Reviewerly/exHarmony huggingface dataset:
A semantic web technology index

Semantic web (SW) technology has been widely applied to many domains such as medicine, health care, finance, geology. At present, researchers mainly rely on their experience and preferences to develop and evaluate the work of SW technology. Although the general architecture (e.g., Tim Berners-Lee's Semantic Web Layer Cake) of SW technology was proposed many years ago and has been well-known, it still lacks a concrete guideline for standardizing the development of SW technology. In this paper, we propose an SW technology index to standardize the development for ensuring that the work of SW technology is designed well and to quantitatively evaluate the quality of the work in SW technology. This index consists of 10 criteria that quantify the quality as a score of [Formula: see text]. We address each criterion in detail for a clear explanation from three aspects: (1) what is the criterion? (2) why do we consider this criterion and (3) how do the current studies meet this criterion? Finally, we present the validation of this index by providing some examples of how to apply the index to the validation cases. We conclude that the index is a useful standard to guide and evaluate the work in SW technology.
Bilinear Scoring Function Search for Knowledge Graph Learning

Learning embeddings for entities and relations in knowledge graph (KG) have benefited many downstream tasks. In recent years, scoring functions, the crux of KG learning, have been human designed to measure the plausibility of triples and capture different kinds of relations in KGs. However, as relations exhibit intricate patterns that are hard to infer before training, none of them consistently perform the best on benchmark tasks. In this paper, inspired by the recent success of automated machine learning (AutoML), we search bilinear scoring functions for different KG tasks through the AutoML techniques. However, it is non-trivial to explore domain-specific information here. We first set up a search space for AutoBLM by analyzing existing scoring functions. Then, we propose a progressive algorithm (AutoBLM) and an evolutionary algorithm (AutoBLM+), which are further accelerated by filter and predictor to deal with the domain-specific properties for KG learning. Finally, we perform extensive experiments on benchmarks in KG completion, multi-hop query, and entity classification tasks. Empirical results show that the searched scoring functions are KG dependent, new to the literature, and outperform the existing scoring functions. AutoBLM+ is better than AutoBLM as the evolutionary algorithm can flexibly explore better structures in the same budget.
Becoming agile together: Customer influence on agile adoption within commissioned software teams

Despite the ongoing expansion of agile practices beyond software firms, agile adoption remains risky, challenging, and poorly understood. Although agile practices emphasize self-organization and customer collaboration, we know little about how customers influence agile adoption within self-organizing teams. Here, we analyze how a commissioned software team engaged in customer collaboration during agile adoption at a Danish IT service provider. Our case study shows that the software team's transition to self-organized teamwork practices, agile planning routines, and active customer engagement was mutually dependent on the customer's trust in the software team and flexible collaborative routines. As a result, we advance a theoretical perspective of customer influence on agile adoption within commissioned software teams, implying that both software teams and customers need to navigate a contradictory tension between self-organization and collaboration to become agile together.
Preliminary fluid geochemical survey in Tete Province and prospective development of geothermics in Mozambique

Abstract An evaluation of the feasible development of geothermal energy in Mozambique is proposed based on some thermal springs geochemical characterization in the Tete region. Chemical and isotopic data suggest that the springs have a meteoric origin and do not show connection with any active magmatic system. The proposed circulation model suggests high depths infiltration of meteoric waters along faults and fractures in a system characterised by discrete permeability and reservoir temperature between 90 and 120 °C. These results, jointly with low salinity fluids and corrosive components absence suggest that the geothermal system may be conveniently exploited for direct and indirect uses.
A dual decomposition with error correction strategy based improved hybrid deep learning model to forecast solar irradiance

An accurate forecasting of solar irradiation plays a vital role in grid balancing, scheduling, and maintenance. With this aim, a deep learning solar forecasting model based on dual decomposition with error correction strategy is proposed in this paper. In this model, historical time series of solar irradiance and error series are decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD), respectively. The bidirectional long short-term memory (BiLSTM) deep learning (DL) network is used with each series to extract the different long- and short-term characteristics of the data. To observe the forecasting accuracy of the proposed model, nine contrast models are built to estimate one to three steps ahead solar irradiance for two different locations. The experimental results demonstrate that the proposed model achieved lower RMSE (1.81 W/m2–28.46 W/m2) and MAPE (0.2–4.40%) compared to the considered models. It improves the RMSE (17.55–72.82%) and MAPE (2.65–74.66%) over a non-error correction strategy-based hybrid model. In addition, the improvement in RMSE (13.82–65.10%) and MAPE (11.46–68.33%) are observed over single decomposition-based error correction model. Moreover, to validate the forecasting performance of the proposed model Diebold–Mariano Hypothesis (DMH) test, percentage improvement and its behavior in different weather conditions are also presented.
Deep Correlated Joint Network for 2-D Image-Based 3-D Model Retrieval

In this article, we propose a novel deep correlated joint network (DCJN) approach for 2-D image-based 3-D model retrieval. First, the proposed method can jointly learn two distinct deep neural networks, which are trained for individual modalities to learn two deep nonlinear transformations for visual feature extraction from the co-embedding feature space. Second, we propose the global loss function for the DCJN, consisting of a discriminative loss and a correlation loss. The discriminative loss aims to minimize the intraclass distance of the extracted features and maximize the interclass distance of such features to a large margin within each modality, while the correlation loss focuses on mitigating the distribution discrepancy across different modalities. Consequently, the proposed method can realize cross-modality feature extraction guided by the defined global loss function to benefit the similarity measure between 2-D images and 3-D models. For a comparison experiment, we contribute the current largest 2-D image-based 3-D model retrieval dataset. Moreover, the proposed method was further evaluated on three popular benchmarks, including the 3-D Shape Retrieval Contest 2014, 2016, and 2018 benchmarks. The extensive comparison experimental results demonstrate the superiority of this method over the state-of-the-art methods.
Blockchain Technology for Securing Internet of Vehicle: Issues and Challenges

Blockchain technology enables safe communication among users in a network of linked automobiles. Intelligent vehicle is a blockchain empowered vehicle, generally alluded to as a self-driving vehicle. This correspondence climate isn&#x0027;t secure and has a few issues and challenges. The significant issues on the Internet of Vehicles (IoVs) correspondence are reliability, exactness, and security. In this article, we acquaint blockchain innovation with assembling trust and dependability in shared organizations with geographies like IoVs. Further, we propose a blockchain-innovation empowered IoV use case. Blockchain innovation is utilized to fabricate a safe, confided-in climate for IoVs. This believed climate gives a protected, dispersed, and decentralized instrument for correspondence between IoVs, without sharing their data in the canny transportation framework. The omnipresence of interfacing advances in shrewd vehicles and the gradual mechanization of its functionalities guarantee critical advantages, remembering a huge decrease in blockage and street fatalities. Here we have also discuss the constraints given in the relevant literature, including the issues of blockchain technology and how they impact its incorporation into the IoV.
GRA-GAN: Generative adversarial network for image style transfer of Gender, Race, and age

Despite a large amount of available data, the datasets that have been recently used in studies on age estimation still entail the age class imbalance problem owing to different age distributions of race or gender. This results in overfitting in which training data aligns toward one side and ultimately reduces the generality of age estimation. Same problems can occur in the cases of race and gender recognition. This problem can be solved if age images that were insufficient in a previously trained distribution or race and gender information that was not considered in the previously trained distribution can be newly created as images that are identical to the previously trained distribution. Therefore, we propose a race, age, and gender image transformation technique by a generative adversarial network for image style transfer of gender, race, and age (GRA-GAN) based on channel-wise and multiplication-based information fusion of encoder and decoder features. Experiments using four open databases (MORPH, AAF, AFAD, and UTK) indicated that our method outperformed the state-of-the-art methods.
A novel multi-objective medical feature selection compass method for binary classification

The use of Artificial Intelligence in medical decision support systems has been widely studied. Since a medical decision is frequently the result of a multi-objective optimization problem, a popular challenge combining Artificial Intelligence and Medicine is Multi-Objective Feature Selection (MOFS). This article proposes a novel approach for MOFS applied to medical binary classification. It is built upon a Genetic Algorithm and a 3-Dimensional Compass that aims at guiding the search towards a desired trade-off between: Number of features, Accuracy and Area Under the ROC Curve (AUC). This method, the Genetic Algorithm with multi-objective Compass (GAwC), outperforms all other competitive genetic algorithm-based MOFS approaches on several real-world medical datasets. Moreover, by considering AUC as one of the objectives, GAwC guarantees the classification quality of the solution it provides thus making it a particularly interesting approach for medical problems where both healthy and ill patients should be accurately detected. Finally, GAwC is applied to a real-world medical classification problem and its results are discussed and justified both from a medical point of view and in terms of classification quality.
Reducing current crowding in meander superconducting strip single-photon detectors by thickening bends

Abstract To facilitate high optical coupling efficiency and absorptance, the active area of a superconducting nano/microstrip single-photon detector (SNSPD/SMSPD) is often designed as a meander configuration with a high filling factor (e.g. ⩾0.5). However, the switching current ( I sw ) of SNSPD/SMSPD, at which the detector switches into the normal state, is significantly suppressed by a geometry-induced ‘current crowding effect’, where there are sharp bends in the strip. Here, we propose and experimentally verify an alternative method to reduce current crowding both in SNSPD and SMSPD by directly increasing the thickness of the bends through the deposition and lift-off of a secondary superconducting film. We measure and compare the performance of SNSPDs and SMSPDs with different filling factors and bend configurations, with or without thickened bends. Improvements for detectors were observed in detection efficiency, intrinsic dark count rate, and time jitter, owing to the enhanced I sw . Our method provides a promising way of optimizing SNSPD/SMSPD detection performance.
Securing medical data by role-based user policy with partially homomorphic encryption in AWS cloud

Cloud technology provides services for storing and accessing a large amount of data with ease of access and less cost. Sensitive data such as patients' electronic health information should be encrypted before outsourcing into the cloud. Many traditional encryption methods are used for protecting data in the cloud, but unable to perform computation on encrypted data. Homomorphic encryption operates directly on the ciphertext. In this study, a Secure Partially Homomorphic Encryption (SPHE) algorithm is proposed to secure the outsourced data and perform multiplication and division operations on the ciphertext. The access control policy in the cloud environment is more flexible. An attacker can easily collect sensitive data by abusing the access policy of another user. Therefore, the database privacy is compromised. Creating a role hierarchy and managing the session is difficult in the cloud environment. The above issues motivate us to develop a model which is the integration of the proposed scheme SPHE with role-based user policy. The model is implemented in Eclipse IDE (Integrated Development Environment) and AWS (Amazon Web Service) Toolkit for Eclipse and deployed in Amazon Elastic Beanstalk (EB) environment. This model is particularly used for securing the patient e-health details and performing computation on outsourced data. The patient details are encrypted by the algorithm SPHE and uploaded in AWS S3 (Simple Storage Service) bucket. The users are created by AWS Identity and Access Management (IAM) service and the access level policy is defined based on user roles in EB environment. The proposed model performance is studied by comparing with other partially homomorphic methods Elgamal, Pailler, and Benaloh. This model achieves data integrity and data confidentiality using the role-based user policy with SPHE.
Ethereum Account Classification Based on Graph Convolutional Network

Accounts in Ethereum are found to be involved in various services or businesses. Account classification can help us detect illegal behavior, track transactions, and de-anonymize the Ethereum transaction system. In this brief, we make use of Graph Convolutional Network (GCN) to solve the account classification problem in Ethereum. We model the Ethereum transaction records as a large-scale transaction network and find that the network is with high heterophily, in which accounts with different features and different labels are connected. In order to solve this problem, we propose a GCN-based model called EH-GCN. The experimental results on a realistic Ethereum dataset show that the proposed method achieves the most advanced classification performance, and results on benchmarks show it produces a competitive performance under homophily.
Light-weighted vehicle detection network based on improved YOLOv3-tiny

Vehicle detection is one of the most challenging research works on environment perception for intelligent vehicle. The commonly used object detection network is too large and can only be realized in real-time on a high-performance server. Based on YOLOv3-tiny, the feature extraction was realized using light-weighted networks such as DarkNet-19 and ResNet-18 to improve accuracy. The K-means algorithm was used to cluster nine anchor boxes to achieve multi-scale prediction, especially for small targets. For automotive applicable scenarios, the proposed vehicle detection network was executed in an embedded device. The KITTI data sets were trained and tested. Experimental results show that the average accuracy is improved by 14.09% compared with the traditional YOLOv3-tiny, reaching 93.66%, and can reach 13 fps on the embedded device.
A Blockchain Based Decentralized Identifiers for Entity Authentication in Electronic Health Records

Over the past two decades, the fast pace of digitization in the healthcare ecosystem led to a phenomenal rise in the creation, storage and sharing of Electronic Health Records (EHRs) across the globe. However, the mechanism of authentication used for proving the identity of entities in EHRs is based on the identifiers issued by centralized identity providers (IDPs). It may lead to a single point of failure, loss of privacy and lack of interoperability. A new wave of decentralized identifiers (DIDs) and verifiable credentials(VCs) data modelled by blockchain has made it possible to achieve entity authentication in a decentralized manner. In this study, a blockchain-based framework with decentralized identifiers for patient authentication and consent management for EHR access using verifiable credentials is proposed. It describes the process of DID generation and authentication credential setup along with workflows for issuing and verifying credentials in the EHR ecosystem. The framework is implemented using Hyperledger Indy blockchain and Aries library. The study evaluates the performance of proposed workflows in terms of scalability, efficiency, resource utilization and conducts security analysis. Specifically, the outcome of this study can be used to realize the decentralized identity management and authentication in EHR systems.
A hybrid high‐order type‐2 FCM improved random forest classification method for breast cancer risk assessment

One of the main challenges in breast cancer risk assessment is to provide a patient with an easily interpretable perspective on her disease's situation. This paper proposes a new method for accessing breast cancer risk, called Hybrid High-order Type-2 Fuzzy Cognitive Map Improved Random Forest Classification (H-HT2 FCM IRFC, in which, by taking an exact analysis, the disease risk is qualitatively obtained in three modes, i.e., each optimistic, realistic and pessimistic. Using either FCM or high-order FCM does not make a favorable response in uncertain situations where applying type-2 fuzzy to obtain the weights of FCM will have much better answers. A hybrid version of high-order type-2 FCM, proposed in this work, enables us to assess the breast cancer risk in three modes of optimistic, realistic, and pessimistic. The proposed method has three levels; at the first level, patient profile, family history, and inherited factors are tested by high-order FCM. At the second level, by examining the mass characteristics obtained from the mammograms, the disease risk is achieved by hybrid high-order type-2 FCM in three modes of optimistic, realistic, and pessimistic. The position of the tumor's effect on breast cancer is obtained in the third level by the fuzzy method. Finally, an overall breast cancer risk is predicted by a new algorithm, called Improved Random Forest Classification, which results in superior performance. Compared with the existing methods, the accuracy of the results obtained from the proposed method is desirable. The three-mode assessment will help the patients and their physician (oncologist) run the best treatment. Finally, the proposed method is successfully tested on an actual medical dataset.
Toward Personalized Answer Generation in E-Commerce via Multi-perspective Preference Modeling

Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the customer shopping experience. Its key function, automatic answer generation for product-related questions, has been studied by aiming to generate content-preserving while question-related answers. However, an important characteristic of PQA, i.e., personalization, is neglected by existing methods. It is insufficient to provide the same “completely summarized” answer to all customers, since many customers are more willing to see personalized answers with customized information only for themselves, by taking into consideration their own preferences toward product aspects or information needs. To tackle this challenge, we propose a novel Personalized Answer GEneration method with multi-perspective preference modeling, which explores historical user-generated contents to model user preference for generating personalized answers in PQA. Specifically, we first retrieve question-related user history as external knowledge to model knowledge-level user preference. Then, we leverage the Gaussian Softmax distribution model to capture latent aspect-level user preference. Finally, we develop a persona-aware pointer network to generate personalized answers in terms of both content and style by utilizing personal user preference and dynamic user vocabulary. Experimental results on real-world E-Commerce QA datasets demonstrate that the proposed method outperforms existing methods by generating informative and customized answers and show that answer generation in E-Commerce can benefit from personalization.
Discrimination of Breast Cancer Based on Ultrasound Images and Convolutional Neural Network

The aim of our study was to establish an artificial intelligence tool for the diagnosis of breast disease base on ultrasound (US) images. A deep learning algorithm Efficient-Det assisted US diagnosis method was developed to determine breast suspicious lesions as benign, malignant, or normal. Totally 1181 US images from 487 patients of our hospital and 694 publicly accessible images were employed for modeling, including 558 benign images, 370 malignant images, and 253 normal tissue images. The actual diagnosis results for the patients were determined by the biopsy or surgery. Efficient-Det was first retrained using an exclusive public breast cancer US dataset with transfer learning techniques. A blind test set consisting of 50 benign, 50 malignant, and 50 normal tissue images was randomly picked from the patients' images as the independent test set to test its searching ability on suspicious tumor regions. Furthermore, the confusion matrix and classification accuracy were employed as evaluation metrics to select the optimal classification models. Efficient-Det has demonstrated remarkable progress in general image recognition tasks with specific advantages of locating and identifying tumor areas simultaneously. Compared to the manual method (mean accuracy: 95.3% and 60 s per image) and traditional feature engineering method (mean accuracy: 90% and 15 s per image), our Efficient-Det has the capability of providing a competitive (mean accuracy: 92.6%) and fast (0.06 s per image) classification result. The deployment of Efficient-Det in our local breast cancer discrimination task exhibits specific applicability within real clinical workflows.