Reviewerly/exHarmony huggingface dataset:
DSNet: A vehicle density estimation network based on multi-scale sensing of vehicle density in video images

It is one of the hot topics in the field of computer vision to estimate the density of vehicles on the road by using UAV equipped camera. Because the vehicle scale in video images is variable, the scene is complex and the change of vehicle density has strong randomness, the estimation results of vehicle density obtained by the existing methods have large errors. To solve these problems, this paper proposes a vehicle density estimation network based on multi-scale sensing of vehicle density (DSNet). The network adaptively senses the vehicle density in video images through an innovatively designed density sensing module and generates feature maps with different densities. Combined with the context information extracted from the network, the appropriate weight for each density-specific feature map is generated. By fusing the feature maps with different weights to enhance the feature of vehicle. Finally, the enhanced feature map is further refined to obtain a high-resolution density map. In addition, to solve the problem of the global counting loss (LC) that the global estimation results are good but the local estimation errors exist, we design and add the new region-aware loss (LG) to enhance the local feature extraction ability of the network and improve the robustness of the network. Experimental results show that the GAME of DSNet on the VisDrone 2019 dataset and DETRAC dataset is reduced to 5.45 and 6.07, respectively. Compared with the current state-of-the-art vehicle density estimation methods, DSNet achieves higher accuracy in the vehicle density estimation tasks of different scenes.
Effects of Architectures on Continual Semantic Segmentation

Research in the field of Continual Semantic Segmentation is mainly investigating novel learning algorithms to overcome catastrophic forgetting of neural networks. Most recent publications have focused on improving learning algorithms without distinguishing effects caused by the choice of neural architecture. Therefore, we study how the choice of neural network architecture affects catastrophic forgetting in class- and domain-incremental semantic segmentation. Specifically, we compare the well-researched CNNs to recently proposed Transformers and Hybrid architectures, as well as the impact of the choice of novel normalization layers and different decoder heads. We find that traditional CNNs like ResNet have high plasticity but low stability, while transformer architectures are much more stable. When the inductive biases of CNN architectures are combined with transformers in hybrid architectures, it leads to higher plasticity and stability of the model. The stability of these models can be explained by their ability to learn general features which are robust against distribution shifts. Experiments with different normalization layers show that Continual Normalization achieves the best trade-off in terms of adaptability and stability of the model, especially in domain-incremental learning. Our experiments suggest that the right choice of architecture can significantly reduce forgetting even with naive fine-tuning and confirm that for real-world applications, the architecture is an important factor in designing a continual learning model.
MlpE: Knowledge Graph Embedding with Multilayer Perceptron Networks

Knowledge graph embedding (KGE) is an efficient method to predict missing links in knowledge graphs. Most KGE models based on convolutional neural networks have been designed for improving the ability of capturing interaction. Although these models work well, they suffered from the limited receptive field of the convolution kernel, which lead to the lack of ability to capture long-distance interactions. In this paper, we firstly illustrate the interactions between entities and relations and discuss its effect in KGE models by experiments, and then propose MlpE, which is a fully connected network with only three layers. MlpE aims to capture long-distance interactions to improve the performance of link prediction. Extensive experimental evaluations on four typical datasets WN18RR, FB15k-237, DB100k and YAGO3-10 have shown the superority of MlpE, especially in some cases MlpE can achieve the better performance with less parameters than the state-of-the-art convolution-based KGE model.
Unsupervised person re-identification by dynamic hybrid contrastive learning

Unsupervised person re-identification (re-ID) aims at cross-camera pedestrian retrieval without manual annotation. Recently, the contrastive learning has been introduced into the field of unsupervised person re-ID. However, existing methods usually focus only on mining the intra-category similarity and neglect the negative effects of clustering noise, which limits the performance in unsupervised person re-ID. In this paper, we propose a Dynamic Hybrid Contrastive Learning (DHCL) method for unsupervised person re-ID. Specifically, we perform the clustering algorithm and the dynamic refinement policy to divide the unlabeled training dataset into two subsets, i.e., clustered samples with pseudo labels and un-clustered independent instances, at each training epoch. Then, the proposed DHCL guides the feature extraction network to mine the intra-category similarity from the clustered samples by applying attraction within samples of the same cluster. Meanwhile, the inter-instance discrimination is also mined by pushing away different instances. Besides, we integrate the two levels of contrastive learning into an end-to-end framework and exploit the complementarity between them to improve the separability of the feature space. To reduce the negative effect of over-focusing on positive samples, a penalty item is added to the hybrid contrastive loss. Extensive experiments demonstrate the effectiveness of the proposed method in unsupervised person re-ID.
Quantifying Object Detection Uncertainty in Autonomous Driving with Test-Time Augmentation

In this paper, we propose the first Test-Time Augmentation (TTA) method to estimate uncertainty and improve accuracy of pre-trained object detectors in the autonomous driving domain. We show, in an autonomous driving dataset, that even simple color-based augmentations are able to improve mean Average Precision (mAP) performance with respect to other state-of-the-art methods with the same purpose (Monte Carlo (MC) Dropout and Output Redundancy). Furthermore, we show that the quality of the estimated uncertainty and distributions can be improved, both in our method and in the state-of-the-art, if some of the parameters of the methods (bounding box selection and clustering criteria) are independently tuned for the classification and the localization subtasks of object detection.
Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive

Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and optimizing ML models to provide accurate solutions to the end user. This article introduces a novel method to initialize the input weights in autoencoder extreme learning machine (AE-ELM), namely factorized input data (FID), which is based on the normalized form of the orthogonal component of the input data. AE-ELM with FID weight initialization is used to efficiently reduce the radio map. Once the dimensionality of the dataset is reduced, we use <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbors to perform the position estimation. This research work includes a comparative analysis with several traditional ways to initialize the input weights in AE-ELM, showing that FID provide a significantly better reconstruction error. Finally, we perform an assessment with 13 indoor positioning datasets collected from different buildings and in different countries. We show that the dimensionality of the datasets can be reduced more than 11 times on average, while the positioning error suffers only a small increment of 15% (on average) in comparison to the baseline.
Performance Comparison of Post-Quantum Signature Algorithms Through An Android Email Application Plug-in

Public key cryptography and signature schemes are essential for reliable digital communication. Asymmetric encryption has been achieved through complex algorithms, such as RSA and Diffie-Hellman, but recent advancements in quantum computing pose threats to the security of these algorithms in the near future as a quantum computer may solve the problems these algorithms are based on. Therefore, Post-Quantum Cryptography (PQC) algorithms have been proposed and investigated by an international community of scientists, especially through the NIST PQC competition ongoing since 2016. They have gathered and classified four different key exchange algorithms and three signature schemes. This paper focuses on implementing the signature schemes inside a popular open-source Android email application as a plug-in and comparing their performances based on this app plug-in utilization. We have used a library, liboqs, from the Open Quantum Safe project as the starting point, which ensures regularity in our results. We also compare the current standard candidate signature schemes with the currently used standard RSA-4096. Our results show that Dilithium has noticeably faster key generation, signing, and verification with the relatively affordable key and signature size. Also, to the best of our knowledge, our study provides the first practical Android email application plug-in supporting PQ-based signatures.
College Student Self-Efficacy in Information Literacy: Influence On Their Research Academic Skills

Self-efficacy in information literacy refers to the individual's confidence in his ability to locate, evaluate, and use information to accomplish a goal. This concurrent nested mixed method explored the college students’ self-efficacy in information literacy in relation to their research academic skills. There were 81 fourth-year college students enrolled in the program of Bachelor of Science in Information Technology in a satellite campus of a state university in Misamis Oriental. The instrument’s validity and reliability were ensured through panel review and reliability test using the Cronbach’s alpha. Data were analyzed using descriptive statistics and multiple regression analysis. Findings reveal that participants’ research academic skills were generally very good, indicating that they performed well on their capstone research project and their self-efficacy on information literacy skills were generally all high. Self-efficacy in information literacy skills in terms of synthesizing and creating significantly influenced the research academic skills. Strengthened research capability emerged as the theme which contributed good in academic research skills. It was concluded that students who had high self-efficacy in information literacy skills were more likely to feel confident in their ability to conduct effective research and use information effectively, thus contributing to effective research academic skills. This study points the need to develop and implement innovative information literacy programs.
Visual-Saliency-Guided Channel Pruning for Deep Visual Detectors in Autonomous Driving

Neural network pruning has become a de facto component for deploying deep networks on resource-constrained devices, which can reduce memory requirements and computation costs. In particular, channel pruning gained more popularity due to its structured nature and direct savings on general hardware. However, most existing pruning approaches utilize importance measures that are not directly related to the task utility. Moreover, few in the literature focus on visual detection models. To fill these gaps, we propose a novel gradient-based saliency measure for visual detection and use it to guide our channel pruning. Experiments on the KITTI and COCO_traffic datasets demonstrate our pruning method’s efficacy and superiority over competing state-of-the-art approaches. It can even achieve better performance with fewer parameters than the original model. Our pruning approach also demonstrates its great potential in handling small-scale objects.
Cryptography as a Bridge to Abstract Mathematics

AbstractWe introduce learning modules in cryptography that can be crafted to motivate many abstract mathematical ideas, and we illustrate with a sample module. These modules can be used in a variety of ways, such as the core for a cryptography course or as motivating topics in other courses such as abstract and linear algebra or number theory.Keywords: Learning modules in cryptographylinear feedback shift registersmotivating abstract mathematics DISCLOSURE STATEMENTNo potential conflict of interest was reported by the author(s).BIOGRAPHICAL SKETCHAaron Wootton received a BSc in mathematics and philosophy at the University of Southampton, England, and his PhD in mathematics from the University of Arizona. His research lies in the areas of Complex Algebraic Geometry and Group Theory, with specific interests in Symmetries of Surfaces. Other interests include participation and fostering of undergraduate research in mathematics and the development of educational resources. In his spare time, when not enjoying time with his family, he plays soccer and he drums for a punk rock band called “Sack of Cats”!
Selected confidence sample labeling for domain adaptation

Unsupervised Domain adaptation (UDA) aims to transfer knowledge from the labeled source domain to the unlabeled target domain. Recently, to achieve reliable knowledge learning, progressively labeling (PL) is proposed to select reliable target samples for training. Although PL achieves fruitful results, there are two problems that limit its performance: (a) PL may neglect to filter out the uncertain samples that lie in the classification boundaries, which might select low-quality target samples for training, and lead to error accumulations; and (b) PL might overlook the consistent selection in the sample selection stage during iteration, which might result in unstable sample selection. To cope with these problems, we propose a novel method called Selected Confidence Sample Labeling (SCSL). SCSL consists of three parts: Discriminative Progressively Labeling (DPL), Consistency Strategy (CS), and Differential Learning (DL). First, DPL selects the high-confidence target samples with the maximum probability difference between the highest and the second-highest probabilities of classification. In this way, the uncertain samples are filtered out, while the qualities of the selected target samples are ensured. Second, CS includes a group of consistency strategies to make the selected high-confidence target samples near to the class-centroids. This further improves the confidence degree of the selected target samples, and ensures that the proposed model does not remove or replace the selected target samples during iteration. At last, DL is a bi-strategic training approach that applies CS and top-k fuzzy probability clustering to train the high-confidence and the remaining target samples, respectively. In doing so, all the target samples are trained simultaneously, and the generalization of the model is improved. Extensive experiments on four benchmark datasets with the comparison of several advanced algorithms demonstrate the superiority of SCSL.
AD-YOLOv5s based UAV detection for low altitude security

UAV (Unmanned Aerial Vehicle) black flight at low altitude could cause serious safety risks. Consequently, it is crucial to detect and manage low altitude small UAVs. The existing methods of low altitude small UAV detection suffer from problems such as high false alarm rate, and poor real-time performance. In order to solve the above problems, we present a novel approach, named AD-YOLOv5s, to achieve low altitude small UAV detection with high precision and high real-time performance. Firstly, the feature enhancement method is used to expand the dataset. We optimize the model feature fusion, the prediction head structure, and the loss function. Based on the CBAM (Convolutional Block Attention Module) attention mechanism, feature enhancement is performed to improve the detection accuracy. Secondly, the ghost module and depthwise separable convolution are used to reduce the number of parameters of the model, and we propose the method of lightweight design of model to improve the detection speed. Compared with the YOLOv5s model, the experiment result shows that our proposed AD-YOLOv5s model improves the value of mAP by 2.2% and the value of Recall by 1.8%, reduces the value of GFLOPs by 29.9% and parameters by 38.8%, and achieves 27.6 FPS when the proposed model deploy on a low-cost edge computing device (jetson nano).
Several classes of Galois self-orthogonal MDS codes and related applications

Let q=ph be a prime power and e be an integer with 0≤e≤h−1. e-Galois self-orthogonal codes are generalizations of Euclidean self-orthogonal codes (e=0) and Hermitian self-orthogonal codes (e=h2 and h is even). In this paper, we propose two general methods to construct e-Galois self-orthogonal (extended) generalized Reed-Solomon (GRS) codes. As a consequence, eight new classes of e-Galois self-orthogonal (extended) GRS codes with odd q and 2e|h are obtained. Based on the Galois dual of a code, we also study its punctured and shortened codes. As applications, new e′-Galois self-orthogonal maximum distance separable (MDS) codes for all possible e′ satisfying 0≤e′≤h−1, new e-Galois self-orthogonal MDS codes via the shortened codes, and new MDS codes with prescribed dimensional e-Galois hull via the punctured codes are derived. Moreover, some new q-ary quantum MDS codes with length greater than q+1 and minimum distance greater than q2+1 are obtained.
I Had a Bad Day: Challenges of Object Detection in Bad Visibility Conditions

Automated vehicles must be able to correctly perceive the environment in every conceivable situation with the help of their sensor stack. The resulting data streams are typically processed by algorithms that detect and classify objects in the scene. In order to ensure safe driving, these algorithms are expected to perform with sufficient accuracy even in the harshest weather conditions, such as rain, fog and snow. However, this is not the case for the current generation of object detection models. A sharp drop in performance can be observed as soon as these are exposed to adverse weather situations. This can be attributed to the weak representation of training data in bad visibility conditions and detection architectures that are not designed to handle them properly. To address this problem, we propose three small scale annotated datasets that include challenging adverse weather conditions. We evaluate state-of-the art object detection models on a variety of datasets and quantify the performance drop for each algorithm in adverse weather conditions. To this end, we show that current object detection models suffer from severe performance loss due to adverse weather effects and identify common challenges of object detection in bad visibility conditions.
Quantum entanglement in an extended Hubbard model as evaluated from a spin concurrence measure

Quantum entanglement is a peculiar feature of quantum mechanics that turns out a powerful resource in quantum communication and information. For this reason, intense research has recently focused on the robustness of quantum entanglement also in strongly correlated electron systems. Here, we consider an extended Hubbard model on a dimer and evaluate the spin entanglement at finite temperature, under the action of an external magnetic field. We show how magnetic field and Coulomb interactions modify the spin concurrence, finding out that the magnetic field may act as a switch from a non-entangled to an entangled state.
ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives

We introduce ShapeCoder, the first system capable of taking a dataset of shapes, represented with unstructured primitives, and jointly discovering (i) useful abstraction functions and (ii) programs that use these abstractions to explain the input shapes. The discovered abstractions capture common patterns (both structural and parametric) across a dataset, so that programs rewritten with these abstractions are more compact, and suppress spurious degrees of freedom. ShapeCoder improves upon previous abstraction discovery methods, finding better abstractions, for more complex inputs, under less stringent input assumptions. This is principally made possible by two methodological advancements: (a) a shape-to-program recognition network that learns to solve sub-problems and (b) the use of e-graphs, augmented with a conditional rewrite scheme, to determine when abstractions with complex parametric expressions can be applied, in a tractable manner. We evaluate ShapeCoder on multiple datasets of 3D shapes, where primitive decompositions are either parsed from manual annotations or produced by an unsupervised cuboid abstraction method. In all domains, ShapeCoder discovers a library of abstractions that captures high-level relationships, removes extraneous degrees of freedom, and achieves better dataset compression compared with alternative approaches. Finally, we investigate how programs rewritten to use discovered abstractions prove useful for downstream tasks.
Matrix operations in Random Permutation Set

In D-S evidence theory, belief and commonality functions are well-defined and deeply researched. However, these concepts have not been extended to Random Permutation Set (RPS) because of the difficulties in giving a suitable definition of containment relationships of permutations. In this paper, we addressed this issue by defining both ordered and unordered containment relationships. Based on the defined containment relationships, the corresponding belief function and commonality functions for RPS are derived naturally. Besides, the plausibility function for RPS is also proposed following the definition in D-S evidence theory. Furthermore, the transformations between them and permutation mass functions are discussed and implemented by matrix converters. Thanks to the properties of commonality functions, the combination process in RPS, including left and right orthogonal sum, are both implemented by matrix operations. Moreover, based on matrix operations, the reverse transformation of combination, that is, the decomposition of fusion results given one information source is achieved for the first time. Numerical examples are listed to illustrate the calculation process. The proposed matrix operations provide a way to implement and accelerate information transformations, combinations and decompositions.