Reviewerly/exHarmony huggingface dataset:
Vulcan: Automatic extraction and analysis of cyber threat intelligence from unstructured text

To counteract the rapidly evolving cyber threats, many research efforts have been made to design cyber threat intelligence (CTI) systems that extract CTI data from publicly available sources. Specifically, indicators of compromise (IOC), such as file hash and IP address, receives the most attention among security researchers. However, the ability of IOC-centric CTI systems to understand and detect threats remains questionable for two reasons. First, IOCs are forensic artifacts that indicate that an endpoint or network has been compromised. They cannot depict the technical details of threats. Second, attackers frequently change infrastructure and static indicators, which makes IOCs have a very short lifespan. Therefore, when designing a CTI system, we should turn our attention to other types of CTI data that are helpful in threat understanding and detection (e.g., attack vector, tool). In this work, we propose Vulcan, a novel CTI system that extracts descriptive or static CTI data from unstructured text and determines their semantic relationships. To do this, we design a neural language model-based named entity recognition (NER) and relation extraction (RE) models tailored for cybersecurity domain. The experimental results confirm that Vulcan is highly accurate with an average F 1 -score of 0.972 and 0.985 for NER and RE tasks, respectively. Vulcan also provides an environment where security practitioners can develop applications for threat analysis. To prove the applicability of Vulcan, we introduce two applications, evolution identification and threat profiling. The applications save time and labor costs to analyze cyber threats and show the detailed characteristics of the threats.
An improved artificial electric field algorithm and its application in neural network optimization

Artificial electric field (AEF) algorithm is a newly developed meta-heuristic optimization technique inspired by the Coulomb's law of electrostatic force. As so far, AEF algorithm has been successfully applied in some scientific research and engineering fields, but it still has the defects of premature convergence and poor search ability in handling complex optimization problems. To address such issues, a novel Coulomb's constant generation scheme is explored to calculate the electrostatic force, and then an improved AEF (IAEF) algorithm is developed to strengthen the global exploration capability. Subsequently, 18 popular test functions with different dimensions are selected as benchmarks for performance evaluation. Furthermore, two nonlinear problems of neural network optimization are employed to further investigate the effectiveness and superiority of IAEF algorithm. Experimental results demonstrate that the proposed IAEF is efficient and effective optimization method in comparison with the conventional AEF and several famous evolutionary algorithms.
Colonial competitive evolutionary Rao algorithm for optimal engineering design

Rao algorithms that include three algorithms are very simple and parameter-less algorithms with effective and desirable performance. This paper modifies these three algorithms, merges them, and establishes a powerful group algorithm. In the first optimization step, the suggested algorithm is tested on 30 standard CEC2014 functions with 50 dimensions to compare it with main algorithms, several well-known algorithms, and modified versions of RAO algorithm. It becomes evident in the first test that the suggested optimizer is effective, and reliable for optimization of real-parameter functions, and it has shown its superiority to original RAO algorithm and several modern and modified versions of RAO algorithms for most of the test functions and achieved more acceptable results than them. Moreover, the suggested algorithm benefits a faster convergence characteristic than original RAO algorithms. The proposed Colonial Competitive RAO (CCRAO) has been applied on five popular engineering problems and its results have been compared with those of recent papers. According to the results, CCRAO is an effective, robust, and reliable optimizer for engineering design problems and can contain all useful features of RAO algorithms altogether. CCRAO has succeeded to converge to the best solution for these engineering problems and surpasses most of the other algorithms.
Construction and evaluation of a 360 degrees panoramic video on the physical examination of nursing students

The aim of this study was to construct and analyze the content validity and usability for a 360-degree panoramic video for physical examination.Acquiring techniques and ability on physical examination (PE) skill is often difficult. Immersion and simulation provide an opportunity for nursing students in the real situation focus on the PE techniques practice and to improve patient care. The combination of digital instructional materials and innovative teaching strategies is crucial to improving education. Therefore, effective and clear instruction of PE techniques that can enable students confidently to use their skills in clinical practice is an issue that nursing education must address.This study used a descriptive research design and the technology acceptance model to create a 360-degree video to teach students to perform PE. Content validity and usability were analyzed by five nursing teachers and 53 nursing students who had completed a physical assessment course were invited to assess its usability.The results of the expert review indicated that the content validity of the video scored 0.98 in terms of content relevance, 0.95 in terms of convenience and 0.97 in terms of suitability, suggesting that the video was useful and appropriate for self-practicing. The experts' qualitative feedback indicated that the technical demonstrations in the video were clear and specific and that the video was easy to use. The video increased learning motivation and streamlined learning. The ability to change the video's perspective also increased learning interest. The results of a satisfaction survey were 8.94 points for perceived ease of use, 8.79 points for perceived usefulness, 8.56 points for user attitude toward the video and 9.02 points for continued intention to use the video, indicating that the participants were satisfied with the video. The participants' qualitative feedback indicated that each PE technique was clear and that the ability to change the video's perspective and zooming was useful and novel.The 360-degree panoramic videos were described as a positive learning method for physical examination of nursing students. It provides easy to use and useful to practice PE technique under realistic conditions. Among these outcomes, the 360-degree video can be learned best when teaching PE in nursing education. Accordingly, nursing educators should construct similar videos for the PE of other parts of the body as soon as possible, improve the videos convenience and usefulness and promote their ability to strengthen professional skills.
Evaluation Methods and Replicability of Software Architecture Research Objects

Context: Software architecture (SA) as research area experienced an increase in empirical research, as identified by Galster and Weyns in 2016 [1]. Empirical research builds a sound foundation for the validity and comparability of the research. A current overview on the evaluation and replicability of SA research objects could help to discuss our empirical standards as a community. However, no such current overview exists.Objective: We aim at assessing the current state of practice of evaluating SA research objects and replication artifact provision in full technical conference papers from 2017 to 2021.Method: We first create a categorization of papers regarding their evaluation and provision of replication artifacts. In a systematic literature review (SLR) with 153 papers we then investigate how SA research objects are evaluated and how artifacts are made available.Results: We found that technical experiments (28%) and case studies (29%) are the most frequently used evaluation methods over all research objects. Functional suitability (46% of evaluated properties) and performance (29%) are the most evaluated properties. 17 papers (11%) provide replication packages and 97 papers (63%) explicitly state threats to validity. 17% of papers reference guidelines for evaluations and 14% of papers reference guidelines for threats to validity.Conclusions: Our results indicate that the generalizability and repeatability of evaluations could be improved to enhance the maturity of the field; although, there are valid reasons for contributions to not publish their data. We derive from our findings a set of four proposals for improving the state of practice in evaluating software architecture research objects. Researchers can use our results to find recommendations on relevant properties to evaluate and evaluation methods to use and to identify reusable evaluation artifacts to compare their novel ideas with other research. Reviewers can use our results to compare the evaluation and replicability of submissions with the state of the practice.
Key point-aware occlusion suppression and semantic alignment for occluded person re-identification

Partial occlusion is a key factor affecting the performance of person re-identification (re-ID). Although some solutions have been specially designed for occluded person re-ID, the ambiguity of pedestrian appearances and complex backgrounds still pose great challenges. Therefore, a key-point-aware occlusion suppression and semantic alignment (POS) method is proposed in this study to alleviate the existing challenges in occluded person re-ID. This method consists of three main modules: key point-aware semantic alignment (KPA-SA), self-similarity guided feature discriminability enhancement (SGFDE), and global feature extraction under occlusion suppression (GFE-UOS). In particular, the proposed KPA-SA semantically aligns the activated areas corresponding to specific pedestrian key points (e.g., head, shoulders, legs, feet) in multiple channels of different images. In addition, according to the paired left and right pedestrian key points, a cross-fusion mechanism can be applied to information compensation to alleviate information loss in occluded areas. The proposed SGFDE utilizes the self-similarity of non-occluded information of the same pedestrian captured from different views to enhance the discriminability of pedestrian identity features and suppress interference information unrelated to pedestrian identity. The proposed GFE-UOS fuses the heat maps of different key points to suppress the negative impact of occlusion on global feature extraction. The comparative experimental results verify the effectiveness of the proposed POS and its superiority over state-of-the-art methods. The related source codes were released on https://github.com/huangdaichui/occ_reid.
Automated Multi-Scale and Multivariate Geological Logging from Drill-Core Hyperspectral Data

Hyperspectral drill-core scanning adds value to exploration campaigns by providing continuous, high-resolution mineralogical data over the length of entire boreholes. However, multivariate mineralogical data must be transformed into lithological domains such that it is compatible with interpolation techniques and be usable for geomodeling. Manual interpretation of multivariate drill-core data is a challenging, time-consuming and subjective task, and automated or semi-automated approaches are needed. However, naive machine-learning techniques that ignore the distinct spatial structure and multi-scale nature of geological systems tend to produce geologically unreasonable results. Automated geological logging and multi-scale hierarchical domaining of drill-cores has been previously addressed in several studies by means of scalograms from a wavelet transform and tessellation, albeit exploiting only univariate information. The methodology involves the extraction of the local first principal component at a neighborhood of each observation, and the segmentation of the resulting series of scores with a continuous wavelet transform for boundary detection. In this way, the correlation pattern between the variables is incorporated into the segmentation. The scalogram accurately locates the geological boundaries at depth and yields hierarchical geological domains with mineralogical composition characteristics. The performance of this approach is demonstrated on a synthetic as well as a real multivariate dataset. The real dataset consists of mineral abundances derived from drill-core hyperspectral imaging data acquired in Elvira, a shale-hosted volcanogenic massive sulfide deposit located in the Iberian Pyrite Belt, where 7000 m of drill-core were acquired along 80 boreholes. The extracted domains are sensible from a geological point of view and spatially coherent across the boreholes in cross-sections. The results at relevant scales were qualitatively validated by comparing against the lithological log. This method is fast, is appropriate for multivariate geological data along boreholes, and provides a choice of scales for hierarchical geological domains along boreholes with mineralogical composition characteristics that can be modeled in 3D. Our approach provides an automatic way to transform hyperspectral image-derived mineral maps into vertically coherent geological units that are appropriate inputs for 3D geological modeling workflows. Moreover, the method improves the boundary detection and geological domaining by making use of multivariate information.
Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning

Problems with inaccurate prediction of electricity generation from photovoltaic (PV) farms cause severe operational, technical, and financial risks, which seriously affect both their owners and grid operators. Proper prediction results are required for optimal planning the spinning reserve as well as managing inertia and frequency response in the case of contingency events. In this work, the impact of a number of meteorological parameters on PV electricity generation in Poland was analyzed using the Pearson coefficient. Furthermore, seven machine learning models using Lasso Regression, K–Nearest Neighbours Regression, Support Vector Regression, AdaBoosted Regression Tree, Gradient Boosted Regression Tree, Random Forest Regression, and Artificial Neural Network were developed to predict electricity generation from a 0.7 MW solar PV power plant in Poland. The models were evaluated using determination coefficient (R2), the mean absolute error (MAE), and root mean square error (RMSE). It was found out that horizontal global irradiation and water saturation deficit have a strong proportional relationship with the electricity generation from PV systems. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed PV farm. Random Forest Regression was the most reliable and accurate model, as it received the highest R2 (0.94) and the lowest MAE (15.12 kWh) and RMSE (34.59 kWh).
Convergence of Blockchain, k-medoids and homomorphic encryption for privacy preserving biomedical data classification

Data privacy on the Internet of Medical Things (IoMT) remains a critical concern when handling biomedical data. While extant studies focus on cryptography and differential privacy, few of them capture the utility and authenticity of data. As a result, data privacy remains the primary concern when training a machine learning (ML) model with IoMT data from various data sources/owners such as k − medoids. To overcome the above-mentioned issues, this study proposes secure ​k − medoids ​that are implemented together with Blockchain and partial homomorphic cryptosystem (Paillier) to ensure authenticity and protect all entities (i.e., data owner and data analyst) data privacy. The homomorphic property of Paillier is utilized to develop secure building blocks (i.e., secure polynomial operations, secure comparison, and secure biasing operations) to ensure data privacy and eliminate dependency on any third parties. We utilized three different biomedical datasets, and these are (I) Heart Disease Data (HDD), (II) Diabetes Data (DD), and (III) Breast Cancer Wisconsin Data (BCWD). Rigorous security analysis demonstrates that secure ​k − medoids ​protect against sensitive data breaches. It also showed superior performance in both BCWD (Accuracy 97.80%, Precision 96.83%, and Recall 99.80%) and HDD (Accuracy 82.50%, Precision 81.28%, and Recall 80.50%) datasets, respectively. However, similar performance was not reflected in the case of the DD dataset. Furthermore, the study explains why such performance results are observed. In addition, the proposed system has been proven to take less execution time compared to the extant studies.
A novel quantitative relationship neural network for explainable cognitive diagnosis model

Cognitive diagnosis is a fundamental task to assist personalized learning in education, and aims to discover learners' proficiency in knowledge concepts. Because cognitive diagnosis models play a very important role in predicting learner performance and recommending personalized learning resources such as exercises, course videos, and course audio, they have received great attention from researchers. However, existing cognitive diagnosis models mostly start from the interactive perspective of learners' answers, ignoring the internal quantitative relationship between exercises and knowledge concepts. This study proposes a novel quantitative relationship-based explainable cognitive diagnosis model called QRCDM. First, learners' concept proficiency was defined based on their answers to objective and subjective questions. Correlation hypotheses are then proposed, which include the explicit correlation between exercises and their corresponding knowledge concepts, as well as the implicit correlation between exercises and the non-inclusive concept. Finally, two contribution matrices of exercises and knowledge concepts through a neural network designed in this study are calculated based on the above hypotheses, which can predict the learner's concept proficiency and answer score. To reduce the noisy data, the learners' faults and guessing factors were also considered. In the experiments, the proposed QRCDM was compared with two classical models, DINA, FuzzyCDF and three latest state-of-the-art models, DeepCDM, NeuralCDM and RCD on five real datasets, and the most experimental results on the majority metrics show the effectiveness and interpretability of this work.
Designs of human–robot interaction using depth sensor-based hand gesture communication for smart material-handling robot operations

With rapid developments in biometric recognition, a great deal of attention is being paid to robots which interact smartly with humans and communicate certain types of biometrical information. Such human–machine interaction (HMI), also well-known as human–robot interaction (HRI), will, in the future, prove an important development when it comes to automotive manufacturing applications. Currently, hand gesture recognition-based HRI designs are being practically used in various areas of automotive manufacturing, assembly lines, supply chains, and collaborative inspection. However, very few studies are focused on material-handling robot interactions combined with hand gesture communication of the operator. The current work develops a depth sensor-based dynamic hand gesture recognition scheme for continuous-time operations with material-handling robots. The proposed approach properly employs the Kinect depth sensor to extract features of Hu moment invariants from depth data, through which feature-based template match hand gesture recognition is developed. In order to construct continuous-time robot operations using dynamic hand gestures with concatenations of a series of hand gesture actions, the wake-up reminder scheme using fingertip detection calculations is established to accurately denote the starting, ending, and switching timestamps of a series of gesture actions. To be able to perform typical template match on continuous-time dynamic hand gesture recognition with the ability of real-time recognition, representative frame estimates using centroid, middle, and middle-region voting approaches are also presented and combined with template match computations. Experimental results show that, in certain continuous-time periods, the proposed complete hand gesture recognition framework can provide a smooth operation for the material-handling robot when compared with robots controlled using only extractions of full frames; presented representative frames estimated by middle-region voting will maintain fast computations and still reach the competitive recognition accuracy of 90.8%. The method proposed in this study can facilitate the smart assembly line and human–robot collaborations in automotive manufacturing.
Opposing Effects of Response Time in Human–Chatbot Interaction

Abstract Research has shown that employing social cues (e.g., name, human-like avatar) in chatbot design enhances users’ social presence perceptions and their chatbot usage intentions. However, the picture is less clear for the social cue of chatbot response time. While some researchers argue that instant responses make chatbots appear unhuman-like, others suggest that delayed responses are perceived less positively. Drawing on social response theory and expectancy violations theory, this study investigates whether users’ prior experience with chatbots clarifies the inconsistencies in the literature. In a lab experiment ( N = 202), participants interacted with a chatbot that responded either instantly or with a delay. The results reveal that a delayed response time has opposing effects on social presence and usage intentions and shed light on the differences between novice users and experienced users – that is, those who have not interacted with a chatbot before vs. those who have. This study contributes to information systems literature by identifying prior experience as a key moderating factor that shapes users’ social responses to chatbots and by reconciling inconsistencies in the literature regarding the role of chatbot response time. For practitioners, this study points out a drawback of the widely adopted “one-design-fits-all” approach to chatbot design.
LiverNet: Diagnosis of Liver Tumors in Human CT Images

Liver cancer contributes to the increasing mortality rate in the world. Therefore, early detection may lead to a decrease in morbidity and increase the chance of survival rate. This research offers a computer-aided diagnosis system, which uses computed tomography scans to categorize hepatic tumors as benign or malignant. The 3D segmented liver from the LiTS17 dataset is passed through a Convolutional Neural Network (CNN) to detect and classify the existing tumors as benign or malignant. In this work, we propose a novel light CNN with eight layers and just one conventional layer to classify the segmented liver. This proposed model is utilized in two different tracks; the first track uses deep learning classification and achieves a 95.6% accuracy. Meanwhile, the second track uses the automatically extracted features together with a Support Vector Machine (SVM) classifier and achieves 100% accuracy. The proposed network is light, fast, reliable, and accurate. It can be exploited by an oncological specialist, which will make the diagnosis a simple task. Furthermore, the proposed network achieves high accuracy without the curation of images, which will reduce time and cost.