Reviewerly/exHarmony huggingface dataset:
Multi-scale pedestrian intent prediction using 3D joint information as spatio-temporal representation

There has been a rise of use of Autonomous Vehicles on public roads.With the predicted rise of road traffic accidents over the coming years, these vehicles must be capable of safely operate in the public domain.The field of pedestrian detection has significantly advanced in the last decade, providing high-level accuracy, with some technique reaching near-human level accuracy.However, there remains further work required for pedestrian intent prediction to reach human-level performance.One of the challenges facing current pedestrian intent predictors are the varying scales of pedestrians, particularly smaller pedestrians.This is because smaller pedestrians can blend into the background, making them difficult to detect, track or apply pose estimations techniques.Therefore, in this work, we present a novel intent prediction approach for multi-scale pedestrians using 2D pose estimation and a Long Short-term memory (LSTM) architecture.The pose estimator predicts keypoints for the pedestrian along the video frames.Based on the accumulation of these keypoints along the frames, spatio-temporal data is generated.This spatio-temporal data is fed to the LSTM for classifying the crossing behaviour of the pedestrians.We evaluate the performance of the proposed techniques on the popular Joint Attention in Autonomous Driving (JAAD) dataset and the new larger-scale Pedestrian Intention Estimation (PIE) dataset.Using data generalisation techniques, we show that the proposed technique outperformed the state-of-the-art techniques by up to 7%, reaching up to 94% accuracy while maintaining a comparable run-time of 6.1 ms.
Automatic Hybrid Access Control in SCADA-Enabled IIoT Networks Using Machine Learning

The recent advancements in the Internet of Things have made it converge towards critical infrastructure automation, opening a new paradigm referred to as the Industrial Internet of Things (IIoT). In the IIoT, different connected devices can send huge amounts of data to other devices back and forth for a better decision-making process. In such use cases, the role of supervisory control and data acquisition (SCADA) has been studied by many researchers in recent years for robust supervisory control management. Nevertheless, for better sustainability of these applications, reliable data exchange is crucial in this domain. To ensure the privacy and integrity of the data shared between the connected devices, access control can be used as the front-line security mechanism for these systems. However, the role engineering and assignment propagation in access control is still a tedious process as its manually performed by network administrators. In this study, we explored the potential of supervised machine learning to automate role engineering for fine-grained access control in Industrial Internet of Things (IIoT) settings. We propose a mapping framework to employ a fine-tuned multilayer feedforward artificial neural network (ANN) and extreme learning machine (ELM) for role engineering in the SCADA-enabled IIoT environment to ensure privacy and user access rights to resources. For the application of machine learning, a thorough comparison between these two algorithms is also presented in terms of their effectiveness and performance. Extensive experiments demonstrated the significant performance of the proposed scheme, which is promising for future research to automate the role assignment in the IIoT domain.
Traversing the uncommon boulevard: entrepreneurial trajectory of decentralised autonomous organisations (DAOs)

ABSTRACTABSTRACTBlockchain technology has revolutionised the ownership structures and business models of young companies, leading to the emergence of new digital entrepreneurship in innovative digital companies known as decentralised autonomous organisations (DAOs). This study examines the influence of blockchain use on the entrepreneurial structure and business models of DAOs. Following an inductive approach guided by the underlying theoretical framework of a nonfungible token (NFT) enabled entrepreneurship and the token economy business model, we conducted a qualitative content analysis of /the whitepapers of 22 selected DAOs obtained from the DeepDAO platform. The results reveal that blockchain assists the growth of digital companies by decentralising their ownership structure and improving their value propositions through NFT and a smart contract-enabled consensus mechanism. However, market uncertainties and the non-liquidity of tokens were identified as significant barriers to implementing DAOs. Three propositions emerged from this study with meaningful contributions to the theory and practice of entrepreneurship.KEYWORDS: Blockchain entrepreneurshipbusiness modeldecentralised autonomous organisationentrepreneurial trajectories AcknowledgementWe acknowledge the reviewers and editors of Technology Analysis & Strategic Management for their comments which helped improve this paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsKunle Francis OguntegbeKunle Francis Oguntegbe (Ph.D) is a Teaching and Research Fellow at the Department of Economics, Management, Institutions (DEMI), University of Naples 'Federico II', Italy. He has co-authored journal articles on management topics such as blockchain implementation, supply chain sustainability and responsible management.Nadia Di PaolaNadia Di Paola, (Ph.D) is a Professor of Blockchain Management and New Venture Creation in the Department of Economics, Management, Institutions (DEMI), University of Naples 'Federico II', Italy. She has authored and co-authored many research articles and books in the fields of Business Management, Entrepreneurship, Business Venturing, Supply Chain Management and Blockchain Management. She serves as Editorial Board member of international scientific journals.Roberto VonaRoberto Vona (Ph.D) is a Full Professor of Business Management at the Department of Economics, Management, Institutions (DEMI), University of Naples 'Federico II', Italy. His research interests include business management, sustainable supply chain management, social innovation and organised crime, amongst others. He has many publications including books and research articles in the areas of Production and Logistics Management, Technology Venturing, Innovation Management and Commercial Enterprises. He is the scientific director of several academic specialisation programmes.
Quality of Work Life, Job Enrichment and their Impact on Employee Retention: Exploratory Research in Private Colleges in Baghdad

Quality of work life (QWL) can be impacted by a variety of factors, including organizational structure, the physical environment, job security, and the opportunity for personal growth and development. As job enrichment is a key component of QWL, it can play an important role in employee retention. The main objective of this research is to determine the dimensions of QWL and job enrichment that promote employee retention and how those dimensions significantly impact employee retention. The research was based on a questionnaire survey with (358) faculty members representing 10 private colleges in Baghdad, the questionnaires’ reliability and validity were examined, and data were described and analysed using a descriptive and analytical approach by using SmartPLS to test the hypothesises. The findings assert both quality of working life and job enrichment had a significant impact on employee retention. The study urges employers to offer highly desirable working conditions, and by utilizing a job enrichment strategy, faculty staff roles are created to keep them.
Information security investment for complementary and substitutable firms: The role of technology similarity

While technology similarity in network security aggravates breach interdependence, it can improve the efficiency of security information sharing. This paper examines the effect of technology similarity on information security investment with mandatory standards through constructing a game-theoretic model with two firms whose information assets are complementary and substitutable respectively. We show that for strict mandatory standards, while technology similarity first decreases and then increases the cost functions of complementary firms, it always decreases the cost functions of substitutable firms. We find that security information sharing always decreases the cost functions of both complementary and substitutable firms and thus should be encouraged. We reveal that when mandatory standards become much stricter, the cost functions of complementary firms always increase but the cost functions of substitutable firms increase if and only if the substitution degree remains relatively high. Finally, we demonstrate that hacker learning, as a factor to intensify breach interdependence, benefits substitutable firms and harms complementary firms in the case of strict mandatory standards.
Human activity recognition from UAV videos using a novel DMLC-CNN model

Human Activity Recognition (HAR) remains a challenging issue that requires to be resolved. Utilizing images, smart phones, or sensors, HAR could be done. Recent studies have explored HAR utilizing Unmanned Aerial Vehicle (UAV) videos. But, owing to numerous restrictions correlated to the platform, HAR from videos captured by drones remains a challenge. To resolve this issue, a novel mechanism for HAR from UAV videos utilizing the Diminutive Multi-Dimensional Locality Coding based Convolutional Neural Network (DMLC-CNN) model is proposed. Primarily, the input video is converted into frames; also, Bounding Boxes (BBs) are produced for humans. For attaining the number of clusters grounded on human presence, the BB is given to the Robust Lucrative Sensitive Amalgam K-Nearest Neighbor (RoLSA-KNN) clustering. After that, utilizing the LR-fit-centric Superior Photogrammetric Silhouette (LR-SPS) segmentation, the humans' silhouettes are segmented. The attained output is divided into patches; then, it is converted into stacked layer frames. Afterward, these frames are converted into a 3D kernel. The Diminutive Multi-Dimensional Hyper Graph (DMHG) extracted the features; then, the Locality-constrained Orthonormal Feature Space Coding (LOFSC) Algorithm encoded the extracted features. Lastly, for recognizing Human Activity (HA), DMLC-CNN is wielded. The proposed framework is contrasted with the conventional techniques. After experimental evaluation, the proposed mechanism was found to be more efficient in HAR.
Normative Mineralogy of 1170 Soil Profiles across Canada

Weathering of soil minerals provides base cations that buffer against acidity, and nutrients that support plant growth. In general, direct observations of soil minerals are rare; however, their abundance can be determined indirectly through soil geochemistry using normative-calculation procedures. This study compiled a data set of major oxide content from published and archived soil geochemical observations for 1170 sites across Canada (averaged over the soil profile [A, B, and C horizons], weighted by depth and bulk density to a maximum depth of 50 cm). Quantitative soil mineralogy (wt%) was systematically determined at each site using the normative method, ‘Analysis to Mineralogy’ (A2M); the efficacy of the approach was evaluated by comparison to X-ray Diffraction (XRD) mineralogy available for a subset of the study sites. At these sites, predicted A2M mineralogy was significantly related to estimated XRD, showing a strong linear relationship for plagioclase, quartz, and K-feldspar, and a moderate linear relationship for chlorite and muscovite. Further, the predicted A2M plagioclase content was almost identical to the estimated XRD soil mineralogy, showing no statistical difference. The Canada-wide predicted quantitative soil mineralogy was consistent with the underlying bedrock geology, such as in north-western Saskatchewan and north-eastern Alberta, which had high amounts of quartz due to the Western Canadian Sedimentary Basin. Other soil minerals (plagioclase, potassium feldspar, chlorite, and muscovite) varied greatly in response to changing bedrock geology across Canada. Normative approaches, such as A2M, provide a reliable approach for national-scale determination of quantitative soil mineralogy, which is essential for the assessment of soil weathering rates.
No-Reference Image Quality Assessment of Magnetic Resonance images with multi-level and multi-model representations based on fusion of deep architectures

Accurate quality assessment of Magnetic Resonance (MR) images is essential for effective medical diagnostics, as it impacts the time spent on image acquisition and image interpretation by radiologists. Therefore, this study aims to provide a novel deep learning-based No-Reference (NR) MR Image Quality Assessment (IQA) method for the quality prediction of MR images. Therefore, in this work, an internal fusion of two complementary deep learning architectures is introduced that offers MR-specific quality-aware features. Apart from obtaining multi-level image representations of joint networks, to further enhance the quality prediction performance, features from re-trained single deep learning architectures are also used. Finally, as one of the main findings of this study, the quality assessment is performed by a high-level quality model trained on scores of quality models obtained for layers of the networks. The superiority of the method against the state-of-the-art techniques is verified by using two publicly available MRIQA benchmarks containing MR images and subjective scores provided by a large number of radiologists. As reported, the method offers superior IQA of MR images as the obtained scores are highly correlated with subjective opinions of medical specialists. It is characterized by the weighted average values of the Spearman Rank-order Correlation Coefficient, Kendall Rank-order Correlation Coefficient, and Pearson Linear Correlation Coefficient of 0.8754, 0.7185, and 0.9062, respectively.
Systematic Literature Review of Social Media Bots Detection Systems

Online social networks (OSNs) are vital to people's daily lives. They offer free services that allow people to connect and interact with family and friends, post comments and images, express views on sports and politics, and influence other users on OSN. The significant risks to OSN security are malicious SMBs, and numerous studies have been done to identify them. This article, a Systematic Literature Review (SLR), aims to determine best practices in SMB recognition. This SLR covers research published between 2008 and 2022. As a result of this study, we classified OSN profiles into real, verified, and fake accounts. The malicious SMBs types are SMBs, spam bots, Sybil and cyborgs, stegobots, political bots, and game bots. We also proposed a classification of SMBs detection techniques, which are ML-based, DL-based, graph-based, anomaly-based, topic-modeling-based, DNA-inspired, genetic-based, and hybrid approaches. Additionally, our study revealed that most public datasets used for SMB detection only include some types of SMB, dependent on Twittersphere, include limited data, and needed to be more extensive, up-to-date and accurate. We studied challenges of SMB detection, like data labeling, features engineering, imbalanced data, etc. Finally, we proposed the opening axis for future works.
Intelligent Mining of Association Rules Based on Nanopatterns for Code Smells Detection

Software maintenance is an imperative step in software development. Code smells can arise as a result of poor design as well as frequent code changes due to changing needs. Early detection of code smells during software development can help with software maintenance. This work focuses on identifying code smells on Java software using nanopatterns. Nanopatterns are method-level code structures that reflect the presence of code smells. Nanopatterns are extracted using a command-line interface based on the ASM bytecode analysis. Class labels are extracted using three tools, namely inFusion, JDeodorant, and iPlasma. Rules are extracted from nanopatterns using the Apriori algorithm and mapped with the extracted class labels. Best rules are selected using the Border Collie Optimization (BCO) algorithm with the accuracy of the k-NN classifier as the fitness function. The selected rules are stored in the rule base to detect code smells. The objective is to detect a maximum number of code smells with a minimum number of rules. Experiments are carried out on Java software, namely jEdit, Nutch, Lucene, and Rhino. The proposed work detects code smells, namely data class, blob, spaghetti code, functional decomposition, and feature envy, with 98.78% accuracy for jEdit, 97.45% for Nutch, 95.58% for Lucene, and 96.34% for Rhino. The performance of the proposed work is competitive with other well-known methods of detecting code smells.
Efficient detection of refugees and migrants in Turkey using convolutional neural network

At the start of the Syrian Civil War in 2011, NGOs played a big part in giving refugees access to aid and distributing that aid so that people could go to school, get a job, or get medical care. Within the last few years, when tensions rose between Syrian refugees and the Turkish community, many non-governmental organizations switched their attention to fostering community among refugees in Turkey. Over the past two decades, family displacement has become a big problem in various countries due to a rise in the frequency with which natural catastrophes, military conflicts, and terrorist strikes occur. It poses severe difficulties for governing bodies and the organizations that oversee them. This research aims to identify and track refugees in surveillance zones by utilizing artificial intelligence. Refugees are vulnerable to acts of nature and human aggression, which makes their random relocation or encampments challenging to manage. To overcome these challenges, a convolutional neural network deep learning model has been proposed to identify and track refugees in surveillance zones. The proposed solution is integrated with Internet of Things (IoT) technology by equipping the system with IoT sensors to capture real-time data on the location and movements of refugees. This combination of AI and IoT has the potential to improve the efficiency and effectiveness of refugee management efforts. The suggested solution uses a convolutional neural network deep learning model, which can quickly identify a refugee’s face. To assist the government in locating a specific refugee, the system simultaneously connects with the refugees and requests that they regularly update their location. The system alerts security to identify the missing immigrant since the refugee does not update their whereabouts. Without human intervention, the deep learning algorithm makes it simple to recognize immigrants and keep an eye on them.
Intelligent Navigation of Indoor Robot Based on Improved DDPG Algorithm

Targeting the problem of autonomous navigation of indoor robots in large-scale, complicated, and unknown environments, an autonomous online decision-making algorithm based on deep reinforcement learning is put forward in this paper. Traditional path planning methods rely on the environment modeling, which can cause more workload of calculating. In this paper, the sensors to detect surrounding obstacles are combined with the DDPG (deep deterministic policy gradient) algorithm to input environmental perception and control the action direct output, which enables robots to complete the tasks of autonomous navigation and distribution without relying on environment modeling. In addition, the algorithm preprocesses the relevant data in the learning sample with Gaussian noise, facilitating the agent to adapt to noisy training environment and improve its robustness. The simulation results show that the optimized DL-DDPG algorithm is more efficient on online decision-making for the indoor robot navigation system, which enables the robot to complete autonomous navigation and intelligent control independently.
Genesis of the Heihaibei gold deposit in the East Kunlun Orogenic Belt, China: Evidence from in situ trace elements of gold-bearing sulfides and stable isotopes

The Heihaibei gold deposit, located in the south part of the East Kunlun orogenic belt (EKOB), is a newly discovered gold deposit, with similar tectonic setting and geological characteristics with typical orogenic gold deposits in the orogenic belt. The aim of this study is to enrich the understanding of metallogeny of the orogenic gold deposit in the EKOB by studying the genesis of the Heihaibei gold deposit. Pyrite and arsenopyrite, as the predominant Au-bearing sulfides, were investigated using a combination of laser ablation–inductively coupled plasma–mass spectrometry (LA–ICP–MS) and laser ablation–multi-collector–inductivity coupled plasma–mass spectrometry (LA–MC–ICP–MS) for in situ trace elements and sulfur isotope. The analyses results provided significant information about genesis for gold mineralization. There are two types of smoky quartz–sulfide veins (barren and mineralized) in this deposit. Three types of pyrite based on host rock can be distinguished, including barren smoky quartz–sulfide vein- (Py0), mineralized altered monzogranite- (Py1) and mineralized smoky quartz–sulfide vein-hosted pyrite (Py2, including Py2a and Py2b). Py1 coexists with arsenopyrite. Py2a and Py2b occurs as disseminated crystals and veinlet assemblage, respectively. Petrographic observations and LA–ICP–MS analyses show that invisible gold in pyrite and arsenopyrite occurs dominantly as solid solution (Au+). Co/Ni ratios of pyrite and S–H–O isotopic compositions exhibit that pyrites (Co/Ni > 1, δ34SV-CDT = −5.28 to 5.08‰) hosted by barren smoky quartz–sulfide vein are consistent with a magmatic origin, whereas those (Co/Ni < 1, δ34SV-CDT = 7.37 to 9.48‰, δ18OH2O = 3.5 to 4.9‰; δDV-SMOW = −97.9‰ to −93.7‰) hosted by mineralized altered monzogranite and mineralized smoky quartz–sulfide vein were derived from a metamorphic source. Combined with regional tectonic evolution, the gold mineralization in the Heihaibei gold deposit may be related to the metamorphism of the regional strata, which was associated with closure of the Paleotethys Ocean during the early Mesozoic. In the following study, we will pay more attention to the metallogenic regularity of orogenic gold deposits in the EKOB.