Reviewerly/exHarmony huggingface dataset:
Kinship Verification Using Color Features and Covariance Descriptor

In today's era, there is a massive growth of image data on social networks and it has become a difficult problem to determine whether the person are related to each other or not. Kinship verification is evidenced to be helpful as a soft biometric modality to reinforce the accuracy performance of face verification by multiplying the chance of similarity and different machine learning approach for classification. Remarkably over a decade now there has been many promising researches in kinship, but the area is still alive to contribute more. In this paper a novel way of feature representation is proposed. Here a nine dimensional feature vector of an image is obtained. These nine dimensional features are cascaded and the most discriminate feature are selected using Linear Discriminative Analysis(LDA). After classification using K-NN classifier, an accuracy of 69.05% is obtained. The effectiveness of the proposed method is measured on two benchmark datasets that is KinFaceW-I and KinFaceW-II. Experimental results shows that the performance has been improved compared to other state-of-the-art methods.
Human Visual Learning Inspired Effective Training Methods

It is claimed that convolutional neural networks are inspired by human vision systems. Based on the literature of development of human visual system, we know that newly born child has blurred vision initially due to rapid eye movements. This rapid eye movement is termed as Nystagmus. This paper is concerned with a novel approach to quantify the nystagmus and implementing an artificial system that can mimic the visual learning of a newly born child or person with nystagmus. To quantify the nystagmus, we have recorded 10 seconds of eye movement videos from 3 subjects and 10 trials. We estimate the eye movement frequency by tracking the eye pupil through image processing, which is then used to create a database. To simulate a suitable learning environment, we have trained our model on gradually decreasing blurriness on Dog vs Cat dataset for classification task. The novelty of the paper is in the type of training which is elicited by human visual learning system. The results show that the performance of the network improves significantly when trained iteratively with increasing level of blur.
Distribution-Dependent Distance of First Two Moments

Closeness measures between distributions, between vectors, and between matrices abound. Many practical problems, however, call for measures of closeness between the first two moments of two unspecified distributions given only a sample of one of them or of a third distribution without other information. We present several metrics for such problems that demand special, distribution-dependent solutions, and show their good qualities. We demonstrate their rich applications in various areas, such as estimation performance analysis, efficiency of distributed fusion, metrized Kullback-Leibler divergence, decision performance evaluation, credibility of estimators, filter initialization, and empirical distribution function problems.
Infographic in relation to the human information - processing system and its effectiveness to deliver complex information

Infographic as a form of presenting information that is designed combining visual with text has become a form that allows the reader easily understand series or complex information. This paper will begin with brief explanation about how the brain processes the information and the history of Infographic that has become part of the visual communication design heritage. Then will be discussed how infographic both on printed and digital media convey comprehensive information that related to the human information - processing system. At the end of the article can be obtained a clearer picture on how infographic with its visual approach makes impacts to the reader, in order to understand complex information.
Hierarchical Distributed Ledger for IoT using Ethereum Blockchain

In the domain of Internet of things there are large number of devices generating data in the network and submitting it to their respective configured data collectors. When the sensors are preconfigured by the data collectors, they can easily verify the data coming from those sensors for its authenticity using cryptography. But if a third party wants to buy that data, how can they trust the data collector for the authenticity of that data i.e whether the data they are buying is real data or some randomly generated stream of numbers. In fact, the only option left is to trust the claim made by the collectors that the data they are providing is authentic. This project resolves the trust issue by creating a distributed application using blockchains by distributing the trust factor to the entire network instead of trusting a single entity [5]. This system provides authenticity of data generated by sensor nodes in the network.
Measuring Students’ Device Specific Competencies Using an Eye-Tracking Study on Oscilloscopes

At the University of Paderborn, a sophisticated Augmented Reality (AR) based application is being developed to help students acquire and deepen practical skills in dealing with electro-technical laboratory equipment. However, evaluating students’ motoric competencies while handling different laboratory devices is a challenging task. Currently, there is less work being done for an objective way to compare or evaluate students’ experimental skills. Hence, we propose an eye-tracking study in this paper with different students and laboratory engineers to identify characteristics in the scanpaths, allowing to draw conclusions from their experimental skills. Firstly, a brief introduction to the project’s context and some theoretical background are given, i.e. known characteristics of experts vs. beginners. In the next step, the task with the oscilloscope is described, which has to be solved by the group of experts (laboratory engineers) and the beginners (first semester students), who have never worked with laboratory devices before. The measured eye-tracking data is presented with respect to fixation times, areas of interest and scanpaths to identify characteristics in the measured data. It turns out that a purely quantitative analysis of the gaze data in this experiment is not very meaningful but should always be verified together with the respective solution strategy, since different solution strategies may evidently lead to similar gaze behaviors.
Diagnosis of Cough and Cancer Using Image Compression and Decompression Techniques

This paper is dedicated to provide a technique with an innovative approach which can efficiently compress and recognize medical images. Since medical images are huge in size, therefore, compression of medical images is needed. Then, recognition capability is tested with the compressed and the uncompressed images. Basically, in this paper, two steps have been used to identify the disease. In the first step, the physical size of the medical image is reduced, and in the second step, differentiation of the image of particularly lung part of the human body at different disease states is performed. The objective of this paper is to detect and analyze the lung part of the human body based on cough state before entering into the cancer state so that the disease can be cured.
Rao-Blackwellised Point-Mass Smoothers for a Class of Conditionally Linear Dynamic Models

The paper deals with the state estimation of nonlinear stochastic dynamic systems. The stress is laid on the numerical solution to the Bayes&#x0027; rule considering a class of conditionally linear Gaussian models typically appearing in navigation. In particular, three novel Rao-Blackwellised smoothers are proposed, where the nonlinear part of the model is solved by a computationally expensive point-mass smoother, whereas the conditionally linear part is solved by a set of linear smoothers. The proposed smoothers offer a tradeoff between the computational complexity and smoothing performance. The properties of the smoothers are theoretically analysed and discussed.
Modern educational process and information technology

The article analyzes the current state of application of information technologies in the educational process, the changes taking place in this direction in connection with the new reform of higher education. The tasks put forward by scientists to information technology in education are indicated. Attention is focused on the further achievements of the global rationalization of intellectual activity in society through the use of new IT. A set of modern methods and tools used in the educational process in developed countries is considered.
Business-Aware ERP Cloud Software Evolution

ERP cloud software and the related issues were focused in this paper. A business-aware framework was proposed in order to improve the flexibility of ERP implementation and evolution, in which users can change or update business processes as required. Such business changes can be captured by the process engine. And then the engine can call the analyzer to determine the complexity of these changes, which can be measured according to the property of forward compatibility defined to evaluate the scale and status of the coming evolution. After that, activity instance scheduler together with change locators can be called to cooperatively complete the process of ERP cloud service evolution by operating the caching sequence of cloud service instances. And practices showed that this mechanism was efficient.
An Automated Ensembled Deep Neural Network Approach towards Accurate Segmentation of Biomedical Images

The automated segmentation of biomedical images is being used increasingly for the clinical practices. Deep learning techniques and networks demonstrate promising results in applications of computer vision. However, the behaviour of different networks are effectively influenced by the choice of architecture and training parameter settings. Ensembles of neural networks are known to be much more efficient and accurate than individual networks. We explore stacking ensemble and snapshot ensembles of different neural network models and architectures for robust performance through the aggregation of predictions for effective biomedical image segmentation tasks. The network models used are inspired from the Fully Convolutional Network(FCN) architectures. The proposed approach is a generic and unbiased deep learning approach that reduces the risk of over fitting the network configuration to a particular dataset. We evaluated our approach on the Boston University-Biomedical Image Library(BU-BIL) Dataset 1. The predicted image segmentation masks were compared to the gold standard ground truth annotations of the dataset to validate the effectiveness of our proposed ensemble architectures.
A Study on the Establishment of a User's Product Attribute Requirement Data Verification Model

Every consumer has different product preferences, so finding a product portfolio that is accepted by most consumers and creating products that most consumers are willing to buy is an important prerequisite for product development. Sporting goods have always been products that require knowledge and technology. They must not only provide users with a good function, but also must provide correct sports knowledge and better health. Therefore, sports products must stand out, except for the products themselves. In addition to functions and special attribute appeals, whether existing product attributes are the same as consumers' perceptions, and whether existing products can really help users to improve their sports needs, etc., are all issues to be explored. This study will take the concept of product attributes as the core, clarify the product's own attribute function, and provide a reference data database for future product design and development, and also introduce the user's physiological reaction experiment design. Construct a model to provide more information to help sporting goods designers find out the user's perception of the product and the functional differences that the product itself gives through experimental verification, providing the driving force for continuous innovation in the future product development process.
Hypothesis Correction Based on Semi-character Recurrent Neural Network for End-to-end Speech Recognition

End-to-end automatic speech recognition (ASR) has become popular because of its simple modeling, but it encounters out-of-vocabulary words more frequently than the conventional hybrid approaches that jointly use acoustic and language models. In particular, word-based end-to-end systems cannot output any words unseen in training data. To address this problem, character-based end-to-end systems have been proposed; however, they are susceptible to noise, and their output words are not necessarily correct in terms of language. This is because language constraints, such as lexicons and language models, are lacking in the decoding process. Thus, errors like misspellings occur frequently. In the field of natural language processing, to correct spelling errors, a semi-character recurrent neural network (scRNN) was proposed whose inputs are the counts of characters in a word and outputs are word ids. To apply scRNN to ASR, extensions are needed because scRNN focuses only on substitution errors. Here, to consider insertion and deletion errors, we introduce blank word symbols, similar to blank symbols in connectionist temporal classification, and word concatenation. Two different ASR tasks, a noisy ASR task and an ASR task with a large vocabulary, showed that scRNN with the proposed extension improved the word error rate.
Divers Tracking with Improved Gaussian Mixture Probability Hypothesis Density filter

The group divers tracking problem with a 2D high-resolution active sonar is studied in this paper. Probability Hypothesis Density (PHD) filter is famous for its good ability in multiple targets tracking. Instead of travelling in a constant velocity motion model, the activity of divers may be, however, affected by the factors such as the destination, activities of surrounded divers and the potential intention of themselves. That is, not only are the motion states of divers correlated with each other but also dependent on the external environment. A solution is proposed to deal with the challenges of a time-varying number of targets, potential interactions by taking advantage of the PHD filter and social forced model (SFM). The diver dynamic model (DDM) is created based on the social force concept. By including the DDM model into the framework of PHD filter, the dependencies from closed group targets and external environments are considered in the recursive Bayesian framework and a different likelihood in prediction stage of a filter can also be obtained. Numerical simulation results show that the proposed method here is able to improve the performance of the PHD filter in the presence of interactions.
Sundanese Aksara Recognition Using Histogram of Oriented Gradients

Indonesia is a famous nation for its wealth in both natural and language resources and culture. Aksara is one of the Indonesian cultures that must be preserved therefore, as not to lose its existence. To avoid the loss of the existence of letters, especially Sundanese aksara, we proposed a new approach Sundanese word recognition with considering rarangkèn characteristic using the Histogram of Oriented Gradients method and support vector machine as a classification method. The datasets used are sourced from a Sundanese dictionary book. Based on the test results obtained an accuracy 81.48 % of the recognition of word Sundanese aksara with the values pixels per cell is 10x10 and cells per block is 1x1 or the values pixels per cell is 20x20 and cells per block is 3x3.
A Fast and Accurate Cluster Center Initialization Algorithm for PolSAR Superpixel Segmentation

Locate iterative clustering (LIC) gets good performance in superpixel segmentation for PolSAR images. However, there are also two problems in its cluster center initialization. One is that there is a lot of computational redundancy in calculating edge map, which slows down the segmentation speed. The other is that the calculated edge is much wider than the real one, which makes the fine-tuning of center seeds in 3 × 3 window not suitable. To solve these problems, a fast and accurate cluster center initialization algorithm for PolSAR superpixel segmentation is proposed in this paper. For the first problem, integral graph is introduced to eliminate the redundancy. For the second problem, a more reasonable window size is selected to conduct fine-tuning. Experiments with measured AIRSAR data sets demonstrate the speed of the proposed edge map calculation method increases 28 times and the center seed is farther away from the edge than before. Furthermore, LIC gets better superpixel segmentation results with the new initialization algorithm.
Extracting Structured Data from Unstructured Text Using Conditional Random Field and Jaccard Similarity

Nowadays, purchasing information is usually found in form of formal messages such as an email. However, most messages are written in natural language form which is difficult to extract data. Our approach paper uses Conditional Random Fields and Words Similarity to extract data from customers' purchase order emails. We started from dividing the words in the email into a sequence of words. Then, we added features of each word. After that, we establish an appropriate template, characteristics and training sample sequences. Next, purchasing information was used for text extraction. From our experiment, it was found that adapting Words Similarity with Conditional Random fields can enhance the accuracy of extracting data significantly.
Quality measure and optimization for grid flow approaches

With increasing calculation power of modern systems, the focus of Stochastic Filtering turns to nonlinear effects. Fully nonlinear solutions to the estimation problem are provided by an approximation of the full probability density function (pdf) in particle filters or the Fokker-Planck equation. Several papers deal with overcoming the problem of degeneration in the measurement update for these nonlinear solutions by particle flow (for the particle filters) or grid flow (for grid based approaches). For the grid flow approach a suggestion for a reasonable choice of the concrete flow exists, but without regarding a measurable quality in comparison to other possible flows. In this contribution a quality measure together with a possible optimization process for the grid flow approach is introduced.
Interest Indices for Human Computer Interaction Applications based on Facial Image Sequences

The paper proposes three metrics for assessing interest levels of users in human-computer interaction (HCI) applications using facial features. The indices, named Expression Index (EI), Ocular Index (OI), and the Head Pose Index (HPI), are based respectively on the three most significant aspects of HCI, viz., facial expression, eyelid dynamics, and head pose. The proposed indices have been computed on a sequence of face images. The region of face and eyes have been localized using Convolutional Neural Networks (CNN) and the indices validated through statistical analysis.
Comparison of Real Time Iterative Deepening Best First Search Algorithm and A* Algorithm on Maze Chase Game NPC

Maze Chase is a game that has a maze background. In this game there are players who have the task, which is to take all the points in the labyrinth. In the Maze Chase game there is also an NPC (Non-Playable Character) that aims to chase players so that players cannot take all the points in the labyrinth. Players can be considered to have won the game is that all the points in the labyrinth have been taken by the player. The author implements the A* and RIBS path search algorithms for NPCs so that NPCs can chase players. That way we get the travel time comparison to the players on each path search algorithm. After testing the average travel time to NPC players with the A* algorithm faster 0.116196% than NPC with the RIBS algorithm.
Using Computational Linguistics to Extract Semantic Patterns from Trolling Data

Trolling is one of the major issues in social media, especially on Twitter. Trolls try to distract, harm and cause discourse between users by posting provoking tweets or posts. The goal of this study is to use state-of-the-art word embedding, computational linguistics, and semantic sentiment extraction techniques to find patterns for the trolling contents. To do so, we applied word embedding to explore patterns in the tweet context. We also apply part of speech extraction and analysis, n-grams and word cloud analysis from different tweet categories. Finally, we apply the SentiStrength approach to explore the sentiment rooted in the semantics of the tweets. Our dataset contains 34,000 tweets. The data are categorized as LeftTroll and RightTroll. We applied different feature extraction techniques to explore the context of trolls with respect to left and right trolls and findings results are promising.
Explaining Black Box Models Through Twin Systems

This paper presents the early stages of my PhD research aiming at advancing the field of eXplainable AI (XAI) investigating the twinsystems, where an uninterpretable black-box model is twinned with a white-box one, usually less accurate but more inspectable, to provide explanations to the classification results.We focus in particular on the twinning occurring between an Artificial Neural Network (ANN) and a Case-Based Reasoning (CBR) system, so-called ANNCBR twins, to explain the predictions in a post-hoc manner taking account of (i) a feature-weighting method for mirroring the ANN results in the CBR, (ii) a set of evaluation metrics that correlate the ANN to other white/grey models supporting explanations for users, (iii) a taxonomy of methods for generating explanations from the twinning for the neural network's predictions.
Bits to Color Image Spin Cryptography for Secure Data Transmission In Cloud Databases

Digital signatures enable the authentication and non-repudiation‖ of digital messages, assuring the recipient of a digital message of both the identity of the sender and the integrity of the message. As Attribute based encryption is better than Identity based encryption in terms of security I have selected ABE for Encryption. But in ABE third trusted party supplies  keys. So to avoid third party ,I have selected quantum Mechanics for key generation and distribution  to improve  security in the cloud. The problem is that when cloud service providers provide service that time might be hacker hacked username and Password. For Prevent this problem we implement the concept of digital Signature. Our proposing scheme is based on generating the image from the text that we want to send securely and spinning the text based quantum QU bit approach as a base. This means no additional data is added to the initial text it is just a change in its format so that we can send it safely. This may be done using different image formats starting with grayscale images to 24 bits. The most important advantage in our proposed cryptographic technique is that it does not require an additional image to hide the text beneath it, besides it has a small size and very fast in comparison with other techniques.