Reviewerly/exHarmony huggingface dataset:
Secure and Efficient Coded Multi-Access Edge Computing with Generalized Graph Neural Networks

We formulate a novel framework to improve security and utility of the coded multi-access edge computing (MEC) network for Internet of Things (IoT) applications where multiple edge servers (ESs) jointly process raw IoT data to obtain the final network output. To correctly recover the final output even when some processing outputs produced by malicious or malfunctioning ESs are erroneous, the network utilizes coded distributed computing (CDC) that enhances security by adding computational redundancy to the data processed by ESs. Within the framework, we propose an advanced approach to address limitations of contemporary CDC-based systems related to their inability to guarantee security when the number of malicious ESs is large and reduced network utility due to redundant computations. In this approach, the processing loads are allocated to ESs based on deep learning (DL) algorithms to identify the unknown ESs types (faithful or malicious) and minimize the load of malicious ESs, thereby optimizing security and utility. The proposed DL algorithms adopt the message passing neural network (NN) a generalized graph NN with lower complexity and faster convergence than conventional NNs. We prove that our framework yields the optimal security and utility, and verify its superior performance compared with the state-of-the-art schemes.
Cross-Subject Activity Detection for COVID-19 Infection Avoidance Based on Automatically Annotated IMU Data

The World Health Organization reported that face touching is a primary source of infection transmission of viral diseases, including COVID-19, seasonal Influenza, Swine flu, Ebola virus, etc. Thus, people have been advised to avoid such activity to break the viral transmission chain. However, empirical studies showed that it is either impossible or difficult to avoid as it is unconsciously a human habit. This gives rise to the need to develop means enabling the automatic prediction of the occurrence of such activity. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeSense</i> , a cross-subject face-touch prediction system that combines the sensing capability of smartwatches and smartphones. The system includes innovative modules for automatically labeling the smartwatches’ sensor measurements using smartphones’ proximity sensors during normal phone use. Additionally, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeSense</i> uses a multi-task learning approach based on autoencoders for learning a subject-invariant representation without any assumptions about the target subjects. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeSense</i> also improves the deep model’s generalization ability and incorporates different modules to boost the per-subject system’s accuracy and robustness at run-time. We evaluated the proposed system on ten subjects using three different smartwatches and their connected phones. Results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeSense</i> can obtain as high as 97.9% prediction accuracy with a F1-score of 0.98. This outperforms the state-of-the-art techniques in all the considered scenarios without extra data collection overhead. These results highlight the feasibility of the proposed system for boosting public safety.
CD-SemMF: Cross-Domain Semantic Relatedness Based Matrix Factorization Model Enabled With Linked Open Data for User Cold Start Issue

Personalized recommendations to cold start user is one of the significant challenges in information filtering systems.Most of the existing systems inherited the idea of collaborative filtering (CF) and avoids the item metadata.For instance, consider a book domain whose metadata are author, publisher, language...etc.The elimination of item metadata is due to the diverse nature of item attributes in a crossdomain environment.Because of this ideology, it is hard for the filtering systems to provide better recommendations to cold start users.Cold start users are people who are new to a domain whose preferences are unknown.A novel model is proposed in this research work called Cross-Domain Semantic Relatedness based Matrix Factorization model (CD-SemMF) resolves the user cold start issue in collaborative filtering recommender system by exploiting Linked Open Data (LOD)."DBpedia" is a widely used LOD resource that contains semantic information about different domains, which is used in this research work to resolve the above-said problem.Here, the metadata available in LOD connects the items preferred by the target user from various domains.The fundamental knowledge graph links items from various domains and also benefits from cross-domain information."LOD Semantic-Relatedness Measure" a new measure is proposed which calculates the closeness of items across domains.Semantic relatedness is measured instead of similarity in this research because the cross-domain item attributes are diverse.The Alternating Least Square method is applied here to learn the user preferences.The proposed model provides relevant, personalized recommendations for the target new user with the user preferences gained from the source domain and by exploiting item semantic relatedness.Experimental evaluation is done on Facebook and Amazon datasets.It is observed from the result that the proposed CD-SemMF model gives better recommendations in target domain for new users than the baseline methods.
Gaze Target Estimation Inspired by Interactive Attention

As an essential nonverbal cue, the human gaze reveals human intentions and plays a crucial role in human daily activities. Therefore, automatic detection of the person’s gaze target has drawn the interests of the computer vision community. This is useful not only for identifying whether children are attentive in class but also for locating items of interest to humans in retail settings. Existing gaze-following methods have only explored and exploited the scenes context and the head cues. Considering the significance of human-object interaction in understanding human intentions, we present the Visual-Spatial Graph and introduce a graph attention network to analyze the interaction probability between the human and elements in the scene. Then the interaction probability inferred from the visual-spatial information that is aggregated by the attention mechanism can be transformed into an interactive attention map that depicts the areas people care about. In addition, we construct a transformer as an encoder to integrate the features extracted by the scene and head pathways aiming to decode the gaze target. After introducing interactive attention, our proposed method achieves outstanding performance on two benchmarks: GazeFollow and VideoAttentionTarget. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nkuhzx/VSG-IA</uri> .
Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation

Abstract Synthetic, i.e., computer-generated imagery (CGI) data is a key component for training and validating deep-learning-based perceptive functions due to its ability to simulate rare cases, avoidance of privacy issues, and generation of pixel-accurate ground truth data. Today, physical-based rendering (PBR) engines simulate already a wealth of realistic optical effects but are mainly focused on the human perception system. Whereas the perceptive functions require realistic images modeled with sensor artifacts as close as possible toward the sensor, the training data has been recorded. This chapter proposes a way to improve the data synthesis process by application of realistic sensor artifacts. To do this, one has to overcome the domain distance between real-world imagery and the synthetic imagery. Therefore, we propose a measure which captures the generalization distance of two distinct datasets which have been trained on the same model. With this measure the data synthesis pipeline can be improved to produce realistic sensor-simulated images which are closer to the real-world domain. The proposed measure is based on the Wasserstein distance (earth mover’s distance, EMD) over the performance metric mean intersection-over-union (mIoU) on a per-image basis, comparing synthetic and real datasets using deep neural networks (DNNs) for semantic segmentation. This measure is subsequently used to match the characteristic of a real-world camera for the image synthesis pipeline which considers realistic sensor noise and lens artifacts. Comparing the measure with the well-established Fréchet inception distance (FID) on real and artificial datasets demonstrates the ability to interpret the generalization distance which is inherent asymmetric and more informative than just a simple distance measure. Furthermore, we use the metric as an optimization criterion to adapt a synthetic dataset to a real dataset, decreasing the EMD distance between a synthetic and the Cityscapes dataset from 32.67 to 27.48 and increasing the mIoU of our test algorithm () from 40.36 to $$47.63\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>47.63</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> .
A Comprehensive Analysis Method for Reversible Data Hiding in Stream-Cipher-Encrypted Images

Reversible data hiding in encrypted images (RDHEI), an essential branch of reversible data hiding (RDH), has been in development for more than a decade. For most existing stream-cipher-based RDHEI algorithms, encryption schemes are often not the same; thus, these schemes have different effects on the encrypted images. As a result, it is not reasonable to compare the embedding rates (ERs) of RDHEI algorithms directly as we do for plaintext RDH algorithms. However, to our knowledge, many studies focus on the performance of embedding but neglect the influence of the encryption. To compare the performance of stream-cipher-based RDHEI algorithms more reasonably, this paper proposes a novel comprehensive measure to evaluate state-of-the-art RDHEI algorithms. First, the characteristics of the stream-cipher-based RDHEI algorithms are divided into two categories according to the encryption and embedding processes. Next, we use correlation and redundancy to evaluate the influence of encryption schemes and also use ER, computation complexity, visual quality, and algorithm stability to evaluate the embedding schemes. In the end, in order to combine all six indexes into the final evaluation measure, we standardize each index before applying the radar chart. The experimental results show the evaluation results of the recent RDHEI algorithms via a comprehensive analysis and demonstrate the guiding significance of the proposed method for the current RDHEI algorithm selection.
Modeling of Critically Ill Patient Pathways to Support Intensive Care Delivery

The COVID-19 pandemic has exposed long standing deficiencies in critical care knowledge and practice in hospitals worldwide. New methods and strategies to facilitate timely and accurate interventions are needed. A virtual counterpart (digital twin) to critically ill patients would allow bedside providers to visualize how the organ systems interact to cause a clinical effect, offering them the opportunity to evaluate the effect of a specific intervention on a virtual patient before exposing an actual patient to potential harm. This work aims at developing a digital simulation that models the clinical pathway of critically ill patients. Using the mixed-methods approach with the support of multiprofessional clinical experts, we first identify the causal and associative relationships between organ systems, medical conditions, clinical markers, and interventions. We record these relationships as structured expert rules, depict them in a directed acyclic graph (DAG) format, and store them in a graph database (Neo4j). These structured expert rules are subsequently utilized to drive a simulation application that enables users to simulate the state trajectory of critically ill patients over a given simulated time period to test the impact of different interventions on patient outcomes. This simulation model will be the engine driving a future digital twin prototype, which will be used as an educational tool for medical students, and as a bedside decision support tool to enable clinicians to make faster and more informed treatment decisions.
Towards design guidance for the digitalisation of work instructions by focusing on technological possibilities and industrial requirements

In consequence of an ongoing change in manufacturing towards cyber-physical production systems (Industry 4.0), there is pressure for industry innovation to maintain and improve competitiveness. One aspect of this process is the digitalisation of work instructions, for example, in assembly or maintenance to use optimization potentials and counteract the increasing complexity and variety of products. A 1:1 transformation of paper-based instructions into a digitalised version often can not assess the full potential of digitalisation and does not fit the underlying hardware. Therefore, the present work deals with a presentation and analysis of existing procedures and possibilities for the digitalisation of work instructions and relates them methodically to the industry’s environmental, work and process-related conditions and requirements. Here, we distinguish between (1) different digitalisation options (text-based, image-based, video/animation-based and Augmented Reality (AR)-based) and (2) different industrial requirements (task and process requirements, user requirements, environmental conditions). A special emphasis is put on AR-based approaches. As a result, we provide design guidance for practitioners’ when chosing digitalisation options for work instructions with respect to present industrial requirements and create a basis for further research work.
RF Fingerprint-Identification-Based Reliable Resource Allocation in an Internet of Battle Things

With the advances of Internet of Things (IoT) technology, the Internet of Battlefield Things (IoBT) has revolutionized the military battlefield. Unlike civilian IoT applications, a few adversarial attackers may sneak into an IoBT and destroy the network infrastructure by generating cyberattacks, e.g., Distributed Denial-of-Service (DDoS) attacks, making the Quality of Service (QoS) of network dramatically decrease. To improve the performance of a network, we propose two schemes of enhancing network QoS: one is user access control with the aid of RF fingerprinting because of small-scaled available signal samples, while the other is the optimization of network performance. Specifically, we address the optimization of network utility with the methods of power allocation and channel allocation. Mathematically, we propose a mixed-integer nonlinear (MIN) problem, which optimizes the allocation of wireless resources. Also, the original optimization problem is decomposed into a power allocation subproblem and a channel allocation subproblem, and both these subproblems are nondeterministic polynomial (NP) hard problems. We propose a few approximate algorithms, which can iteratively converge to the optimum of the original problem in polynomial time. The simulation results show that our proposed algorithms can improve network utility and various network QoS than the other algorithms in an IoBT environment.
Safety Compliant Control for Robotic Manipulator With Task and Input Constraints

Increasingly, robots are working in close proximity with humans, and in dynamically changing environments. We present a new control architecture that guarantees safety under such conditions, while simultaneously ensuring that task goals are satisfied. Our approach combines Control Barrier Functions (to provide safety guarantees) with Rapidly Exponentially Stabilizing Control Lyapunov functions (to satisfy task constraints and ensure exponential convergence). We formulate the problem completely in the operational space, using super ellipsoids to define safe sets which are dynamically adapted to restrict the robot’s operational space in response to moving obstacles (including humans). Control barrier functions, which can be implemented in real time using quadratic programming, ensure the forward invariance of these safe sets, while minimally perturbing prescribed control trajectories. We demonstrate our approach on a seven degree-of-freedom Kuka LBR iiwa robotic manipulator, both in a physics engine-based simulation and with real hardware experiments.
ENSURE: A Deep Learning Approach for Enhancing Situational Awareness in Surveillance Applications With Ubiquitous High-Dimensional Sensing

Judicious processing of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">High-Dimensional Sensing (HDS)</i> data streams is often the first step in a data-driven decision-support system. However, how one goes about processing large amounts of incoming HDS data streams for enhancing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">situational awareness (SA)</i> is a non-trivial, and often challenging task due to the interwoven nature of operational environments, especially in complex surveillance applications ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> predictive policing, urban reconnaissance). Toward addressing this challenge, a novel data processing framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ENSURE (ENhanced Situational Understanding with Ubiquitous-sensing REsources)</i> is presented. In particular, by using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">density-based clustering</i> in tandem with variable length sequence decoding methods, ubiquitous HDS data streams are clustered and then processed for early identification of ‘events’ and their sequences of interest to a (surveillance) task at hand. Then, by leveraging notions in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data association,</i> ENSURE ‘tracks’ the occurrence of such event sequences and their evolution over time to ‘perceive’ and ‘comprehend’ what’s happening in the operational environment. Therefore, ENSURE can be interpreted as an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">information fusion</i> approach for HDS systems for object recognition, classification, tracking, and decision-making tasks. Use of ENSURE is illustrated via a simulated experiment to provide insights into its behavior, performance and sensitivity to parameters.
Adversarial Analysis of ML-Based Anomaly Detection in Multi-Layer Network Automation

The fast development of multi-layer packet-over-optical networks has made network monitoring and troubleshooting increasingly complicated. This has stimulated people to combine machine learning (ML) and software-defined networking (SDN) to realize multi-layer network automation. Despite its initial successes, the vulnerabilities of multi-layer network automation have not been fully explored. This work studies how to mislead the ML-based classifiers for anomaly detection. Specifically, we design two adversarial-sample-based attack schemes based on the white-box attack (WBA) and black-box attack (BBA) strategies, respectively, to eavesdrop and tamper legitimate telemetry data samples and generate adversarial samples adaptively, for disturbing ML-based classifiers and in turn misleading network automation to make incorrect decisions. Compared with WBA, BBA makes the attack scheme more practical by minimizing the dependency on pre-knowledge of the target ML-based classifiers. Considering different types of ML-based classifiers, we build a real-world packet-over-optical testbed and leverage the telemetry samples collected in it to demonstrate that our proposed BBA scheme can interact with the network quietly to train itself, generate well-crafted adversarial samples to tamper legitimate telemetry samples in the hard-to-detect way, and mislead ML-based classifiers in the network automation system to severely affect their performance on anomaly detection.
IoT Data Blockchain-Based Transaction Model Using Zero-Knowledge Proofs and Proxy Re-encryption

While IoT devices provide people with richer and more complete services, they also generate a large amount of device data. The potential value of these data has spawned IoT data transactions. In the blockchain environment, due to its decentralization characteristics, the validity of transactions can be guaranteed, but the transaction information of the blockchain will be stored and visible by all nodes. The use of asymmetric encryption to realize data sharing on the blockchain faces the challenge of privacy. The risk of key leakage and flexible data sharing cannot be achieved. Aiming at this problem, this paper proposes an IoT data transaction model in the blockchain environment, realizes multi-party IoT data sharing with the help of proxy re-encryption, and uses zero-knowledge proof to realize the identity verification mechanism. Finally, experiments show that the model is effective and feasible in terms of cost and efficiency.
Multiaccess Edge Integrated Networking for Internet of Vehicles: A Blockchain-Based Deep Compressed Cooperative Learning Approach

Recently, Internet of Vehicles (IoV) and Machine Learning (ML) have attracted more and more attention. Considering inefficient real-time training and high requirements on computing capabilities of centralized data collection, performing Distributed Machine Learning (DML) in IoV has become an important research branch. However, the heterogeneity, mobility, and distrust among IoV nodes affect how to execute DML effectively, securely, and in a salable manner. In this paper, a blockchain-based Cooperative Learning framework combined with a Deep Compression method (CLDC) is proposed. First, we improve the local training efficiency of lightweight IoV nodes by using deep compression method. Meanwhile, we have introduced a blockchain system in CLDC, the significance of which is that we have completed the transformation from centralized architecture to distributed framework through the blockchain, and shared local training results in a verifiable manner. The framework uses non-tamperable features of the blockchain to ensure the security of local training results. Moreover, we propose a Learning-based Redundant Byzantine Fault Tolerance (L-RBFT) protocol, in which the primary node needs to confirm the loss percentage of learning in the transaction before forwarding the RBFT messages. The significance of L-RBFT is to ensure that IoV nodes obtain the best training results through the consensus of blockchain nodes. We use it to solve the computing and communication resource allocation problem in IoV to clarify the operating mechanism of the proposed framework. The experimental results prove that this scheme performs better when compared with the traditional centralized deep reinforcement learning method.