Information

Clustering analysis is a significant technique in various fields, including unsupervised machine learning, data mining, pattern recognition, and image analysis. Many clustering algorithms are currently used, but almost all of them encounter various challenges, such as low accuracy, required number of clusters, slow processing, inability to produce non-spherical shaped clusters, and unstable performance with respect to data characteristics and size. In this research, a novel clustering algorithm called the fast component density clustering in spatial databases (FCDCSD) is proposed by utilizing a density-based clustering technique to address the aforementioned existing challenges. First, from the smallest to the largest point in the spatial field, each point is labeled with a temporary value, and the adjacent values in one component are stored in a set. Then, all sets with shared values are merged and resolved to obtain a single value that is representative of the merged sets. These values represent final cluster values; that is, the temporary equivalents in the dataset are replaced to generate the final clusters. If some noise appears, then a post-process is performed, and values are assigned to the nearest cluster based on a set of rules. Various synthetic datasets were used in the experiments to evaluate the efficiency of the proposed method. Results indicate that FCDCSD is generally superior to affinity propagation, agglomerative hierarchical, k-means, mean-shift, spectral, and density-based spatial clustering of applications with noise, ordering points for identifying clustering structures, and Gaussian mixture clustering methods. Full article

An important topic in the field of geometric optimization is finding the largest rectangle separating two different points sets, which has significant applications in circuit design and data science. We consider some extensions of the maximum bichromatic separating rectangle (MBSR) problem. In one of the extensions, the optimal rectangle may include up to k outliers, where k is given as part of the input. This extension and is called MBSR with outliers or MBSR-O. In this paper, we improve the current known time bounds for MBSR-O from $O(k^{7}mlogm+n)$ to $O(k^{3}m+kmlogm+n)$ using a clever staircase sweep approach. We also propose another extension, which is named MBSR among circles or MBSR-C and asks for the largest rectangle separating red points from blue unit circles. Our solution to MBSR-C is an $O(m^2+n)$-time algorithm that involves an optimized scanning of all candidate circle arcs for locations of potential optimal solutions. Full article

In today’s world, heart disease is the leading cause of death globally. Researchers have proposed various methods aimed at improving the accuracy and efficiency of the clinical diagnosis of heart disease. Auxiliary diagnostic systems based on machine learning are designed to learn and predict the disease status of patients from a large amount of pathological data. Practice has proved that such a system has the potential to save more lives. Therefore, this paper proposes a new framework for predicting heart disease using the smote-xgboost algorithm. First, we propose a feature selection method based on information gain, which aims to extract key features from the dataset and prevent model overfitting. Second, we use the Smote-Enn algorithm to process unbalanced data, and obtain sample data with roughly the same positive and negative categories. Finally, we test the prediction effect of Xgboost algorithm and five other baseline algorithms on sample data. The results show that our proposed method achieves the best performance in the five indicators of accuracy, precision, recall, F1-score and AUC, and the framework proposed in this paper has significant advantages in heart disease prediction. Full article

Enterprise resource planning (ERP) systems have become the most important tool for integrating businesses and achieving the “once only” principle in data entry, which contributes to resource efficiency, the enhancement of numerous organizational processes and capabilities, and, ultimately, improved business performance. In this study, we examine the ERP system’s quality as the company’s dynamic capability, contributing to business performance according to the dynamic capability perspective. Thus, we incorporate theoretical mechanisms into the model of the ERP system’s dimensions as a function of financial and non-financial organizational performance. We hypothesized that companies with a better ERP system, with all three dimensions, information, system, and services, will achieve better non-financial and financial performance. The model was tested using primary data collected using a survey method in the environment of a developing country, where the digital transformation of companies is still at a lower level. Structural equation modeling was employed for data analysis, and the results suggest a positive relationship between ERP system quality and both types of organizational business performance. The results indicate that not all dimensions have the same effect. The quality of information and service is particularly important for business performance until the system’s technical characteristics have no significant effect. Full article

The aim of this paper is to present the involvement of public Higher Education Institutions (HEIs) in Poland in promoting the Sustainable Development Goals (SDGs) in 2021. The reason for undertaking the research was a noticeable lack of studies in the literature on the engagement of universities in the concept of sustainability. The research conducted was mainly qualitative in nature. The aim was realized using three groups of research methods: collection of empirical data, data processing, and the presentation of study outcomes. The study used 16 indicators, constructed from data taken from SciVal and RAD-on reports. These indicators presented the number of publications promoting each of the SDGs in 2021 per academic staff and other persons carrying out and participating in teaching or research activities. The research was conducted on 57 universities from Poland. Four clusters were distinguished as a result of the cluster analysis that was performed. The first cluster comprised only universities of life sciences. This group had the highest publication productivity for the following goals: SDG2, SDG3, SDG6, SDG11, SDG12, SDG13, SDG14, SDG15, and SDG16. The universities that made up cluster 2 (predominantly universities of technology) specialized in publications related to goals such as SDG7 and SDG9. The highest publication productivity for SDG1, SDG4, SDG5, SDG8, SDG10, and SDG16 belonged to cluster 3 (consisting mainly of universities of economics). The HEIs (mainly universities of physical education) that made up cluster 4 did not have the highest publication productivity in any of the SDGs. Therefore, it can be concluded that, in 2021, there was a correlation between the profile of a public HEI in Poland and the SDGs it was involved in promoting. Full article

Since their inception, deep-learning architectures have shown promising results for automatic segmentation. However, despite the technical advances introduced by fully convolutional networks, generative adversarial networks or recurrent neural networks, and their usage in hybrid architectures, automatic segmentation in the medical field is still not used at scale. One main reason is related to data scarcity and quality, which in turn generates a lack of annotated data that hinder the generalization of the models. The second main issue refers to challenges in training deep models. This process uses large amounts of GPU memory (that might exceed current hardware limitations) and requires high training times. In this article, we want to prove that despite these issues, good results can be obtained even when using a lower resource architecture, thus opening the way for more researchers to employ and use deep neural networks. In achieving the multi-organ segmentation, we are employing modern pre-processing techniques, a smart model design and fusion between several models trained on the same dataset. Our architecture is compared against state-of-the-art methods employed in a publicly available challenge and the notable results prove the effectiveness of our method. Full article

The main steps in a graph neural network are message propagation and aggregation between nodes. Message propagation allows messages from distant nodes in the graph to be transmitted to the central node, while feature aggregation allows the central node to obtain messages regarding its neighbors and update itself, so that it can express deep-layer features. Because the graph structure data have no local translation invariance, the number of neighbors of each central node is different, and there is no order, there are two difficulties: (1) how to design a reliable message propagation method to better express all network topologies; (2) how to design a feature aggregation function so that it can weigh local features and global features. In this paper, a new adaptive propagation graph convolutional network model based on the attention mechanism (APAT-GCN) is proposed, which enables GNNs to adaptively complete the process of message propagation and feature aggregation, according to the neighbors of the central node, and set the influence degree of local and global messages on the aggregation of the central node. Compared with other classical models, this method is superior to the baseline model and can improve the accuracy of node- and graph-level classification tasks in downstream tasks. Full article

The existing usability models have been used primarily for evaluation, not for usability engineering. The models were found to be general for specific mobile applications. They also lack appropriate guidelines to apply the usability models to m-government applications. Earthquake information is an example of critical information delivered to citizens via m-government applications. Usability design is considered a very important key factor to the success of such applications. This research addresses the challenges in finding the usability factors important to m-government applications and choosing appropriate factors for specific m-government applications. A questionnaire was administered to 49 citizens. The results include six usability factors which are learnability, simplicity, satisfaction, security, privacy, and memorability. Descriptions of the usability factors were later added to provide a clearer definition for each factor. This paper proposes the usability design framework for m-government applications. The use of the framework was illustrated based on the user interface redesign of the EarthquakeTMD application. The main aim was to demonstrate the applicability of the framework. The quality of the original UI design of the application in the case study was assessed with a questionnaire which was administered to 57 Thai citizens who lived in the areas affected by the disasters. Four designers participated in UI redesigning and produced four different UI designs. The new UI designs were evaluated via two usability tests on two sample groups of representative users. The first usability test was conducted with 24 participants. Twenty-four test cases were used. The second usability test was conducted with 351 representative users. After the tests, both sample groups were given a questionnaire based on the SUS (System Usability Scale). The same two UI designs by experienced and inexperienced designers who used the framework received the highest scores: 89.58 and 87.60 on the first usability test. They also received the highest score on the second usability test: 89.10 and 90.88. The results reveal that the citizens preferred the new user interfaces designed using the framework. It was found that the scores of the UI designed by inexperienced designers who used the framework were as high as the scores of the UI designed by experienced designers, whereas the UI designs from the designers who did not use the framework received the lowest scores: 63.23 and 54.27 on the first usability testing and 59.34 and 46.53 on the second usability testing. Full article

Within the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions characterized by deep uncertainty, such as the need for policy support within socio-technical systems, leads to the necessity to revisit the way simulation can be applied in this new area. Similar observations can be made for complex adaptive systems that constantly change their behavior, which is reflected in a continually changing solution space. Deep uncertainty describes problems with inadequate or incomplete information about the system and the outcomes of interest. Complex adaptive systems under deep uncertainty must integrate the search for robust solutions by conducting exploratory modeling and analysis. This article visits both domains, shows what the new challenges are, and provides a framework to apply methods from operational research and complexity science to address them. With such extensions, simulation-based approaches will be able to support these new areas as well, although optimal solutions may no longer be obtainable. Instead, robust and sufficient solutions will become the objective of optimization processes. Full article

On Instagram, we have all seen memes. Honestly, what would you do if you encountered a meme in a museum? The purpose of the study is to evaluate the nexus between posts uploaded by museum visitors and emotions, as well as the popularity of artworks and memes. We gathered N = 4.526 (N = 1.222 for memes and N = 3.304 for museum posts) entire posts using API. We selected the total number of likes, comments, frequency, nwords, and text emotions as indicators for several supervised machine learning tasks. Moreover, we used a ranking algorithm to measure meme and artwork popularity. Our experiments revealed the most prevalent emotions in both the memes dataset and museum posts dataset. The ranking task showed the most popular meme and museum post, respectively, that can influence the aesthetic experience and its popularity. This study provided further insight into the social media sphere that has had a significant effect on the aesthetic experience of museums and artwork’s popularity. As a final point, we anticipate that our outcomes will serve as a springboard for future studies in social media, art, and cultural analytics. Full article

The Museo Egizio’s collection contains 200 precious and unique leather artifacts belonging to different historical periods. The materials used during the tanning and curing procedures affect the chemical and elemental composition of the surface of the samples as well as their preservation status, specifically through the hydration level within the bulk. Here we provide an experimental characterization of a series of samples from Museo Egizio that document an extensive denaturation phenomenon (gelatinization), by combining non-destructive techniques including surface probes (X-ray fluorescence, Raman scattering, and scanning electron microscopy enhanced by X-ray energy spectroscopy) and neutron-based bulk techniques (inelastic and deep-inelastic neutron scattering). Results show partial dehydration of the samples in the bulk, affecting the morphology of their surface, the presence of potassium alum, and iron oxides, as well as phosphates and hydroxides related to the tanning and curing procedures. Finally, we briefly discuss the need for a versatile and adaptable software package that is capable of combining quantitative analyses with complementary techniques including morphological, elemental, and chemical composition. Full article

News dissemination in social media causes fluctuations in financial markets. (Scope) Recent advanced methods in deep learning-based natural language processing have shown promising results in financial market analysis. However, understanding how to leverage large amounts of textual data alongside financial market information is important for the investors’ behavior analysis. In this study, we review over 150 publications in the field of behavioral finance that jointly investigated natural language processing (NLP) approaches and a market data analysis for financial decision support. This work differs from other reviews by focusing on applied publications in computer science and artificial intelligence that contributed to a heterogeneous information fusion for the investors’ behavior analysis. (Goal) We study various text representation methods, sentiment analysis, and information retrieval methods from heterogeneous data sources. (Findings) We present current and future research directions in text mining and deep learning for correlation analysis, forecasting, and recommendation systems in financial markets, such as stocks, cryptocurrencies, and Forex (Foreign Exchange Market). Full article

The deep model is widely used and has been demonstrated to have more hidden security risks. An adversarial attack can bypass the traditional means of defense. By modifying the input data, the attack on the deep model is realized, and it is imperceptible to humans. The existing adversarial example generation methods mainly attack the whole image. The optimization iterative direction is easy to predict, and the attack flexibility is low. For more complex scenarios, this paper proposes an edge-restricted adversarial example generation algorithm (Re-AEG) based on semantic segmentation. The algorithm can attack one or more specific objects in the image so that the detector cannot detect the objects. First, the algorithm automatically locates the attack objects according to the application requirements. Through the semantic segmentation algorithm, the attacked object is separated and the mask matrix for the object is generated. The algorithm proposed in this paper can attack the object in the region, converge quickly and successfully deceive the deep detection model. The algorithm only hides some sensitive objects in the image, rather than completely invalidating the detection model and causing reported errors, so it has higher concealment than the previous adversarial example generation algorithms. In this paper, a comparative experiment is carried out on ImageNet and coco2017 datasets, and the attack success rate is higher than 92%. Full article

Interactive machine learning (IML) enables the incorporation of human expertise because the human participates in the construction of the learned model. Moreover, with human-in-the-loop machine learning (HITL-ML), the human experts drive the learning, and they can steer the learning objective not only for accuracy but perhaps for characterisation and discrimination rules, where separating one class from others is the primary objective. Moreover, this interaction enables humans to explore and gain insights into the dataset as well as validate the learned models. Validation requires transparency and interpretable classifiers. The huge relevance of understandable classification has been recently emphasised for many applications under the banner of explainable artificial intelligence (XAI). We use parallel coordinates to deploy an IML system that enables the visualisation of decision tree classifiers but also the generation of interpretable splits beyond parallel axis splits. Moreover, we show that characterisation and discrimination rules are also well communicated using parallel coordinates. In particular, we report results from the largest usability study of a IML system, confirming the merits of our approach. Full article

In recent years, sharing threat information has been one of the most suggested solutions for combating the ever-increasing number of cyberattacks, which stem from the system-wide adoption of Information and Communication Technology (ICT) and consequently endangers the digital and physical assets of organizations. Several solutions, however, were proposed to facilitate data exchange between different systems, but none were able to address the main challenges of threat sharing such as trust, privacy, interoperability, and automation in a single solution. To address these issues, this paper presents a secure and efficient threat information sharing system that leverages Trusted Automated Exchange of Intelligence Information (TAXII^TM) standard and private blockchain technology to automate the threat sharing procedure while offering privacy, data integrity, and interoperability. The extensive evaluation of the solution implementation indicates its capability to offer secure communication between participants without sacrificing data privacy and overall performance as opposed to existing solutions. Full article

Community search is a basic problem in graph analysis. In many applications, network nodes have certain properties that are important for the community to make sense of the application; hence, attributes are associated with nodes to capture their properties. Community influence is an important community property that can be used to rank communities in a network based on the relevance/importance of a particular attribute. Unfortunately, most of the community search algorithms introduced previously in attributed networks research work ignored the community influence. When searching for influential communities, two potential data sources can be used: network attributes and nodes. Dealing with structure-related attributes is a challenge. Recently, the graph neural network (GNN) has completely changed the field of graph representation learning by effectively learning node embedding and has achieved the most advanced results in tasks such as node classification and connection prediction. In this paper, we investigate the problem of searching for the influential communities in attributed networks. We propose an efficient algorithm for retrieving the influential communities in a large attributed network. The proposed approach contains two main steps: (1) Community detection using a graph convolutional network in a semi-supervised learning setting considering the correlation between attributes and the overall graph information, and (2) constructing the influential communities resulting from step 1. The proposed approach is evaluated on various real datasets. The experimental results show the efficiency and effectiveness of the proposed implementations. Full article

Interaction is the main feature of social media, while in recent years, frequent privacy disclosure events of the social media user affect users’ privacy disclosure behavior. In this paper, we explore the mechanism of interaction characteristics by social media apps on users’ privacy disclosure behavior. Using SOR theoretical models and the privacy calculus theory, the effects of privacy disclosures on TikTok are examined. Structural equation modeling is used to analyze the data from 326 questionnaires. We concluded that human–computer interaction (perceived personalization, perceived control) and interpersonal interaction (perceived similarity) positively and negatively affected perceived benefits and perceived risks, respectively, and had positive effects on intention to disclose privacy through perceived benefits and perceived risks, respectively, except that perceived personalization had no effect on perceived risk and intention to disclose privacy. In addition, perceived benefits and perceived risks played an intermediary role in interactivity and privacy disclosure intention. Finally, we provided countermeasures and suggestions for social media operators and policy makers. Full article

Human–robot collaboration (HRC) is a new and challenging discipline that plays a key role in Industry 4.0. Digital transformation of industrial plants aims to introduce flexible production lines able to adapt to different products quickly. In this scenario, HRC can be a booster to support flexible manufacturing, thus introducing new interaction paradigms between humans and machines. Augmented reality (AR) can convey much important information to users: for instance, information related to the status and the intention of the robot/machine the user is collaborating with. On the other hand, traditional input interfaces based on physical devices, gestures, and voice might be precluded in industrial environments. Brain–computer interfaces (BCIs) can be profitably used with AR devices to provide technicians solutions to effectively collaborate with robots. This paper introduces a novel BCI–AR user interface based on the NextMind and the Microsoft Hololens 2. Compared to traditional BCI interfaces, the NextMind provides an intuitive selection mechanism based on visual cortex signals. This interaction paradigm is exploited to guide a collaborative robotic arm for a pick and place selection task. Since the ergonomic design of the NextMind allows its use in combination with the Hololens 2, users can visualize through AR the different parts composing the artifact to be assembled, the visual elements used by the NextMind to enable the selections, and the robot status. In this way, users’ hands are always free, and the focus can be always on the objects to be assembled. Finally, user tests are performed to evaluate the proposed system, assessing both its usability and the task’s workload; preliminary results are very encouraging, and the proposed solution can be considered a starting point to design and develop affordable hybrid-augmented interfaces to foster real-time human–robot collaboration. Full article

Building trust and transparency in healthcare can be achieved using eXplainable Artificial Intelligence (XAI), as it facilitates the decision-making process for healthcare professionals. Knowledge graphs can be used in XAI for explainability by structuring information, extracting features and relations, and performing reasoning. This paper highlights the role of knowledge graphs in XAI models in healthcare, considering a state-of-the-art review. Based on our review, knowledge graphs have been used for explainability to detect healthcare misinformation, adverse drug reactions, drug-drug interactions and to reduce the knowledge gap between healthcare experts and AI-based models. We also discuss how to leverage knowledge graphs in pre-model, in-model, and post-model XAI models in healthcare to make them more explainable. Full article