Reviewerly/exHarmony huggingface dataset:
SSC-KA: A Framework of Space Situational Knowledge Acquisition for Status-Cognition of Satellites

With the rapid development of space technology, the environment of the space domain has become more and more complex and changeable, which brought great difficulties in cognition of space domain activity. As space domain awareness (SDA) required, any relevant information and knowledge from various sources are needed as much as possible, while all of those can be sorted and integrated for effective cognition of space objects, including the cognition of their status. This paper proposes a knowledge integration framework (SSC-KA) designed for the cognition of space target and its status. In the framework (SSC-KA), open-source information and data acquired from multi-kinds of sensors are sent into parallel channels, and then processed by the algorithm this paper designed into sequence data as the channels’ output. Furthermore, the rules and four statuses defined in this paper can be judged for the anomaly detection of satellites. Based on the space domain knowledge acquisition framework of SSC-KA, this paper describes a complete abnormal state detection method for satellites step by step through multi-level feature engineering. Therefore, the method is used to analyze four different statuses of satellites in this paper, to verify the validity and feasibility of the application of the method in the cognition of spatial events, thus laying the foundation for the cognition of Space Domain Awareness.
A Method for Designing an Embedded Human Activity Recognition System for a Kitchen Use Case Based on Machine Learning

Human activity recognition enables technical systems to analyse human behaviour in various settings. For example, it can be directly used to support the user in elder care, healthcare or training environments. Nevertheless, human activities are often times highly variable and therefore pose a challenge for any technical system to correctly classify and, even more importantly, generate a feedback that is valuable to the user. In this paper the process for designing a system that uses machine learning on the sensor node itself is presented in order to improve human activity recognition within a sensor network. Each sensor node of the network consists of a Bluetooth capable system on module and an accelerometer. The acceleration data is used to distinguish between several slicing techniques of different vegetables with the aim to help the network to distinguish the different dishes cooked with those vegetables. Various steps were taken to find the best possible machine learning model and sensor configuration to infer the cut vegetable on the sensor hardware, which is based on a standard microcontroller and therefore poses a challenge with its limited memory. Overall, the system is able to correctly infer the correct class most of the times while enabling a sufficient battery run time. Within this paper these steps and tests for the design and implementation of the embedded machine learning algorithm is described and its capability for activity recognition evaluated
Cross-Domain Attention and Center Loss for Sketch Re-Identification

Matching all RGB photos of the target person in the gallery database with the full-body sketch image drawn by the professional is defined as Sketch re-identification (Sketch Re-id). The big gap between the sketch domain and RGB domain makes Sketch Re-id challenging. This paper addresses the problem by proposing a new framework to obtain domain-invariant features, which uses CNN as the backbone. To make the model focus more on the regions related to the sketch image in the RGB photo, we propose a novel cross-domain attention (CDA) mechanism. It uses different ways of splitting feature maps in its two branches and calculates the relationship between different parts in the sketch images and RGB photos. Moreover, we designed the cross-domain center loss (CDC), which breaks through the limitations that datasets need to be in the same domain in the traditional center loss. It effectively reduces the gap between two domains and makes the features with the same ID closer. The experiment is performed on the Sketch Re-id dataset. Each person has one sketch image and two RGB photos. To evaluate the generalization, we also experimented on two popular sketch-photo face datasets. The result in the Sketch Re-id dataset shows the model performs 3.7% higher than the previous methods. And the result in the CUHK student dataset performs 0.38% higher than the state-of-the-art methods.
Underwater Object Detection Based on Enhanced YOLO

As an important research topic in the field of computer vision, object detection has been successfully applied to several fields. YOLO is one of the popular frameworks for detection, but the traditional YOLO detection method lacks the processing of anchor points with detection and recognition features. In addition, most detection methods seldom consider of complex environments, especially for underwater images with high turbidity. Therefore, a YOLO based underwater object detection method for underwater images is proposed. An improved YOLO detection method without anchor points is introduced, where the detection features are separated from the recognition features to reduce the mutual interference between features and improve the detection accuracy. Further, a Retinex-based image enhancement algorithm is also proposed for underwater images enhancement. Relevant experiments based on underwater datasets are conducted to verify the effectiveness of the proposed enhanced YOLO detection method.
Heterogeneous Feature Alignment and Fusion in Cross-Modal Augmented Space for Composed Image Retrieval

Composed image retrieval (CIR) aims at fusing a reference image and text feedback to search for the desired images. Compared to general image retrieval, it can model the users' search intent more comprehensively and search the target images more accurately, which has significant impacts in various real-world applications, such as E-commerce and Internet search. However, because of the existing heterogeneous semantic gap, the synthetic understanding and fusion of both image and text are difficult to implement. In this work, to tackle this difficult problem, we propose an end-to-end framework MCR, which uses text and images as retrieval queries. The framework mainly includes four pivotal modules. Specifically, we introduce the Relative Caption-aware Consistency (RCC) constraint to align text pieces and images in the database, which can effectually bridge the heterogeneous gap. The Multi-modal Complementary Fusion (MCF) and Cross-modal Guided Pooling (CGP) are constructed to mine multiple interactions between image local features and text word features and learn the complementary representation of the composed query. Furthermore, we develop a plug-and-play Weak-text Semantic Augment (WSA) module for datasets with short or incomplete query texts, which can supplement the weak-text features and is conducive to modeling an augmented semantic space. Extensive experiments demonstrate the practical superior performance over the existing state-of-the-art empirical algorithms on several benchmarks.
A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management

Cloud workloads today are typically managed in a distributed environment and processed across geographically distributed data centers. Cloud service providers have been distributing data centers globally to reduce operating costs while also improving quality of service by using intelligent workload and resource management strategies. Such large scale and complex orchestration of software workload and hardware resources remains a difficult problem to solve efficiently. Researchers and practitioners have been trying to address this problem by proposing a variety of cloud management techniques. Mathematical optimization techniques have historically been used to address cloud management issues. But these techniques are difficult to scale to geo-distributed problem sizes and have limited applicability in dynamic heterogeneous system environments, forcing cloud service providers to explore intelligent data-driven and Machine Learning (ML) based alternatives. The characterization, prediction, control, and optimization of complex, heterogeneous, and ever-changing distributed cloud resources and workloads employing ML methodologies have received much attention in recent years. In this article, we review the state-of-the-art ML techniques for the cloud data center management problem. We examine the challenges and the issues in current research focused on ML for cloud management and explore strategies for addressing these issues. We also discuss advantages and disadvantages of ML techniques presented in the recent literature and make recommendations for future research directions.
Uncertainty-Guided Semi-Supervised Few-Shot Class-Incremental Learning With Knowledge Distillation

Class-Incremental Learning (CIL) aims at incrementally learning novel classes without forgetting old ones. This capability becomes more challenging when novel tasks contain one or a few labeled training samples, which leads to a more practical learning scenario, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> ., Few-Shot Class- Incremental Learning (FSCIL). The dilemma on FSCIL lies in serious overfitting and exacerbated catastrophic forgetting caused by the limited training data from novel classes. In this paper, excited by the easy accessibility of unlabeled data, we conduct a pioneering work and focus on a Semi-Supervised Few-Shot Class-Incremental Learning (Semi-FSCIL) problem, which requires the model incrementally to learn new classes from extremely limited labeled samples and a large number of unlabeled samples. To address this problem, a simple but efficient framework is first constructed based on the knowledge distillation technique to alleviate catastrophic forgetting. To efficiently mitigate the overfitting problem on novel categories with unlabeled data, uncertainty-guided semi-supervised learning is incorporated into this framework to select unlabeled samples into incremental learning sessions considering the model uncertainty. This process provides extra reliable supervision for the distillation process and contributes to better formulating the class means. Our extensive experiments on CIFAR100, miniImageNet and CUB200 datasets demonstrate the promising performance of our proposed method, and define baselines in this new research direction.
A Homomorphic Signcryption-Based Privacy Preserving Federated Learning Framework for IoTs

Federated learning (FL) enables clients to train a machine learning model collaboratively by just aggregating their model parameters, which makes it very useful in empowering the IoTs with intelligence. To prevent privacy information leakage from parameters during aggregation, many FL frameworks use homomorphic encryption to protect client’s parameters. However, a secure federated learning framework should not only protect privacy of the parameters but also guarantee integrity of the aggregated results. In this paper, we propose an efficient homomorphic signcryption framework that can encrypt and sign the parameters in one go. According to the additive homomorphic property of our framework, it allows aggregating the signcryptions of parameters securely. Thus, our framework can both verify the integrity of the aggregated results and protect the privacy of the parameters. Moreover, we employ the blinding technique to resist collusion attacks between internal curious clients and the server and leverage the Chinese Remainder Theorem to improve efficiency. Finally, we simulate our framework in FedML. Extensive experimental results on four benchmark datasets demonstrate that our framework can protect privacy without compromising model performance, and our framework is more efficient than similar frameworks.
Secure Inter-Container Communications Using XDP/eBPF

While the use of containerization technologies for virtual application deployment has grown at an astonishing rate, the question of the robustness of container networking has not been well scrutinized from a security perspective, even though inter-container networking is indispensable for microservices. Thus, this paper first analyzes container networks from a security perspective, discussing the implications based on their architectural limitations. Then, it presents Bastion+, a secure inter-container communication bridge. Bastion+ introduces ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> ) a network security enforcement stack that provides fine-grained control per container application and securely isolates inter- container traffic in a point-to-point manner. Bastion+ also supports ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ii$ </tex-math></inline-formula> ) selective security function chaining, enabling various security functions to be chained between containers for further security inspections (e.g., deep packet inspection) according to the container’s network context. Bastion+ incorporates ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$iii$ </tex-math></inline-formula> ) a security policy assistant that helps an administrator discover inter-container networking dependencies correctly. Our evaluation demonstrates how Bastion+ can effectively mitigate several adversarial attacks in container networks while improving the overall performance up to 25.4% within single-host containers and 17.7% for cross-host container communications.
Modulation Classification Based on Eye Diagrams and Deep Learning

New emerging technologies such as the Internet of Things (IoT) and fifth generation wireless communication New Radio (5G NR) are introducing challenges in spectrum and systems' complexity. Radio spectrum awareness is overcoming many challenges through tasks related to signal detection and channel identification that improves overall the system's reliability, efficiency, and security. The eye diagram is one of the signal representations useful in many applications for simulation and debugging of the system. The eye diagram shows vital parameters such as timing jitter and inter-symbol interference. The eye diagram contains essential features that could be used for spectrum awareness tasks. This paper uses deep learning with an eye diagram to study and identify modulated signals in narrowband fading channels, e.g., Rayleigh and Rician fading. Our results show that deep learning neural networks can classify modulated signals with the impact of the fading channel using an eye diagram.
Evaluation of Political Classroom Teaching Quality in Universities Based on DA-BP Algorithm

In order to improve the evaluation accuracy of political classroom teaching quality in universities, a DA-BP-based evaluation method of political classroom teaching quality in universities is proposed. First of all, in constructing the teaching quality evaluation system of university political courses using the framework of behavior theory, the system consists of 4 primary indicators and 15 secondary indicators. Second, the university’s 2008–2013 annual teaching quality assessment scores for the teaching quality of political courses were used as the input data of the DA-BP network, and the 2013–2016 teaching evaluation scores were used as the algorithm’s test data. In order to test the prediction accuracy of the DA-BP algorithm, it is compared with the GA-BP algorithm, PSO-BP algorithm, and BP algorithm. The research results show that DA-BP has higher prediction accuracy than other algorithms and can better evaluate the teaching quality of political courses.
Environmental Monitoring and Restoration of Ecological Cycle Index System Based on IoT Agriculture

Environmental factors have a direct impact on the development of agriculture, so it is particularly important to detect the environment of the agroecological cycle index system. Although there are some plans for environmental monitoring, most of them are environmental monitoring for a larger concept, but this study is specific to the actual object. This article takes farmland as the research object and comprehensively uses embedded technology, information monitoring technology, and network technology based on the analysis of the research status and system application of the farmland environmental monitoring system. This article studies and designs a real-time online remote monitoring system for farmland ecological environment based on embedded architecture and GPRS technology. By using sensors to obtain farmland information, it is displayed on the monitoring center and mobile client, and data information is obtained in real time. It manages and protects the farmland environment in advance. This article measures and analyzes the data from the final experiment. The experimental results show that the monitoring system can accurately collect farmland data in real time. And through the embedded server and the Internet, the data can be remotely transmitted in real time and displayed on the monitoring center and mobile client software. The results showed that PM2.5 was 30 μg/m3 at 20:00. The experimental data have the characteristics of real time and stability, which can meet the requirements of real-time network remote monitoring and transmission of data.
Generative Adversarial Network and Its Application in Energy Internet

Energy Internet (EI) can provide consumers with flexible energy-sharing services. Recently, artificial intelligence (AI) technology has been widely used in the field of EI. Generative adversarial network (GAN) is one of the hottest research directions in the field of AI in recent years, and its excellent data generation ability has attracted wide attention. First, this paper introduces the framework, advantages, disadvantages, and improvement of classic GAN. Then, the application of GAN in the field of EI is reviewed. Finally, the paper is summarized, and the possible application of GAN in EI is prospected.
Generic Construction of Trace-and-Revoke Inner Product Functional Encryption

A traitor tracing system is a multi-receiver encryption that allows an authority or an arbitrary party (in the case of public traceability) to identify malicious users (traitors) that collude to create a pirate decoder. A trace-and-revoke system is an extension of the traitor tracing system where there is an additional user revocation mechanism that the content distributor can use to disable the decryption capabilities of compromised keys. Trace-and-revoke systems have been extensively studied in the settings of broadcast encryption (BE), identity-based encryption (IBE), and attribute-based encryption (ABE), but not functional encryption (FE). Recently, Do, Phan and Pointcheval (CT-RSA'20) studied traitor tracing for FE and proposed the first traceable inner-product functional encryption (IPFE) scheme. However, their scheme is selectively secure against chosen-plaintext attacks and supports one-target black-box traceability (a weaker notion of black-box traceability). In addition, their scheme does not support public traceability nor user revocation. In this work, we study trace-and-revoke mechanisms for FE and propose the first efficient trace-and-revoke IPFE systems from standard assumptions. Our schemes support public, black-box traceability, and are proven adaptively secure against chosen-plaintext attacks in the standard model. Technically, our construction is generic and relies on a generic transformation from IPFE schemes to trace-and-revoke IPFE systems. For traitor tracing systems, our generic construction also implies the first traceable IPFE schemes that simultaneously support public, black-box traceability, and achieve adaptive security. This provides a significant improvement over the previous traceable IPFE construction by Do, Phan and Pointcheval.
Scaling up GAEN Pseudorandom Processes: Preparing for a More Extensive Pandemic

“Exposure Notification (EN) Systems” which have been envisioned by a number of academic and industry groups, are useful in aiding health authorities worldwide to fight the COVID-19 pandemic spread via contact tracing. Among these systems, many rely on the BLE based Google-Apple Exposure Notification (GAEN) API (for iPhones and Android systems). We assert that it is now the time to investigate how to deal with scale issues, assuming the next pandemic/ variant will be more extensive. To this end, we present two modular enhancements to scale up the GAEN API by improving performance and suggesting a better performance-privacy tradeoff. Our modifications have the advantage of affecting only the GAEN API modules and do not require any change to the systems built on top of it, therefore it can be easily adopted upon emerging needs. The techniques we suggest in this paper (called “dice and splice” and “forest from the PRF-tree”) are general and applicable to scenarios of searching values within anonymous pseudo-randomly generated sequences.
Iterative self-transfer learning: A general methodology for response time-history prediction based on small dataset

There are numerous advantages of deep neural network surrogate modeling for response time-history prediction. However, due to the high cost of refined numerical simulations and actual experiments, the lack of data has become an unavoidable bottleneck in practical applications. An iterative self-transfer learningmethod for training neural networks based on small datasets is proposed in this study. A new mapping-based transfer learning network, named as deep adaptation network with three branches for regression (DAN-TR), is proposed. A general iterative network training strategy is developed by coupling DAN-TR and the pseudo-label strategy, and the establishment of corresponding datasets is also discussed. Finally, a complex component is selected as a case study. The results show that the proposed method can improve the model performance by near an order of magnitude on small datasets without the need of external labeled samples,well behaved pre-trainedmodels, additional artificial labeling, and complex physical/mathematical analysis.
DenseCL: A simple framework for self-supervised dense visual pre-training

Self-supervised learning aims to learn a universal feature representation without labels. To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for dense prediction tasks due to the discrepancy between image-level prediction and pixel-level prediction. To fill this gap, we aim to design an effective, dense self-supervised learning framework that directly works at the level of pixels (or local features) by taking into account the correspondence between local features. Specifically, we present dense contrastive learning (DenseCL), which implements self-supervised learning by optimizing a pairwise contrastive (dis)similarity loss at the pixel level between two views of input images. Compared to the supervised ImageNet pre-training and other self-supervised learning methods, our self-supervised DenseCL pre-training demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection, semantic segmentation and instance segmentation. Specifically, our approach significantly outperforms the strong MoCo-v2 by 2.0% AP on PASCAL VOC object detection, 1.1% AP on COCO object detection, 0.9% AP on COCO instance segmentation, 3.0% mIoU on PASCAL VOC semantic segmentation and 1.8% mIoU on Cityscapes semantic segmentation. The improvements are up to 3.5% AP and 8.8% mIoU over MoCo-v2, and 6.1% AP and 6.1% mIoU over supervised counterpart with frozen-backbone evaluation protocol. Code and models are available at: https://git.io/DenseCL