Reviewerly/exHarmony huggingface dataset:
Skill Movement Trajectory Recognition of Freestyle Skiing U-Shaped Field Based on Deep Learning and Multitarget Tracking Algorithm

Freestyle skiing U-shaped field is a snow sport that uses double boards to perform a series of action skills in a U-shaped pool, which requires very high skills for athletes. In this era of deep learning, in order to develop a more scientific training method, this paper combines multitarget tracking algorithm and deep learning to conduct research in freestyle skiing U-shaped venue skills motion capture. Therefore, this paper combines the convolutional neural network and multitarget tracking algorithm in deep learning to study the human action recognition technology, and then uses the LSTM module to study the freestyle skiing U-shaped venue skills. Finally, this paper designs the training method of the action recognition algorithm of the freestyle U-shaped skiing skills multitarget tracking algorithm based on deep learning. This paper also designs multitarget tracking dataset experiments and model updating experiments. Based on the data of experimental analysis, the training method designed in this paper is optimized, and finally compared with the traditional training method. Compared with the traditional freestyle U-shaped skiing skills training method, the experimental results show that the training method of the freestyle U-shaped skiing skills multitarget tracking algorithm action recognition algorithm is based on deep learning designed in this paper and this improves the skill score by 14.48%. Most professional students are very satisfied with the training method designed in this paper.
A Cloud Native SLA-Driven Stochastic Federated Learning Policy for 6G Zero-Touch Network Slicing

eration (6G)-enabled massive network slicing is a strong enabler for the expected pervasive digitalization of the vertical market. In such a context, artificial intelligence (AI)-driven zero-touch network automation should present a high degree of scalability and sustainability, especially when deployed in live production networks wherein the collected monitoring datasets at different points are non-independent and identically distributed (non-IID). This paper presents a new cloud-native service-level agreement (SLA)-driven stochastic policy to guarantee a scalable and fast operation of constrained federated learning (FL)-based analytic engines that perform statistical slice- level resource provisioning at RAN-Edge in a non-IID setup. Both simulated and cloud-native emulated scenarios are implemented to demonstrate the superiority of the solution in reducing SLA violation, convergence time and computation cost compared to different FL baselines, showcasing thereby a higher scalability.
Artificial intelligence and Big Data in neurology

Recent advances in technology have allowed us access to a multitude of datasets pertaining to various dimensions in neurology. Together with the enormous opportunities, we also face challenges related to data quality, ethics and intrinsic difficulties related to the application of data science in healthcare. In this article we will describe the main advances in the field of artificial intelligence and Big Data applied to neurology with a focus on neurosciences based on medical images. Real-World Data (RWD) and analytics related to large volumes of information will be described as well as some of the most relevant scientific initiatives at the time of this writing.
Neural network on interval‐censored data with application to the prediction of Alzheimer's disease

Abstract Alzheimer's disease (AD) is a progressive and polygenic disorder that affects millions of individuals each year. Given that there have been few effective treatments yet for AD, it is highly desirable to develop an accurate model to predict the full disease progression profile based on an individual's genetic characteristics for early prevention and clinical management. This work uses data composed of all four phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, including 1740 individuals with 8 million genetic variants. We tackle several challenges in this data, characterized by large-scale genetic data, interval-censored outcome due to intermittent assessments, and left truncation in one study phase (ADNIGO). Specifically, we first develop a semiparametric transformation model on interval-censored and left-truncated data and estimate parameters through a sieve approach. Then we propose a computationally efficient generalized score test to identify variants associated with AD progression. Next, we implement a novel neural network on interval-censored data (NN-IC) to construct a prediction model using top variants identified from the genome-wide test. Comprehensive simulation studies show that the NN-IC outperforms several existing methods in terms of prediction accuracy. Finally, we apply the NN-IC to the full ADNI data and successfully identify subgroups with differential progression risk profiles. Data used in the preparation of this article were obtained from the ADNI database.
Binary multi-modal matrix factorization for fast item cold-start recommendation

Hashing technology can support large-scale recommendation very effectively due to its advantages of low storage cost and high recommendation efficiency. However, existing hashing-based recommendation methods often suffer from item cold-start problem. This is because they simply consider the user-item interaction and the single content information of the items, but the full interaction history is not always available and the single auxiliary information may be missing. To solve this issue, in this paper we propose a Binary Multi-modal Matrix Factorization (BMMF) method. First, we propose an efficient consensus multi-modal mapping to transform the heterogeneous multi-modal features to the unified factors by exploiting the complementarity of multiple modalities. Then, binary matrix factorization is simultaneously performed on the multi-modal features of the items and past user preferences to learn the compact binary codes of the users/items in a common Hamming space. In addition, inspired by the observation that similar instances often have similar binary codes within a short Hamming distance, we formulate a semantic structure regularization term to preserve the similarities of the items during the binary embedding process. Finally, we develop an effective Discrete Coordinate Descent (DCD) approach to tackle the formulated discrete hash optimization problem directly. Experiments on three publicly available real-world datasets demonstrate the superiority of the proposed method against the state-of-the-art methods. Our source codes and testing datasets are available at https://github.com/pcm1217/BMMF .
Implementation of a Secure Storage Using Blockchain for PCA-FRF Sensor Data of Plate-Like Structures

Structural Health Monitoring (SHM) systems have widely been used to guarantee the safe functioning of electrical, mechanical, civil, and aerospace engineering assets. It is required to improve the SHM systems from different aspects in terms of smartification, performance, beneficiation, sustainment, automation, cost-effectivity, and safety using cutting-edge technologies. Blockchain is currently the most revolutionary technology in computer science. Implementation of Blockchain in different fields of academia and industry has recently given very good results. However, application of Blockchain in SHM is still in the infancy stage. Therefore, many challenges are still ahead. One of the main potential applications of Blockchain is to secure the sensor data. In this study, a local Blockchain approach is developed as a secure storage using the extracted Principal Components (PCs) of Frequency Response Function (FRF) data obtained from modal analysis of a plate-like structure. In general, Secure Hash Algorithm (SHA) is one of the most practical hash functions with efficient performance which has been employed in Bitcoin. Therefore, in this research, SHA-256 is considered to generate the hash for each block. To the best of our knowledge, this is the first attempt to develop a secure storage for SHM data using Blockchain.
Exploring Transformer-Based Learning for Negation Detection in Biomedical Texts

NLP techniques have been widely adopted in the biomedical domain to perform various textanalytics tasks, such as searching biomedical literature and extracting and deriving new knowledge from biomedical data.One type of biomedical data is clinical texts (e.g., clinical cases and medical records), which typically contain physicians' notes about a patient's health, including previous medical history (symptoms, diseases, lab exams, treatments, etc.), as every visit to the hospital leads to the addition of more information to the patient's record.Another type of biomedical data is biological articles, which typically discuss and explore a certain phenomenon, such as the behavior of biological entities (e.g., genetic relations and interactions among them) and the roles of specific biological processes in causing diseases (e.g., how genetic amplification can cause tumorous diseases).For both types of biomedical data, negation detection is an essential analytics task that can be applied to identify negated contexts in biomedical text (e.g., detecting the presence of a statement establishing that a patient does not have/fit a certain clinical condition or detecting statements that indicate the nonexistence of certain relations among biological entities).This task has been addressed in prior work by considering a variety of approaches such as rule-based systems, conventional machine-learning classifiers, and deep learning approaches.In this work, we propose applying transformerbased learning for negation detection in biomedical texts.We use pre-trained BERT and other similar models (such as ALBERT, XLNet, and ELECTRA) to address two negation-detection subtasks: negation sentence identification and negation scope recognition.We evaluated our approach using the BioScope corpus and relying on measures such as accuracy, precision, recall, F1, and percentage of correct scopes (PCS).Our findings show the potential of transformer-based learning for negation detection, reaching an accuracy of 99% for negation identification and a PCS of 95% for negation scope recognition.
PrintQueue

When diagnosing performance anomalies, it is often useful to reason about why a packet experienced the queuing that it did. To that end, we observe that queuing is both a result of historical effects and the current state of the network. Further, both factors involve short and long timescales by nature. Existing work fails to provide insight that satisfies all of these needs.
FedAux: An Efficient Framework for Hybrid Federated Learning

As an enabler of sixth-generation communication technology (6G), Federated Learning (FL) triggers a paradigm shift from "connected things" to "connected intelligence". FL implements on-device learning, where massive end devices jointly and locally train a model without private data leakage. However, FL suffers from problems of low accuracy and convergence rate when no data is shared to the central server and the data distribution is non-IID. In recent years, attempts have been made on hybrid FL, where very small amounts of data (e.g., less than 1%) is shared from the participants. With the opportunities brought by shared data, we notice that the server is capable of receiving the data in order to assist the FL process and mitigate the challenge of non-IID. Notably, existing hybrid FL only applies the model-level technologies belonging to the traditional FL and does not make full use of the characteristics of shared data to make targeted improvements. In this paper, we propose FedAux, a novel hybrid FL method at knowledge-level, which utilizes shared data to construct an auxiliary model and then transfer general knowledge to traditional aggregated model or client model for enhancing the accuracy of global model and speeding up the convergence of global model. We also propose two specific knowledge transfer strategies named c-transfer and i-transfer. We conduct extensive analysis and evaluation of our methods against the well-known FL methods, FedAvg and Hybrid-FL protocol. The results indicate that FedAux shows higher accuracy (10.89%) and faster convergence rate compared with other methods.
Efficient multi-tier, multiple entry PBFT consensus algorithm for IoT

An implementation of a blockchain-based data storage and Internet of Things (IoT) system is described in this paper. A Practical Byzantine Fault Tolerance (PBFT)-like protocol is used to achieve consensus. The proposed approach consists of two layers, the lower layer with a number of clusters and the upper layer. The upper layer consists of virtual cluster composed of delegate nodes from lower clusters. Each cluster in the lower layer allows its member nodes to initiate simultaneous consensus rounds, implemented using a dedicated overlay network per node. Each overlay network is rooted in one node and connects it with every other node. This allows concurrent multiple entry PBFT consensus sessions in each lower layer cluster. In the upper layer, the virtual cluster members have to contend for linking their accepted blocks into the blockchain ledger. Performance analysis of the proposed approach is performed using a discrete-time Markov Chain (DTMC) and M/G/1 queuing-based analytical model. The efficiency of the proposed model is verified by testing over a wide range of parameter values.
Scale-Balanced Real-Time Object Detection With Varying Input-Image Resolution

Current object-detection methods for small-scale objects are often marred by poor performance. Using relatively high-resolution input images can be considered a remedy for this issue, but it usually leads to performance degeneration for large-scale objects. We define this problem as the imbalance of detection performance for multi-scale objects when the resolution of input images varies. In addition, the use of high-resolution images results in significant computational resource consumption and inference-speed impairment. In this paper, we propose a friendly varying-resolution object-detection method for multi-scale objects. We analyze in detail the reasons leading to the performance degradation in the detection of large-scale objects with increasing input-image resolution, and propose a novel lightweight bidirectional feature-flow module to enhance the performance of multi-scale object detection in high-resolution images, especially for large-scale objects. The proposed approach can also ease the problems of computational resource consumption and inference-speed impairment caused by high-resolution images. Additionally, a decoupled detection head is designed to further improve performance by separating classification and regression sub-tasks, and an adaptive feature-fusion module is designed to better fuse different feature levels. The proposed scheme alleviates the negative effects of using high-resolution input images and achieves an excellent balance between inference speed and precision. Experiments on the MS COCO dataset show that the scheme achieves 44.6 AP at 42.6 FPS and 47 AP at 26.7 FPS, showing significant advantages over the methods to which it is compared.
EMG-Based Hand Gesture Dataset to Control Electronic Wheelchair for SCI Patients

This paper presents electromyography (EMG)-based hand gesture dataset to control electric wheelchair for the patient with spinal cord injury (SCI). We have recorded eight-channel surface EMG (sEMG) signals from EMG sensor placed at the forearm of the SCI patient. These signals were collected from six hand gesture-based wheelchair control movements (forward, backward, left, right, start and stop). We collected hand gesture data containing different EMG signals from 12 healthy subjects and 7 SCI subjects. Later on, The EMG signals were segmented and the time-domain feature extraction technique was applied to generate 18000 training samples and 10500 testing samples. We then classified the hand gestural EMG signals using 5 different classical machine learning models. We analyze the classification results in two ways. The first one is, training the models using only data of healthy subjects and cross-validated using data from 7 SCI patients. And the second one is by including six SCI patient’s data in the training process along with healthy subjects we performed leave one out cross-validation. From this analysis we were able to achieve highest 95.42% accuracy using decision tree (DT) and Random Forest(RF) algorithms.
Clustered Hierarchical Distributed Federated Learning

In recent years, due to the increasing concern about data privacy security, federated learning, whose clients only synchronize the model rather than the personal data, has developed rapidly. However, the traditional federated learning system still has a high dependence on the central server, an unguaranteed enthusiasm of clients and reliability of the central server, and extremely high consumption of communication resources. Therefore, we propose Clustered Hierarchical Distributed Federated Learning to solve the above problems. We motivate the participation of clients by clustering and solve the dependence on the central server through distributed architecture. We apply a hierarchical segmented gossip protocol and feedback mechanism for in-cluster model exchange and gossip protocol for communication between clusters to make full use of bandwidth and have good training convergence. Experimental results demonstrate that our method has better performance with less communication resource consumption.
Defending Data Poisoning Attack via Trusted Platform Module and Blockchain Oracle

With the development of Internet of Things (IoT) technology, the digital pill has been employed as an IoT system for emerging remote health monitoring to detect the impact of medicine intake on patients’ biological index. The medical data is then used for model training with federated learning. An adversary can launch poisoning attacks by tampering with patients’ medical data, which will lead to misdiagnosis of the patients’ conditions. Lots of studies have been conducted to defend against poisoning attacks based on blockchain or hardware. However, 1) Blockchain-based schemes can only exploit on-chain data to deal with poisoning attacks due to the lack of off-chain trusted entities. 2) Typical hardware-based schemes have the bottleneck of single point of failure. To overcome these defects, we propose a defense scheme via multiple Trusted Platform Modules (TPMs) and blockchain oracle. Benefitting from multiple TPMs verification results, a distributed blockchain oracle is proposed to obtain off-chain verification results for smart contracts. Then, the smart contracts could utilize the off-chain verification result to identify poisoning attacks and store the unique identifiers of the non-threatening IoT device immutably on the blockchain as a whitelist of federated learning participants. Finally, we analyze the security features and evaluate the performance of our scheme, which shows the robustness and efficiency of the proposed work.
MED-GPVS: A Deep Learning-Based Joint Biomedical Image Classification and Visual Question Answering System for Precision e-Health

General Purpose Vision System (GPVS) is a task-agnostic vision-language system that inputs an image and a question from which the system recognizes the tasks to be performed and outputs bounding boxes, confidence scores, and text outputs to answer the question. While much attention to GPVS has been recently given in the computer vision field, its medical field applications are still in their infancy. This paper presents MED-GPVS, a customized deep learning-based GPVS on biomedical images to perform various vision tasks, such as object detection and visual question answering, on medical images to facilitate precision medicine/e-health services. Our envisioned MED-GPVS takes an image and a natural language text as inputs, and then outputs bounding boxes, confidence scores, and generates a caption (i.e., the answer to the posed query). For example, if a medical image of a patient’s abdomen is presented to MED-GPVS followed by the question: "does the picture contain stomach?", MED-GPVS should ideally provide the answer "yes" along with a prediction box and prediction score on the image. We utilize the multilingual SLAKE dataset, which was annotated by expert physicians with a full semantic label, to validate the performance of MED-GPVS under various scenarios involving different biomedical image-based diagnoses. For the visual question answering (VQA) task, MED-GPVS demonstrates encouraging performance with significantly high accuracy of 82.41%.
Outsourced and Practical Privacy-Preserving K-Prototype Clustering supporting Mixed Data

Aiming to the data and model privacy issue in outsourced clustering tasks, this paper proposes an practical privacy-preserving k-prototype clustering scheme (referred to PriKPM) supporting mixed numerical and categorical attributes data. In PriKPM scheme, the users only randomly split the data sample into two shares and send them to two non-collusive servers, without interacting with the servers online. The two servers can cooperate to perform secure sample distance calculation, cluster center selection, and in-cluster sample update operations over two randomness data shares, and finally obtain the clustering distribution of samples. Specifically, we design an efficient secure comparison protocol based on additive/arithmetic secret sharing, which can switch freely between "greater than or equal" and "equal" functions, providing two comparison forms for PriKPM scheme. Theoretical analysis indicates the security and efficiency of our PriKPM scheme. Experimental results further show that compared to prior work, the clustering time of PriKPM scheme is reduced by 3 orders of magnitude.
NestedGNN: Detecting Malicious Network Activity with Nested Graph Neural Networks

Network attacks are dramatically increasing over the years. A graph can accurately model the network activities. Therefore, graph-based techniques are frequently used to detect network threats. Motivated by the strong representation of graph neural networks (GNNs), many GNN-based techniques have been proposed for various security problems, such as network threat detection, malware detection, insider threat detection, and fraud detection. Most GNNs work on the classical attributed graph structure, while we observe that a nested graph structure is a more accurate representation for modelling enterprise network, where the communications between hosts form a graph, while the local activities of each host, e.g., local event graph, form an inner graph. Observing no existing GNNs can directly learn on such a nested graph, in this paper, we designed NestedGNN, the first graph neural network for nested graphs. NestedGNN consists of three layers, i.e., inner GNN layers, nested graph layers, and outer GNN layers. We successfully applied it to compromised host detection. NestedGNN can significantly improve the performance over traditional methods on a publicly available cybersecurity dataset.