Reviewerly/exHarmony huggingface dataset:
Captioning model based on meta-learning using prior-convergence knowledge for explainable images

Big data has a variety of data types, including image and text. In particular, image data-based research on face recognition and objection detection has been conducted in diverse areas. Deep learning needs a massive amount of data for learning a model accurately. The amount of data collected is different in each area, and thus it is likely to lack data for analysis through deep learning. Accordingly, it is necessary a method of learning a model effectively and predicting a result accurately with the use of a small amount of data. Also, captions and tags are generated to obtain image information. In the case of tagging, an image is expressed with words, while, in the case of captioning, a sentence can be created in connection with words. For this reason, it is possible to obtain image information in detail through captioning, compared to tagging. However, when a caption is created with words, there is the limitation of end-to-end to lower performance if labeled data are not sufficient. As a solution to the problem, meta-learning, in which a small amount of data can be used, is applied. This study proposes the captioning model based on meta-learning using prior-convergence knowledge for explainable images. The proposed method collects multimodal image data for predicting image information. From the collected data, the attributes representing object information and context information are used. After that, with the use of a small amount of data, meta-learning is applied in a bottom-up approach to creating a sentence for captioning. It can solve the problem caused by data shortage. Lastly, for the extraction of image features, LSTM for convolution network and captioning is established, and the basis for explanation is generated through the reverse operation. The generated basis is an image object. An appropriate explanation sentence is displayed in line with a particular object. Performance evaluation is conducted in two ways for accuracy. Firstly, BLEU score is evaluated according to whether there is meta-learning. Secondly, the proposed captioning model based on prior knowledge, RNN-based captioning model, and bidirectional RNN-based captioning model is evaluated in terms of BLEU score. Therefore, through the proposed method, LSTM’s bottom-up method reduces the cost of improving image resolution and solves the data shortage problem through meta-learning. In addition, it is possible to find the basis of image information using subtitles and to more accurately describe information about photos based on XAI.
Digital image security based on the chaotic logistic map

A digital image is used in every field, i.e., security, medical, entertainment, and many more. To save time and space the image was transmitting and storage through internet. There is a need for security guaranty during the image's transmission and storage, therefore the data secure and authentic. This study was conducted to protect the digital image during transmission and storage by encrypted it with a secure image protection algorithm based on the chaotic logistic map. The results compared the histograms of plain image, permutated image, chipper image, decoded image, and plain image. The histogram of the encrypted image is uniform, which means the image pixels were distributed uniformly. Therefore, plain and encrypted images were totally different. It was also shown by the correlation coefficient of the encrypted image; it was 0.0269, which closed to 0.
Augmented Reality-based ecolabel model for environmental awareness

Abstract The purpose of this research is to develop Augmented Reality-based ecolabel model for environmental awareness. This research and development were done in Jakarta, the capital city of Indonesia. The product of this research is augmented reality called ecolabel on the Android Smartphone having eight logos to give environmental awareness to the consumers. This product giving the consumers some 2-dimension information containing accurate, fast, verifiable, and not misleading information related to environmental aspect of the certain products. This product should be supported by sticker as marker to turn on augmented reality. Consequently, the society will be more appropriate in making decision to choose the product used. Revision on the basis of expert validation reaching 3,67 from maximum score 4 is that pictures given to the nine logos in this product should be added more to provide more examples to the consumer. The conclusion of this research is that Augmented Reality-based ecolabel model for environmental awareness is already valid.
EDM-DRL: Toward Stable Reinforcement Learning Through Ensembled Directed Mutation

Deep reinforcement learning (DRL) has experienced tremendous growth in the past few years. However, training stability of agents continues to be an open research question. Here, the authors present Ensembled Directed Mutation of Deep Reinforcement Learning (EDM-DRL) - a hybridization of evolutionary computing (EC), ensemble learning, and DRL methods as a means of mitigating training instability in DRL agents. We show that our method trains more consistently than synchronous Advantage Actor Critic (A2C). We also show that by employing our novel mutation and ensemble methods, performance of DRL agents can be improved during test time without sacrificing training stability. Further, though a similar number of time steps are used, we show that the EDM-DRL algorithm uses a mere 1% or less of the network parameter updates used in A2C. Finally, we conduct an ablation study to identify components within the EDM-DRL algorithm responsible for highest contribution. Code and experimental logs are available at: https://github.com/Linked-Liszt/EDM-DRL .
Spectral clustering based on high‐frequency texture components for face datasets

Spectral clustering is one of the most widely used technologies for clustering tasks, which represents data as a weighted graph, and aims to find an appropriate way to cut the graph apart in order to categorize the raw data. The pivotal step of spectral clustering is to find out the accurate information to estimate the relationship of pairwise data, based on which a graph can be constructed. According to the cognition that different faces are distinguished by the edge contour which can be represented by high-frequency texture components, a novel spectral clustering algorithm via high-frequency signal of human face, named high-frequency spectral clustering (HFSC) is proposed. In HFSC, first the local high-frequency texture components are extracted from samples. Then the relationship of pairwise samples can be estimated with the degree of correlation, which is produced from the image high-frequency information. The graph will be set up with the correlation information. Subsequently the graph cut will be implemented to achieve the final clustering results. Experimental results show that this algorithm outperforms the state-of-the-art clustering methods on several datasets.
Arrows and their modern versions: narrativity signalled by lines in data visualizations

For creating and reading data visualizations, visual literacy is crucial. This article advances the knowledge about graphical variations and conventions related to the basic graphical element of the graphical line used as a connector in data visualizations. Some visual characteristics of connecting lines can be used to show directionality and thus signal a narrative claim. Arrowheads may be one way to do so. However, particularly in a digital environment, other techniques may be used as well. This article presents a corpus study investigating the ways in which narrativity is signalled by connecting lines in current, publicly available digital data visualizations. The central connecting lines of 163 award-winning data visualizations are analysed with focus on their visual forms and how they represent actions and situations. The repeated occurrence of some visual techniques points to conventions formed both by a long tradition of analogue visualization and the advent of digital production techniques and output devices. The presented results are relevant to researchers, educators and practitioners in the data visualization field, as they provide novel empirical data on the use of an omnipresent graphical element.
Distilling and Transferring Knowledge Via Cgan-Generated Samples for Image Classification and Regression

Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student model based on the knowledge from a teacher model. However, applying KD in image regression with a scalar response variable is also important (e.g., age estimation) yet has been rarely studied. Besides, existing KD methods often require a practitioner to carefully select or adjust the teacher and student architectures, making these methods less flexible in practice. To address the above problems in a unified way, we propose a comprehensive KD framework based on conditional generative adversarial networks (cGANs), termed cGAN-KD. Fundamentally different from existing KD methods, cGAN-KD distills and transfers knowledge from a teacher model to a student model via specifically processed cGAN-generated samples. This novel mechanism makes cGAN-KD suitable for both classification and regression tasks, compatible with other KD methods, and insensitive to the teacher and student architectures. An error bound for a student model trained in the cGAN-KD framework is derived in this work, providing a theory for why cGAN-KD is effective as well as guiding the practical implementation of cGAN-KD. Extensive experiments on CIFAR-100 and ImageNet-100 (a subset of ImageNet with only 100 classes) datasets show that the cGAN-KD framework can leverage state of the art KD methods to yield a new state of the art. Moreover, experiments on Steering Angle and UTKFace datasets demonstrate the effectiveness of cGAN-KD in image regression tasks, where existing KD methods are inapplicable. Notably, in classification, incorporating cGAN-KD into training improves the state of the art SSKD by an average of 1.32 in test accuracy on ImageNet-100 across five different teacher-student pairs. In regression, cGAN-KD decreases the test mean absolute error of a ResNet20 student model from 5.51 to 1.60 degrees (i.e., 71% drop) on Steering Angle.
Statistical Modeling of High Dimensional Counts

Statistical modeling of count data from RNA sequencing (RNA-seq) experiments is important for proper interpretation of results. Here I will describe how count data can be modeled using count distributions, or alternatively analyzed using nonparametric methods. I will focus on basic routines for performing data input, scaling/normalization, visualization, and statistical testing to determine sets of features where the counts reflect differences in gene expression across samples. Finally, I discuss limitations and possible extensions to the models presented here.
Usability Study of Mobile Applications with Cognitive Load Resulting from Environmental Factors

The main goal of this work was to design and conduct usability tests of mobile applications, with particular emphasis on cognitive load. Not only was the cognitive load due to user interface design errors considered, but also the cognitive load due to external factors such as the real world. The study involved 12 participants, who were young adults, i.e. people aged 20–30 who regularly use mobile devices. The participants were randomly divided into two groups of 6 people. These groups independently carried out the scenario of activities using two different applications, in two different environments: laboratory and field. The collected results were statistically analysed in terms of task completion time, number of actions, number of errors and satisfaction with regard to net promoters score and system usability scale. The analysis of these features allowed to determine the relationship between the quality of the graphical user interface, the impact of the environmental factors and user satisfaction.
Extraction of Interpretable Decision Sets in Graph Databases

In recent years, research on knowledge discovery in graph structured databases has regained attention, and many studies have been conducted. While recently developed kernel-based methods and graph neural networks show high performance in accuracy for the classification tasks, they have a problem with interpretability since the obtained model is usually a black box that is difficult for humans to understand. In this paper, in order to build accurate predictive models with high interpretability, we propose an extension of Interpretable Decision Sets (IDS), one of representative frameworks in the field of interpretable machine learning, for graph databases. IDS employs two stage framework. After the enumeration of association rules in the first stage, IDS selects an optimal set of rules under a function on interpretability and accuracy in the second stage. In this paper, as an extension of the first stage, we give a method for deriving a set of classification rules on subgraph patterns. In addition, we develop new optimization functions for selecting a small number of simple and accurate rules which have dissimilar subgraph patterns to each other. The effectiveness of the proposed framework is confirmed through the experiments using two real datasets.
Probability-Constrained robust estimation for a random parameter with stochastic model uncertainties

This paper investigates the problem of estimating a random parameter in a linear observation model when the transformation matrix suffers stochastic uncertainties. In practice, the uncertainties can be either the bounded uncertainties or the Gaussian uncertainties. The existing minimax robust estimation method is only applicable to the bounded uncertainties, and minimizes the worst-case mean squared error (MSE) over the region of uncertainties, irrespective of the probability of such worst-case scenario, so it tends to be overly conservative. A probability-constrained (PC) robust linear estimation method is proposed in this paper to minimize the MSE with a guaranteed probability, which can handle bounded uncertainties as well as Gaussian uncertainties. However, the formulated problem is not analytically tractable due to the unspecified distribution of the uncertainties, so two approximate PC robust estimators are derived by tackling two upper bounds of the original optimization problem and converting them to semidefinite programming problems with the safe tractable approximation techniques. The comparative analysis reveals that there is a tradeoff between the MSE performance and computation complexity in the proposed two PC estimators. The Monte Carlo simulations are used to corroborate the theoretical results, which demonstrate that the proposed PC estimators are robust to the uncertainties compared to the nominal linear minimum mean squared error (LMMSE) estimator and the nominal best linear unbiased estimator (BLUE), and are less conservative compared to the traditional minimax robust estimator.
Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net

Breast cancer is a major disease in the world and is detected by histopathological image analysis. The structure and characteristics of nuclei contributes largely in the decision of malignancy of a tumor. There exists several medical image processing techniques based on traditional and CNN methods to segment nuclei from breast histopathological images. However, these algorithms use hand crafted features and depend on availability of large annotated dataset. Moreover, heterogeneous structure and characteristic of nuclei makes it non trivial task. In this context, this paper presents an encoder decoder based CNN architecture to semantically segment nuclei from breast histopathological images. A new attention mechanism is used to extract feature from the nuclei regions at multiple scales. The proposed architecture is evaluated on breast histopathological images and achieved an mIoU of 0.77.
A Cloud-Based Diagnosis Framework Of Dyslexia For The Arabic Language using Large Scale Ontologies Reasoning over Apache Spark

In this paper we present our Cloud-based Framework for Knowledge Sharing on Early Diagnosis of Dyslexia for the Arabic Language. The Framework allows the intelligent generation of sentences containing forms of dyslexia that are susceptible to being mispronounced by the child. The Framework also extracts the forms of dyslexia present in a sentence and evaluates the patient's achievements. We exploit the innovative techniques of knowledge representation and parallel processing of big data to ensure the efficiency and speed necessary for such real time applications. The Framework architecture is based on Apache HDFS, Apache SPARK and HBase. Our system is designed to be easily integrated into mobile games and applications. The results of the tests carried out are encouraging.
ExtraFerns: Fully Parallel Ensemble Learning Technique with Non-Greedy yet Minimal Memory Access Training

Training machine learning models on edge devices is always a conflict with power consumption and computing cost. This paper proposes a hardware-oriented training method called extraFerns for a unique subset of decision tree ensembles, which drastically decreases memory access and optimizes each tree in parallel. The extraFerns gets the best of both worlds: extraTrees and randomFerns. As extraTrees does, it generates nodes by randomly selecting attributes and generating thresholds. After that, as randomFerns does, it builds ferns that are decision trees sharing an identical node in each depth. In contrast to other ensemble methods using greedy optimization, extraFerns try searching global optimization of each fern. The experimental results show that extraFerns requires only 4.3% and 4.1% memory access for training models with 3.0% and 1.2% accuracy drop compared with randomForest and extraTrees, respectively.
Collaborative filtering with q-divergence-based fuzzy clustering for spherical data

Abstract Although recommendation systems are the most powerful tool to help people choose items, a higher recommendation accuracy is required to satisfy the needs of the people. Motivated by this requirement, this study proposes a novel collaborative filtering (CF) algorithm, which is the underlying technology of a recommendation system. It filters items for a target user based on the reactions of similar users. Cluster analysis helps detect similar users by grouping a set of users such that users in the same group are more similar to each other than to those in other groups. However, in most representative CF algorithms such as GroupLens algorithm, users are considered as spherical data, and as categorical multivariate data in the clustering phase of a previous study. This study overcomes this logic gap by proposing a novel CF method using fuzzy clustering for spherical data based on q-divergence as both the clustering phase and the GroupLens algorithm consistently deal with users as spherical data. Experiments were conducted on six real datasets—BookCrossing, Epinions, Jester, LibimSeTi, MovieLens, and SUSHI, to compare the performance of the proposed method with GroupLens and the method using fuzzy clustering for categorical multivariate data based on q-divergence, which are conventional methods, where the performance is measured by the area under the receiver operating curve. The results of the experiments indicate that the proposed algorithm outperforms the others in terms of recommendation accuracy.
Secure Voting for Democratic Elections: A Blockchain-Based Approach

Blockchain technology, due to its lucrative features like immutability, transparency, and security, is definitely at the forefront of an impending digital revolution. Numerous organizations have already started integrating blockchain with their applications to enhance security. Currently, blockchain is being used in a wide spectrum of industries ranging from the energy domain to the automobile industry to the financial sector. Blockchain technology can be used in any domain that requires transparency, flexibility, and immutability as a fundamental requirement. One such domain targeted in this paper is the voting domain. Building a secure electronic voting system that offers transparency, immutability and security is the challenge that has been faced for a long time. This paper proposes a novel Blockchain-as-a-Voting-Service (BaaVS) solution that solves all the issues that are currently existing in the electronic-based voting system by delivering a secured and transparent voting approach.
A Meta-modeling Approach to Take into Account Data Domain Characteristics and Relationships in Information Visualizations

Visual explanations are powerful means to convey information to large audiences. However, the design of information visualizations is a complex task, because a lot of factors are involved (the audience profile, the data domain, etc.). The complexity of this task can lead to poor designs that could make users reach wrong conclusions from the visualized data. This work illustrates the process of identifying features that could make an information visualization confusing or even misleading with the goal of arranging them into a meta-model. The meta-model provides a powerful resource to automatically generate information visualizations and dashboards that take into account not only the input data, but also the audience’s characteristics, the available data domain knowledge and even the data context.
STEM and STEAM Technologies in Problem Solving with Python

The article deals with the structure and methods of construction of the course "Problem Solving with Python" using STEM and STEAM technologies. The course is built on the principle of "visualization and problem solving head first". The techniques are offered to make the course easy to understand, including operations with NumPy arrays and slices, libraries of the Python ecosystem, domain-specific languages. It is shown that visualization in problem solving increases student interest. The techniques of conducting the course in the conditions of Covid-19 pandemic are offered.
A Novel Embedding Model for Knowledge Graph Completion Based on Multi-Task Learning

Knowledge graph completion is the task of predicting missing relationships between entities in knowledge graphs. State-of-the-art knowledge graph completion methods are known to be primarily knowledge embedding based models, which are broadly classified as translational models and neural network models. However, both kinds of models are single-task based models and hence fail to capture the underlying inter-structural relationships that are inherently presented in different knowledge graphs. To this end, in this paper we combine the translational and neural network methods and propose a novel multi-task learning embedding framework (TransMTL) that can jointly learn multiple knowledge graph embeddings simultaneously. Specifically, in order to transfer structural knowledge between different KGs, we devise a global relational graph attention network which is shared by all knowledge graphs to obtain the global representation of each triple element. Such global representations are then integrated into task-specific translational embedding models of each knowledge graph to preserve its transition property. We conduct an extensive empirical evaluation of multi-version TransMTL based on different translational models on two benchmark datasets WN18RR and FB15k-237. Experiments show that TransMTL outperforms the corresponding single-task based models by an obvious margin and obtains the comparable performance to state-of-the-art embedding models.