Reviewerly/exHarmony huggingface dataset:
Digit Dataset Generation Using DCGAN: A ResNet Experimentation

GANs or generative adversarial networks are deep generative models that have the capability to imitate a given data distribution and generate realistic data, that can be in the form of images, music, and even outcomes of scientific experiments. An important application of GANs is data creation and augmentation in cases where there is not enough data or the variety in the data is less for training a model to perform well in real-world testing. A lot of applications ranging from license plate recognition, bank check processing, auto filling form details, etc., make use of digit recognition. The digit recognition and classification accuracy of machine learning models, generally if not always, can be improved when more data (images of digits in this case) is provided. It can help the model to learn distinct features from the wide variety of data for better predictions and generalization. In this study, we use GANs and the SVHN dataset of 73 thousand colored, house number images to generate new and unseen, similar, realistic looking images of digits and then evaluate our GAN model using the Inception Score as the metric for evaluating quality and diversity of newly generated images. Later with an intent to further improve the quality of the newly generated images, we study the effects of modifying the discriminator (CNN) architecture by adding residual blocks to make it deeper, along with some other minor experimentation with hyperparameters, with an expectation of improving the classification accuracy of the discriminator, thus helping the generator improvise in generating more realistic images and finally discuss the results.
Tweakable Block Mode of Operation for Disk Encompression Using Cipher Text Stealing

In this paper, we study a particular class of symmetric algorithms that aim to ensure confidentiality by using a functionality that is tweakable enciphering scheme. A tweakable enciphering scheme is a length preserving encryption protocol which can encrypt messages of varying lengths. The security goal is to satisfy the notion of the tweakable strong pseudorandom permutation (SPRP). Our proposed work is a modified version of XTS that is Xor-Encrypt-Xor with Cipher Text Stealing. This work includes a Galois Field multiplier GF (2128) that can operate in any common field representations. This allows very efficient processing of consecutive blocks in a sector. To handle messages whose length is greater than 128-bit but not a multiple of 128-bit.
Policy Augmentation: An Exploration Strategy For Faster Convergence of Deep Reinforcement Learning Algorithms

Despite advancements in deep reinforcement learning algorithms, developing an effective exploration strategy is still an open problem. Most existing exploration strategies either are based on simple heuristics, or require the model of the environment, or train additional deep neural networks to generate imagination-augmented paths. In this paper, a revolutionary algorithm, called Policy Augmentation, is introduced. Policy Augmentation is based on a newly developed inductive matrix completion method. The proposed algorithm augments the values of unexplored state-action pairs, helping the agent take actions that will result in high-value returns while the agent is in the early episodes. Training deep reinforcement learning algorithms with high-value rollouts leads to the faster convergence of deep reinforcement learning algorithms. Our experiments show the superior performance of Policy Augmentation. The code can be found at: https://github.com/arashmahyari/PolicyAugmentation.
Keynote

Blockchain is a novel distributed ledger technology. Through its features and smart contract capabilities, a wide range of application areas opened up for blockchain-based innovation [5]. In order to analyse how concrete blockchain systems as well as blockchain applications are used, data must be extracted from these systems. Due to various complexities inherent in blockchain, the question how to interpret such data is non-trivial. Such interpretation should often be shared among parties, e.g., if they collaborate via a blockchain. To this end, we devised an approach codify the interpretation of blockchain data, to extract data from blockchains accordingly, and to output it in suitable formats [1, 2]. This work will be the main topic of the keynote. In addition, application developers and users of blockchain applications may want to estimate the cost of using or operating a blockchain application. In the keynote, I will also discuss our cost estimation method [3, 4]. This method was designed for the Ethereum blockchain platform, where cost also relates to transaction complexity, and therefore also to system throughput.
Comparison of deep learning and hand crafted features for mining simulation data

Computational Fluid Dynamics (CFD) simulations are a very important tool for many industrial applications, such as aerodynamic optimization of engineering designs like cars shapes, airplanes parts etc. The output of such simulations, in particular the calculated flow fields, are usually very complex and hard to interpret for realistic three-dimensional real-world applications, especially if time-dependent simulations are investigated. Automated data analysis methods are warranted but a non-trivial obstacle is given by the very large dimensionality of the data. A flow field typically consists of six measurement values for each point of the computational grid in 3D space and time (velocity vector values, turbulent kinetic energy, pressure and viscosity). In this paper we address the task of extracting meaningful results in an automated manner from such high dimensional data sets. We propose deep learning methods which are capable of processing such data and which can be trained to solve relevant tasks on simulation data, i.e. predicting drag and lift forces applied on an airfoil. We also propose an adaptation of the classical hand crafted features known from computer vision to address the same problem and compare a large variety of descriptors and detectors. Finally, we compile a large dataset of 2D simulations of the flow field around airfoils which contains 16000 flow fields with which we tested and compared approaches. Our results show that the deep learning-based methods, as well as hand crafted feature based approaches, are well-capable to accurately describe the content of the CFD simulation output on the proposed dataset.
DAPC:Domain Adaptation People Counting via Style-level Transfer Learning and Scene-aware Estimation

People counting concentrates on predicting the number of people in surveillance images. It remains challenging due to the rich variations in scene type and crowd density. Besides, the limited closed-set with ground truth from reality significantly increase the difficulty of people counting in actual open-set. Targeting to solve these problems, this paper proposes a domain adaptation people counting via style-level transfer learning (STL) and scene-aware estimation (SAE). The style-level transfer learning explicitly leverages the style constraint and content similarity between images to learn effective knowledge transfer, which narrows the gap between closed-set and open-set by generating domain adaptation images. The scene-aware estimation introduces scene classifier to provide scene-aware weights for adaptively fusing density maps, which alleviates interference of variations in scene type and crowd density on domain adaptation people counting. Extensive experimental results demonstrate that images generated by STL are more suitable for domain adaptation learning and our proposed approach significantly outperforms the state-of-the-art methods on multiple cross-domain pairs.
Constructing a New Display Laboratory Management Model Based on Internet of Things Technology

With the development of Internet technology, display experiments seem to be gradually replaced by virtual experiments. However, in the actual teaching, it is found that the display experiment plays an irreplaceable role in cultivating ability of student to maintain and innovate. A new idea of management of display laboratory starting from the relationship between entity model and virtual model based on internet of things technology was presented in this paper, in order to solve the difficult problems that have long puzzled the development of laboratory. It lays a foundation for the establishment of a “student-centered” basic teaching environment.
Shape Consistent 2D Keypoint Estimation under Domain Shift

Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex problems involving structured predictions (e.g. semantic segmentation, depth estimation). Following this trend, in this paper we present a novel deep adaptation framework for estimating keypoints under domain shift, i.e, when the training (source) and the test (target) images significantly differ in terms of visual appearance. Our method seamlessly combines three different components: feature alignment, adversarial training and self-supervision. Specifically, our deep architecture leverages from domain-specific distribution alignment layers to perform target adaptation at the feature level. Furthermore, a novel loss is proposed which combines an adversarial term for ensuring aligned predictions in the output space and a geometric consistency term which guarantees coherent predictions between a target sample and its perturbed version. Our extensive experimental evaluation conducted on three publicly available benchmarks shows that our approach outperforms state-of-the-art domain adaptation methods in the 2D keypoint prediction task.
Correlation of Qualitative User Experience Feedback and Interface-Generated Quantitative Data during Human-Robot Interaction

My research is on Human-Robot Interaction (HRI) from a User Centered Design (UCD) perspective and values the user’s qualitative assessment of their interaction under the concept of User Experience (UX). Traditionally, UX is retrieved in a written questionnaire as a satisfaction survey, nevertheless, with the hike in electronic devices equipped with a diversity of sensors, actuators and processing capabilities (turning everyday appliances into robots) comes an intention to migrate from qualitative to quantitative assessment of the UX; for example, visual recognition of emotional facial expressions. In this regard, what I present as a novelty in the research area is the study of interaction data generated through the robot’s interface during its intended use (everyday use), while providing an anonymous experience (no cameras, no mics) to the user.
From abyssal plains to stratospheric clouds: a geological flight

Geology is many things, but agile is not one of them. It takes its own course of time, sometimes a lot of it. Geologists are not too different either. Afterall, the phrase ‘you are what you eat’ is not just hearsay. Times, however, have changed, and this is even more pronounced in an industrial set up. Disruptive technology, rate of return, net present value, non‐productive time, incremental gain, upside potential, Infrastructure Led Exploration, easy barrels, ensemble modeling, scrum, agility, digital and now artificial intelligence (AI) are no longer just buzz words imported from some Silicon Valley—they are the rules of the game. And who better than geology and its disciples to know that you either play by the rules or prepare to fossilize.
Convolutional Neural Network for Image Compression with Application to JPEG Standard

In this paper the authors propose a novel structure of convolutional neural network for lossy compression of images intended to be used as an extension of JPEG image compression standard. The convolutional network is trained on the set of images randomly selected from the database of high-quality images representing human faces and its effectiveness is verified experimentally using both human faces images as well as standard test images. The performance of the proposed network expressed in terms of its compression capabilities and image reconstruction quality is compared to other approaches utilizing the standard Discrete Cosine Transform, Lapped Orthogonal Transform, Modulated Lapped Transform and Karhunen-Loeve Transform, also incorporated into JPEG image compression standard. The obtained experimental results indicate that the proposed approach not only performs significantly better than the remaining approaches in terms of objective image quality measures, but also enables significant reduction of the blocking defects, which was verified by visual examination, when compared to the remaining tested transforms.
Alternating Conditional Expectation (ACE) Algorithm for Permeability Estimation in Bahariya Formation

Summary The nonparametric transformation is a data-driven technique, which can be used to estimate optimal correlations between a dependent variable (response) and a set of independent parameters (predictors). This study introduces a systematic methodology using the nonparametric transformation concept and the alternating conditional expectation (ACE) algorithm to estimate the effective gas permeability using conventional logs and the core data. The ACE algorithm was employed in the current work using the MATLAB® (The MathWorks, Inc., Natick, Massachusetts, USA) code and the open-source GRaphical ACE (GRACE) software (Xue et al. 1997) for deriving the optimal nonparametric correlations for predicting the permeability. The methodology was applied to a heterogeneous formation [Bahariya (BAH)] in Egypt to understand its characteristics and predict its permeability more accurately. The BAH Formation is considered one of the main sources for oil production throughout the Western Desert (WD) of Egypt. The cumulative oil production from the BAH Formation is estimated to be approximately 40% of the total WD production. The reservoir characteristics of the BAH Formation range from highly permeable to tight sandstone interbedded with shale and siltstone. It usually depicts low-resistivity and low-contrast (LRLC) log behavior. Thus, regional and accurate determination of the reservoir permeability for the different rock units of the BAH Formation across the WD is a challenge. Conventional well log data from approximately 100 cored wells and corresponding 5,500 core measurements were used to provide a regional permeability correlation that can be used in a large number of reservoirs. The methodology of this work included two main steps: Applying the nonparametric transformation technique to identify the collective log responses for deriving optimal correlation Predicting the permeability profiles using the selected log responses The model was applied to many wells that address different petrophysical characteristics of the BAH Formation. The established permeability profiles showed reliable correlation coefficients relative to the measured core data. The correlation coefficient was 0.893 for the training data points (75% of the collected database) and 0.913 for the testing data points (25% of the collected database). In addition, the mean absolute percentage error (MAPE) between the predicted and the measured permeability for the training and testing data points were 5.93 and 4.14%, respectively. Permeability prediction using ACE is compared with other techniques such as k-ϕ crossplots, multiple linear regression (MLR), Coates, and Wyllie-Rose correlations. This work is considered an original contribution to present regional permeability prediction correlations using the conventional well logs for reservoir characterization and simulation applications. The ACE algorithm was successfully applied to the BAH Formation and proved its capability to identify the best predictors that are required to establish a rigorous model.
An Integrated Customer Knowledge Management Framework for English Teaching Training

With the continuous deepening of reform and opening up and the sustainable development of society, great changes have taken place in the fields of economy, culture and education in China. Especially in the context of quality education and new curriculum reform, higher requirements are put forward for the abilities of middle school teachers in all aspects. However, there is an enormous gap between the reality that some teachers' abilities are insufficient and the ideal of high requirements for quality education. In order to improve the professional level of teachers as soon as possible, this study aims to identify the problem of English teaching training and develop the proposed training model in order to improve Chinese middle school teachers' teaching performance. Through the research and analysis of the factors that affect the training of middle school teachers, this paper expounds the problems existing in the training of middle school teachers and their causes, and probes into the scientific and reasonable training of middle school teachers in theory, which has certain practical guiding significance. In addition, with the development of society and the advancement of educational modernization, the all-round development of educators directly affects the development of education. Input, Process and Output Interview Structure interview structure is applied with the aim to identify the current situation of English teaching training in Chinese middle school, while Text mining is used for finding and extracting valuable information from interview results to find or solve some practical problems. And then Kano Model is used for proposing a training model based on the results from Text mining and to text the training model is work or not.
Non-Parallel Many-To-Many Voice Conversion Using Local Linguistic Tokens

The VQ-VAE based voice conversion models have lately received increasing attention in non-parallel many to many voice conversion, where the encoder extracts the speaker- invariant linguistic content from the input speech using vector quantization and the decoder produces the target speech from the encoder output, conditioned on the target speaker representation. However, it is challenging for the encoder to find a proper balance between removing the speaker information and preserving the linguistic content, which degrades the converted speech quality. To address this issue, we propose the Local Linguistic Tokens (LLTs) model to learn high-quality speaker-invariant linguistic embeddings using the multi-head attention module, which has shown great success in extracting speaking style embeddings in Global Style Tokens (GSTs). Instead of vector quantization, the multihead attention module makes the encoder preserve more linguistic content to enhance the converted speech quality. Both objective and subjective experimental results revealed that, compared with the state-of-the-art VQ-VAE model, the proposed LLTs model achieved significantly better speech quality and comparable speaker similarity. The converted samples are available online for listening. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
LegitANN: Neural Network Model with Unbiased Robustness

One of the widely used models in Machine Learning is an Artificial Neural Network. Ingenuous and legitimate results are the utmost need of every model. These models work like a black box, and no factor as trust is considered. Machine Learning algorithms are stuck with the risk of relying on biases encoded in the data or algorithms used for predictions. Prejudice or Bias is the root cause of the poor performance of the model. A novel approach is proposed, named legitANN, which has used three levels of bias control and an AvgNew loss function to make the model legitimate and reliable. Median Absolute deviation is a statistical measure that can guarantee the learning of neural networks to achieve unbiased robustness. Median Absolute deviation used here to prove the effectiveness of the method legitANN. The work proved that feed forward neural network is better at approximating. But for uniformly dense sets or preprocessed data, a radial basis network may be a right choice. This paper presents legitANN a new model that work on bias control using radial basis network and achieve better performance in comparison to other Artificial Neural Network in terms of improved evaluation metrics values for link prediction.
Comparative Solutions of Exact and Approximate Methods for Traveling Salesman Problem

There are two major optimization methods: Exact and Approximate methods. A well known exact method, Branch and Bound algorithm (B&amp;B) and approximate methods, Elimination-based Fruit Fly Optimization Algorithm (EFOA) and Artificial Atom Algorithm (A3) are used for solving the Traveling Salesman Problem (TSP). For 56 destinations, the results of total distance, processing time, and the deviation between exact and approximate method will be compared where the distance between two destinations is a Euclidean distance and this study shows that the distance of B&amp;B is 270 , EFOA is 270 and A3 is 288.38 which deviates 6.81%. For time processing aspect, B&amp;B needs 12.5 days to process, EFOA needs 36.59 seconds, A3 needs 35.34 seconds. But for 29 destinations, exact method is more powerful than approximate method.
Qualified Specialists’ Readiness for Digitalization Risks

The authors analyze the working population readiness for training, the possibility of obtaining a new profession in the fields of professional activity and occupational groups. The purpose of this study is to identify the characteristics of labor attitudes of Russian specialists with higher and secondary education connected with the changes in their professional activities under digitalization of the economy and society. As a result, a different degree of the qualified specialists’ readiness for continuous professional development, digitalization risks, depending on the professional sphere of activity and the level of education was revealed. A higher propensity of specialists with the highest level of qualification for labor mobility was revealed. Qualification groups with the least fear of disappearance of their profession in connection with digitalization were identified. Research results can be useful in designing corporate training and government employment programs.
Explainable Feature Embedding using Convolutional Neural Networks for Pathological Image Analysis

The development of computer-assisted diagnosis (CAD) algorithms for pathological image analysis is an important research topic. Recently, convolutional neural networks (CNNs) have been used in several studies for the development of CAD algorithms. Such systems are required to be not only accurate but also explainable for their decisions, to ensure reliability. However, a limitation of using CNNs is that the basis of the decisions made by them are hardly interpretable by humans. Thus, in this paper, we present an explainable diagnosis method comprising two CNNs playing different roles. This method allows us to interpret the basis of the decisions made by the CNN from two perspectives: statistics and visualization. For the statistical explanation, the method constructs a dictionary of representative pathological features. It performs diagnoses based on the occurrence and importance of learned features referred from its dictionary. To construct the dictionary, we introduce a vector quantization scheme for CNN. For the visual interpretation, the method provides images of learned features embedded in a feature space as an index of the dictionary by generating them using a conditional autoregressive model. The experimental results showed that the proposed network learned pathological features that contributed to the diagnosis and yielded an area under the receiver operating curve (AUC) of approximately 0.93 for detecting atypical tissues in pathological images of the uterine cervix. Moreover, the proposed method demonstrated that it could provide visually interpretable images to show the rationales behind its decisions. Thus, the proposed method can serve as a valuable tool for pathological image analysis in terms of both its accuracy and explainability.