Reviewerly/exHarmony huggingface dataset:
ML-Based Advantage Distillation for Key Agreement in Underwater Acoustic Channels

Underwater networks have become an important topic of research due to their potential impact on several military and civil applications. To secure these communications, many protocols require the users to have symmetric secret keys. To distribute the keys, we can resort to physical layer key generation schemes, where the keys are generated by each user from the channel itself, by exploiting the environment as a source of randomness. We focus on the advantage distillation step of the secret key agreement (SKA) procedure, where the users extract the bit sequence from the channel observations. We propose an adversarial autoencoder (AAE) model for advantage distillation, which has multiple tasks: it enhances the reciprocity between legitimated channel features, decreases the reciprocity to the attacker channel features, and provides uniformly distributed bit sequences. The proposed approach is adaptive and directly derived from the training dataset, thus it does not require the a priori knowledge of the channel statistics. The solution is tested using channel observations collected from a sea experiment and compared to existing advantage distillation solutions, showing a significant improvement over the state of the art.
Predicting Generalization in Deep Learning Using Data Augmentation and posterior Probability Estimators

We propose a new method for predicting generalization in deep learning, which is based on data augmentation procedure in conjunction with posterior probability generalization predictors reminiscent of bolstered posterior probability error estimators. The proposed method calculates a penalty for each augmented data point depending on whether or not it is correctly classified by the trained network. A posterior probability estimator is used to define the penalty for correctly classified augmented images, whereas for incorrectly classified data, the penalty is computed as a function of the strength and amount of augmentation. Experimental results suggest that our proposed approach can effectively predict the generalization error for different network architectures and training datasets.
Point Multiplication ZigZag: An Optimized Co-Processor Architecture Design for SM2/3 Based on RISC-V

With the explosive growth of network information, demands for hardware implementation of cryptographic algorithms is increasing rapidly in recent years. However, the greatest limitation impeding extensive data encryption lies in that, there lack of efficient and lightweight cryptography implementations adaptable for ubiquitous smart devices, especially for those IoT devices, whose power and computations are both limited. In that case, a well-designed processing unit for cryptography computations is the critical way to boost ubiquitous usages of cryptography-based data protections. In this paper, we take SM2/SM3 algorithms as an example, to propose a brand new RISC-V compatible coprocessor design, named as ZigZag, aiming at easy usages in IoT environments. More specifically, we adopt two-fold optimizations for both SM2/3 computations on paralleled calculations in ZigZag, one is the paralleled double point multiplication design for SM2 computation acceleration, and the other is the optimization of SM3 in terms of message extension and compression functions. To justify the correctness and efficiency of the proposed ZigZag architecture, we implement a coprocessor prototype on the basis of RISC-V compatible development toolchains, running on Xilinx FPGA VCU118 when testing the prototype's performance. Our experiments show that, the ZigZag prototype takes 0.106 ms for a 256-bit point multiplication operation with 29.2k LUTs + 128 DSP hardware consumptions, which is 3.49 times faster in FPGA implementations than the state-of-the-art design.
Spatial Sampling in Mixed Reality

Spatial Sampler XR is a new musical instrument linking gesture capture to sound processing. In the same way that a sampler is an empty keyboard filled with sounds, Spatial Sampler XR uses gesture capture to transform the surrounding physical space into an area of keys for, recording, indexing and playing back samples. Spatial Sampler XR let the musician arrange the sound around him or her through gesture, creating a spatialized and interactive soundstage. A virtual reality headset adds to the instrument the ability to visualize the layout of sounds. The 3D immersion greatly facilitates their organization and increases the precision of the interaction. Several modes of play are possible and the interaction modalities vary according to the type of performance and the number of performers. This article first introduces the Synekine project, a ten-year research project from which the concept of spatial sampling is derived. It presents the technical devices used, the instruments created, the different modes of play in performative situations. The instrument relies on movement to link time and space. Thus, the Spatial Sampler XR is particularly suitable for movement artists as well as for extra-musical applications.
Dynamic nsNET2: Efficient Deep Noise Suppression with Early Exiting

Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resourceconstrained devices still proved challenging. Therefore, we present an early-exiting model based on nsNet2 that provides several levels of accuracy and resource savings by halting computations at different stages. Moreover, we adapt the original architecture by splitting the information flow to take into account the injected dynamism. We show the trade-offs between performance and computational complexity based on established metrics.
Generative Adversarial Network-Based Network Anomaly Detection with Unlabeled Data

In the rapidly evolving landscape of 5G networks, effective anomaly detection is becoming increasingly important to ensure network stability and security. Traditional methods often encounter issues due to the inherent complexity and diversity of network data, coupled with the lack of labeled data. Although there are some feasible artificial intelligence (AI) techniques available, their performance is still limited. To address these challenges, this paper proposes a network anomaly detection scheme. First, the strengths of Autoencoder (AE) and generative adversarial network (GAN) architectures is combined. Through feature transformation, it calculates anomaly scores for network data at specific time points, significantly enhancing detection accuracy. Second, the scheme is deployed in a distributed architecture, thereby improving the robustness and flexibility of network anomaly detection. Finally, the simulation results are provided in two distinct 5G vertical applications to show the performance gains of our proposed scheme.
IoT Data Security: An Integration of Blockchain and Federated Learning

Technological advancement has led to a rapid increase in the growth of IoT devices leading to a vast amount of generated data. Manufacturers of such devices utilize machine learning algorithms to extract valuable insights from user data. However, this can give rise to critical issues surrounding data leakage and privacy. To tackle these issues, utilizing blockchain as a decentralized database to securely store data and employing federated learning to extract useful insights from user data can provide a viable solution. In this paper, we propose a three-layered, decentralized architecture that uses a traditional federated learning mechanism in conjunction with the Ethereum blockchain. Moreover, for data management, we use Inter-Planetary File System (IPFS) which is a peer-to-peer network used to store data in a decentralized manner. We tested our model's feasibility by using CIFAR-10 dataset and Python as the programming language with a framework for federated learning on a general purpose computer. We used Ganache_v2.5.4 and Truffle_v5.4.22 for developing smart contracts and testing and deploying them over the Ethereum blockchain.
Dual Quaternion Rotational and Translational Equivariance in 3D Rigid Motion Modelling

Objects' rigid motions in 3D space are described by rotations and translations of a highly-correlated set of points, each with associated x, y, z coordinates that real-valued networks consider as separate entities, losing information. Previous works exploit quaternion algebra and their ability to model rotations in 3D space. However, these algebras do not properly encode translations, leading to sub-optimal performance in 3D learning tasks. To overcome these limitations, we employ a dual quaternion representation of rigid motions in the 3D space that jointly describes rotations and translations of point sets, processing each of the points as a single entity. Our approach is translation and rotation equivariant, so it does not suffer from shifts in the data and better learns object trajectories, as we validate in the experimental evaluations. Models endowed with this formulation outperform previous approaches in a human pose forecasting application, attesting to the effectiveness of the proposed dual quaternion formulation for rigid motions in 3D space.
Software Tools for Flexible Control of Radiation Synthesis

Compact spherical loudspeaker arrays are new tools for the creation of auditory objects in a space. Originally designed for musical and performance purposes – as a substitute for conventional cabinet loudspeakers lacking naturalness in playback – they intent to promote spatial presence and to emulate lifelike directional features of natural sources such as acoustic instruments and performers. They are typically used to reproduce the measured radiation pattern of sound sources, or to steer directional sound beams in order to excite the acoustic environment. The directivity pattern of the sound beams can be adjusted, in orientation and shape, by modal beamforming techniques.In electroacoustic music, it is most often desirable to apply dynamic beamforming, i.e. to vary the directional attributes over time, in order to effectively enable a sense of spatial presence or immersion. This paper presents a software tool, intended for creative applications, for the flexible design and intuitive manipulation of spherical beampatterns. In particular, we propose a simple parametric model that allows to synthesize a variety of high-order radiation patterns, and to smoothly transition between shapes. The tool is implemented in the Max environment, making it easy to extend and to interface with remote controllers for interactive applications.
Perception-Map Generation for Autonomous Driving Vehicle

With the development of deep learning technology, algorithms related to computer vision used for autonomous driving are becoming faster and more accurate. The software architecture of autonomous driving consists of perception, decision, and control stages. In this paper, the perception stage is composed by combining several sensors and algorithms. In the perception stage, obstacle recognition and traffic signal detection are main tasks for path determination. This work creates a map that includes all of this information, helping more efficient determination in the decision stage. The experiments were conducted using a 3D simulator with ROS-noetic. The performance of obstacle recognition is evaluated for accuracy, recall, precision by confusion matrix. This work achieves 95.6% of accuracy, 88.6% of recall, 76.6% of precision. The experiments of traffic light detection task are evaluated as error ratio of distance estimation by stereo vision. Average error rate was calculated as 7.25%.
Uplink Scheduling in Federated Learning: An Importance-Aware Approach via Graph Representation Learning

Federated Learning (FL) has emerged as a promising framework for distributed training of AI-based services, applications, and network procedures in 6G.One of the major challenges affecting the performance and efficiency of 6G wireless FL systems is the massive scheduling of user devices over resource-constrained channels.In this work, we argue that the uplink scheduling of FL client devices is a problem with a rich relational structure.To address this challenge, we propose a novel, energy-efficient, and importance-aware metric for client scheduling in FL applications by leveraging Unsupervised Graph Representation Learning (UGRL).Our proposed approach introduces a relational inductive bias in the scheduling process and does not require the collection of training feedback information from client devices, unlike state-of-the-art importance-aware mechanisms.We evaluate our proposed solution against baseline scheduling algorithms based on recently proposed metrics in the literature.Results show that, when considering scenarios of nodes exhibiting spatial relations, our approach can achieve an average gain of up to 10% in model accuracy and up to 17 times in energy efficiency compared to state-of-the-art importance-aware policies.
Significant Feature Dimensionality Reduction for Histopathology Image Retrieval as a Tool for Healthcare Decisions Support

Content-Based Histopathology Image Retrieval (CBHIR) is often used as a powerful tool to support decision making by clinicians, and facilitate the disease detection and diagnosis. In CBHIR, feature extraction plays a key role for representing and interpreting histopathological images of several tissues. To enhance the retrieval performance, combining several features is commonly used in CBHIR; however, the high-dimension of the combined features often makes the process of retrieving very time-consuming. To resolve this challenge, we propose in this paper a method for reducing the dimension of a combined feature vector formed of several features extracted using the Improved local binary patterns (ILBP) descriptor, and coded in different color spaces. To reduce the size of the combined color ILBP feature, we adopt a selection scheme for picking only the most important patterns of the latter according to optimal weights associated to the patterns. The optimal weights are generated using the Particle Swarm Optimization (PSO) algorithm. The results of the experiments performed on Kimia Path960 dataset confirm the efficiency of the proposed method in reducing the feature dimensionality and the retrieval time while maintaining as good as or better retrieval performance than the combined feature.
Mem-DeepSC: A Semantic Communication System with Memory

While semantic communications succeed in effectively transmitting due to the strong capability to extract the essential semantic information, it is still far from intelligent communications. In this paper, we introduce an essential component, memory, into semantic communications to mimic human communications. Particularly, we propose a deep learning (DL) based semantic communication system with memory, named Mem-DeepSC, by considering the scenario question answer as the task at the receiver. We exploit universal Transformer based transceiver to extract the semantic information and introduce the memory module to enhance the semantic decoding capability at the receiver. Moreover, we derive the semantic channel capacity and propose a consecutive dynamic transmission method to minimize the transmission latency. Numerical results show that Mem-DeepSC is superior to benchmarks in terms of answer accuracy and the number of transmitted symbols.
A Two-Step Feature Selection Procedure to Handle High-Dimensional Data in Regression Problems

A wide range of fields are interested in high-dimensional data, including finance, tomography, genetics, etc. Feature selection facilitates the handling of this type of data in Machine Learning. This improves prediction performance by separating relevant variables from irrelevant ones. In this paper, we propose a two-step feature selection method for regression. An initial feature space is reduced by feature screening. To remove extra noise and irrelevant variables, the resulting reduced dataset is fitted to an elastic net model. The proposed method improves elastic net models' prediction results. This is demonstrated in this paper through simulations and real datasets. We demonstrate our method's application to gene co-expression data analysis using the Eyedata dataset.
Hybrid Coded MapReduce for Latency-Constrained Tasks with Straggling Servers

Coded distributed computing (CDC) can efficiently improve the performance of Map Reduce by mitigating the effects of straggling servers and decreasing the communication load simultaneously. In this paper, for the latency-constrained computation tasks, we use hybrid coded MapReduce and optimize the hybrid coded scheme to minimize the total latency, including the computation latency in Map phase and the communication latency in Shuffle phase. For the practical scenarios where the number of straggling servers is relatively small compared with the number of all servers, we reformulate the original optimization problem into a non-convex optimization problem. Then we use the successive convex approximation (SCA) algorithm to approximate the non-convex optimization problem into the iterations of a convex optimization problem. We prove the effectiveness and convergence of the proposed iterative SCA algorithm. Besides, the proposed SCA-based algorithm shows the effective convergence and outperforms the enumeration algorithm of the original problem with the approximated total latency and the short execution time via numerical experiments. Furthermore, the hybrid coded scheme achieves significant performance improvement in terms of decreasing the total latency compared with baseline schemes.
LARR: A Localization-Assisted Method to Conceal Latency-Induced Position Errors in MR Remote Rendering

The future of social interaction lies in the Metaverse, where mixed reality (MR) plays a key role. With the growth of the Metaverse, there is a growing demand for volumetric videos in the media industry. However, rendering complex volumetric content on mobile devices with limited computing power presents a challenge. To address this, we propose a localization-assisted remote rendering (LARR) architecture for MR applications, which can effectively conceal delay-induced position error and display streamed objects accurately. The LARR architecture uses dedicated object streaming cameras (OSC) at the server for each 3D object to enable real-time position updates. In addition, stream data optimization is used to minimize unnecessary data transfer. The LARR approach reduces streaming data by 81% and 46% compared to non-optimized and existing MR streaming architectures in real-world experiments.
Private Status Retrieval for Blockchain-Based Certificate Revocation System

Blockchain is the most promising technology to tackle the security challenges of certificate revocation schemes, such as vulnerability to the single point of failure and lack of accountability systems. However, current blockchain-based certificate revocation systems suffer from privacy problems as the blockchain nodes can learn which website the client is going to connect with and infer the end-user's private information, such as identity, location, and health condition. In this paper, we propose a decentralized certificate revocation scheme that allows clients to securely and privately verify revocation information. We not only take advantage of blockchain to provide security guarantees but also further craft a novel multi-server offline/online private information retrieval (PIR) protocol named MOO-PIR to preserve query privacy for clients even if a subset of servers collude. Finally, we provide security analysis and performance evaluation, demonstrating that our scheme can protect client privacy without compromising efficiency.
Decentralized Two-Stage Federated Learning with Knowledge Transfer

Recently, federated learning has been widely applied, but the performance of the local model will be greatly decreased when the data distribution across different clients is heterogeneous. In this paper, we propose a decentralized two-stage federated learning scheme with knowledge transfer. Specifically, a ring federated learning model with knowledge transfer is constructed, by which users can acquire accurate service results. Meanwhile, a knowledge accumulation method based on conditional generative adversarial networks (CGAN) is designed, which can accumulate information from the previous edge nodes without compromising privacy. Furthermore, the accumulated global knowledge is learned by edge nodes through knowledge distillation to improve the performance of models on the global test dataset. Finally, the simulation results demonstrate that our proposed scheme achieves better classification accuracy and stability compared with the state-of-the-art methods.
Blockchain and Supply Chain in Algeria: Enhancing Transparency and Security of Operations

This article specializes in the implementation of Blockchain generation in deliver chain control in Algeria, with the aim of improving transparency and security of operations. The article highlights the importance of supply chains for local businesses and the global economy, introducing the decentralized architecture and secure capabilities of blockchain's distributed ledger. The work implements blockchain technology in supply chain management in Algeria, with a view to improving transparency and security of operations. We begin by highlighting the importance of blockchain as a key business priority and its potential to reshape the future of business through the process of reform and reconstruction. Next, we present an extensive overview of the advantages and limitations of blockchain technology. By analyzing its advantages and disadvantages, we also look at existing solutions to address these drawbacks. Finally, a use case is presented to validate this technology in the pharmaceutical sector in Algeria.
A Robustness Measure for Neural Networks

This paper introduces a novel method that can be used to determine relative robustness of multiple networks using only training data set without any need for regular or adversarial test data. Proposed method is based on measuring the curvature of the data manifold at the network output layer which is a full black-box approach and does not require any knowledge of network topology or parameters. The proposed method was tested using 19 different network models published in literature using CIFAR-10 dataset.
Vertex-Based Networks to Accelerate Path Planning Algorithms

Path planning plays a crucial role in various autonomy applications, and RRT* is one of the leading solutions in this field. In this paper, we propose the utilization of vertex-based networks to enhance the sampling process of RRT*, leading to more efficient path planning. Our approach focuses on critical vertices along the optimal paths, which provide essential yet sparser abstractions of the paths. We employ focal loss to address the associated data imbalance issue, and explore different masking configurations to determine practical tradeoffs in system performance. Through experiments conducted on randomly generated floor maps, our solutions demonstrate significant speed improvements, achieving over a 400% enhancement compared to the baseline model.