Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing User-Level Privacy Attack Against Federated Learning

Federated learning has emerged as an advanced privacy-preserving learning technique for mobile edge computing, where the model is trained in a decentralized manner by the clients, preventing the server from directly accessing those private data from the clients. This learning mechanism significantly challenges the attack from the server side. Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i.e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client. To analyze the privacy leakage of federated learning, this paper gives the first attempt to explore user-level privacy leakage by the attack from a malicious server. We propose a framework incorporating GAN with a multi-task discriminator, called multi-task GAN – Auxiliary Identification (mGAN-AI), which simultaneously discriminates category, reality, and client identity of input samples. The novel discrimination on client identity enables the generator to recover user specified private data. Unlike existing works interfering the federated learning process, the proposed method works “invisibly” on the server side. Furthermore, considering the anonymization strategy for mitigating mGAN-AI, we propose a beforehand linkability attack which re-identifies the anonymized updates by associating the client representatives. A novel siamese network fusing the identification and verification models is developed for measuring the similarity of representatives. The experimental results demonstrate the effectiveness of the proposed approaches and the superior to the state-of-the-art.
Remaining useful life prognostics for the rolling bearing based on a hybrid data-driven method

Rolling bearing is the core part of rotating mechanical equipment, so developing an effective remaining useful life prognostics method and alarming the impending fault for rolling bearing are of necessity to guarantee the reliable operation of mechanical equipment and schedule maintenance. The relevance vector machine is one of the substantially used methods for remaining useful life prognostics of rolling bearing. However, the accuracy generated by relevance vector machine drops rapidly in the long-term prognostics. To remedy this existing shortcoming of relevance vector machine, a novel hybrid method combining grey model, complete ensemble empirical mode decomposition and relevance vector machine are put forward. In the hybrid prognostics framework, the grey model is applied to gain a “raw” prediction result based on a trained model and produce an original error sequence. Subsequently, a new smoother error sequence reconstructed by complete ensemble empirical mode decomposition method is used to train relevance vector machine model, by which the future prediction error applied to correct the raw prediction results of grey model is projected. Ultimately, the online learning technique is used to implement dynamic updating of the “old” hybrid model, so that the remaining useful life of rolling bearing throughout the run-to-failure data set could be accurately predicted. The experimental results demonstrate the satisfactory prognostics performance.
Round Trip Routing for Energy-Efficient Drone Delivery Based on a Public Transportation Network

Focusing on using drones to deliver packages, this article considers a new delivery mode exploiting public transportation vehicles. Due to the battery capacity constraint, which limits the delivery area of drones, the public transportation vehicles are used to transport a drone to someplace unreachable by flying only. The problem under investigation is the round trip routing problem that finds the shortest path in terms of time in a time-dependent network, subject to the delivery deadline constraint and the energy budget constraint. The time-dependent network composes of the transit network and some extra drone flights. An exact solution algorithm is developed to find the optimal round trip path, and then, it is extended to accommodate the randomness of the considered network. The effectiveness is demonstrated via computer simulations.
Controlling Fast Height Variation of an Actively Articulated Wheeled Humanoid Robot Using Center of Mass Trajectory

Hybrid wheel-legged robots have begun to demonstrate the ability to adapt to complex terrain traditionally inaccessible to purely wheeled morphologies. Further research is needed into how their dynamics can be optimally controlled for developing highly adaptive behaviours on challenging terrain. Using optimal center of mass (COM) kinematic trajectories, this work examines the nonlinear dynamics control problem for fast height adaptation on the hybrid humanoid platform known as Aerobot. We explore the dynamics control problem through experimentation with an offline trajectory optimisation (TO) method and a task-space inverse dynamics (TSID) controller for varying the robot's height. Our TO approach uses sequential quadratic programming (SQP) to solve optimal 7th order spline coefficients for the robot’s kinematics. The nonlinear Zero Moment Point (ZMP) is used to model a stability criterion that is constrained in the TO problem to ensure dynamic stability. Our TSID controller follows motion plans based on using task jacobians and a simplified passive dynamics model of the Aerobot platform. Results exhibit fast height adaptation on the Aerobot platform with significantly differing results between the control methods that prompts new research into how it may be controlled online.
A Data Augmentation Scheme for Geometric Deep Learning in Personalized Brain–Computer Interfaces

Electroencephalography signals inherently deviate from the notion of regular spatial sampling, as they reflect the coordinated action from multiple distributed overlapping cortical networks. Hence, the observed brain dynamics are influenced both by the topology of the sensor array and the underlying functional connectivity. Neural engineers are currently exploiting the advances in the domain of graph signal processing in an attempt to create robust and reliable brain decoding systems. In this direction, Geometric Deep Learning is a highly promising concept for combining the benefits of graph signal processing and deep learning towards revolutionising Brain-Computer Interfaces (BCIs). However, its exploitation has been hindered by its data-demanding character. As a remedy, we propose here a novel data augmentation approach that combines the multiplex network modelling of multichannel signal with a graph variant of the classical Empirical Mode Decomposition (EMD), and which proves to be a strong asset when combined with Graph Convolutional Neural Networks (GCNNs). As our graph-EMD algorithm makes no assumptions with respect to linearity and stationarity, it appears as an appealing solution towards analysing brain signals without artificially imposing regularities in either temporal or spatial domain. Our experimental results indicate that the proposed scheme for data augmentation leads to substantial improvement when it is combined with GCNNs. Using recordings from two distinct BCI applications and comparing against a state-of-the-art augmentation method, we illustrate the benefits from its use. By making it available to BCI community, we hope to further foster the application of geometric deep learning in the field.
Design of Sliding Mode Control for Complex Nonlinear Models: A Numerical Approach

In this work, a numerical approach of formulating Sliding Model Control (SMC) is proposed to deal with complex nonlinear models of certain physical systems. Analytical formulation of SMC for such models is not possible as SMCs require algebraic manipulation of system equations, which is not achievable when the system model has strong nonlinearities. For such models, it is proposed to solve the SMC design using a numerical method that deals with numerical values instead of variables or functions in the equations. Mostly, SMC control law is dependent on the bounds of variables that are present in the mathematical expression of time derivatives of the sliding variable. To compute these bounds, a numerical approach is carried out using uncertainty bounds of the system’s parameters. Also, a numerical approach is used to compute time derivatives of nonlinear functions that are required in the formulation of SMC. Various forms of SMCs are investigated in this respect, including the basic first-order SMC and a second-order SMC. The proposed framework is generalized as well, making it compatible with a wide range of nonlinear models whose algebraic manipulation is not possible. A prototype example of a nonlinear model of a boiler is used as a proof-of-concept. The simulation results are promising and prove the efficacy of the proposed approach.
High-Sensitive Refractive Index Sensing Enabled by Topological Charge Evolution

A high-sensitive refractive index sensor is proposed and demonstrated by utilizing merging bound states in the continuum, namely a set of integer topological charges in the momentum space. Through varying cladding refractive index <inline-formula><tex-math notation="LaTeX">$n_c$</tex-math></inline-formula>, the topological charges continuously depart from the merging state, and result in the robust and considerable high-<inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> factors (above <inline-formula><tex-math notation="LaTeX">$7\times 10^4$</tex-math></inline-formula>) in a large detection range of 0.456. In such a range, sensing sensitivity of <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>36 nm/RIU and figure-of-merit from <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>5990 (air) to 1607 (<inline-formula><tex-math notation="LaTeX">$n_c$</tex-math></inline-formula> = 1.456) are achieved. Meanwhile, the detection limit on the order of <inline-formula><tex-math notation="LaTeX">$10^{-5}$</tex-math></inline-formula> RIU is clearly distinguishable from the measured spectrum under tiny variation of cladding refractive index. Our work paves the way for promising integrated high-sensitive sensors in many biological and chemical applications.
Normalization of Severity Rating for Automated Context-aware Vulnerability Risk Management

In the last three years, the unprecedented increase in discovered vulnerabilities ranked with critical and high severity raise new challenges in Vulnerability Risk Management (VRM). Indeed, identifying, analyzing and remediating this high rate of vulnerabilities is labour intensive, especially for enterprises dealing with complex computing infrastructures such as Infrastructure-as-a-Service providers. Hence there is a demand for new criteria to prioritize vulnerabilities remediation and new automated/autonomic approaches to VRM.In this paper, we address the above challenge proposing an Automated Context-aware Vulnerability Risk Management (ACVRM) methodology that aims: to reduce the labour intensive tasks of security experts; to prioritize vulnerability remediation on the basis of the organization context rather than risk severity only. The proposed solution considers multiple vulnerabilities databases to have a great coverage on known vulnerabilities and to determine the vulnerability rank. After the description of the new VRM methodology, we focus on the problem of obtaining a single vulnerability score by normalization and fusion of ranks obtained from multiple vulnerabilities databases. Our solution is a parametric normalization that accounts for organization needs/specifications.
Towards Enhancement of Network Communication Architectures and Routing Protocols for FANETs: A Survey

The popularity of Unmanned Aerial Vehicles (UAVs), also known as drones, has been growing rapidly in the recent years. Their applications extend to using a group of UAVs, forming what is known as the Flying Ad hoc Network (FANET), to perform a certain mission in various applications such as search and rescue and various military applications and many others. In order for the UAVs to communicate, a network communication architecture with the implementation of suitable routing protocols needs to be established while taking into consideration the limitation of this system. Therefore, in this paper, we survey in details the latest state-of-the-art network communication architectures and various ad-hoc routing protocols, mainly those falling under the proactive, reactive and hybrid routing protocols and their enhanced versions which are used currently for FANETs. Furthermore, we propose a unique network communication architecture based on the concept of clustering with the aim of overcoming the limitation in the state-of-theart. Moreover, we put forward a new idea of a routing mechanism to aid in implementing an efficient and effective routing system for FANETs.
CCAN: Constraint Co-Attention Network for Instance Grasping

Instance grasping is a challenging robotic grasping task when a robot aims to grasp a specified target object in cluttered scenes. In this paper, we propose a novel end-to-end instance grasping method using only monocular workspace and query images, where the workspace image includes several objects and the query image only contains the target object. To effectively extract discriminative features and facilitate the training process, a learning-based method, referred to as Constraint Co-Attention Network (CCAN), is proposed which consists of a constraint co-attention module and a grasp affordance predictor. An effective co-attention module is presented to construct the features of a workspace image from the extracted features of the query image. By introducing soft constraints into the co-attention module, it highlights the target object’s features while trivializes other objects’ features in the workspace image. Using the features extracted from the co-attention module, the cascaded grasp affordance interpreter network only predicts the grasp configuration for the target object. The training of the CCAN is totally based on simulated self-supervision. Extensive qualitative and quantitative experiments show the effectiveness of our method both in simulated and real-world environments even for totally unseen objects.
FFANN Weight Initialization: A New Method

In this paper a new weight initialization scheme is proposed. The proposed scheme, distributes the biases / thresholds at a equal intervals in an interval $(-\lambda, \lambda)$; while the weights are distributed uniformly in the same interval. The value of the $\lambda$ is chosen such that the expected value of the net inputs is 0 while the variance is 1. On a set of 10 tasks (5 function approximation and 5 real life benchmark regression tasks), the proposed weight initialization routine was compared to three existing weight initialization routine. From the results obtained we may infer that the proposed weight initialization is almost always better than these three existing routines and is never worse on the basis of generalization performance (result over the test data set).
Path-Following Model Predictive Control of Ballbots

This paper introduces a novel approach for model predictive control of ballbots for path-following tasks. Ballbots are dynamically unstable mobile robots which are designed to balance on a single ball. The model presented in this paper is a simplied version of a full quaternion-based model of ballbots’ underactuated dynamics which is suited for online implementation. Furthermore, the approach is extended to handle nearby obstacles directly in the MPC formulation. The presented controller is validated through simulation on a high fidelity model as well as through real-world experiments on a physical ballbot system.
Multi-GPU Design and Performance Evaluation of Homomorphic Encryption on GPU Clusters

We present a multi-GPU design, implementation and performance evaluation of the Halevi-Polyakov-Shoup (HPS) variant of the Fan-Vercauteren (FV) levelled Fully Homomorphic Encryption (FHE) scheme. Our design follows a data parallelism approach and uses partitioning methods to distribute the workload in FV primitives evenly across available GPUs. The design is put to address space and runtime requirements of FHE computations. It is also suitable for distributed-memory architectures, and includes efficient GPU-to-GPU data exchange protocols. Moreover, it is user-friendly as user intervention is not required for task decomposition, scheduling or load balancing. We implement and evaluate the performance of our design on two homogeneous and heterogeneous <inline-formula><tex-math notation="LaTeX">${\sf NVIDIA}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">NVIDIA</mml:mi></mml:math><inline-graphic xlink:href="qaisarahmadalbadawi-ieq1-3021238.gif"/></alternatives></inline-formula> GPU clusters: <inline-formula><tex-math notation="LaTeX">${\sf K80}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="sans-serif">K</mml:mi><mml:mn mathvariant="sans-serif">80</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="qaisarahmadalbadawi-ieq2-3021238.gif"/></alternatives></inline-formula>, and a customized <inline-formula><tex-math notation="LaTeX">${\sf P100}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="sans-serif">P</mml:mi><mml:mn mathvariant="sans-serif">100</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="qaisarahmadalbadawi-ieq3-3021238.gif"/></alternatives></inline-formula>. We also provide a comparison with a recent shared-memory-based multi-core CPU implementation using two homomorphic circuits as workloads: vector addition and multiplication. Moreover, we use our multi-GPU Levelled-FHE to implement the inference circuit of two Convolutional Neural Networks (CNNs) to perform homomorphically image classification on encrypted images from the <inline-formula><tex-math notation="LaTeX">${\sf MNIST}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">MNIST</mml:mi></mml:math><inline-graphic xlink:href="qaisarahmadalbadawi-ieq4-3021238.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">${\sf CIFAR-10}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="sans-serif">CIFAR</mml:mi><mml:mo>-</mml:mo><mml:mn mathvariant="sans-serif">10</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="qaisarahmadalbadawi-ieq5-3021238.gif"/></alternatives></inline-formula> datasets. Our implementation provides 1 to 3 orders of magnitude speedup compared with the CPU implementation on vector operations. In terms of scalability, our design shows reasonable scalability curves when the GPUs are fully connected.
Brain Signal Analysis for Mind Controlled Type-Writer Using a Deep Neural Network

The prime objective of this work is to develop a notable methodology for modeling a mind controlled type writing system to cater the needs of individuals suffering from various communication related disorders using EEG signal analysis technique. First, the EEG signals are procured from twelve subjects who were involved in mental utterance of seven vowel sounds. The eLORETA analysis of the acquired signals confirms the involvement of occipital, parietal and pre-frontal lobes for this cognitive activity. The procured signals are then filtered to circumvent the effects of various artifacts and are transferred to a novel deep multilayer perceptron classifier for categorization of seven class labels. Performance analysis undertaken confirms that the proposed classifier is able to distinctly categorize the seven class levels with a very high precision level. A coding mechanism has also been proposed to represent the consonants by the concoction of two vowel sounds segregated by a space. Thus, the proposed scheme can be effectively utilized as a mind controlled type writing system to serve the basic requirements of disabled individuals. Additionally, this technique can also be used in certain military scenarios that demand non-verbal communication as a secure option and in various gaming applications that will aid in exhilarating the player's experience.
Hardware Design of Concatenated Zigzag Hadamard Encoder/Decoder System With High Throughput

Both turbo Hadamard codes and concatenated zigzag Hadamard codes are ultimate-Shannon-limit-approaching channel codes. The former one requires the use of Bahl-Cocke-Jelinek-Raviv (BCJR) in the iterative decoding process, making the decoder structure more complex and limiting its throughput. The latter one, however, does not involve BCJR decoding. Hence its decoder structure can be much simpler and can potentially operate at a much higher throughput. In this paper, we investigate the hardware design of a concatenated zigzag Hadamard encoder/decoder system and implement it onto an FPGA board. We design a decoder capable of decoding multiple codewords at the same time, and the proposed system can operate with a throughput of 1.44 Gbps — an increase of 50% compared with the turbo Hadamard encoder/decoder system. As for the error performance, the encoder/decoder system with a 6-bit quantization achieves a bit error rate of <inline-formula> <tex-math notation="LaTeX">$2\times 10^{-5}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">$E_{b}/N_{0} = -0.2$ </tex-math></inline-formula> dB.
Fault-Tolerant Trust Model for Hybrid Attack Mode in Underwater Acoustic Sensor Networks

With the boost of ocean development, the study of UASNs has been gaining much attention from researchers. Nevertheless, UASNs are particularly vulnerable to malicious attacks because of their open and unattended nature. Hence, efficient and reliable security mechanisms are required to maintain the normal operation of UASNs. To defend against sudden network faults and malicious attacks, a novel trust model is proposed. The model is divided into three phases. First, a quantified environment model is developed to reflect the impact of the underwater environment on trust evaluation. Then, a trust update model is constructed based on reinforcement learning to confront hybrid attacks. Finally, a trust redemption model is given to reinstate low trust sensor nodes for improving resource utilization of the network. The experimental results prove that this trust model is effective in achieving highly efficient trust updating in the face of changing attack modes.