Reviewerly/exHarmony huggingface dataset:
Subjective Evaluation of Egocentric Human Segmentation for Mixed Reality

Augmented Virtuality (AV) represents the combination of an immersive environment and a selection of the egocentric local reality. The latter is carried out by applying a particular image segmentation method selected according to the specific application requirements. These image segmentation algorithms can be evaluated from a computer vision perspective using traditional quantitative metrics such as Intersection over Union (1oU), Pixel Accuracy (PA), among others. As AV applications are designed to create experiences for real users, it is also desired to bare in mind their subjective feedback. Thus, in this work we compare segmentation methods from a perceptual point of view, by conducting subjective experiments with a group of users. Subsequently, we compare obtained perceptual results with algorithmic metrics, showing a significant correlation between them. However, we also found subtle differences that could influence decision-making.
Intelligent driving system at opencast mines during foggy weather

The fog in mining operations minimises the visibility, preventing drivers from a clear view, causing accidents and vehicle collisions. This paper provides an intelligent driving system for heavy earthmoving machinery operators in opencast mines, including hardware and software. Hardware contains high definition and thermal cameras, a global navigation satellite system (GNSS), radar, laser light, wireless devices, graphical processing unit, touch screen, etc. The software covers image stitching, image enhancement, and convolution neural network-based object detection. The display dashboard is divided into four windows. Each window represents a different view, i.e. 180° panorama view of the driving lane, GNSS tracking map, proximity radar detection view, and rear thermal camera view. An additional colour transfer method has been used in the existing image stitching method to reduce misalignment and ghost effect in the panorama output. The proposed method outperformed the existing methods, namely contrast limited adaptive histogram equalisation (CLAHE) and dark channel prior (DCP). The proposed image enhancement technique has increased contrast, entropy, and colour average by 0.069, 0.43, and 13.96, respectively, than CLAHE, and 0.994, 0.43, and 42.07 than DCP. The accuracy of the object detection model is 97%, and the overall processing time of all the algorithms is 0.44949 seconds.
The impact of formal and informal organizational norms on susceptibility to phishing: Combining survey and field experiment data

Phishing is one of the most common forms of social engineering that exploits human vulnerabilities and causes immense personal and organizational costs. This study advances the research on the factors of susceptibility to phishing in three regards. First, it addressed the role of organizational norms in susceptibility to phishing. Second, it aimed for high external and ecological validity by combining survey and phishing experiments data on large samples of organizations and their employees. Third, it employed a two-level design that considered explanatory variables at the individual and organizational levels. The study chiefly explored how formal, descriptive, injunctive, and personal norms influence employee interactions with phishing emails. To this end, an explanatory model was tested on 83,269 employees in 510 organizations using a multilevel modeling approach. Clicking on links in simulated phishing emails and entering personal information in simulated fraudulent websites were deemed as two types of susceptibility to phishing. Formal norms and collectively shared injunctive norms were found to exert the strongest effects on susceptibility to phishing; in contrast, personal norms exert a weak influence, and descriptive norms can result in a boomerang effect. These results have significant theoretical and practical implications for both researchers and managers seeking organizational-level mechanisms to reduce the threat of phishing emails.
Decentralized, Unlabeled Multi-Agent Navigation in Obstacle-Rich Environments using Graph Neural Networks

We propose a decentralized, learning-based solution to the challenging problem of unlabeled multi-agent navigation among obstacles, where robots need to simultaneously tackle the problems of goal assignment, local collision avoidance, and navigation. Our method has each robot infer their desired action by communicating with each other as well as a set of position-fixed routers. The inference is carried out on a graph neural network (GNN) with both robot and router nodes. We train our GNN using imitation learning on a small group of robots, where we modify the centralized version of the concurrent goal assignment and planning algorithm (CAPT) as our expert. By sharing weights among all robots and routers, our model can scale to unseen environments with any number of possibly kinodynamic agents during test time. We have achieved a success rate of 91.2% and 85.6% for point and car-like robots, respectively. Source code will be publicly available upon the publication of the work.
Integration of Information Technology Certifications into Undergraduate Computing Curriculum

This innovative practice full paper describes our experiences of integrating information technology certifications into an undergraduate computing curriculum. As the technology landscape evolves, a common challenge for educators in computing programs is designing an industry-relevant curriculum. Over the years, industry practitioners have taken technology certifications to validate themselves against a base level of technical knowledge currently in demand in industry. Information technology (IT) certifications can also offer paths for academic computing programs to stay relevant to industry needs. However, identifying relevant IT certifications and integrating it into an academic curriculum requires a careful design approach as substantial efforts are needed by educators to design and deliver courses. This paper describes our proposed approach and experiences of identifying and mapping IT certifications for an undergraduate Information Systems (IS) curriculum. We first review our IS curriculum with an academic standard and an industry skills framework to identify relevant job roles for students. Next, we perform a web search in the Google Job Search engine for job postings relevant to these job roles and apply text analytics techniques to identify the IT certifications referenced in these job postings. We also share our implementation experiences of integrating two highly referenced IT certifications - Amazon Web Services (AWS) Cloud Practitioner and Solutions Architect - Associate certification exams in two undergraduate computing courses. We learnt that there is a need to allocate efforts for the faculty to manage the logistics matters. Students appreciate the teaching efforts and are motivated to train themselves for the certifications. In the first year of the implementation, we achieved 90% and 96% exam pass rates in AWS Cloud Practitioner certification and Solutions Architect - Associate certification, respectively. We hope that our approach and the lessons learnt can help other educators consider integrating IT certifications into their computing curricula.
Evidential Data Fusion for Characterization of Pavement Surface Conditions during Winter Using a Multi-Sensor Approach

The role of a service that is dedicated to road weather analysis is to issue forecasts and warnings to users regarding roadway conditions, thereby making it possible to anticipate dangerous traffic conditions, especially during the winter period. It is important to define pavement conditions at all times. In this paper, a new data acquisition approach is proposed that is based upon the analysis and combination of two sensors in real time by nanocomputer. The first sensor is a camera that records images and videos of the road network. The second sensor is a microphone that records the tire-pavement interaction, to characterize each surface's condition. The two low-cost sensors were fed to different deep learning architectures that are specialized in surface state analysis; the results were combined using an evidential theory-based data fusion approach. This study is a proof of concept, to test an evidential approach for improving classification with deep learning, applied to only two sensors; however, one could very well add more sensors and make the nanocomputers communicate together, to analyze a larger urban environment.
Software Defect Prediction Using SMOTE and Artificial Neural Network

defect prediction (SDP) is process of identifying software defect on the early testing stage of SDLC. SDP can saving time software tester on the development process. There are some issues on the way to develop SDP to be more effective, one of the issues is how to increase accuracy for predicting whereas most of dataset for SDP typically has imbalanced data for the defect class. In other words the dataset will naturally affecting appearance of prediction error on the classification model. This paper is to proposed Synthetic Minority Oversampling Technique (SMOTE) and artificial neural network to address the issues. The SMOTE is used to handling imbalance data and the artificial neural network is used to build predicting model. SMOTE and artificial neural network are applied to discover result of classification performance. The scenario is comparing imbalanced dataset that already processed with SMOTE and without SMOTE than classifying using artificial neural network and measured using value of precision, recall and f-measure. The experimental result show that the proposed SMOTE and Artificial Neural Network (ANN) and Association Rule Mining (ARM) method increase predicting performance for software defect prediction comparing with only Artificial Neural Network method. For parameter precision, recall, accuracy and F-measure improving increase are 2.2%, 18.4%, 22.4% and 8.8% At comparing ANN+ARM with ANN+ARM+SMOTE and beside that comparison about ANN+ARM with ANN+ARM+SMOTE also improve by 32.2%, 70.24%, 7% and 63.38%. The last comparison show different results from the others, the ANN+ARM+SMOTE get smaller performance value than combination ANN + SMOTE with gap score 17.2%, 7.8%, 1%, 15.6%. It happen because feature selection with Association Rule Mining didn’t help to improve predicting accuracy performance.
Devanagari Handwritten Character Recognition using CNN as Feature Extractor

This paper proposes the use of Convolutional Neural Networks as feature extractor for extraction of features from handwritten Devanagari characters. For classification, classifiers employed are SVM (Linear, Polynomial and RBF), KNN, RF, DT, MLP and XGB. Use of CNN model for feature extraction eliminates the need of handcrafted features by traditional pattern recognition methods. XGB Classifier, which is a very recent technique, has also been explored (which has not been done previously to the best of our knowledge). The experiments with these eight techniques have been done on the DHCD dataset proposed in year 2015. Use of CNN proved to be very effective for Devanagari characters recognition as all the models achieved recognition accuracy of over 92% and total training time including feature extraction and classification did not exceed a total of 12.16 minutes.
Systematization of Password ManagerUse Cases and Design Paradigms

Despite efforts to replace them, passwords remain the primary form of authentication on the web. Password managers seek to address many of the problems with passwords by helping users generate, store, and fill strong and unique passwords. Even though experts frequently recommend password managers, there is limited information regarding their usability. To aid in designing such usability studies, we systematize password manager use cases, identifying ten essential use cases, three recommended use cases, and four extended use cases. We also systematize the system designs employed to satisfy these use cases, designs that should be examined in usability studies to understand their relative strengths and weaknesses. Finally, we describe observations from 136 cognitive walkthroughs exploring the identified essential use cases in eight popular managers. Ultimately, we expect that this work will serve as the foundation for an explosion of new research into the usability of password managers.
Bankline detection of GF-3 SAR images based on shearlet

The GF-3 satellite is China's first self-developed active imaging C-band multi-polarization synthetic aperture radar (SAR) satellite with complete intellectual property rights, which is widely used in various fields. Among them, the detection and recognition of banklines of GF-3 SAR image has very important application value for map matching, ship navigation, water environment monitoring and other fields. However, due to the coherent imaging mechanism, the GF-3 SAR image has obvious speckle, which affects the interpretation of the image seriously. Based on the excellent multi-scale, directionality and the optimal sparsity of the shearlet, a bankline detection algorithm based on shearlet is proposed. Firstly, we use non-local means filter to preprocess GF-3 SAR image, so as to reduce the interference of speckle on bankline detection. Secondly, shearlet is used to detect the bankline of the image. Finally, morphological processing is used to refine the bankline and further eliminate the false bankline caused by the speckle, so as to obtain the ideal bankline detection results. Experimental results show that the proposed method can effectively overcome the interference of speckle, and can detect the bankline information of GF-3 SAR image completely and smoothly.
A Trusted Distribution Mechanism of Tasks for the Internet of Vehicles Based on Blockchain

In order to obtain higher reward, vehicles may report fake computing capability, which can easily lead to poor performance and data security problems. To solve this problem, based on blockchain, a trusted distribution mechanism of tasks for the Internet of Vehicles is proposed. Firstly, the scoring model and the reputation model are established to grade the computing tasks completed by vehicles and update their reputation values in real time. At the same time, an incentive mechanism is set up to encourage vehicles to compute tasks. In order to ensure the fairness of task distribution, the task distribution method which based on reputation model and incentive mechanism is proposed. In order to assure the non-tampering of data information, a blockchain consensus algorithm of multi-weight based on vehicle reputation value is proposed. The miners are selected according to the weighted evaluation method of vehicle reputation value, historical tasks amount which stored in the RSU (Road Side Unit) and the reputation value of the RSU. After miner publishes blocks, broadcasts and verified by other RSU through the P2P (Peer to Peer) network, they can be tamper-proofing data. Finally, the simulation results verify the practicability and effectiveness of the proposed mechanism.
Based on Consortium Blockchain to Design a Credit Verifiable Cross University Course Learning System

In recent years, the attention of online cross-university courses has been increasing, and students in universities want to increase their knowledge and professional skills by taking online courses from different universities, which raises the issue of course credit verification. In the past, the credits obtained by students in online courses lack endorsement from the education department, and the students’ learning process could not be verified. Therefore, the credits of online courses in one university could not be recognized by other universities. The education departments of some countries and regions implement credit conversion rules to convert the credits obtained by students in online courses into university credits or certificates endorsed by the education department. However, these schemes rely too much on the authority of the education department, and the process of students obtaining credits cannot be verified. In addition, the centralized storage method makes the data of education departments at risk of leakage or tampering. With the emergence of blockchain technology, some researchers have proposed the use of blockchain to store students’ credits, making it possible to reach consensus among multiple parties on the blockchain while ensuring that credits are not tampered with, but these schemes cannot test the learning process of students and the recognition of credits still relies on the authority of the education department. To solve the above problems, this paper proposes a cross-university course learning system with verifiable credits based on Hyperledger Fabric consortium blockchain technology, and the consortium includes many universities. The credits obtained by students in the course and the hash value of the learning records are stored on the blockchain, and the data on the blockchain is jointly maintained by the universities in the system. One university can verify the homework and final examination of students to check the real ability of students, thus recognizing the credits from other universities, and at the same time, to protect the privacy of students, the important data of students are encrypted for transmission.
Ensemble-based deep meta learning for medical image segmentation

Deep learning methods have led to the state-of-the-art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders for further collaboration. However, limited labeled data set limits the deep learning algorithms to be generalized for one domain into another. To handle the problem, meta-learning helps to solve this issue especially it can learn from a small set of data. We proposed a meta-learning-based image segmentation model that combines the learning of the state-of-the-art models and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segment part and remove noise from the new test images. The proposed model can achieve 0.94 precision and 0.92 recall. The ability is to increase 3.3% among the state-of-the-art algorithms.
A multi‐objective approach for local path planning of autonomous mobile robot based on metaheuristics

Abstract The traditional methods of mobile robot (MR) path planning very often fail in finding the optimal path in a cluttered, complex, and unknown environment. Therefore, in this article, a novel constrained multi‐objective function involving route length, smoothness of the path, and path safety is formulated for a disc‐shaped MR integrated with sensors. The imposed constraints to the problem are the size of the robot and the collision‐free path. A new population‐based algorithm called the chemical reaction optimization algorithm is used to solve the path planning problem. The results obtained through the proposed controller are compared with the results of other methods available in the literature. To demonstrate the effectiveness and usefulness of the proposed algorithm in finding the solution to the path planning problem, statistically significant tests, that is, the coefficient of variation analysis and Friedman's test are conducted. It is observed that the proposed method outperforms the state‐of‐the‐art methods in terms of route length, path smoothness, average sensing, and planning time, and time is taken to reach the goal.
Augmented reality for virtual training in the construction industry

Technology has evolved in surprising ways, and augmented reality (AR) has positioned itself as one of the technologies with outstanding value. Its importance in education is still being debated, but its incorporation in business training has been left out.This study aims to determine variables that explain the intention to use this technology in construction training, focused on preventing injuries and disabilities.This study was carried out using the Technology Acceptance Model (TAM) through the structural equation method. The variables: perceived ease of use, perceived usefulness, attitude toward using, and behavioral intention to use were incorporated. An AR app was developed that addresses the teaching of safety elements on scaffolding, and we collected data from Chilean construction companies.The results show that perceived usefulness and perceived ease of use explain the attitude towards using augmented reality, while perceived usefulness and attitude towards using explain behavioral intention to use.These findings enrich AR's literature in the construction industry and have high business managers' implications. It may allow them to implement this technology more likely to succeed in their virtual business training to prevent injury-related disability.
An efficient method for mining sequential patterns with indices

In recent years, mining informative data and discovering hidden information have become increasingly in demand. One of the popular means to achieve this is sequential pattern mining, which is to find informative patterns stored in databases. Its applications cover different areas and many methods have been proposed. Recently, pseudo-IDLists were proposed to improve both runtime and memory usage in the mining process. However, the idea cannot be directly used for sequential pattern mining as it only works on clickstream patterns, a more distinct type of sequential pattern. We propose adaptations and changes to the original idea to introduce SUI ( S equential pattern mining U sing I ndices). Comparing SUI with two other state-of-the-art algorithms on six test databases, we show that SUI has effective and efficient performance and memory usage.
An Edge-device Based Fast Fall Detection Using Spatio-temporal Optical Flow Model

The elderly fall detection is one critical function in health of the elderly. A real-time fall detection for the elderly has been a significant healthcare issue. The traditional video analysis on cloud has large communication overhead. In this paper, a fast fall detection system based on the spatio-temporal optical flow model is proposed, which is further deeply compressed by a structured tensorization towards an implementation on edge devices. Firstly, an object extractor is built to extract motion objects from video clips. The spatio-temporal optical flow model is formed to estimate optical flow fields of motion objects. It can extract features from objects and their corresponding optical flow fields. Then these two features are fused to form new spatio-temporal features. Finally, the tensor-compressed model processes the fused features to determine fall detection, where the strongest optical field would indicate the fall. We conduct experiments with Multicam and URFD datasets.Clinical relevance- It demonstrates that the proposed model achieves the accuracy of 96.23% and 99.37%, respectively. Besides, it attains the inference speed of 83.3 FPS and storage reduction of 210.9×. Our work is further implemented on an AI acceleration core based edge device, and the runtime is reduced by 9.21×.This high performance system can be applied to the field of clinical monitoring in the future.
Clustering of non-annotated data

Clustering of non-annotated data is a common problem in many areas. One of the main uses for clustering is finding structures and models in data. Because the data are unlabeled, we cannot tell if the result from clustering is the desired result. That is why we need annotated data. This can be provided from trained neural network, which works with the same or related data. (Abstract)