Reviewerly/exHarmony huggingface dataset:
Combining Label-wise Attention and Adversarial Training for Tag Prediction of Web Services

Tagging is well regarded as one of the best ways of managing web services, in which keywords are assigned by users to describe the published services. As users are required to select multiple tags from a large set of candidate tags based on their own understanding, such user-attached tags are not always reliable and may affect the efficiency of service discovery. To alleviate the issue, tag prediction can suggest users appropriate tags for web services based on the textual descriptions of their functionality. Therefore, it is necessary to design tag prediction methods to support service search and recommendation. In this work, we propose a tag prediction model that adopts BERT-based label-wise attention mechanism, and use adversarial training to further improve the model performance. Experimental results on the service datasets collected from ProgrammableWeb show that the proposed method can achieve better prediction performance than other state-of-art methods.
Computer vision algorithm for identifying the post-irradiated nuclear fuel in a hotcell

Post-irradiation testing of nuclear fuel must be carried out in a special installation called a hot laboratory (Hotcell). The main component is the manipulator, which functions as a robotic arm that holds nuclear fuel samples in all of its displacement processes. As technology advances, manipulators can be operated automatically. The integration of a camera in the manipulator allows the application of computer vision technology in identifying the geometric shape of nuclear fuel, so that in the future a manipulator can be created that can run part of the testing process automatically. In this study, a system that can recognize the shape of a nuclear fuel plate in the form of PEB (Pelat Elemen Bakar) U3Si2Al has been developed. The algorithm approach was built using YOLO (You Only Look Once). The results obtained are that the geometric shape of the sample can be recognized with an accuracy value of 95% and can distinguish if the sample held by the manipulator is not nuclear fuel.
Rethinking an ICT-mediated teaching strategy during the COVID-19 pandemic

The COVID-19 pandemic has positioned the issue of the virtual environments in the "new normal" of the teaching and learning processes. During these last times, the rapid transition from traditional face-to-face learning to online learning or ICT-based learning has been viewed as a paradigm shift in higher education. Based on the theoretical fundamentals of teaching-learning methodologies and strategies, educational resources design, and assessment, the present study describes an ICT-mediated teaching strategy to the Universidad de Medellín (Colombia) and its adaptation to the curricular innovation model of the institution. The present approach aims to contribute from two perspectives: (i) in a theoretically way to the digital learning area or ICT-based learning for higher education institutions, and (ii) in an empiric way with the practical implications of formulating strategies and processes of digital transformation in academic context and the relevant reflection about the teachers and students needs inside the virtual learning environments.
Augmenting Code Review Experience Through Visualization

Code review is a systematic inspection of the code-base. It ensures that the software satisfies the required functionalities and standards; thus, it is an essential stage in the modern development process and is used frequently in the industry. For a successful review, the reviewer should be able to identify defects; therefore, the process is highly dependent on the awareness of the reviewer. Currently, code review is done by comparing the line-by-line differences on the codebase. However, this does not give structural information, such as design pattern changes or dependency changes between services.To address this problem, we propose an augmented next-generation code review experience using visual and holistic approaches to streamline the reviewing process. The proposed code review experience shows the structural representation of the change by visualizing it into a UML-like relationship diagram. Along with this diagram, the risk percentage for each commit is highlighted to emphasize the differences that possibly can be most affected by the change. An artifact map is also produced that includes issue-commit relation for bug tracking, to make reviewers aware of the frequent issues that are brought up. This map also highlights the change frequency of the files, for focusing the reviewers on the possibly vulnerable parts of the software for better quality reviews. The proposed visualizations have been implemented in a plugin-based proof-of-concept tool integrated within GitHub, supporting the visual reviewing of changes in codebases developed with Java language.
Sustainable Digital Transformation of Heritage Tourism

Tourism has always meant big business. With the travel service sector being the economic backbone of several countries, heritage tourism becomes a key fiscal factor for growth. So far, technology in the travel and tourism industry primarily focused on customer engagement and service, aiming at a simplified digital experience from booking a trip and planning the itinerary to organizing payments and post-trip memories. However, with the world coming to terms with the devastation of the COVID-19 pandemic, digital heritage tourism becomes critical to local economies mainly to revive global interest and to keep domestic businesses going. A sustainable transformation to digital heritage tourism can support nations to vault over physical travel challenges during times such as these, and help governments collaborate with international organizations for positive future change.
Research and Modeling Electronic Data Tracing Scheme Based on Blockchain

Abstract From the point of view that the traditional electronic data (documents in image, PDF, etc) is difficult to track and trace, taking image copyright as an example, this paper studies a new scheme to solve the problem of replication detection and traceability based on blockchain. With the decentralization and trust removing of blockchain, both copyright storage and evidence of infringement are stored in a tamper proof and transparent way. Based on the judgment of image similarity, a “resource reverse tracing strategy” is designed to control the infringement timely. The infringement evidence can reach the creator quickly, which provides convenience for the creator’s rights protection. This method changes traditional tracing methods of image copyright. Also, it can be applied to the tracing problem of reuse of electronic invoice, short video, music, etc, and brings new ideas for the related problem.
Functional Brain Image Clustering and Edge Analysis of Acute Stroke Speech Arrest MRI

In the area of neural imaging the statistical and Mathematical analysis plays an important role in supervising the fMRI images of human brain. In this work, we have performed a cluster analysis of fMRI images of human brain with a connectivity architecture that takes the sequence of brain images. In particular, the edge extent is a major challenging piece of work in brain edge detection planning and it’s quantitative estimation. Edge detection is a fundamentally focus on to distinguish the tissues :WM, GM and CSF the signal intensities are abruptly changes along edge. Here, we have discussed the effectiveness of the procedure to location intensity of brain image and edge detection tasks. Cluster analysis for fMRI images with brain connectivity architecture that takes the sequence of brain image data. (i) Simulation of how clustering can be used for neuroimaging atlas to parcellate the brain. (ii) Investigated the quantitative evaluation of ROI. (iii) Analyzed the intensity in arbitrary frame from video data of speech arrest MRI. Due to overlapped voxel at the edges of brain region are not defined by specific intensities therefore we focus on intensities of MR images of the and performed the clustering using Python. Finally, we have compared the intensities of the MR images histogram.In the distribution of all the voxel intensities the realted intensities of and WM, GM,and CSF voxel respectively in the histogram are used the gradient based method to distinguish the distribution respectively with spatial prior = 0.1. We demonstrate the gradient information contains contrast and intensity of the brain image.
Stacked AMR Parsing with Silver Data.

Lacking sufficient human-annotated data is one main challenge for abstract meaning representation (AMR) parsing. To alleviate this problem, previous works usually make use of silver data or pre-trained language models. In particular, one recent seq-to-seq work directly fine-tunes AMR graph sequences on the encoder-decoder pre-trained language model and achieves new state-of-the-art results, outperforming previous works by a large margin. However, it makes the decoding relatively slower. In this work, we investigate alternative approaches to achieve competitive performance at faster speeds. We propose a simplified AMR parser and a pre-training technique for the effective usage of silver data. We conduct extensive experiments on the widely used AMR2.0 dataset and the results demonstrate that our Transformer-based AMR parser achieves the best performance among the seq2graph-based models. Furthermore, with silver data, our model achieves competitive results with the SOTA model, and the speed is an order of magnitude faster. Detailed analyses are conducted to gain more insights into our proposed model and the effectiveness of the pre-training technique.
Chinese Relation Extraction with Phrase Component Representations

Recently, the Lattice structure has been proved to be effective for Chinese entity relation extraction by incorporating the word information. However, this method cannot utilize phrases that contain sentence structure information. In order to solve the problem, we introduced a Lattice model with phrase component representation, which can merge word, phrase and character input. We also apply the multi-head self-attention mechanism in the classification layer to effectively learn the dependencies between distant words. After comparative experiments, our model achieves better results.
LiDAR-Based Autonomous Exploration Using Local and Global RRT for MAV

Exploring and mapping an unknown space is a fundamental task for autonomous robots. Various exploration planners employ the scheme which directing the robots to the frontiers. Frontiers are the boundaries between the known and unknown space. This paper presents a new exploring strategy based on local and global Rapidly-exploring Random Trees (RRTs) for frontier detecting. The global RRT starts from the starting position and extends outwards looking for frontiers. The local RRT starts from the position of the MAV. It resets the local tree while finding the frontier and output the desired path for MAV. The proposed exploring planner can find frontiers faster and the global tree ensures the complete exploration results. The proposed planner is capable of running online. It is implemented and tested on the GAZEBO with a PX4 quadrotor. For future effort, it can be extended to multi-agent systems.
Unrealistic Promises and Urgent Wording Differently Affect Suspicion of Phishing and Legitimate Emails

Phishing emails have certain characteristics, including wording related to urgency and unrealistic promises (i.e., “too good to be true”), that attempt to lure victims. To test whether these characteristics affected users’ suspiciousness of emails, users participated in a phishing judgment task in which we manipulated 1) email type (legitimate, phishing), 2) consequence amount (small, medium, large), 3) consequence type (gain, loss), and 4) urgency (present, absent). We predicted users would be most suspicious of phishing emails that were urgent and offered large gains. Results supporting the hypotheses indicate that users were more suspicious of phishing emails with a gain consequence type or large consequence amount. However, urgency was not a significant predictor of suspiciousness for phishing emails, but was for legitimate emails. These results have important cybersecurity-related implications for penetration testing and user training.
Design of Reconfigurable Signed Dual Modulo Multiplier Function (DMMF) for RNS

Residue Number System (RNS) is an alternative number representation of a Binary weighted system to process the data at a higher rate with lower hardware. Modular Multiplication operations are extensively utilized in RNS Processors. To increase the processor's performance, a new configurable signed dual modulo <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2^{\mathrm{n}}\pm 1$</tex> multiplication that uses array architecture is proposed in this work. The multiplier could be used to perform successive modulo multiplications on the same hardware. Mathematical modelling, VLSI architecture and real-time verification are done in this work. Proposed DMMF saves the area by 40%, reduces the power consumption by 37%, and increases the operation speed by 33% compared to the architecture operated separately.
PageRank Centrality based Graph Convolutional Networks for Semi-supervised Node Classification

Graph convolutional neural networks(GCNs) have achieved impressive results on a variety of graph-based tasks. However, due to the problem of over-smoothing, the output representation of each node in the graph converges to the same asymptotically with increasing depth of GCN, so the vast majority of GCN models in practical applications are shallow. In this paper, we investigate deep GCNs and propose a PageRank-GCN(PR-GCN) model, which is an extension of DropEdge model. PR-GCN uses PageRank to get the top e neighbors that are most easily accessed by nodes and adds them back to the adjacency matrix after DropEdge processing. The results of a series of experiments on real-world datasets show that the PR-GCN model outperforms the state-of-the-art baseline approach in semi-supervised node classification tasks. The experiments also validate that the proposed method can prevent over-smoothing in deep GCNs while mitigating the information loss caused by DropEdge.
Neon: Low-Latency Streaming Pipelines for HPC

Real time data analysis in the context of e.g. realtime monitoring or computational steering is an important tool in many fields of science, allowing scientists to make the best use of limited resources such as sensors and HPC platforms. These tools typically rely on large amounts of continuously collected data that needs to be processed in near-real time to avoid wasting compute, storage, and networking resources. Streaming pipelines are a natural fit for this use case but are inconvenient to use on high-performance computing (HPC) systems because of the diverging system software environment with big data, increasing both the cost and the complexity of the solution. In this paper we propose Neon, a clean-slate design of a streaming data processing framework for HPC systems that enables users to create arbitrarily large streaming pipelines. The experimental results on the Bebop supercomputer show significant performance improvements compared with Apache Storm, with up to 2x increased throughput and reduced latency.
Static Gesture Classification and Recognition Using HOG Feature Parameters and k-NN and SVM-Based Machine Learning Algorithms

The paper analyzes the effectiveness of histogram-oriented gradient (HOG) features in static hand gesture recognition (SHGR) using k-nearest neighbor (k-NN) and support vector machine (SVM) classifiers. The experiment is carried out on a database of 10 different static gestures built on environmental conditions that are limited. A histogram-oriented gradient is extracted from the segmented gestures after performing various color image enhancements on the original database. For both classifiers k-NN and SVM, 80% of the extracted features are used for training and the remaining 20% are used for testing. For k-NN an average recognition accuracy of 96% is obtained. On the other hand, for SVM an average recognition accuracy of 98% is obtained on the same set of training and test data and found that the SVM classifier gives better performance than the k-NN classifier.
Augmented and Mixed Reality based Decision Support Tool for the Integrated Resource Plan

In today’s era, enormous amounts of data are generated and the means to visualize this data is becoming a challenge. Data visualization is an important support tool that allows one to make informed decisions. This paper attempts to provide a possible solution to this major challenge. The solution presented is a data analytic tool capable of visualizing complex data by leveraging the fourth industrial revolution technologies, augmented and mixed reality. Using the disruptive nature of these technologies, it could provide an application that is more intuitive and immersive to the end-user. Moreover, we focus the solution in the energy domain by exploring methods in enhancing energy data visualization to support decision-making of South Africa’s Integrated Resource Plan (IRP).
A Fast Real-Time QoS-aware Service Selection Algorithm

The mutual invocation and composition of services conduct the Internet of Services (IoS). Dynamic QoS is a prominent feature of IoS. Existing QoS-aware methods always consider the QoS as a static indicator to differentiate similar services, and attributes of QoS are independent of each other, which may lead to poor performance of service selection. In this paper, we proposed a real-time QoS-aware service selection method to find good service to match the users’ requirements, in which an FM-based dynamic evaluation model was employed to predict the real-time QoS, and then a hash space segment-based service matching algorithm is adopted to select service resources rapidly. Extensive experimental results show that mining the dynamic secondary characteristic relationship among the QoS attributes can improve the service selection effect on the real-world scene.
A Japanese Character Flick-Input Interface for Entering Text in VR

This paper presents new flick input interfaces to improve the usability of Japanese character input in VR space. We designed three different interfaces called TouchFlick, EyeFlick, and RoundFlick, which make use of various controller interactions and eye gestures. To investigate the effectiveness of these methods, we compared them with a conventional VR QWERTY keyboard with ray-based selection. We found that TouchFlick was significantly faster and RoundFlick had a significantly lower error rate for experienced users. On the other hand, the input efficiency was the same as that of the conventional method for those who had no experience with flick input. Regarding subjective evaluation, there was no significant difference in usability and mental workload.
Adapting a Floor Sensor for Autonomous Robots in a Smart Building

We demonstrate a real time recognition of autonomous robots using a floor sensor. In these years, many types of autonomous mobility such as delivery robots and guiding robots are introduced to building facilities. To provide smooth operation of robots in a building, accurate position detection of robots by infrastructural sensors is required.
An improved upper bound on self-dual codes over finite fields GF(11), GF(19), and GF(23)

This paper gives new methods of constructing symmetric self-dual codes over a finite field GF(q) where q is a power of an odd prime. These methods are motivated by the well-known Pless symmetry codes and quadratic double circulant codes. Using these methods, we construct an amount of symmetric self-dual codes over GF(11), GF(19), and GF(23) of every length less than 42. We also find 153 new self-dual codes up to equivalence: they are [32, 16, 12], [36, 18, 13], and [40, 20, 14] codes over GF(11), [36, 18, 14] and [40, 20, 15] codes over GF(19), and [32, 16, 12], [36, 18, 14], and [40, 20, 15] codes over GF(23). They all have new parameters with respect to self-dual codes. Consequently, we improve bounds on the highest minimum distance of self-dual codes, which have not been significantly updated for almost two decades.
The Influence of the Pandemic on the Motivation of EAP Learners in Studying IELTS

Various sectors have been affected by the Covid-l9 pandemic, including education. Many institutions have not yet applied face-to-face learning in full to prevent the transmission of the virus. The aims of this study are (1) to examine the motivation of EAP learners to learn IELTS in online and offline classes during a pandemic on an English course, and (2) to identify changes in student motivation during the learning process that are affected by the pandemic situation. This study uses a qualitative descriptive method with a sampling technique in which students are sampled randomly. As many as 10 students have been taken as participants. Furthermore, a questionnaire has been used as a data collection method, and it has been shared through Google Forms . It consists of 25 statements divided based on intrinsic and extrinsic motivation. The result of it has been classified based on four learning motivation aspects by Gowing’s theory. Overall, the findings revealed that three of the four aspects of learning motivation are medium-level (49,4%) in this case. In conclusion, there is no substantial motivation for EAP pupils in the English learning process, specifically for IELTS in online and offline sessions, for an ongoing pandemic.
The Implications of Virtual Reality (VR) for the Aged

As virtual reality (VR) technology continuously evolves in this century, the number of such applications have undoubtedly increased significantly. Nevertheless, the VR market for the aged is still in a blue ocean. The Hong Kong population is aging more quickly than ever before. This puts a huge stress to the healthcare systems in Hong Kong. In this chapter, the authors describe the updated VR technologies and their applications in a series of emerging healthcare services such as training and green burial for the aged. To this end, the chapter will provide management staff in homes for the aged, hospitals, healthcare service providers, and other health service professionals with the latest concepts and good practices in VR for the aged. The humanistic, holistic, and integrated care for the aged will be achieved in the future.
An investigation into the effectiveness of student-led experiential learning for UG architects and the implications of incorporating AR into such pedagogical exercises

Abstract This paper focuses on a collaboration between three researchers from two universities in Zhejiang province to explore the pedagogical value of hands-on experiential learning using non-standard materials (in this case, bamboo). The second aim of the project was to explore how Augmented Reality (AR) might be used to improve the efficiency of the learning process for the participants. As a building project, the outcome was largely successful- the undergraduate (UG) student participants were able to construct a 4-metre-tall bamboo structure in the summer of 2020, using active bending, from a 3D computer model, within the timeframe. They were given complete autonomy as part of the project which resulted in problems but also arguably improved the learner autonomy and problem-solving abilities of the respondents over the timeframe. Concerning the use of AR in such projects, this researched identified the value for this purpose but also some very major obstacles, including cost and complexity.