Reviewerly/exHarmony huggingface dataset:
Multiview Spectral Clustering With Bipartite Graph

Multi-view spectral clustering has become appealing due to its good performance in capturing the correlations among all views. However, on one hand, many existing methods usually require a quadratic or cubic complexity for graph construction or eigenvalue decomposition of Laplacian matrix; on the other hand, they are inefficient and unbearable burden to be applied to large scale data sets, which can be easily obtained in the era of big data. Moreover, the existing methods cannot encode the complementary information between adjacency matrices, i.e. similarity graphs of views and the low-rank spatial structure of adjacency matrix of each view. To address these limitations, we develop a novel multi-view spectral clustering model. Our model well encodes the complementary information by Schatten p-norm regularization on the third tensor whose lateral slices are composed of the adjacency matrices of the corresponding views. To further improve the computational efficiency, we leverage anchor graphs of views instead of full adjacency matrices of the corresponding views, and then present a fast model that encodes the complementary information embedded in anchor graphs of views by Schatten p-norm regularization on the tensor bipartite graph. Finally, an efficient alternating algorithm is derived to optimize our model. The constructed sequence was proved to converge to the stationary KKT point. Extensive experimental results indicate that our method has good performance.
Cyber risk management in the US banking and insurance industry: A textual and empirical analysis of determinants and value

Abstract In this paper, we first construct a cyber risk consciousness score using a text mining algorithm, applied to annual reports of large‐ and mid‐cap US banks and insurers from 2011 to 2018. We next categorize the firms' cyber risk management based on keywords to study determinants and value‐relevance. Our results show an increasing cyber risk consciousness, regardless of the industry. In addition, for the entire sample we find that firms belonging to the banking industry, with a higher cyber risk consciousness score and a higher general risk awareness are more likely to implement cyber risk management, which also holds for both industries separately. We find the opposite in the case of profitable firms for the entire sample and the insurer subsample. Finally, we observe a significant positive relationship between cyber risk management and firm value measured by Tobin's Q for the entire sample and the subsamples of banks and insurers.
A meta-analysis of the effects of E-books on students' mathematics achievement

The 21st century allows the use of e-books to substitute printed books in educational context, thereby adapt teaching and learning to the potentialities of our digital era. Several studies examined the effect of mathematics e-books compared to traditional printed books on students' mathematics achievement, however, the results are still inconclusive. Therefore, this research conducted a meta-analysis to determine the effects of mathematics e-books on students' mathematics achievement. This is a quantitative research with data collected from 17 studies with 26 effect sizes (N = 3115) published between 2010 and 2021. The collected data were used to analyze the moderating effects of publication year, sample size, level education, treatment duration and type of e-books on students' mathematics achievement. The result showed that overall using mathematics e-books has a high effect (g = 0.82) on students' mathematics achievement. Furthermore, using e-Books does not affect students' mathematics achievement in year publication and significantly affects the pre-school level, who are more proficient in using technology. Mathematics e-books are more effective when used for less than 4 weeks and also effective when the sample is less than 30 students. There is no significant difference between non-interactive and interactive mathematics e-books. Moreover, this research provided a detailed description of the findings and implications as well as highlighted some suggestions for future studies.
PSImple

Private set intersection (PSI) protocols allow a set of mutually distrustful parties, each holding a private set of items, to compute the intersection over all their sets, such that no other information is revealed. PSI has a wide variety of applications including online advertising (e.g., efficacy computation), security (e.g., botnet detection, intrusion detection), proximity testing (e.g., COVID-19 contact tracing), and more. Private set intersection is a rapidly developing area and there exist many highly efficient protocols. However, almost all of these protocols are for the case of two parties or for semi-honest security. In particular, despite the high interest in this problem, prior to our work there has been no concretely efficient, maliciously secure multiparty PSI protocol.
Two-stage generative adversarial networks for binarization of color document images

• We propose a two-stage color document image enhancement and binarization method using generative adversarial networks. • Four color-independent adversarial networks extract color foreground information from an input image for document image enhancement. • Two independent adversarial networks with global and local features are trained for image binarization of documents of variable size. • The proposed method outperforms state-of-the-art algorithms on various datasets. Document image enhancement and binarization methods are often used to improve the accuracy and efficiency of document image analysis tasks such as text recognition. Traditional non-machine-learning methods are constructed on low-level features in an unsupervised manner but have difficulty with binarization on documents with severely degraded backgrounds. Convolutional neural network (CNN)based methods focus only on grayscale images and on local textual features. In this paper, we propose a two-stage color document image enhancement and binarization method using generative adversarial neural networks. In the first stage, four color-independent adversarial networks are trained to extract color foreground information from an input image for document image enhancement. In the second stage, two independent adversarial networks with global and local features are trained for image binarization of documents of variable size. For the adversarial neural networks, we formulate loss functions between a discriminator and generators having an encoder–decoder structure. Experimental results show that the proposed method achieves better performance than many classical and state-of-the-art algorithms over the Document Image Binarization Contest (DIBCO) datasets, the LRDE Document Binarization Dataset (LRDE DBD), and our shipping label image dataset. We plan to release the shipping label dataset as well as our implementation code at github.com/opensuh/DocumentBinarization/ .
Reducing Ransomware Crime: Analysis of Victims’ Payment Decisions

In this paper, the decision-making processes of victims during ransomware attacks were analysed. Forty-one ransomware attacks using qualitative data collected from organisations and police officers from cybercrime units in the UK were examined. The hypothesis tested in this paper is that victims carefully analyse the situation before deciding whether to pay a ransom. This research confirms that victims often weigh the costs and benefits of interventions before making final decisions, and that their decisions are based on a range of reasons. As ransomware attacks become more prevalent globally, the findings should be highly relevant to those developing guidance and policies to prevent or minimise ransom payments.
OD-XAI: Explainable AI-Based Semantic Object Detection for Autonomous Vehicles

In recent years, artificial intelligence (AI) has become one of the most prominent fields in autonomous vehicles (AVs). With the help of AI, the stress levels of drivers have been reduced, as most of the work is executed by the AV itself. With the increasing complexity of models, explainable artificial intelligence (XAI) techniques work as handy tools that allow naive people and developers to understand the intricate workings of deep learning models. These techniques can be paralleled to AI to increase their interpretability. One essential task of AVs is to be able to follow the road. This paper attempts to justify how AVs can detect and segment the road on which they are moving using deep learning (DL) models. We trained and compared three semantic segmentation architectures for the task of pixel-wise road detection. Max IoU scores of 0.9459 and 0.9621 were obtained on the train and test set. Such DL algorithms are called “black box models” as they are hard to interpret due to their highly complex structures. Integrating XAI enables us to interpret and comprehend the predictions of these abstract models. We applied various XAI methods and generated explanations for the proposed segmentation model for road detection in AVs.
Mining social media text for disaster resource management using a feature selection based on forest optimization

Resource management is an essential task that needs to be performed by the government or any disaster management agency during natural disasters. During these critical circumstances, people mostly depend upon a social media platform to share and collect information about the situation of the affected localities. The huge volume of real-time data can be useful in disaster assessment, response, and relief activities. We have presented a system which analyzes tweets during natural disasters and categorizes them according to the availability or need for general or medical resources along with their location information (if any) mentioned in the tweets. Several statistical classifiers are applied to show their usefulness for a better solution. Optimal feature representation is the heart of any machine learning based classification model. Here, we have applied a forest optimization-based wrapper feature selection algorithm to improve the classification accuracy. FIRE, SMERP, and CrisisLex dataset are used to evaluate our system and its effectiveness is demonstrated for smooth management of the resources. From the experimentation, it is found that forest optimization algorithm (FOA) wrapped multinomial naive bayes classifier gives an accuracy of 91.41 percent and f-measure of 88.33 percent on the FIRE dataset. The execution time of the model is quite less which will be very helpful for this challenging task.
Construction of situational teaching mode in ideological and political classroom based on digital twin technology

With the increased use of information networks and the barrage of media such as Video of Things (VoT), various new ideas and concepts affect college students' behavior and value exposure. It necessitates analyzing the current problems in the VoT-enabled teaching processes in colleges and universities, continuously reforming teaching methods, and seeking to improve the relevance and effectiveness of teaching ideological and political theory courses in colleges and universities. To end this, we propose an ideological VoT model using digital twin technology in this paper. Furthermore, we design three progressive models such as physical simulation scenes, character experience scenes, and simulation experiment scene design. We discuss experiments to validate the model developed in this paper. The simulation results revealed the efficiency of the proposed model compared to similar techniques.
Efficient federated multi-view learning

Multi-view learning aims to explore a global common structure shared by different views collected from multiple individual sources. The nascent field of federated learning tries to learn a global model over distributed networks of devices. This paper shows that multi-view learning is naturally suited to address the feature heterogeneity of the federated setting. We propose a novel model, namely robust federated multi-view learning (FedMVL), which is considered in the following formulation: given a dataset with M views, it is required to train machine learning models while the M views are distributed across M devices or nodes. Considering the unique challenges like stragglers and fault tolerance in federated setting, we derive an iterative federated optimization algorithm that allows each node with the flexibility to approximately address its subproblem. To the best of our knowledge, our model for the first time considers the issues including high communication cost, fault tolerance, and stragglers for distributed multi-view learning. The proposed model also achieves encouraging performance on clustering task compared to closely related methods, as we illustrate through simulations on several real-world datasets.
Block-HPCT: Blockchain Enabled Digital Health Passports and Contact Tracing of Infectious Diseases like COVID-19

Due to its significant global impact, both domestic and international efforts are underway to cure the infection and stop the COVID-19 virus from spreading further. In resource-limited environments, overwhelmed healthcare institutions and surveillance systems are struggling to cope with this epidemic, necessitating a specific strategic response. In this study, we looked into the COVID-19 situation and to establish trust, accountability, and transparency, we employed blockchain's immutable and tamper-proof properties. We offered a smart contract (SC)-based solution (Block-HPCT) that has been successfully tested to preserve a digital health passport (DHP) for vaccine recipients; also, for contact tracing (CT) we employed proof of location concept, which aids in a swift and credible response directly from the appropriate healthcare authorities. To connect on-chain and off-chain data, trusted and registered oracles were integrated and to provide a double layer of security along with symmetric key encryption; both Interplanetary File System (IPFS) and Hyperledger Fabric were merged as storage center. We also provided a full description of the suggested solution's system design, implementation, experiment results, and evaluation (privacy and cost analysis). As per the findings, the suggested approach performed satisfactorily across all significant assessment criteria, implying that it can lead the way for practical implementations and also can be used for similar types of situations where contact tracing of infectious can be crucial.
Classification of open-ended responses to a research-based assessment using natural language processing

Surveys have long been used in physics education research to understand student reasoning and inform course improvements. However, to make analysis of large sets of responses practical, most surveys use a closed-response format with a small set of potential responses. Open-ended formats, such as written free response, can provide deeper insights into student thinking, but take much longer to analyze, especially with a large number of responses. Here, we explore natural language processing as a computational solution to this problem. We create a machine learning model that can take student responses from the Physics Measurement Questionnaire as input, and output a categorization of student reasoning based on different reasoning paradigms. Our model yields classifications with the same level of agreement as that between two humans categorizing the data, but can be done by a computer, and thus can be scaled for large datasets. In this work, we describe the algorithms and methodologies used to create, train, and test our natural language processing system. We also present the results of the analysis and discuss the utility of these approaches for analyzing open-response data in education research.2 MoreReceived 16 June 2021Accepted 14 April 2022DOI:https://doi.org/10.1103/PhysRevPhysEducRes.18.010141Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAssessmentResearch methodologyPhysics Education Research
Addressing the Challenges of Electronic Health Records Using Blockchain and IPFS

Electronic Health Records (EHR) are the healthcare sector’s core digital strategy meant to improve the quality of care provided to patients. Despite the benefits afforded by this digital transformation initiative, adoption among healthcare organizations has been slower than desired. The sheer volume and sensitive nature of patient records compel these organizations to exercise a healthy amount of caution in implementing EHR. Cyberattacks have also increased the risks associated with non-optimal EHR implementations. An influx of high-profile data breaches has plagued the sector during the COVID-19 pandemic, which put the spotlight on EHR cybersecurity. One objective of this research project is to aid the acceleration of EHR adoption. Another objective is to ensure the robustness of the system to resist malicious attacks. For the former, a systematic review was used to unearth all the possible causes why the adoption of EHR has been anemic. In this paper, sixty-five existing proposed EHR solutions were analyzed and it was found that there are fourteen major challenges that need to be addressed to reduce friction and risk for health organizations. These were privacy, security, confidentiality, interoperability, access control, scalability, authentication, accessibility, availability, data storage, data ownership, data validity, data integrity, and ease of use. We propose EHRChain, a new framework that tackles all the listed challenges simultaneously to address the first objective while also being designed to achieve the second objective. It is enabled by dual-blockchains based on Hyperledger Sawtooth to allow patient data decentralization via a consortium blockchain and IPFS for distributed data storage.
A novel image encryption algorithm based on DNA strand exchange and diffusion

This article proposes an improved tent-dynamic cross coupled map lattice, which has superior ergodicity, broader chaotic range and higher information entropies than cross coupled map lattice and tent-dynamic cross coupled map lattice, and has high suitability for chaos-based image encryption algorithm. Traditional encryption scheme has two significant stages, including image confusion and diffusion. Our encryption algorithm utilizes DNA coding and sequences to construct short DNA strand and long DNA strand, where short DNA strand is used for DNA strand exchange, and long DNA strand is used for DNA strand diffusion. Compared to general image confusion, DNA strand exchange permutates parts of two diverse DNA strands, where the length of exchange is uncertain. Compared to image diffusion, DNA strand diffusion is divided into two categories according to Watson-Crick base pairing property, and different category applies diverse DNA computation operation. Through security analysis and statistical analysis, our encryption scheme can resist different attacks. Experimental results confirm that the proposed algorithm has a high degree of security and the excellent efficiency.
Human identification based on accelerometer sensors obtained by mobile phone data

In order to achieve secure usage digitally, many different methodologies (i.e., pin code, fingerprint, face recognition) have been employed. In this study, a novel way of user identification, which can be expressed as a biometrical method, has been proposed. The proposed approach was based on the characteristics of mobile phone usage (position changes in carrying, talking, and other actions). To assess and validate the proposed method, a dataset, which consists of millions of data collected from users with the help of accelerometers for several months during their ordinary smartphone usage, was obtained. This large dataset was reduced by randomly taking 3000 samples from each of the 387 devices in the dataset. The arbitrarily selected signals were labeled according to “one against all” (or “one vs. all”) strategies. Extracted features were classified by the k nearest neighbor (kNN) and the randomized neural network (RNN), machine learning methods. It has been seen that behavior-based biometric recognition can be accomplished with mobile phone accelerometer data, with 99.994% success rates for kNN and 99.97% for RNN.
GRAN3SAT: Creating Flexible Higher-Order Logic Satisfiability in the Discrete Hopfield Neural Network

One of the main problems in representing information in the form of nonsystematic logic is the lack of flexibility, which leads to potential overfitting. Although nonsystematic logic improves the representation of the conventional k Satisfiability, the formulations of the first, second, and third-order logical structures are very predictable. This paper proposed a novel higher-order logical structure, named G-Type Random k Satisfiability, by capitalizing the new random feature of the first, second, and third-order clauses. The proposed logic was implemented into the Discrete Hopfield Neural Network as a symbolic logical rule. The proposed logic in Discrete Hopfield Neural Networks was evaluated using different parameter settings, such as different orders of clauses, different proportions between positive and negative literals, relaxation, and differing numbers of learning trials. Each evaluation utilized various performance metrics, such as learning error, testing error, weight error, energy analysis, and similarity analysis. In addition, the flexibility of the proposed logic was compared with current state-of-the-art logic rules. Based on the simulation, the proposed logic was reported to be more flexible, and produced higher solution diversity.
Multiclass Classification of Different Glass Types using Random Forest Classifier

Multiclass Classification is an inherent technique for any classification model, especially pertaining to real-life scenarios. There are various scenarios in day-to-day life where we need to classify the results based on multiple output domains. In such a scenario there is a need to have an efficient model which can proceed for multiclass classification with elevated accuracies. The research work proposed in this paper proposes a multiclass classification model with highly accurate predictions results. The results are noble to the best of our knowledge as we have been able to achieve highly meticulous predicted output values using the proposed model. The work is compared with existing values especially with the Random Forest Classification model which has the maximum accuracy till now in the case of the multiclass classification model. Our results show better prediction accuracy and other statistical values as compared to the existing work done in literature till now. The comparison is shown with all the existing approaches. All the steps for data pre-processing have been taken care of very minutely. To balance the multiclass data the SMOTE oversampling technique is applied so that the prediction accuracy is quite elevated. The real-life example which we have taken is to classify the glasses in various types so that the people involved in this manufacturing can use them most appropriately for the designing purpose.