Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-supervised Community Detection using Graph Embedding

Community detection is one of the fundamental problems in network analysis. The target of most methods in community detection is finding dense subgraphs using metrics like modularity. However, in real applications, communities are not always consistent with dense subgraphs. Recently, semi-supervised community detection methods have been proposed to address the issue. It focuses on finding more communities by learning directly from some tagged communities. Following this setting, we designed a semi-supervised community detection method by utilizing graph neural networks to learn graph em-beddings. We use unsupervised graph embedding method to learn embeddings of communities. Efficient community search and generation methods are adopted to find more similar communities in the whole graph. We show the effectiveness of our method on several public datasets by similarity matching between generated communities and ground-truth communities.
Intruder Detection System for Digital Device using Computer Vision

Probably, the most valuable asset of a digital device is its data and their security. An intruder detection system is a modern concept that is absolutely necessary for a computer system to have a secured data and its features. This intruder detection system detects intruders and sends an alarm to the appropriate party. As a result, the incident responder can quickly evaluate the problem and take appropriate action. Here in the system the image of the person is captured through a web camera. The extracted face is recognized and then stored in a separate folder. The captured image is compared to the authorized person’s saved image in the database. By comparing, the system can distinguish between authorized and illegitimate users. If an unauthorized person is discovered, the system sends the owner the recognized image through email. Along with that this model also sends the recorded screen video of the intruder, which will give information about what the intruder tried to do with the device. Then to alert the authorized person a normal message is also sent to the authorized person mobile number because one may not check the mail frequently so sending an SMS will alert the authorized person even if their phone is out of internet. Then at last the device will make an alarm to alert the people around the device and then it will be locked, so the intruder can no long access the device.
Control and Instability Analysis of Multiple Inverters Parallel Based on Droop Control

As a power conversion interface for new energy generation into the power grid, grid-connected inverters are more and more widely used in modern power systems, and multiple inverters parallel technology has become one of the most mature technologies. When multiple inverters operate in parallel, droop control can be used to solve the problem of power sharing without relying on communication equipment. In order to ensure the stability of the inverter, three factors should be considered in the parameter design of the droop control, which are stability of drooping power outer loop, stability of voltage control double closed-loop and effects of current limiting on system stability. In this paper, the root locus method, bode diagram analysis method and simulation analysis method are used to theoretically analyze the stability problems caused by the above three factors. Meanwhile, the theoretical analysis is verified in Matlab/Simulink environment, which proves the correctness of the theoretical analysis.
A Review of Sequential Control & Monitoring of Distribution Lines in Substations

The substation is a high-voltage electrical facility with several switchgear and equipment pieces necessary for the operation of AC current and voltage. In this place, we convert AC voltages to various levels and change the AC voltage to DC voltage as needed. This article ensure that these values being created are properly and effectively monitored when numerous processes are occurring at a single location and need to ensure that there is no energy waste, which is why electrical parameters to be monitored and maintained at the right values. As transformers and other equipment have ratings beyond which it might harm them, excessive current flow could have a negative impact on how well they perform. This would disturb the flow of power, which is not at all acceptable. The windings of the various pieces of equipment in the power system could be damaged if the temperature rises as a result of heat dissipation. Additionally, differences in the frequency may result in variations in the power factor, which would result in excessive power loss and worse efficiency. In order to ensure that the power transfer occurs effectively, it is crucial that all of the values are kept at their proper values. Any changes to the parameters could have a negative impact on both the efficiency and the machines involved.
Traffic-Adaptive Scheme for SDN Control Plane with Containerized Architecture

Software-Defined Networking (SDN) is gaining attention for its flexibility in programmability. It enhances network configuration and provides global visibility for administrators via a single interface. However, the centralized nature of SDN exposes many issues in scalability and resiliency. In this paper, with the advent of cloud computing, we present a containerized architecture capable of fast scaling up and down based on traffic load for SDN control plane. With our novel traffic-adaptive algorithm, the results show that the proposed system is able to fit performance with high incoming new-flow requests and scale down underused controllers for resource efficiency.
High-Resolution ISAR Imaging Based on 2-D Fast Sparse Bayesian Learning

We address the problem of high-resolution ISAR imaging in a Bayesian inference framework. Firstly, the probabilistic graphical model is constructed by imposing the sparsity-promoting Gamma-Gaussian prior to the distribution of scattering centers. In order to eliminate the issue of matrix inversion in conventional Sparse Bayesian Learning (SBL), a 2-D fast SBL is then proposed to improve the computational efficiency. Experimental results have demonstrated the effectiveness of the proposed method in sparse-aperture and low signal-to-noise ratio scenarios.
Multivariate Time Series Analysis: An Interpretable CNN-based Model

Deep neural networks, especially the Convolutional Neural Network (CNN) models, have shown promising results in multivariate time series data analysis. However, the predictions of these data-driven black-box models are tough to interpret from a human perspective, making it questionable to trust and rely on the predictions made by these models, specifically for time series data with the append-only feature. This paper proposes a new approach to interpret the CNN outputs by extracting and clustering the activated time series sequences learned from a trained network. These sequences show the representative features for each output label and form interpretable representations from the original time series data. Our approach is the first framework to identify each signal’s role and dependencies, consider all possible combinations of signals in the multivariate time-series input, and visualize the data representative features. Our experiments on the Baydogan’s archive indicate remarkable improvements in the interpretability of the network predictions and relation identification of each input signal to the output label and the channels of the network layers. Furthermore, the conducted experiments confirm that the extracted patterns are representative of the multivariate input and changing them results in a drastic reduction in the prediction accuracy.
Self-Organizing Map-Based Graph Clustering and Visualization on Streaming Graphs

Many real-world networks, such as computer networks, social networks, and the Internet of Things (loT), can be represented by streaming (or dynamic) graphs. Analysis of these streaming graphs serves as the basis for classification, anomaly detection, community detection, clustering, and visual-ization tasks. This paper uses a Self-Organizing Map (SOM), an unsupervised learning model, to cluster and visualize streaming graphs. As a result, a SOM is used to visualize and interpret the anomaly detection technique on high-dimensional graph-structured data. For this, the SOM-based graph clustering and visualization technique is divided into two phases. In the first phase, we use various existing graph sketching techniques like StreamS pot, SpotLight, and SnapSketch to embed streaming graphs into sketched vectors. Later, in the second phase, we pass the sketched vector inputs into a SOM to cluster and visualize the normal and anomalous graph streams to interpret the anomaly detection technique. In addition, the SOM-based visualization also helps to estimate the quality of embedding (or sketching) techniques.
Positive emotions help rank negative reviews for sellers and producers in e-commerce

Negative reviews, the poor ratings in postpurchase evaluation, play an indispensable role in e-commerce, especially in shaping future sales and firm equities. However, extant studies seldom examine their potential value for sellers and producers in enhancing capabilities of providing better services and products. For those who exploited the helpfulness of reviews in the view of e-commerce keepers, the ranking approaches were developed for customers instead. To fill this gap, in terms of combining description texts and emotion polarities, the aim of the ranking method in this study is to provide the most helpful negative reviews under a certain product attribute for online sellers and producers. By applying a more reasonable evaluating procedure, experts with related backgrounds are hired to vote for the ranking approaches. Our ranking method turns out to be more reliable for ranking negative reviews for sellers and producers, demonstrating a better performance than the baselines like BM25 with a result of 8% higher. In this paper, we also enrich the previous understandings of emotions in valuing reviews. Specifically, it is surprisingly found that positive emotions are more helpful rather than negative emotions in ranking negative reviews. The unexpected strengthening from positive emotions in ranking suggests that less polarized reviews on negative experience in fact offer more rational feedbacks and thus more helpfulness to the sellers and producers. The presented ranking method could provide e-commerce practitioners with an efficient and effective way to leverage negative reviews from online consumers.
The Contribution of Phonological and Fluency Factors to Chinese L2 Comprehensibility Ratings: A Case Study of Urdu-speaking Learners

The present study set out to investigate the crucial phonological and fluency aspects that influence listeners’ judgement of Chinese L2 comprehensibility. 180 speech samples elicited from 20 Urdu-speaking learners of Chinese were subjectively rated by native speakers of Chinese for comprehensibility scores, and then objectively analyzed in terms of phonological (segment error ratio, high & low FL error ratio, FL of segment substitutions, tone error ratio, and average maximum posterior possibility) and fluency features (articulation rate, pause ratio, FL of pause errors, and sentence length). The results showed that comprehensibility was significantly related to most of the features, and tone error ratio has the strongest correlation with comprehensibility judgements $(r=-.588)$. Furthermore, multiple linear regression analyses revealed that these features have a combined contribution to the comprehensibility $(r^{2}=.6286)$. This study offers: (1) an empirical evidence for comprehensibility-related features of Chinese L2 speech, (2) the adaptation of Brown’s FL principle on Chinese, (3) a strong proof that lexical tone errors greatly impair understanding when the segmental information is not sufficient.
ESM and Detection Cooperated Jamming Strategy

In this paper, to improve the jamming efficiency of the existing jamming strategy, an ESM and detection cooperated jamming strategy is proposed. With the strategy, precise selective barrage jamming can be implemented to the emitters. In the strategy, the information sharing between different functions is considered to realize multi-function cooperation. Firstly, the jamming strategy is described and the influence of position accuracy on jamming efficiency is analyzed. Then, to quantitatively analyze the jamming efficiency, the probability models of ESM and detection are established. Finally, numerical results are provided to verify the validity of the proposed strategy.
An Inclusive Model for a Practical Low-Voltage Feeder with Explicit Multi-Grounded Neutral Wire

With increasing penetration of distributed energy resources in the distribution network, voltage levels become a concern. However, power flow models for these networks are rare due to the complexity to collect and manage large amounts of feeder data in a way that suits engineering analysis. Unfortunately, even the network model exists, the quality of data may not be high enough for power flow analysis. In this paper, a comprehensive 4-wire low-voltage network is built, starting from reliable topology and approximate sequence impedances, and simulation results are compared to the uncleaned data and a simplified 3-wire representation. Power flow with photovoltaic penetration is also carried out. Results indicate that the improved network data has a higher occurrence of undervoltage situations. Using a 4-wire network with neutral wire can more accurately reflect the influence of neutral-to-ground voltage, even in the presence of frequent grounding. Both undervoltage and overvoltage deteriorate with PV penetration due to neutral voltage fluctuation.
Predicting Stock Price with LSTM Networks

Stock market strategies are complicated and rely on a vast quantity of data. As a result, stock price forecasting has always been challenging for many experts and investors. As a result of significant research, many machine learning algorithms have been built without being explicitly written to handle complicated computational issues and improve prediction capabilities. This study, looks into a mechanism for forecasting stock price swings. To estimate, The estimation was done with a recurrent neural network (RNN) based on long short-term memory (LSTM) if the S & P500 will rise (up) or fall (down) over the following trading month, based on the volatility of the variable. The three pieces of time sequence data are return, trading volume, and trading volume. The accuracy and the area under the (ROC) curves are used to alter hyper-parameters and assess prediction performance (AUC). It finds out that LSTM models perform comparably to baseline linear classification models.
RFI Localization Algorithm Based on Sparse Bayesian Learning for Synthetic Aperture Radiometers

For synthetic aperture imaging radiometer (SAIR), the effect of radio frequency interference (RFI) is affecting the quality of the brightness temperature (BT) retrieval. RFI detection and localization are essential for RFI mitigation, which contributes to the retrieved data improvement. Sparse Bayesian spatial spectrum estimation algorithm has been proposed and simulated for traditional radar DOA, and experiments shows that its performance is better than traditional compressed sensing methods. Considering the common characteristic of traditional array signal processing and SAIR, This paper proposes an RFI localization method based on sparse Bayesian learning. The undersampling information is and combine it with Expectation-Maximum (EM) algorithm for joint parameter estimation. The proposed method is validated through simulation of generated RFIs and SMOS L1a data, which shows the precise performance of the proposed method for RFI detection and localization.
Autonomous Distributed Frequency Control for the Management of Energy Storage Systems in Microgrid

This paper proposes a novel frequency control method based on autonomous distributed control scheme. The operating frequency is controlled by battery energy storage systems (BESSs) in AC microgrid (MG) where an efficient coordination of BESS’s state-of-charges (SOCs) is performed without communications. The BESS-SOCs of the individual prosumers are automatically managed to a same value, while the frequency is deviated in a specified bandwidth as a function of the total BESS-SOCs in MG. The proposed method can increase the reliability of MG operation specially in emergency state where communication is lost. The method is useful not only for the management of BESS-SOCs but also for any distributed generations (DGs) with limited energy storages. The simulation results show that the effectiveness of the proposed method where the MG survives until all energy storages are consumed.
Joint DOD and DOA Estimation via Coupled CPD with Sparse MIMO Radar for IIoT

Coarray tensor-based joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation with sparse multiple-input multiple-output (MIMO) radar for industrial internet of things (IIoT) has been a heated topic, where the canonical polyadic decomposition (CPD) is utilized to retrieve angle information. However, the conventional CPD approach ignores the spatial relevance among the coarray tensors corresponding to the collocated transmitters and receivers of the sparse MIMO radar. In this paper, we propose a coupled CPD-based joint DOD and DOA estimation method for the sparse MIMO radar in IIoT. Firstly, multiple tensors are modeled to preserve the multi-dimensional signal structure of the sparse MIMO radar. Then, tensor-based spatially smoothed coarray statistics corresponding to augmented virtual transmit and receive arrays are derived. After that, a segmented coarray tensorization approach is proposed to formulate multiple coupled coarray tensors. To make full use of the relevance among these coarray tensors, they are jointly decomposed via coupled CPD for joint DOD and DOA estimation with a high accuracy and resolution. The effectiveness of the proposed method is verified by simulations.
Towards Fair Representation Learning in Knowledge Graph with Stable Adversarial Debiasing

With graph-structured tremendous information, Knowledge Graphs (KG) aroused increasing interest in aca-demic research and industrial applications. Recent studies have shown demographic bias, in terms of sensitive attributes (e.g., gender and race), exist in the learned representations of KG entities. Such bias negatively affects specific popu-lations, especially minorities and underrepresented groups, and exacerbates machine learning-based human inequality. Adversariallearning is regarded as an effective way to alleviate bias in the representation learning model by simultaneously training a task-specific predictor and a sensitive attribute-specific discriminator. However, due to the unique challenge caused by topological structure and the comprehensive re-lationship between knowledge entities, adversarial learning-based debiasing is rarely studied in representation learning in knowledge graphs. In this paper, we propose a framework to learn unbiased representations for nodes and edges in knowledge graph mining. Specifically, we integrate a simple-but-effective normalization technique with Graph Neural Networks (GNNs) to constrain the weights updating process. Moreover, as a work-in-progress paper, we also find that the introduced weights normalization technique can mitigate the pitfalls of instability in adversarial debasing towards fair-and-stable machine learning. We evaluate the proposed framework on a benchmarking graph with multiple edge types and node types. The experimental results show that our model achieves comparable or better gender fairness over three competitive baselines on Equality of Odds. Importantly, our superiority in the fair model does not scarify the performance in the knowledge graph task (i.e., multi-class edge classification).
Polarimetric Characteristic Analysis of Complex Marine Aquaculture in Sanduao Coastal Zone

Synthetic aperture radar (SAR) data make up for the deficiency of visible light data in marine aquaculture monitoring due to its imaging characteristics. However, the characteristics of marine aquaculture can not be effectively extracted by single polarization and multi polarization data. In this paper, Freeman polarization decomposition is utilized to extract the characteristics of full-polarimetric GF-3 and radarsat-2 data. We analyze the polarization characteristics of complex marine aquaculture in dense marine aquaculture area and the factors affecting the effect of polarization characteristics. Finally, we set threshold according to the polarization characteristics of marine aquaculture and extract the floating raft aquaculture and cage culture in Sanduao coastal zone.
Capless Low-Dropout Regulator with a Dual Feedback Loop and Voltage Dampers

This paper presents a capless low-dropout regulator (LDO) using a dual feedback loop and voltage dampers for fast load transient. For small load transient, a low-speed error amplifier controls the gate of a pass transistor and allows for high DC gain. To extend bandwidth, most of power budget is allocated to the high-speed loop in which the body terminal of the pass transistor is controlled. When large load transient detected, the proposed voltage dampers are turned on temporarily and control the gate of the pass transistor to regulate the output. The proposed LDO regulator is fabricated in a 0.18 $\mu \mathrm{m}$ CMOS process with the active area of 0.027 $\text{mm}^{2}$ and on-chip capacitance of 9.33 $\text{pF}$. Experimental results show that the proposed LDO is superior to others by having figure-of-merit (FOM) of 1.264 $\text{mV}/\mu \mathrm{m}^{2}$ at the quiescent current of 29 $\mu \mathrm{A}$.
SAR Target Classification Based on Knowledge Distillation

Synthetic aperture radar (SAR) classification methods based on deep learning usually rely on powerful computing resources and deep convolution neural networks. Although the large deep neural network has high accuracy, it needs a lot of computing and storage resources, which limits the application of SAR classification in mobile embedded devices. The small network has high efficiency but poor accuracy. To deal with the above problems, a ResNet8 network combined with knowledge distillation is proposed in this paper. ResNet18 and ResNet8 are selected as the teacher network and student network, respectively. The response-based knowledge of the ResNet18 network is used to guide the ResNet8 network learning via offline distillation. The experimental results based on the MSTAR real dataset demonstrate that the proposed method can obtain almost the same high accuracy as ResNet18. Compared with ResNet18, the model parameters of ResNet8 is about one-tenth of ResNet18, and it has higher classification efficiency while maintaining relatively high accuracy.
Parameter Estimation of Layered Media Based on GPR Time-Frequency Spectrum Inversion

Conventional ground-penetrating radar (GPR) has limitations in estimating the dielectric properties of individual layers of multi-layer media. In this work, we propose a new inversion method based on the time-frequency spectrum to address this problem. First, the time-domain reflected signal is reconstructed using the generalized reflection coefficient. Then, the actual reflected signal is analyzed in the time-frequency domain by using the wavelet transform, and the reflection time delays of the layered interfaces are determined. Finally, an improved cost function is optimized by using the genetic algorithm (GA) to obtain the inversion parameters. The accuracy and anti-noise capability of the method is validated by the simulation data generated using gprMax software.