Reviewerly/exHarmony huggingface dataset:
Recursive Nonlinear Filtering via Gaussian Approximation With Minimized Kullback–Leibler Divergence

Authors:
raw_author_name: Liping Guo
raw_affiliation_string: College of Mathematics, Sichuan University, Chengdu, China
raw_author_name: Sanfeng Hu
raw_affiliation_string: School of Mathematics and Statistics and the Yunnan Key Laboratory of Statistical Modeling and Data Analysis, Yunnan University, Kunming, China
raw_author_name: Jie Zhou
raw_affiliation_string: College of Mathematics, Sichuan University, Chengdu, China
raw_author_name: X. Rong Li
raw_affiliation_string: Department of Electrical Engineering, University of New Orleans, New Orleans, LA, USA

Abstract: In order to solve various problems in a Bayesian framework efficiently, it is critical to approximate a posterior distribution. This work provides a Gaussian approximation of a general distribution via Kullback–Leibler (KL) divergence minimization by deterministic sampling. Two algorithms, feasible direction method and linearized alternating direction method of multipliers, each having its strengths, are proposed for the Gaussian approximation. Theoretical results of complexity, convergence, convergence rate, and guidelines for parameter selection of the proposed algorithms are also provided. Based on the Gaussian approximation, two recursive filters are developed for nonlinear dynamic systems. Examples are given to demonstrate the effectiveness and efficiency of the proposed Gaussian approximation and the related filters.
Point cloud optimization of multi-view images in digital image correlation system

Authors:
raw_author_name: Pengxiang Ge
raw_affiliation_string: School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology, Hefei 230009, China; School of mechanical and electrical engineering, Anhui Jianzhu University, Hefei 230009, China
raw_author_name: Yonghong Wang
raw_affiliation_string: School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology, Hefei 230009, China
raw_author_name: Jie Zhou
raw_affiliation_string: School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology, Hefei 230009, China
raw_author_name: Biao Wang
raw_affiliation_string: School of Instrument Science and Opto-electronics Engineering, Hefei University of Technology, Hefei 230009, China

Abstract: Post-processing of the initial point cloud is one of the important ways to improve the measurement accuracy of digital image correlation (DIC). In this study, a multi-level point cloud optimization scheme is proposed for processing point clouds of multi-view images in DIC systems. Dense point clouds obtained after interpolation of primary point clouds from multi-view images contain noise. First, the local Random Sample Consensus (RANSAC) algorithm is proposed to retain local features while removing coarse matching points. Defects are filled in by the adaptive cavity repair technique based on information around the cavities and restores the original surface shape to the greatest extent. Finally, a multi-layer surface filtering is executed to smooth the mesh. Experiments verify the comprehensive performance of the multi-level optimization scheme, providing excellent results in point cloud fusion, cavity repair, and mesh smoothing, regardless of static 3D reconstruction or dynamic deformation measurement.
Multivariate time series anomaly detection by fusion of deep convolution residual autoencoding reconstruction model and ConvLstm forecasting model

Authors:
raw_author_name: Hongsong Chen
raw_affiliation_string: Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China; University of Science and Technology Beijing, School of Computer and Communication Engineering, Beijing, 100083, China

Abstract: Multivariate time-series anomaly detection is very essential to ensure the normal operation of physical equipment. Great achievements have been made in this field in recent years. However, two critical challenges limit the generalization ability of the model. Graph attention networks without prior knowledge are unable to extract the distribution features of dependencies among variables in a fine granularity. This reduces the accuracy of the dependence matrix to represent the correlations between variables. Beyond this challenge, the reconstruction model loses the fine-grained seasonal feature information in the spatial dimension during the reconstruction analysis process of the sample. In order to solve the above challenges, we propose a multivariate time-series anomaly detection model consisting of characterization network and forecasting network. In the characterization network, we first remove non-existent dependencies between variables by using prior knowledge. This approach can accurately capture the fine-grained dependency distribution features between variables and improve the accuracy of the dependence matrix representation of the correlations between variables. We then construct a deep convolutional residual autoencoder to reduce the loss of seasonal feature information in the spatial dimension. In forecasting network, we construct a temporal attention-based ConvLstm forecasting network to make fine-grained anomaly decisions on the output of the characterization network in the time dimension. We perform systematic experiments on six open-source datasets, in social network security scenario,the malicous acount behavior can be detected. Experimental results show that the proposed method outperforms the baseline anomaly detection methods with excellent detection performance and robustness. Specifically, our method achieves an average F1 score improvement of 0.23 over the baseline method at its highest level.
Spurious heritability of ability tilts: A comment on

Authors:
raw_author_name: Gustav Nilsonne
raw_affiliation_string: Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Psychology, Stockholm University, Stockholm, Sweden

Abstract: Ability tilts refer to within-individual differences between two abilities, e.g. math ability – verbal ability. Coyle et al. (2023) found ability tilts to be genetically heritable and concluded that ability tilts are genuine and, presumably, genetically coded individual characteristics. Moreover, Coyle et al. found a large portion of variance in ability tilts to be attributable to non-shared environmental factors (i.e. environmentability), which they interpreted to indicate that ability tilts are potentially generated by niche-picking. However, through simulations we show that heritability and environmentability of X-Y tilts are spurious consequences of heritability and environmentability of the constituent variables X and Y. Furthermore, we reanalyzed data used by Coyle et al. and show that the logic of their arguments would lead to the conclusions, for example, that the human genome codes for a difference between head circumference and verbal ability and that some individuals have picked a niche that includes a long nose at the expense of spatial ability. We do not find these conclusions tenable and propose, instead, that heritability and environmentability of tilts are spurious consequences of heritability and environmentability of the constituent variables.
Small object detection in unmanned aerial vehicle images using multi-scale hybrid attention

Authors:
raw_author_name: Gang Song
raw_affiliation_string: School of Mathematics, Shandong University, Jinan, 250100, China
raw_author_name: Hongwei Du
raw_affiliation_string: School of Mathematics, Shandong University, Jinan, 250100, China
raw_author_name: Xinyue Zhang
raw_affiliation_string: School of Mathematics, Shandong University, Jinan, 250100, China
raw_author_name: Yunfeng Zhang
raw_affiliation_string: School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China

Abstract: Small object detection in unmanned aerial vehicle images is always challenging due to the low resolution and the limited amount of information that they contain. Many feature enhancement effects have been introduced to improve the detection of small objects, but the extracted effective information is still insufficient, and redundant information interference is an issue. In this paper, we propose a new multi-scale hybrid attention based detector (MHA-YOLOv5), which integrates the similarity relationships between objects into you only look once version 5 (YOLOv5) for small object detection. Specifically, a novel multi-scale hybrid attention (MHA) structure is proposed to enhance the feature representation of small objects. This structure contains three modules: multi-scale attention (MsA), foreground enhancement module (FEM) and depthwise separable channel attention (DSCA). The MsA module is designed to build connections between large objects with abundant details and small objects with insufficient features on multiple scale features and capture the similarity relationships between objects. To reduce the interference of redundant information, the FEM is used to focus on the foreground features of multiple scale features, and the DSCA module is utilized to effectively extract multidimensional channel information. Sufficient experiments on the challenging VisDrone2019-DET, UAVDT and CARPK datasets demonstrate the effectiveness and superiority of the proposed approach. Specifically, compared with the performance of YOLOv5, MHA-YOLOv5 demonstrates a 2.82% mean average precision (mAP) improvement on the VisDrone2019-DET dataset, a 2.25% mAP improvement on the UAVDT dataset, and a 3.07% mAP improvement on the CARPK dataset.
Detecting security vulnerabilities with vulnerability nets

Authors:
raw_author_name: Pingyan Wang
raw_affiliation_string: Hiroshima University, Higashi Hiroshima, Japan
raw_author_name: Shaoying Liu
raw_affiliation_string: Hiroshima University, Higashi Hiroshima, Japan
raw_author_name: Ai Liu
raw_affiliation_string: Hiroshima University, Higashi Hiroshima, Japan
raw_author_name: Wen Jiang
raw_affiliation_string: Hiroshima University, Higashi Hiroshima, Japan

Abstract: Detecting security vulnerabilities is a crucial part in secure software development. Many static analysis tools have proved to be effective in finding vulnerabilities, but generally there are some complex and subtle vulnerabilities that can escape from detection. Manual audits are a complementary approach to using tools. Unfortunately, most manual analyses are tedious and error prone. To benefit from both the tools and manual audits, some approaches incorporate the auditor's expertise into a static analysis tool during vulnerability discovery. Following this strategy, this paper presents a representation of source code called a vulnerability net, which is a special Petri net that integrates with data dependence graphs and control flow graphs. The combined representation can facilitate the detection of taint-style vulnerabilities such as buffer overflows and injection vulnerabilities. We test the proposed approach on Securibench Micro and demonstrate that it has the capability to identify a variety of vulnerabilities while keeping the rates of false negatives and positives low.
Mental image reconstruction from human brain activity: Neural decoding of mental imagery via deep neural network-based Bayesian estimation

Authors:
raw_author_name: Naoko Koide-Majima
raw_affiliation_string: Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka 565-0871, Japan; Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan
raw_author_name: Shinji Nishimoto
raw_affiliation_string: Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka 565-0871, Japan; Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan
raw_author_name: Kei Majima
raw_affiliation_string: Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba 263-8555, Japan; JST PRESTO, Saitama 332-0012, Japan

Abstract: Visual images observed by humans can be reconstructed from their brain activity. However, the visualization (externalization) of mental imagery is challenging. Only a few studies have reported successful visualization of mental imagery, and their visualizable images have been limited to specific domains such as human faces or alphabetical letters. Therefore, visualizing mental imagery for arbitrary natural images stands as a significant milestone. In this study, we achieved this by enhancing a previous method. Specifically, we demonstrated that the visual image reconstruction method proposed in the seminal study by Shen et al. (2019) heavily relied on low-level visual information decoded from the brain and could not efficiently utilize the semantic information that would be recruited during mental imagery. To address this limitation, we extended the previous method to a Bayesian estimation framework and introduced the assistance of semantic information into it. Our proposed framework successfully reconstructed both seen images (i.e., those observed by the human eye) and imagined images from brain activity. Quantitative evaluation showed that our framework could identify seen and imagined images highly accurately compared to the chance accuracy (seen: 90.7%, imagery: 75.6%, chance accuracy: 50.0%). In contrast, the previous method could only identify seen images (seen: 64.3%, imagery: 50.4%). These results suggest that our framework would provide a unique tool for directly investigating the subjective contents of the brain such as illusions, hallucinations, and dreams.
Hierarchical features extraction and data reorganization for code search

Authors:
raw_author_name: Manman Peng
raw_affiliation_string: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China
raw_author_name: Yuanyuan Shen
raw_affiliation_string: College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China

Abstract: According to a natural language query, code search aims to retrieve relevant code snippets from a codebase. Recent works mainly rely on transformer-based pretraining models to measure the matching degree of queries and codes. Compared with works that rely on earlier deep learning methods, such as LSTM and Attention, they can significantly improve the performance of code search tasks. However, the different layers of the transformer-based models have different features that are intuitive and efficient for understanding the semantics of codes and queries but are rarely considered. Moreover, existing methods do not consider further increasing the amount of training data during training to improve the model’s performance. Toward this end, we propose a novel method called HFEDR, which utilizes the hierarchical features of transformer-based models and reorganizes original training data during a training phase. Specifically, we first extract high-level and low-level features of queries and codes from the higher and lower layers of GraphCodeBERT, respectively, achieving multi-view and comprehensive semantic representation. After that, we organize the original training data into hierarchical-uncorrelated feature pairs and then reorganize them into hierarchical-correlated feature pairs, achieving training the model with more data. Finally, we update the model’s parameters using a contrastive training method. We conduct extensive experiments on CodeSearchNet, demonstrating the effectiveness and rationality of our proposed approach.
The b-symbol weight hierarchy of the Kasami codes

Authors:
raw_author_name: Hongwei Zhu
raw_affiliation_string: School of Mathematical Sciences, Anhui University, Hefei 230601, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
raw_author_name: Minjia Shi
raw_affiliation_string: Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, School of Mathematical Sciences, Anhui University, Hefei 230601, China; State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China

Abstract: The symbol-pair read channel was first proposed by Cassuto and Blaum. Later, Yaakobi et al. generalized it to the b-symbol read channel. It is motivated by the limitations of the reading process in high density data storage systems. One main task in b-symbol coding theory is to determine the b-symbol weight hierarchy of codes. In this paper, we study the b-symbol weight hierarchy of the Kasami codes, which are well known for their applications to construct sequences with optimal correlation magnitudes. The complete symbol-pair weight distribution of the Kasami codes is determined.
Self-calibrated defocused speckle imaging for remote surface motion measurements

Authors:
raw_author_name: Juuso Heikkinen
raw_affiliation_string: Department Mechanical Engineering, University of British Columbia, Vancouver, Canada
raw_author_name: Gary S Schajer
raw_affiliation_string: Department Mechanical Engineering, University of British Columbia, Vancouver, Canada

Abstract: Defocused Speckle Imaging (DSI) is a non-contact optical method that can measure multiaxial object motions at microscopic sensitivity using a simple and mechanically robust optical setup. The object surface is illuminated by a laser beam, and the scattered interference speckle pattern is tracked with a defocused camera. DSI measurement sensitivity increases with distance, and the sensitivity can be tuned by simple camera defocus adjustment. These characteristics make DSI an attractive choice for tracking remote objects in field conditions. However, the use of DSI for practical measurements is limited because the speckle signals due to linear and rotational surface motions mix together, and because the instrument calibration requires accurate object range and surface orientation parameters. To address these issues, the work described here combines two recently presented concepts: 1) a defocused camera pair, and 2) a diffraction-based self-calibration principle. The proposed approach was demonstrated by a series of self-calibrated multiaxial motion measurements performed at an extended (30.7-m) distance. The studied motions included in-plane displacements and out-of-plane rotations (tilts), whereas out-of-plane displacements and in-plane rotations were excluded. The experiments showed DSI's capability for high tilt sensitivity down to 0.0006°, standalone remote angle measurements, and suitability for retroreflective surfaces. The diffraction-based self-calibration approach could monitor sampling distances at a 6.4 % accuracy and the relative surface angles of 2.5–7.4° at a 0.2° accuracy. The defocused camera pair tracked microscopic in-plane displacements (400 µm) and very fine out-of-plane tilts (0.003°) at a high accuracy, with a maximum uncertainty of 6.0 %. The proposed DSI approach is particularly suited for monitoring large objects and for operating in hazardous environments. The findings pave a way for interesting new applications, like high-range remote angle measurements, high-sensitivity straightness measurements, and 3D-positioning utilizing retroreflective markers that already exist in the built infrastructure.
On triangular biregular degree sequences

Authors:
raw_author_name: Benjamin Egan
raw_affiliation_string: Department of Mathematical and Physical Sciences, La Trobe University, VIC 3086, Australia
raw_author_name: Yuri Nikolayevsky
raw_affiliation_string: Department of Mathematical and Physical Sciences, La Trobe University, VIC 3086, Australia

Abstract: A simple graph is called triangular if every edge of it belongs to a triangle. We conjecture that any graphical degree sequence all terms of which are greater than or equal to 4 has a triangular realisation, and establish this conjecture for a class of biregular graphical degree sequences.
Calculating the distance between languages with deep learning

Authors:
raw_author_name: Man-ni Chu
raw_affiliation_string: Graduate Institute of Cross-Cultural Studies, Fu Jen Catholic University, Taiwan
raw_author_name: Ming-lei Yu
raw_affiliation_string: Department of Computer Science and Information Engineering, Fu Jen Catholic University, Taiwan
raw_author_name: Jia-lien Hsu
raw_affiliation_string: Department of Computer Science and Information Engineering, Fu Jen Catholic University, Taiwan

Abstract: Artificial intelligence (AI) has been implemented in various fields, including speech recognition. In this paper, a computational method is proposed for calculating the similarity between different languages or language varieties, with their similarity represented in terms of distance. In this process, we extracted mel spectrogram features from speech signals to provide the feature vectors and derived pairs of signal tokens based on vectors. Then, we trained a Siamese time-delay neural network to calculate the distance between two signal tokens. If the token pairs are from the same language group, the distance obtained using this Siamese network model is zero. In this preliminary experiment, three types of regional Mandarin Chinese (BJ, FJ, GD) were used as the dataset. The results gave the F1-score of 0.794, 0.623, and 0.715 for the classification task with respect to BJ, FJ, and GD dataset. In addition, 10 Taiwan Mandarin (TM) native speakers participated in identification and a pair-wise discrimination experiment to allow comparison with the Siamese network model. The 10 TM natives tended to misidentify GD-accented Mandarin as FJ-accented Mandarin resulting in a much greater distance between the two in the FJ-GD discrimination task compared to the Siamese network model. Overall, the results show that the performance of our model is better than the comparative experiment in completing the identification task, with the distance between the Siamese network model and the experiment having a mean absolute error (MAE) of 0.35. The familiarity might be the reason why the 10 TM natives displayed a bias towards BJ-accented Mandarin Chinese. To sum up, we provide a computational method to calculate the distance between two languages or language varieties, which can help linguists pre-classify sound files.