Reviewerly/exHarmony huggingface dataset:
An architecture for the dynamic adaptation of an intelligent multi-tutoring system

The articulation between machine tutoring and human tutoring remains a productive research within in the context of Intelligent Tutoring Systems (ITS), particularly in the context of e-learning where the dropout rate is high. We explore an innovative approach, the automation of tutoring as it is done in the classroom to respond to the difficulties of the learner. We propose a generic Intelligent Multi-Tutoring System (IMTS) architecture composed of two modules COMES and MAT. The Communication Entry Service (COMES) module manages communications between the IMTS and a Learning Management System (LMS). The module Multi-Agent Tutoring (MAT) is the multi-agent system developed with JADE, which allows the dynamic adaptation of tutoring (Machine, Peer, Teacher) according to the profile of the learner. We offer a configurable system to customize tutoring to the individual needs of each learner. It can be grafted onto any learning platform, making it multidisciplinary and easy to integrate into existing learning environments. The teacher will be able to devote more time to learners which need greater his intervention.The peer will develop human and relational qualities linked to their know-how, transversal skills sought by recruiters. To validate this architecture, we provide an application and results that integrate the elements of the described model. The results of the experiment prove the feasibility and reliability of our approach.
A survey of topic models: From a whole-cycle perspective

With the rapid development of information science and social networks, the Internet has accumulated various data containing valuable information and topics. The topic model has become one of the primary semantic modeling and classification methods. It has been widely studied in academia and industry. However, most topic models only focus on long texts and often suffer from semantic sparsity problems. The sparse, short text content and irregular data have brought major challenges to the application of topic models in semantic modeling and topic discovery. To overcome these challenges, researchers have explored topic models and achieved excellent results. However, most of the current topic models are applicable to a specific model task. The majority of current reviews ignore the whole-cycle perspective and framework. It brings great challenges for novices to learn topic models. To deal with the above challenges, we investigate more than a hundred papers on topic models and summarize the research progress on the entire topic model process, including theory, method, datasets, and evaluation indicator. In addition, we also analyzed the statistical data results of the topic model through experiments and introduced its applications in different fields. The paper provides a whole-cycle learning path for novices. It encourages researchers to give more attention to the topic model algorithm and the theory itself without paying extra attention to understanding the relevant datasets, evaluation methods and latest progress.
A novel hash based feature descriptors for content based image retrieval in large database

For retrieving the relevant images from the internet, CBIRs (content based image retrievals) techniques are most globally utilized. However, the traditional image retrieval techniques are unable to represent the image features semantically. The CNNs (convolutional neural networks) and DL has made the retrieval task simpler. But, it is not adequate to consider only the finalized aspect vectors from the completely linked layers to fill the semantic gap. In order to alleviate this problem, a novel Hash Based Feature Descriptors (HBFD) method is proposed. In this method, the most significant feature vectors from each block are considered. To reduce the number of descriptors, pyramid pooling is used. To improve the performance in huge databases, the hash code like function is introduced in each block to represent the descriptors. The proposed method has been evaluated in Oxford 5k, Paris 6k, and UKBench datasets with the accuracy level of 80.6%, 83.9% and 92.14% respectively and demonstrated better recall value than the existing methods.
Ways forward in the teaching of Physical Education: The private and public university experiences

In recent years, the integration of digital technology in education has become increasingly prevalent, with many institutions adopting e-learning tools to enhance the teaching and learning experience. Such practice was intensified with the kickoff of the pandemic in 2020 when most, if not all, educational institution around the world were forced to come up with a more innovative technique in teaching to continue learning. Schools and universities, be it private or public, opted to shift to online education. This study seeks to the variations in the technological gadgets and e-learning tools in the teaching of physical education classes through identification, chi-square testing, and Focus Group Discussion using the Replacement and Amplification of (RAT) Framework. Specifically, it examines the availability of these resources in different types of universities and investigates their effectiveness in promoting student engagement and improving pedagogical practices while exploring the differences between digital gadgets and e-learning tools and their availability in universities, with a specific focus on the effectiveness of teaching of physical education classes. The findings indicate that the types of digital gadgets (X2, 57.793, df=8), p &lt;.000 &lt;.05) and e-learning tools (X2, 63.36, df=19, p &lt;.001 &lt;.05) used by the students vary depending on the type of university- whether private or public, due to the accessibility for the students, and the popularity of the tools and apps. Furthermore, the use of e-learning tools is beneficial in enhancing pedagogical practices in physical education classes and promoting student engagement in learning tasks because of their benefits. Additionally, the study recommends further research on the use of these tools in the new educational landscape.
An Efficient Human Activity Recognition Using Hybrid Features and Transformer Model

Human activity recognition is a challenging and active research topic in computer science due to its applications in video surveillance, health monitoring, rehabilitation, human-robot interaction, robotics, gesture and posture analysis, and sports. In the past, various studies have utilized manual features to identify human activities and obtained good accuracy. Nonetheless, the performance of such features degraded in complex situations. Therefore, recent research used deep learning (DL) techniques to capture the local features automatically from given activity instances. Though automatic feature extraction overcomes the problems of manual features, there is still a need to enhance the efficiency and accuracy of existing techniques. The motivation behind this research is to improve the efficiency and accuracy of HAR systems. This research proposed a HAR system, which applies data enhancement techniques before capturing robust and discriminative features set from each activity instance. The captured feature set is given to the transformer model for activities recognition using the PAMAP2, UCI HAR, and WISDM datasets. The achieved results revealed that the proposed HAR model outperformed the baseline methods. Specifically, the proposed HAR achieved 98.2% accuracy for PAMAP2 with all instances in 12 activities, 98.6% accuracy for UCI HAR with all instances in 6 activities, 97.3% for WISDM with all instances in 6 activities. The advantage of the proposed hybrid features is the capability to capture both low-level and high-level information from the sensor data, potentially enhancing the discriminative power of the system. In addition, this study employed a transformer a model due to its ability to capture long-range dependencies, which are beneficial in recognizing complex human activities patterns.
Enhancing Black-Box Attacks With Self-Supervised Features and Luminance-Aware Visual Model

The practical utility of black-box adversarial attacks in deep learning security has gained significant attention. However, current black-box attacks face challenges related to overfitting, leading to limited transferability of adversarial samples. This limitation arises from excessive iterations, limited input diversity during sample generation, and indiscriminate perturbation addition. To address these issues, this paper proposes a novel approach. Adversarial perturbations are generated using a self-supervised model with strong generalization capabilities, resulting in enhanced transferability of adversarial samples. Furthermore, dynamic perturbation range maps are generated based on nonlinear characteristics of human eye luminance perception. A random gamma transform is also introduced to increase input diversity and mitigate overfitting. Extensive experiments on ImageNet datasets demonstrate the significant improvement in adversarial sample transferability achieved by our method. Moreover, our approach can be effectively combined with other techniques, achieving an average success rate of 91.70% against normal models and 74.90% against defensive models when using solely normally trained models.
Multi-Region Asynchronous Swarm Learning for Data Sharing in Large-Scale Internet of Vehicles

To provide various intelligent services in Internet of Vehicles (IoVs), such as autonomous driving, data sharing technologies enable vehicles to overcome information barriers and provide a big data foundation. Federated Learning (FL), which shares models instead of raw data, has emerged as a popular solution to address privacy concerns. However, current approaches have limited scalability and security, which are not suitable for the dynamic network topology of IoV scenarios. In this letter, we propose a Multi-Region Asynchronous Swarm Learning (MASL) framework in IoVs, which is empowered by the hierarchical blockchain and executed parallel between multiple regions. The MASL integrates identity verification and asynchronous model training while ensuring secure aggregation as well as data privacy. Through intra-regional and cross-regional sharing, the security and efficiency during large-scale data sharing in IoVs are effectively improved while alleviating the not Independent and Identically Distributed (Non-IID) data problem. Finally, both the simulation and hardware testbed results demonstrate that the proposed MASL framework could achieve better performances in terms of efficiency and security compared with the existing algorithms.
Adaptive Multi-Dimensional Shrinkage Block for Automatic Modulation Recognition

Low Signal-to-Noise Ratio (SNR) conditions pose significant challenges in Automatic Modulation Recognition (AMR) tasks. In this letter, we propose an innovative Multi-Dimensional Shrinkage Block (MDSB) to address these challenges. MDSB is a novel Convolutional Neural Network (CNN) architecture that effectively enhances the noise robustness of CNNs by employing a unique denoising mechanism, which tackles the limitations of CNNs in extracting temporal information. Leveraging the MDSB, a new AMR network named the Spatial and Channel-wise Shrinkage Neural Network (SCSNN) is introduced. Comprehensive experiments on multiple public datasets demonstrate the superior recognition performance of the proposed SCSNN model in comparison to other methods.
Straggler-Aware In-Network Aggregation for Accelerating Distributed Deep Learning

In-network aggregation facilitates accelerated distributed deep learning by utilizing a programmable switch to aggregate gradient packets. However, a straggler problem should be addressed to avoid performance degradation in terms of training time. In this paper, we propose a straggler-aware in-network aggregation (SAINA) scheme to mitigate the straggler problem while preventing accuracy degradation. In SAINA, the programmable switch aggregates local gradients of the fastest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> workers to exclude stragglers and changes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> adaptively to balance the tradeoff between training speed and accuracy. To this end, we design a switch-friendly convergence detection (SFCD) algorithm which detects a convergence point and determines <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> at the convergence point. SAINA is implemented over a software programmable switch and experimental results show that the accuracy of SAINA can reach a target accuracy up to 2.84x faster than the existing in-network aggregation scheme.
HAT: A Visual Transformer Model for Image Recognition Based on Hierarchical Attention Transformation

In the field of image recognition, Visual Transformer (ViT) has excellent performance. However, ViT, relies on a fixed self-attentive layer, tends to lead to computational redundancy and makes it difficult to maintain the integrity of the image convolutional feature sequence during the training process. Therefore, we proposed a non-normalization hierarchical attention transfer network (HAT), which introduces threshold attention mechanism and multi head attention mechanism after pooling in each layer. The focus of HAT is shifted between local and global, thus flexibly controlling the attention range of image classification. The HAT used the smaller computational complexity to improve it’s scalability, which enables it to handle longer feature sequences and balance efficiency and accuracy. HAT removes layer normalization to increase the likelihood of convergence to an optimal level during training. In order to verify the effectiveness of the proposed model, we conducted experiments on image classification and segmentation tasks. The results shows that compared with classical pyramid structured networks and different attention networks, HAT outperformed the benchmark networks on both ImageNet and CIFAR100 datasets.
Grey Wolf Optimization Algorithm Based on Follow-Controlled Learning Strategy

This paper analyzes the OBL strategy’s impact on optimizing the GWO algorithm and identifies three shortcomings. the specific limitations of the OBL optimization approach. To address these three shortcomings and enhance both global optimization and local exploration capabilities of GWO, this paper introduces a follow-controlled opposition learning strategy. then, the paper analyzes the control parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> of the grey wolf algorithm to investigate its impact on global optimization and local exploration. Based on these properties, a new control parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> is proposed. The proposed learning strategy and control parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> are introduced into the traditional grey wolf algorithm to obtain the FCGWO algorithm. Finally, this paper conducts a comparative analysis of the FCGWO algorithm in comparison to other meta-heuristic algorithms, as well as the enhanced grey wolf algorithm, utilizing 23 benchmark test functions and 2 engineering problems. The results indicate that FCGWO effectively avoids the shortcomings of the traditional OBL, while also outperforming other algorithms significantly in terms of solution quality.
Variational Nonlinear Kalman Filtering With Unknown Process Noise Covariance

Motivated by the maneuvering target tracking with sensors such as radar and sonar, this article considers the joint and recursive estimation of the dynamic state and the time-varying process noise covariance in nonlinear state-space models. Due to the nonlinearity of the models and the nonconjugate prior, the state estimation problem is generally intractable as it involves integrals of general nonlinear functions and unknown process noise covariance, resulting in the posterior probability distribution functions lacking closed-form solutions. This article presents a recursive solution for joint nonlinear state estimation and model parameters identification based on the approximate Bayesian inference principle. The stochastic search variational inference is adopted to offer a flexible, accurate, and effective approximation of the posterior distributions. We make two contributions compared to existing variational inference-based noise adaptive filtering methods. First, we introduce an auxiliary latent variable to decouple the latent variables of dynamic state and process noise covariance, thereby improving the flexibility of the posterior inference. Second, we split the variational lower bound optimization into conjugate and nonconjugate parts, whereas the conjugate terms are directly optimized that admit a closed-form solution and the nonconjugate terms are optimized by stochastic gradient, achieving the tradeoff between inference speed and accuracy. The performance of the proposed method is verified on radar target tracking applications by both simulated and real-world data.
Can Anaphora Resolution Improve Extractive Query-Focused Multi-Document Summarization?

Query-Focused Multi-Document Summarization (QF-MDS) is the task of automatically generating a summary from a collection of documents that answers a specific user’s query. Extractive methods are mainly based on identifying, selecting, and ranking sentences according to their relevance to the pre-given query. These methods have shown promising results, however, they may yield incoherent summaries, as pronominal anaphoric expressions may appear unbound. To deal with this issue, this paper proposes a novel method that leverages the potential of both contextual embeddings as well as anaphora resolution methods. More specifically, Sentence-BERT (SBERT) model is used o generate contextual embeddings for the document’s sentences and the user’s query. Additionally, the SpanBERT model is used to resolve unbound pronominal references in the input documents’ sentences with the aim of improving the cohesiveness of the generated summaries. We conducted a comprehensive comparative analysis using quantitative and qualitative evaluations with other state-of-the-art systems on the standard DUC’2005 and DUC’2007 datasets. The obtained results have shown that the proposed method is competitive and performs better than recent query-focused multi-document summarization systems on some ROUGE evaluation measures. In addition, human evaluation results further verify that our method was able to generate more informative, cohesive, and less-redundant summaries.
High-Level Data Abstraction and Elastic Data Caching for Data-Intensive AI Applications on Cloud-Native Platforms

Nowdays, it is prevalent to train deep learning models in cloud-native platforms that actively leverage containerization and orchestration technologies for high elasticity, low and flexible operation cost, and many other benefits. However, it also faces new challenges and our work is focusing on those related to I/O throughput for training, including complex data access, lack of matching dynamic I/O requirement, and inefficient I/O resource scheduling across different jobs. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fluid</i> , a cloud-native platform that provides DL training jobs with high-level data abstraction called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fluid Dataset</i> to access training data from heterogeneous sources with elastic data acceleration. In addition, it comes with an on-the-fly cache system autoscaler that can match the online training speed and increase the number of cache replicas adaptively to alleviate I/O bottlenecks. To improve the overall performance of multiple DL jobs, Fluid co-orchestrate the data cache and DL jobs by arranging job scheduling in an appropriate order and can also schedule data cache and DL jobs on the same node to realize cache affinity. Experimental results show significant performance improvement of each individual DL job which uses dynamic computing resources with Fluid. For scheduling multiple DL jobs with same datasets, Fluid achieves around 2x performance speedup when integrated with existing widely-used and cutting-edge scheduling solutions through the appropriate job scheduling order. Besides, the cache affinity scheduling policy also improves job execution performance significantly. Fluid is now an open source project hosted by Cloud Native Computing Foundation (CNCF) with many production adopters.
Information Bottleneck and Aggregated Learning

We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation learning problem, which we call "IB learning". We show that IB learning is, in fact, equivalent to a special class of the quantization problem. The classical results in rate-distortion theory then suggest that IB learning can benefit from a "vector quantization" approach, namely, simultaneously learning the representations of multiple input objects. Such an approach assisted with some variational techniques, result in a novel learning framework, "Aggregated Learning", for classification with neural network models. In this framework, several objects are jointly classified by a single neural network. The effectiveness of this framework is verified through extensive experiments on standard image recognition and text classification tasks.
Intelligent medical image reconstruction: PET image denoising and super-resolution joint processing based on generating adversarial network

PET (Positron Emission Tomography) is one of the advanced imaging technologies in nuclear medicine. It is now widely used in diagnosing many diseases, determining conditions, evaluating efficacy, performing organ function research, and developing new drugs. However, due to the characteristics of nuclear medicine technology, PET is still a low-count and low- resolution imaging method. Therefore, solving the high noise and low resolution of PET images is essential. This paper proposes a multitask least square generative Adversarial Network (MultiLSGAN) for joint noise-reduction-super-resolution reconstruction of low-quality PET images. This paper proposes a Multitask Least Square Generative Adversarial Network (MultiLSGAN) for joint noise-reduction-super-resolution reconstruction of low-quality PET images. The method is targeted for noise-reduction- super-resolution imaging tasks with supervised learning. Based on the loss function of the original GAN, we introduce the VGG model to calculate the perceptual loss and add mean square error (MSE), gradient, total variance, and other loss function constraints to generate images. Finally, experiments are conducted using the data provided by Neusoft Medical, and the quantitative and qualitative results of MultiLSGAN are better compared with the advanced methods.