Reviewerly/exHarmony huggingface dataset:
Organizational Trust in the Age of the Fourth Industrial Revolution: Shifts in the Form, Production, and Targets of Trust

In this essay, we argue that the advent of the Fourth Industrial Revolution calls for a reexamination of trust patterns within and across organizations. We identify fundamental changes in terms of (1) what form organizational trust takes, (2) how it is produced, and (3) who needs to be trusted. First, and most broadly, trust is likely to become more impersonal and systemic. Trust between actors is increasingly substituted by trust in a system based on digital technology. Second, in terms of trust production modes, characteristic- and institution-based trust production will gain in importance. Third, despite the move toward system trust, there will nonetheless be a need to trust certain individuals; however, these trustees are no longer the counterparts to the interaction but rather third parties in charge of the technological systems and data. Thus, the focal targets of interpersonal trust are changing.
BIoMT Modular Infrastructure: The Recent Challenges, Issues, and Limitations in Blockchain Hyperledger-Enabled E-Healthcare Application

This paper presents a layered hierarchy that depicts the progressive relationship between data, information, knowledge, and wisdom. To begin with, data is gathered and organized into information. Information is gathered, filtered, refined, and put through an investigation process to create knowledge. Wisdom is attained after knowledge discovery through the process of filtration and aggregation through experience. The layered hierarchy in the domain of e-healthcare necessitates higher scheduling costs for data collection, processing wisdom, and management, which is also an insecure and untrustworthy process for progressive medical service. The medical industry faces a difficult problem in providing collected data integrity, information reliability, and knowledge trustworthiness for the service of progressive medical relationships in the face of an increasing number of day-to-day records. The blockchain consortium hyperledger (fabric) has been used in this paper to act as a bridge that bridges the gap between electronic data, information, knowledge, and wisdom (DIKW) movement and processes by enabling the process of the layered hierarchy of schedule information and management and providing security and transparency. For e-healthcare information management and privacy, the DIKW-ledger, such as patients’ consultancy information, availing medical services, personal records, appointments, treatment details, and other health-related transactions, a consortium hyperledger fabric-enabled efficient architecture is proposed. This proposed architecture creates two networks: a public network for medical stakeholders to exchange and agree on specific medical activities before being preserved on distributed storage (read-only after record registration) and a private network for complete DIKW process scheduling and management. We designed and created smart contracts for this purpose, as well as use-case diagrams to describe the overall execution process. The proposed architectural solution provides more efficient information integrity, provenance, and storage procedures to immutably preserve the medical ledger in a permissioned hash-encrypted structure.
Spectral graph theory-based virtual network embedding for vehicular fog computing: A deep reinforcement learning architecture

In the intelligent transportation system (ITS), the vehicular fog computing network (VFCN) can effectively alleviate the bottleneck existing in the cloud computing framework, such as high latency-sensitive applications, through edge computing offloading. It uses vehicles as the infrastructure, and fog nodes can communicate, perceive and share resources, so resource orchestration has become an essential issue of VFCN. To reduce the communication transportation cost and improve the resource utilization of VFCN, we propose a spectral graph theory-based resource orchestration algorithm by combining Virtual Network Embedding (VNE) and Deep Reinforcement Learning (DRL). Specifically, we propose a four-layer strategy network based on Graph Convolutional Networks (GCNs) for computing node embedding probability, where fog nodes fully mine spatial structure information by fusing themselves with neighborhood information to compensate for the lack of traditional heuristic VNE. Moreover, fog link embedding is performed by breadth-first search (BFS). Finally, the effectiveness of the proposed strategy is scientifically and rigorously proved in terms of long-term average revenue, long-term average revenue-cost ratio, and VNR acceptance rate through simulation cases, which can reasonably arrange the resources of VFCN.
HealthGuard: An Intelligent Healthcare System Security Framework Based on Machine Learning

Utilization of the Internet of Things and ubiquitous computing in medical apparatuses have “smartified” the current healthcare system. These days, healthcare is used for more than simply curing patients. A Smart Healthcare System (SHS) is a network of implanted medical devices and wearables that monitors patients in real-time to detect and avert potentially fatal illnesses. With its expanding capabilities comes a slew of security threats, and there are many ways in which a SHS might be exploited by malicious actors. These include, but are not limited to, interfering with regular SHS functioning, inserting bogus data to modify vital signs, and meddling with medical devices. This study presents HealthGuard, an innovative security architecture for SHSs that uses machine learning to identify potentially harmful actions taken by users. HealthGuard monitors the vitals of many SHS-connected devices and compares the vitals to distinguish normal from abnormal activity. For the purpose of locating potentially dangerous actions inside a SHS, HealthGuard employs four distinct machine learning-based detection approaches (Artificial Neural Network, Decision Tree, Random Forest, and k-Nearest Neighbor). Eight different smart medical devices were used to train HealthGuard for a total of twelve harmless occurrences, seven of which are common user activities and five of which are disease-related occurrences. HealthGuard was also tested for its ability to defend against three distinct forms of harmful attack. Our comprehensive analysis demonstrates that HealthGuard is a reliable security architecture for SHSs, with a 91% success rate and in F1-score of 90% success.
Attention augmented distance regression and classification network for nuclei instance segmentation and type classification in histology images

Nuclei instance segmentation and classification in histology plays a major role in routine pathology image examination, which enable morphological features analysis that further facilitates streamlined diagnosis and prognosis quantification. However, the nuclei in the tissue images obtained from different human organs are characterized with high variability in shape, size, morphology and spatial arrangements. Moreover, during digitization of tissue slide, the image quality is degraded because of added artifacts, poor contrast, blurred regions due to failed auto-focus and inconsistent staining procedure. Owing to these challenges, it is difficult to build a generalized feature representation that can achieve precise segmentation and classification of nuclei instances in complex tumor micro-environment of tissue specimens obtained from various organs. To address these problems, we propose a novel deep learning model, that harnesses horizontal and vertical distance information hidden among the nuclei instances to successfully delineate the challenging nuclei. Our proposed methodology uses soft attention mechanism to generate relevant feature activation and prune irrelevant and noisy information. These attention units produce more precise and refined feature maps resulting in finer instances segmentation and accurate classification in the overlapping nuclei, the nuclei with touching boundaries and reduction in false positives. We train our model on publicly available data-sets (Kumar, CoNSep, CPM-17 and a new data-set PanNuke). Our methodology shows superior performance in nuclei classification and segmentation in comparison with recently published methods. The code and the obtained results have been made public at the following link: http://t.ly/eGkV.
The global cooperation in asteroid mining based on AHP, entropy and TOPSIS

In this paper we introduce an equity evaluation index (EEI) to measure the global equity on asteroid mining, and present a global equity evaluation model by applying the analytic hierarchy process (AHP), entropy weight method and the technique for order preference by similarity to ideal solution (TOPSIS). Based on multi-objective optimization, we provide a global cooperative decision model to allocate the inputs and outputs among countries based on their strengths and demands. Furthermore, we analyze the the trends of EEI of each country under the influence of the outbreak events by using the autoregressive integrated moving average model. Our results show that among all countries with foundations in aerospace, China has the highest EEI while Russia ranks the lowest. Humans mining minerals in a cooperative manner will achieve a win-win situation.
Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model

Skin cancer is one of the most prevalent of all cancers. Because of its being widespread and externally observable, there is a potential that machine learning models integrated into artificial intelligence systems will allow self-screening and automatic analysis in the future. Especially, the recent success of various deep machine learning models shows promise that, in the future, patients could self-analyse their external signs of skin cancer by uploading pictures of these signs to an artificial intelligence system, which runs such a deep learning model and returns the classification results. However, both patients and dermatologists, who might use such a system to aid their work, need to know why the system has made a particular decision. Recently, several explanation techniques for the deep learning algorithm’s decision-making process have been introduced. This study compares two popular local explanation techniques (integrated gradients and local model-agnostic explanations) for image data on top of a well-performing (80% accuracy) deep learning algorithm trained on the HAM10000 dataset, a large public collection of dermatoscopic images. Our results show that both methods have full local fidelity. However, the integrated gradients explanations perform better with regard to quantitative evaluation metrics (stability and robustness), while the model-agnostic method seem to provide more intuitive explanations. We conclude that there is still a long way before such automatic systems can be used reliably in practice.
An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.
SCTD: A spatiotemporal correlation truth discovery scheme for security management of data platform

The continuous development of mobile sensing devices enables them to quickly perceive a large amount of data, forming a promising mobile crowd sensing (MCS) platform for large-scale data collection. The raw data stored in the data platform is further synthesized into a variety of Internet of Things (IoT) services to data consumers through the processing of the data platform. However, due to the wide range of data sources, sensing devices may contribute corrupted data, or maliciously spread forged data, causing the data platform to make wrong decisions and damage the quality of service. Therefore, collecting high-quality data is critical for the security of the data platform and the quality of IoT applications. In this paper, a novel Spatiotemporal Correlation Truth Discovery (SCTD) scheme is proposed, which adopts historical data as verifiable evidence to identify the truth of reported data and gain the trust of workers, consequently recruiting high-trust devices to collect data. First, Unmanned Aerial Vehicles (UAVs) are sent to collect Gold Ground Truth Data (GGTD), which is used as the benchmark to verify the data truth of the minority sensing devices. Then a trust evaluation method is proposed to calculate the trust of devices. Second, the data reported by trusted devices as Silver Ground Truth Data (SGTD) is utilized to verify the trust of most devices, so the method proposed in this paper can discover the truth of massive data. Third, to reduce the cost of truth discovery, a low-cost method of data fitting is proposed to collect massive historical data of the trusted device, thereby verifying the truth of data in the same time and space. Since historical data contributes little value to IoT services, the platform can obtain a large amount of historical data by paying low rewards to the devices. Finally, we propose to select mobile sensing devices to collect truthful data in different spaces, which can effectively cover the spatiotemporal correlation data truth discovery in time and space, thereby verifying as much data submitted to the platform as possible. Based on the trust relationships constructed in this paper, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy workers to participate in data-sensing tasks. The experimental results show that our solution can accurately identify the trust of more workers and verify the truth of data in a wider range while minimizing the cost of the data platform.
An Improved African Vulture Optimization Algorithm for Feature Selection Problems and Its Application of Sentiment Analysis on Movie Reviews

The African Vulture Optimization Algorithm (AVOA) is inspired by African vultures’ feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model’s performance. A multi-strategy version of the AVOA called BAVOA-v1 is presented. In the proposed approach, i.e., BAVOA-v1, different strategies such as IPRS, mutation neighborhood search strategy (MNSS) (balance between exploration and exploitation), multi-parent crossover (increasing exploitation), and Bitwise (increasing diversity and exploration) are used to provide solutions with greater variety and to assure the quality of solutions. The proposed methods are evaluated on 30 UCI datasets with different dimensions. The simulation results showed that the proposed BAOVAH algorithm performed better than other binary meta-heuristic algorithms. So that the proposed BAOVAH algorithm set is the most accurate in 67% of the data set, and 93% of the data set is the best value of the fitness functions. In terms of feature selection, it has shown high performance. Finally, the proposed method in a case study to determine the number of neurons and the activator function to improve deep learning results was used in the sentiment analysis of movie viewers. In this paper, the CNNEM model is designed. The results of experiments on three datasets of sentiment analysis—IMDB, Amazon, and Yelp—show that the BAOVAH algorithm increases the accuracy of the CNNEM network in the IMDB dataset by 6%, the Amazon dataset by 33%, and the Yelp dataset by 30%.
TSFNFR: Two-stage fuzzy neighborhood-based feature reduction with binary whale optimization algorithm for imbalanced data classification

Recently developed imbalanced data classification models are mainly focused on the majority class samples. In addition, several whale optimization-based feature reduction models are inefficient for high-dimensional data, readily fall into the local optimum, and are subject to difficulties associated with achieving a global optimal feature subset, due to high costs. To overcome the drawbacks, in this study, a two-stage feature reduction model using fuzzy neighborhood rough sets (FNRS) and the binary whale optimization algorithm (BWOA) was developed for imbalanced data classification. First, to indicate the sample fuzziness of mixed data, a similarity measure between samples based on fuzzy neighborhood was defined to investigate the similarity matrix and fuzzy neighborhood granule, and a new FNRS model was presented by constructing lower and upper approximations. By considering the uneven distribution of classes, the boundary-based feature significance measure was developed to minimize the influence of the uncertainty in boundary region for imbalanced data. Second, fuzzy neighborhood roughness and decision entropy were investigated based on FNRS, and by integrating these above measures, fuzzy neighborhood decision entropy was proposed to evaluate the fuzziness and roughness of the fuzzy neighborhood for imbalanced data. The external and internal significance metrics were proposed to achieve the preselected feature subset in the first stage. Third, in this second stage, a new control factor was defined to control the position of whales, and a novel fitness function was developed to evaluate the selected feature subset from imbalanced datasets. Thereafter, the immune regulation strategy of artificial immune was introduced into the BWOA to design the mixed selection probability, to divide the whale population. Two local interference strategies were applied to adjust the whale position and prevent BWOA trapped in the local optimum. Thus, an optimal feature subset was achieved by constantly iterating the BWOA. Finally, a two-stage feature reduction algorithm was designed to handle imbalanced and high-dimensional data, where the particle swarm optimization (PSO) algorithm was employed to determine the different optimized parameters for this two-stage algorithm. Experiments conducted on 22 datasets revealed that the proposed algorithm is efficient for two-class and multiclass datasets. • The boundary-based feature significance measure was developed to minimize the influence of the uncertainty. • Fuzzy neighborhood decision entropy was proposed to evaluate the fuzziness and roughness of imbalanced data. • A fitness function was developed to evaluate the selected feature subset from imbalanced data. • A two-stage feature reduction algorithm was designed to handle imbalanced and high-dimensional data.
What Is Immersive Learning?

The article investigates possible perspectives on Immersive Learning. After discussing the need for the term, the perception of non-mediation is identified as its key concept. Immersive Learning can be defined as learning with artificial experiences that are perceived as non-mediated. The research area Immersive Learning (Immersive Education) investigates educational benefits provided by such experiences. The internal process Immersive Learning is the active construction and adaption of cognitive, affective, and psychomotor models through these experiences. Immersive Learning as an educational method (Immersive Teaching) uses these experiences as a learning supply.
Explainable AI and Interpretable Machine Learning: A Case Study in Perspective

Explainable AI, as the word implies is a type of artificial intelligence which enables the explanation of learning models and focuses on why the system arrived at a particular decision, exploring its logical paradigms, contrary to the inherent black box nature of artificial intelligence. Similarly, machine learning interpretability allows users to comprehend the results of the learning models by providing reasoning for the decisions that it has arrived at. This nature of Explainable AI(XAI) and Interpretable Machine Learning (IML) is particularly helpful in the context of AI applications pertaining to healthcare and medical diagnosis. In this paper, we present a case study wherein we have focused on using the ELI5 XAI toolkit in conjunction with LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) algorithmic frameworks in Python, for determining if a patient is diabetic or not, based on a randomized clinical trial dataset. We also endeavor to point out trends and most vital factors that can help clinicians and researchers in analyzing patient data, in conjunction with machine learning and artificial intelligence outputs. Having explanations for machine learning models allows for higher degree of interpretability and paves the way for accountability and transparency in medical and other fields of data analysis. We explore the aforementioned paradigms in the context of this research paper, paving the way for developing an accountable, transparent and robust data analytics framework using XAI & IML.