Reviewerly/exHarmony huggingface dataset:
A Unified Federated Learning Framework for Wireless Communications: towards Privacy, Efficiency, and Security

Training high-quality machine learning models on distributed systems is a critical issue to achieve edge intelligence in wireless communications. Conventional data-driven machine learning approaches are infeasible due to non-IID data caused by privacy issues and the limited communication resources in wireless networks. Besides, considering the complex user identities, the training process also faces the challenges of Byzantine devices, which can inject poisoning information into models. In this paper, we propose a two-step federated learning framework, robust federated augmentation and distillation (RFA-RFD), to enable privacy-preserving, communication-efficient, and Byzantine-tolerant on-device machine learning in wireless communications. RFA is a method to tackle the problem of non-IID local data, which firstly trains local data generators on edge devices, then trains a global generator in the cloud server according to the IID dataset generated by the uploaded local generators, and finally, devices rectify non-IID dataset by downloading the global generator. After obtaining IID local data in edge devices, RFD is implemented to improve the performance of local models, in which devices only share the local information of models' outputs to reduce communication overhead. By employing a detection and discard mechanism in both RFA and RFD, our framework achieves robustness to the influence of Byzantine devices. Experiments show the effectiveness of RFA-RFD on preserving privacy, correcting non-IID data, reducing communication overhead, and resisting Byzantine devices, without much loss of accuracy compared with existing state-of-the-art methods.
Internet Public Opinion Evolution in the COVID-19 Event and Coping Strategies

In this study, we carried out a text analysis on the information disseminated and discussed among netizens on the Baidu Post Bar (the world's largest Chinese forum) during the coronavirus disease 2019 (COVID-19) epidemic, to create a policy basis for health administrative departments.We used Python tools to search for the relevant data on the Baidu Post Bar. Next, a text analysis was performed on the posts' contents using a combination of latent Dirichlet allocation (LDA), sentiment analysis, and correlation analysis.According to the LDA analysis, the public was highly interested in topics such as COVID-19 prevention, infection symptoms, infection and coping measures, sources of transmission and treatments, community management, and work resumption. The majority of the public had negative emotional values, yet a portion of the public held positive emotional values. We also performed a correlation analysis of the influencing factors was established.Netizens' degree of concern shown in their posts was greatly associated with the spread of COVID-19. With the rise, diffusion, outbreak, and mitigation of COVID-19 in China, netizens have successively created a large number of posts, and the topics of discussion varied over time. Therefore, the media and the government have the responsibility to distribute positive information, to correctly guide the public's emotions to bring some sort of reassurance to the public.
Dynamic Object Removal and Spatio-Temporal RGB-D Inpainting via Geometry-Aware Adversarial Learning

Dynamic objects have a significant impact on the robot’s perception of the environment which degrades the performance of essential tasks such as localization and mapping. In this work, we address this problem by synthesizing plausible color, texture and geometry in regions occluded by dynamic objects. We propose the novel geometry-aware DynaFill architecture that follows a coarse-to-fine topology and incorporates our gated reccurrent feedback mechanism to adaptively fuse information from previous timesteps. We optimize our architecture using adversarial training to synthesize fine realistic textures which enables it to hallucinate color and depth structure in occluded regions online in a spatially and temporally coherent manner, without relying on future frame information. Casting our inpainting problem as an image-to-image translation task, our model also corrects regions correlated with the presence of dynamic objects in the scene, such as shadows or reflections. We introduce a large-scale hyperrealistic dataset with RGB-D images, semantic segmentation labels, camera poses as well as groundtruth RGB-D information of occluded regions. Extensive quantitative and qualitative evaluations show that our approach achieves state-of-the-art performance, even in challenging weather conditions. Furthermore, we present results for retrieval-based visual localization with the synthesized images that demonstrate the utility of our approach.
AGUnet: Annotation-guided U-net for fast one-shot video object segmentation

The problem of semi-supervised video object segmentation has been popularly tackled by fine-tuning a general-purpose segmentation deep network on the annotated frame using hundreds of iterations of gradient descent. The time-consuming fine-tuning process, however, makes these methods difficult to use in practical applications. We propose a novel architecture called Annotation Guided U-net (AGUnet) for fast one-shot video object segmentation (VOS). AGUnet can quickly adapt a model trained on static images to segmenting the given target in a video by only several iterations of gradient descent. Our AGUnet is inspired by interactive image segmentation, where the interested target is segmented by using user annotated foreground. However, in AGUnet we use a fully-convolutional Siamese network to automatically annotate the foreground and background regions and fuse such annotation information into the skip connection of a U-net for VOS. Our AGUnet can be trained end-to-end effectively on static images instead of video sequences as required by many previous methods. The experiments show that AGUnet runs much faster than current state-of-the-art one-shot VOS algorithms while achieving competitive accuracy, and it has high generalization capability.
MEM-OPT: A Scheduling and Data Re-Use System to Optimize On-Chip Memory Usage for CNNs On-Board FPGAs

In the last years, Convolutional Neural networks (CNNs) found applications in many fields from computer vision to speech recognition, showing outstanding results in terms of accuracy. Field Programmable Gate Arrays (FPGAs) proved to be a promising platform for running CNN algorithms because they offer a remarkable trade-off between power consumption and computational power. However, an efficient implementation of CNN models on-board an FPGA represents a complex task since CNN massive parallel processing is often limited by FPGA storage capabilities and design congestion. This article introduces MEM-OPT, a scheduling algorithm and data re-use system that aims to optimize on-chip memory usage on-board FPGAs for what concerns input feature maps storage and Processing Elements multiply and accumulation process. The work presents MEM-OPT implementations results on a Xilinx XC7Z020, including hardware resources, maximum clock frequency and power consumption. MEM-OPT memory requirements are analyzed for LeNet-5, MobileNet, VGG-16 and other state-of-the-art CNNs, showing, a reduction up to 80% of the overall on-chip memory necessary for storing input feature maps and accumulating output results with respect to alternative solutions available in the literature.
SVM-Boosting based on Markov resampling: Theory and algorithm

In this article we introduce the idea of Markov resampling for Boosting methods. We first prove that Boosting algorithm with general convex loss function based on uniformly ergodic Markov chain (u.e.M.c.) examples is consistent and establish its fast convergence rate. We apply Boosting algorithm based on Markov resampling to Support Vector Machine (SVM), and introduce two new resampling-based Boosting algorithms: SVM-Boosting based on Markov resampling (SVM-BM) and improved SVM-Boosting based on Markov resampling (ISVM-BM). In contrast with SVM-BM, ISVM-BM uses the support vectors to calculate the weights of base classifiers. The numerical studies based on benchmark datasets show that the proposed two resampling-based SVM Boosting algorithms for linear base classifiers have smaller misclassification rates, less total time of sampling and training compared to three classical AdaBoost algorithms: Gentle AdaBoost, Real AdaBoost, Modest AdaBoost. In addition, we compare the proposed SVM-BM algorithm with the widely used and efficient gradient Boosting algorithm-XGBoost (eXtreme Gradient Boosting), SVM-AdaBoost and present some useful discussions on the technical parameters.
EBM+: Advancing Evidence-Based Medicine via two level automatic identification of Populations, Interventions, Outcomes in medical literature

Evidence-Based Medicine (EBM) has been an important practice for medical practitioners. However, as the number of medical publications increases dramatically, it is becoming extremely difficult for medical experts to review all the contents available and make an informative treatment plan for their patients. A variety of frameworks, including the PICO framework which is named after its elements (Population, Intervention, Comparison, Outcome), have been developed to enable fine-grained searches, as the first step to faster decision making. In this work, we propose a novel entity recognition system that identifies PICO entities within medical publications and achieves state-of-the-art performance in the task. This is achieved by the combination of four 2D Convolutional Neural Networks (CNNs) for character feature extraction, and a Highway Residual connection to facilitate deep Neural Network architectures. We further introduce a PICO Statement classifier, that identifies sentences that not only contain all PICO entities but also answer questions stated in PICO. To facilitate this task we also introduce a high quality dataset, manually annotated by medical practitioners. With the combination of our proposed PICO Entity Recognizer and PICO Statement classifier we aim to advance EBM and enable its faster and more accurate practice.
Pseudo Label Based on Multiple Clustering for Unsupervised Cross-Domain Person Re-Identification

Person re-identification (Re-ID) has achieved great improvement with the development of deep learning. However, domain adaptation in unsupervised Re-ID has always been a challenging task. Most existing works based on clustering only cluster once, which may lead to pseudo labels of poor quality. In this letter, we propose a Pseudo Label based on Multiple Clustering (PLMC) approach, which makes full advantage of multiple clustering to obtain more robust pseudo labels. In particular, our PLMC framework consists of two stages, namely, global training stage, and local training stage. We adopt the training strategy that combines the information learned from global features, and local features by training two stages alternately. Extensive experiments are carried out on three standard benchmark datasets (e.g., Maket1501, DukeMTMC-ReID, CUHK03). The results demonstrate that our PLMC method is superior to the previous methods based on single clustering, and achieves state-of-the-art person Re-ID performance under the unsupervised cross-domain setting.
A Survey on Deep Learning-Based Automatic Text Summarization Models

Text summarization is referred to the process of rewriting a particular content into its brief version by understanding it. It is a serious need to obtain concise and relevant information from huge information. Automatic text summarization is fast-growing research area in natural language processing from last few years. It has improved from simple heuristics to neural network-based text summarization techniques. The extractive and abstractive text summarization generate the condensed text which saves time in reading the entire document. The abstractive approach is more complex as it needs natural language processing and neural network. Due to the availability of huge unsupervised data, traditional approach fails to provide accuracy in text summarization. The deep learning-based text summarization model gives a good performance as compared to the conventional techniques. In this paper, we have reviewed the recent work on text summarization based on deep learning techniques.
Interpretable Real-Time Win Prediction for Honor of Kings—A Popular Mobile MOBA Esport

With the rapid prevalence and explosive development of Multiplayer Online Battle Arena electronic sports (MOBA esports), much research effort has been devoted to automatically predicting game results (win predictions). While this task has great potential in various applications, such as esports live streaming and game commentator artificial intelligence systems, previous studies fail to investigate the methods to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interpret</i> these win predictions. To mitigate this issue, we collected a large-scale dataset that contains real-time game records with rich input features of the popular MOBA game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Honor of Kings</i> . For interpretable predictions, we proposed a two-stage spatial–temporal network (TSSTN) that can not only provide accurate real-time win predictions but also attribute the ultimate prediction results to the contributions of different features for interpretability. Experiment results and applications in real-world live streaming scenarios showed that the proposed TSSTN model is effective in both prediction accuracy and interpretability.
An efficient semi-supervised manifold embedding for crowd counting

Crowd counting is one of the most paramount tasks for safety and security. Many existing methods mainly focus on the predicted accuracy but ignore the efficiency, which hinders their applications in practice. Moreover, their performance heavily depends on the learning from a large number of labeled scene data, which is cost-expensive for crowd counting. In this paper, we present a novel crowd counting approach called semi-supervised manifold embedding (SSME) to address the above weaknesses. In the newly proposed method, we formulate the crowd counting as a semi-supervised classification problem and learn a linear mapping from the high-dimensional scene feature space to the low-dimension label space by simultaneously imposing the label fitness and the manifold smoothness, where the learned linear mapping facilitates the efficiency of crowd counting. In order to alleviate the issue that most supervised approaches to crowd counting require sufficient labeled data for improving the performance, we exploit the first neighbor propagation to select informative samples in the proposed SSME-based algorithm. Thorough validation experiments on three challenging benchmark datasets indicate that the proposed method is capable of achieving more impressive prediction accuracy on the number of pedestrians in a monitoring scene than other state-of-the-art competitors.
Update Recovery Attacks on Encrypted Database within Two Updates using Range Queries Leakage

Recently, reconstruction attacks on static encrypted database supporting range queries have been proposed. However, attacks on encrypted database within two updates in the similar setting have not been studied extensively. As far as we know, the only work is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">update recovery attack</i> presented by Grubbs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (CCS 2018). Following their seminal work, we present new update recovery attacks for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dense</i> dataset (i.e., at least one record corresponding to each value in the range), which enable a deeper understanding of the impact caused by leakages due to updates on dynamic encrypted database. Our first attack aims at recovering the value of a newly added record in the case of one database update. We further demonstrate that the attack can fully reconstruct the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">database counts</i> if the updated value is either the minimum or maximum in the range. We then consider a setting where two distinct records are added separately, which leads to our second attack. We next extend our attacks to the setting where the update operation is deletion. To the best of our knowledge, update recovery attack on database supporting deletion has not been considered before. We demonstrate practicality of our attack via extensive simulations using real dataset.
Automated evaluation of COVID-19 risk factors coupled with real-time, indoor, personal localization data for potential disease identification, prevention and smart quarantining

Since the beginning of the current COVID-19 pandemic, more than five million people have been infected and the numbers are still on the rise.Early symptom detection and proper hygienic standards are thus of utmost importance, especially in venues where people are in random or opportunistic contact with each other.To this end, automated systems with medical-grade body temperature measurement, hygienic compliance evaluation and individualized, person-to-person tracking, are essential, not only for disease spread intervention and prevention, but also to assure economic stability.Herein, we present a system that encapsulates all of the mentioned functionality via readily-available components (both hardware and software) and is further enhanced with preliminary RTLS data acquisition, enabling post-symptom detected, person-toperson interaction identification to asses potential infection vectors and mitigate further propagation thereof by means of smart quarantine.
A Novel Data-Driven Specific Emitter Identification Feature Based on Machine Cognition

Machine learning becomes increasingly promising in specific emitter identification (SEI), particularly in feature extraction and target recognition. Traditional features, such as radio frequency (RF), pulse amplitude (PA), power spectral density (PSD), and etc., usually show limited recognition effects when only a slight difference exists in radar signals. Numerous two-dimensional features on transform domain, like various time-frequency representation and ambiguity function are used to augment information abundance, whereas the unacceptable computational burden usually emerges. To solve this problem, some artfully handcrafted features in transformed domain are proposed, like representative slice of ambiguity function (AF-RS) and compressed sensing mask (CS-MASK), to extract representative information that contributes to machine recognition task. However, most handcrafted features only utilizing neural network as a classifier, few of them focus on mining deep informative features from the perspective of machine cognition. Such feature extraction that is based on human cognition instead of machine cognition may probably miss some seemingly nominal texture information which actually contributes greatly to recognition, or collect too much redundant information. In this paper, a novel data-driven feature extraction is proposed based on machine cognition (MC-Feature) resort to saliency detection. Saliency detection exhibits positive contributions and suppresses irrelevant contributions in a transform domain with the help of a saliency map calculated from the accumulated gradients of each neuron to input data. Finally, positive and irrelevant contributions in the saliency map are merged into a new feature. Numerous experimental results demonstrate that the MC-feature can greatly strengthen the slight intra-class difference in SEI and provides a possibility of interpretation of CNN.
Encryption and Steganography a secret data using circle shapes in colored images

Abstract Since the earliest times, people have used the encryption and hiding data to achieve a safe and reliable transfer of important data. This paper propesed a new method for encrypting important data based on the circular shapes information that extracted from the cover image, The encryption process is done using an update of a well known traditional method with simple calculations taking advantage of the coordinates of the center of the main circle as keys extracted from the cover image to reduce the number of keys exchanged between the sender and the recipient and to increase the level of security and confidentiality. The hiding process of the encrypted data is done in pixels that located in the circular areas of the cover image and in three forms of concealment, which providing second encryption, choosing the hiding form depending on the appearing sequence of the character in the text, which makes decode the secure data will be so difficult, the experiments showed that the proposed method was achieved excellent encryption and hiding depending on the coefficients Peak Signal to Noise Ratio analyses (PNSR), Mean Square Error (MSE), and other measurements. The proposed method achieves a complete data recovery ratio where it was Bit Error Rate BER=0.
Observing and tracking bandlimited graph processes from sampled measurements

A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signals that are sparse in a well-defined graph Fourier domain. Current works focused on sampling time-invariant graph signals and ignored their temporal evolution. However, time can bring new insights on sampling since sensor, biological, and financial network signals are correlated in both domains. Hence, in this work, we develop a sampling theory for time varying graph signals, named graph processes, to observe and track a process described by a linear state-space model. We provide a mathematical analysis to highlight the role of the graph, process bandwidth, and sample locations. We also propose sampling strategies that exploit the coupling between the topology and the corresponding process. Numerical experiments corroborate our theory and show the proposed methods trade well the number of samples with accuracy.
Several alternative term weighting methods for text representation and classification

Text representation is one kind of hot topics which support text classification (TC) tasks. It has a substantial impact on the performance of TC. Although the most famous TF–IDF is specially designed for information retrieval rather than TC tasks, it is highly useful in the field of TC as a term weighting method to represent text contents. Inspired by the IDF part of TF–IDF which is defined as the logarithmic transformation, we proposed several alternative methods in this study to generate unsupervised term weighting schemes that can offset the drawback confronting TF–IDF.​ Moreover, owing to TC tasks are different from information retrieval, representing test texts as a vector in an appropriate way is also essential for TC tasks, especially for supervised term weighting approaches (e.g., TF–RF), mainly due to these methods need to use category information when weighting the terms. But most of current schemes do not clearly explain how to represent test texts with their schemes. To explore this problem and seek a reasonable solution to these schemes, we analyzed a classic unsupervised term weighting method and three typical supervised term weighting methods in depth to illustrate how to represent test texts. To investigate the effectiveness of our work, three sets of experiments are designed to compare their performance. Comparisons show that our proposed methods can indeed enhance the performance of TC, and sometimes even outperform existing supervised term weighting methods.