Reviewerly/exHarmony huggingface dataset:
VTBC: Privatizing the Volume and Timing of Transactions for Blockchain Applications

Existing privacy-preserving blockchain solutions have shown how to maintain the anonymity and confidentiality of the contents of blockchain transactions. However, due to blockchains needing to be stored and updated in a decentralized manner, metadata like the volume of transactions and the timestamp of each transaction can always be publicly observed, even with state-of-the-art solutions. Blockchain applications, especially ones with time-sensitive or volume-sensitive outcomes, may require this volume and timing information to be privatized. One example is not leaking the lateness of students' exam submissions because this could violate student privacy laws. In this paper, we propose VTBC, a blockchain system to privatize such volume and timing information for multi-party privacy-preserving blockchain applications through decoy blockchain transactions which a) do not contribute at all to the execution of the application and b) are indistinguishable from real (non-decoy) transactions. Even though the volume and timing metadata of all transactions must be public, volume and timing information for an application can be indirectly privatized (even after the application has been finalized) by carefully deciding when and how many decoy transactions are added to the blockchain. We demonstrate how these decoy transactions can be created without sacrificing the application's integrity, functionality, or verifiability, without making changes to the underlying blockchain's architecture, and always using the blockchain as the trusted timekeeper. We implemented our approach via a Dutch auction that supports decoy bid transactions and evaluated its performance on a private Ethereum blockchain network.
Human-Centered Explainable AI at the Edge for eHealth

Explainable Artificial Intelligence (XAI) is a new paradigm of Artificial Intelligence (AI) that is giving different AI/ Machine Learning (ML) models a boost to penetrate sectors where people are thinking about adopting AI. This work focuses on the adoption of XAI in the health sector. It portrays that careful integration of XAI in both cloud and edge could change the whole healthcare industry and make humans more aware of their present health conditions, which is the need of the hour. To demonstrate the same, we have done an experiment based on the prediction of a particular medical condition called "cardiac arrest" in a specific subject group (patients who are 70 years old). Here, based on the explanation provided by the XAI model (e.g., SHAP, LIME) at Cloud and Edge, our system can predict the chances of a "cardiac arrest" for the subject with a valid explanation. This type of model will be the next big upgrade in the healthcare industry in terms of automation and a self-explanatory system that works as a personal health assistant for individuals.
Transfer-Once-For-All: AI Model Optimization for Edge

Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches use evolutionary search to extract models of different sizes from a supernet trained on a very large data set, and then fine-tune the extracted models on the typically small, real-world data set of interest. The computational cost of training thus grows linearly with the number of different model deployment scenarios. Hence, we propose Transfer-Once-For-All (TOFA) for supernet-style training on small data sets with constant computational training cost over any number of edge deployment scenarios. Given a task, TOFA obtains custom neural networks, both the topology and the weights, optimized for any number of edge deployment scenarios. To overcome the challenges arising from small data, TOFA utilizes a unified semi-supervised training loss to simultaneously train all subnets within the supernet, coupled with on-the-fly architecture selection at deployment time.
Data-Driven Next-Generation Wireless Networking: Embracing AI for Performance and Security

New network architectures, such as the Internet of Things (IoT), 5G, and next-generation (NextG) cellular systems, put forward emerging challenges to the design of future wireless networks toward ultra-high data rate, massive data processing, smart designs, low-cost deployment, reliability and security in dynamic environments. As one of the most promising techniques today, artificial intelligence (AI) is advocated to enable a data-driven paradigm for wireless network design. In this paper, we are motivated to review existing AI techniques and their applications for the full wireless network protocol stack toward improving network performance and security. Our goal is to summarize the current motivation, challenges, and methodology of using AI to enhance wireless networking from the physical to the application layer, and shed light on creating new AI-enabled algorithms, mechanisms, protocols, and system designs for future data-driven wireless networking.
Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids

False data injection attacks (FDIAs) pose a significant security threat to power system state estimation. To detect such attacks, recent studies have proposed machine learning (ML) techniques, particularly deep neural networks (DNNs). However, most of these methods fail to account for the risk posed by adversarial measurements, which can compromise the reliability of DNNs in various ML applications. In this paper, we present a DNN-based FDIA detection approach that is resilient to adversarial attacks. We first analyze several adversarial defense mechanisms used in computer vision and show their inherent limitations in FDIA detection. We then propose an adversarial-resilient DNN detection framework for FDIA that incorporates random input padding in both the training and inference phases. Our simulations, based on an IEEE standard power system, demonstrate that this framework significantly reduces the effectiveness of adversarial attacks while having a negligible impact on the DNNs' detection performance. Index Terms-False Data Injection Attack, Smart Grid Communication, Deep Learning, Adversarial Attacks
CNN Feature Map Augmentation for Single-Source Domain Generalization

In search of robust and generalizable machine learning models, Domain Generalization (DG) has gained significant traction during the past few years. The goal in DG is to produce models which continue to perform well when presented with data distributions different from the ones available during training. While deep convolutional neural networks (CNN) have been able to achieve outstanding performance on downstream computer vision tasks, they still often fail to generalize on previously unseen data Domains. Therefore, in this work we focus on producing a model which is able to remain robust under data distribution shift and propose an alternative regularization technique for convolutional neural network architectures in the single-source DG image classification setting. To mitigate the problem caused by domain shift between source and target data, we propose augmenting intermediate feature maps of CNNs. Specifically, we pass them through a novel Augmentation Layer to prevent models from overfitting on the training set and improve their cross-domain generalization. To the best of our knowledge, this is the first paper proposing such a setup for the DG image classification setting. Experiments on the DG benchmark datasets of PACS, VLCS, Office-Home and TerraIncognita validate the effectiveness of our method, in which our model surpasses state-of-the-art algorithms in most cases.
Illicit item detection in X-ray images for security applications

Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours make it a Big Data analysis task. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage, anchor-based object detectors. This paper proposes a two-fold improvement of such algorithms for the X-ray analysis domain, introducing two complementary novelties. Firstly, more efficient anchors are obtained by hierarchical clustering the sizes of the ground-truth training set bounding boxes; thus, the resulting anchors follow a natural hierarchy aligned with the semantic structure of the data. Secondly, the default Non-Maximum Suppression (NMS) algorithm at the end of the object detection pipeline is modified to better handle occluded object detection and to reduce the number of false predictions, by inserting the Efficient Intersection over Union (E-IoU) metric into the Weighted Cluster NMS method. E-IoU provides more discriminative geometrical correlations between the candidate bounding boxes/Regions-of-Interest (RoIs). The proposed method is implemented on a common single-stage object detector (YOLOv5) and its experimental evaluation on a relevant public dataset indicates significant accuracy gains over both the baseline and competing approaches. This highlights the potential of Big Data analysis in enhancing public safety.
PAROS: The Missing “Puzzle” in Smart Home Router Operating Systems

The Internet of Things (IoT) devices have been increasingly deployed in smart homes for automation. Unfortunately, extensive recent research shows that external on-path adversaries can infer and fingerprint user sensitive in-home activities by analyzing IoT network traffic rates alone. Most recent traffic padding-based defending approaches cannot sufficiently protect user privacy with reasonable traffic overhead. In addition, these approaches typically assume the installation of additional hub hardware in smart homes to host their traffic padding-based defending approaches. To address these problems, we design a new open-source traffic reshaping system—privacy as a router operating system service (PAROS) that enables smart home users to significantly reduce private information leaked through IoT network traffic rates. PAROS does not assume the installation of any additional hardware device. We evaluate PAROS on open-source router Operating System (OS)—OpenWrt enabled virtual machine and also two real best-selling home routers. We find that PAROS can effectively prevent a wide range of state-of-the-art adversarial machine learning-based user in-home activity inference attacks, with near-zero system overhead increasing.
Early Marking for Controllable Maximum Queue Length in Data Center Networks

In data center networks (DCNs), numerous congestion control schemes utilize explicit congestion notification (ECN) to achieve low average queue delay. Such schemes generally mark packets based on the current queue length exceeding a marking threshold. However, due to the delay of ECN feedback, the queue length may further increase before the congestion notification is delivered to senders, which may lead to uncontrollable maximum queue length when bursts occur. In this paper, we propose an early ECN marking scheme based on prediction, E-ECN, to control the maximum queue length in DCNs. E-ECN uses predicted queue length rather than the current to indicate congestion with an advance time which offsets the hysteresis of ECN. We theoretically and experimentally demonstrate that early marking does not impact the throughput with appropriate selection of the advance time, and we provide guidelines for the selection in DCNs. Our simulation results show that E-ECN achieves shorter average queue delay and controllable maximum queue length in general with a bandwidth utilization guarantee. E-ECN greatly reduces queue overflow and improves the robustness of DCNs.
$\mathrm{P}^{3}$: A Privacy-Preserving Perception Framework for Building Vehicle-Edge Perception Networks Protecting Data Privacy

With the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as perception using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge units. However, data privacy becomes a major concern for vehicular edge computing, as sensor data with sensitive information from vehicles can be observed and used by edge servers. We aim to address the privacy problem by protecting both vehicles' sensor data and the detection results. In this paper, we present a privacy preserving perception <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{P}^{3})$</tex> framework which provides a secure version of every commonly used layers in various perception CNN networks. They server as the building blocks to facilitate the construction of a privacy preserving CNN for any existing or future network. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{P}^{3}$</tex> leverages the additive secret sharing theory to develop secure functions for perception networks. A vehicle's sensor data is split and encrypted into multiple secret shares, each of which is processed on an edge server by going through the secure layers of a detection network. The detection results can only be obtained by combining the partial results from the participating edge servers. We present two use cases where the secure layers in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{P}^{3}$</tex> are used to build privacy preserving both single-stage and two-stage object detection CNNs. Experimental results indicate data privacy for vehicles is protected without comprising the detection accuracy and with a reasonable amount of performance degradation. To the best of our knowledge, this is the first work that provides a generic framework to ease the development of vehicle-edge perception networks protecting data privacy.
An Orchestrator Architecture for Multi-tier Edge/Cloud Video Streaming Services

Video streaming has become a prevalent form of entertainment and a vital means of communication, but the challenges of delivering high quality video content over the internet are numerous. One of the key challenges is the varying network conditions that can significantly impact video streaming quality, such as bandwidth fluctuations and packet loss. To overcome these challenges, an adaptive video streaming architecture is needed to adjust the video streaming in real-time to match the changing network conditions and ensure a high Quality of Experience (QoE) for the end-user. This article presents MIGRATE, an orchestrator architecture for video streaming services capable of adapting to user demand in real-time. The study considers an edge/cloud multi-tier network infrastructure. In addition, an Integer Linear Programming (ILP) model and a Greedy solution are proposed to decide the distribution of connections between users and services. Experimental results show that based on the optimization strategy used, it is observed that there is a trade-off between the resources used and the QoE provided to users. Further, we discuss the importance of considering QoE metrics and user engagement in designing video streaming systems.
Tree-based Optimization for Image-to-Image Translation with Imbalanced Datasets on the Edge

Image-to-image (I2I) translation models typically refer to a class of adversarial architectures aiming to transfer an image content from a source domain to a target domain. To increase the image quality, data augmentation techniques or collecting new samples represent valid options yet lack of diversity and overfitting may negatively impact on the final results. In this regard, several practical scenarios do not permit to include new samples, or to employ powerful hardware, due to privacy policies or insufficient financial resources, leading to use imbalanced sets of images and favoring the more populated domain. To overcome these issues, we propose a simple and effective procedure to take advantage of the combination of critical learning parameters and demonstrate that averaging weights of multiple pre-trained <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{I}2\mathrm{I}$</tex> models is beneficial for increasing model performance, which can be optimized for edge computing without hurting the quality of synthesized images. To this end, we define a tree-based structure, including multiple <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{I}2\mathrm{I}$</tex> translation models, that outputs a single and more reliable network. We demonstrate that this strategy increases image quality and also show that our binary-tree learning procedure has a beneficial impact on edge devices, and it can be easily applied to architectures trained on different domains.
Incremental Instance Segmentation for Cluttered Baggage Threat Detection

Identification of contraband items from highly oc-cluded baggage of air travelers is a challenging task even for human experts with very high experience. Many researchers have been working rigorously to develop computer vision-based techniques for baggage screening through X-ray images. Nu-merous machine learning and deep learning-based frameworks have been proposed by researchers in the last two decades. However, all of these techniques face limitations in segmenting prohibited items from highly occluded and cluttered baggage. In this paper, we propose a novel framework based on semantic segmentation to automatically detect concealed prohibited items from X-ray baggage scans. Furthermore, to detect different overlapping instances of the same contraband item, we propose an instance-aware segmentation model that enables the semantic segmentation model to identify multiple instances of the same threat category through incremental learning without requiring additional overhead. The proposed framework is computationally lighter compared to other similar approaches as it requires min-imal training examples and leverages previous knowledge. The proposed model has outperformed state-of-the-art instance seg-mentation techniques when tested on publicly available GDXray and SIXray datasets, giving mean average precision scores of 0.50 and 0.47, respectively. In addition, the proposed framework leads other instance segmentation baseline models in terms of mean inference time.
Detection Transformer Framework for Recognition of Heavily Occluded Suspicious Objects

Baggage scanning for potential dangers has become a significant worldwide concern in the aviation industry. The manual process of identifying prohibited items can be tedious and chaotic. Researchers have created automated systems to detect potential threats in baggage using X-ray scans, but these systems can still miss items that are hidden or surrounded by clutter. This paper proposes a novel Detection Transformer (DETR) framework for detecting and classifying highly cluttered suspicious items. The proposed framework consists first of extracting the features from the CNN backbone using object proposals that are obtained based on coherent contour maps. These weights are then passed to the CNN model in the DETR to extract the features from the original scan and, therefore, enhance the feature extraction process. In this stage, we feed the transformer encoder-decoder with the representative features for predicting cluttered and concealed prohibited items bounding boxes. The proposed framework has been rigorously evaluated and tested using a total of 47,677 X-ray scans from the publicly available PIDray dataset, where it outperformed the state-of-the-art scheme by 2.10%, 4.05% and 3.64% in terms of mean average precision for easy, hard, and hidden subsets from PIDray dataset, respectively.
Research on Zero Trust Evaluation Method for Network Security

With the rapid progress and development in fields such as cloud computing, Internet of Things, and mobile offices, the traditional fortress-style network architecture has exposed many problems. In response to the increasingly severe network security situation, zero-trust security proposes a new network architecture based on the idea of “continuous verification, never trust”. The zero-trust network architecture can effectively reduce traditional network security threats and better protect network systems and sensitive resources. Based on the zero-trust security concept, this article proposed a new access control model based on trust and attributes, introduced the mechanisms and algorithms of the access control model, and designed a zero-trust simulation platform for testing. This model can determine whether to allow the request based on the trust value and relevant security attributes of the access request, which greatly improves the security of the network system and reduces security risks from internal and external sources.
Joint Graph Convolutional Network and Convolutional Neural Network for Recommendation

The recommendation algorithms based on graph convolutional network can extract the association relationship between users and items by interaction data, but most of these algorithms ignore the general preferences in the review data of users and items. The recommendation algorithms based on convolutional neural network with review can learn user behaviors and item properties. In order to combine the advantages of two kinds of algorithm and make use of both review text data and user-item interaction data fully, we proposed a new model called Joint Graph Convolutional Network and Convolutional Neural Network for Recommendation (JGCR) in this paper. This model used the graph convolutional network to extract association relations, and used convolutional neural network to extract the general preferences from review text, and carried out feature fusion to improve the recommendation effect. This paper conducted comparative experiments on four datasets of amazon dataset to verify the effectiveness of the proposed model. The evaluation metric is mean square error. Experiments results demonstrate that JGCR significantly outperforms all baseline recommendation algorithms. The feature from jointing graph convolutional network and convolutional neural network can improve the recommendation effect.
Progressively Coding the Human Visual Information for Auroral Substorm Recognition

For a long time, selecting specific events from a large number of auroral sequences and analyzing them has been a routine process for space physics experts to study space activities between the Sun and the Earth. As a common space event with greater impact on the magnetosphere and ionosphere, substorms are a hot topic in the space physical field. Therefore, the automated identification of substorm sequences is an ongoing task. At the same time, due to the particularity of substorm sequence data, people who do not belong to the field of space physics cannot label and identify them, which largely limits the application of artificial intelligence techniques in this field. Therefore, this paper proposes a progressive method for encoding visual information to automatically identify substorm sequences. Based on a small amount of substorm visual map labeled by manually, this method designs a stream to learn and predict these visual maps. Meanwhile, the predicted visual map will assist the network in extracting representative sequence features and finally getting the identification results of substorm sequences automatically. The series of experimental results show the effectiveness of the proposed method.