Reviewerly/exHarmony huggingface dataset:
Categorization of the Models Based on Structural Information Extraction and Machine Learning

As various engineering fields increasingly use modelling techniques, the number of provided models, their size, and their structural complexity increase. This makes model management, including finding these models, with state of the art very expensive computationally, i.e., leads to non-tractable graph comparison algorithms. To handle this problem, modelers can organize available models to be reused and overcome the development of the new and more complex models with less cost and effort. Therefore, we utilized a model classification using baseline machine learning approaches on a dataset including 555 Ecore metamodels. In our proposed system, the structural information of each model was summarized in its elements through generating their simple labelled graphs. The proposed solution is to transform the complex attributed graphs of the models to simply labelled graphs so that graph analysis algorithms can be applied to them. The labelled graphs (models) were structurally compared using graph comparison techniques such as graph kernels, and the results were used as a set of features for similarity search. After generating feature vectors, the performance of six machine learning classifiers (Naïve Bayes (NB), k Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) were evaluated on the feature vectors. The presented model yields promising results for the model classification task with a classification accuracy over 87%.
Autonomous Generation of Service Strategy for Household Tasks: A Progressive Learning Method With A Priori Knowledge and Reinforcement Learning

Human beings tend to learn unknown knowledge in a gradual process, from the basic to the complex. Based on this point, we propose a progressive learning method for producing service strategies according to requests, with a hierarchical priori knowledge and reinforcement learning. Service strategy aims to guide how to perform home services and takes into consideration the relationship between actions and objects in home environment. In this paper, strategy generation is regarded as a text generation problem in question answering (QA). Firstly, a hierarchical priori knowledge with service-object correlation at the bottom and action-object correlation at the top is constructed to assist the understanding on the relationship of objects and actions in service strategies. Service-object correlation guides how to select proper objects with the correct order, while action-object correlation associates actions in strategies according to selected objects. Based on the hierarchical priori knowledge, a progressive learning method is proposed to make the model produce effective strategies with a sequential cognition, from service-object correlation (objects) to action-object correlation (actions). After that, reinforcement learning is employed to enhance the progressive guidance, by designing rewards in terms of the hierarchical priori knowledge. Finally, the proposed method is tested with both comparative experiments and ablation studies, and the experimental results demonstrate the superiority in producing comprehensive and logical strategies, indicating that the progressive learning method in our paper can further improve the QA performance.
SVD Mark: A Novel Black-Box Watermarking for Protecting Intellectual Property of Deep Neural Network Model

With the rapid development of deep learning technology, more and more researchers have paid attention to protecting the intellectual property rights of the deep neural network (DNN) model. So far, various methods have been proposed to construct black-box watermarking copyright protection based on trigger sets. Since extant black-box watermarking methods are backdoor-based, the watermark embedding process inevitably distorts the decision boundary of the DNN model, which leads to a decline in the performance of the DNN model. We propose a novel scheme for constructing black-box watermarking based on Singular Value Decomposition (SVD) to compensate for shortcomings. We select an appropriate number of image samples as watermark key samples in the training dataset by employing the Mersenne-Twister algorithm, which strengthens the relevance of the process watermarking embedding to the original classification task and extends the perceptual domain of the DNN model. Subsequently, the SVD algorithm extracts the primary feature information of the watermark key samples, thereby constructing more stable and covert watermark samples. Next, the classification labels corresponding to the watermark samples are specified as the classification labels of their corresponding watermark key samples, which is unlike most existing DNN watermarking schemes. It can effectively reduce the distortion of the DNN model decision boundary caused by watermarking during the embedding process. As such, the proposed scheme has a low impact on the performance of the DNN model and is highly robust. We have validated the proposed watermarking scheme on two benchmark datasets. The experimental results show that our scheme, besides meeting the functional requirements of watermarking, also does not affect the test accuracy of the DNN model. Moreover, the proposed watermarking is robust to the common watermarking attacks.
Deep Denoising for Multiview Depth Cameras

A novel method for noise removal in multi-camera depth sensing systems is proposed in this work. The method employs a combination of convolutional neural networks applied on each depth camera separately, followed by depth reprojection and joint processing of the resulting point clouds using a 3D neural network. Both depth and point cloud processing networks are designed to preserve the structure of the depth maps by appropriately correcting noise in the direction of the viewing rays. The proposed method accommodates any depth unit and noise intensity thanks to adequate normalization in both processing steps. The proposed approach is shown to outperform state-of-the-art methods for both synthetic and real data captured with a multi-camera setup, and can reduce inter-camera inconsistencies while preserving depth map structures.
Artificial Intelligence Framework for Content-Based Image Retrieval: Performance Analysis

Feature extraction, representation, and similarity estimation are all essential to measure the performance of a content-based image retrieval (CBIR) system, and they have all been widely studied for decades. Although numerous solutions have been projected, the semantic gap remains one of the most challenging problems in the ongoing research of CBIR. The semantic gap talks about how pixels in an image are perceived by computers and how humans perceive images. In recent years, machine learning and deep learning approaches have made considerable progress in addressing this issue. Proposed research work uses deep architectures to model high-level abstractions in data. Deep learning is modeled as an intelligent architecture that integrates data and information through various transformations and representations. Deep learning techniques enable a computer to learn many complicated functions that link preprocessed input data to output data without domain knowledge or human-crafted features. We have used a multi-class weather dataset and Wang’s dataset to measure the effectiveness of retrieval efficiency. The AlexNet and VGG16 are used for training and testing. Developed systems are tested with a testing dataset, and the results are compared with state-of-the-art technology. The VGG16 outperformed category-wise and also concerning mean average precision.
Explainable AI (XAI) for Social Good: Leveraging AutoML to Assess and Analyze Vital Potable Water Quality Indicators

Water is the most fundamental need of mankind, and its demand has been ever increasing concomitant with growth in world’s population. Regrettably, planet earth is witnessing a steep decrease in water quality leading to various diseases and deficiencies in human body. An immense pressure to meet the demand has not only led to the reduction of important minerals in water but also their appropriate proportion in it. This neglect of fundamental need of good quality water has reached a point that global attention is needed for it and consequently steps are being taken to enhance awareness, and research is being conducted to make healthy potable water within the reach of everyone. This paper attempts to align with the philosophy of ‘AI for Social Good’ to address this problem. Experimental results of this paper include Accuracy of 97.58%, AUC of 0.9939, Recall of 0.8521, Precision of 0.9163, F1 score of 0.8831, Kappa of 0.8696, MCC of 0.8703 with a run time of 150 s.
<i>Fisher</i> Regularized ε-Dragging for Image Classification

Discriminative least-squares regression (DLSR) has been shown to achieve promising performance in multiclass image classification tasks. Its key idea is to force the regression labels of different classes to move in opposite directions by means of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -dragging technique, yielding a discriminative regression model exhibiting wider margins. However, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -dragging technique ignores an important problem: its relaxation matrix is dynamically updated in optimization, which means the dragging values can also cause the labels from the same class to be uncorrelated. In order to learn a more powerful projection, as well as regression labels, we propose a Fisher regularized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -dragging framework (Fisher- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> ) for image classification by constraining the relaxed labels using the Fisher criterion. On the one hand, the Fisher criterion improves the intraclass compactness of the relaxed labels during relaxation learning. On the other hand, it is expected further to enhance the interclass separability of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -dragging. Fisher- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> for the first time ever attempts to integrate the Fisher criterion and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -dragging technique into a unified model because they are complementary in learning discriminative projection. Extensive experiments on various data sets demonstrate that the proposed Fisher- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> method achieves performance that is superior to other state-of-the-art classification methods. The MATLAB codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/chenzhe207/Fisher-epsilon</uri> .
The Predictability and Analysis of CNY to USD Exchange Rate Based on ARMA Model

The paper uses AR, MA, and ARMA models to analyze the CNY to USD exchange rate, and forecasts the possible future value of the CNY to USD exchange rate. Based on the results of ACF and PACF, I found that ARMA (1, 2) was the best model. Furthermore, using the estimation results of AR (1), MA (2), and ARMA (1, 2) and the mean of forecast error, I found that the predictability of ARMA (1, 2) was the best.
Style Transfer Method Based on Deep Learning for Chinese Ink Painting

Image style transfer technology learn different drawing styles by making full use of the abstract and learning ability of the neural network, and then automatically generates similar style images from the target image. However, traditional style migration methods are not fully effective in learning the details of Chinese ink painting styles. In this paper, we propose a new style transfer method for Chinese ink painting, which uses the VGG19 model to extract the features of the input style image and the input content image, merge the extracted features to generate the content image with the input style. And we also design two regular items according to the characteristics generated by the Chinese ink painting style in the loss function. A series of experiment results demonstrate that our method can generate Chinese ink-and-ink-style images better.
Purchasing Behavior Analysis Model that Considers the Relationship Between Topic Hierarchy and Item Categories

With the spread of EC sites, it has become an important work for companies to analyze user preferences contained in accumulated purchase history data and utilize them in marketing measures. A topic model is well known as a method for analyzing user preferences from purchase history data, and a model assuming hierarchy of topics has been proposed as an extension method. The previously proposed PAM (Pachinko Allocation Model) is a highly expressive model in which all upper and lower topics are connected by a network and the relationships between multiple topics can be analyzed. However, PAM is easily affected by the initial values of learning parameters, and it is difficult to obtain stable topics, so the interpretation of the estimated topics becomes unstable. It is dangerous to make business decisions based on the interpretation of such unstable results. Therefore, in this research, instead of using the hierarchy of topics estimated based on the user's purchasing behavior, we use information with a hierarchical structure of "product categories" given by the EC site for managing items. Therefore, we propose a method that is useful for studying measures and that enables hierarchical topic analysis. Finally, the proposed method is applied to the evaluation history data of the actual EC site to analyze the user's preference and show its usefulness.
Effects of Virtual Character’s Eye Movements in Reach Motion on Target Prediction

In human communication, nonverbal behaviors play an important role in realizing smooth communication. However, it is not clear that the effects of reliability for the information transmission of the motion in each body part such as eye movements and body movements. In this study, we designed and developed a virtual character with movable right arm, head, and eyes to investigate the effects of reliable motion. The virtual character acted simply as reaching a cup on the table, while participants were asked to predict which cup the character would grab. Participants’ predictions were highly consistent with where the character gazed, especially when the eye movements were oriented differently from the face. The results suggest that eye movements affect target prediction.
New Advanced Approach for Data Flows Prioritization at an Output of a User Terminal

Multimedia services are among the most developed segments of services.User behavior is changing with a significant number being very active.They utilize services that produce large amounts of data traffic in uplinks.Consequently, this is quite a new phenomenon that service providers should consider.The Quality of Service (QoS) of real-time multimedia services is crucial for all users, and research has been devoted to satisfying these requirements.Although fulfilling the QoS requirements is challenging, most of the research has focused on the downlink.The goal of this study was to focus on uplinks because this direction is crucial to modern attractive multimedia services such as streaming of videos and video gaming.This study proposed a solution that prioritizes traffic at the end of the user terminal and fixes the issue with real-time multimedia services.This effectively eliminates the first bottleneck in the entire system, regardless of the access network.The investigated solution is independent of the type of access network because it is implemented at the user terminal.
Efficient and Secure Outsourced Image Watermarking in Cloud Computing

Image watermark has played an important role in image copyright protection. With the development of cloud computing in recent years, more and more people are willing to outsource the watermark embedding processing to cloud servers. However, the usual watermark embedding is performed in the plaintext domain, which will lead to the leakage of private information. We propose an effective privacy-preserving outsourcing watermark embedding method. We firstly present the batched implementation of discrete cosine transform (DCT) in the encrypted domain using fully homomorphic encryption integrated with Single Instruction Multiple Data (SIMD). The watermark bits are embedded in the DC components of the DCT coefficients in the encrypted domain without any leakage of the original image data. Our experimental results indicate that our secure watermarking scheme has higher efficiency than the compared work.
Can Formal Languages Help Pangenomics to Represent and Analyze Multiple Genomes?

Graph pangenomics is a new emerging field in computational biology that is changing the traditional view of a reference genome from a linear sequence to a new paradigm: a sequence graph (pangenome graph or simply pangenome) that represents the main similarities and differences in multiple evolutionary related genomes. The speed in producing large amounts of genome data, driven by advances in sequencing technologies, is far from the slow progress in developing new methods for constructing and analyzing a pangenome. Most recent advances in the field are still based on notions rooted in established and quite old literature on combinatorics on words, formal languages and space efficient data structures. In this paper we discuss two novel notions that may help in managing and analyzing multiple genomes by addressing a relevant question: how can we summarize sequence similarities and dissimilarities in large sequence data? The first notion is related to variants of the Lyndon factorization and allows to represent sequence similarities for a sample of reads, while the second one is that of sample specific string as a tool to detect differences in a sample of reads. New perspectives opened by these two notions are discussed.
Regression for Predicting Effort in Object-Oriented Software Projects

Software estimation is one of the pivotal activities in software engineering projects. However, predicting effort is challenging due to the complexity, intangibility, and diversity of software solutions and their involved expertise and applied technologies. This work aims at improving the accuracy of software estimation using regression algorithms and based on the Use-Case Points analysis (UCP). Five regression algorithms were applied including Random Forests, Stochastic Gradient Decent (SGD), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Neural Networks (NN). These algorithms were applied to a historical dataset that represents several object-oriented software projects. The dataset consists of 70 projects that are described using 37 attributes which are related to software requirement specifications, development process, and domain application in addition to a set of technical and environmental factors. The experimental results in this work showed that Random Forests and SGD algorithms were successful in achieving significant improvement in the predicting software effort estimation compared to Use-Case Point analysis. The random forest model scored 0.86 in R2 , while the SGD model scored 0.83. It was concluded that regression improves software effort prediction compared to other estimation methods, particularly if adequate data is collected, processed, and analysed using the right algorithms.
Case Study on the Use of the SafeML Approach in Training Autonomous Driving Vehicles

The development quality for the control software for autonomous vehicles is rapidly progressing, so that the control units in the field generally perform very reliably. Nevertheless, fatal misjudgments occasionally occur putting people at risk: such as the recent accident in which a Tesla vehicle in Autopilot mode rammed a police vehicle. Since the object recognition software which is a part of the control software is based on machine learning (ML) algorithms at its core, one can distinguish a training phase from a deployment phase of the software. In this paper we investigate to what extent the deployment phase has an impact on the robustness and reliability of the software; because just as traditional, software based on ML degrades with time. A widely known effect is the so-called concept drift: in this case, one finds that the deployment conditions in the field have changed and the software, based on the outdated training data, no longer responds adequately to the current field situation. In a previous research paper, we developed the SafeML approach with colleagues from the University of Hull, where datasets are compared for their statistical distance measures. In doing so, we detected that for simple, benchmark data, the statistical distance correlates with the classification accuracy in the field. The contribution of this paper is to analyze the applicability of the SafeML approach to complex, multidimensional data used in autonomous driving. In our analysis, we found that the SafeML approach can be used for this data as well. In practice, this would mean that a vehicle could constantly check itself and detect concept drift situation early.
On the Impact of Location-related Terms in Neural Embeddings for Content Similarity Measures in Cultural Heritage Recommender Systems

Analysing text to detect semantic similarities is a recent breakthrough of Natural Language Processing that brought many novel applications in different fields. A domain which could greatly benefit of this innovation is the one regarding Location-based and/or Touristic Recommender Systems, where the user receives suggestions based on his/her past liked items. In this work, we consider the use of neural embeddings weighted using Smooth-Inverse Frequency (SIF) to detect semantic similarities in textual descriptions found in a large graph database covering Italian cultural Points of Interests (POIs). Of all detected similar pairs on a national scale, 19% are composed by POIs that do not belong to the same ontological category, highlighting the potential neural embeddings have to match POIs beyond the categories they have been assigned to. However, since text descriptions also contain references to the places where POIs are found, similarities can be detected among POIs sharing the same location, especially in the case of low-frequency geographical terms. While this may be desirable, in some cases, it may harm location-aware applications, as POIs positions are already known. By comparing city names occurrence probabilities both in the full text corpus and in location-constrained sub-corpora, we observed probability shifts, on average, of 232%. This suggests that, for the specific case of location-aware services, SIF-weighted neural embeddings should use location-constrained sub-corpora for term occurrence probability computation in order to efficiently remove uninteresting information.