Reviewerly/exHarmony huggingface dataset:
Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification

The features of a dataset play an important role in the construction of a machine learning model. Because big datasets often have a large number of features, they may contain features that are less relevant to the machine learning task, which makes the process more time-consuming and complex. In order to facilitate learning, it is always recommended to remove the less significant features. The process of eliminating the irrelevant features and finding an optimal feature set involves comprehensively searching the dataset and considering every subset in the data. In this research, we present a distributed fuzzy cognitive map based learning-based wrapper method for feature selection that is able to extract those features from a dataset that play the most significant role in decision making. Fuzzy cognitive maps (FCMs) represent a hybrid computing technique combining elements of both fuzzy logic and cognitive maps. Using Spark’s resilient distributed datasets (RDDs), the proposed model can work effectively in a distributed manner for quick, in-memory processing along with effective iterative computations. According to the experimental results, when the proposed model is applied to a classification task, the features selected by the model help to expedite the classification process. The selection of relevant features using the proposed algorithm is on par with existing feature selection algorithms. In conjunction with a random forest classifier, the proposed model produced an average accuracy above 90%, as opposed to 85.6% accuracy when no feature selection strategy was adopted.
SnTiEmd: Sentiment Specific Embedding Model Generation and Evaluation for a Resource Constraint Language

Sentiment analysis is primitive natural language processing (NLP) research for resource-constrained languages where feature extraction in a specific domain is a challenging issue. The word embeddings are good intermediate text to feature extraction methods where capturing semantic regularities between words. However, the performance of general word embeddings is limited in capturing domain-specific semantics or knowledge in sentiment analysis. Moreover, for low-resource languages like Bengali, no sentiment-specific word embedding research has been conducted to date. This study developed a domain-based, e.g., sentiment-specific embedding corpus (SeC) and built an intrinsic evaluation dataset. The three embedding methods (i.e., GloVe, FastText, Word2Vec) are investigated to develop the sentiment-based embedding model (SnTiEmd). The SnTiEmd (i.e., GloVe, fastText, Word2Vec) models are evaluated using an intrinsic evaluation dataset (i.e., semantic and syntactic). The highest accuracy of Pearson correlation for syntactic similarity is ( $$55.66\%$$ ) and semantic similarity ( $$52.97\%$$ ), whereas the maximum accuracy for spearman correlation is ( $$52.28\%$$ ) and ( $$55.19\%$$ ) for syntactic and semantic word similarity using GloVe-based SnTiEmd, respectively.
Securing electronic health records against insider-threats: A supervised machine learning approach

The introduction of electronic health records (EHR) has created new opportunities for efficient patient data management. For example, preventative medical practice, rather than reactive, is possible through the integration of machine learning to mine digital patient record datasets. Furthermore, within the wider smart cities’ infrastructure, EHR has considerable environmental and cost-saving benefits for healthcare providers. Yet, there are inherent dangers to digitising patient records. Considering the sensitive nature of the data, EHR is equally at risk of both external threats and insider attacks, but security applications are predominantly facing the outer boundary of the network. Therefore, in this work, the focus is on insider data misuse detection. The approach involves the use of supervised classification (decision tree, random forest and support vector machine) based off pre-labelled real-world data collated from a UK-based hospital for the detection of EHR data misuse. The results demonstrate that by employing a machine learning approach to analyse EHR data access, anomaly detection can be achieved with a 0.9896 accuracy from a test set and 0.9908 from the validation set using a support vector machine classifier. The emphasis of this research is on the detection of EHR data misuse, through the detection of anomalous behavioural patterns. Based on the results, the recommendation is to adopt an SVM for data misuse/insider threat detection.
Blockchain applications in smart sustainable city context—A systematic mapping study

The advancement in blockchain applications in the smart sustainable city infrastructure is evaluated in this paper through a comprehensive mapping review. The evaluation is carried out by posing four research questions that address current developments in blockchain technology in the context of smart cities and point out areas where additional study is needed. This study also includes a scoping of blockchain applications in a smart city context to highlight the obstacles to incorporating blockchain technology into smart city infrastructure. Finally, some suggestions for overcoming the problems of incorporating blockchain technology with smart city infrastructure are offered. This research will help researchers and policymakers gain a better understanding of blockchain applications in smart cities.
Open world long-tailed data classification through active distribution optimization

Real-world data exhibits a long-tailed label distribution, which leads to classification bias. Popular re-sampling or re-weighting methods usually require known category information. However, learning from long-tailed data with open categories is a challenging issue. In this paper, we propose an active distribution optimization algorithm (DALC) to handle the interesting issue. Through clustering, querying and classification iterations, we explore new categories and balance label distribution. For clustering, we present an exploration technique that adaptively obtains optimal data distribution with minimal total distance/cost. For each query, we design a critical instance selection strategy with the cluster information. For classification, we establish an ensemble model to continuously balance the label distribution. We conducted experiments on synthetic, benchmark and domain datasets. The results of the significance test verified the effectiveness of DALC and its superiority over state-of-the-art long-tailed data classification and open set classification algorithms.
Global horizontal irradiance modeling from environmental inputs using machine learning with automatic model selection

Solar radiation prediction is essential in load control on electrical networks allowing a balanced energy supply in accordance with the network demand. However, stations needing specific equipment, which is typically expensive and rare, to collect the data needed for such predictions. As a result of the tension between the demand for data and the challenge of acquiring it, the adoption of novel, low-cost technological solutions is necessary to address pressing issues. Successful predictions of solar radiation at many sites across the world have been made using machine learning (ML) models. This research develops a more straightforward strategy for efficient input variable selection. The proposed framework uses the most relevant environmental information to build an efficient model to predict global horizontal irradiance for the Dar es Salaam region, Tanzania. Four different ML models are developed and compared to predict solar radiation, i.e., Multivariate Adaptive Regression Spline (MARS), Support Vector Regression (SVR), ensemble Extreme Gradient Boosting (XGB), and polynomial Ridge Regression (RR). A total of 252 different scenarios are analyzed in the search for the best combination between the ML models and variables subset. The results showed that all optimized machine learning models could predict global horizontal irradiation with small error rates. The research also indicates that the proper choice of the available environmental variables can lead to less complex modeling with improved predictions.
Advancing sustainability with blockchain-based incentives and institutions

Despite significant efforts over many decades, humanity faces daunting challenges in the governance, management and sustainability of natural resources. Perhaps the most obvious is our global inability to collectively act and control or reduce greenhouse gases that are warming the planet. Another example, occurring at finer geographic scales, is the overuse of groundwater aquifers. Institutions—defined in Political Science and Economics as formal and informal rules that guide and incentivize socio-economic activities—are humanity’s general approach toward addressing these and other environmental challenges. Institutional arrangements typically specify whom they apply to, under what circumstances, and what penalty the breaking of the rule involves. Effective institutional design requires the ability to properly incentivize human behavior in the context of socio-economic systems, and establish systems to monitor behavior and sanction when rules are broken. From time to time, technological advances come along that complement institutional designs and improve our ability to incentivize and monitor behavior. We believe that the invention of Blockchain or Distributed Ledger technology—increasingly touted as the beginning of the fourth industrial revolution–could provide new ways to incentivize behavior of resource users, establish innovative monitoring capacity, and help to avoid corrupt governmental behavior. In this Perspective article, we summarize Proof-of-Stake Blockchain technology and provide two examples—deforestation and groundwater management—to describe how this new revolution could provide new solutions for the sustainable management of natural resources at local to global scales.
On-device federated learning with fuzzy logic based client selection

With the rapid development of IoT, more and more advanced sensor devices are used to collect and process a large number of datasets. In order to protect user privacy while training machine learning models with multiple datasets, federated learning (FL) is introduced. Due to the heterogeneity of different IoT devices, the client selection method may greatly impact the performance of federated learning. In this paper, we introduce an enhanced version of our previous approach to address the client selection issue in a realistic FL environment. The proposed method considers the number of local data, computing capability, and network resources of each client participating in FL based on fuzzy logic. Then, the most suitable clients are selected for federated learning to get higher performance. Moreover, we use Raspberry Pi and laptops as FL clients and verify the superiority of the proposed method through extensive experiments.
Adaptive fault-tolerant visual control of robot manipulators using an uncalibrated camera

In this paper, we propose an adaptive fault-tolerant visual control scheme for robotic manipulators with possible actuator failures in an uncalibrated environment. Most existing visual control approaches for robot system do not take into account actuator failures, which may prominently affect the transient performance of the system in practice. In order to moderate the detrimental and adverse effects of actuator failures on the system, a new adaptive algorithm is proposed to compensate stuck-type failures occurred in actuators. Moreover, by proposing a decoupling method, the uncertain parameter model of actuator failure is successfully separated from the dynamics model while the two models are coupled in most existing results [e.g., (Rugthum and Tao in Robotica 34(7):1529–1552, 2016; Rugthum in: International conference on engineering, applied sciences, and technology (ICEAST), pp 1–4, 2018)]. And the stability of the dynamic system and the convergence of the image error are proved by the Lyapunov analysis method. Finally, the effectiveness of the proposed control scheme is verified by comparing and analysing the tracking performance of the 3-DOF manipulator under different failure parameters.
Automatically detecting human-object interaction by an instance part-level attention deep framework

Automatically detecting human-object interactions (HOIs) from an image is a very important but challenging task in computer vision. One of the significant problems in HOI detection is that similar human-object interactions are difficult to distinguish. Recently, many instance-centric HOI detection schemes, based on appearance features and coarse spatial information, have been proposed. These methods, however, lack the capacity of capturing and analyzing the fine-grained context between human poses and object parts, which plays a crucial role in HOI detection. To address these problems, we propose a novel instance part-level attention deep framework for HOI detection. Specifically, our approach consists of a human/object-part detection phase and an HOI detection phase. In the former phase, a part-level visual pattern estimation model is designed for capturing the fine-grained human body parts and object parts. In the latter phase, a self-attention-based deep network is proposed to learn the visual composite around the human-object pair that implicitly expresses the consistent spatial, scale, co-occurrence, and viewpoint relationships among human body parts and object parts across images, which are effective for predicting HOI. To the best of our knowledge, we are the first to propose a framework where the fine-grained part-level mutual context of a human-object pair is extracted to improve HOI detection. By comparing our approach with state-of-the-art HOI detection methods on benchmark datasets, we demonstrated that our proposed framework outperformed the existing HOI detection methods, such as significantly improving the performance of part-level visual pattern estimation, HOI detection, and the quality of the self-attention-based deep network structure.
The valued coexistence of protection motivation and stewardship in information security behaviors

Organizations need to consider different managerial approaches to motivate security compliance behavior as more employees work outside of the office. This study examines two approaches for motivating security compliance behaviors in a changing work environment. We examine protection motivation and stewardship-based strategic approaches to determine their effectiveness towards influencing information security behaviors. Independently, each approach has received demonstrable evidence of contributing to organizational behaviors. However, as the nature of work changes (e.g., hoteling, traveling, working from home), it also changes the security risk landscape for organizations, rendering the effectiveness of previous security controls debatable. This study contends there is value to management in integrating both protection motivation and stewardship approaches in this context. A research model is developed that integrates both approaches; protection motivation and stewardship concepts. We find that constructs consistent with protection motivation theory are more likely to explain decreases in security policy violations than stewardship theory. Further, by combining protection motivation and stewardship perspectives, this study's results supplement our understanding of security policy violations in a changing work environment. We show that the two together account for 51 percent of the variance, and thus both have value. Our findings suggest that a PMT-based strategy is a stronger approach for influencing compliance behavior. Therefore, it is important for organizations to continue focusing their efforts on threat communications as well as enhancing employees' abilities to respond. However, we also show that organizations should build a sense of belonging with their employees such that their employees become stewards of the organization's resources.
KBHN: A knowledge-aware bi-hypergraph network based on visual-knowledge features fusion for teaching image annotation

Teaching images, as an important auxiliary tool in teaching and learning, are fundamentally different from the general domain images. Besides visually similar images being more likely to share common labels, teaching images also face the challenge of visual-knowledge inconsistency, including intra-knowledge visual difference and inter-knowledge visual similarity. To address the above challenges, we present KBHN, a knowledge-aware bi-hypergraph network, which not only considers coarse-grained visual features, but also extracts fine-grained knowledge features that reflect knowledge intention hidden in teaching images. In detail, a visual hypergraph is constructed to connect images with visual similarity. It further enriches coarse-grained visual features by modeling the high-order visual relations among teaching images. Moreover, a knowledge hypergraph based on typical images is built to aggregate images with similar knowledge information, which innovatively extracts fine-grained knowledge features by modeling high-order knowledge correlations between local regions. Furthermore, a multi-head attention mechanism is adopted to fuse visual-knowledge features for enriching image representation. A teaching image dataset is constructed to train and validate our model, which contains 20744 real-world images annotated with 24 knowledge points. Experimental results demonstrate that KBHN, incorporating visual-knowledge features, achieves state-of-the-art performance compared to existing methods.
Decentralizing Cyber Physical Systems for Resilience: An Innovative Case Study from A Cybersecurity Perspective

The integration of blockchain with various business scenarios in cyber physical systems, where resilience is critical for the stability of system operation, can provide decentralized, constant validation and a consistent view of the system state. However, there are significant managerial and cybersecurity challenges in the integration process. The physical elements in the industrial infrastructure are heterogeneous with limited communication and computation capabilities. This paper aims to investigate the blockchain assured innovative business models in the power grid as a distributed industrial network and explore the blockchain consensus design that enables a state-aware power grid with high scalability. Following design science and interdisciplinary research methodology, a multi-level technical framework is developed to explore the blockchain integration strategies, specifically with a customized Proof of Stake consensus, as a case study. Technical evaluation is performed to reveal the performance improvement and the resilience of the business models in the detection of false data injection attacks.
A Hybrid Multi-Cloud Framework Using the IBBE Key Management System for Securing Data Storage

Information storage and access in multi-cloud environments have become quite prevalent. In this paper, a multi-cloud framework is presented that secures users’ data. The primary goal of this framework is to secure users’ data from untrusted Cloud Service Providers (CSPs). They can collude with other malicious users and can hand over users’ data to these malicious users for their beneficial interests. In order to achieve this goal, the data are split into parts, and then each part is encrypted and uploaded to a different cloud. Therefore, client-side cryptography is used in this framework. For encrypting users’ data, the BDNA encryption technique is used. This framework presents a hybrid cryptographic approach that uses Identity-based Broadcast Encryption (IBBE) for managing the keys of the symmetric key algorithm (BDNA) by encrypting them with the particular version of IBBE. The work presented in this research paper is the first practical implementation of IBBE for securing encryption keys. Earlier, IBBE was only used for securely broadcasting data across many users over a network. The security of this hybrid scheme was proved through Indistinguishable Chosen-Ciphertext Attacks. This double encryption process makes the framework secure against all insiders and malicious users’ attacks. The proposed framework was implemented as a web application, and real-time storage clouds were used for storing the data. The workflow of the proposed framework is presented through screenshots of different working modules.
Learning to simultaneously enhance field of view and dynamic range for light field imaging

Light field (LF) imaging, which simultaneously captures the intensity and direction information of light rays, enabling many vision applications, has received widespread attention. However, limited by the optical structure of the LF camera, the acquired LF images usually suffer from narrow field of view (FOV) and low dynamic range. To address these problems, this paper proposes an unsupervised wide-FOV high dynamic range (HDR) LF imaging method, which can effectively reconstruct a wide-FOV HDR LF image from a set of source LF images captured from different perspectives and simultaneously with different exposures. Specifically, the proposed method first exploits tensor decomposition to obtain a compact representation of high-dimensional LF image, so as to enable a computationally efficient 2D neural network for LF registration. Subsequently, an exposure restoration network is constructed to recover the multi-exposure information of the registered non-overlapping regions, which is then linearly fused with the previous registered results to generate the stitched wide-FOV multi-exposure LF images. Finally, an HDR LF blending network with two ingenious unsupervised losses is designed to blend the stitching results to generate the desired wide-FOV HDR LF image. Experimental results show that the proposed method achieves superior performance compared with the state-of-the-art methods in both qualitative and quantitative evaluation. Moreover, a series of ablation studies effectively validate the performance of each module in the proposed method.
Do data breach notification laws reduce medical identity theft? Evidence from consumer complaints data

Abstract As the number of data breaches in the United States grows each year, cybersecurity has become an increasingly important policy area. The primary mechanism for regulating and deterring data breaches is the “data breach notification law.” Every US state now has such a law that mandates that certain organizations disclose data breaches to their data subjects. Despite the popularity of these laws, there is relatively little evidence about their effectiveness at deterring breaches, and therefore reducing identity theft. Using medical identity theft panel data collected from the Consumer Financial Protection Bureau, this study implements an augmented synthetic control approach to analyze the effect of California's 2016 data breach notification standards on medical identity theft. This approach suggests that medical identity theft reports in California were reduced by 3.5 reports/100,000 people.