Reviewerly/exHarmony huggingface dataset:
A Live Speech-Driven Avatar-Mediated Three-Party Telepresence System: Design and Evaluation

Abstract In this article, we present a live speech-driven, avatar-mediated, three-party telepresence system, through which three distant users, embodied as avatars in a shared 3D virtual world, can perform natural three-party telepresence that does not require tracking devices. Based on live speech input from three users, this system can real-time generate the corresponding conversational motions of all the avatars, including head motion, eye motion, lip movement, torso motion, and hand gesture. All motions are generated automatically at each user side based on live speech input, and a cloud server is utilized to transmit and synchronize motion and speech among different users. We conduct a formal user study to evaluate the usability and effectiveness of the system by comparing it with a well-known online virtual world, Second Life, and a widely-used online teleconferencing system, Skype. The user study results indicate our system can provide a measurably better telepresence user experience than the two widely-used methods.
Healthcare Internet of Things (HIoT) data security enhancement using blockchain technology

In recent years, the Internet of Things (IoT) has attracted more attention after the integration of IoT devices with the cloud for data management. IoT is used for sharing data in healthcare services. However, security and privacy vulnerabilities still exist in data transfers to and from cloud environments. Due to memory limitations, it is difficult to implement security protocols in IoT devices. So an intermediate secure node is established to enhance data security and privacy while handling healthcare data. Blockchain is a renowned technology that is adopted in various data management and security applications, but its potential is not unleashed effectively in healthcare data management. Therefore in this research work, firstly, the data is pre-processed and then the Ant colony optimization method is used to find the shortest path for efficient data delivery. And, a blockchain based data security approach for healthcare data management is presented. The main objective is to enhance security against healthcare data threats. In the IoTs fog layer, a public-permissioned blockchain security process with elliptical curve cryptography and digital signature is presented as a distributed ledger database that provides immutable security, transparency in transactions, and prevents data tampering. The performance of the proposed approach is verified through various parameters like certificate generation time and size, data retrieval time, and size for better validation.
ADFNet: Attention-based Fusion Network for Few-shot RGB-D Semantic Segmentation

research-article Share on ADFNet: Attention-based Fusion Network for Few-shot RGB-D Semantic Segmentation Authors: Chengkai Zhang School of Electronic Engineering, Beijing University of Posts and Telecommunications, China School of Electronic Engineering, Beijing University of Posts and Telecommunications, ChinaSearch about this author , Jichao Jiao School of Electronic Engineering, Beijing University of Posts and Telecommunications, China School of Electronic Engineering, Beijing University of Posts and Telecommunications, ChinaSearch about this author , Wei Xu China Research Institute of Radio Wave Propagation, China China Research Institute of Radio Wave Propagation, ChinaSearch about this author , Ning Li School of Electronic Engineering, Beijing University of Posts and Telecommunications, China School of Electronic Engineering, Beijing University of Posts and Telecommunications, ChinaSearch about this author , Min Pang School of Electronic Engineering, Beijing University of Posts and Telecommunications, China and China Research Institute of Radio Wave Propagation, China School of Electronic Engineering, Beijing University of Posts and Telecommunications, China and China Research Institute of Radio Wave Propagation, ChinaSearch about this author , Jianye Dong China Research Institute of Radio Wave Propagation, China China Research Institute of Radio Wave Propagation, ChinaSearch about this author Authors Info & Claims ICMLC 2022: 2022 14th International Conference on Machine Learning and Computing (ICMLC)February 2022 Pages 91–96https://doi.org/10.1145/3529836.3529864Online:21 June 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Improved noise-filtering algorithm for AdaBoost using the inter-and intra-class variability of imbalanced datasets

Boosting methods are known to increase performance outcomes by using multiple learners connected sequentially. In particular, Adaptive boosting (AdaBoost) has been widely used owing to its comparatively improved predictive results for hard-to-learn samples based on misclassification costs. Each weak learner minimizes the expected risk by assigning high misclassification costs to suspect samples. The performance of AdaBoost depends on the distribution of noise samples because the algorithm tends to overfit noisy samples. Various studies have been conducted to address the noise sensitivity issue. Noise-filtering methods used in AdaBoost remove samples defined as noise based on the degree of misclassification to prevent overfitting to noisy samples. However, if the difference in the classification difficulty between classes is considerable, it is easy for samples from classes that are difficult to classify to be defined as noise. This situation is common with imbalanced datasets and can adversely affect performance outcomes. To solve this problem, this study proposes a new noise detection algorithm for AdaBoost that considers differences in the classification difficulty of classes and the characteristics of iteratively recalculated sample weight distributions. Experimental results on ten imbalanced datasets with various degrees of imbalanced ratios demonstrate that the proposed method defines noisy samples properly and improves the overall performance of AdaBoost.
SRT: Shape Reconstruction Transformer for 3D Reconstruction of Point Cloud from 2D MRI

There has been some work on 3D reconstruction of organ shapes based on medical images in minimally invasive surgeries. They aim to help lift visualization limitations for procedures with poor visual environments. However, extant models are often based on deep convolutional neural networks and complex, hard-to-train generative adversarial networks; their problems about stability and real-time plague the further development of the technique. In this paper, we propose the Shape Reconstruction Transformer (SRT) based on the self-attentive mechanism and up-down-up generative structure to design fast and accurate 3D brain reconstruction models through fully connected layer networks only. Point clouds are used as the 3D representation of the model. Considering the specificity of the surgical scene, a single 2D image is limited as the input to the model. Qualitative demonstrations and quantitative experiments based on multiple metrics show the generative capability of the proposed model and demonstrate the advantages of the proposed model over other state-of-the-art models.
‘Unified Side-Channel Attack - Model’ (USCA-M): An Extension with Biometrics Side-Channel Type

This paper presents the ‘Unified Side-Channel Attack Model’ (USCA-M) with an additional side-channel type of ‘Biometrics.’ The original published paper ‘Introducing the Unified Side-Channel Attack–Model (USCA-M)’ [1] was presented and published through the International Symposium on Digital Forensics and Security (ISDFS) conference in 2020 [1]. The USCA-M model was initially compiled by research on side-channel attacks (SCAs) from published journal articles and conference papers between 2015 and 2020. The study found that SCAs can be categorized into three main areas: SCA types, SCA methods, and SCA techniques. The USCA-M provides a unified model to categorize present and future SCA vulnerabilities and exploit techniques found. Its future use would provide a reference point for organizations to identify and place a found SCA within a standard or unified categorization. It can also be used to granulate SCA techniques into identifiable components to assist in defending SCAs, such as code pattern recognition and intrusion detection systems (IDS).
Incorporating geometry knowledge into an incremental learning structure for few-shot intent recognition

Few-shot incremental intent recognition aims at continually identifying users’ intents from utterances with limited labeled novel data. To mitigate the effect of catastrophic forgetting inherent in incremental learning, existing methods generally resort to storing informative base exemplars as the model memory that are further replied to when learning novel classes. However, merely preserving these base exemplars and ignoring the relationship between them can easily trigger some concerns in the incremental training process. First, in each iteration, the participation of novel samples further updates the embedding space to make the novel labels well separated, which instead poisons the learned labels, reducing their separability. Second, the overfitting risk is further amplified in incremental learning, causing models to be overconfident on seen base classes but hardly generalized to unseen novel classes. In view of these problems, a geometry-aware learning (GAL) model is proposed to address few-shot incremental intent recognition and bridge the two research gaps mentioned above. Specifically, in our proposal, for the embedding shifting issue, GAL constructs a geometric structure of selected exemplars based on their spatial distribution in the embedding space, which is treated as a strong constraint in the following training. For the overfitting issue, GAL introduces an episodic-based pretraining strategy as well as a multisource contrastive-based loss to enhance the lack of supervised signals in the classification of data-scarce novel labels. Experimental results on a public dataset OOS (CLINC-150) verify the effectiveness of our proposal by beating the state-of-the-art baselines. Specifically, our model outperforms the best baseline by 1.34% and 0.39% on the 5-way 1-shot meta task for the base and novel classes, respectively. It also outperforms the best baseline by 1.89% and 0.80% on the 5-way 5-shot meta task for the base and novel classes, respectively. Furthermore, cross-domain experiments between two datasets (CLINC-150 and ATIS) reflect that GAL has better generalization ability across different domains. The method is superior to traditional intent recognition models in the application scenario where the model is required to accurately distinguish both new and trained categories under the low-data dilemma. Analogously, this common application scenario can also provide a broad developing direction for the dialog recommender system, which helps it recommend continuously and accurately with only a few labeled samples.
Revisiting initial sets in abstract argumentation

We revisit the notion of initial sets by Xu and Cayrol (In Proceedings of the 1st Chinese Conference on Logic and Argumentation (CLAR’16) 2016), i. e., non-empty minimal admissible sets in abstract argumentation frameworks. Initial sets are a simple concept for analysing conflicts in an abstract argumentation framework and to explain why certain arguments can be accepted. We contribute with new insights on the structure of initial sets and devise a simple non-deterministic construction principle for any admissible set, based on iterative selection of initial sets of the original framework and its induced reducts. In particular, we characterise many existing admissibility-based semantics via this construction principle, thus providing a constructive explanation on the structure of extensions. We also investigate certain problems related to initial sets with respect to their computational complexity.
Research on Improvement of Substation Monitoring Capability Based on AI Algorithm

Abstract The limitations of video surveillance in traditional substations have a serious impact on the intelligent monitoring of substations. In order to improve the level of monitoring intelligence, this paper conducts research on the improvement of substation monitoring capabilities based on AI algorithms. First, it analyzes the low pixels of video surveillance and the problems existing in the monitoring system, and then provides hardware support through ARM-based CPU and deep learning algorithms. Improve the recognition ability of pictures, and then solve the problem of low pixels and reduce the pressure of data processing. Finally, the effectiveness of the method in this paper is verified by the actual operation field test.
Transfer Learning based Classification of Plasmodium Falciparum Parasitic Blood Smear Images

A transfer learning-based convolutional neural network (CNN) architecture is used in the current study to differentiate parasitic malaria cell images from the healthy ones and localize the parasites in infected images using global average pooling(GAP) and heat map. Malaria is a serious malady that can even lead to death in the absence of timely diagnosis. With the use of computerized malaria diagnosis, the suggested solution tackles the problem of timely detection and eases the strain on health care. Three transfer learning-based neural network architectures are studied and compared in terms of their accuracy, precision, sensitivity and specificity. The optimal model with less number of false negatives was then interfaced with a newly developed web service which can be easily accessed and used by common people. The studied models were trained and evaluated on 27,558 single cell images, yielding a maximum accuracy of 96.88%, with 97.35% sensitivity, 96.41% specificity, 96.89% F1-Score, and 96.44% precision.
Brain MR Images Super-Resolution with the Consistent Features

Magnetic resonance imaging plays an important role in auxiliary diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it’s challenging to acquire high-resolution (HR) magnetic resonance (MR) image clinically. In this paper, consistent feature generative adversarial network (CFGAN) is proposed to produce HR MR images from the low-resolution counterparts. Specifically, a consistent-features encoder is employed to extract the multi-scales features and encode them into latent codes. Then, a progressive generator is utilized to decode the latent codes from high-level to low-level features. With the encoder and generator, the shared consistent features between low-resolution and high-resolution can be fully extracted and recovered. Experiments on ADNI dataset demonstrate that CFGAN outperforms the competing methods quantitatively and qualitatively.
Real-time Bitcoin price tendency awareness via social media content tracking

Cryptocurrency has been introduced as a relatively new financial system that is widely spread among traders and investors all over the globe. This type of digital currency is hugely attracting social media attention; Social media community along with investors and traders interact to share knowledge or to predict the price tendency of the market. Bitcoin, the leading cryptocurrency nowadays, has the highest market capitalization among other currencies. Which means that any major change in its price tendency will definitely affect the whole market and therefore other coins’ prices will surely rise or fall accordingly. We can assume that the greatest concern ever of all the traders around the world is to be alerted or aware of such major price tendency shifts in real time, in which may help them gain more profit or cut losses before it is late, or in either way predict the potential market movement of other digital currencies. It is reported that emotional interactions of the social media users especially on Twitter (one of the most globally used micro-blogging social networks especially related to cryptocurrency topics) have a great influence on the trend of the Bitcoin price. The huge number of daily active Twitter users with the enormous volume of tweets related to Bitcoin price tendency makes it remarkable regarding its impact on Bitcoin market interaction. In this paper, we will implement Apache Spark logistic regression model to process the large-scale data after having a sentimental analysis of Twitter tweets regarding Bitcoin, to predict the upcoming price tendency and classify an awareness level to alert potential traders and investors in real time about such potential market changes.
Asking nicely: Best practices for requesting data

Compiling disparate datasets into publicly available composite databases helps natural resource communities explore ecological trends and effectively manage across spatiotemporal scales. Though some studies have reported on the database construction phase, fewer have evaluated the data acquisition and distribution process. To facilitate future data sharing collaborations, Louisiana State University surveyed data providers and requestors to understand the characteristics of effective data requests and sharing. Data providers were largely U.S. natural resource agency personnel, and they reported that unclear data requests, privacy issues, and rigid timelines and formats were the greatest barriers toward providing data, but that they were motivated by improving science and collaboration. Data requestors identified challenges such as evolving needs, standardization issues, and insufficient resources (time and funding) as barriers to compiling data for these types of efforts. In a time of big data, open access, and collaboration, significant scientific advances can be made with effective requests and inclusion of data sets into larger and more powerful databases.
Self-Renewal Consortium Blockchain Based on Proof of Rest and Strong Smart Contracts

Focusing on the business alliance scenario in blockchains, this paper proposes a new consensus mechanism named proof of rest (PoR) and strong smart contracts. The block structure and logic of PoR consensus are described. And a consortium blockchain system supporting strong smart contracts is designed. We modify the difficulty value algorithm based on proof of work (PoW) and add adjustable parameters. The longer a node rests after creating a block, the less difficult it is to create another new block, hence the term PoR. The penalty for slack nodes, the joining and quitting of nodes, and the adjustment of the expected block creation time can all be accomplished using the strong smart contracts, so the consortium blockchain can realize self-renewal.
Comparison of Two Microcontroller Boards for On-Device Model Training in a Keyword Spotting Task

Machine learning applications on resource-constrained devices such as microcontroller units often use models trained externally on more powerful devices. This approach, however, limits a later adaptation of the machine learning model in the device to changing data. Differently, on-device training allows the model to be updated for new datasets, but the training process needs to take into account the resource limitations of the device. This paper compares on-device training performance for a keyword spotting task using two popular microcontroller boards, Arduino Nano 33 BLE Sense and Arduino Portenta H7, in terms of inference accuracy, training latency, and current consumption. We use feedforward neural networks having a single hidden layer for models. The inference accuracy has been significantly improved using the Portenta H7 board by employing more neurons fitted to its memory budget, compared to the Nano board. With a neural network having 25 neurons for a hidden layer, the 5.0 x inference and 4.2 x training speedups are achieved using the Arduino Portenta H7 board, compared to the Arduino Nano 33 BLE Sense. While the memory of the Arduino Nano 33 BLE Sense is capable to train a neural network for the keyword spotting task, the Arduino Portenta H7 gives new possibilities for exploring more complex models for more complex problems thanks to a larger memory budget and adapting a model to new data in lower latency.
Visual Attention Relates to Operator Performance in Spacecraft Docking Training

BACKGROUND: Manually controlled docking of a spacecraft to a space station is an operational task that poses high demands on cognitive and perceptual functioning. Effective processing of visual information is crucial for success. Eye tracking can reveal the operator's attentional focus unobtrusively and objectively. Therefore, our aim was to test the feasibility of eye tracking during a simulation of manual docking and to identify links between visual information processing and performance.METHODS: We hypothesized that duration and number of gazes to specific regions of interest of the simulation (total dwell time and number of dwells) would be associated with docking accuracy. Eye movements were recorded in 10 subjects (30% women, M = 33.4 yr old) during the 6° head-down tilt bed rest study AGBRESA during 20 training sessions with the 6df learning program for spacecraft docking.RESULTS: Subjects' gaze was directed most frequently and longest to the vizor (185 dwells and 22,355 ms per task) followed by the two instrument displays (together 75 dwells and 4048 ms per task). We observed a significant positive relationship between number and duration of visual checks of speed and distance to the docking point and the accuracy of the docking maneuver.DISCUSSION: In conclusion, eye tracking provides valuable information related to docking accuracy that might prospectively offer the opportunity to improve docking training effectiveness.Piechowski S, Johannes B, Pustowalow W, Arz M, Mulder E, Jordan J, Wolf OT, Rittweger J. Visual attention relates to operator performance in spacecraft docking training. Aerosp Med Hum Perform. 2022; 93(6):480-486.
Visualizing the school organic chemistry course with augmented reality

Abstract Nowadays, studying natural sciences, as well as chemistry, is impossible without good-quality visualization of the theoretic data. Supplying mobile apps with augmented reality give the opportunity to visualize the study information for the students and make its perception and learning easier. The paper is dedicated to developing a lap book and mobile app LiCo.STEAM Sugar with augmented reality and studying the “Carbohydrates” topic according to the 10 th grade chemistry program, and also to investigate it’s effciency within the chemistry lessons. The developed lap book includes the theories with carbohydrate molecules’ images, an experimental part designed for performing chemical experiments and studying properties of organic compounds, and also tasks of different levels. Molecules of carbohydrates, their structure can be visualized with AR, and also video-experiments on this subjets can be played. Using the lap book “Carbohydrates” with augmented reality together with LiCo.STEAM Sugar mobile app allows to upgrade the content and the volumes of the theories, apply modern ICT within the study in order to build students’ skills of a new level. Applying educational data with augmented reality give students the ability to memorize the theories in a better way, which is shown with the increased results of educational achievements of students in chemistry.