Reviewerly/exHarmony huggingface dataset:
Sign Language Recognition with Quaternion Moment Invariants: A Comparative Study

In this paper, we aim to carry out a brief study of a new sets of Quaternion Discrete Moment Invariants (QDMI) in uniform lattice named: Quaternion Tchebichef Moment Invariants (QTMI), Quaternion Krawtchouk Moment Invariants (QKMI), and Quaternion Hahn Moment Invariants (QHMI), for hand gesture-based sign language recognition. For this purpose, we present briefly the principles of those moment invariants. Then, we test them on several datasets that contain challenging attributions, with regard to invariability and recognition under noise-free and noisy conditions. We conclude our paper by discussing the obtained results and the future works in this field.
Incremental Ensemble Gaussian Processes

Belonging to the family of Bayesian nonparametrics, Gaussian process (GP) based approaches have well-documented merits not only in learning over a rich class of nonlinear functions, but also in quantifying the associated uncertainty. However, most GP methods rely on a single preselected kernel function, which may fall short in characterizing data samples that arrive sequentially in time-critical applications. To enable online kernel adaptation, the present work advocates an incremental ensemble (IE-) GP framework, where an EGP assembler employs an ensemble of GP learners, each having a unique kernel belonging to a prescribed kernel dictionary. With each GP expert leveraging the random feature-based approximation to perform online prediction and model update with scalability, the EGP assembler capitalizes on data-adaptive weights to synthesize the per-expert predictions. Further, the novel IE-GP is generalized to accommodate time-varying functions by modeling structured dynamics at the EGP assembler and within each GP learner. To benchmark the performance of IE-GP and its dynamic variant in the adversarial setting where the modeling assumptions are violated, rigorous performance analysis has been conducted via the notion of regret, as the norm in online convex optimization. Last but not the least, online unsupervised learning for dimensionality reduction is explored under the novel IE-GP framework. Synthetic and real data tests demonstrate the effectiveness of the proposed schemes.
Evaluating the user experience of a fuzzy-based Intelligent Tutoring System

Nowadays, the scientific interest for educational software and Intelligent Tutoring Systems increases. The main goal of an Intelligent Tutoring System is to offer a personalized learning experience, adapting the learning content and process to each individual student, and maximizing the learning outcomes. However, for ensuring the system’s quality the user experience evaluation is equally important to the learning effectiveness evaluation. Concerning the above, in this paper we evaluate thoroughly the user experience of a fuzzy-based Intelligent Tutoring System, which teaches the logic of computer programming and the programming language ‘C’. For the evaluation’s needs, we used observation and questionnaires. Furthermore, for ensuring the validity of the evaluation’s results, t-tests were conducted. The evaluation results are very positive and show that the embedded fuzzy-based mechanism realizes lesson’s sequence and learning content adaptation to the learners’ needs in such a way that they consider the system as a useful pleasant and helpful tool for learning computer programming.
Fiducial free correction of mapping distortion in null test of aspheres and freeforms

Aspheres and freeform surfaces are well measured with null optics but the transverse coordinates on the test surface are not linear with the pixels on the imaging plane. Such a mapping distortion must be corrected before the measured surface error map is used for corrective figuring process. Conventional mapping distortion correction involves tediously finding the coordinates of a sequence of fiducial marks on both the test surface and the imaging plane. This paper presents a pure ray tracing method without use of fiducial marks by introducing a dummy spherical surface between the point source and the null optics, acting like a virtual reference sphere (RS). It is linearly mapped to the imaging plane with the linear scale easily calibrated. On the other hand, the virtual RS is exactly mapped to the test surface by tracing a bundle of dense rays. This virtual surface thus bridges the gap between coordinates on the imaging plane and those on the test surface. Two experimental examples of null test of on-axis and off-axis aspheres demonstrate the effectiveness of mapping distortion correction.
Multi-robot Informative Path Planning using a Leader-Follower Architecture

We consider typical scenarios where an autonomous multi-robot team is used for surveying a large region. The desired output is a spatial map of the physical values of interest. Accounting for spatial correlation and uncertainty, the map is modeled using a Gaussian Process. Considering real-world constraints such as limited time budget and collision avoidance, we model team’s mission as a joint informative path planning problem which is tackled using a leader-follower architecture balancing centralized and fully distributed computation of plans. The leader first identifies a convex containment region that is to be sampled by the team. Next, through a combination of Bayesian optimization and Monte Carlo simulation, distinct sampling locations are identified and assigned to the followers. Each follower independently solves an orienteering problem to find a collision-free path maximizing information gain. A team-level adaptive replanning criterion is designed to keep redirecting sampling towards the most informative regions. The algorithm has been validated in computational experiments for map estimation. Compared to a baseline reference algorithm, it has shown a significantly higher accuracy. Moreover, the approach has shown good ability to support network connectivity, as well as good scalability in computation.
XRmas: Extended Reality Multi-Agency Spaces for a Magical Remote Christmas

The COVID-19 pandemic has raised attention toward remote and hybrid communications. Currently, one highly-studied solution lets a remote user use virtual reality (VR) to enter an immersive view of a local space, and local users use augmented reality (AR) to see the remote user's representation and digital contents. Such systems give the remote user a sense of 'being there', but we identify two more challenges to address. First, current systems provide remote users with limited agency to control objects and influence the local space. It is necessary to further explore the relationship between users, virtual objects, and physical objects, and how they can play a role in providing richer agency. Second, current systems often try to replicate in-person experiences, but hardly surpass them. We propose XRmas: an AR/VR telepresence system that (1) provides a multi-agency space that allows a remote user to manipulate both virtual and physical objects in a local space, and (2) introduces three family activities in a Christmas context that adopt holographic animation effects to create a 'magical' experience that takes users beyond merely the feeling of 'being there'. We report on preliminary insights from the use of such a system in a remote family communication context.
Improving Text Classification with Transformer

Huge amounts of text data are produced every day. Processing text data that accumulates and grows exponentially every day requires the use of appropriate automation tools. Text classification, a Natural Language Processing task, has the potential to provide automatic text data processing. Many new models have been proposed to achieve much better results in text classification. The transformer model has been introduced recently to provide superior performance in terms of accuracy and processing speed in deep learning. In this article, we propose an improved Transformer model for text classification. The dataset containing information about the books was collected from an online resource and used to train the models. We witnessed superior performance in our proposed Transformer model compared to previous state-of-art models such as LSTM and CNN.
Characterizing real-time dense point cloud capture and streaming on mobile devices

Point clouds are a dense compilation of millions of points that can advance content creation and interaction in various emerging applications such as Augmented Reality (AR). However, point clouds consist of per-point real-world spatial and color information that are too computationally intensive to meet real-time specifications, especially on mobile devices. To stream dense point cloud (PtCl) to mobile devices, existing solutions encode pre-captured point clouds, yet with PtCl capturing treated as a separate offline operation.
SSSGAN: Satellite Style and Structure Generative Adversarial Networks

This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area.
Hierarchical Reinforcement Learning for Waypoint-based Exploration in Robotic Devices

The training of Deep Reinforcement Learning algorithms on robotic devices is challenging due to their large number of actuators and limited number of feasible action sequences. This paper addresses this challenge by extending and transferring existing approaches for waypoint-based exploration with Hierarchical Reinforcement Learning to the domain of robotic devices. The resulting algorithm utilizes a top-level policy, which suggests waypoints to a bottom-level policy that controls the system actuators. The waypoints can either be provided to the top-level policy as domain knowledge or be learned from scratch. The algorithm explicitly accounts for the low number of feasible waypoints and waypoint transitions that are characteristic of robotic devices. The effectiveness of the approach is evaluated on the simulation of a research demonstrator, and a separate ablation study proves the importance of its components.
Fast Ship Detection Method for Sar Images in the Inshore Region

Due to the sizes of synthetic aperture radar (SAR) images are very large, traditional deep learning methods usually require the dense sliding window pre-processing to obtain sub-images for ship detection. Generally, most sub-images contain no ship targets, and they may bring a large amount of redundant information that greatly affects the detection efficiency. To deal with the above problems, this paper proposes a fast ship detection method for SAR images in the inshore region, which mainly consists of two stages: scene classification and ship detection. The two stages employ two cascade deep convolutional networks, i.e., scene classification network (SCN) and single shot detector (SSD), respectively. The SCN can quickly eliminate the sub-images that may not contain ships, and then the remaining sub-images are input into the SSD to implement refined ship target detection. The experimental results based on the real dataset AIR-SARShip-1.0 show that the proposed method has higher efficiency than the original SSD method while maintaining relative high accuracy.
SoftMP: Attentive feature pooling for joint local feature detection and description for place recognition in changing environments

Visual place recognition is the task of finding matchings of images that show the same place in the world. Combinations of appearance changes (e.g. changing illumination or weather) and geometric changes (e.g. viewpoint changes or occlusions) challenge existing approaches. Learning-based local image feature pipelines are a promising approach to this type of problem. We present a novel attentive feature pooling method that can be used to train a CNN to jointly detect and describe local image features. It can be trained on small or moderately sized datasets with weak supervision in a classification training setup (e.g. we use a set of 24k images of publicly available web-camera images in our experiments). We propose to use a joint loss function that combines the cross-entropy loss for the classification task with a mean squared error in order to increase the repeatability of feature detections. We show how the approach can be integrated in a place recognition pipeline and run experiments on several standard place recognition datasets. Despite the small training dataset, we demonstrate a 15% improvement in the average performance compared to the best of a number of compared state-of-the-art approaches, and, probably more importantly, a 3x improvement in the worst-case performance. Open source code is available.
A systematic review on privacy-preserving distributed data mining

Combining and analysing sensitive data from multiple sources offers considerable potential for knowledge discovery. However, there are a number of issues that pose problems for such analyses, including technical barriers, privacy restrictions, security concerns, and trust issues. Privacy-preserving distributed data mining techniques (PPDDM) aim to overcome these challenges by extracting knowledge from partitioned data while minimizing the release of sensitive information. This paper reports the results and findings of a systematic review of PPDDM techniques from 231 scientific articles published in the past 20 years. We summarize the state of the art, compare the problems they address, and identify the outstanding challenges in the field. This review identifies the consequence of the lack of standard criteria to evaluate new PPDDM methods and proposes comprehensive evaluation criteria with 10 key factors. We discuss the ambiguous definitions of privacy and confusion between privacy and security in the field, and provide suggestions of how to make a clear and applicable privacy description for new PPDDM techniques. The findings from our review enhance the understanding of the challenges of applying theoretical PPDDM methods to real-life use cases, and the importance of involving legal-ethical and social experts in implementing PPDDM methods. This comprehensive review will serve as a helpful guide to past research and future opportunities in the area of PPDDM.
Virtual Reality platform for functional magnetic resonance imaging in ecologically valid conditions

Functional magnetic resonance Brain Imaging (fMRI) is a key non-invasive imaging technique for the study of human brain activity. Its millimetric spatial resolution is at the cost of several constraints: participants must remain static and experience artificial stimuli, making it difficult to generalize neuroscientific results to naturalistic and ecological conditions. Immersive Virtual Reality (VR) provides alternatives to such stimuli through simulation, but still requires an active first-person exploration of the environment to evoke a strong sense of presence in the virtual environment. Here, we report how to compensate for the inability to freely move in VR by leveraging on principles of embodiment for a virtual avatar, to eventually evoke a strong sense of presence with a minimal motion of the participant. We validated the functionality of the platform in a study where healthy participants performed several basic research tasks in an MR-specific immersive virtual environment. Our results show that our approach can lead to high sense of presence, strong body ownership, and sense of agency for a virtual avatar, with low movement-related MRI artifacts. Moreover, to exemplify the versatility of the platform, we reproduced several behavioral and fMRI results in the perceptual, motor, and cognitive domains. We discuss how to leverage such technology for neuroscience research and provide recommendations on efficient ways to implement and develop it successfully.
SaPus: Self-Adaptive Parameter Update Strategy for DNN Training on Multi-GPU Clusters

Parameter server architecture has been identified as an efficient framework for scaling DNNs training on clusters. For large-scale deployment, communication becomes the bottleneck, and the parameter updating strategy strongly impacts the training performance and accuracy. Recent state-of-art solutions have adopted the local SGD approach, which enables workers to update their local version of models and only aggregate them to update the global parameters after finishing a number of iterations, to alleviate heavy communication pressure on the parameter server and improving the training performance. We identify three limitations of these works. First, these works do not provide an approach for determining when the worker is to update the parameter with the server under asynchronous communication strategies that can guarantee the training performance. Second, local SGD suffers from the problem of unbounded gradient delay. Previous work works well for a short delay while can not guarantee the performance with an increase of gradient delay. Third, they do not consider the system performance when determining the update interval of the local SGD, including the CPU, memory, and network, which affects the training performance extremely. We provide a self-adaptive parameter updating strategy called SaPus, which allows each worker to detect their training results through quantification of the accumulated gradient updates and determine when to update the parameter with the server adaptively and individually. Theoretical lower and upper bound of the update interval is also provided. We also propose a weighted aggregation algorithm based on a global-loss window, which is used to collect the most recent loss value of other workers to calculate a <i>weight</i> for the accumulated gradients of each worker to solve the unbounded delay problem in asynchronous local SGD. To increase the robustness of our parameter updating strategy, a performance model is built to provide a resource-aware lower bound for the update interval. Extensive experimental results generated on GPU cluster indicate that our model improves the training performance of DNNs, achieving up to <inline-formula><tex-math notation="LaTeX">$66.67\%$</tex-math></inline-formula> speedup as compared with state-of-art solutions. Further, results show the CPU utilization of server dropped by up to <inline-formula><tex-math notation="LaTeX">$81.1\%$</tex-math></inline-formula> and network bandwidth usage reduced to less than <inline-formula><tex-math notation="LaTeX">$1~Gbps$</tex-math></inline-formula> on an average during the training.
DCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework

The emergence of social media platforms like Twitter has become a prominent communication source in disaster outbreak.NGOs, Government agencies leverage twitter's open and public features to provide immediate relief.Nevertheless, situational information gets immersed in millions of tweets with varying characteristics.Examining each tweet can be cumbersome and time-consuming.Thus, the efficient extraction of disaster-related tweets and getting information from all the extracted tweets is required.In the current paper, we have developed a novel framework that uses a deep learning-based classification model to separate the situational tweets from others and summarize them in real-time.Our system is a three-phase process: (a) Creating tweet clusters using a representative set of tweets from the initial set of extracted tweets using a multi-objective optimization concept; (b) When a new tweet arrives, the clusters are updated.The new tweet is classified as situational vs. non-situational.If situational, it is assigned to the closest cluster or new cluster.This assignment is based on its weighted average of syntactic and semantic distances and relevancy to the cluster; (c) Summary is formulated by extracting tweets from each cluster.The proposed approach's superior performance on four datasets related to different disaster-related events indicates the developed framework's efficiency over state-of-the-art techniques.
Quality design for maritime studies programme in the digital era

Competition among academic institutions for students and Industry 4.0 have spurred institutions to actively improve undergraduates' learning experiences and outcomes. The objectives of this research are to identify the quality dimensions of maritime studies programmes from students' perspectives and examine their impacts on learning outcomes which include satisfaction, engagement, and academic performance. After reviewing the literature, six quality dimensions containing 29 measurement items were generated. An online survey was conducted on maritime students from Nanyang Technological University, Singapore. Thereafter, exploratory factor analysis and path analysis were used to analyse the survey data. The exploratory factor analysis produces a six-factor solution that confirms the proposed quality dimensions. Further, the quality dimension with the most impact on student satisfaction is (1) technology integration, followed by (2) industrial exposure, (3) learning and teaching environment, (4) industry-aligned & innovative curriculum, (5) faculty member competencies, and finally (6) supporting activities. In addition, student satisfaction positively influences engagement and academic performance. Further, engagement positively influences academic performance. The developed quality dimension model can serve as a measurement tool for maritime institutions to assess the quality of their programme and update their curriculums respectively to meet students' expectations and learning outcomes.
Novel Quantum Algorithms to Minimize Switching Functions Based on Graph Partitions

After Google reported its realization of quantum supremacy, Solving the classical problems with quantum computing is becoming a valuable research topic.Switching function minimization is an important problem in Electronic Design Automation (EDA) and logic synthesis, most of the solutions are based on heuristic algorithms with a classical computer, it is a good practice to solve this problem with a quantum processer.In this paper, we introduce a new hybrid classic quantum algorithm using Grover's algorithm and symmetric functions to minimize small Disjoint Sum of Product (DSOP) and Sum of Product (SOP) for Boolean switching functions.Our method is based on graph partitions for arbitrary graphs to regular graphs, which can be solved by a Grover-based quantum searching algorithm we proposed.The Oracle for this quantum algorithm is built from Boolean symmetric functions and implemented with Lattice diagrams.It is shown analytically and verified by simulations on a quantum simulator that our methods can find all solutions to these problems.