Reviewerly/exHarmony huggingface dataset:
A Dialogue State Tracking Model with Slot Embedding

Abstract Dialogue State Tracking (DST) is the core component of the task-oriented dialogue systems. Although recent neural DST models have made great progress, they often ignore the phenomenon that the current dialogue state is closely related to the earlier dialogue states. In this paper, we try to introduce the slot embedding into the transformer to focus on those special tokens, which ever appear in earlier dialogue states. On the basis, we leverage the copy mechanism to predict the state over the dialogue utterances. Our model also imitates the architecture of reading comprehension model to make full use of the current utterances. The experimental results verify the benefits of the slot embedding, and our model achieves significant improvements than baselines on MultiWOZ 2.0 and MultiWOZ 2.1 datasets.
3D Experience on a 2D Display

As we witness an explosion in terms of image data availability, the need for richer visualization also is increasing. While there are significant and successful efforts about creation of 3D datasets (point clouds) and visualization, most systems are today available only for industrial or commercial use, and a full end-to-end economically viable solution is not available for the common man. The solution proposed in this paper focuses on filling this gap - offering solutions to create 3D datasets and interactively visualizing the same on normal 2D displays. This is truly inter-disciplinary in nature - an amalgamation of 3D geometry, computer vision, machine learning, computer science, etc. We report a successful implementation of a prototype satisfying all these aspects.
Connection of Diagonal Hessian Estimates to Natural Gradients in Stochastic Optimization

With massive resurgence of artificial intelligence, statistical learning theory and information science, the core technology of AI, are getting growing attention. To deal with massive data, efficient learning algorithms are required in statistical learning. In deep learning, natural gradient algorithms, such as AdaGrad and Adam, are widely used, motivated by the idea of Newton's approach that applies second-order derivatives to rescale gradients. By approximating the second-order geometry of the empirical loss with the empirical Fisher information matrix (FIM), natural gradient methods are expected to obtain extra efficiency of learning. However, the exact curvature of the empirical loss is described by the Hessian matrix, not the FIM, and biases between the empirical FIM and the Hessian always exist before convergence, which will affect the expected efficiency. In this paper, we present a new stochastic optimization algorithm, diagSG (diagonal Hessian stochastic gradient), in the setting of deep learning. As a second-order algorithm, diagSG estimates the diagonal entries of the Hessian matrix at each iteration through simultaneous perturbation stochastic approximation (SPSA) and applies the diagonal entries for the adaptive learning rate in optimization. By comparing the rescaling matrices in diagSG and in natural gradient methods, we argue that diagSG possess advantages in characterizing loss curvature with better approximation of Hessian diagonals. In practical part, we provide a experiment to endorse our argument.
An Evaluation and Validation of Contemporary Approaches in Scene Text Extraction and Recognition

In recent decades, Scene Text Extraction and Recognition (STER) has remarkably attracted many researchers from the area of image processing and computer vision. Most of the methods discussed in this paper are subjected to evaluation and validation based on different feature properties such as size, orientation, color information, edges, texture property, connected components, and geometry of scene text. Combining different features like CNN and region-based methods together have gained importance and increase in performance of STER. Critical analysis of several state-of-the-art techniques along with benchmark datasets has been carried out with standard testing procedures and evaluation metrics in order to rationalize the decision of selecting an efficient algorithm for better and fast scene text extraction and recognition. Findings of this paper are discussed exclusively by specifying open area for future research in the field of STER system.
Survey on examining the student’s cognizance on usage of Cloud Computing in Higher Education

Due to the advent of technology classroom teaching is adopting changes in the modern education system. Today, students have become more tech savvy as teaching and learning uses more advance technology day in, day out. The economic growth of any country is usually maintained and enhanced by education in terms of quality and level. Cloud computing is an exciting development in modern education system. Understanding the role of higher education institutions in Cloud Computing adoption is a necessary research area. Higher education institutions must consider opportunities afforded by cloud computing. The main purpose of this paper is to examine the student’s awareness on usage of Cloud Computing in Education. This study emphasizes the need for the relevance of improvising education by creating awareness among the students of Mangalore city. These researches encourage higher education institutions to adopt cloud computing technology.
Quaternary generalized cyclotomic sequences with high linear complexity and even period

Abstract In this paper, we consider quaternary generalized cyclotomic sequences with high linear complexity for arbitrary even period. These sequences we construct through using new generalized cyclotomic classes, the order of that depends on choice of parameter set.
Evaluasi Metode Pendugaan Laju Evapotranspirasi Standar (ETo) Menggunakan Bahasa Pemograman Visual Basic Microsoft Excel di Kabupaten Nagan Raya Aceh

Estimation of evaporation rate is needed in hydrological data, especially in irrigation and drainage planning data. The method for estimating the evapotranspiration rate recommended by FAO is the Penman - Monteith method, however apart from this method there are still several methods that can be used if the data obtained in the field are different. Evaluation of the method of estimating the rate of evapotranspration is expected to help in choosing the right method when different data is obtained, so with this evaluation, we can see the accuracy of the methods. The methods that are closest to accuracy with the Penman - Monteith method are Hargreaves, Makkink, Turc, Blaney-Cridlle and Penman. If ordered based on the ranking of the evaluation results, the method that is closest to the Penman - Monteith method is Hargreaves, because of all the evaluation variabels Hargreaves has the best evaluation value. While the closest model based on the best R2 value is the Jensen-Haise model, but the error value of this model is very high.
EvoGAN

In biology, evolution is the gradual change in the characteristics of a species over several generations. It has two properties: 1) The change is gradual, and 2) long-term changes are relied on short-term changes. Face aging/rejuvenation, which renders younger or elder facial images, follows the principles of evolution. Inspired by this, we propose an <u>Evo</u>lutionary <u>GAN</u>s (EvoGAN) for face aging/rejuvenation by making each age transformation smooth and decomposing a long-term transformation into several short-terms. Specifically, since short-term facial changes are gradual and relatively easy to render, we first divide the ages into several groups (i.e., chronologically from child, adult to elder). Then, for each pair of adjacent groups, we design two age transforms for face aging and rejuvenation, which are supposed to preserve personal identify information and predict age-specific characteristics. Compared with the mainstream for face aging/rejuvenation, i.e., conditional
Optimized frequent pattern mining algorithm based on Can Tree

Abstract Due to the continuous dynamic changes of data in the current era, research on incremental association rules is necessary. Among them, frequent pattern mining has always been the subject of research. The research found that among the existing algorithms, Can Tree is very suitable for incremental mining because of its superior nature that it does not require adjustment, merging, and/or splitting of tree nodes during maintenance. In this paper, a new method of mining Can Tree is proposed to solve the problem of time consuming caused by repeatedly traversing paths when obtaining conditional mode basis. The path only needs to be traversed once to meet the requirements and verify it. Experimental results show that the performance of the algorithm is better than the traditional Can Tree algorithm, reducing time consumption to a certain extent.
A Terms Interrelationship Approach to Query Expansion Based on Terms Selection

The term selection is a potential challenge in query expansion. It concerns with the inability of query expansion techniques to select appropriate terms for expanding query. Most of the existing term selection for query expansion techniques allows the expansion of terms with synonyms from a single semantic dictionary, and this doesn’t provide an efficient information retrieval system due to low precision results. This paper proposed terms interrelationship of term selection value (TI-TSV) for query expansion where terms are expanded based on synonyms from many semantic dictionaries. The proposed TI-TSV employs existing TSV method of term selection based on the three cases. The three Cases for term selection are based on the retrieved top-ranked documents to increase precision results. The experiments are performing on Yusuf Ali, Sarwar and Arberry English Quran translation datasets collected from Tanzil. The results indicate that the proposed TI-TSV has significantly improved the performance of the search system.
Concentration and ROC Curves, Revisited

Abstract This work is aimed at illustrating the strict relationship between a general definition of concentration function appeared quite some time ago on this journal and a widely used measure of the diagnostic strength of a family of binary classifiers indexed by a threshold parameter, the so-called ROC curve. The ROC curve is a common work tool in Statistics, Machine Learning and Artificial Intelligence, appearing in many applications where a binary classification (diagnosis) procedure is of interest. Hence, it is worth remarking that diagnostic strength and concentration are two sides of the same coin: the higher the concentration of one probability measure with respect to another, the higher the diagnostic strength of the likelihood ratio classification rule.
Incorporating a graph-matching algorithm into a muscle mechanics model

Differential models for the simulation of the muscle mechanics are based on iteratively updating a mesh grid and deducing its new state through a finite element model. Models usually assume that the mesh grid is almost regular, and this makes a degradation of the simulation accuracy in long simulation sequences, since the mesh tends to be less regular when the number of iterations increases. We present a model that has the aim of reducing this accuracy degradation. It is based on recomputing the mesh grid returned by the model in each iteration through the concept of graph matching. The new model is currently in use to analyse the dynamics of the human heart when some pressure is applied to it. The final goal of the project (which is not shown in this paper) is to deduce the optimal position and strength pressure applied to the heart that increases the chance of reviving it with the minimum tissue damage. Experimental validation shows that our model returns a higher accuracy of the muscle position through some iterations than classical differential models with an insignificant increase of runtime. Thus, it is worth recomputing the mesh grid since the simulation accuracy drastically increases at the expense of a low runtime increase.
Landmark Localization in Occluded Faces Using Deep Learning Approach

Detecting and localizing facial landmark in occluded faces is a challenging problem for face landmark detection in computer vision. The challenge turns to be more difficult when the occlusion is high where most of the face is veiled. High occluded faces landmark localization is an ongoing research gap which motivates more accurate and highly efficient solutions. This paper presents a review of recent advances in facial landmark detection and localization, discusses available datasets and investigates the influence of occlusion on the accuracy, performance, and robustness on landmark detection. It outlines existing challenges in dealing with and controlling of occlusion.
Prediction of the Response Time in Web Services Based on Matrix Factorization

This paper concerns the prediction of the response time of internet resources depending on a group of various factors, such as agent location, web browser and cookies, using algorithms related to matrix factorization techniques. The experiment was based on testing ten university websites from five different agent locations. The collected data was used to predict the load time of pages using the SVD (Singular Value Decomposition), SVD++ (Singular Value Decomposition++) and NMF (Non-negative Matrix Factorization) algorithms. The quality of the prediction was evaluated using RMSE (Root Mean Square Error), MSE (Mean Square Error) and MAE (Mean Absolute Error) indexes.
Incorporation of Augmented-Reality Technology into Smartphone App for Large-Scale Performance Art

Performance art is generally physical in nature. In recent years, the development of augmented-reality (AR) technology has enabled the merge of the virtual and the real to present novel visual effects. However, incorporations of AR into performance art are generally shown to audiences on large screens, which designates the audience a passive role. Using the example of ‘Epitome of Time’, a performance at the Da Guan International Performing Arts Festival, the researchers experimented with a smartphone application (app) that can incorporate AR into stage performances. The app features the following functions: (1) A projected image in the performance hall served as the AR recognition target; audience members could scan the image with their smartphones, generating virtual objects that they could interact with in the space around them. (2) The AR served as a marketing method before the performance. (3) Push notifications sent audience members reminders of show times. This study adopted the spiral model of action research method presented by Lewin, the steps of which include planning, action, observation, and reflection. The interactive re-examination of each process forms a feedback loop for app development. We then put forward suggestions to serve as reference for future incorporations of smartphones and AR into large-scale performance art. We also predict which emerging AR equipment could create more possibilities for AR interaction in this field.
A Global Data Pre-processing Technique for Automatic Speech Recognition

Automatic speech recognition (ASR) is one of the major research field in computer science. ASR is one of the most necessary tools, which is required for, almost every languages. But every language has its own techniques and highlights. In past years, significant number of mentionable research has already been done in this field. And the exactness of ASR mostly depends on the noise reduction process. This is the fundamental requirement of Speech data preprocessing . There are various way of data preprocessing for speech recognition. In this paper we discuss very effective way for preprocessing technique of Bangla speech data. We demonstrate the whole process of recording, then digitizing the data, removing noise with respect to signal (SNR), transforming the signal into Fourier transformation (FFT) for time domain to frequency domain, it would be better if result frames are chunked into small window, and finally apply Mel frequency cepstral coefficients (MFCC) algorithm to make data into respective vector representation so that, data is ready for training in neural network.
Research of the microseismicity location algorithm

In this paper, we propose the algorithm for locating microseismic events by the method of directed summation of energy envelopes over a network of stations. The algorithm is based on a back projection technology, which allows visualizing the intensity of seismic emission by layer. A comparative stability and robustness analysis of the proposed method with the method using the semblance is carried out.
Research on mail classification algorithm based on improved convolutional neural network

Abstract Aiming at the problems of high dimension and sparse data in traditional text classification methods, this paper proposes an e-mail classification method based on i-cnn model by combining convolutional neural network (CNN) and inception V1 model. In the convolution and pooling operation, 1 * 1 convolution kernel is added to reduce the thickness of eigenvectors, reduce the parameters and improve the computational performance. Through data validation, i-cnn model for e-mail classification results as high as 92.18%, in the comparative experiment, i-cnn model compared with several machine learning classification models, achieved the highest classification accuracy, in the comparison with or without the emergence structure model, i-cnn model accuracy is higher than CNN model. It shows that the model has a good classification effect, and the integration of inception V1 model can improve the accuracy of text classification.
The influence of the distance season on teaching students of senior courses to program in 1C

The article discusses the features of the organization of the educational process of teaching 4th year bachelor students of the “Fundamental informatics and information technologies” direction to program in 1C, in the context of a rapid transition from fulltime to distance learning and vice versa. A brief plan of distributing lectures and practical classes in the discipline “Automation of enterprise activities” is given, the place of the 1C certified courses in the development of the curriculum of the discipline is noted. The structure of practical lessons for the course is given. The system of assessment of student work is considered, indicating the number of points for each work. Methods for increasing the uniqueness of student work are analyzed in the conditions of absence of personal contact between the teacher and the students during classes (with distance learning). The experience of organizing student training using combined full-time and distance learning during a pandemic is presented. An analysis of the progress of students studying with the help of this approach is given, and on the basis of this analysis it is shown that for students of senior courses the use of a combined face-to-face and distance approach to the organization of training sessions is effective. The article notes that with a clear schedule for organizing the educational process and a transparent assessment system, distance educational technologies can become an effective addition to any course, especially in the last semesters of training.
다감각환경이 자폐범주성장애 아동의 행동과 생리적 반응에 미치는 영향

The prevalence of autism spectrum disorder (ASD) in South Korea and around the world is increasing every year, but there is no cure for ASD. The multi-sensory environment (MSE), a non-pharmaceutical behavioral treatment, is one promising way. The purpose of this study was to investigate the effects of the MSE on the target behaviors and heart rates of children with ASD. The participants of this study were two children with ASD with hypo-visual and hypo-auditory sensitivities. A single-subject experimental design with alternating treatment design was used for the study. Based on the sensory characteristics of the participants, the MSE interventions were manipulated by a visually stimulating MSE, an auditorily stimulating MSE, and an integration of visually and auditorily stimulating MSE. The participating children showed the decreases in target behaviors in all types of MSE interventions, indicating that sensory experiences in MSE positively affected the negative daily behaviors of children with ASD. Regarding the physiological response, there was a statistical significance in the heart rates of the participants according to each MSE intervention type. However, the relationship between the heart rate and the target behavioral change was indeterminable, and the trends in heart rate could not be concluded with the same effect as the decrease in the target behavior. The present study suggests that sensory environment design plays an integral role in MSE therapy, since the design can support customized environments according to the individual sensory needs of users.
A Fault Resistant AES via Input-Output Differential Tables with DPA Awareness

Nowadays, hardware-based AES faces more than one type of Side-Channel-Attacks (SCA), such as the Differential Power Analysis (DPA) attack and the Differential Fault Analysis (DFA) attack. However, most of the current DFA-resistant implementations of AES only focus on resisting the DFA attack but with minimal concurrent considerations on their DPA resistance capability. In this paper, we propose a fault resistant AES with DPA awareness. First, we evaluate the DPA resistance for different implementation architectures of AES before the implementation with S-Box over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is selected. Second, we evaluate the DPA resistance for different fault detection architectures before the concurrent fault detection method is selected. Third, we propose a novel fault resistant technique for AES using input-output differential tables over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . We have performed tests based on Partial Guessing Entropy (PGE) to evaluate the DPA resistance for the existing DFA-resistant designs and our proposed design. Experimental results prove that our design has a slower convergence speed (around 33%) with a 100% fault coverage rate and less area than the existing countermeasure designs for fault injection. Results also show that the fault detection designs weaken their DPA resistance, which indicates the importance of co-design of DFA and DPA to achieve less power information leakage.
An Improved Non-Face-to-Face, Contactless Preference Survey System

Conventional election-related public opinion polls have utilized the automated response system (ARS) method. The ARS public opinion polls are predicated on the convenience of use and require random telephonic responses. However, the actual response rate is less than 5%. As a result, discrepancies between recent public opinion polls and the actual election results have become an issue. In this study, we propose a system that quantifies the preferences by region, age, and gender by quantifying emotions based on the behaviors and facial expressions of the citizens passing by at the campaign site and utilizes them as basic statistics. Furthermore, a previously published facial recognition artificial intelligence (AI) was used to obtain age, gender, and various facial recognition data, along with citizens’ emotions. The published facial recognition AI produced stability of over 99% recognition rate. The data structure followed a weighted reverse tree structure, and facial expressions, gender, and age were analyzed using the published facial recognition algorithm. Moreover, the expressions as well as the behaviors showing emotions were merged to gather and analyze data with weights.