Reviewerly/exHarmony huggingface dataset:
Gaze estimation with semi-supervised eye landmark detection as an auxiliary task

The changes in gaze are often reflected in the movements of eye landmarks, highlighting the relevance of eye landmark learning for accurate gaze estimation. To leverage eye landmarks, we propose a gaze estimation framework that incorporates eye landmark detection as an auxiliary task. However, obtaining eye landmark annotations for real-world gaze datasets is challenging. To address this, we exploit synthetic data, which provides precise eye landmark labels, by jointly training an eye landmark detector using labeled synthetic data and unlabeled real-world data in a semi-supervised manner. To reduce the influence of discrepancy between synthetic and real-world data, we improve the generalization ability of the landmark detector by performing a self-supervised learning strategy on a large scale of unlabeled real-world images. The proposed method outperforms other state-of-the-art gaze estimation methods on three gaze datasets, indicating the effectiveness of leveraging eye landmark detection as an auxiliary task to enhance gaze estimation performance.
The variance of a restricted sum-of-squares function over short intervals in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:mo stretchy="false">[</mml:mo><mml:mi>T</mml:mi><mml:mo stretchy="false">]</mml:mo></mml:math>

For B∈Fq[T] of degree 2n≥2, consider the number of ways of writing B=E2+γF2, where γ∈Fq⁎ is fixed, and E,F∈Fq[T] with degE=n and degF=m<n. We denote this restricted sum-of-squares function by Sγ;m(B). We obtain an exact formula for the variance of Sγ;m(B) over short intervals in Fq[T] where q is an odd prime power. Interestingly, when m+1≤n≤2m, the variance vanishes on some (not all) short intervals, which is in contrast to the standard function that counts representations as a sum of two squares. We use the method of additive characters and Hankel matrices that we previously used for the variance and correlations of the divisor function. In Section 2, we give a short overview of our approach. Section 3 gives new results relating to values of quadratic forms, over Fq, that are defined from Hankel matrices.
DFLNet: Deep Federated Learning Network With Privacy Preserving for Vehicular LoRa Nodes Fingerprinting

The rapid advancement of the Internet of Things (IoT) and intelligent vehicle networks has led to the widespread deployment of massive long-range (LoRa) nodes. These nodes play a crucial role in facilitating the sharing of critical information towards various vehicles. However, a significant challenge that persists is the secure authentication of LoRa. Recently, radio frequency fingerprinting (RFF) emerges as a robust potential solution, offering both rapid and scalable identification capabilities. By harnessing machine learning (ML) techniques, the inherent hardware-level, unique, and resilient features of RFF can be throughly explored. Given the inherent non-identical (heterogeneity) of LoRa networks and the non-stationary characteristics of chirp signals, conventional approaches prove inadequate in effectively capturing the RFF features. To surmount these challenges, a novel scheme termed as “deep federated learning network (DFLNet)” is proposed. DFLNet facilitates distributed RFF learning while ensuring privacy preservation within diverse LoRa networks. Addressing the heterogeneity of datasets, we incorporate an adaptive optimizer that contributes to the efficient convergence improvement. The DFLNet adeptly manages computational demands and privacy concerns through the utilization of mobile edge computing (MEC) servers, thus enabling distributed learning. Empirical evaluations substantiate the efficacy of DFLNet, attaining an impressive identification accuracy of up to approximately 96.7% utilizing merely around 5000 samples per client, while concurrently ensuring a robust privacy preservation.
AGRIC: Artificial-Intelligence-Based Green Routing for Industrial Cyber–Physical System Pertaining to Extreme Environment

Industrial Cyber-Physical Systems (ICPS) can play a crucial role in damage assessment during extreme conditions by leveraging their integration of physical infrastructure, sensing capabilities, and advanced analytics. However, due to the wireless sensing devices that are made to operate in ICPS, there is a dire need to address the green routing (energy-efficient) challenges through an optimized solution. In recent times, Artificial Intelligence (AI) has had a significant impact on Wireless Sensor Networks (WSNs) designed to operate as ICPS components. In this research work, we present AGRIC: AI-based Green Routing for ICPS. While following the cluster-based routing, the election of Cluster Head (CH) is executed using our proposed AI-inspired Extended Spotted Hyena Levy Flight Optimization (ESHLFO) algorithm. Furthermore, to address the energy hole problem, four energy-unlimited data collection nodes are used around the periphery of the network. The results of the experiment demonstrate the fact AGRIC delivers network longevity and supreme performance in the context of stability time, throughput, and energy left over in the network as important performance indicators.
Graph-Based Data Publication via Differentially Structural Inference

Due to the rapid development of Internet of Things, a large number of data are collected and published. Nevertheless, the process of data publication entails the risk of data privacy disclosure. Most of the related works can be largely classified into data publication based on anonymity, differential privacy, and graph. However, these existing works either cannot provide theoretically provable privacy protection, or only considered one kind of data attribute and thus cannot guarantee the desirable data utility. To this end, we propose a graph-based data publication scheme via differentially structural inference that can provide theoretically provable differential privacy for individuals, and maintain desirable data utility in many practical applications rather than a certain kind of statistics or data mining results. The main idea is to map the dataset to be published into a data graph, and further use the hierarchical random graph model in statistics to encode the structure of the data graph into dendrograms. Then, we use the Markov Chain Monte Carlo to infer an optimal dendrogram, and moreover design threshold strategy to differentially disturb the optimal dendrogram. Finally, we generate the sanitized data graph based on the disturbed optimal dendrogram, and further map the sanitized data graph to the sanitized dataset to be published. Thereafter, we theoretically prove the performance boundaries of both the privacy preservation and the data utility guarantees provided in our work. Furthermore, the extensive experimental results on two real-world datasets demonstrate that the proposed scheme is superior to the existing work and Baseline, guaranteeing the data utility and preserving the data privacy in many practical applications.
Matching Game for Multi-Task Federated Learning in Internet of Vehicles

To overcome the inherent defects of massive data uploading and processing in traditional machine learning, federated learning is emerged as a promising tool given that it enables to implement privacy-preserved distributed machine learning in Internet of Vehicles (IoV). However, the performance of the federated learning suffers from several challenges, especially ineffective execution of delay-sensitive tasks triggered simultaneously by moving vehicles. To minimize the total execution delay of multiple tasks, we propose a multi-task federated learning framework which improves task completion rate and enables each task to be completed in time. Moreover, we also aim to improve the network utility of the IoV. The algorithm of joint optimization algorithm is proposed to achieve a stable partition of vehicle coalitions based on the block coordinate descent (BCD) method, the matching game-based coalition method, and gradient projection method. The performance of the proposed multi-task federated learning is evaluated through numerical simulations in terms of total latency, network utility, and accuracy of federated learning tasks. The results show that our proposed multi-task federated learning framework and algorithm guarantees the completion of multiple delay-sensitive tasks effectively while improving vehicular network utility.
Quantum remote control utilizing multiple degrees of freedom

We present protocols for remote implementation of general operators between two nodes, which are considered: the first where an arbitrary single-qubit operation acts on photon state in the polarization degree of freedom, and the second with photon state in the temporal degree of freedom. By employing simultaneous entanglement in two independent degrees of freedom, both protocols are fulfilled near deterministically in two steps. In the first step, the effective cross-Kerr interaction and X-quadrature measurements of the coherent states are main tools for achieving the goal. Together with the available existing optical elements and common single-photon detectors, the construction circuit of the second step is simple and feasible with current technologies. Our protocols are beneficial to long-distance quantum communication and quantum metrology.
Improved error tolerance of programmable photonic integrated circuits for MNIST handwritten digit classification

Programmable photonic integrated circuits (PPICs) can realize linear optical unitary transformation between arbitrary multi-port input and multi-port output for optical computing and signal processing. With the scale increase of PPICs, the accumulation of manufacturing errors will lead to overall performance deviations. A symmetrical Mach-Zehnder interferometer (MZI) circuit with a shorter optical path and lower propagation loss has been proposed recently to replace the traditional asymmetrical MZI circuit. However, no systematic research on the error compensation of symmetrical MZI circuits has been reported yet. In this paper, the error tolerance characteristics of symmetrical MZI circuits and asymmetrical MZI circuits are analyzed and compared in detail. An effective strategy to compensate the 3 dB beam splitter error of a symmetrical MZI circuit is proposed. A deep learning task of the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset classification was carried out with two hidden layers, which are implemented by rectangular symmetrical MZI circuit structures. The recognition accuracy can be significantly improved with this error compensation method.
Unified Representation Learning for Multi-View Clustering by Between/Within View Deep Majorization

Multi-view data are characterized by a variety of features that are semantically coherent, with different views usually complementing each other. Multi-view clustering seeks to learn a unified representation that is comprehensive yet discriminative to partition the samples. However, existing research ignored the possible redundancy of latent feature in each view, then yielded a unified task-oriented feature based on the correlation of these multi-view latent features without explicit consideration of the influence of the unified feature. Such a way failed to accurately measure the association among views. To fully take the association into account, we propose a deep multi-view Unified Representation Learning network, named by deep-URL, using measurement between multi-view data to realize desirable unified representation. Specifically, deep-URL employs a multi-view encoder-decoder framework that maps multi-scale data into a same latent space to obtain view-specific latent representation. Within each view, the view-specific features are pruned to eliminate the redundant information in the latent space by minimizing the within-view mutual information. Between the multiple views, a unified informative representation is derived to accurately estimate the nonlinear associations between views via maximizing conditional mutual information of the view-specific features. The deep unified representation is further elaborated to achieve the full reconstruction of each view to ensure its reliability. The proposed network is trained by between/within view deep representation majorization. It achieves redundant representation removal within each view, unified features integration across views, and universal reconstruction via decoding. Extensive experiments on five real datasets with largely varied sample sizes demonstrate that the deep-URL achieved superior clustering performance by comparing with fourteen baseline methods.
Distributed Bayesian target tracking with reduced communication: Likelihood consensus 2.0

The likelihood consensus (LC) enables Bayesian target tracking in a decentralized sensor network with possibly nonlinear and non-Gaussian sensor characteristics. Here, we propose an evolved LC methodology—dubbed “LC 2.0”—with significantly reduced intersensor communication. LC 2.0 uses multiple refinements of the original LC including a sparsity-promoting calculation of expansion coefficients, the use of a B-spline dictionary, a distributed adaptive calculation of the relevant state-space region, and efficient binary representations. We consider the use of the proposed LC 2.0 within a distributed particle filter and within a distributed particle-based probabilistic data association filter. Our simulation results demonstrate that a reduction of intersensor communication by a factor of about 190 can be obtained without compromising the tracking performance.
Enhanced D-CNN architecture and centroid-based algorithm for real-time vehicle tracking and accident detection from surveillance videos

In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc.
GAF-MAE: A Self-Supervised Automatic Modulation Classification Method Based on Gramian Angular Field and Masked Autoencoder

With the development of deep learning (DL), several fields have ushered in leapfrog development, such as image classification and natural language processing. Combing the powerful feature extraction tool, DL-based Automatic Modulation Classification (AMC) emerges. However, most DL-based AMC methods require massive labeled samples for training, which is difficult for non-cooperative scenarios. In this paper, we propose a novel self-supervised AMC method called GAF-MAE. Gramian Angular Field (GAF) is applied for domain transformation from time series to images first, then a Masked Autoencoder (MAE) is built for self-supervised reconstruction tasks through unlabeled samples. After self-supervised pretrain, a small number of labeled samples are used for downstream classification finetuning. We conduct a comprehensive evaluation of a public dataset RadioML.2016.10a. The simulation results show that GAF-MAE can achieve a relatively high average accuracy of 54.85% even if the label proportion is 5% and 61.38% when the label proportion reaches 100%, which outperforms other well-known models.
Towards constrained optimization of cloud applications: A hybrid approach

A database system is one of the most common cloud applications where elasticity allows to dynamically allocate resources in response to changing workload demands. In such systems, users usually configure resources based on empirical decisions, for example by boosting computational resources with the expectation of improving database throughput. The latter also suggests that users manually scale resources and tune the database configurations offline to meet the application demands. However, this approach could rapidly increase infrastructure expenditures while this is also time consuming. In this paper, we propose SONA, a framework to support constrained performance optimization of cloud applications using a hybrid approach of artificial neural networks and genetic algorithms. The proposed framework monitors the source system to identify the optimal configurations that maximize application performance based on genuine workload executions. As a result, the experimental analysis presents a novel dataset collected from TPC-C runs on MySQL server. The optimization process is being constrained to satisfy user and application requirements including the cloud infrastructure expenditures, the cost-performance ratio, the baseline performance and the average percentage of idle CPU resources. Furthermore, SONA uses a cloned containerized environment that replicates the main application to avoid system overhead during the optimization process. Our results demonstrate the effectiveness of SONA framework to optimize application performance of OLTP applications deployed on cloud.
Controllable Style Transfer via Test-time Training of Implicit Neural Representation

Existing CNN-based style transfer frameworks have suffered from inaccurate control of pixel-wise stylization. As the CNN operation is designed based on kernel-wise operation, such a design unavoidably makes pixels affect each other. To mitigate this problem, we propose a controllable style transfer framework that leverages Implicit Neural Representation to encode each pixel respectively and optimize each style and content pair via test-time training. Unlike previous CNN-based style transfer frameworks, this formulation naturally enables accurate pixel-wise stylization control. In addition, to give explicit controllability on the degree of stylization, we define two vectors that represent the content and style respectively, enabling control by interpolating these vectors. We further demonstrate that, after being test-time trained once, our framework can show a various range of applications by precisely controlling the stylized images pixel-wise and freely adjusting image resolution and degree of stylization without further optimization or training.
Information geometry based extreme low-bit neural network for point cloud

Deep learning has significantly advanced three-dimensional computer vision applied to point clouds. Nevertheless, the substantial consumption of time, storage, and energy substantially limits its deployment on edge devices with constrained resources. Extremely low bit quantization has received wide attention due to its extremely high compression ratio, but the problem of a significant drop in accuracy cannot be ignored. To alleviate the obvious accuracy degradation for extreme low-bit quantization, this paper proposes a novel compression framework for binary and ternary neural networks applied to point clouds, which introduces information geometry to model quantization to compensate the severe feature manifold distortion. It applies differential geometry on manifolds to study the implicit information of the point cloud feature data. Based on the theoretical analysis from the novel perspective of information geometry, two optimization modules are proposed to alleviate severe geometry distortions on differential manifolds. The first module, scaling recovery, provides layer-wise scaling parameters to reduce geometric distortion caused by quantization. The second module, Pooling Recovery, is specially designed to alleviate more severe pooling geometry distortions in point clouds. These two modules benefit both binary and ternary neural networks with ignored overheads. For ternary quantization, optimizations on convolution weights and gradients are additionally introduced. The proposed self-adaptive gradient estimation provides a more accurate approximation to the non-differential ternary staircase function. Convolution weight optimization is implemented on an information-geometry optimized model to achieve even higher accuracy and less memory consumption. Experimental results validate that the proposed models significantly outperform state-of-the-art methods and demonstrate better scalability. Overall, this compression framework has the potential to facilitate the deployment of deep learning models on edge devices with limited resources, opening up new opportunities for applications of three-dimensional computer vision.
Blind quality-based pairwise ranking of contrast changed color images using deep networks

Next-generation multimedia networks are expected to provide systems and applications with top Quality of Experience (QoE) to users. To this end, robust quality evaluation metrics are critical. Unfortunately, most current research focuses only on modeling and evaluating mainly distortions across the pipeline of multimedia networks. While distortions are important, it is also as important to consider the effects of enhancement and other manipulations of multimedia content, especially images and videos. In contrast to most existing works dedicated to evaluating image/video quality in its traditional context, very few research efforts have been devoted to Image Quality Enhancement Assessment (IQEA) and more specifically, Contrast Enhancement Evaluation (CEE). Our contribution fills this gap by proposing a pairwise ranking scheme for estimating and evaluating the perceptual quality of image contrast change (contrast enhancement and/or contrast-distorted images) process. We propose a novel Deep Learning-based Blind Quality pairwise Ranking scheme for Contrast-Changed (Deep-BQRCC) images. This method provides an automatic pairwise ranking of a set of contrast-changed images. The proposed framework is based on using a pair of Convolutional Neural Networks (CNN) together with a saliency-based attention model and a color-difference visual map. Extensive experiments were conducted to validate the effectiveness of the proposed workflow through an ablation analysis. Different combinations of CNN models and pooling strategies were analyzed. The proposed Deep-BQRCC approach was evaluated over three dedicated publicly available datasets. The experimental results showed an increase in performance within a range of 3–10% compared to state-of-the-art IQEA measures.