Reviewerly/exHarmony huggingface dataset:
Video spatio-temporal generative adversarial network for local action generation

Generating action videos in future scenes based on static images can make computer vision systems to be better applied for video understanding and intelligent decision-making. However, current models pay more attention to the motion trend of the generated objects, and the processing effect on local details is not ideal. The local features of the generated video will have the problem of blurred frames and incoherent motion. This paper proposes a two-stage model, video spatio-temporal generative adversarial network (VSTGAN), which consists of two GAN networks, such as temporal network and spatial network (S-net). The model fully combines the advantages of CNNs, recurrent neural networks (RNNs), and GANs to decompose the complex spatiotemporal generation problem into temporal and spatial dimensions. Therefore, VSTGAN can focus on local features from the above dimensions respectively. In the temporal dimension, we propose an RNN unit, the convolutional attention unit (ConvAU), which uses the convolutional attention module to dynamically generate weights to update the hidden state. Thus, T-net uses the ConvAU to generate local dynamics. In the spatial dimension, S-net uses CNNs and attention modules to perform resolution reconstruction of the generated local dynamics for video generation. We build two small-sample datasets and validate our approach on these two new datasets and the KTH public dataset. The results show that our approach can effectively generate local details in future action videos and that the model performance on small-sample datasets is competitive with the state-of-the-art in video generation.
Sound board's Frequency Response Analysis of Classical Guitars

The instrument's sound quality assessment is an arduous task to be performed computationally. This work aims to compare high-quality sound characteristics among instruments. The impulse response of the guitar is recording in the time domain and, by means of the Fourier and Continuous Wavelet Transforms the characteristics among high-quality guitars, in the frequency domain, are shown. To discriminate high quality, through experimentation, the criteria are: 1) tuning of notes, sound projection and definition of notes; 2) sound balance at all frequencies and timbre. Comparison of the frequency response (intensity, density, shape, range) gives a good intuition regarding sound quality of the instruments that shares similar characteristics, according to the criteria defined. Note that, among the high-quality classical guitars, it is not possible to define what is better sound-wise due to the fact that high-quality instruments are hearing-perceptual defined, therefore, depends on the criteria of the musician.
Deep Learning Based Image Steganalysis

Various cryptographic and steganographic techniques are used to hide digital information during its processing, storage, and transmission. While cryptography hides the information content of digital data (by converting them into a meaningless set of noise-like sequences), steganography hides the very existence of information messages. In other words, steganographic techniques hide digital messages by embedding them in so-called. containers. Containers are other digital data or physical objects. To do this, containers (covers, media) must be highly redundant data. Revealing the fact of steganographic hiding and detecting an embedded message is usually extremely difficult. In fact, hidden messages are some noise added to the container, and we must, based on the study of this noise, decide on the presence or absence of an embedded message. In this article, we consider deep learning methods for steganoanalysis of digital cover images. We have considered several deep learning models and conduct numerous tests on various datasets. Our experiments show that deep learning does indeed make it possible to design effective stego-detectors, but this requires fine-tuning of model hyperparameters and optimization of the neural network architecture.
Methods of Digital-To-Analog Conversion for Reproduction of Sound Waves

In this paper we are considering a method of digital-to-analog conversion with which it is possible to implement the transfer of sound information using air flows generated by fans. The architecture of the software-hardware complex with which this is done and the algorithm with all stages of data preparation, processing and transmission are also considered. Experiments conducted to verify the effectiveness of using this method in real life are also described. People participated in the experiment, who needed to identify the class of sound that is reproduced by means of the created complex. For this, the combination of air flows for each class must be previously learned by a person. Also, a separate stage of experiments with sounds typical of wartime was conducted. The results were formed, in which it was found that certain combinations of the duration of sounds and their count can indeed be correctly identified by a person.
Real-Time Page Extraction for Document Digitization

The paper analyzes existing methods of processing photo and video images to solve the problem of extracting pages from a video stream. The concept of an algorithm for video segmentation for use in real-time optical character recognition (OCR) tasks is proposed. Possibilities for implementation of the approach, potential shortcomings, and challenges, as well as ways to solve them, are described. The algorithm can become the basis for other algorithms for digitization and analysis of printed and handwritten texts, which can later be used for future research in the field. The novelty of the approach is the use of video semantics algorithms to solve the topical question of text recognition.
Self-Adaptive Bandwidth Allocation to Address Dynamic Traffic Concept Drift in Optical Access Networks

In this invited talk, we will review our recent work on online reinforcement learning-based dynamic bandwidth allocation (ORL-DBA) scheme that addresses concept drift in ML-enhanced optical access networks for supporting low-latency applications against network traffic dynamicity. The scheme improves bandwidth decisions via exploration or exploits existing decision knowledge to keep a low latency, with extensive simulations showing rapid and self-adaptive bandwidth decisions that yield up to 60% latency improvement in dynamic traffic environments.
On-board Hybrid Heterogeneous Distributed Computing Resource Virtualization

It is necessary to develop a real-time fault-tolerant high-performance computing architecture to enhance the efficiency and performance of on-board applications, the architecture would provide a shared, unified, and scalable computing platform utilizing various hybrid heterogeneous resources such as CPU, DSP, FPGA, and GPU. In this paper, we propose an abstract method for modeling the energy consumption of heterogeneous computing resources and establish performance models for each resource. By enabling dynamic allocation and elastic expansion of on-board hybrid heterogeneous distributed computers based on user needs, we aim to achieve maximum computing energy efficiency and service quality.
An Analysis of European Union Cybersecurity Higher Education Programs Through the Crowd-Sourced Database CyberHEAD

In this work, we present an analysis of the data of the Cybersecurity Higher Education Database (CyberHEAD) of the European Union Agency for Cybersecurity (ENISA) to highlight the state of play in the European Union regarding the cybersecurity skills supply.
Verifiable anonymous quantum communication with authentication based on d-level single-particle states

Abstract Anonymous quantum communication (AQC) enables the secure transmission of classical and quantum messages while preserving the anonymity of the sender, receiver, or both. Qudits, quantum states with more advantages than qubits in terms of information transmission rate and eavesdropping detection, offer promising capabilities. In this paper, we propose a verifiable AQC (VAQC) protocol utilizing d -level single-particle states, establishing anonymous entanglement between a public sender and an anonymous receiver. By using quantum teleportation, the public sender can transmit private information to his/her chosen anonymous receiver. The verifiable function of the proposed protocol guarantees the legitimacy of all participants’ identities and verifies the correctness of the anonymous entanglement. Furthermore, we demonstrate that the proposed VAQC protocol satisfies the requirements of correctness, anonymity, and security. The proposed VAQC protocol exhibits extensibility and can be extended to enable private communication between an anonymous sender and an anonymous receiver, as well as anonymous entanglement among multiple participants. This work lays the foundation for practical applications in achieving d -level anonymous entanglement and anonymous transmission of qudits.
Personalized anomaly detection using deep active learning

Abstract Anomaly detection algorithms are typically applied to static, unchanging, data features hand-crafted by the user. But how does a user systematically craft good features for anomalies that have never been seen? Here we couple deep learning with active learning – in which an Oracle iteratively labels small amounts of data selected algorithmically over a series of rounds – to automatically and dynamically improve the data features for efficient outlier detection. This approach, Anomaly Hunt (ahunt), shows excellent performance on MNIST, CIFAR10, and Galaxy-DECaLS data, significantly outperforming both standard anomaly detection and active learning algorithms with static feature spaces. Beyond improved performance, ahunt also allows the number of anomaly classes to grow organically in response to the Oracle’s evaluations. Extensive ablation studies explore the impact of Oracle question selection strategy and loss function on performance. We illustrate how the dynamic anomaly class taxonomy represents another step towards fully personalized rankings of different anomaly classes that reflect a user’s interests, allowing the algorithm to learn to ignore statistically significant but uninteresting outliers (e.g. noise). This should prove useful in the era of massive astronomical data sets serving diverse sets of users who can only review a tiny subset of the incoming data.
Lessons Learned From a Learning Program for Software Architects

About 15 years ago, a role-based learning program aimed primarily at software architects was established. In the early days, we had extensive discussions to what extent to build on existing materials available outside of the company or to “grow our own” learning program. Due to the nature of our businesses, especially having many complex and cyber-physical systems with a strong focus on quality attributes, we established our own program. This architect-based learning program, known as the “Software Curriculum” has evolved over time and continues to be a key part of our company’s learning landscape. Especially as systems today and in future get more and more complex, the importance of learning, and especially in the software architecture area, has never been higher.
CS<sup> <i>n</i> </sup>Net: A Remote Sensing Detection Network Breaking the Second-Order Limitation of Transformers With Recursive Convolutions

In recent years, transformer-based networks, known for their ability to model long-range dependencies, have been widely used in downstream computer vision tasks, surpassing certain neural network architectures. However, transformer-based networks suffer from issues such as large parameter size, high computational complexity, and difficulties in extending spatial and channel features to the third or even higher orders, resulting in convergence challenges for small to medium-sized datasets and limited effectiveness in extracting high-order detailed features. In this paper, we propose a high-order spatial and channel controllable convolution module, named CS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , which can replace standard convolutions in any convolutional network. In the context of remote sensing small object detection, CS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> demonstrates superior performance compared to Self-Attention structures embedded in neural networks. Moreover, it introduces long-range dependency relationships among pixels, similar to Self-Attention, and adopts a cascaded recursive approach to extend spatial and channel features to arbitrary higher orders without introducing significant additional computation. This extension captures crucial information from high-order spatial and channel dimensions, resulting in improved accuracy for small object detection. Additionally, we construct a novel, versatile CS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -(FPN+PAN) structure for object detection networks, referred to as CS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> Net. Finally, our proposed model exhibits significant advantages in remote sensing detection when compared to state-of-the-art methods on publicly available SAR datasets (SSDD, HRSID) and optical remote sensing dataset (NWPU-10), achieving respective improvements of 3.4%, 4.5%, and 0.4% in mAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> compared to baseline models.
LFD-Net: Lightweight Feature-Interaction Dehazing Network for Real-Time Remote Sensing Tasks

Currently, remote sensing equipments are evolving towards intelligence and integration, incorporating edge computing techniques to enable real-time responses. One of the key challenges in enhancing downstream decision-making capabilities is the pre-processing step of image dehazing. Existing dehazing methods usually suffer from steep computational costs with densely connected residual modules, as well as difficulties in maintaining visual quality. To tackle these problems, we designed a lightweight Atmosphere Scattering Model (ASM) based network structure to extract, fuse and weight multiscale features. Our proposed LFD-Net demonstrates strong interpretability by exploiting the gated fusion module and attention mechanism to realize feature interactions between multi-level representations. The experimental results of LFD-Net on SOTS dataset reach an average Frequency Per Second (FPS) of 54.41, approximately 8 times faster than seven most popular methods with equivalent metrics. After image dehazing by LFD-Net, the performance of object detection is significantly improved. The mean Average Precision when IoU = 0.5 (mAP@0.5) based on YOLOv5 is improved by 4.73% on DAIR-V2X dataset, which verified the practicability and adaptability of LFD-Net for real-time vision tasks. Our codes are available at DD00DD <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/RacerK/LFD-Net</uri>
Learning From Partially Labeled Data for Multi-Organ and Tumor Segmentation

Medical image benchmarks for the segmentation of organs and tumors suffer from the partially labeling issue due to its intensive cost of labor and expertise. Current mainstream approaches follow the practice of one network solving one task. With this pipeline, not only the performance is limited by the typically small dataset of a single task, but also the computation cost linearly increases with the number of tasks. To address this, we propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple partially labeled datasets. Specifically, TransDoDNet has a hybrid backbone that is composed of the convolutional neural network and Transformer. A dynamic head enables the network to accomplish multiple segmentation tasks flexibly. Unlike existing approaches that fix kernels after training, the kernels in the dynamic head are generated adaptively by the Transformer, which employs the self-attention mechanism to model long-range organ-wise dependencies and decodes the organ embedding that can represent each organ. We create a large-scale partially labeled Multi-Organ and Tumor Segmentation benchmark, termed MOTS, and demonstrate the superior performance of our TransDoDNet over other competitors on seven organ and tumor segmentation tasks. This study also provides a general 3D medical image segmentation model, which has been pre-trained on the large-scale MOTS benchmark and has demonstrated advanced performance over current predominant self-supervised learning methods.
Efficient Propagation and Remapping of Sound Through a Geometric Approach in Virtual Environments and Terrains

In this paper, we propose an efficient sound propagation and remapping technique based on geometry to enhance immersive sound effects in virtual environments, and suggest a method to interactively represent sound by considering the altitude and slope of the terrain. The proposed technique can represent real-time patterns of sound such as waves and flows of sound, as well as diffraction that occur in a physical environment. Our approach involves identifying the position of obstacles from the sound source and calculating the new position of sound that has been refracted or diffracted due to the obstacles. Using a ray-tracing technique, we determine whether there is a collision between obstacles and sound, and calculate the reflected and refracted vectors due to the collision to predict the magnitude of sound that agents beyond the obstacles would hear. In this process, the number of reflected and refracted rays affects the magnitude of sound, allowing us to model sound attenuation based on the distance to the obstacle and the material of the obstacle. In this process, we control the magnitude of sound based on the altitude difference from the sound source and the gradient difference depending on the shape of the surrounding terrain. As a result, we efficiently and quickly represented diffraction patterns expressed in a physically-based approach, and demonstrated that the magnitude of sound diffuses naturally according to the deformed diffraction pattern as the obstacles move and rotate. Furthermore, we realistically represented the sound heard by the audience in terrains with altitude differences, and demonstrated that the magnitude of sound varies depending on the terrain.
FeLebrities: A User-Centric Assessment of Federated Learning Frameworks

Federated Learning (FL) is a new paradigm aiming to solve the data access problem. It provides a solution by moving the focus from sharing data to sharing models. The FL paradigm involves different entities (institutions) holding proprietary datasets, contributing with each other to train a global Artificial Intelligence (AI) model using their own locally available data. Although several studies propose ways to distribute the computation or aggregate results, fewer efforts have been made on how to implement FL pipelines.With the ambition of helping accelerate the exploitation of FL frameworks, this paper proposes a survey of public tools that are currently available for building FL pipelines, an objective ranking based on the current state of user preferences, and the assessment of the growing trend of the tool’s popularity over a one year time window, with measurements performed every six months. These measurements include objective metrics, like the number of "Watch," "Star" and "Follow" available from software repositories as well as thirteen custom metrics grouped into three main categories: Usability, Portability, and Flexibility. Finally, a ranking of the maturity of the tools is derived based on key aspects to consider when building a FL pipeline.
Sign4all: A Low-Cost Application for Deaf People Communication

One of the main barriers for deaf people is communication due to the lack of understanding between them and the hearing society.This fact can considerably affect their daily life by leading to their social exclusion.On the way to an inclusive society, this paper presents a low-cost application to assist people to communicate by means of the Sign Language alphabet.For that, this application includes two functionalities: 1) a Sign Language recognizer, which is in charge of typing the letters being signed by deaf people; and 2) a virtual avatar signing what is written letter by letter.So, a comparative analysis of different techniques resulted in an accuracy of 79.96% when using the Convolutional Neural Network ResNet50 for sign recognition from an RGB image.
Optimizing Multi-Modal Electromagnetic Design Problems Using Quantum Particle Swarm Optimization With Differential Evolution

Many versatile and promising swarm intelligence evolutionary algorithms are being developed to solve engineering optimization problems. Although evolutionary algorithms have been implemented in various optimization fields, there is still potential for enhancement in the domain of complex, electromagnetic, and multimodal objective problems. To effectively address the shortcomings and slow convergence speed observed in both smart quantum particle swarm optimization (QPSO) and differential evolution (DE), a hybrid strategy is proposed. In the proposed QPSODE, apart from the smart strategy of QPSO for improving the exploration as a whole, more additional features such as non-linear adaptive control parameter, the partition of the swarm to apply smart and gaussian mutation mechanism, crossover and selection of best particle using Boltzmann strategy to avoid premature convergence are introduced. Consequently, applying the new design algorithm to several benchmark-constrained, mostly non-convex, and superconducting magnetic energy storage (SMES) electromagnetic problems shows a marked performance improvement. The performances of the QPSODE is compared with those of many other widely recognized population-based swarm intelligence optimizers. Experimental results and statistical analysis using Friedman test show that the search accuracy and the convergence of the hybrid QPSODE strategy are advantageous over other optimization approaches.
A Study on the Effect of Ageing in Facial Authentication and the Utility of Data Augmentation to Reduce Performance Bias Across Age Groups

This work presents a study on the effects of aging on the performance and reliability of facial authentication methods. First, a brief review of the literature on the effect of age on face recognition algorithms is presented, followed by a detailed description of the face aging datasets. In contrast with some recent studies, we demonstrate significant variations in authentication robustness between age groups. The second part of this paper focuses on a comprehensive comparative assessment on the effects across age groups. Four different face recognition algorithms are studied of which three are state-of-the-art neural network based models and the fourth one is a conventional machine learning model. Two different age range threshold settings (±3 in Experiment Category A and ±5 in Experiment Category B) of the age groups are adopted in the experimental analysis to get a proper comparison. Moreover, a synthetic aging method has been incorporated to augment the age data. Experimental result shows that the older adults group are easier to identify with higher levels of accuracy and robustness compared to other age groups, while younger adults are the most challenging and false authentications are more likely to occur.