) and enhanced inhibition by oxotremorine-M. Inhibition could not be attributed to activation of mAChRs, calcium influx, or block of M channels but was antagonized by intracellular diC8-PIP2 (dioctanoyl-phosphatidylinositol-4,5-bisphosphate), suggesting that it disrupted PIP2–M channel gating. A fluorescently tagged PIP2 palpeptide was highly targeted to the plasma membrane but did not accumulate in the cytoplasm. We suggest that these palpeptides are anchored in the plasma membrane via the palmitoyl group, such that the peptide moiety can interact with target molecules on the inner face of the membrane. The G-protein-replicating palpeptides were sequence specific and probably compete with the receptor for the cognate G-protein. The PIP2 palpeptide was not sequence specific so probably interacts electrostatically with anionic PIP2 head groups.
Time Warping Invariant Neural Networks

We proposed a model of Time Warping Invariant Neural Networks (TWINN) to handle the time warped continuous signals. Although TWINN is a simple modification of well known recurrent neural network, analysis has shown that TWINN completely removes time warping and is able to handle difficult classification problem. It is also shown that TWINN has certain advantages over the current available sequential processing schemes: Dynamic Programming(DP)[1], Hidden Markov Model(HMM)[2], Time Delayed Neural Networks(TDNN) [3] and Neural Network Finite Automata(NNFA)[4]. We also analyzed the time continuity employed in TWINN and pointed out that this kind of structure can memorize longer input history compared with Neural Network Finite Automata (NNFA). This may help to understand the well accepted fact that for learning grammatical reference with NNFA one had to start with very short strings in training set. The numerical example we used is a trajectory classification problem. This problem, making a feature of variable sampling rates, having internal states, continuous dynamics, heavily time-warped data and deformed phase space trajectories, is shown to be difficult to other schemes. With TWINN this problem has been learned in 100 iterations. For benchmark we also trained the exact same problem with TDNN and completely failed as expected.
Comparative study of Avalanche effect in DES using binary codes

With the continuing progression of digital data exchange in electronic way, information security is becoming more vital in data storage and transmission. Cryptography has come up as a solution which plays a vital role in information security system against malicious attacks. This security system uses some algorithms to scramble data into incomprehensible text which can be only being decoded or decrypted by party those possesses the associated key. In order to apply an appropriate technique in a particular application we are required to know strengths and weakness of the technique. Therefore analysis of these techniques is critically necessary. In this paper a most widely used symmetric encryption technique Data encryption standard (DES) has been implemented using MATLAB software. After the implementing this encryption technique, analysis is done by using a parameter called Avalanche effect. Plaintext and encryption key are mapped in binary code before encryption process. Avalanche Effect is calculated by changing one bit in plaintext keeping the key constant and by changing one bit in encryption key keeping the key constant, Experimental results shows that the proposed algorithm exhibit significant high Avalanche Effect which improves the level of the security.
Revision of an Argumentation System

In this paper, we address the problem of revising a Dung-style abstract argumentation system, when we add a new argument which interacts with one previous argument. We study the impact of such an addition on the outcome of the argumentation system, more particularly on the set of its extensions. Different kinds of revision are defined according to the change induced on the number or on the contents of the extensions. Two particular revisions are studied, for which we propose characterization theorems.
A lookback scheduling framework for long-term quality of service over multiple cells

In current cellular networks, schedulers allocate wireless channel resources to users based on instantaneous channel gains and short-term moving averages of user rates and queue lengths. By using only such short-term information, schedulers ignore the users' service history in previous cells and, thus, cannot guarantee long-term quality of service QoS when users traverse multiple cells with varying load and capacity. In this paper, we propose a new long-term lookback scheduling LLS framework, which extends conventional short-term scheduling with long-term QoS information from previously traversed cells. We demonstrate the application of LLS for common channel aware, as well as channel and queue-aware schedulers. The developed long-term schedulers also provide a controllable trade-off between emphasizing the immediate user QoS or the long-term measures. Our simulation results show high gains in long-term QoS without sacrificing short-term user requirements. Therefore, the proposed scheduling approach improves subscriber satisfaction and increases operational efficiency. Copyright © 2014 John Wiley & Sons, Ltd.
A Real-time DSP-Based Optical Character Recognition System for Isolated Arabic characters using the TI TMS 320 C 6416 T

Optical Character Recognition (OCR) is an area of research that has attracted the interest of researchers for the past forty years. Although the subject has been the center topic for many researchers for years, it remains one of the most challenging and exciting areas in pattern recognition. Since Arabic is one of the most widely used languages in the world, the demand for a robust OCR for this language could be commercially valuable. There are varieties of software based solutions available for Arabic OCR. However, there is little work done in the area of hardware implementation of Arabic OCR where speed is a factor. In this research, a robust DSP-based OCR is designed for recognition of Arabic characters. Since the scope of this research is focused on hardware implementation, the system is designed for recognition of isolated Arabic characters. An efficient recognition algorithm based on feature extraction and using a Fuzzy ART Neural Network as well as the hardware implementation is also proposed in this research. A recognition rate of 95% is reported.
Overexpression of c‐myc in the liver prevents obesity and insulin resistance

Alterations in hepatic glucose metabolism play a key role in the development of the hyperglycemia observed in type 2 diabetes. Because the transcription factor c‐Myc induces hepatic glucose uptake and utilization and blocks gluconeogenesis, we examined whether hepatic overexpression of c‐myc counteracts the insulin resistance induced by a high‐fat diet. After 3 months on this diet, control mice became obese, hyperglycemic, and hyperinsulinemic, indicating that they had developed insulin resistance. In contrast, transgenic mice remained lean and showed improved glucose disposal and normal levels of blood glucose and insulin, indicating that they had developed neither obesity nor insulin resistance. These findings were concomitant with normalization of hepatic glucokinase and pyruvate kinase gene expression and enzyme activity, which led to normalization of intrahepatic glucose‐6‐phosphate and glycogen content. In the liver of control mice fed a high‐fat diet, the expression of genes encoding proteins that control energy metabolism, such as sterol receptor element binding protein 1‐c, peroxisome proliferator activated receptor α, and uncoupling protein‐2, was altered. In contrast, in the liver of transgenic mice fed a high‐fat diet, the expression of these genes was normal. These results suggest that c‐myc overexpression counteracted the obesity and insulin resistance induced by a high‐fat diet by modulating the expression of genes that regulate hepatic metabolism.
Language model adaptation using auto-induced semantic structures in a voice search system

In this paper, we study how to generate in-domain data for statistical language model adaptation in a Chinese voice search dialogue system. Given limited amount of in-domain data, we use unsupervised clustering to induce semantic classes and structures from the first part of test data. These structures are further augmented with domain information to generate large amount of in-domain data. Lastly we test on the second part of test data and get a improvement of speech recognition for 6.2%.