Reviewerly/exHarmony huggingface dataset:
LISN @ SIGMORPHON 2023 Shared Task on Interlinear Glossing

This paper describes LISN's submission to the second track (open track) of the shared task on Interlinear Glossing for SIGMORPHON 2023.Our systems are based on Lost, a variation of linear Conditional Random Fields initially developed as a probabilistic translation model and then adapted to the glossing task.This model allows us to handle one of the main challenges posed by glossing, i.e. the fact that the list of potential labels for lexical morphemes is not fixed in advance and needs to be extended dynamically when labelling units are not seen in training.In such situations, we show how to make use of candidate lexical glosses found in the translation and discuss how such extension affects the training and inference procedures.The resulting automatic glossing systems prove to yield very competitive results, especially in low-resource settings.
Probing neural language models for understanding of words of estimative probability

Words of Estimative Probability (WEP) are phrases used to express the plausibility of a statement.Examples include terms like probably, maybe, likely, doubt, unlikely, and impossible.Surveys have shown that human evaluators tend to agree when assigning numerical probability levels to these WEPs.For instance, the term highly likely equates to a median probability of 0.90±0.08 according to a survey by Fagen-Ulmschneider (2015).In this study, our focus is to gauge the competency of neural language processing models in accurately capturing the consensual probability level associated with each WEP.Our first approach is utilizing the UNLI dataset (Chen et al., 2020), which links premises and hypotheses with their perceived joint probability p. From this, we craft prompts in the form: "[PREMISE].[WEP], [HYPOTHESIS]."This allows us to evaluate whether language models can predict if the consensual probability level of a WEP aligns closely with p.In our second approach, we develop a dataset based on WEP-focused probabilistic reasoning to assess if language models can logically process WEP compositions.For example, given the prompt "[EVENTA] is likely.[EVENTB] is impossible.", a wellfunctioning language model should not conclude that [EVENTA&B] is likely.Through our study, we observe that both tasks present challenges to out-of-the-box English language models.However, we also demonstrate that fine-tuning these models can lead to significant and transferable improvements.
Real-Time Object Detection in Video for Traffic Monitoring

This chapter presents the application of YOLO, a deep learning-based object detection algorithm, for traffic monitoring. The algorithm was applied to real-time video streams from roadway cameras to detect and track vehicles. The results were compared with traditional computer vision methods and showed superior accuracy and processing speed. This study highlights the potential of YOLO for traffic monitoring and the significance of incorporating deep learning into intelligent transportation systems. YOLO V7 outperforms all other real-time object detectors on the GPU V100 in terms of speed and accuracy in the range of 5 to 160 frames per second and has the highest accuracy of 56.8% AP. YOLO V7 also introduces a new training methodology that improves the convergence rate and the generalization capabilities of the model. Experimental results show that YOLO V7 outperforms existing methods in terms of accuracy, speed, and efficiency, making it an attractive solution for real-world applications.
Detection of Phishing Websites

Phishing attacks are one of the biggest security threats to personal and financial information on the internet. They are a type of cyber-attack where attackers pretend to be a trusted entity in order to trick people into revealing sensitive information, such as passwords and credit card numbers. To address this issue, a web application has been developed for detecting phishing websites. This application utilizes the Beautiful Soup library in Python to extract HTML and XML content and machine learning algorithms for detection. The application was created using the Streamlit software, making it user-friendly and easy to access through a web interface.
Deep learning-based detection of insulator defects in power grids

In order to quickly and accurately identify insulator defects and solve the problems such as labor waste and misjudgment of traditional detection methods, this paper proposes an insulator detection algorithm based on YOLOv3(You Only Look Once). Firstly, the K-means algorithm is improved by the genetic algorithm to generate anchors, then the YOLOv3algorithm is trained using the anchors, and finally the trained model is used to detect the insulator images. After experimental comparison, the algorithm in this paper is more accurate than the original K-means algorithm to generate anchors, and the YOLOv3defect detection model is more accurate than the original algorithm.
Research on filtering method of telemetry data based on whale optimization and wavelet transform

Ground based telemetry stations are usually used to acquire real-time information of flight vehicles, and monitor the flying states in order to guarantee the safety of flight tests. However, when the telemetry ground station tracks the aerial vehicle, some wild values are inevitably included in the received telemetry data due to various interference factors, which can seriously affect the interpretation of the telemetry data and the evaluation of the vehicle performance. In order to make up for the shortage of existing telemetry data wild value elimination algorithms, this paper uses the wavelet transform threshold method to eliminate the wild values in telemetry data based on the principle of wavelet transform, and introduces the swarm intelligence optimization algorithm to obtain the optimal thresholds for different telemetry data adaptively. The optimal threshold and threshold function coefficients are obtained for different telemetry data to achieve better filtering effect. Corresponding results show that the proposed method can effectively eliminate the wild values in the telemetry data and realize the filtering of the telemetry data.
Tracking moving targets of interest-based deep learning

Tracking specific target is an important research area in machine vision. When tracking specific target encounters the same local features with light and dark variations, convolutional neural networks based on deep learning require large data sets to extract target feature information. To address these problems, this paper proposes a target of interest tracking scheme based on specific object imaging. The method allows for fast capture and processing of important information about the target of interest. In addition, small data sets are used to track the target and extract the target of interest efficiently. The target of interest can also be accurately tracked when the background and light changes. The results show that our method reduces the processing of redundant information, reduces the storage overhead, and tracks the target accurately.
Multimodal diffusion model for increments of electroencephalogram data

Abstract We propose a new strictly stationary strong mixing diffusion model with marginal multimodal (three-peak) distribution and exponentially decaying autocorrelation function for modeling of increments of electroencephalogram data collected from Ugandan children during coma from cerebral malaria. We treat the increments as discrete-time observations and construct a diffusion process where the stationary distribution is viewed as a mixture of three non-central generalized Gaussian distributions and we state some important properties related to the moments of this mixture. We estimate the distribution parameters using the expectation-maximization algorithm, where the added shape parameter is estimated using the higher order statistics approach based on an analytical relationship between the shape parameter and kurtosis. The derived estimates are then used for prediction of subsequent neurodevelopment and cognition of cerebral malaria survivors using the elastic net regression. We compare different predictive models and determine whether additional information obtained from multimodality of the marginal distributions can be used to improve the prediction.
ViBe algorithm based on background fusion and channel calculation

ViBe (Visual background extractor) algorithm is a motion target detection algorithm by background modeling, uses domain pixels for background modeling and updates the background model in real time. However, the traditional ViBe algorithm will produce disadvantages such as ghosting and shadow part false detection. This paper solves the problem of ghosting by image fusion mechanism, through the characteristics of the shadow itself, using the shadow part of the three-channel variance value for shadow detection and eliminate the shadow. The experiments show that our algorithm can quickly and effectively solve the ghost and shadow problems in the ViBe algorithm
Research on the improved bat algorithms

The bat algorithm (BA) was a kind of meta-heuristic algorithm that was simple and efficient in optimization, it had been widely applied in various fields. To improve the capability of the original BA, two methods used frequently in improvements were proposed in this paper: the gradient and sub-gradient methods, together with the Levy flights. Simulation experiments were carried out on the representatives of unimodal and multimodal benchmark functions. Results confirmed that not all traditional improvements were effective, some of the improved BA even work worse than the original one. However, the Levy flights could be considered a better replacement of randomness in applications.
Two-sided fairness-aware recommendation method under many-to-many relation schema

With the widespread use of recommendation systems, researchers have gradually started to focus on fairness issues including two-sided fairness along with recommendation accuracy. However, these works often consider the relationship between the provider and the recommended items as one-to-one or one-to-many, without considering the situation that each item may have multiple related individuals on the provider side. In this paper, we implement a two-sided fair perception recommendation method based on fair allocation in a context where the relationship pattern between providers and items is many-to-many relationship. Specifically, on the one hand, the attention of all consumers is considered as the total exposure available to the provider and is fairly allocated to each provider based on the fairness criterion, and on the other hand, each exposure of each item is allocated to each relevant provider based on the fairness criterion, and the sum of the exposure obtained by each provider in all relevant items is taken as its final exposure. We conducted experiments on a real-world dataset, and the results show that the approach in this paper provides better two-sided fairness compared to the benchmark approach while maintaining good recommendation quality.
A swin transformer-based asymmetrical network for light field salient object detection

In this paper, we propose a Swin Transformer-based Asymmetrical Network (SwinAN) for light field salient object detection (SOD). SwinAN is a bifurcated backbone network composed of a 2D branch and a 3D branch. The 2D branch is driven by Swin Transformer blocks for all-in-focus images, while the 3D branch is constructed by a 3D convolutional neural network (CNN) for focus stacks. Then, the high-level features extracted by the asymmetric backbone network are integrated using cascaded Multi-Level feature Fusion (MLF) modules, which consist of channel attention mechanisms and a residual block. Finally, the Multi-Attention Fusion (MAF) module is used to fuse multi-modal features to generate the predicted saliency maps. Experimental results show that after comparing 10 different SOD models, our SwinAN achieves excellent performance on 3 datasets, confirming the superiority as an efficient and accurate method for detecting saliency
The Interplay of Task Characteristics, Linguistic Complexity, and Language Proficiency in High‐Stakes <scp>English as a Foreign Language</scp> Writing

Abstract The linguistic characteristics of text productions depend on various factors, including individual language proficiency as well as the tasks used to elicit the production. To date, little attention has been paid to whether some writing tasks are more suitable than others to represent and differentiate students' proficiency levels. This issue is especially relevant in the context of high‐stakes language examinations. In this study, we investigated task effects in English as a foreign language (EFL) Abitur examinations, the high‐stakes test qualifying for higher education admission in Germany. Based on texts produced by 362 students, we examined (a) if and how student writings differ in their linguistic complexity and (b) if the EFL Abitur tasks are equally suited to differentiate the students' proficiency levels. We used a broad operationalization of linguistic complexity, including measures from various linguistic domains in the computational analysis of the texts. The results of our mixed‐effects models show that student texts differ in their linguistic complexity primarily by the functional needs of the task types employed ( summary , analysis , and argumentation ). Furthermore, writing tasks that demand high independence in language performance are best suited to differentiate between proficiency levels, which becomes especially evident in vocabulary choice and usage.
Behavior recognition algorithm based on motion capture and enhancement

Motion modeling and temporal modeling are crucial issues for video behavior recognition. When extracting motion information in two-stream network, the optical flow diagram needs to be calculated in advance and the end-to-end training cannot be realized. 3D CNNs can extract spatiotemporal information, but it requires huge computational resources. To solve these problems, we propose a plug-and-play motion capture and enhancement network (MCE) in this paper, which consists of a temporal motion capture module (TMC) and a multi-scale spatiotemporal enhancement module (MSTE). The TMC module calculates the temporal difference of the feature-level and captures the key motion information in the short temporal range. The MSTE module simulates long-range temporal information by equivalent enlarging the temporal sensitive field through multi-scale hierarchical sub-convolution architecture, and then further enhances the significant motion features by referring to the maxpooling branch. Finally, several experiments are carried out on the behavior recognition standard datasets of Something-Something-V1 and Jester, and the recognition accuracy rates are 49.6% and 96.9%, respectively. Experimental results show that the proposed method is effective and efficient.
Blurred image recognition based on GLCM

With the development of artificial intelligence, the fault detection of substations has changed from manual to artificial intelligence detection. The higher the image quality, the better the recognition accuracy of the model, but when the inspection image is acquired, transmitted and saved, it is usually affected by bad weather, relative motion, imaging equipment shaking and other factors, which makes the acquired image blurry. Therefore, a fuzzy image screening model is constructed to screen and remove blurred images. In this paper, the grayscale co-occurrence matrix is used to extract the characteristics of the texture feature information of the image, extract the four feature values of the image, take the feature values as the input of the MLP neural network, and use the TID2013 dataset for the training dataset, and finally realize the quality scoring and screening of the blurred image.
A sea cucumber recognition network based on improved YOLOv5

A lightweight network based on YOLOv5 is proposed in this paper to improve the real-time detection ability of underwater targets for fishing gear and to solve the difficulty of deploying model algorithms on embedded devices. First, the Shuffle_Block module replaces the leading feature extraction network in YOLOv5, reducing parameters and improving the algorithm's inference speed. Second, this module is combined with depthwise separable convolution to construct the feature fusion Shuffle-PANet, significantly reducing network parameters and improving detection speed while ensuring accuracy. The proposed method in this paper has been verified to reduce the parameter count by 89% compared to the YOLOv5 source code while doubling the detection speed of the source code. Additionally, the weight file size is reduced by 83%. The mAP50 reaches 96.3%, which is only a 2% decrease compared to YOLOv5. The lightweight network proposed in this paper can recognize sea cucumbers well and has fast recognition speed and lightweight design characteristics. Shuffle-YOLOv5 has significant advantages compared to the original model and can complete real-time target detection on low-power embedded devices.
Multi-view consistency for multi-hop knowledge base question answering

The task of Knowledge Base Question Answering (KBQA) is to answer a question in natural language over a Knowledge Base. And multi-hop KBQA aims to reason over multiple hops of facts in KB to answer a complex question. Step-wised reasoning has been an important schema to solve multi-hop KBQA. But previous approaches suffer from lacking reasoning paths, causing models may answer in an incorrect way. To address the issue, we present a novel approach to enhance the KBQA model by leveraging consistency between different views of the data, with few intermediate-relation-labeled data. Previous retrieval-based methods proceeded by utilizing the data view of (question, intermediate entities, answer entities). In our method, we introduce the data view of (question, intermediate relations) and enhance the KBQA model through the consistency of different data views. Concretely, we first implement a question-to-intermediate relations(Q2R) model to obtain intermediate relations’ distributions. By utilizing a pretrained text generation model, it performs well using a small part of relation-labeled data. Then we devise a map function to map distributions of intermediate entities to distributions of intermediate. Finally, a constraint that metrics the consistency between the intermediate path distributions obtained from the Q2R model and the original KBQA model is constructed to enhance the KBQA model. Experiments over three datasets of multi-hop KBQA are conducted, and the results demonstrate the effectiveness of our method.
Image deblurring based on double blur kernel

Image deblurring refers to the process of restoring sharp images from blurry images, which is one of the pivotal technologies in many domains. In this paper, we introduce a novel neural network, which contains DIP network, double blur kernel processing network and blur kernel size estimation network. First, DIP network mainly extracts the prior information of blur kernel and sharp image. Then, double blur kernel processing network conduct similarity processing on a pair of blur kernel information collected. Compared with state-of-the-art methods. our method has a great improvement in running time and deblurring performance
Modeling the topographic lateral resolution of interferometers

Performance characteristics for interferometers that measure surface topography include the ability to resolve closely spaced surface features, referred to as topographic spatial resolution. Within well-defined limits, scalar diffraction theory and classical Fourier optics provide a software model for prediction of the resolution and spatial frequency response for interference phase-based measurements of surface topography. Analytical solutions and adaptive sampling allow for rapid simulation of both the nominal linear transfer function and an estimate of intrinsic residual nonlinearities.
Forecasting method for photovoltaic power based on data preprocessing and temporal convolutional network

Power prediction of photovoltaic generation is very important to ensure the safety of the power system. It can assist the scheduling of smart grid and the operation and maintenance of photovoltaic stations. In order to improve the accuracy of photovoltaic power prediction, a temporal convolution network (TCN) model is constructed, which is combined with adaptive white noise complete integrated empirical mode decomposition (CEEMDAN) and wavelet threshold denoising. In the data preprocessing, the sequence of sample data is decomposed as modal components of different time and frequency scales with CEEMDAN method and the high-frequency modal components are denoised through wavelet threshold. By reconstructing the denoised components, the noise-reduced data set is obtained. In the process of TCN construction, the sample data used for modeling is from DKASC photovoltaic power generation public dataset. The experiment of modeling results show that the designed model can improve the accuracy of power prediction compared with the traditional prediction model. And the modeling method has feasibility and effectiveness.
Research on image feature point extraction and matching algorithm based on Key.Net

Image feature point extraction and matching are important branches in the field of computer vision. Based on the extracted image feature points, the image descriptor is calculated, and the matching information between two images is generated using the image descriptor. The relative position relationship between the two images is then recovered using homography transformation, which is the basis of monocular visual odometry. In this study, the Key.Net method is used to extract image feature points, and SIFT and SURF methods are used to calculate feature point descriptors. The images are matched using a brute-force matching method, and the relative position relationship between the two frames is solved. Multiple image feature point extraction and descriptor calculation methods are compared on the KITTI dataset using the distance information between two frames as the error benchmark. The proposed method has the lowest total error and the best performance, and it has good robustness, laying a foundation for visual navigation algorithms.
Application of two-dimensional linear piecewise full mapping function in chaotic image encryption

In recent years, information security has become more and more important. Image encryption technology based on chaos theory has become one of the hot issues in this field. Chaotic system has the characteristics of initial value sensitivity and sequence ergodicity, which is very suitable for image encryption. In this paper, the folded surface is generated by connecting the randomly generated points on the bottom surface and the top surface in the unit space, and the surjective binary function is further constructed. Use this type of function and functions such as planes to construct discrete dynamical system. It is experimentally analyzed that the function has good chaotic characteristics by drawing bifurcation diagram and Lyapunov exponent diagram. The chaotic sequence of the discrete dynamic system is used for image encryption, and its information entropy and correlation coefficient before and after encryption are calculated. It is proved that this kind of system has good chaotic characteristics. This is a new type of chaotic system, which needs further research, analysis and expansion.
Speaker recognition based on improved ECAPA-TDNN network

Speaker recognition, also known as voiceprint recognition, is a biometric technology with wide practicability at present. This paper summarizes and compares and analyzes the main research methods of speaker recognition at home and abroad at this stage, and proposes an improved ECAPA_TDNN algorithm. It is proved by experiments that the improved ECAPA_TDNN algorithm in this paper is superior to the classical algorithm in terms of accuracy and loss.