Reviewerly/exHarmony huggingface dataset:
Asymptotically Optimal Quasi-Complementary Code Sets of Length $p_{1}^{m_{1}}p_{2}^{m_{2}}$

In this paper we propose a construction of quasi-complementary code sets (QCCSs) of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p_{1}^{m_{1}}p_{2}^{m_{2}}$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p_{1},p_{2}$</tex> are primes and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m_{1}, m_{2}$</tex> are positive integers, and show that the proposed QCCSs achieve optimality asymptotically. The construction generalizes a recent work by Sarkar et al. on QCCSs of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p^{m}$</tex> , which has more flexible alphabet size.
A new class of AMDS symbol-pair codes

Symbol-pair codes introduced by Cassuto and Blaum in 2010 are designed to protect against the pair errors in symbol-pair read channels. In this paper, we give a new class of AMDS symbol-pair codes with the length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4p$</tex> by virtue of repeated-root cyclic codes. The main results are obtained by determining the solutions of certain equations over finite fields.
Equivalence Classes and Balance Properties of Interleaved Sequences

An interleaved sequence is created from a base sequence and a shift sequence. Interleaved sequences have been studied by several researchers. In this paper we study the relationship between periodicity, reversal and balance proper-ties of the base, shift, and interleaved sequences. We look at equivalence classes of interleaved sequences under the operations of left shift and reversal, and give complete necessary and sufficient conditions to determine the size of the equivalence classes. Additionally, we consider several types of balance. The balance properties we study are more nuanced than previous investigations.
Quantum Synchronizable Codes from Generalized Cyclotomy

Quantum synchronizable codes can correct the ef-fects of quantum noise on qubits as well as misalignment in block synchronization. In this paper, dual-containing cyclic codes of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2pq$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p^{2}$</tex> are constructed by generalized cyclotomy, where some of the cyclic codes of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p^{2}$</tex> are optimal or almost optimal. Based on these cyclic codes and their augmented codes, we obtain two new classes of quantum synchronizable codes whose synchronization capabilities are the best.
Interval-based Cost-sensitive Classification Tree Induction as a Bi-level Optimization Problem

Cost-sensitive learning is one of the most adopted approaches to deal with data imbalance in classification. Unfortunately, the manual definition of misclassification costs is still a very complicated task, especially with the lack of domain knowledge. To deal with the issue of costs' uncertainty, some researchers proposed the use of intervals instead of scalar values. This way, each cost would be delimited by two bounds. Nevertheless, the definition of these bounds remains as a very complicated and challenging task. Recently, some researches proposed the use of genetic programming to simultaneously build classification trees and search for optimal costs' bounds. As for any classification tree there is a whole search space of costs' bounds, we propose in this paper a bi-level evolutionary approach for interval-based cost-sensitive classification tree induction where the trees are constructed at the upper level while misclassification costs intervals bounds are optimized at the lower level. This ensures not only a precise evaluation of each tree but also an effective approximation of optimal costs intervals bounds. The performance and merits of our proposal are shown through a detailed comparative experimental study on commonly used imbalanced benchmark data sets with respect to several existing works.
New Correlations of m-sequences over the finite field $\mathbb{F}_{4}$ compatible with a new bijection to $\mathbb{Z}_{4}$

In this paper we obtain a new method to compute the correlation values of two arbitrary sequences defined by a mapping from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{4^{n}}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{4}$</tex> . We apply this method to demon-strate that the usual nonbinary maximal length sequences have almost ideal correlation under the canonical complex correlation definition and investigate some decimations giving good cross correlation.
3-adic complexity of ternary sequences with period pq

We study the 3-adic complexity of ternary sequences with period equal to the product of two different primes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> . These sequences are constructed from two ternary Legendre sequences and have good autocorrelation properties. Using the generalized cyclotomic classes modulo <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$pq$</tex> , we show that these sequences have high 3-adic complexity. It is good enough to resist the attack of the rational approximation algorithm.
Boolean Functions with Few Walsh Transform Values

In this paper, we revisit the construction given by Pang et al. in Advances in Mathematics of Communications, 2021, 15(4): 757–775. We simplify the original construction as a product of a Boolean function with a linear function. The relationship between the Walsh transform values of the original Boolean function and those of the resulted Boolean function is established. The virtue of our proposed construction is that it can be generalized to the case of the product of a Boolean function with two linear functions. By using the bent function satisfying property <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{P}_{\tau}$</tex> with a fixed defining set, which was first introduced by Tang et al. in 2017, Boolean functions with five or seven Walsh transform values can be derived.
A New Class of Optimal Wide-Gap One-Coincidence Frequency-Hopping Sequence

The frequency-hopping sequence(FHS) plays a crucial role in achieving the anti-jamming capability of the FH communication system. Therefore, it is necessary to try to ensure that the wide-gap between two adjacent frequency slots is larger enough and the number of collisions is small enough. In this paper, we present a new class of wide-gap one-coincidence FHS set. Such WG-OC-FHS set is optimal with respect to WG-Peng-Fan Bound. And each sequence of the set is optimal with respect to WG-Lempel-Greenberger Bound. Compared with previous construction, this construction has good randomness and use all frequency slots as much as possible.
Humanized Management of College Students Based on Machine Perspective and Intelligent Monitoring System

In order to improve the effect of humanized management of college students, this article builds a humanized management system for college students based on the machine perspective and intelligent monitoring system, studies its hardware and software systems, and introduces the basic principle of evanescent mode resonators. Moreover, the formulas of the resonant frequency and the unloaded Q value of the resonator are deduced by the equivalent circuit of the evanescent mode resonator, and the variation curve of the unloaded Q value and the resonant frequency of the actual resonator is obtained by HFSS simulation. In addition, this article constructs a humanized management model for college students based on the machine perspective and intelligent monitoring system. The experimental research results show that the intelligent monitoring system based on the machine perspective proposed in this article has good effects and can effectively improve the quality of humanized management of college students.
On the Effectiveness of Visible and MWIR-Based Periocular Human Authentication When Wearing Face Masks

In the COVID-19 (Coronavirus Disease-19) era, everyone is advised to wear a face mask. According to the Centers for Disease Control and Prevention (CDC) published on 19th of April 2021 (Centers for Disease Control and Prevention: Guidance for wearing masks, 2021), masks are most effective when everyone wears one to completely cover vital facial features, i.e., nose and mouth, and to fit correctly against the sides of the face, so that there is no air gap. While the benefits of masks to control the spread of a pandemic or even an endemic are known, many challenges are imposed on state-of-the-art face recognition systems. According to a study published by the National Institute of Standards and Technology (NISTIR 8311), the accuracy of facial recognition algorithms is reduced between 5 and 50% when compared to the accuracy yielded by the same algorithms when the subjects are not wearing face masks. It has been observed that the periocular region is the feature-rich region around the eye, which includes features such as eyelids, eyelashes, eyebrows, tear ducts, eye shape, and skin texture. The report from NIST also states that face images of subjects wearing masks can increase the failure to enroll rate (FER) more frequently than before. In addition, masked face images lower the efficiency of surveillance (unconstrained) face recognition systems, which become even more prone to error due to occlusion, distance, camera quality, outdoors, and low light. In this study, we focus on the effectiveness of periocular-based (combining both eye regions and not just one eye, i.e. left or right) face recognition algorithms when the subjects are wearing face masks under controlled and challenging conditions both for visible and MWIR (mid-wave infrared) band face images. We utilize MILAB-VTF(B), a challenging multi-spectral face dataset composed of thermal and visible videos collected at the University of Georgia (the largest and most comprehensive dual band face dataset to date) (Peri et al (2021) A synthesis-based approach for thermal-to-visible face verification. In: 2021 16th IEEE international conference on automatic face and gesture recognition (FG 2021). IEEE, pp 01–08). We manually crop the faces from the images and use the existing pre-trained face recognition algorithms to perform periocular face recognition. The FR models used in this research study are FaceNet, and VGG-Face. After manually cropping the faces, we perform same-spectral periocular face recognition. FaceNet yields a Rank-1 face identification accuracy of 87.54 and 83.54% in the thermal and visible bands respectively, while VGG-Face yields superior performance with 100 and 99.52% in the thermal and visible bands respectively. Additionally, we also perform same-spectral face recognition experiments (visible-to-visible and thermal-to-thermal) and report the results.
Citation Graph Analysis and Alignment Between Citation Adjacency and Themes or Topics of Publications in the Area of Disease Control Through Social Network Surveillance

This paper presents a Data-Network Science study on a dataset of publications archived in The Semantic Scholar Open Research Corpus (S2ORC) database and categorized under the area of “Disease Control through Social Network Surveillance,” an area abbreviated from now on as “DCSNS.” In particular, our dataset consists of 10,866 documents (which are articles and reviews), retrieved through a Boolean search, published in the period from 1983, the first year of cataloguing such publications in S2ORC, to 2020. Retrieving also the corpus of abstracts of these documents (publications) and applying the standard LDA Topic Modeling technique, we found an optimal number of six topics producing the maximum topic coherence score among the corresponding topic models with varying numbers of topics. In that matter, the network of our study becomes a directed citation graph of publications in the area of DCSNS, with nodes/publications labeled by the Topics (into which Topic Modeling categorizes words from their abstracts). Our aim is to study global and local network properties with regards to clustering under triadic relationships amongst connected nodes/publications, and with regards to the assortativity of attributes related to the content of publications (such as types of publications and themes in the employed keyword searches). Thus, we have succeeded in analyzing the interplay between semantics and structure in the area of publications on DCSNS, by examining and discovering the occurrence of certain important attributes of publications in such a way that the aggregation of publications according to these attributes is associating the meaning of attribute affiliations to certain structural patterns of clustering, exhibited by the bibliographic citation network of the collected publications.
Multispectral Face Mask Compliance Classification During a Pandemic

In this paper, we investigate the problem of face mask compliance classification in response to the Corona Virus Disease (COVID-19) pandemic. Since the start of the pandemic, many governments and businesses have been continually updating policies to help slow the spread of the virus, including requiring face masks to use many public and private services. In response to these policies, many researchers have developed new face detection and recognition techniques for masked faces. Many of the developed approaches proposed to address the problem of masked face detection have been relatively successful. However, these approaches almost exclusively focus on detecting the presence or absence of someone wearing a face mask. It is understood and broadly discussed in various reports and the media that there are people not always following the suggested guidelines provided by public health authorities for wearing face masks, which include ensuring the face mask properly covers the nose and mouth. To date, very few research publications exist that investigate the capability of modern classification algorithms to efficiently distinguish between masked faces in the visible band that are either compliant or non-compliant with the suggested guidelines. Furthermore, to the best of our knowledge, there is no publication in the open literature that focuses on the investigation of face mask classification in the thermal band. As thermal sensors continue to improve in quality and decrease in cost, surveillance applications using thermal imagery are expected to continue to grow and benefit those organizations considering the automation of face mask detection and compliance. In this study, we propose an investigation on face mask compliance in both the thermal and visible bands. It is composed of the following salient steps; (1) the creation of a multi-spectral masked face database from subjects wearing or not wearing face masks, (2) the augmentation of the generated database with synthetic face masks to simulate two different levels of non-compliant wearing of face masks, and (3) the assessment of a variety of CNN architectures on the previous augmented database to investigate any differences between classifying thermal and visible masked faces. Experimental results show that face mask compliance classification in both studied bands yield a classification accuracy that reaches 100% for most models studied, when experimenting on frontal face images captured at short distances with adequate illumination.
The Effects of Face Masks on the Performance of Modern MWIR Face Detectors

Since the pandemic is still a challenge all over the world, wearing a mask has become the new norm. Even if we step out of this pandemic soon, another one may come in the future, and thus, wearing a mask will always be considered a step towards the right direction in preventing the spread of a virus. Wearing face masks poses a challenge for existing biometric systems that are passive and depend on the full facial information to perform well. This book chapter explains the impact of the usage of face masks on the performance of various face detectors trained using some of the most efficient object detection models to-date in terms of accuracy and computational complexity. The chapter presents an insight into the effects of face masks on face detection in one of the most challenging spectral bands, i.e. the Mid-Wave Infrared (MWIR; 3–5 μm) and addresses the problem by training new models using masked face image data. Initially, we train and test two of each model, one with masked and one with unmasked data, to establish a baseline of performance. Then, we test those model’s abilities to generalize the face boundaries on the opposite face data from what it was trained on. Subsequently, we determine a percent drop in performance, called a penalty, when comparing a model’s performance when tested on the same category of data it was trained on versus tested on the opposite category of data. For unmasked trained models tested on masked data, we noticed an average precision and recall penalty of 37.27% and 37.25%, respectively. Similarly, the computational time for the detection of a single face image increased by an average of 12.72%. Then, this process is reversed, where we test the models we trained on masked data with unmasked face data and determine the drop in performance when each model is asked to detect faces on this unfamiliar data. We notice a near identical drop or penalty in detection time at 11.81% as well as an average precision drop of 30.24% and a recall drop of 29.50%. Faster R-CNN Inception-ResNet V2 was determined to be superior in terms of performance. Across all experiments performed, it outperforms all other models in terms of precision and recall. The downside is the fact that it is slow compared to all other models, making it a less attractive solution when working in mobile or time sensitive operations. Our fastest model is the CenterNet RestNet50 V2, but performs poorly when the train and test data are different. This shows that unmasked train models are considerably disadvantaged in scenarios where masks are required and face detection algorithms are used. Models trained on solely masked face data perform marginally better, but largely cannot stand on their own. This highlights the need for using masked face data when training face detection models in today’s ever changing Covid-19 landscape.
Implementing Binarized Neural Network Processor on FPGA-Based Platform

Binarized neural networks (BNNs) have 1-bit weights and activations, which are well suited for FPGAs. The BNNs suffer from accuracy loss compared with conventional neural networks. Shortcut connections are introduced to address the performance degradation. This work proposes a BNN processor supporting the shortcut connects. To evaluate the performance of the processor, we implement the system on an FPGA (Xilinx Kintex UltraScale). Our experiments show that the proposed processor achieves state-of-the-art energy efficiency.
Perceiver Hopfield Pooling for Dynamic Multi-modal and Multi-instance Fusion

Deep network architectures are usually based on domain-specific assumptions and are specialized to the modalities under consideration. This conceptual behavior also applies to multimodal networks, leading to modality-specific subnetworks. In this paper, we introduce a novel dynamic multi-modal and multi-instance (MM-MI) network based on Perceiver and Hopfield pooling which can learn intrinsic data fusion. We further introduce a novel composite dataset for evaluating MM-MI problems. We successfully show that our proposed architecture outperforms the late fusion baseline in all multi-modal setups by more than 40% accuracy on noisy data. Our simple, generally applicable, yet efficient architecture is a novel generalized approach for data fusion with high potential for future applications.
Towards Both Accurate and Robust Neural Networks Without Extra Data

Deep neural networks have achieved remarkable performance in various applications but are extremely vulnerable to adversarial perturbation. The most representative and promising methods that can enhance model robustness, such as adversarial training and its variants, substantially degrade model accuracy on benign samples, limiting practical utility. Although incorporating extra training data can alleviate the trade-off to a certain extent, it remains unsolved to achieve both robustness and accuracy under limited training data. Here, we demonstrate the feasibility of overcoming the trade-off, by developing an adversarial feature stacking (AFS) model, which combines multiple independent feature extractors with varied levels of robustness and accuracy. Theoretical analysis is further conducted, and general principles for the selection of basic feature extractors are provided. We evaluate the AFS model on CIFAR-10 and CIFAR-100 datasets with strong adaptive attack methods, significantly advancing the state-of-the-art in terms of the trade-off. The AFS model achieves a benign accuracy improvement of $$\sim $$ 6% on CIFAR-10 and $$\sim $$ 10% on CIFAR-100 with comparable or even stronger robustness than the state-of-the-art adversarial training methods.
A Novel Deep Learning Based Method for Doppler Spectral Curve Detection

This paper proposes a novel doppler spectral curve detection method. First, U-net model is used to obtain the coarse segmentation map from the spectral image. Then, in order to solve the problem of curve deviation and curve defect, two components, curve correction and curve filling, which adopt deep regression and Generative adversarial networks, are devised for spectrum curve refining operation. These two outputs are fused for final segmentation. The experiments are validated on a private collected Spectral Doppler Spectrum dataset. The results demonstrate the proposed method has achieved satisfactory performance.
MUST Augment: Efficient Augmentation with Multi-stage Stochastic Strategy

Data augmentation has been widely used for enhancing data diversity and deep-learning model generalization. Recent research achieved better accuracies on image classification tasks by introducing automated search for optimal augmentation policies, but incurred high computation cost and long search time because of large search spaces and complex searching algorithms. In this paper, we present an augmentation method called MUST (MUlti-Stage sTochastic) Augment which completely skips policy searching. Instead of searching, our method applies a multi-stage augmentation strategy on top of a simple stochastic augmentation mechanism. This multi-stage complexity driven augmentation strategy ensures the whole training process converges smoothly to a good quality model; and within individual stages, it applies augmentation in a stochastic manner and provides both scalability and diversity by introducing more augmentation operations without extra search cost. Our extensive experiments with state-of-the-art results show that our method has advantages in both accuracy and efficiency compared to search-based augmentation methods. Besides image classification, we also examine the general validity of MUST on Face Recognition and Text Detection tasks, and demonstrate the effectiveness of our method across various CV tasks.