Reviewerly/exHarmony huggingface dataset:
A Location Privacy and Query Privacy Joint Protection Scheme for POI Query in Vehicular Networks

In vehicular networks, the Point of Interest (POI) query was widely used in Location-Based Services (LBS) for vehicle applications. However, since the attackers can easily access the location, query content, and other information, it is difficult to protect the LBS privacy of vehicle users only using location privacy protection or query privacy protection independently. Therefore, we propose a location privacy and query privacy joint protection scheme based on dummy sequences. According to the limitations of the POI query, we model the correlations between location privacy and query privacy into semantic correlation, temporal correlation, and spatial-temporal attribute correlation, characterized by the Euclidean distance and the association rule algorithm. Moreover, we formulate the dummy sequence selection as a constrained multi-objective optimization problem to obtain the query sequence with a high level of anonymity and a big cloaking region. Experimental results demonstrate that our scheme can resist joint attacks on location privacy and query privacy and protect users' LBS privacy more efficiently compared to the existing schemes.
Weighted Sum-Rate Maximization with Causal Inference for Latent Interference Estimation

The paper investigates the weighted sum-rate maximization (WSRM) problem with latent interfering sources outside the known network, whose power allocation policy is hidden from and uncontrollable to optimization.The paper extends the famous alternate optimization algorithm weighted minimum mean square error (WMMSE) [1] under a causal inference framework to tackle with WSRM.Specifically, with the possibility of power policy shifting in the hidden network, computing an iterating direction based only on the observed interference inherently implies that counterfactual is ignored in decision making.A method called synthetic control (SC) is used to estimate the counterfactual.For any link in the known network, SC constructs a convex combination of the interference on other links and uses it as an estimate for the counterfactual.Power iteration in the proposed SC-WMMSE is performed taking into account both the observed interference and its counterfactual.SC-WMMSE requires no more information than the original WMMSE in the optimization stage.To our best knowledge, this is the first paper explores the potential of SC in assisting mathematical optimization in addressing classic wireless optimization problems.Numerical results suggest the superiority of the SC-WMMSE over the original in both convergence and objective.
Social Networks Based Robust Federated Learning for Encrypted Traffic Classification

The encrypted traffic classification based on federated learning has become one of the key concerns since it can effectively provide expansion and privacy protection for traffic dataset. However, existing classification models suffer from low robustness and slow convergence in the presence of abnormal traffic data on the client side. We note that the clients participating in the training are operated by humans in social networks, and their communication with each other generate social traffic. By introducing the traffic data into the federated learning classification model, the correlation between the respective small model parameters of the clients can be increased, which can be leveraged to quickly detect abnormal clients and improve the model performance. The effectiveness of the scheme is verified on a classical public dataset and the results show that this WS network structure converges the fastest and the degree distribution has an overall linear relationship with the convergence speed. Our scheme is still highly robust with abnormal data and the model convergence speed is significantly better than other methods. Compared with the existing method, the model of social networks based classification of federated encrypted traffic has 2.5 % higher accuracy, 6.1 % higher recall, and more than 39.3% fewer communication rounds, respectively.
A Hybrid CNN-RF Classifier with Multi-Dimensional Early-Exit Strategy for Radio Frequency Fingerprinting

With the development of wireless communication technology and the increasingly complex electromagnetic environment, radio frequency fingerprinting (RFF) plays a vital role in improving the security of communication and information systems. Recently, many RFF algorithms based on machine learning have emerged. However, most of them focus on improving the accuracy of RFF identification but ignore the computational cost. This paper proposes a novel classifier composed of a convolutional neural network (CNN) backbone with two random forest branches, called hybrid CNN-RF. Under the scheduling of the proposed multi-dimensional early exit strategy, hybrid CNN-RF can end early when processing “easy” samples to reduce the computational cost, and activate the inference of the subsequent network layer when processing “hard” samples to ensure accuracy. Finally, our experiments show that the proposed algorithm can reduce the computational cost by a factor of 3.41 while improving the accuracy by 1.5%.
Learning-Based Route Selection in Noisy Quantum Communication Networks

Finding a path with the least overall noise from quantum memories, fibers, and gate operations in a quantum network involves the challenge of acquiring knowledge of these noises, their sources, and the time of occurrences. In this paper, we propose a reinforcement learning-based route selection approach that uses a multi-arm bandit algorithm to find the least noisy path from a transmitter (Tx) to a receiver (Rx), without considering any information on qubit decoherence due to probabilistic noises inherent in quantum memories and imperfect gate operations. It only uses network deployment knowledge to find a set of feasible paths from Tx to Rx. We provide a key finding from a network design perspective which says that performing entanglement swapping on nodes within a path in a non-synchronized and parallel manner not always reduces the decoherence experienced in achieving the end-to-end entanglement on that path. Further, we design and open-source a new simulator for simulating probabilistic noises encountered during entanglement distribution between Tx-Rx on a path, which has supporting callable functions for connecting the unknown network environment required for interaction with the multi-arm bandit agent. The simulation results demonstrate that our proposed route selection approach provides a path up to ~ 33% better fidelity (less noise) compared to conventional, distance-based route selection approach for the considered quantum network.
A Topological Semantic Mapping Method Based on Text-Based Unsupervised Image Segmentation for Assistive Indoor Navigation

Recently, much effort has gone into developing travel technology aids in indoor scenes, intending to increase the autonomy and quality of life of Blind or Visually Impaired (BVI) people. However, existing indoor navigation methods require accurate prior maps. Yet such maps are not always available and often difficult for people, especially the BVI, to acquire or construct on site in real-time. To tackle such important issue, this work proposes a topological semantic mapping method for the BVI such that they can well navigate such indoor environment as hospitals. The distinctive feature of this approach is its utilization of non-standard floor plans, which can be easily obtained from real-world settings, as the primary data source. The proposed method that generating a sparse Topological Semantic Map (TSM) from a floor plan includes: 1) preprocessing of captured floor plan images to extract a map area and correct a map view; 2) segmentation of an accessible area through a proposed Text-based Unsupervised Image Segmentation (TUIS) network; and 3) node calculation and semantic matching. Experimental results demonstrate the validity of the constructed TSM as well as TUIS’s superiority to existing methods.
Analysis and Optimization of Model Aggregation Performance in Federated Learning

Recently, federated learning (FL), which replaces data sharing with model sharing, has emerged as an efficient and privacy-friendly machine learning paradigm. One of the main challenges in FL is the huge communication cost for model aggregation. Many compression/quantization schemes have been proposed to reduce the communication cost of model aggregation, with remarkable results. However, the following two questions remain unanswered: What are the performance limits of model aggregation? How much can FL convergence performance be improved by modifying existing model aggregation schemes? In this paper, we manage to answer these two questions. Specifically, for the first question, we put forth a general model aggregation performance analysis framework based on the rate-distortion theory. We then derive an inner bound of the rate-distortion region of model aggregation. For the second question, we develop an algorithm to search for the minimum achievable aggregation distortion, which, combined with the achievability scheme of the derived inner bound, induces a method to approximate the limits of FL convergence performance numerically. Numerical results demonstrate that the baseline model aggregation schemes still have great potential for further improvement.
Interpreting Neural Min-Sum Decoders

In decoding linear block codes, it was shown that noticeable reliability gains can be achieved by introducing learnable parameters to the Belief Propagation (BP) decoder. Despite the success of these methods, there are two key open problems. The first is the lack of interpretation of the learned weights, and the other is the lack of analysis for non-AWGN channels. In this work, we aim to bridge this gap by providing insights into the weights learned and their connection to the structure of the underlying code. We show that the weights are heavily influenced by the distribution of short cycles in the code. We next look at the performance of these decoders in non-AWGN channels, both synthetic and over-the-air channels, and study the complexity vs. performance trade-offs, demonstrating that increasing the number of parameters helps significantly in complex channels. Finally, we show that the decoders with learned weights achieve higher reliability than those with weights optimized analytically under the Gaussian approximation.
Enhancing Virtual Audio Immersion Using Binaural Mesh

Binauralization of sound sources has become a dominant form of virtual spatialization in arts, entertainment, and industry. One of the greatest challenges associated with binauralization of virtual sources has been balancing scalability with fidelity. In the following paper the author presents a novel approach to binauralizing virtual sound sources using D4 library's Binaural Mesh. The paper presents the rationale for the Binaural Mesh, outlines its newfound affordances, including Motion Blur, Spatial Mask, as well as ultradirectionality and a capacity for improved spatial image. Further, the paper offers a feasibility study focusing on Binaural Mesh's potential to scale by optimizing computational overhead in source rich environments that exceed the capacity of the existing high-fidelity HRTF-based counterparts.
Adaptive User Scheduling and Resource Allocation in Wireless Federated Learning Networks: A Deep Reinforcement Learning Approach

Federated Learning (FL) is widely regarded as a leading distributed machine learning paradigm, owing to its outstanding performance in preserving privacy and conserving communication resources. To use it efficiently in wireless communication networks, novel transmission schemes that jointly consider the model propagation and training features are required. In this paper, a novel joint user scheduling and resource allocation scheme is proposed to reduce the communication cost in terms of the weighted sum of energy and time consumption while ensuring the convergence of FL. The time-varying channels and unpredictable model loss in the system make it difficult to use conventional optimization methods for this problem. Furthermore, considering optimal transmission policy in FL is to train a qualified model in the dynamic iterative process, a deep reinforcement learning based Proximal Policy Optimization (PPO) approach is employed to train an automatic policy maker. Specifically, the dynamic policy is decided in each training round based on the observed model accuracy and the time-varying channel gains, aiming at minimizing the total cost. Simulation results verify the proposed scheme can reduce the defined communication cost and improve the training efficiency compared with the traditional greedy and random benchmarks.
DRL-Based Workload Allocation for Distributed Coded Machine Learning

Over the past years, Distributed Machine Learning (DML) has been employed to tackle the high complexity problem with many Machine Learning (ML) algorithms. With DML, the original computation task involved in an ML algorithm is first split into multiple subtasks, which are forwarded to a group of computing devices in a distributed environment. Thereafter, the subtask results are collected in order to arrive at the final result for the original task. Despite the advantages of DML, the overall computation time can be seriously increased if some subtask results cannot be collected in a timely manner due to device malfunctions or network glitches. Recently, Distributed Coded Machine Learning (DCML) has been proposed to mitigate the problem with DML. Specifically, DCML employs coding techniques to inject redundancy into the original computation task. With the injected redundancy, DCML does not need to collect all subtask results to construct the result for the original task. So far, how to split the original task and thereafter assign an appropriate workload to each computing device in DCML has been a challenging problem. In this paper, we propose a novel work allocation scheme for DCML, DWA, to tackle the challenging problem. Our experimental results indicate that DWA outperforms the existing DCML schemes.
Dynamic Pruning for Distributed Inference via Explainable AI: A Healthcare Use Case

The healthcare sector has undergone a significant transformation with the widespread adoption of Deep Neural Networks (DNN). However, due to privacy constraints and stringent latency requirements, online remote inference is not a viable option in healthcare scenarios. Many efforts have been conducted to enable local computation, such as network compression using pruning or DNN distribution among multiple resource-constrained devices. Yet, it is still challenging to conduct distributed inference due to the latency and energy overheads resulting from intermediate shared data. On the other hand, given that realistic healthcare systems use pre-trained models, local pruning and fine-tuning relying only on the scarce and biased data is not possible. Even pre-pruned DNNs are not efficient as they are not customized to the local load of data and the dynamics of devices. The dynamic and online pruning of DNN without fine-tuning is a promising solution; however, it was not considered in the literature as most well-known techniques do not perform well without adjustment. In this paper, driven by the data restrictions in healthcare sector, we propose a novel pruning strategy based on Explainable AI (XAI), with a target to enhance the pruned DNN performance without fine-tuning. Moreover, to maintain the highest possible accuracy, we propose to combine distribution and pruning techniques to perform online distributed inference assisted by dynamic pruning only when needed. Our experiments show the performance of our pruning criterion compared to other reference techniques, in addition to its ability to assist the distribution by reducing the shared data, while keeping high accuracy.
Optimal Privacy Preserving in Wireless Federated Learning Over Mobile Edge Computing

Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve the user differential privacy while reducing the wireless resources. Specifically, an FL learning process can be fused with quantized Binomial mechanism-based updates contributed by multiple users to reduce the communication overhead/cost as well as to protect the privacy of participating users. However, the optimization of wireless transmission and quantization parameters (e.g., transmit power, bandwidth, and quantization bits) as well as the added noise while guaranteeing the privacy requirement and the performance of the learned FL model remains an open and challenging problem. In this paper, we aim to jointly optimize the level of quantization, parameters of the Binomial mechanism, and devices' transmit powers to minimize the training time under the constraints of the wireless networks. The resulting optimization turns out to be a Mixed Integer Non-linear Programming (MINLP) problem, which is known to be NP-hard. To tackle it, we transform this MINLP problem into a new problem whose solutions are proved to be the optimal solutions of the original one. We then propose an approximate algorithm that can solve the transformed problem with an arbitrary relative error guarantee. Intensive simulations show that for the same wireless resources the proposed approach achieves the highest accuracy, close to that of the conventional FL with no quantization and no noise added. This suggests the faster convergence/training time of the proposed wireless FL framework while optimally preserving users' privacy.
Camera Scheduling Techniques for Optimal Camera Placement

Recently, various research and developments have been conducted to utilize real information in virtual reality. When producing 3D data provided in virtual space, it is necessary to arrange a camera capable of obtaining real object information in order to restore real object information to virtual space. At this time, it is necessary to correct how the arrangement of the cameras should be configured in order to secure information by arranging the cameras in the optimal position. It is necessary to apply the scheduling technique to prevent a situation in which only one camera among the deployed cameras is continuously calibrated and the remaining cameras are not calibrated within a limited time. In this paper, in order to restore 3D information in virtual space, in the process of acquiring information by arranging multiple unstructured cameras, when calibrating unstructured cameras, each camera is optimized so that each camera can be placed in the optimal position. In order to prevent the situation in progress, we propose a camera allocation resource distribution method that applies Round-Robin Scheduling to camera optimization task allocation. Through the results in this paper, it is possible to obtain high-quality real information, secure high accuracy of the information, and optimize camera placement required for 3D data restoration in virtual space through efficient resource distribution of camera placement.
NFlowGAN: High-Utility Privacy-Preserving Network Flow Synthesis Based on GAN

The sensitivity of network traffic data has led to the scarcity of public traffic datasets, hindering the development of data-driven research in this field. Researchers proposed publishing synthetic network traffic instead of the original dataset. However, existing traffic synthesis methods are inadequate in data utility and seldom consider privacy protection. For this reason, we propose NFlowGAN for high-utility privacy-preserving network flow synthesis. We introduce spectral normalization in the network structure to improve training stability, thus improving the data utility. In addition, we add a Gaussian noise layer to the discriminator of NFlowGAN to provide higher privacy guarantees for the synthesized flow. The experimental evaluation results on the Darknet2020 dataset demonstrate that our proposed NFlowGAN achieves a significant improvement in data utility with privacy preservation compared to the two baselines. The synthesized high-utility dataset can be widely shared for research and educational purposes.
Unsupervised Wireless Diarization: A Potential New Attack on Encrypted Wireless Networks

We present a new threat model enabling a passive adversary to infer which overheard packets belong to which transmitters. We call this threat model unsupervised wireless diarization (UWD) where the adversary assigns transmitter identity (label) to received packets in an encrypted wireless network without access to the MAC headers. To demonstrate the feasibility of such an attack, we develop UWDNet, a wireless diarization pipeline comprised of a Siamese neural network to extract embeddings from received packets, a similarity metric to compare embeddings, and unsupervised clustering. We evaluate UWDNet on both synthetic datasets and datasets of real wireless transmissions collected using Rice University's configurable massive MIMO testbed RENEW. Via various experimentation scenarios, our initial results show that UWDNet achieves a diarization accuracy of above 90% on synthetic data of transmitters it has never seen. To push the limits of performance evaluation, we collected a real radio transmissions dataset representing a worst-case (almost pathological) setting where all nodes are co-located. Even in this near-pathological case, UWDNet accuracy is > 60% – well above a random label assignment, indicating the feasibility of unsupervised wireless diarization in real-life scenarios. We also analyzed different factors such as the spatial channel and transmit parameters, which impact diarization accuracy in real-world scenarios.
Semantic Text Compression for Classification

We study semantic compression for text where meanings contained in the text are conveyed to a source decoder, e.g., for classification.The main motivator to move to such an approach of recovering the meaning without requiring exact reconstruction is the potential resource savings, both in storage and in conveying the information to another node.Towards this end, we propose semantic quantization and compression approaches for text where we utilize sentence embeddings and the semantic distortion metric to preserve the meaning.Our results demonstrate that the proposed semantic approaches result in substantial (orders of magnitude) savings in the required number of bits for message representation at the expense of very modest accuracy loss compared to the semantic agnostic baseline.We compare the results of proposed approaches and observe that resource savings enabled by semantic quantization can be further amplified by semantic clustering.Importantly, we observe the generalizability of the proposed methodology which produces excellent results on many benchmark text classification datasets with a diverse array of contexts.