Reviewerly/exHarmony huggingface dataset:
CRM-SBKG: Effective Citation Recommendation by Siamese BERT and Knowledge Graph

Citation recommendation focuses on recommending references to the given document automatically. While the existing works have achieved improvements in citation recommendation, their quality may still be degraded due to the cold-start problem or insufficient use of features. To alleviate these problems, we investigate the possibility of fully utilizing a document's features from different dimensions. Specifically, we present a citation recommendation model based on Siamese BERT and Knowledge Graph (CRM-SBKG), a mixture model that mines the text feature and entity feature to obtain the recommendation list. We use the BERT model with the Siamese network for text feature extraction and build a citation knowledge graph where entity feature is extracted. We apply the proposed model on Open Academic Graph (OAG) and DataBase systems and Logic Programming (DBLP). The results show that our model outperforms the baselines significantly. It reflects that the proposed model can extract features more effectively and make better citation recommendations.
Automatic Fluency Assessment Method for Spontaneous Speech without Reference Text

The automatic fluency assessment of spontaneous speech without reference text is a challenging task that heavily depends on the accuracy of automatic speech recognition (ASR). Considering this scenario, it is necessary to explore an assessment method that combines ASR. This is mainly due to the fact that in addition to acoustic features being essential for assessment, the text features output by ASR may also contain potentially fluency information. However, most existing studies on automatic fluency assessment of spontaneous speech are based solely on audio features, without utilizing textual information, which may lead to a limited understanding of fluency features. To address this, we propose a multimodal automatic speech fluency assessment method that combines ASR output. Specifically, we first explore the relevance of the fluency assessment task to the ASR task and fine-tune the Wav2Vec2.0 model using multi-task learning to jointly optimize the ASR task and fluency assessment task, resulting in both the fluency assessment results and the ASR output. Then, the text features and audio features obtained from the fine-tuned model are fed into the multimodal fluency assessment model, using attention mechanisms to obtain more reliable assessment results. Finally, experiments on the PSCPSF and Speechocean762 dataset suggest that our proposed method performs well in different assessment scenarios.
Adversarial Machine Learning Attacks in Internet of Things Systems

Researchers are looking into solutions to support the enormous demand for wireless communication, which has been exponentially increasing along with the growth of technology. The sixth generation (6G) Network emerged as the leading solution for satisfying the requirements placed on the telecommunications system. 6G technology mainly depends on various machine learning and artificial intelligence techniques. The performance of these machine learning algorithms is high. Still, their security has been neglected for some reason, which leaves the door open to various vulnerabilities that attackers can exploit to compromise systems. Therefore, it is essential to evaluate the security of machine learning algorithms to prevent them from being spoofed by malicious hackers. Prior research has shown that the decision tree is one of the most popular algorithms used by 80% of researchers for classification problems. In this work, we collect the dataset from a laboratory testbed of over 100 Internet of things (IoT) devices. The devices include smart cameras, smart light bulbs, Alexa, and others. We evaluate classifiers using the original dataset during the experiment and record a 98% accuracy. We then use the label-flipping attack approach to poison our dataset and record the output. As a result, flipping 10%, 20%, 30%, 40%, and 50% of the poison data generated accuracies of 86%, 74%, 64%, 54%, and 50%, respectively.
Contrastive-Regularized U-Net for Video Anomaly Detection

Video anomaly detection aims to identify anomalous segments in a video. It is typically trained with weakly supervised video-level labels. This paper focuses on two crucial factors affecting the performance of video anomaly detection models. First, we explore how to capture the local and global temporal dependencies more effectively. Previous architectures are effective at capturing either local and global information, but not both. We propose to employ a U-Net like structure to model both types of dependencies in a unified structure where the encoder learns global dependencies hierarchically on top of local ones; then the decoder propagates this global information back to the segment level for classification. Second, overfitting is a non-trivial issue for video anomaly detection due to limited training data. We propose weakly supervised contrastive regularization which adopts a feature-based approach to regularize the network. Contrastive regularization learns more generalizable features by enforcing inter-class separability and intra-class compactness. Extensive experiments on the UCF-Crime dataset shows that our approach outperforms several state-of-the-art methods.
A Novel Semantic Segmentation Algorithm for RGB-D Images Based on Non-Symmetry and Anti-Packing Pattern Representation Model

With the rapid development of deep learning technology, the accuracy of image semantic segmentation tasks has been greatly improved. However, indoor RGB-D semantic segmentation remains a challenging problem because of the complexity of indoor environments. The emergence of depth sensors makes depth information gradually used to improve the effect of semantic segmentation. The splicing of weights such as between the RGB features and the depth features which are used as the input features of the neural network can effectively improve the accuracy of the indoor semantic segmentation tasks. Most previous researches focused on improving the performance of semantic segmentation by adjusting the structure of convolutional neural network. These researches have either added attention mechanism or performed data augmentation on input features, but they didn’t make full use of the boundary information and the texture information of the original RGB image. In this paper, we propose a semantic segmentation algorithm for RGB-D images based on Non-symmetry and Anti-packing pattern representation Model (NAM). The core idea of the proposed algorithm is to take the channel-wise concatenation of pre-segmentation labels provided by the traditional hierarchical image segmentation and RGB-D features as the input of the neural network so as to guide the semantic segmentation tasks. The extensive experiments are conducted on the popular indoor RGB-D semantic segmentation datasets. When compared with the state-of-art algorithms, the experimental results presented in this paper show that our proposed method has improved the performance of image semantic segmentation networks on several popular neural network architectures.
Research on China Academic Library and Information System

China Academic Library & Information System (CALIS) is the world's largest university library consortium, which is responsible for resource coordination and joint construction, document information sharing services, etc.It uses cloud computing technology to build a platform for resource sharing, which is a model for the development of China's university library alliances (CULAS) in the past 30 years.CALIS has played an important role in promoting the CULAS development, which has independently designed and developed an online cooperative cataloging system and an online public retrieval (OPAC) system.This article mainly adopts the method of case analysis to study the development process, organizational structure, operation mode, and development trend of CALIS, to reveal the general laws of CULAS construction.
Evaluation of Memory Capacity and Time Series Prediction Using a Spin Hall Oscillator as Reservoir

Spintronic devices, such as spin Hall oscillators (SHOs) and spin transfer torque oscillators, have become popular candidates for use as the reservoir in the reservoir computing (RC) architecture. The memory capacity of a reservoir quantifies the amount of information retained at any given time, indicating its significance for different RC tasks. In this work, we consider an SHO, consisting of a bilayer structure of platinum/Permalloy, as the reservoir. Using micromagnetic simulations, we evaluate the change in memory capacity of the SHO system as the magnetization dynamics varies from transient state oscillations to limit cycle oscillations. We also perform a three-bit parity task to study the performance of the oscillator in carrying out a nonlinear task. A time series prediction task, namely, the nonlinear auto regressive moving average (NARMA)2 task, is also performed. The results of both tasks confirm a correlation between the evaluated memory capacity of the reservoir and its efficiency in performing temporal tasks. The best performance in RC tasks is observed when the output magnetization dynamics of the oscillator consists of both transient state and limit cycle oscillations.
DEMOS: Clustering by Pruning a Density-Boosting Cluster Tree of Density Mounts

Most existing clustering algorithms require presetting cluster number and often fail to capture complex shapes. Herein, we propose a clustering algorithm by pruning a density-boosting cluster tree of density mounts—DEnsity MOuntains Separation clustering algorithm (DEMOS). A cluster is assumed to be a density-connected area with multiple (or a single) density mounts (i.e., single-peak clusters) and a relatively large dis-connectivity from density-connected areas of higher densities. Based on this assumption, DEMOS can easily detect the number of clusters and robustly reconstruct their complex shapes. It first builds the dataset into a peak graph, where each density peak represents a density mount. A multi-valley-link-based connectivity estimation method is embedded to efficiently estimate the connectivity between density peaks during peak graph building. Then, by applying a new linkage metric designed based on our assumption, DEMOS builds density mounts into a reasonably density-boosting cluster tree. After obtaining a robust center detection in a clarity-enhancing decision graph (i.e., a two-dimensional plot for detecting centers), DEMOS prunes the cluster tree into final clusters to finish clustering. Experimental results on both synthetic and real datasets demonstrated the effectiveness of DEMOS and its applicability to large-scale data clustering.
Quantum computing of the pairing Hamiltonian at finite temperature

In this work, we study the pairing Hamiltonian with four particles at finite temperatures on a quantum simulator and a superconducting quantum computer. The excited states are obtained by the variational quantum deflation. The error-mitigation methods are applied to improve the noisy results. The simulation of thermal excitation states is performed using the same variational circuit as at zero temperature. The results from quantum computing become close to exact solutions at high temperatures, and demonstrate a smooth superfluid-normal phase transition as a function of temperatures as expected in finite systems.
Use and sustainability of wood in acoustic guitars: An overview based on the global market

Since the dawn of humanity, wood has been the material of choice for crafting musical instruments due to its favorable acoustic properties, workability, and aesthetic value. Acoustic guitars are among the most widespread musical instruments worldwide, and today are industrially manufactured by large companies with global markets. This study investigates the use of wood in modern acoustic guitars, with a focus on wood species and sustainability. The websites of the 14 most relevant manufacturers on a global scale were analyzed collecting data on 874 guitars and 4506 components. The purpose of the research is to provide a novel overview of the variety and relevance of woods employed in the sector, of the most common environmental issues associated with their use, and of the strategies adopted by companies to deal with sustainability concerns and to communicate their environmental commitment to the public. The results show that, overall, wood remains the material of choice for acoustic guitars, and a wide number of wood species are used. Sustainability aspects are often highlighted on websites. However, the taxonomy is ambiguous in several cases, and some endangered wood species are used with limited information about preservation. Therefore enhancing the research of alternative wood species and increasing the efforts on environmental commitment and on promoting end consumer sustainability awareness are relevant targets to further enhance the use of wood in a sector that already provides a great valorization of this renewable material.
High-Performance Soft Decision Decoding for Compound Channel Using RS-SPC Concatenated Codes

A novel single parity check-assisted ordered statistic decoding (SPC-OSD) algorithm for the compound channel is proposed for Reed-Solomon (RS) codes. The algorithm can effectively use the channel soft information to accurately determine the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N-K$ </tex-math></inline-formula> unreliable symbol positions to be erased by using single parity checking and threshold comparison. Besides that, the second-burst error symbol is also proposed as a supplement to unreliable symbols. Based on these predetermined errors, the algorithm can be dynamically adjusted according to the channel environment to achieve the best decoding performance. Compared with burst-error correcting algorithm-based ordered-statistics decoding (BC-OSD) and burst error correcting hard-decision decoding-based low-complexity chase algorithm (BCHDD-LCC), simulation results show that the proposed algorithm can give a coding gain reaching up to 2.0dB and 2.1dB respectively, when symbol error rate (SER) is 10−4.
ECAsT: a large dataset for conversational search and an evaluation of metric robustness

The Text REtrieval Conference Conversational assistance track (CAsT) is an annual conversational passage retrieval challenge to create a large-scale open-domain conversational search benchmarking. However, as of yet, the datasets used are small, with just more than 1,000 turns and 100 conversation topics. In the first part of this research, we address the dataset limitation by building a much larger novel multi-turn conversation dataset for conversation search benchmarking called Expanded-CAsT (ECAsT). ECAsT is built using a multi-stage solution that uses a combination of conversational query reformulation and neural paraphrasing and also includes a new model to create multi-turn paraphrases. The meaning and diversity of paraphrases are evaluated with human and automatic evaluation. Using this methodology, we produce and release to the research community a conversational search dataset that is 665% more extensive in terms of size and language diversity than is available at the time of this study, with more than 9,200 turns. The augmented dataset not only provides more data but also more language diversity to improve conversational search neural model training and testing. In the second part of the research, we use ECAsT to assess the robustness of traditional metrics for conversational evaluation used in CAsT and identify its bias toward language diversity. Results show the benefits of adding language diversity for improving the collection of pooled passages and reducing evaluation bias. We found that introducing language diversity via paraphrases returned up to 24% new passages compared to only 2% using CAsT baseline.
Discriminative feature encoding for intrinsic image decomposition

Abstract Intrinsic image decomposition is an important and long-standing computer vision problem. Given an input image, recovering the physical scene properties is ill-posed. Several physically motivated priors have been used to restrict the solution space of the optimization problem for intrinsic image decomposition. This work takes advantage of deep learning, and shows that it can solve this challenging computer vision problem with high efficiency. The focus lies in the feature encoding phase to extract discriminative features for different intrinsic layers from an input image. To achieve this goal, we explore the distinctive characteristics of different intrinsic components in the high-dimensional feature embedding space. We define feature distribution divergence to efficiently separate the feature vectors of different intrinsic components. The feature distributions are also constrained to fit the real ones through a feature distribution consistency. In addition, a data refinement approach is provided to remove data inconsistency from the Sintel dataset, making it more suitable for intrinsic image decomposition. Our method is also extended to intrinsic video decomposition based on pixel-wise correspondences between adjacent frames. Experimental results indicate that our proposed network structure can outperform the existing state-of-the-art.
Application of LV's distribution on the parameter estimation of multicomponent radar emitter signals

Lv&apos;s distribution (LVD) can achieve high resolution while effectively suppressing cross-term and is widely used for signal detection. Aiming at the problem of the detection of multi-component radar emitter signals of a low SNR, a parameter estimation method with weighted LVD is proposed in this paper. The parametric symmetric instantaneous autocorrelation function (PSIAF) of the signal to be estimated is developed first. A scale transformation is then required to decouple the variables. The decoupled PSIAF is followed by a 2D-FFT to obtain the classic LVD result. Then the curve between the LVD results and signal parameters can be fitted. From the curve, we can calculate the weight to correct the power attenuation caused by the limited signal length. Massive simulation experiments show that the proposed method can significantly enhance the robustness of LVD on the premise of maintaining accuracy.
Attention Weighted Local Descriptors

Local features detection and description are widely used in many vision applications with high industrial and commercial demands. With large-scale applications, these tasks raise high expectations for both the accuracy and speed of local features. Most existing studies on local features learning focus on the local descriptions of individual keypoints, which neglect their relationships established from global spatial awareness. In this paper, we present AWDesc with a consistent attention mechanism (CoAM) that opens up the possibility for local descriptors to embrace image-level spatial awareness in both the training and matching stages. For local features detection, we adopt local features detection with feature pyramid to obtain more stable and accurate keypoints localization. For local features description, we provide two versions of AWDesc to cope with different accuracy and speed requirements. On the one hand, we introduce Context Augmentation to address the inherent locality of convolutional neural networks by injecting non-local context information, so that local descriptors can "look wider to describe better". Specifically, well-designed Adaptive Global Context Augmented Module (AGCA) and Diverse Surrounding Context Augmented Module (DSCA) are proposed to construct robust local descriptors with context information from global to surrounding. On the other hand, we design an extremely lightweight backbone network coupled with the proposed special knowledge distillation strategy to achieve the best trade-off in accuracy and speed. What is more, we perform thorough experiments on image matching, homography estimation, visual localization, and 3D reconstruction tasks, and the results demonstrate that our method surpasses the current state-of-the-art local descriptors. Code is available at: https://github.com/vignywang/AWDesc.
Deep Learning based Breast Image Classification Study for Cancer Detection

Many human beings are losing life yearly due to Breast Cancer. Breast Cancer detection is a challenging task where skilled radiologists are essential to detect it. The manual identification of Breast Cancer illness involves a significant amount of time, and the manual treatment of disease also demands a significant amount of time. So automated detection is needed, to help in giving early treatment and, in some cases, prevents life risk. Recently, many advances have been made in the health care domain. Because of resource availability and computation capacity, these technological improvements are helpful for early treatment. This survey paper covers all the modern approaches applied on various datasets, which helps the researchers to improve the outcomes in the effective identification of Breast Cancer. This is a review study that examines approximately 30 deep learning-based classification mechanisms for Breast Cancer detection with different types of modalities.
BP Neural Network Algorithm for Computer Network Security Evaluation

BP neural network algorithm has been used in the evaluation of computer network security. This algorithm is also called back propagation learning. It is a supervised learning technology, which can be used to identify the most important nodes in a given neural network, and then use this information to determine how these nodes should change their behavior. The application of back propagation (BP) neural network algorithm helps us understand the working principle behind this principle. The main goal of this algorithm is to detect any malicious activities in the computer network. Its working principle is learning and pattern recognition. It is very fast and efficient, and does not require much time for training or testing. It can be easily implemented in any type of system, such as embedded system, desktop computer, etc. It has been proved that it can be applied to identify malware with high accuracy without much impact on performance. This paper analyzes the existing information system risk assessment methods in the process of evaluating complex information systems. Due to the high complexity and strong interdependence of information systems, it is difficult to carry out risk assessment work. A computer network security assessment study based on BP neural network algorithm is proposed to improve the traditional analytic hierarchy process risk assessment method, which can complete the dynamic assessment process of network security risks, It can greatly improve the accuracy of evaluation and meet the actual needs of network security; Applying the improved analytic hierarchy process to the evaluation process of computer network information systems in Tibet can better identify hidden risk factors in computer network information systems, and comprehensively defend and improve network operation indicators.