Reviewerly/exHarmony huggingface dataset:
Prepended Domain Transformer: Heterogeneous Face Recognition Without Bells and Whistles

Heterogeneous Face Recognition (HFR) refers to matching face images captured in different domains, such as thermal to visible images (VIS), sketches to visible images, near-infrared to visible, and so on. This is particularly useful in matching visible spectrum images to images captured from other modalities. Though highly useful, HFR is challenging because of the domain gap between the source and target domain. Often, large-scale paired heterogeneous face image datasets are absent, preventing training models specifically for the heterogeneous task. In this work, we propose a surprisingly simple, yet, very effective method for matching face images across different sensing modalities. The core idea of the proposed approach is to add a novel neural network block called Prepended Domain Transformer (PDT) in front of a pre-trained face recognition (FR) model to address the domain gap. Retraining this new block with few paired samples in a contrastive learning setup was enough to achieve state-of-the-art performance in many HFR benchmarks. The PDT blocks can be retrained for several source-target combinations using the proposed general framework. The proposed approach is architecture agnostic, meaning they can be added to any pre-trained FR models. Further, the approach is modular and the new block can be trained with a minimal set of paired samples, making it much easier for practical deployment. The source code and protocols will be made available publicly.
Sentinels and Twins: Effective Integrity Assessment for Distributed Computation

Distributed computing supports large scale and data-intensive computations with the cooperation of a multitude of parties, each responsible for a portion of the workload. Such parties are often not fully reliable and may return incorrect results. In this article, we address the problem of assessing the integrity of the computation results. We provide a comprehensive characterization of two techniques, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sentinels</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">twins</i> , evaluating their effectiveness and synergy. Sentinels are pre-computed tasks whose result is known apriori, and enable checking returned results against a ground truth. Twins are replicated tasks assigned to different workers, and enable cross-checking returned results for a same task. The analysis considers many questions that arise in the design of a concrete integrity assessment strategy and identifies the parameters that have a critical impact on the overall protection. Our model enables to tune the integrity controls so to achieve best effectiveness. The model can be applied to a variety of scenarios and offers guidelines that can find extensive application.
An efficient evolutionary optimizer for solving complex dairy feed optimization problems

Dairy feed ration optimization is one of the most debated problems of the present time. The presence of linear and non-linear constraints, along with their various potential blends, turns the problem into Non-deterministic Polynomial (NP) hard. This triggers the need for efficient evolutionary algorithms to figure out feed mix at minimum cost and maximum shelf life for dairy cattle. Despite its exclusive importance in a real-life scenario, few papers have developed algorithms to solve this problem. Therefore, an attempt is made in this study to introduce a Self-adaptive multi-population approach with the recently proposed Quadratic Approximation based Jaya (JaQA) algorithm (Das et al., 2022), where care has been taken for the maintenance of diversity in each of its sub-population along with enhancement of convergence. However, considering the difficulty in setting the size of the sub-population, the Self-adaptive Multi-Population Quadratic Approximation guided Jaya (SMP-JaQA) follows an automated learning approach based on the past data to step in the next generation, hence making it a user-friendly approach. The performance of this approach is tested on problems in the benchmark set of Congress on Evolutionary Computation (CEC) 2006 and three popular engineering design functions. Numerical results, statistical tests (including Friedman Rank Test, Iman-Davenport Test, and Wilcoxon signed-rank test), and the convergence analysis confirm the better efficacy of the proposed technique over some recent algorithms. Later, this approach is incorporated into two real-life case studies to optimize the cost of feedstuffs for dairy cattle. The improved results under the feasible domain ascertain the proposed method to be farmer-friendly.
<i>VACSEN</i>: A <u>V</u>isualization <u>A</u>pproa<u>c</u>h for Noi<u>s</u>e Awaren<u>e</u>ss in Qua<u>n</u>tum Computing

Quantum computing has attracted considerable public attention due to its exponential speedup over classical computing. Despite its advantages, today's quantum computers intrinsically suffer from noise and are error-prone. To guarantee the high fidelity of the execution result of a quantum algorithm, it is crucial to inform users of the noises of the used quantum computer and the compiled physical circuits. However, an intuitive and systematic way to make users aware of the quantum computing noise is still missing. In this paper, we fill the gap by proposing a novel visualization approach to achieve noise-aware quantum computing. It provides a holistic picture of the noise of quantum computing through multiple interactively coordinated views: a Computer Evolution View with a circuit-like design overviews the temporal evolution of the noises of different quantum computers, a Circuit Filtering View facilitates quick filtering of multiple compiled physical circuits for the same quantum algorithm, and a Circuit Comparison View with a coupled bar chart enables detailed comparison of the filtered compiled circuits. We extensively evaluate the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VACSEN</i> through two case studies on quantum algorithms of different scales and in-depth interviews with 12 quantum computing users. The results demonstrate the effectiveness and usability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VACSEN</i> in achieving noise-aware quantum computing.
FPGA-Based Implementation of Multidimensional Reconciliation Encoding in Quantum Key Distribution

We propose a multidimensional reconciliation encoding algorithm based on a field-programmable gate array (FPGA) with variable data throughput that enables quantum key distribution (QKD) systems to be adapted to different throughput requirements. Using the circulatory structure, data flow in the most complex pipeline operation in the same time interval, which enables the structural multiplexing of the algorithm. We handle the calculation and storage of eight-dimensional matrices cleverly to conserve resources and increase data processing speed. In order to obtain the syndrome more efficiently, we designed a simplified algorithm according to the characteristics of the FPGA and parity-check matrix, which omits the unnecessary operation of matrix multiplication. The simplified algorithm could adapt to different rates. We validated the feasibility and high speed of the algorithm by implementing the multidimensional reconciliation encoding algorithm on a Xilinx Virtex-7 FPGA. Our simulation results show that the maximum throughput could reach 4.88 M symbols/s.
Factor Analysis of Students’ Knowledge Assessment Based on the Results of Online Entrance Testing in Mathematics to the University under the Conditions of COVID-19

After the COVID-19 pandemic emerged, many educational institutions switched to distance learning, which led to the problem of organizing entrance examinations for universities. When conducting entrance examinations online, applicants have many more opportunities for fraudulent activities (cheating on the exam and using outside help). This article is devoted to the factor analysis of the assessment of students’ knowledge in mathematics enrolled in 2020 at the Altai State Technical University (Barnaul, Russia) based on the results of online entrance testing during the COVID-19 pandemic using an Internet browser and the webcams of applicants. The study used statistical methods, including groupings and comparisons. The analysis revealed that the spread of students’ entrance ratings and their grades at the end of examination sessions in the first year increased by 2.7 times compared to previous years when entrance testing was held offline at the university. Such a large spread can be justified by 37% of the personal problems of applicants (difficulty adapting to the educational process at the university, lack of time, change of interests, etc.) and by 17% of a partial transition to distance learning in the current COVID-19 pandemic. There is a 46% imperfection rate of online testing as a mechanism for competitive selection of applicants. Despite the moderator’s constant control over the progress of testing by monitoring the video from the cameras of applicants, some students probably used outside help in the exam. A correlation analysis showed that the propensity to such behavior is influenced by such characteristics as the age of the applicant, the level of previous education, knowledge of Russian for foreigners, degree of adaptation to life, and education in another country. In addition, the analysis of the correctness of answers to the tasks of the online test made it possible to identify several tasks that can additionally serve as a detector of independence when passing the test, called “traps”.
XANet: An Efficient Remote Sensing Image Segmentation Model Using Element-Wise Attention Enhancement and Multi-Scale Attention Fusion

Massive and diverse remote sensing data provide opportunities for data-driven tasks in the real world, but also present challenges in terms of data processing and analysis, especially pixel-level image interpretation. However, the existing shallow-learning and deep-learning segmentation methods, bounded by their technical bottlenecks, cannot properly balance accuracy and efficiency, and are thus hardly scalable to the practice scenarios of remote sensing in a successful way. Instead of following the time-consuming deep stacks of local operations as most state-of-the-art segmentation networks, we propose a novel segmentation model with the encoder–decoder structure, dubbed XANet, which leverages the more computationally economical attention mechanism to boost performance. Two novel attention modules in XANet are proposed to strengthen the encoder and decoder, respectively, namely the Attention Recalibration Module (ARM) and Attention Fusion Module (AFM). Unlike current attention modules, which only focus on elevating the feature representation power, and regard the spatial and channel enhancement of a feature map as two independent steps, ARM gathers element-wise semantic descriptors coupling spatial and channel information to directly generate a 3D attention map for feature enhancement, and AFM innovatively utilizes the cross-attention mechanism for the sufficient spatial and channel fusion of multi-scale features. Extensive experiments were conducted on ISPRS and GID datasets to comprehensively analyze XANet and explore the effects of ARM and AFM. Furthermore, the results demonstrate that XANet surpasses other state-of-the-art segmentation methods in both model performance and efficiency, as ARM yields a superior improvement versus existing attention modules with a competitive computational overhead, and AFM achieves the complementary advantages of multi-level features under the sufficient consideration of efficiency.
HT-Fed-GAN: Federated Generative Model for Decentralized Tabular Data Synthesis

In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated generative models with differential privacy are used by the existing methods. Unfortunately, the existing models apply only to images or text data and not to tabular data. Unlike images, tabular data usually consist of mixed data types (discrete and continuous attributes) and real-world datasets with highly imbalanced data distributions. Existing methods hardly model such scenarios due to the multimodal distributions in the decentralized continuous columns and highly imbalanced categorical attributes of the clients. To solve these problems, we propose a federated generative model for decentralized tabular data synthesis (HT-Fed-GAN). There are three important parts of HT-Fed-GAN: the federated variational Bayesian Gaussian mixture model (Fed-VB-GMM), which is designed to solve the problem of multimodal distributions; federated conditional one-hot encoding with conditional sampling for global categorical attribute representation and rebalancing; and a privacy consumption-based federated conditional GAN for privacy-preserving decentralized data modeling. The experimental results on five real-world datasets show that HT-Fed-GAN obtains the best trade-off between the data utility and privacy level. For the data utility, the tables generated by HT-Fed-GAN are the most statistically similar to the original tables and the evaluation scores show that HT-Fed-GAN outperforms the state-of-the-art model in terms of machine learning tasks.
On Attacking Future 5G Networks with Adversarial Examples: Survey

The introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to dynamically create and deploy multiple services which function under various requirements in different vertical sectors while operating on top of the same physical infrastructure. The recent progress in artificial intelligence and machine learning is theorized to be a potential answer to the arising resource allocation challenges. It is therefore expected that future generation mobile networks will heavily depend on its artificial intelligence components which may result in those components becoming a high-value attack target. In particular, a smart adversary may exploit vulnerabilities of the state-of-the-art machine learning models deployed in a 5G system to initiate an attack. This study focuses on the analysis of adversarial example generation attacks against machine learning based frameworks that may be present in the next generation networks. First, various AI/ML algorithms and the data used for their training and evaluation in mobile networks is discussed. Next, multiple AI/ML applications found in recent scientific papers devoted to 5G are overviewed. After that, existing adversarial example generation based attack algorithms are reviewed and frameworks which employ these algorithms for fuzzing stat-of-art AI/ML models are summarised. Finally, adversarial example generation attacks against several of the AI/ML frameworks described are presented.
Fractal Texture Enhancement of Simulated Infrared Images Using a CNN-Based Neural Style Transfer Algorithm with a Histogram Matching Technique

Here, we propose a CNN-based infrared image enhancement method to transform pseudo-realistic regions of simulation-based infrared images into real infrared texture. The proposed algorithm consists of the following three steps. First, target infrared features based on a real infrared image are extracted through pretrained VGG-19 networks. Next, by implementing a neural style-transfer algorithm to a simulated infrared image, fractal nature features from the real infrared image are progressively applied to the image. Therefore, the fractal characteristics of the simulated image are improved. Finally, based on the results of fractal analysis, peak signal-to-noise (PSNR), structural similarity index measure (SSIM), and natural image quality evaluator (NIQE) texture evaluations are performed to know how the simulated infrared image is properly transformed as it contains the real infrared fractal features. We verified the proposed methodology using a simulation with three different simulation conditions with a real mid-wave infrared (MWIR) image. As a result, the enhanced simulated infrared images based on the proposed algorithm have better NIQE and SSIM score values in both brightness and fractal characteristics, indicating the closest similarity to the given actual infrared image. The proposed image fractal feature analysis technique can be widely used not only for the simulated infrared images but also for general synthetic images.
Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment

To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model's understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model.
Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments

In recent years, discriminative correlation filters (DCF) based trackers have been widely used in mobile robots due to their efficiency. However, underground coal mines are typically a low illumination environment, and tracking in this environment is a challenging problem that has not been adequately addressed in the literature. Thus, this paper proposes a Low-illumination Long-term Correlation Tracker (LLCT) and designs a visual tracking system for coal mine drilling robots. A low-illumination tracking framework combining image enhancement strategies and long-time tracking is proposed. A long-term memory correlation filter tracker with an interval update strategy is utilized. In addition, a local area illumination detection method is proposed to prevent the failure of the enhancement algorithm due to local over-exposure. A convenient image enhancement method is proposed to boost efficiency. Extensive experiments on popular object tracking benchmark datasets demonstrate that the proposed tracker significantly outperforms the baseline trackers, achieving high real-time performance. The tracker’s performance is verified on an underground drilling robot in a coal mine. The results of the field experiment demonstrate that the performance of the novel tracking framework is better than that of state-of-the-art trackers in low-illumination environments.
Register variation explains stylometric authorship analysis

Abstract For centuries, investigations of disputed authorship have shown that people have unique styles of writing. Given sufficient data, it is generally possible to distinguish between the writings of a small group of authors, for example, through the multivariate analysis of the relative frequencies of common function words. There is, however, no accepted explanation for why this type of stylometric analysis is successful. Authorship analysts often argue that authors write in subtly different dialects, but the analysis of individual words is not licensed by standard theories of sociolinguistic variation. Alternatively, stylometric analysis is consistent with standard theories of register variation. In this paper, I argue that stylometric methods work because authors write in subtly different registers. To support this claim, I present the results of parallel stylometric and multidimensional register analyses of a corpus of newspaper articles written by two columnists. I demonstrate that both analyses not only distinguish between these authors but identify the same underlying patterns of linguistic variation. I therefore propose that register variation, as opposed to dialect variation, provides a basis for explaining these differences and for explaining stylometric analyses of authorship more generally.
Structure-transferring edge-enhanced grid dehazing network

The problem of image dehazing has received a great deal of attention in the computer vision community over the past two decades. Under haze conditions, due to the scattering of water vapor and dust particles in the air, the sharpness of the image is seriously reduced, making it difficult for many computer vision systems, such as those for object detection, object recognition, surveillance, driver assistance, etc. to do further process and operation. However, the previous dehazing methods usually have shortcomings such as poor brightness, color cast, removal of uncleanliness, halos, artifacts, and blurring. To address these problems, we propose a novel Structure-transferring Edge-enhanced Grid Dehazing Network (SEGDNet) in this study. An edge-preserving smoothing operator, a guided filter, is used to efficiently decompose the images into low-frequency image structure and high-frequency edges. The Low-frequency Grid Dehazing Subnetwork (LGDSn) is proposed to effectively preserve the low-frequency structure while dehazing. The High-frequency Edge Enhancement Subnetwork (HEESn) is also proposed to enhance the edges and details while removing the noise. The Low-and-High frequency Fusion Subnetwork (L&amp;HFSn) is used to fuse the low-frequency and high-frequency results to obtain the final dehazed image. The experimental results on synthetic and real-world datasets demonstrate that our method outperforms the state-of-the-art methods in both qualitative and quantitative evaluations.
NumCap: A Number-controlled Multi-caption Image Captioning Network

Image captioning is a promising task that attracted researchers in the last few years. Existing image captioning models are primarily trained to generate one caption per image. However, an image may contain rich contents, and one caption cannot express its full details. A better solution is to describe an image with multiple captions, with each caption focusing on a specific aspect of the image. In this regard, we introduce a new number-based image captioning model that describes an image with multiple sentences. An image is annotated with multiple ground-truth captions; thus, we assign an external number to each caption to distinguish its order. Given an image-number pair as input, we could achieve different captions for the same image under different numbers. First, a number is attached to the image features to form an image-number vector (INV). Then, this vector and the corresponding caption are embedded using the order-embedding approach. Afterward, the INV’s embedding is fed to a language model to generate the caption. To show the efficiency of the numbers incorporation strategy, we conduct extensive experiments using MS-COCO, Flickr30K, and Flickr8K datasets. The proposed model attains 24.1 in METEOR on MS-COCO. The achieved results demonstrate that our method is competitive with a range of state-of-the-art models and validate its ability to produce different descriptions under different given numbers.