Reviewerly/exHarmony huggingface dataset:
Conducting Research in Virtual Reality: Experiences of Interviewing Inside the Metaverse

Recent technological developments in the “Metaverse” have made the immersive virtual reality an imminent reality. Most existing metaverse research focuses on the use case and business potential of the metaverse and virtual reality technologies, but little research has been conducted to understand the experiences of early adopters and the motivators and inhibitors which affect their adoption and continued use. We set out to research the use cases of the metaverse among early adopters. A qualitative approach was employed for research, given its strengths in exploring unforeseen themes and allowing early adopters to raise issues which matter most to them. However, we adopted a novel, albeit entirely appropriate, research methodology which offers possible emulation: our interviews were conducted within a popular metaverse application, RecRoom, with semi-structured interviews taking place between avatars. We discovered some interesting differences between interviews in virtual reality, as compared to face-to-face and online interviews, due to the unique affordances of the metaverse. We discuss some of our experiences with interviewing in virtual reality. These include positive experiences which build on the affordances of the virtual reality space; apart from the anonymity (not one’s real face), interview participants experienced a greater social presence afforded by gestures indicating emphasis and emotional expressions. However, we also found some potential problems, such as harassment of the researcher and the difficulties with multi-tasking. Alongside capturing metaverse use cases by early adopters at this juncture of metaverse development, our experiences provide some insights and suggestions which might be useful for future metaverse researchers who intend conducting their research with participants while immersed in a virtual reality space.
Promoting sustainable and personalised travel behaviours while preserving data privacy

Cities worldwide have agreed on ambitious goals regarding carbon neutrality; thus, smart cities face challenges regarding active and shared mobility due to public transportation's low attractiveness and lack of real-time multimodal information. These issues have led to a lack of data on the community's mobility choices, traffic commuters' carbon footprint and corresponding low motivation to change habits. Besides, many consumers are reluctant to use some software tools due to the lack of data privacy guarantee. This paper presents a methodology developed in the FranchetAI project that addresses these issues by providing distributed privacy-preserving machine learning models that identify travel behaviour patterns and respective GHG emissions to recommend alternative options. Also, the paper presents the developed FranchetAI mobile prototype.
Confidential Certified Notification Protocol using Rejectable Soulbound Tokens and Identity-Based Cryptography

Blockchain technology can be used in several e-commerce applications, like the creation of certified notifications. This kind of service allows a user, which acts as the sender, to prove that he has sent some data to a receiver. With the use of blockchain technology, we can take advantage of the fact that it offers a transparent and decentralized way to certify the authenticity and integrity of notifications. In this proposal, we combine the use of Soulbound tokens (SBTs), a special kind of Non-Fungible Tokens (NFTs) whose owners cannot transfer or trade to other users, with an improvement of the ERC-721 standard to include the possibility that the receiver could reject a token, allowing the selective reception of SBTs. Representing the certified notifications as Rejectable SBTs (RejSBT), we can track when the sender sends a notification, and when the receiver accepts it, providing both non-repudiation of origin (NRO) and reception (NRR) proofs. The notification process require only two steps. Once the receiver has accepted the RejSBT that represents the ownership of a notification, we will use Identity-Based Cryptography to let the owner decrypt the confidential information represented by the RejSBT.
Radio frequency fingerprinting identification using semi-supervised learning with meta labels

Radio frequency fingerprinting (RFF) is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things (IoT) systems. Deep learning (DL) is a critical enabler of RFF identification by leveraging the hardware-level features. However, traditional supervised learning methods require huge labeled training samples. Therefore, how to establish a highperformance supervised learning model with few labels under practical application is still challenging. To address this issue, we in this paper propose a novel RFF semi-supervised learning (RFFSSL) model which can obtain a better performance with few meta labels. Specifically, the proposed RFFSSL model is constituted by a teacher-student network, in which the student network learns from the pseudo label predicted by the teacher. Then, the output of the student model will be exploited to improve the performance of teacher among the labeled data. Furthermore, a comprehensive evaluation on the accuracy is conducted. We derive about 50 GB real long-term evolution (LTE) mobile phone's raw signal datasets, which is used to evaluate various models. Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97% experimental testing accuracy over a noisy environment only with 10% labeled samples when training samples equal to 2700.
Semantic Segmentation Based on Multiple Granularity Learning

Accurate and robust coarse semantic segmentation plays a key role in the pursuit of autonomous driving. We present an algorithm that regularizes the representation space of Semantic Segmentation by Multiple Granularity Learning (SSMGL). This approach explores multiple levels of semantic knowledge in an unified framework, where the fine-grained semantic information can be either labeled or unlabeled. In our experiments, we find that SSMGL can achieve better results (1) on both on-road and off-road benchmarks, (2) under different segmentation architectures, or (3) with different backbones. The method is plug-and-play, not specialized for autonomous driving applications, and can be easily extended to any other segmentation scenario. Moreover, our SSMGL approach does not increase the computational overhead in the inference stage.
Aerial Manipulator Systems Gain a New Skill: Achieve Contact-based Landing on a Mobile Platform

This paper studies a novel application of an aerial manipulator (AM)-the contact-based landing on a mobile platform. An AM is inherently unstable, under-actuated, and usually loses some DOFs while contacting environments. Meanwhile, the AM's flight state is susceptible to uncertain movements of the mobile platform, such as acceleration, sudden stopping, and reversing. To accomplish the contact-based landing mission, a robust controller is first designed to maintain a steady contact-based flight. Then a hierarchical control framework is applied, integrating the controllers in free-flight and restricted-flight stages. An AM and a mobile platform are developed for contact-based flight experiments. The proposed scheme is reliable and has good repeatability in experiments. To the best of our knowledge, this is the first time an AM has been implemented to conduct a contact-based landing, which is also an innovative landing approach for rotorcraft UAVs.
AdaptSeqVPR: An Adaptive Sequence-Based Visual Place Recognition Pipeline

Visual Place Recognition (VPR) is essential for autonomous robots and unmanned vehicles, as an accurate identification of visited places can trigger a loop closure to optimize the built map. The most prevalent methods tackle VPR as a single-frame retrieval task, which uses a CNN-based encoder to describe and compare each individual frame. These methods, however, overlook the temporal information between frames. Other methods improve this by searching the database with consecutive frames, which can greatly reduce false positives. Nevertheless, current sequence-based methods typically assume the consecutive image frames to be captured at an approximately constant speed, which is not always the case in practice. Therefore, we propose an adaptive sequence search strategy (AdaptSeq), which can dynamically alter the step size of adjacent frames in the retrieved sequence trajectory. Furthermore, to address false positive retrieval of input frames, we propose a CNN-based discriminator named DDsNet. It can determine whether the top retrieved candidates are true positives based on the learned statistics rather than an artificial threshold. Overall, we construct a novel sequence-based VPR pipeline named AdaptSeqVPR. It utilizes a CNN-based encoder for frame descriptions, and encompasses AdaptSeq and DDsNet for sequence matching. The experimental results indicate that our AdaptSeqVPR exhibits superior performance compared to the baseline SeqSLAM and SeqVLAD. Notably, our method can robustly handle the sequence-based VPR for vehicles traveling at non-uniform speeds in changing environments.
Can Quadruped Guide Robots be Used as Guide Dogs?

Quadruped robots have the potential to guide blind and low vision (BLV) people due to their highly flexible locomotion and emotional value provided by their bionic forms. However, the development of quadruped guide robots rarely involves BLV users' participatory designs and evaluations. In this paper, we conducted two empirical experiments both in indoor controlled and outdoor field scenarios, exploring the benefits and drawbacks of quadruped guide robots. The results show that the nowadays commercial quadruped robots exposed significant disadvantages in usability and trust compared with wheeled robots. It is concluded that the moving gait and walking noise of quadruped robots would limit the guiding effectiveness to a certain extent, and the empathetic effect of its bionic form for BLV users could not be fully reflected. Based on the findings of wheeled robots and quadruped robots' advantages, we discuss the design implications for the future guide robot design for BLV users. This paper reports the first empirical experiment about quadruped guide robots with BLV users and preliminary explores their potential improvement space in substituting guide dogs, which can inspire the further specialized design of quadruped guide robots.
Polynomial-Based Online Planning for Autonomous Drone Racing in Dynamic Environments

In recent years, there is a noteworthy advance-ment in autonomous drone racing. However, the primary focus is on attaining execution times, while scant attention is given to the challenges of dynamic environments. The high-speed nature of racing scenarios, coupled with the potential for unforeseeable environmental alterations, present stringent requirements for online replanning and its timeliness. For racing in dynamic environments, we propose an online replanning framework with an efficient polynomial trajectory representation. We trade off between aggressive speed and flexible obstacle avoidance based on an optimization approach. Additionally, to ensure safety and precision when crossing intermediate racing waypoints, we formulate the demand as hard constraints during planning. For dynamic obstacles, parallel multi-topology trajectory planning is designed based on engineering considerations to prevent racing time loss due to local optimums. The framework is integrated into a quadrotor system and successfully demonstrated at the DJI Robomaster Intelligent UAV Championship, where it successfully complete the racing track and placed first, finishing in less than half the time of the second-place <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://pro-robomasters-hz-n5i3.oss-cn-hangzhou.aliyuncs.com/sass/event-list.html.
Encoding Molecular Docking for Quantum Computers

Molecular docking is important in drug discovery but is burdensome for classical computers. Here, we introduce Grid Point Matching (GPM) and Feature Atom Matching (FAM) to accelerate pose sampling in molecular docking by encoding the problem into quadratic unconstrained binary optimization (QUBO) models so that it could be solved by quantum computers like the coherent Ising machine (CIM). As a result, GPM shows a sampling power close to that of Glide SP, a method performing an extensive search. Moreover, it is estimated to be 1000 times faster on the CIM than on classical computers. Our methods could boost virtual drug screening of small molecules and peptides in future.
Development Of Electronic Teaching Materials Writing Explanatory Texts Assisted By Quick Response Code Class XI Superior SMAN

This study aims to determine (1) the process of developing electronic teaching materials for writing explanatory texts assisted by quick response codes, (2) the form of teaching materials, (3) the feasibility of teaching materials, (4) the effectiveness of teaching materials. The results showed t
Text Sentiment Analysis Based on a Dynamic Pruning Capsule Network

Dynamic pruning of capsules and a variable weight BIGRU model (DP-CAPS-VW-BIGRU) are proposed in this paper to address the problem of unnecessary capsule connections that cause noise and the insufficient utilization of bidirectional GRU network information. The input text is vectorized using the ERNIE pretraining model, and the convolutional layers are used to extract context information from the text. The main capsule layer uses vector output instead of the scalar output of the convolutional network to preserve instantiation parameters, and the convolutional capsule layer further extracts deeper feature information. To reduce the impact of noise caused by unnecessary capsule connections on model performance, dynamic pruning of the capsule network is proposed in this paper. At the same time, a variable weight bidirectional GRU network is proposed to improve the utilization of forward and backwards information. Experimental results show that the proposed model can effectively improve the performance of text sentiment analysis tasks.
Language guided 3D object detection in point clouds for MEP scenes

Abstract In recent years, contrastive language‐image pre‐training (CLIP) has gained popularity for processing 2D data. However, the application of cross‐modal transferable learning to 3D data remains a relatively unexplored area. In addition, high‐quality, labelled point cloud data for Mechanical, Electrical, and Plumbing (MEP) scenarios are in short supply. To address this issue, the authors introduce a novel object detection system that employs 3D point clouds and 2D camera images, as well as text descriptions as input, using image‐text matching knowledge to guide dense detection models for 3D point clouds in MEP environments. Specifically, the authors put forth the proposition of a language‐guided point cloud modelling (PCM) module, which leverages the shared image weights inherent in the CLIP backbone. This is done with the aim of generating pertinent category information for the target, thereby augmenting the efficacy of 3D point cloud target detection. After sufficient experiments, the proposed point cloud detection system with the PCM module is proven to have a comparable performance with current state‐of‐the‐art networks. The approach has 5.64% and 2.9% improvement in KITTI and SUN‐RGBD, respectively. In addition, the same good detection results are obtained in their proposed MEP scene dataset.
Design of an image-based BRDF measurement method using a catadioptric multispectral capture and a real-time Lambert calibration

By utilizing a catadioptric system and a calibration Lambertian sample, a compact measurement method of bidirectional reflectance distribution function (BRDF) has been proposed for rapid and accurate measurement. With the help of an ellipsoidal dome mirror, a hyperboloid mirror, and a high-resolution camera, spatial reflectance distributions from reflected directions with a large field of view (FOV) can be obtained. The built-in Lambertian standard allows for real-time calibration to account for fluctuations in the illumination spectrum, effectively reducing the measurement drift and achieving a high accuracy. Moreover, a multispectral camera captures images at 8 spectral bands for accurate spectral color reconstruction from different directions. To verify the method, a prototype capable of fast, high-resolution measurements with a large FOV has been developed for characterizing the scattering properties of objects. It achieves a measured angular range up to 160°. Multispectral BRDF data for each sample can be obtained within 5 minutes with an angular resolution of less than 0.6°. Eight ceramic samples with different colors were selected for the verification of measurement accuracy, and their mean relative bias of BRDF measurement was found to be as low as 2.5%.
Identifying Valuable Knowledge Topics in Innovation Communities Using Innovation-LDA

&lt;p&gt;Researchers and practitioners have recognized that user-generated content in the innovation community plays an important role. However, it is challenging to automatically identify valuable knowledge from these unstructured texts. Thus, in this study, we propose an efficient model for extracting innovation-oriented topics and, simultaneously, for assigning discovered topics to each post in the online innovation community. Specifically, we introduce a variant of the latent Dirichlet allocation (LDA) topic model, called the Innovation-LDA model, which comprehensively considers users&amp;rsquo; interests (reflected by pageviews and replies) and the structure of threads (e.g., header or body) to generate the valuable topics. We access the quality of discovered information through statistical fit as well as substantive fit. Based on our experimental results, we can conclude that our proposed method exhibits better performance than that of the contrasted method and can locate more meaningful innovation topics; that is, our innovation-LDA model is capable of not only identifying more rigorous topics for each thread by utilizing the text structure but is also capable of learning more semantic and coherent themes from user interests. This investigation expands topic identification research by providing both a new theoretical perspective and useful guidance for enterprises in product innovation.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Pengembangan Media Pembelajaran Interaktif Menggunakan Powerpoint Berbasis Animasi Pada Materi Sel Di Kelas XI SMAN 8 Medan

This study aims to determine how to develop interactive learning media using powerpoint-based animation in cell biology lessons and determine the feasibility of the media developed based on the results of validation by the validator. This study uses the ADDIE model of research and development (R&amp;D) (Analysis, Design, Development, Implementation, and Evaluation). The sample in this study were students of class XI MIPA 1, 2 and 4 SMAN8 Medan. The trial was carried out in small groups to 15 students and large group trials to 50 students through a response questionnaire sheet. Based on the results of media expert validation, the overall aspect obtained an average score of 86.2% with a very decent category. The results of material expert validation obtained an average score of 75% with a decent category. The results of the validation of linguists obtained an average score of 93.6% with a very decent category. The results of the teacher's assessment response obtained an average score of 90.625% with a very decent category. The results of the student assessment responses in the small group trial obtained an average score of 94% and in the large group trial an average score of 87% was obtained in the very feasible category.
Multimedia Interaktif Sebagai Sarana Pembelajaran Dalam Peningkatan Prestasi Siswa Di Sekolah Pada Masa Pandemi

Learning media is anything that helps humans in order to understand or learn some theory or material that is useful in order to train abilities or skills. In addition, multimedia is a useful tool for selecting individuals based on the material displayed. Interactive multimedia is designed to have interactive characteristics and is easy to use to help people interact with media content. Interactive multimedia can add meaning to the process because it allows users to interact with the material. Interactive multimedia can also increase student interest in the learning process during a pandemic. This study aims to analyze interactive multimedia in improving students during a pandemic.
Penggunaan E-Learning Sebagai Media Interaktif Dalam Pembelajaran Dalam Jaringan (Daring)

Technology was an important role in the world of education, especially in the teaching and learning process where during the spread of the Covid-19 virus, all activities were transferred to remote or online (online) including learning carried out remotely through various technological media, one of the technologies used This is E-Learning, where e-learning is considered efficient in collecting assignments and providing learning materials, but even though online learning is considered to have advantages and looks fun because it can be done from home, it is not easy to do. This study aims to find out how important the use of E-learning is in the teaching-learning process during the Covid-19 period.
Incorporating Expert Knowledge into Artificial Intelligence Algorithms for Geological Exploration

Preview this conference paper: Incorporating Expert Knowledge into Artificial Intelligence Algorithms for Geological Exploration, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/2214-4609/2023/third-eage-workshop-on-mineral-exploration-in-latin-america-the-role-of-mining-in-the-energy-transition/EAGE_ExtendedAbstractFNV-14-26-Navarro-Felipe-1.gif
DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference

To accelerate the inference of deep neural networks (DNNs), quantization with low-bitwidth numbers is actively researched. A prominent challenge is to quantize the DNN models into low-bitwidth numbers without significant accuracy degradation, especially at very low bitwidths (< 8 bits). This work targets an adaptive data representation with variablelength encoding called DyBit. DyBit can dynamically adjust the precision and range of separate bit-fields to be adapted to the DNN weights/activations distribution. We also propose a hardware-aware quantization framework with a mixed-precision accelerator to trade-off the inference accuracy and speedup. Experimental results demonstrate that the ImageNet inference accuracy via DyBit is 1.97% higher than the state-of-the-art at 4-bit quantization, and the proposed framework can achieve up to 8.1× speedup compared with the original ResNet-50 model.
Demonstrating efficiency through data connectivity between asset management systems and IM

The CoDEC (Connected Data for Effective Collaboration) project has developed standardised tools – an Asset Data Dictionary and associated Ontology – to support data integration between Building Information Modelling (BIM) and Asset Management Systems (AMS). CoDEC has also developed an OpenAPI (Application Protocol Interface) to enable automatic data connection between the two systems. Having developed the standardised process, the potential benefits offered for connecting and linking data have been shown in three pilot projects to demonstrate (1) how tunnel monitoring data can enrich BIM models of tunnels (2) how data from bridge sensors can be linked and visualised and (3) how highway data from a BIM model can be linked to GIS-based AMS for the operational phase. This paper describes the project outcomes.