Reviewerly/exHarmony huggingface dataset:
Development of a Virtual Wetland Ecological System Using VR 360° Panoramic Technology for Environmental Education

In this study, the VR 360° panoramic technology is used to develop a virtual wetland ecological system for applications in environmental education. The system provides a virtual wetland environment for autonomous exploration, where the user can conduct inquiry-based learning by cardboard VR to obtain essential information and concepts in ecological protection. It contains the learning contents of wetland ecology and creatures in Hsinchu coastal areas, northern Taiwan. To investigate the learning effectiveness of the VR system, we recruited 42 seventh graders as participants and divided them evenly into two groups. The experimental group used the virtual wetland ecological system for learning and the control group learned with conventional teaching materials. The differences of the experimental results indicated: (1) the experimental group showed a high degree of satisfaction after using the virtual wetland ecological system; (2) the learning achievement of the experimental group was significantly higher than that of the control group; (3) the learning motivation of the experimental group was significantly higher than that of the control group. The virtual wetland ecological system can enhance the learning effectiveness and learning motivation using low-cost cardboard glasses, so it is a suitable tool for promoting environmental education in remote areas.
Semi-supervised partial multi-label classification with low-rank and manifold constraints

Multi-Label Classification (MLC), processing data annotated with multiple labels simultaneously, is usually based on the assumption that the instances are all correctly labeled with the ground-truth labels. However this assumption is often not satisfied in real-world scenarios due to the high cost of manual labeling. In this paper, we consider a classification scenario in which only a part of the instances are redundantly annotated, and call this scenario a Semi-supervised Partial Multi-Label Classification (SPMLC). We propose a novel method Lion (semi-supervised partial multi-label classification with Low-rank and manIfOld coNstraints) to handle this particular scenario. Especially, the not exactly known ground-truth labels are regarded as latent labels, and the prediction is achieved by estimating the confidences of latent labels (i.e., the latent ground-truth confidences) in future data. Lion jointly learns the latent ground-truth confidences and the prediction model. The low-rank and manifold constraints are utilized to capture local label correlations and neighboring structure of instances, so as to accurately estimate the latent ground-truth confidences. Extensive experimental results validate the effectiveness of Lion.
A Video Abnormal Behavior Recognition Algorithm Based on Deep Learning

Aiming at the problems of low detection accuracy and slow processing speed of crowd abnormal behavior in public places, this paper proposes a method of crowd abnormal behavior recognition based on three-dimensional spatiotemporal convolution neural network. Three dimensional convolution neural network is used to model the temporal and spatial characteristics of the captured video based on deep learning. Multiple convolution cores with variable temporal depth are used, and the temporal transition layer is redesigned combined with depth separable convolution layer. The temporal information in the input signal is used to classify abnormal behavior and normal behavior, and then the abnormal behavior is identified. Experimental results show that the accuracy of the proposed method can reach more than 91%, and the accuracy and generalization performance of the improved model are further improved than the existing algorithms.
Structured Cooperative Reinforcement Learning With Time-Varying Composite Action Space

In recent years, reinforcement learning has achieved excellent results in low-dimensional static action spaces such as games and simple robotics. However, the action space is usually composite, composed of multiple sub-action with different functions, and time-varying for practical tasks. The existing sub-actions might be temporarily invalid due to the external environment, while unseen sub-actions can be added to the current system. To solve the robustness and transferability problems in time-varying composite action spaces, we propose a structured cooperative reinforcement learning algorithm based on the centralized critic and decentralized actor framework, called SCORE. We model the single-agent problem with composite action space as a fully cooperative partially observable stochastic game and further employ a graph attention network to capture the dependencies between heterogeneous sub-actions. To promote tighter cooperation between the decomposed heterogeneous agents, SCORE introduces a hierarchical variational autoencoder, which maps the heterogeneous sub-action space into a common latent action space. We also incorporate an implicit credit assignment structure into the SCORE to overcome the multi-agent credit assignment problem in the fully cooperative partially observable stochastic game. Performance experiments on the proof-of-concept task and precision agriculture task show that SCORE has significant advantages in robustness and transferability for time-varying composite action space.
Landscape of biomedical informatics standards and terminologies for clinical sleep medicine research: A systematic review

A systematic literature review was conducted to understand the current landscape of standards and terminologies used in clinical sleep medicine. Literature search on PubMed, EMBASE, Medline and Web of Science was performed in March 2021 using terms related to sleep, terminologies, standards, harmonization, semantics, ontology, and electronic health records (EHR). Systematic review was carried out according to PRISMA. Among 128 included studies, 35 were eligible for review. Articles were broadly classified into six topics: standard terminology efforts, reporting standards, databases and resources, data integration efforts, EHR abstraction and standards for automated sleep scoring. This review highlights the progress and challenges related to establishing computable terminologies in sleep medicine, and identifies gaps, limitations and research opportunities related to data integration that could improve adoption of clinical research informatics in this field. There is a need for the systematic adoption of standardized terminologies in all areas of sleep medicine. Existing data aggregation resources could be leveraged to support the development of an integrated infrastructure and subsequent deployment in EHR systems within sleep centers. Ultimately, the adoption of standardized practices for documenting sleep disorders and related traits facilitates data sharing, thus accelerating discovery and clinical translation of informatics approaches applied to sleep medicine.
Based on the improved YOLOV3 small target detection algorithm

Target detection is one of the core problems in the field of machine vision. However, the different appearance, shape, and size of objects and the influence of interference factors such as illumination and occlusion during the imaging process pose a considerable challenge to the task of target detection. This article introduces YOLOV3 network structure and related research, proposes a small target detection algorithm based on improved YOLOV3, describes the flow of YOLOV3 model to achieve small target detection algorithm, analyzes the experiment and results, and summarizes the problems in the implementation process and shortcomings, provide a reference for the continuous improvement of small target detection in the future.
Amelia—A new security protocol for protection against false links

In recent months, the demand for services provided on the Internet (scientific conferences, training, webinars) has increased. As this activity grew, so did cybercrime. Social networks share invitations to various training and webinars. We also receive similar announcements via e-mail. It is not difficult to accidentally click on the attached link. The submitted URL may contain a script. The script may infect our device or steal our login details. We have developed a new security protocol — Amelia, to protect against responding to a false invitation to a web event. Our protocol makes it possible to check if the link sent is valid. Also, it enables the generation of unique user identifiers. Amelia protocol provides users’ verification and distribution of symmetric session keys. We conducted a study of our protocol. We checked its vulnerability to Intruder attacks. The obtained results are promising. We did not find an attack on this protocol.
Rural Uncommoning

Shared use of small-scale natural commons is vital to the livelihoods of billions of rural inhabitants, particularly women, and advocates propose that local telecommunications systems that are oriented by the commons can close rural connectivity gaps. This article extends insights about women's exclusion from such Community Networks (CNs) by considering ‘commoning’, or practices that produce, reproduce and use the commons and create communality. I generated data in interviews and observations of rural CNs in seven countries in the Global South and in multi-sited ethnography of international advocacy for CNs. Male biases in technoculture and rural governance limit women's participation in CNs, and women adopt different approaches to performing their communal identity while using technology. This situation contributes to detaching CNs from relations that are produced in women's commoning. It also illustrates processes that co-opt the commons in rural technology endeavours and the diverse ways commoners express their subjectivities in response.
ScaleG: A Distributed Disk-based System for Vertex-centric Graph Processing

Designing distributed graph systems has drawn a lot of research interests due to the strong expressiveness of the graph model and rapidly increasing graph volume. Most of them require the graph data and all intermediate messages to reside in main memory, which may sacrifice the scalability. Even though several disk-based systems have been studied to remedy such issue, several challenges still exist in achieving both high computational efficiency and low network communication under the limitation of memory usage. In this paper, we design a novel disk-based distributed graph system, called ScaleG. The system provides a series of user-friendly programming interfaces. Unlike previous systems, the programmer in ScaleG does not need to concern any logic regarding the communication between vertices like sending messages and combining messages. In addition to a simple and clear programming model, we propose several techniques to reduce both disk I/Os in each machine and message I/Os via the network. We manage all messages in memory and bound all messages by the number of vertices. We also carefully design the data structure to support partial computation and automatic vertex activation. We conduct extensive experiments on six big graphs to show the high efficiency of our system.
Gaze Gesture Recognition by Graph Convolutional Networks

Gaze gestures are extensively used in the interactions with agents/computers/robots. Either remote eye tracking devices or head-mounted devices (HMDs) have the advantage of hands-free during the interaction. Previous studies have demonstrated the success of applying machine learning techniques for gaze gesture recognition. More recently, graph neural networks (GNNs) have shown great potential applications in several research areas such as image classification, action recognition, and text classification. However, GNNs are less applied in eye tracking researches. In this work, we propose a graph convolutional network (GCN)–based model for gaze gesture recognition. We train and evaluate the GCN model on the HideMyGaze! dataset. The results show that the accuracy, precision, and recall of the GCN model are 97.62%, 97.18%, and 98.46%, respectively, which are higher than the other compared conventional machine learning algorithms, the artificial neural network (ANN) and the convolutional neural network (CNN).
Efficient Boolean Methods for Preparing Uniform Quantum States

As each quantum algorithm requires a specific initial quantum state, quantum state preparation is an important task in quantum computing. The preparation of quantum states is performed by a quantum circuit consisting of controlled-NOT (CNOT) and single-qubit gates. Known algorithms to prepare arbitrary <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -qubit quantum states create quantum circuits in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(2^n)$</tex-math></inline-formula> runtime and use <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(2^n)$</tex-math></inline-formula> CNOTs, which are more expensive than single-qubit gates in NISQ architectures. To reduce runtime and the number of CNOTs, we simplify the problem by considering an important family of quantum states, which are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uniform quantum states</i> (UQSs). We map UQSs to Boolean functions and propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UQS preparation</i> (UQSP) method. Preparing UQSs using Boolean functions allows us to utilize different representations of Boolean functions. We utilize decision diagrams to reduce runtime and enable preparation for a larger number of qubits where the state-of-the-art methods are not applicable. To further reduce the number of CNOTs, we utilize variable reordering and functional dependencies among the variables. Our state preparation method requires an exponential number of CNOTs in the worst case but it reduces CNOTs significantly for practical benchmarks. Moreover, our method generates an exact representation of quantum states without using free-qubits. We compare our algorithm with QisKit. The comparison shows that our UQSP method is capable to reduce the average number of CNOTs by 75.31% for the practical benchmarks. The runtime is almost reduced by a factor of 2.
Exponentially Decaying Explosion in Fireworks Algorithm

Fireworks algorithm (FWA) as an efficient and robust swarm intelligence algorithm can successfully deal with complex multi-modal problems. In this paper, a novel new explosion operator called exponentially decaying explosion is proposed to enhance the local search ability of fireworks algorithm based on the principle of utilizing more information. The proposed method takes the idea of guided mutation a step further and dismantled the explosion process into an exponentially decaying series of guided explosion. The FWA variant with this explosion operator is called exponentially decaying fireworks algorithm (EDFWA). Theoretical analysis proved the superiority of EDFWA in terms of information utilization ratio compared with GFWA. Experimental results showed that EDFWA not only surpassed LoTFWA in low dimensional situations, but also exhibited powerful searching capability on 1000 dimensional high dimensional problems compared with multiple representative optimizers specially designed for large-scale problems.
Latent Space Inpainting for Loss-Resilient Collaborative Object Detection

Edge devices, such as cameras and mobile units, are increasingly capable of performing sophisticated computation in addition to their traditional roles in sensing and communicating signals. The focus of this paper is on collaborative object detection, where deep features computed on the edge device from input images are transmitted to the cloud for further processing. We consider the impact of packet loss on the transmitted features and examine several ways for recovering the missing data. In particular, through theory and experiments, we show that methods for image inpainting based on partial differential equations work well for the recovery of missing features in the latent space. The obtained results represent the new state of the art for missing data recovery in collaborative object detection.
Design and Simulation of Medical Assistance Robot for combating COVID-19

The number of applications for a humanoid robot is at an all-time high, and one such area where humanoid robots could be used is the hospital sector. The Covid-19 pandemic which started in 2019 has accelerated the research in this field. This paper proposes a novel design and simulation of a semi-humanoid robot which could be used in a hospital environment. The robot includes a 5 degrees of freedom robotic arm which could be used for pick and place functionality. The pick and place functionality of the robot is viewed in MoveIt. The robot is simulated in a hospital environment using gazebo to view how the robot interacts in it.
Non-malleable Codes for Bounded Parallel-Time Tampering

Non-malleable codes allow one to encode data in such a way that once a codeword is being tampered with, the modified codeword is either an encoding of the original message, or a completely unrelated one. Since the introduction of this notion by Dziembowski, Pietrzak, and Wichs (ICS ’10 and J. ACM ’18), there has been a large body of works realizing such coding schemes secure against various classes of tampering functions. It is well known that there is no efficient non-malleable code secure against all polynomial size tampering functions. Nevertheless, no code which is non-malleable for bounded polynomial size attackers is known and obtaining such a code has been a major open problem. We present the first construction of a non-malleable code secure against all polynomial size tampering functions that have bounded parallel time. This is an even larger class than all bounded polynomial size functions. In particular, this class includes all functions in non-uniform $$\mathbf {NC}$$ (and much more). Our construction is in the plain model (i.e., no trusted setup) and relies on several cryptographic assumptions such as keyless hash functions, time-lock puzzles, as well as other standard assumptions. Additionally, our construction has several appealing properties: the complexity of encoding is independent of the class of tampering functions and we can obtain (sub-)exponentially small error.
Design for Immersive Experience: Role of Spatial Audio in Extended Reality Applications

The incredible growth of extended reality (XR) applications will be leading us to a world beyond our imaginations in the coming decades. Extended reality is an umbrella term that encompasses different categories of immersive technologies like virtual reality (VR), augmented reality (AR), and mixed reality (MR). From the traditional applications like entertainment and training, XR has been spreading its wings into a large number of applications in health care, aerospace, product design and prototyping, e-commerce, workspace productivity, architecture, and building industries. Immersibility of the virtual reality scene into the physical world will be crucial for its acceptance by mainstream industries and future development. In addition to the virtual scene's visual perception, spatial audio is a key feature in designing truly immersive XR. Hearing is the fastest sense of humans, which makes virtual auditory display (VAD) an ineluctable part of any XR application. In this work, the importance of three-dimensional spatial audio in XR applications is explored in the user perspective approach. User experience (UX) is improved to a large extent when the applications make use of spatial audio compared to direction-less hearing experience. Spatial sound has a crucial role in giving information regarding actions in the background and beyond the field of view (FOV), and thus in making proper three-dimensional realism. However, designing user-dependent virtual audio reality is challenging because of its parametric dependence on human anthropometric features. This work also suggests the possibilities of utilizing computer-aided designing (CAD) and computer-aided engineering (CAE) tools in producing personalized virtual audio reality. While the immersive extended reality experience evolves as the next frontier in user experience designing, a sophisticated 3D audio experience will be there at the heart of it.
An End-to-End Traffic Visibility Regression Algorithm

Traffic visibility detection plays a vital role in intelligent transportation, autonomous driving, safe driving, etc. Convolutional neural networks (CNNs) based regression and classification algorithms have been shown competitive performance in many applications, but little attention has been paid to traffic visibility identification.In this paper, we propose a trainable end-to-end system called traffic visibility regression network (TVRNet).TVRNet takes a road image as input and outputs its visibility value.TVRNet adopts CNNs based deep architecture, uses appropriate filters to extract fog density-related features, and exploits the parallel convolution for multi-scale mapping.Later, a new type of non-linear activation function called Modified_sigmoid function is used.We synthesize labeled visibility datasets comprised of multi-scene and single-scene based on the actual road sense to train the visibility regression network.Extensive experiments and comparisons with other popular algorithms are performed to verify our method in road visibility estimation.
Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks

In typical point cloud delivery, a sender uses octree-based and graph-based digital video compression to send three-dimensional (3D) points and color attributes. However, the digital-based schemes have an issue called the cliff effect, where the 3D reconstruction quality will be a step function in terms of wireless channel quality. To prevent the cliff effect subject to channel quality fluctuation, we have proposed a wireless point cloud delivery called HoloCast inspired by soft delivery. Although the HoloCast realizes graceful quality improvement according to instantaneous wireless channel quality, it requires large communication overheads. In this paper, we propose a novel scheme for soft point cloud delivery to simultaneously realize better 3D reconstruction quality and lower communication overheads. The proposed scheme introduces an end-to-end deep learning framework based on graph neural network (GNN) to reconstruct high-quality point clouds from its distorted observation under wireless fading channels. We demonstrate that the proposed GNN-based scheme can reconstruct a clean 3D point cloud with low overheads by removing fading and noise effects.