Search

Various methods have been proposed for authentication, including password or pattern drawing, which is clearly visible on personal electronic devices. However, these methods of authentication are more vulnerable, as passwords and cards can be forgotten, lost, or stolen. Therefore, a great curiosity has developed in individual authentication using biometric methods that are based on physical and behavioral features not possible to forget or be stolen. Authentication methods are used widely in portable devices since the lifetime of battery and time response are essential concerns in these devices. Due to the fact that these systems need to be fast and low power, designing efficient methods is still critical. We, in this paper, proposed a new low power and fast method for pupil segmentation based on approximate computing that under trading a minor level of accuracy, significant improvement in power assumption and time saving can be obtained and makes this algorithm suitable for hardware implementation. Furthermore, the experimental results of PSNR and SSIM show that the error rate in this method is negligible., Comment: 5 pages, 6 figures

We obtain the exact solution to the Dirac equation with the Poschl-Teller double ring-shaped Coulomb (PTDRSC) potential for any spin-orbit quantum number K. The relativistic scattering amplitude for spin 1/2 particles in the field of this potential has been studied. The wave functions are expressed in terms of the hyper-geometric series of the continuous states on the K/2Pi scale. A formula for the phase shifts has also been found. In the nonrelativistic limits, our solution to the Dirac system converges to that of the Schr\"odinger one. At the high temperature, the partition function is calculated in order to study the behavior of some thermodynamic properties.

Goal-oriented dialogue systems are now being widely adopted in industry where it is of key importance to maintain a rapid prototyping cycle for new products and domains. Data-driven dialogue system development has to be adapted to meet this requirement --- therefore, reducing the amount of data and annotations necessary for training such systems is a central research problem. In this paper, we present the Dialogue Knowledge Transfer Network (DiKTNet), a state-of-the-art approach to goal-oriented dialogue generation which only uses a few example dialogues (i.e. few-shot learning), none of which has to be annotated. We achieve this by performing a 2-stage training. Firstly, we perform unsupervised dialogue representation pre-training on a large source of goal-oriented dialogues in multiple domains, the MetaLWOz corpus. Secondly, at the transfer stage, we train DiKTNet using this representation together with 2 other textual knowledge sources with different levels of generality: ELMo encoder and the main dataset's source domains. Our main dataset is the Stanford Multi-Domain dialogue corpus. We evaluate our model on it in terms of BLEU and Entity F1 scores, and show that our approach significantly and consistently improves upon a series of baseline models as well as over the previous state-of-the-art dialogue generation model, ZSDG. The improvement upon the latter --- up to 10% in Entity F1 and the average of 3% in BLEU score --- is achieved using only the equivalent of 10% of ZSDG's in-domain training data., Comment: EMNLP 2019

This study focuses on enhancing the electrical and thermoelectric properties of tungsten trioxide (WO$_3$) thin films using the spray pyrolysis technique. Three samples, namely A$_1$, A$_2$, and A$_3$, were prepared by depositing the films onto glass substrates at different deposition rates: R$_1=1$ mL/min, R$_2=3$ mL/min, and R$_3=7$ mL/min, respectively. Subsequently, the films underwent annealing at 500 $^\circ$C for 2 hours in air. A detailed investigation was conducted to analyze the structural, morphological, optical, and electrical properties of the $\mathrm{WO}_3$ thin films. The FE-SEM images revealed the formation of circular strings with varying diameters for all layers. The diameter of each string decreased as the deposition rate increased. XRD structural analysis indicated that, before annealing, sample R$_1$ exhibited a polycrystalline nature with mixed tetragonal-hexagonal phases, while the other two samples were amorphous. After annealing, sample A$_3$ also became polycrystalline. Moreover, the UV-Vis spectra analysis demonstrated a monotonic decrease in the band gap of the layers with increasing spray rate, potentially attributed to the attenuation of the quantum confinement effect. The Seebeck effect was used to authenticate the $n$-type conductivity of the WO$_3$ layers, as the Seebeck effect and conductivity exhibited a direct connection. Furthermore, the current-voltage variation was found to align with the oxygen vacancies sites. Remarkably, the sample A$_3$ with the highest deposition rate exhibited the lowest resistivity., Comment: 10 pages, 12 figures

Dialogue technologies such as Amazon's Alexa have the potential to transform the healthcare industry. However, current systems are not yet naturally interactive: they are often turn-based, have naive end-of-turn detection and completely ignore many types of verbal and visual feedback - such as backchannels, hesitation markers, filled pauses, gaze, brow furrows and disfluencies - that are crucial in guiding and managing the conversational process. This is especially important in the healthcare industry as target users of Spoken Dialogue Systems (SDSs) are likely to be frail, older, distracted or suffer from cognitive decline which impacts their ability to make effective use of current systems. In this paper, we outline some of the challenges that are in urgent need of further research, including Incremental Speech Recognition and a systematic study of the interactional patterns in conversation that are potentially diagnostic of dementia, and how these might inform research on and the design of the next generation of SDSs., Comment: Published at Dialog for Good 2019 - Workshop on Speech and Language Technology Serving Society

Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems. In a real-world enterprise setting where dialogue systems are developed rapidly and are expected to work robustly for an ever-growing variety of domains, products, and scenarios, efficient learning from a limited number of examples becomes indispensable. In this paper, we introduce a technique to achieve state-of-the-art dialogue generation performance in a few-shot setup, without using any annotated data. We do this by leveraging background knowledge from a larger, more highly represented dialogue source --- namely, the MetaLWOz dataset. We evaluate our model on the Stanford Multi-Domain Dialogue Dataset, consisting of human-human goal-oriented dialogues in in-car navigation, appointment scheduling, and weather information domains. We show that our few-shot approach achieves state-of-the art results on that dataset by consistently outperforming the previous best model in terms of BLEU and Entity F1 scores, while being more data-efficient by not requiring any data annotation., Comment: Accepted at SigDial 2019

We study the spherical quantum pseudodots in the Schrodinger equation using the pseudo-harmonic plus harmonic oscillator potentials considering the effect of the external electric and magnetic fields. The finite energy levels and the wave functions are calculated. Furthermore, the behavior of the essential thermodynamic quantities such as, the free energy, the mean energy, the entropy, the specific heat, the magnetization, the magnetic susceptibility and the persistent currents are also studied using the characteristic function. Our analytical results are found to be in good agreement with the other works. The numerical results on the energy levels as well as the thermodynamic quantities have also been given., Comment: 18 Figs

We solve the Schr\"odinger wave equation for the generalized Morse and Cusp molecular potential models. In the limit of high temperature, at first, we need to calculate the canonical partition function which is basically used to study the behavior of the thermodynamic functions. Based on this, we further calculate the thermodynamic quantities such as the free energy, the entropy, the mean energy and the specific heat. Their behavior with the temperature has been investigated. In addition, the susceptibility for two level systems is also found by applying the incident time dependent field., Comment: 6 Figures

We have recently seen the emergence of several publicly available Natural Language Understanding (NLU) toolkits, which map user utterances to structured, but more abstract, Dialogue Act (DA) or Intent specifications, while making this process accessible to the lay developer. In this paper, we present the first wide coverage evaluation and comparison of some of the most popular NLU services, on a large, multi-domain (21 domains) dataset of 25K user utterances that we have collected and annotated with Intent and Entity Type specifications and which will be released as part of this submission. The results show that on Intent classification Watson significantly outperforms the other platforms, namely, Dialogflow, LUIS and Rasa; though these also perform well. Interestingly, on Entity Type recognition, Watson performs significantly worse due to its low Precision. Again, Dialogflow, LUIS and Rasa perform well on this task., Comment: Accepted by IWSDS2019