Reviewerly/exHarmony huggingface dataset:
Eye center localization in a facial image based on geometric shapes of iris and eyelid under natural variability

The estimation of the human eye centers is one of the important step in several computer vision applications such as driver drowsiness detection, eye tracking, face recognition etc. Most of the existing techniques are able to localize eyes in frontal faces only while they fail to localize eye pairs in complex scenarios such as changes in head pose, scale, and illumination. In this paper, an eye localization method has been proposed that can locate the eye centers more precisely in facial images captured under the above-mentioned complexities. The proposed method consists of three stages: eye candidate detection, eye candidate verification, and post-processing. In eye candidate detection, the possible eye candidates are extracted using two new features namely Semi-Circular Edge Shape (sCES) and Semi-Ellipse Edge Shape (sEES) features. These features take into consideration the semi-circular and semi-ellipse edges of iris and eyelid and hence are able to localize eye centers more precisely. In verification, the extracted eye candidates are verified using a Support Vector Machine (SVM) based classifier. A scale-space framework is also included in the verification stage to handle the scale variations of images. In post-processing, the eye centers are paired using some geometrical constraints and then a modified gradient-based method is proposed to detect the required eye pair. The proposed system is evaluated on different databases to check its robustness to changes in head pose, scale, illumination etc. The experimental results suggest that the proposed method shows better accuracy in challenging environments and also outperforms some state-of-the-art methods.
Design of Fuzzy Controller rule base using Bat Algorithm

The work in this paper revolves fundamentally around the main axes of fuzzy control of the type Takagi-Sugeno (T-S) zero order for dynamic, complex nonlinear systems. In this paper, we present method for designing Fuzzy controller rule base using a new swarm intelligence algorithm, which is based on the Bat algorithm. The Bat algorithm is one of the most recent swarm intelligence based algorithms that simulates the intelligent hunting behavior of the bats found in nature. The main objective is to design the fuzzy rule base of fuzzy controller respecting the desired performance. To demonstrate the efficiency of the suggested approach, a control of a Magnetic Ball Suspension System is selected. Simulation results shows that the proposed approach could be employed as a simple and effective optimization method for achieving optimum determination of fuzzy rule base parameters.
Metamorphic Robustness Testing: Exposing Hidden Defects in Citation Statistics and Journal Impact Factors

We propose a robustness testing approach for software systems that process large amounts of data.Our method uses metamorphic relations to check software output for erroneous input in the absence of a tangible test oracle.We use this technique to test two major citation database systems: Scopus and the Web of Science.We report a surprising finding that the inclusion of hyphens in paper titles impedes citation counts, and that this is a result of the lack of robustness of the citation database systems in handling hyphenated paper titles.Our results are valid for the entire literature as well as for individual fields such as chemistry.We further find a strong and significant negative correlation between the journal impact factor (JIF) of IEEE Transactions on Software Engineering (TSE) and the percentage of hyphenated paper titles published in TSE.Similar results are found for ACM Transactions on Software Engineering and Methodology.A software engineering field-wide study reveals that the higher JIF-ranked journals are publishing a lower percentage of papers with hyphenated titles.Our results challenge the common belief that citation counts and JIFs are reliable measures of the impact of papers and journals, as they can be distorted simply by the presence of hyphens in paper titles.
Engaging Museum Visitors with AI: The Case of Chatbots

This chapter explores the application of artificial intelligence (AI) Artificial Intelligence (AI) in museums and galleries in engaging their audiences, specifically through the development and use of chatbotChatbot technologies. Through a case studyCase study approach, the chapter further provides a practical focus on the design and implementation of an audience development pilot in Milan involving four historic house museumsHistoric house museums (Case Museo di Milano). The pilot aimed to find new and interesting ways to engage teenagersTeenagers in visiting these museums through visualizing narrative using a convergence of chatbot and gamificationGamification platforms.
NPUFort

Deep neural network (DNN) models are widely used for inference in many application scenarios. DNN accelerators are not designed with security in mind, but for higher performance and lower energy consumption. Hence, they are suffering from the security risk of being attacked. The insecure design flaws of existing DNN accelerators can be exploited to recover the structure of DNN model from the plain instructions, thus the runtime environment can be controlled to obtain the weights of DNN model. Furthermore, the structure of DNN model running on the accelerator is acquired by the side channel information and interrupt status register. To protect general DNN accelerator from being attacked by model inversion attack, this paper proposes a secure and general architecture called NPUFort, which guarantees the confidentiality of the parameters of DNN model and mitigates side-channel information leakage. The experimental results demonstrate the feasibility and effectiveness of the secure architecture of DNN accelerators with negligible performance overhead.
Deep asymmetric video-based person re-identification

In this paper, we investigate the problem of video-based person re-identification (re-id) which matches people’s video clips across non-overlapping camera views at different time. A key challenge of video-based person re-id is a person’s appearance and motion would always display differently and take effects unequally at disjoint camera views due to the change of lighting, viewpoint, background and etc., which we call the “view-bias” problem. However, many previous video-based person re-id approaches have not quantified the importance of different types of features at different camera views, so that the two types of important features (i.e. appearance and motion features) do not collaborate effectively and thus the “view-bias” problem remains unsolved. To address this problem, we propose a Deep Asymmetric Metric learning (DAM) method that embeds a proposed asymmetric distance metric learning loss into a two-stream deep neural network for jointly learning view-specific and feature-specific transformations to overcome the “view-bias” problem in video-based person re-id. As learning these view-specific transformations become expensive when there are large amount of camera views, a clustering-based DAM method is developed to make our DAM scalable. Extensive evaluations have been carried out on three public datasets: PRID2011, iLIDS-VID and MARS. Our results verify that learning view-specific and feature-specific transformations are beneficial, and the presented DAM has empirically performed more effectively overall for video-based person re-id on challenging benchmarks.
Sentiment Analysis of Saudi Dialect Using Deep Learning Techniques

Nowadays Twitter and Facebook are the most popular social media platforms to express opinions about various topics. Sentiment analysis (SA) aims to extract the opinions from tweets, reviews and comments. Almost all prior research has used machine learning algorithms to classify the Arabic text into positive, negative or neutral. Recently, the deep learning techniques have shown promising accuracy for SA on English, Thai, Persian, and Tamil. Motivated by the results, we propose the first study applying deep learning to classify sentiments of Saudi dialect texts. We have collected a dataset of 32063 tweets and then applied two deep learning techniques to perform SA: Long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). For the sake of comparison, we have also applied the well-known support vector machine (SVM) algorithm to classify the sentiments of the collected data. The results show that the deep learning techniques outperform the SVM algorithm. The experimental result of Bi-LSTM is 94% outperforming that of the LSTM (92%), while the SVM has the lowest performance of 86.4%.
Classifier Selection for Highly Imbalanced Data Streams with Minority Driven Ensemble

The nature of analysed data may cause the difficulty of the many practical data mining tasks. This work is focusing on two of the important research topics associated with data analysis, i.e., data stream classification as well as data analysis with imbalanced class distributions. We propose the novel classification method, employing a classifier selection approach, which can update its model when new data arrives. The proposed approach has been evaluated on the basis of the computer experiments carried out on the diverse pool of the non-stationary data streams. Their results confirmed the usefulness of the proposed concept, which can outperform the state-of-art classifier selection algorithms, especially in the case of high imbalanced data streams.
Empirical Evaluation on Synthetic Data Generation with Generative Adversarial Network

Data release has been proven to be impactful in scientific research and business innovation. Nevertheless, the valuable data often contains personal information so that the data release also leads to privacy leakage. Releasing a synthetic data may be a solution for the problem of private data release. In this paper, we consider a generative adversarial networks (GAN)-based synthetic data generation. Furthermore, we perform extensive experiments to evaluate the data utility and risk of re-identification of our GAN-based solution.
Online education: key trends and barriers

The key trends and obstacles in the development of education in Russia have been considered in the article. It has been shown, that the education system, formed in the previous technological way, does not meet the needs of modern society. The main modern trend in the development of education is the introduction of digital technologies and in particular the transition from the traditional “classroom” model of learning to online education. This direction is now actively developing abroad and seems prospective in Russia. At the same time, there is a number of obstacles, that significantly inhibit its development. The main one is still not the existing system of recognition of equality of online education to its traditional forms. This problem is typical not only for our country and can be solved at the legislative level by developing the system of legislative regulation and standardization of sphere of education. Another obstacle is the continuing lack of implementation of online technologies in the educational process by educational organizations themselves, due to both the lack of development of the digital infrastructure and the need to train teachers of the new formation, who can work effectively in the digital environment. This problem can be solved by full-scale digitalization of universities with the involvement of both public financing and private investment. The third major obstacle to the full transition to online education is the very low percentage of students, who complete courses successfully. At the same time, opinion polls show, that Russian students themselves are not ready to accept digital education as a full-fledged one. The correction of this situation requires the introduction of technological and organizational solutions in the field of education, aimed at adapting the educational system to the dynamically changing needs of the labor market, individualization of educational trajectories and increasing the involvement of students in the educational process.
A novel unsupervised corpus-based stemming technique using lexicon and corpus statistics

Word Stemming is a widely used mechanism in the fields of Natural Language Processing, Information Retrieval, and Language Modeling. Language-independent stemmers discover classes of morphologically related words from the ambient corpus without using any language related rules. In this article, we proposed a fully unsupervised language-independent text stemming technique that clusters morphologically related words from the corpus of the language using both lexical and co-occurrence features such as lexical similarity, suffix knowledge, and co-occurrence similarity. The method applies to a wide range of inflectional languages as it identifies morphological variants formed through different linguistic processes such as affixation, compounding, conversion, etc. The proposed approach has been tested in Information Retrieval application for four languages (English, Marathi, Hungarian, and Bengali) using standard TREC, CLEF, and FIRE test collections. A significant improvement over word-based retrieval, five other corpus-based stemmers, and rule-based stemmers has been achieved in all the languages. Besides, information retrieval, the proposed approach has also been tested in text classification and inflection removal tasks. Our algorithm excelled over other baseline methods in all the test scenarios. Thus, we successfully achieved the objective of developing a multipurpose stemming algorithm that cannot only be used for information retrieval task but also for non-traditional tasks such as text classification, sentiment analysis, inflection removal, etc.
Automatic Hand Skeletal Shape Estimation From Radiographs

Rheumatoid arthritis (RA) is an autoimmune disease whose common manifestation involves the slow destruction of joint tissue, a damage that is visible in a radiograph. Over time, this damage causes pain and loss of functioning, which depends, to some extent, on the spatial deformation induced by the joint damage. Building an accurate model of the current deformation and predicting potential future deformations are the important components of treatment planning. Unfortunately, this is currently a time-consuming and labor-intensive manual process. To address this problem, we propose a fully automated approach for fitting a shape model to the long bones of the hand from a single radiograph. Critically, our shape model allows sufficient flexibility to be useful for patients in various stages of RA. Our approach uses a deep convolutional neural network to extract low-level features and a conditional random field (CRF) to support shape inference. Our approach is significantly more accurate than previous work that used hand-engineered features. We provide a comprehensive evaluation for various choices of network hyperparameters, as current best practices lack significantly in this domain. We evaluate the accuracy of our pipeline on two large datasets of hand radiographs and highlight the importance of the low-level features, the relative contribution of different potential functions in the CRF, and the accuracy of the final shape estimates. Our approach is nearly as accurate as a trained radiologist and, because it only requires a few seconds per radiograph, can be applied to large datasets to enable better modeling of disease progression.
An evolutionary hyper-heuristic to optimise deep belief networks for image reconstruction

Deep Belief Networks (DBN) have become a powerful tools to deal with a wide range of applications. On complex tasks like image reconstruction, DBN's performance is highly sensitive to parameter settings. Manually trying out different parameters is tedious and time consuming however often required in practice as there are not many better options. This work proposes an evolutionary hyper-heuristic framework for automatic parameter optimisation of DBN. The hyper-heuristic framework introduced here is the first of its kind in this domain. It involves a high level strategy and a pool of evolutionary operators such as crossover and mutation to generates DBN parameter settings by perturbing or modifying the current setting of a DBN. Providing a large set of operators could be beneficial to form a more effective high level strategy, but in the same time would increase the search space hence make it more difficulty to form a good strategy. To address this issue, a non-parametric statistical test is introduced to identify a subset of effective operators for different phases of the hyper-heuristic search. Three well-known image reconstruction datasets were used to evaluate the performance of the proposed framework. The results reveal that the proposed hyper-heuristic framework is very competitive when compared to the state of art methods.
Towards learning a partitioning advisor with deep reinforcement learning

In this paper we introduce a partitioning advisor for analytical workloads based on Deep Reinforcement Learning. In contrast to existing approaches for automated partitioning design, an RL agent learns its decisions based on experience by trying out different partitionings and monitoring the rewards for different workloads. In our experimental evaluation with a distributed database and various complex schemata, we show that our learned partitioning advisor is thus not only able to find partitionings that outperform existing approaches for automated data partitioning but is also able to find non-obvious partitionings.
Reinforced Fuzzy Clustering-Based Ensemble Neural Networks

In this paper, we propose reinforced fuzzy clustering-based ensemble neural networks (FCENNs) classifier. The objective of this paper is focused on the development of the design methodologies of ensemble neural networks classifier for constructing the network structure and enhancing the learning methods of fuzzy clustering-based neural networks through the combination of the probabilistic model and its learning mechanism. The proposed FCENNs classifier takes into consideration a cross-entropy error function to improve learning while L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm regularization is used to reduce overfitting as well as enhance generalization abilities. The essential points of the proposed reinforced FCENNs classifier can be enumerated as follows: First, in the proposed classifier, the cross-entropy error function is used as a cost function; to do this, a softmax function is applied to represent a categorical distribution located at the nodes of the output layer. Second, the learning mechanism is composed of two parts. First, fuzzy C-means clustering forms the connections (weights) of the hidden layer while the connections of the output layer are adjusted with the aid of the nonlinear least squares method using Newton's method-based learning. Third, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm-regularization is considered to avoid the degradation of generalization ability caused by overfitting. The learning mechanism similar to ridge regression is realized by adding L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> penalty term to the cross-entropy error function. From the viewpoint of performance improvement achieved through the proposed novel learning method, the design methodology for the ensemble neural networks classifier is discussed and analyzed with the aid of a diversity of two-dimensional synthetic data and machine learning datasets.
Implications of Blockchain in Industry 4.O

Rapid advancements in Information Technology and industrialization methods have expedited the advent of 4th Industrial revolution also known as integrated industry industrial internet or smart manufacturing. The notion of Industry 4.0 promises unprecedented progress in next generation of manufacturing technology by fundamentally changing the ways of production and value creation with the help of digital transformation in product/service offerings and hori-zontaVvertical value chains. Industry4.0 is underpinned by a spectrum of emerging technologies such as internet of things, cloud computing, machine learning, adaptive robotics, cyber physical systems, artificial intelligence, Industrial Integration, and Service Oriented Computing. Distributed ledger technology also known as blockchain can not only affect Industry4.0 but also has its direct implications in the above mentiond set of technologies. Lack of powerful tools for visibility, accountability and auditing is a major obstacle in complex processes and supply chains of Industry 4.0. In particular, issues of cloning of products, counterfeiting, trickier maintenance and IP theft are crucial teething issues for realizing Industry 4.0, which poses unique challenges. This paper is an effort to break the ground for demonstrating and presenting the use of Blockchain technology in 4th industrial era.In this paper we explore some concepts of industry 4.0 using blockchain technology and see how blockchain enablement is beneficial for industry 4.0. we explore different areas where we can use blockchain technlogy to foster the development in Industry4.0.
Adaptively Tuning a Convolutional Neural Network by Gate Process for Image Denoising

The conventional image denoising methods based on the convolutional neural network (CNN) focus on the non-blind training, and hence many networks are required to cope with various noise levels at the test. Although there are blind training methods that deal with multiple noise levels with a single network, their performance gain is generally lower than the non-blind ones, especially at low noise levels. In this paper, we propose a new denoising scheme that controls the feature maps of a single denoising network according to the noise level at the test phase, without changing the network parameters. This is achieved by employing a gating scheme where the feature maps of the denoising network are multiplied with appropriate weights from a gate-weight generating network which is trained along with the denoising network. We train the overall network on a wide range of noise level such that the proposed method can be used for both blind and non-blind cases. The experiments show that the proposed system yields better denoising performance than the other CNN-based methods, especially for the untrained noise levels. Finally, it is shown that the proposed system can manage spatially variant unknown noises and real noises without changing the whole CNN parameters.
Learning action models with minimal observability

This paper presents FAMA, a novel approach for learning Strips action models from observations of plan executions that compiles the learning task into a classical planning task. Unlike all existing learning systems, FAMA is able to learn when the actions of the plan executions are partially or totally unobservable and information on intermediate states is partially provided. This flexibility makes FAMA an ideal learning approach in domains where only sensoring data are accessible. Additionally, we leverage the compilation scheme and extend it to come up with an evaluation method that allows us to assess the quality of a learned model syntactically, that is, with respect to the actual model; and, semantically, that is, with respect to a set of observations of plan executions. We also show that the extended compilation scheme can be used to lay the foundations of a framework for action model comparison. FAMA is exhaustively evaluated over a wide range of IPC domains and its performance is compared to ARMS, a state-of-the-art benchmark in action model learning.