Reviewerly/exHarmony huggingface dataset:
Wavelength- and Time-Division Multiplexing via Pump Current Variation of a Pulsed Semiconductor Laser—A Method of Synchronization for Quantum Key Distribution

The dependence of the semiconductor laser wavelength on the pump current is a well-known phenomenon, which is generally attributed to a change in the refractive index of the active layer due to carrier injection. This effect is usually considered to be a drawback as it causes frequency chirping of the pulses produced via direct current modulation. Here, we show that at high values of thermal resistance of a laser diode, the lasing wavelength is red-shifted due to the change of the refractive index caused by a thermal effect and this shift may significantly exceed (in absolute value) the blue shift related to a refractive index change induced by the carrier injection. We propose to benefit from the ability to tune wavelength by the pump current variation and use the same laser to generate qubits and synchronization optical pulses for quantum key distribution at different wavelengths. To demonstrate the proposed method of synchronization, we perform numerical simulations as well as proof-of-principle experiments.
Quitting Ratio-Based Bitrate Ladder Selection Mechanism for Adaptive Bitrate Video Streaming

To improve users' experience and decrease their likelihood of quitting watching videos, this paper addresses the question of how to encode the videos used in adaptive bitrate (ABR) video streaming. When addressing ABR video streaming, a lot of effort has been put into developing ABR control schemes. However, ways to appropriately encode videos also need to be defined. Unlike previous approaches that focus on coding quality, this paper considers the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user quitting ratio</i> . The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user quitting ratio</i> is the percentage of users still watching videos at a given time and enables us to address the consequences of quality and stimulus duration on the decision of a user to quit. Considering the value of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user quitting ratio</i> , this paper describes a method that uses content analysis, as well as a network's historical throughput data, to define how video should be encoded to decrease the likelihood of users quitting watching. Unlike previous approaches, the method is independent of the ABR control scheme used by the video player, and the selected ladders perform equivalently across different players with different behaviors. Results of experiments based on real-world network traces demonstrate the usefulness of the proposed method.
Multilevel Heterogeneous Domain Adaptation Method for Remote Sensing Image Segmentation

Due to more abundant data sources, more various objects of interest, and more time-consuming annotations, there is a large amount of out-of-distribution (OOD) data in the remote sensing field, on which the performance of high-accuracy image segmentation models trained under ideal experimental conditions generally degrades dramatically. Domain adaptation (DA) consequently comes into being, which aims to learn the predictor for the label-scarce target domain of interest with the help of the label-sufficient source domain in the presence of the distribution difference, namely, domain shift, between the two domains. However, the off-the-shelf DA methods for image segmentation not only struggle to cope with the more complex domain shift problems in remote sensing imagery but also almost cannot process heterogeneous data directly without information loss. While the current heterogeneous DA methods mostly still rely on some supervision information from the target domain, which is typically inaccessible in the real world. To overcome these drawbacks, we propose the multilevel heterogeneous unsupervised DA (UDA) method, termed MHDA, which unifies the instance-level DA based on cycle consistency, the feature-level DA based on contrastive learning, and the decision-level DA based on task consistency into a framework to more effectively handle the complex domain shift and heterogeneous data. After that, extensive DA experiments are conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) dataset, the BigCity dataset constructed by ourselves, and the Wuhan University (WHU) dataset, to explore the effect of each module in MHDA, the necessity of heterogeneous DA, and the effectiveness of multilevel DA. And the results demonstrate that MHDA can achieve superior performance on the remote sensing image segmentation task, compared with several state-of-the-art DA methods.
Inverse-GMM: A Latency Distribution Shaping Method for Industrial Cooperative Deep Learning Systems

The front deployed deep learning is a promising technology of the next generation industrial applications, which can extract essential information from high dimension sensors. However, part of these heavy computation tasks at resource constrained front devices have to be offloaded to the edge or cloud devices, which forms the cooperative deep learning system through the exchange of intermediate data. The inference efficiency of cooperative deep learning system will then be highly correlated with the communication latency caused by the non-stationary industrial multipath-rich fading channel. This paper proposes a novel method to control the distribution of communications latency, which is able to support efficient cooperative deep learning architecture in the harsh industrial environment. The proposed method is essentially an inverse process of Gaussian Mixture Model (GMM), which adjusts latency samples to approach the given arbitrary shape function. To achieve this objective, a new variation of Expectation-Maximization (EM) algorithm in analytical domain is derived to decompose arbitrary distribution shape with multiple Gaussian kernels and an optimized stochastic resource allocation algorithm is proposed to approximate each Gaussian kernels. The performance of proposed method is verified by both classical Rician channel model and field measured industrial fading channel responses.
Interpreting Graph-Based Sybil Detection Methods as Low-Pass Filtering

Online social networks (OSNs) are threatened by Sybil attacks, which create fake accounts (also called Sybils) on OSNs and use them for various malicious activities. Therefore, Sybil detection is a fundamental task for OSN security. Most existing Sybil detection methods are based on the graph structure of OSNs, and various methods have been proposed recently. However, although almost all methods have been compared experimentally in terms of detection performance and noise robustness, theoretical understanding of them is still lacking. In this study, we show that existing graph-based Sybil detection methods can be interpreted in a unified framework of low-pass filtering. This framework enables us to theoretically compare and analyze each method from two perspectives: filter kernel properties and the spectrum of shift matrices. Our analysis reveals that the detection performance of each method depends on the effectiveness of the low-pass filtering. Furthermore, on the basis of the analysis, we propose a novel Sybil detection method called SybilHeat. Numerical experiments on synthetic graphs and real social networks demonstrate that SybilHeat performs consistently well on graphs with various structural properties. This study lays a theoretical foundation for graph-based Sybil detection and leads to a better understanding of Sybil detection methods.
Personalized federated learning with model interpolation among client clusters and its application in smart home

Abstract The proliferation of high-performance personal devices and the widespread deployment of machine learning (ML) applications have led to two consequences: the volume of private data from individuals or groups has exploded over the past few years; and the traditional central servers for training ML models have experienced communication and performance bottlenecks in the face of massive amounts of data. However, this reality also provides the possibility of keeping data local for ML training and fusing models on a broader scale. As a new branch of ML application, Federated Learning (FL) aims to solve the problem of multi-party joint learning on the premise of protecting personal data privacy. However, due to the heterogeneity of devices, including network connection, network bandwidth, computing resources, etc., it is unrealistic to train, update and aggregate models in all devices in parallel, while personal data is often not independent and identically distributed (Non-IID) due to multiple reasons. This reality poses a challenge to the speed and convergence of FL. In this paper, we propose the pFedCAM algorithm, which aims to improve the robustness of the FL system to device heterogeneity and Non-IID data, while achieving some degree of federation model personalization. pFedCAM is based on the idea of clustering and model interpolation by classifying heterogeneous clients and performing FedAvg algorithm in parallel, and then combining them into personalized federated global models by inter-cluster model interpolation. Experiments show that the accuracy of pFedCAM improves 10.3% on Fashion-MNIST and 11.3% on CIFAR-10 compared to the benchmark in the case of Non-IID data. In the end, we applied pFedCAM in HomeProtect, a smart home privacy protection framework we designed, and achieved good practical results in the case of flame recognition.
An RF Fingerprint Extraction Method based on Time-frequency Domain Feature Fusion

Abstract Identification and authentication of communication radiation sources using RF fingerprint feature extraction technology is widely used in civilian and military applications and has become a research hotspot. In this paper, an RF fingerprint extraction method based on time-frequency domain feature fusion is proposed. The frequency domain features of WVD and the time domain statistical features (variance, skewness, kurtosis) of the signal are first extracted, and then fused and downscaled using multivariate discriminant analysis to obtain new time-frequency fusion features, which are finally input to the support vector machine for classification and recognition. This method is used to analyze signals of walkie-talkies and signals of cell phones with and without noise, and the experimental results show that this method effectively improves the recognition rate and has good noise immunity compared with using frequency domain features or time domain features alone, and the accuracy is improved by about 5% under the condition of low SNR. When the SNR is -10dB, the recognition accuracy of walkie-talkies can reach 81.6% and that of cell phones can reach 86.1%.
Automatic velocity picking with restricted weighted k-means clustering using prior information

Automatic picking of seismic velocity can be performed using k-means clustering. In simple k-means clustering, the number of clusters needs to be predetermined, while the picking result is affected by the initial value of each cluster center. In this study, we present an unsupervised weighted k-means clustering velocity-picking method that picks the centers of the energy clusters instead of the geometric centers of the clusters. This method works on the semblance velocity spectrum and requires an initial velocity function and three user-defined thresholds to limit the search area. The number of cluster centers and their initial times are obtained according to a rectangular signal resulting from the three thresholds, while the initial velocities of the cluster centers can be subsequently obtained using their initial times and the initial velocity function. Inaccurate selection of thresholds may merge two clusters wrongly, in which case only a stronger event is selected. In the weighted k-means clustering algorithm, weights are calculated by using the amplitudes of the velocity points. Meanwhile, points far from the center are gradually removed to ensure that each cluster center coincides with the respective energy cluster center. We also propose a method for ignoring non-primary velocities, such as multiples, by removing points that create sudden changes in the slope of the reference velocity beyond a user-defined limit. The processing of the model and real data show that the proposed seismic velocity-picking method has high efficiency and picking accuracy.
Statistical machine learning techniques of weather simulation for the fishery-solar hybrid systems

OPINION article Front. Energy Res., 16 January 2023Sec. Energy Storage Volume 10 - 2022 | https://doi.org/10.3389/fenrg.2022.1073976
On Scene Engineering and Domain Randomization: Synthetic Data for Industrial Item Picking

Synthetic data for training deep neural networks is increasingly used in computer vision. Different strategies, such as domain randomization or domain adaptation, exist to bridge the gap between synthetic training data and the real application. Despite recent progress and gain in knowledge in this area, the following question remains: How much adjustment to reality is required and which degree of randomization is useful for transferring precise object detectors to real use cases? In this paper, we present a detailed study with more than 100 datasets and 2,700 trained convolutional neural networks (CNNs), comparing the influence of different degrees of manual optimization (scene engineering) and domain randomization techniques. To distinguish precision and robustness, the trained object detectors are evaluated on different domain shifts with respect to scene environment and object appearance. Using the example of robot-based industrial item picking, we show that the scene context and structure as well as realistic textures are crucial for the simulation to reality transfer. The combination with well-chosen randomization parameters, especially lighting and distractor objects, improves the robustness of the CNNs at higher domain shifts.
Real2Sim or Sim2Real: Robotics Visual Insertion Using Deep Reinforcement Learning and Real2Sim Policy Adaptation

Reinforcement learning has shown a wide usage in robotics tasks, such as insertion and grasping. However, without a practical sim2real strategy, the policy trained in simulation could fail on the real task. There are also wide researches in the sim2real strategies, but most of those methods rely on heavy image rendering, domain randomization training, or tuning. In this work, we solve the insertion task using a pure visual reinforcement learning solution with minimum infrastructure requirement. We also propose a novel sim2real strategy, Real2Sim, which provides a novel and easier solution in policy adaptation. We discuss the advantage of Real2Sim compared with Sim2Real.
An explainable deepfake detection framework on a novel unconstrained dataset

Abstract In this work, we created a new large-scale unconstrained high-quality Deepfake Image (DFIM-HQ) dataset containing 140K images. Compared to existing datasets, this dataset includes a variety of diverse scenarios, pose variations, high-quality degradations, and illumination variations, making it a particularly challenging dataset. Since computer vision models learn to perform a task by capturing relevant statistics from training data, they tend to learn spurious age, gender, and race correlations leading to learning biases. To account for AI bias in our proposed DFIM-HQ dataset, we design a simple yet effective image recognition benchmark for studying bias mitigation. Our detection system makes use of an Inception-based network to extract frame-level features and automatically detect manipulated content. We also propose an explainability framework that provides a better understanding of the model’s prediction. Such informed decisions provide insights that can be used to improve the model and, thereby, helps to add trust to the model. Our evaluation illustrates that our frameworks can achieve competitive results in detecting deepfake images using deep learning architectures.
Value of deep learning models based on ultrasonic dynamic videos for distinguishing thyroid nodules

This study was designed to distinguish benign and malignant thyroid nodules by using deep learning(DL) models based on ultrasound dynamic videos.Ultrasound dynamic videos of 1018 thyroid nodules were retrospectively collected from 657 patients in Zhejiang Cancer Hospital from January 2020 to December 2020 for the tests with 5 DL models.In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 0.929(95% CI: 0.888,0.970) for the best-performing model LSTM Two radiologists interpreted the dynamic video with AUROC values of 0.760 (95% CI: 0.653, 0.867) and 0.815 (95% CI: 0.778, 0.853). In the external test set, the best-performing DL model had AUROC values of 0.896(95% CI: 0.847,0.945), and two ultrasound radiologist had AUROC values of 0.754 (95% CI: 0.649,0.850) and 0.833 (95% CI: 0.797,0.869).This study demonstrates that the DL model based on ultrasound dynamic videos performs better than the ultrasound radiologists in distinguishing thyroid nodules.
M2ASR-KIRGHIZ: A Free Kirghiz Speech Database and Accompanied Baselines

Deep learning has significantly boosted the performance improvement of automatic speech recognition (ASR) with the cooperation of large amounts of data resources. For minority languages, however, there are almost no large-scale data resources, limiting the development of ASR technologies in these languages. In this paper, we publish a free Kirghiz speech database accompanied by associated language resources. The entire database involves 128 h of speech data from 163 speakers and corresponding transcriptions. To our knowledge, this is the largest Kirghiz speech database that is dedicated to the ASR task and is publicly free so far. In addition, we also provide several baseline systems based on Kaldi and WeNet to demonstrate how these public data resources can be used to facilitate the Kirghiz ASR research. This publication is a part of the M2ASR project, and all the resources can be downloaded at the project webpage.
A Novel Transformer-Based Adaptive Object Detection Method

To accurately detect multi-scale remote sensing objects in complex backgrounds, we propose a novel transformer-based adaptive object detection method. The backbone network of the method is a dual attention vision transformer network that utilizes spatial window attention and channel group attention to capture feature interactions between different objects in complex scenes. We further design an adaptive path aggregation network. In the designed network, CBAM (Convolutional Block Attention Module) is utilized to suppress background information in the fusion paths of different-level feature maps, and new paths are introduced to fuse same-scale feature maps to increase the feature information of the feature maps. The designed network can provide more effective feature information and improve the feature representation capability. Experiments conducted on the three datasets of RSOD, NWPU VHR-10, and DIOR show that the mAP of our method is 96.9%, 96.6%, and 81.7%, respectively, which outperforms compared object detection methods. The experimental results show that our method can detect remote-sensing objects better.
Irregular Scene Text Detection Based on a Graph Convolutional Network

Detecting irregular or arbitrary shape text in natural scene images is a challenging task that has recently attracted considerable attention from research communities. However, limited by the CNN receptive field, these methods cannot directly capture relations between distant component regions by local convolutional operators. In this paper, we propose a novel method that can effectively and robustly detect irregular text in natural scene images. First, we employ a fully convolutional network architecture based on VGG16_BN to generate text components via the estimated character center points, which can ensure a high text component detection recall rate and fewer noncharacter text components. Second, text line grouping is treated as a problem of inferring the adjacency relations of text components with a graph convolution network (GCN). Finally, to evaluate our algorithm, we compare it with other existing algorithms by performing experiments on three public datasets: ICDAR2013, CTW-1500 and MSRA-TD500. The results show that the proposed method handles irregular scene text well and that it achieves promising results on these three public datasets.
Accounting for the knowledge gained during a web search: An empirical study on learning transfer indicators

Searches with learning intent typically require the users to interact with the searching environment and perform knowledge acquisition features such as scan, read, and process the online content to fulfill their information needs. To capture indicators from searching behaviors that could account for the knowledge gained during a Web search, a qualitative study was performed using the Concurrent Think-Aloud protocol to observe the mechanisms of transfer and map knowledge flows during 78 search sessions. Findings indicate evidence of transfer of learning in the form of sixteen online information searching strategy indicators. This research aids the understanding of how knowledge is gained during search sessions and how to identify behaviors that could indicate that learning has occurred, which could be used to represent knowledge gain on Web search engines. In this way, it can aid search engines to become not only better tools of searching, but also tools of learning.
Short attribute-based signatures for arbitrary Turing machines from standard assumptions

Abstract This paper presents the first attribute-based signature () scheme supporting signing policies representable by Turing machines (), based on well-studied computational assumptions . Our work supports arbitrary s as signing policies in the sense that the s can accept signing attribute strings of unbounded polynomial length and there is no limit on their running time, description size, or space complexity. Moreover, we are able to achieve input-specific running time for the signing algorithm. All other known expressive schemes could at most support signing policies realizable by either arbitrary polynomial-size circuits or s having a pre-determined upper bound on the running time. Consequently, those schemes can only deal with signing attribute strings whose lengths are a priori bounded, as well as suffers from the worst-case running time problem. On a more positive note, for the first time in the literature, the signature size of our scheme only depends on the size of the signed message and is completely independent of the size of the signing policy under which the signature is generated. This is a significant achievement from the point of view of communication efficiency. Our construction makes use of indistinguishability obfuscation () for polynomial-size circuits and certain -compatible cryptographic tools. Note that, all of these building blocks including for polynomial-size circuits are currently known to be realizable under well-studied computational assumptions.