Reviewerly/exHarmony huggingface dataset:
Let's get everyone involved! The effects of transformational leadership and organizational culture on organizational excellence

Purpose This paper posited a dynamic relationship between transformational leadership, organizational culture, and organizational excellence in order to develop a better understanding of the casual linkages between these three areas. Design/methodology/approach The research methodology includes a multi-item scale questionnaire survey which included 448 samples from United Arab Emirates (UAE) with an average response rate of 61.1%. The hypotheses were tested by applying structural equation modeling (SEM) and path analyses. Analyses used the Mplus software package. Findings The key finding in this study suggests that creating the involvement culture provides the all-inclusive participation and holistic engagement from employees, which consequently leads to organizational excellence. Research limitations/implications The study is more focused on particular type of leadership and can extend to other types of leadership as well the other regions. The study extends the findings of previous studies that suggested authoritative was essential initially but that this would change with the emerging culture. Practical implications Managers should foster an involvement culture and adopt transformational leadership style in order to reap the benefits of the quality management approaches. Originality/value From existing research on leadership in QM context, we adopt transformational leadership in connection with organizational culture and organizational excellence. The originality of the study lies in its quantitative approach to test an already demonstrated phenomenon about the relationship between transformational leadership, organizational culture and business excellence. This study significantly contributes to the literature on QM by discovering that organizations with transformational leadership styles and involvement or adaptability cultures can perform well and achieve organizational excellence.
Multi-Authority CP-ABE-Based user access control scheme with constant-size key and ciphertext for IoT deployment

With the ever-increasing rate of adoption of internet-enabled smart devices, the allure of greater integration of technologies, such as smart home, smart city, and smart grid into everyday life is undeniable. However, this trend inevitably leaves a massive amount of information and infrastructure connected to the public Internet, which exposes the data to many security threats and challenges. In this paper, we discuss the need for fine-grained user access control for IoT smart devices. The inherently distributed nature of IoT environment necessitates the support of multi-authority attribute-based encryption (ABE) for the implementation of fine-grained access control. Therefore, we present a secure fine-grained user access control scheme for data usage in the IoT environment. The proposed scheme is a three-factor user access control scheme, which supports multi-authority ABE and it is highly scalable as both the ABE key size stored in the user’s smart card and ciphertext size needed for authentication request are constant with respect to the number of attributes. Through the formal and informal security analysis, we show that the proposed scheme is secure and robust against several potential attacks required in an IoT environment. Moreover, we demonstrate that the proposed scheme performs at par or better than existing schemes while providing greater functionality features.
Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling

• The difficulty of sharing patient data hinders training machine learning models. • Distributed learning trains models locally, sidestepping the need to share data. • Ensembling is a simple way to perform distributed learning. • ANNs, SVMs, and RF can be used for distributed learning by ensembling. • Small local datasets, such as the case with rare diseases, can be used. The development of machine learning solutions in medicine is often hindered by difficulties associated with sharing patient data. Distributed learning aims to train machine learning models locally without requiring data sharing. However, the utility of distributed learning for rare diseases, with only a few training examples at each contributing local center, has not been investigated. The aim of this work was to simulate distributed learning models by ensembling with artificial neural networks (ANN), support vector machines (SVM), and random forests (RF) and evaluate them using four medical datasets. Distributed learning by ensembling locally trained agents improved performance compared to models trained using the data from a single institution, even in cases where only a very few training examples are available per local center. Distributed learning improved when more locally trained models were added to the ensemble. Local class imbalance reduced distributed SVM performance but did not impact distributed RF and ANN classification. Our results suggest that distributed learning by ensembling can be used to train machine learning models without sharing patient data and is suitable to use with small datasets.
Virtual Block Group: A Scalable Blockchain Model with Partial Node Storage and Distributed Hash Table

Abstract The inability to scale is one of the most concerning problems looming in blockchain systems, where every node has to store all contents of the ledger database locally, leading to centralization and higher operation costs. In this paper, we propose a model named virtual block group (VBG), which aims at addressing the node storage scalability problem. Adopting the VBG model, each node only needs to store part of block data and saves the VBG storage index to distributed hash table by taking block data as a resource, thus improving the query efficiency of block data. With the incentive mechanism of block data storage, and the storage verification and audit mechanism of block data, the security and reliability of block data storage can be ensured. The analysis and calculation show that this model saves hard drive storage space of the node to a greater extent with a shorter time of requesting block data, in the premise of ensuring secure and reliable block data. Compared to other technologies such as sharding, our model does not change the consensus mechanism or the network topology and retains the reliability and security of the original blockchain system.
VocBench 3: A collaborative Semantic Web editor for ontologies, thesauri and lexicons

VocBench is an open source web platform for the collaborative development of datasets complying with Semantic Web standards. Since its public release – five years ago – as an open source platform, VocBench has attracted a growing user community consisting of public organizations, companies and inde pendent users looking for open source solutions for maintaining their thesauri, code lists and authority resources. The focus on collaboration, the differentiation of user roles and the workflow management for content validation and publication have been the strengths of the platform, especially for those organizations requiring a distributed, yet centrally controlled, publication environment. In 2017, a new, completely reengineered, version of the system has been released, broadening the scope of the platform: funded by the ISA2 programme of the European Commission, VocBench 3 offers a general-purpose collaborative environment for development of any kind of RDF dataset (with dedicated facilities for ontologies, thesauri and lexicons), improving the editing capabilities of its predecessor, while still maintaining the peculiar aspects that determined its success. In this article, we review the requirements and the new objectives set for version 3, and then introduce the new characteristics that were implemented for this new incarnation of the platform
Systematic Literature Review to Investigate the Application of Open Source Intelligence (OSINT) with Artificial Intelligence

Open Source Intelligence (OSINT) is a concept to describe the search, collection, analysis, and use of information from open sources, as well as the techniques and tools used. OSINT emerges out of a military need to collect relevant and publicly available information. Through the use of OSINT, it is possible to find specific information that has some knowledge or provides an advantage. Since its emergence, some studies have been done proposing and developing new ways of using OSINT in different areas. In addition to OSINT, another field of study that has also been a worldwide trend and is being used together with other areas is Artificial Intelligence (AI). AI is the area of ​​computer science responsible for the development of intelligent systems. However, a systematic literature review that investigates the use of OSINT over the years and your application with AI was not found. So, this work has an objective to develop a systematic literature review on OSINT to investigate the application of OSINT with AI. This work was motivated to fill this research gap, for this, consolidate the publications on OSINT divided into the publication bases. As for its contribution, this work presents a systematic literature review composed of 9-step and also brings consolidated information to support the next OSINT studies. This research searched for publications between January 1990 and October 2019, finding a total of 244 publications. The 9-steps of the systematic literature review are Definition of Keywords, Query string definition, the definition of publication bases, the search on the publications bases, the base search results analysis, download of publications, importing the publications into Mendeley, Importing. Ris file into VOSviewer and Keyword Map Analysis. Analyzing the results, we find some relevant information about the publications that address OSINT and OSINT with AI or other areas. With this information, it was possible to understand where the largest concentration of publications, which countries and continents develop the most research and the characteristics of these publications. What are the trends for the next studies on OSINT with AI. Which AI subareas are used with OSINT. What are the most used keywords, and how do these keywords relate to others over the years. Which publication bases have the highest concentration of publications and what are the types of these publications? Also, a timeline describing the application of OSINT. It also became evident how OSINT has been used with AI to solve problems in different areas with different objectives. Based on these results, it is concluded that the application of a systematic literature review can show the application of OSINT with AI.
Graph embedding-based approach for detecting group shilling attacks in collaborative recommender systems

Over the past decade, many approaches have been presented to detect shilling attacks in collaborative recommender systems. However, these approaches focus mainly on detecting individual attackers and rarely consider the collusive shilling behaviors among attackers, i.e., a group of attackers working together to bias the output of collaborative recommender systems by injecting fake profiles. Such shilling behaviors are generally termed group shilling attacks, which are more harmful to collaborative recommender systems than traditional shilling attacks. In this paper, we propose a graph embedding-based method to detect group shilling attacks in collaborative recommender systems. First, we construct a user relationship graph by analyzing the user rating behaviors and use a graph embedding method to obtain the low-dimensional vector representation of each node in the user relationship graph. Second, we employ the k-means++ clustering algorithm to obtain candidate groups based on the generated user feature vectors. Finally, we calculate the suspicious degree of each candidate group according to the attack group detection indicators and use the Ward’s hierarchical clustering method to cluster the candidate groups according to their suspicious degrees and obtain the attack groups. The experimental results on the Amazon and Netflix datasets show that the proposed method outperforms the baseline methods in detection performance.
Model Evasion Attack on Intrusion Detection Systems using Adversarial Machine Learning

Intrusion Detection Systems (IDS) have a long history as an effective network defensive mechanism. The systems alert defenders of suspicious and / or malicious behavior detected on the network. With technological advances in AI over the past decade, machine learning (ML) has been assisting IDS to improve accuracy, perform better analysis, and discover variations of existing or new attacks. However, applications of ML algorithms have some reported weaknesses and in this research, we demonstrate how one of such weaknesses can be exploited against the workings of the IDS. The work presented in this paper is twofold: (1) we develop a ML approach for intrusion detection using Multilayer Perceptron (MLP) network and demonstrate the effectiveness of our model with two different network-based IDS datasets; and (2) we perform a model evasion attack against the built MLP network for IDS using an adversarial machine learning technique known as the Jacobian-based Saliency Map Attack (JSMA) method. Our experimental results show that the model evasion attack is capable of significantly reducing the accuracy of the IDS, i.e., detecting malicious traffic as benign. Our findings support that neural network-based IDS is susceptible to model evasion attack, and attackers can essentially use this technique to evade intrusion detection systems effectively.
Suppressed K-Anonymity Multi-Factor Authentication Based Schmidt-Samoa Cryptography for privacy preserved data access in cloud computing

Security and privacy-preservation is a significant problem to be solved in the cloud when accessing data services. The existing authentication technique verifies the client identity using single authentication factor. But, the performance of conventional single factor authentication technique is not efficient since the client’s sensitive information is highly intrusive and exposed to third parties. In order to overcome these limitations, a novel multifactor authentication called Suppressed K-Anonymity Multi-Factor Authentication Based Schmidt-Samoa Cryptography (SKMA-SC) technique is proposed. The SKMA-SC technique comprises three key processes namely registration, authentication and data access. During the registration phase, the clients register their personal identification information’s and securely stores it in the cloud server (CS) with support of suppression method. This helps for SKMA-SC technique to preserve the client sensitive information’s from adversaries or third parties in a cloud environment. During the authentication phase, SKMA-SC technique validates the identity of clients by considering multifactor such as password, one-time token and conditional attributes. At last in the data access phase, SKMA-SC technique allows the clients to get requested data services when she or he is authorized by performing Schmidt-Samoa data encryption/decryption process . From that, SKMA-SC technique avoids illegal access from the unauthorized party over insecure communications in a cloud environment with a lower amount of time. Result analysis of SKMA-SC technique is carried out with the metrics authentication accuracy (AA), computational complexity (CC) and privacy-preserving rate (PPR) with different number of clients and cloud data. The experimental result proves that SKMA-SC technique enhances the PPR and lessens the CC of authentication as evaluated with state-of-the-art works.
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning

Transfer learning enables solving a specific task having limited data by using the pre-trained deep networks trained on large-scale datasets. Typically, while transferring the learned knowledge from source task to the target task, the last few layers are fine-tuned (re-trained) over the target dataset. However, these layers are originally designed for the source task that might not be suitable for the target task. In this paper, we introduce a mechanism for automatically tuning the Convolutional Neural Networks (CNN) for improved transfer learning. The pre-trained CNN layers are tuned with the knowledge from target data using Bayesian Optimization. First, we train the final layer of the base CNN model by replacing the number of neurons in the softmax layer with the number of classes involved in the target task. Next, the CNN is tuned automatically by observing the classification performance on the validation data (greedy criteria). To evaluate the performance of the proposed method, experiments are conducted on three benchmark datasets, e.g., CalTech-101, CalTech-256, and Stanford Dogs. The classification results obtained through the proposed AutoTune method outperforms the standard baseline transfer learning methods over the three datasets by achieving 95.92%, 86.54%, and 84.67% accuracy over CalTech-101, CalTech-256, and Stanford Dogs, respectively. The experimental results obtained in this study depict that tuning of the pre-trained CNN layers with the knowledge from the target dataset confesses better transfer learning ability. The source codes are available at https://github.com/JekyllAndHyde8999/AutoTune_CNN_TransferLearning.
Performance Evaluation of Deep Learning Algorithms in Biomedical Document Classification

Document classification is a prevalent task in Natural Language Processing (NLP), which has an extensive range of applications in the biomedical domains such as biomedical literature indexing, automatic diagnosis codes assignment, tweets classification for public health topics, and patient safety reports classification. Nevertheless, manual classification of biomedical articles published every year into specific predefined categories becomes a cumbersome task. Hence, building an automatic document classification for biomedical databases emerges as a significant task among the scientific community. In recent years, Deep Learning (DL) models like Deep Neural Networks (DNN), Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), and Ensemble Deep Learning models are widely used in the area of text document classification for better classification performance compared to Machine Learning (ML) algorithms. The major advantage of using DL models in document classification is that it provides rich semantic and grammatical information for document representation through pre-trained word embedding. Hence, this paper investigates the deployment of the various state-of-the-art DL based classification models in automatic classification of benchmark biomedical datasets. Finally, the performance of all the aforementioned constitutional classifiers is compared and evaluated through the well-defined performance evaluation metrics such as accuracy, precision, recall, and f1-measure.
Integrated navigation for doppler velocity log aided strapdown inertial navigation system based on robust IMM algorithm

Global navigation satellite system (GNSS) is the most widely used position, navigation and time service system. However, GNSS such as BeiDou Navigation Satellite System (BDS) cannot provide the external assistance information for the autonomous underwater vehicle (AUV) because of the blocking effect of water on electromagnetic wave. The uncertain and non-Gaussian characteristics of the measurement noise may degrade the performance of SINS/DVL integrated navigation under the standard Kalman filter (KF) framework. To solve this problem, this paper proposes a robust interacting multiple model algorithm (IMM-RKF) based on the Mahalanobis distance algorithm. Furthermore, two switching sub-models will depict the uncertain parameters. The contributions of the work presented here are twofold. First, this paper robustifies the IMM algorithm under non Gaussian condition, and second the research method is verified in the problem of SINS/DVL integrated navigation. Simulation experimental results for the problem of SINS/DVL integrated navigation under non-Gaussian and uncertain conditions demonstrate the validity and superiority of the proposed method.
A swipe-based unlocking mechanism with supervised learning on smartphones: Design and evaluation

With the rapid development of mobile devices, smartphones have become common in people's daily lives, i.e., retrieving community happenings and connecting with peers. Due to the convenience, users often store a large amount of private information on their phones (e.g., photos) and use the phone to process sensitive operations (e.g., financial transactions). Thus, there is a great need to protect the devices from unauthorized access in order to avoid privacy leakage and financial loss. Passwords are the most widely used authentication method, but attackers can take over the phone after it is unlocked. Instead, behavioral authentication can verify current users in a continuous way, which can complement the existing authentication mechanisms like passwords. With the increasing capability of smartphone sensors, users can perform various touch actions to interact with their devices. Motivated by this, in this work, we focus on swipe behavior and develop SwipeVlock, a supervised unlocking mechanism on smartphones, which can authenticate users based on their way of swiping the phone screen with a background image. In the evaluation, we measure several typical supervised learning algorithms and conduct two user studies with over 150 participants. As compared with similar schemes, it is found that participants could perform well with SwipeVLock, i.e., with a success rate of 98% during login and retention.
Sentiment aware word embeddings using refinement and senti-contextualized learning approach

Most pre-trained word embeddings are achieved from context-based learning algorithms trained over a large text corpus. This leads to learning similar vectors for words that share most of their contexts, while expressing different meanings. Therefore, the complex characteristics of words cannot be fully learned by using such models. One of the natural language processing applications that suffers from this problem is sentiment analysis. In this task, two words with opposite sentiments are not distinguished well by using common pre-trained word embeddings. This paper addresses this problem and proposes two simple, but empirically effective, approaches to learn word embeddings for sentiment analysis. The both approaches exploit sentiment lexicons and take into account the polarity of words in learning word embeddings. While the first approach encodes the sentiment information of words into existing pre-trained word embeddings, the second one builds synthetic sentimental contexts for embedding models along with other semantic contexts. The word embeddings obtained from the both approaches are evaluated on several sentiment classification tasks using Skip-gram and GloVe models. Results show that both approaches improve state-of-the-art results using basic deep learning models over sentiment analysis benchmarks.