Miracle-dz/UniFAR-FTUs huggingface dataset:
Channel Interaction with Local Enhancement for Few-Shot Semantic Segmentation

State-of-the-art semantic segmentation algorithms are based on deep convolutional neural networks, which aim to exploit a great quantity of labeled samples to predict the class label for each pixel in an image. Few-shot segmentation alleviate the data labeling task, learning a model given only a few labeled samples, which adapts well to unseen classes. Previous works only extract spatial pixels relationship to build the guidance, which ignores the important feature channel information that contain abundant local semantic structure in the images. In this paper, we propose a novel channel interaction network (CINet) for few-shot semantic segmentation. It mainly consists of three novel steps: a local enhancement module that aggregates the spatial pixels connection in both query images and support images; a channel interaction module that captures the similar channel from relationship between each pair of images for segmentation guidance; and a residual attention module that exploiting residual connection of feature scales to alleviate spatial inconsistency between query images and corresponding example images. Extensive experiments on PASCAL-5idataset demonstrate that our model outperforms state-of-the-art methods without parameters increase sharply.
Adaptive Dila-DenseNet for Image based Time Series Classification in IoT

As a critical problem in time series data mining, time series classification (TSC) has always been a challenging task for high-dimensional and high-frequency time sequences in internet of things (IoT), Recently, convolutional neural networks (CNNs) have exhibited great superiority on TSC tasks in deep learning models, but not effective in capturing the long-term temporal dependency of time series. In this paper, we transform time series into GM-images by Gramian Angular Field (GAF) and Markov Transition Field (MTF), which can well preserve the temporal dependency and transition statistics of raw time series, respectively. We further propose an efficient Adaptive Dila-DenseNet (ADDN) to extract various and discernable patterns from GM-images for TSC. In ADDN, we devise an adaptive feature aggregation method for combining static and dynamic information flexibly from GAF and MTF representations. Moreover, inspired by the dilated residual networks, we design the Dila-Dense block in ADDN to preserve local spatial information for GM-images. The significant decrease of GM-images resolution in common deep learning models may lead to performance degradation. Nevertheless, our Dila-Dense block can address this resolution issue without decreasing the receptive field. Experiments evaluated on 24 benchmark datasets demonstrate that our approach shows greater efficiency and efficacy over the compared deep learning baselines, in IoT.
Deep Multi-View Learning for Tire Recommendation

We are constantly using recommender systems, often without even noticing. They build a profile of our person in order to recommend the content we will most likely be interested in. The data representing the users, their interactions with the system or the products may come from different sources and be of a various nature. Our goal is to use a multi-view learning approach to improve our recommender system and improve its capacity to manage multi-view data. We propose a comparative study between several state-of-the-art multi-view models applied to our industrial data. Our study demonstrates the relevance of using multi-view learning within recommender systems.
Geographically Weighted Regression using Fixed and Adaptive Gaussian Kernel Weighting for Maternal Mortality Rate Analysis

The weights have a very large influence on the parameter estimation of the geographically weighted regression (GWR). The weights show the relationship between observations or locations in the model. Types of weights that are often used in GWR are Gaussian kernels. This weighting can also be arranged into two forms. There are the fixed Gaussian kernel and the adaptive Gaussian kernel. Fixed is used when each location has the same bandwidth value. Adaptive is used when each location has a different bandwidth value. This study compares the results of the estimated parameters of the GWR model using the two weights. The modelling is carried out using data on maternal mortality in Central Sulawesi Province, consisting of 13 regencies/cities. The data used is secondary data. The result is that the adaptive Gaussian kernel weighting gives better results on the GWR model. It can be seen from the smaller standard error value (0.000398), the larger coefficient of determination (0.5468), and the smaller AIC (-152.52) than the model with the fixed Gaussian kernel weighting.
Deciphering the contributions of episodic and working memories in increasingly complex decision tasks

Augmenting the representation of the current state of the external world with internal states corresponding to working and episodic memories has been proposed as a bioinspired solution to apply models of reinforcement learning to non-Markovian tasks. But, transposing these results to behavioral and experimental neuroscience, it is not completely clear how each of these memories can contribute to learning the augmented representations and when they must act in association for more complex tasks. Choosing an elementary implementation of these memories and experimental tasks of decision making in rodents, we explore these pivotal situations and make concrete the underlying mechanisms and criteria. We also specify cases where additional mechanisms must be envisaged.
Adversarial Domain Adaptation with Correlation-Based Association Networks for Longitudinal Disk Fault Prediction

Disk fault is known to be the key cause of data loss in the modern large-scale data center, which affects the reliability and stability of the server and even the whole IT infrastructure, resulting in high financial cost. Recent works on disk fault prediction demonstrate the ability of machine learning techniques in the early prediction of disk failure. However, two limitations hinder the real-world application of current methods. First, they ignore the data heterogeneity in the data center, where distribution shifts commonly exist across different disk model types that decrease the model's performance. Second, the number of disks of different disk model types varies greatly in the data center, and current methods failed to deliver an acceptable performance over disk model types with few training samples. To address these limitations, we propose adversarial domain adaptation with correlation-based association networks (ADA-CBAN) to both mitigate the distribution shift problem and also to boost the performance on small-scaled data. Extensive experiments prove that our proposed model is effective and achieves new state-of-the-art. In addition, our post model analysis can also reveal important feature interactions and highlight the crucial period before disk faults, where both are useful information in real-world applications.
Hyperspectral Anomaly Detection With Otsu-Based Isolation Forest

Hyperspectral anomaly detection involves in many practical applications. Traditional anomaly detection methods are mainly proposed based on statistical models and geometrical models. This article proposes an Otsu-based isolation forest method, which applies the assumption that anomaly pixels are more sensitive to be isolated from the alternative pixels. The proposed article trains an isolation forest by assembling multiple binary trees. To construct a more discriminative binary tree, Otsu-based splitting criterion is applied to split subsamples into two groups at each division. Then, it feeds each tested pixel into isolation forest and obtains its anomaly score via the average path length throughout isolation forest. Considering the pixels with anomaly attribute values, path length refinement strategy based on distance weight is applied to better distinguish anomaly scores of tested pixels. Experimental results on three datasets reveal that the proposed method can effectively separate anomalies from backgrounds compared with other anomaly detection methods.
A telehealth framework for dementia care: an ADLs patterns recognition model for patients based on NILM

The ageing of the population and the increasing number of patients with dementia in modern society undoubtedly put tremendous pressure on the medical system. Providing telehealth care for potential patients and patients with dementia can reduce the burden on both the health system and care-givers. This paper describes a telehealth framework for dementia early detection and dementia care. Specifically, we propose an improved deep neural network model for Non-Intrusive Load Monitoring (NILM), which disaggregates the household's overall energy usage into those of individual appliances based on the sequence-to-point model and transfer learning. The daily behaviour regularities of patients are then inferred by combining principal component analysis and $K$-means clustering based on the disaggregated appliance-level consumptions. Experiments show that the proposed model can significantly improve training efficiency and maintain load disaggregation accuracy, and the inferred behaviour regularities have great potential to be used as useful inputs and prior knowledge to the dementia condition detection platform for early detection and real-time monitoring of patient's conditions.
Two-Stage Adaptive and Compressed CSI Feedback for FDD Massive MIMO

Channel state information (CSI) feedback is a key technical challenge for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. We propose a two-stage adaptive and compressed CSI feedback scheme to reduce the CSI feedback overhead and improve the feedback accuracy. Specifically, a binary angle-of-departure (AoD) vector and the associated angular-domain channel coefficients are first extracted from the massive MIMO channel. Then, by exploiting the dynamic burst-sparsity of the massive MIMO channel in the joint time-angular domain, a two-stage differential feedback scheme is proposed to compress and feed back the AoD vector, where the feedback overhead is adaptively adjusted according to the AoD changing rate. In addition, an adaptive-codebook-based feedback scheme is proposed for the angular-domain channel coefficients. Compared with the 5G New Radio (NR) Type-2 feedback scheme in 3GPP R15, the proposed scheme can significantly reduce the feedback overhead and achieve better block error rate (BLER) performance.
EMI Reduction Via Resonator Coils in Glassless Integrated Domestic Induction Systems

This paper explores the magnetic flux emissions of induction heating systems compared with inductively coupled heating systems. Inductively coupled heating uses a resonator coil attached to the ferromagnetic load in order to improve energy transfer from the appliance to the load. The magnetic flux emissions of both kinds of systems are simulated, and their dependence on coil current and turn number is outlined. The paper focuses on emitted near field, whose measurement and limits are determined by norm. Several prototypes are developed and tested to verify the simulations.
Current Pulsation Suppression Method Based on Power Current Closed-Loop Control for a PMSM Under Fluctuating DC-Link Voltage

For a single-phase rectifier, a common issue is that there exists a component fluctuating at twice the grid frequency in dc-link voltage due to the fluctuating input power. This fluctuating dc-link voltage causes modulation error, which induces severe phase current pulsation at low frequency. An additional LC filter can suppress the voltage fluctuation at the cost of increasing volume and reducing power density. The conventional software method is achieved by the modulation ratio or frequency compensation. However, the compensation methods have limited performance. In this article, a cascaded power current controller is proposed to suppress the current pulsation. The power current is constructed and used as the control target. Accordingly, the power current loop is cascaded between the speed and current loops. Maintaining a constant power current beneficially rejects the current ripple. Compared to the conventional closed-loop method, the proposed method does not need a phase-locked loop or resonant controller. Therefore, it has the ability to suppress current pulsation within a wide frequency range, and can achieve improved dynamic performance due to the fast convergence. Experimental results confirm the expected performance of the proposed method.
High-Density SRAM Read Access Yield Estimation Methodology

As high-density SRAMs must be designed to ensure a substantially small failure rate, the accurate yield estimation with practically acceptable runtime of circuit simulations is highly challenging. Here, a read access yield estimation method for high-density static random access memory (SRAM) is proposed. Instead of performing SPICE runs for the entire SRAM circuit, the proposed method partitions the SRAM into three parts—the control signal generation circuit, bitcell array, and sense amplifier (SA)—that determine three key parameters: word-line to SA enable delay, bit-line voltage difference, and SA offset voltage. Subsequently, the proposed method derives the probability density of these key parameters from each of the three partitioned circuits. Here, different methods are applied to derive the probability of the key parameters, considering the respective characteristics of each circuit part and parameter. According to our experimental results, the proposed method can accelerate the yield estimation by 500– $3000\times $ , compared with the brute-force Monte Carlo simulation method, and 10– $100\times $ compared with the other state-of-art methods. In addition, the proposed method can accelerate the circuit optimization procedure accompanied by multiple circuit revisions, that is, the circuit revisions can be reflected with SPICE runs only for the revised circuit part, unlike the previous methods that require SPICE runs for the entire SRAM.
Blockchain-enabled Federated Learning: A Survey

Federated learning (FL) has experienced a boom in recent years, which is jointly promoted by the prosperity of machine learning and Artificial Intelligence along with emerging privacy issues. In the FL paradigm, a central server and local end devices maintain the same model by exchanging model updates instead of raw data, with which the privacy of data stored on end devices is not directly revealed. In this way, the privacy violation caused by the growing collection of sensitive data can be mitigated. However, the performance of FL with a central server is reaching a bottleneck, while new threats are emerging simultaneously. There are various reasons, among which the most significant ones are centralized processing, data falsification, and lack of incentives. To accelerate the proliferation of FL, blockchain-enabled FL has attracted substantial attention from both academia and industry. A considerable number of novel solutions are devised to meet the emerging demands of diverse scenarios. Blockchain-enabled FL provides both theories and techniques to improve the performance of FL from various perspectives. In this survey, we will comprehensively summarize and evaluate existing variants of blockchain-enabled FL, identify the emerging challenges, and propose potentially promising research directions in this under-explored domain.
Analytical Model for Brushless Double Mechanical Port Flux-Switching Permanent Magnet Machines

In this article, a 2-D analytical model is presented for brushless double mechanical port flux-switching permanent magnet machines (BDMPFSPMMs). The BDMPFSPMM is an appropriate candidate to be employed as the electric motor in hybrid electric vehicles (HEVs) due to integrating two rotors and two stators into a compact structure. The radial and tangential components of the magnetic flux density are calculated due to permanent magnets and armature currents in each active region of the machine based on the subdomain method. The effects of the saliency on both the rotor and stator structures as well as their interactions are considered. The approach can be used for the magnetic field calculation in BDMPFSPMMs with any combination of rotor- and stator-pole numbers. Based on the computed magnetic flux density, electromagnetic torque, self and mutual inductances, flux linkage, induced voltage, local traction, and unbalanced magnetic force (UMF) are obtained. To validate the proposed model, the analytical results are compared with those obtained from the finite element method (FEM).
Link Prediction with Multiple Structural Attentions in Multiplex Networks

Many real networks can be viewed as multiplex networks with more than one layers. As different layers are usually not independent from each other, they can provide complementary information in the task of link prediction. In this paper, with the help of attention mechanism, we dig the structural correlations among different layers of the multiplex network as well as the network structural information of the target layer to make more precise link predictions. Specifically, we introduce three different attentions, namely the intra-layer distance/degree attention, the intra-layer neighbourhood attention, and the interlayer structural attention, to calculate both the influence among nodes in the same layer and the link correlations in different layers. Compared with other state-of-the art methods which usually require the information of node attributes or edge types, we only utilize the topological information of the network and thus provide a more general link prediction solution for multiplex network. We conduct comprehensive experiments on several real-world datatsets of different scales. By comparing with the state-of-the-art link prediction algorithms, we show the advantages of our algorithm, and the effectiveness of different attentions. Also, through visual case studies we uncover some intuitions about the relationship between the graph structure and the existence of a link. We make our source code anonymously available at: (will be released after review)
U-ASD Net: Supervised Crowd Counting Based on Semantic Segmentation and Adaptive Scenario Discovery

Crowd counting considers one of the most significant and challenging issues in computer vision and deep learning communities, whose applications are being utilized for various tasks. While this issue is well studied, it remains an open challenge to manage perspective distortions and scale variations. How well these problems are resolved has a huge impact on predicting a high-quality crowd density map. In this study, a hybrid and modified deep neural network (U-ASD Net), based on U-Net and adaptive scenario discovery (ASD), is proposed to get precise and effective crowd counting. The U part is produced by replacing the nearest upsampling in the encoder of U-Net with max-unpooling. This modification provides a better crowd counting performance by capturing more spatial information. The max-unpooling layers upsample the feature maps based on the max locations held from the downsampling process. The ASD part is constructed with three light pathways, two of which have been learned to reflect various densities of the crowd and define the appropriate geometric configuration employing various sizes of the receptive field. The third pathway is an adaptation path, which implicitly discovers and models complex scenarios to recalibrate pathway-wise responses adaptively. ASD has no additional branches to avoid increasing the complexity. The designed model is end-to-end trainable. This integration provides an effective model to count crowds in both dense and sparse datasets. It also predicts an elevated quality density map with a high structural similarity index and a high peak signal-to-noise ratio. Several comprehensive experiments on four popular datasets for crowd counting have been carried out to demonstrate the proposed method’s promising performance compared to other state-of-the-art approaches. Furthermore, a new dataset with its manual annotations, called Haramain with three different scenes and different densities, is introduced and used for evaluating the U-ASD Net.
An Improved Dense V-Network for Fast and Precise Segmentation of Left Atrium

Knowledge of the underlying anatomy of the left atrium can promote improved diagnostic protocols and clinical interventions; therefore, automatic segmentation of the left atrium on magnetic resonance imaging (MRI) can support diagnosis, treatment and surgery planning of the heart. Due to the small size of the left atrium with respect to the whole MRI volume, most of the current deep learning approaches are based on cropping or cascading networks. Dense V-Network is an encoder-decoder model designed for volumetric images by introducing a specialised dense feature stack to the standard V-Net model. A minor manipulation in parameters of the Dense V-Network can make it suitable for the fast and efficient segmentation of the left atrium. We present a brief review showing the ability of the Dense V-Network to deal with the issue of class imbalance and the unavailability of a large dataset of left atrium segmentation. We conclude that Dense V-Network can be tailored to left atrium MRI segmentation showing the accuracy that can surpass current methods, potentially supporting cardiac diagnosis and surgery.
Multi-fault Detection and Isolation for Lithium-Ion Battery Systems

Various faults in the lithium-ion battery system pose a threat to the performance and safety of the battery. However, early faults are difficult to detect, and false alarms occasionally occur due to similar features of the faults. In this article, an online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults. An interleaved voltage measurement topology is adopted to distinguish voltage sensor faults from battery short-circuit or connection faults. Based on the established comprehensive battery model, structural analysis is performed to develop diagnostic tests that are sensitive to different faults. Residual generation based on the extended Kalman filter and residual evaluation based on the statistical inference are conducted to detect and isolate sensor faults. Sample entropy is used to further distinguish between the short-circuit faults and connection faults. The effectiveness of the proposed diagnostic method is verified by multiple fault tests with different fault types and sizes. The results also show that the proposed method has good robustness to noise and inconsistencies in the state of charge and temperature.