Reviewerly/exHarmony huggingface dataset:
Investigations into Temporal Variation Characteristics of Near-shore Waves

Analysis of the temporal distribution characteristics of waves can evaluate the temporal distribution status of regional wave energy resources. In this paper, wave dataset from an offshore wave buoy was used, annual and seasonal variation characteristics of significant wave height and zero-crossing wave period were analyzed. Joint frequency distribution between wave height and wave period was established by using Copula function to analyze their correlations.
Real-Time Detection and Tracking of Multiple Ground Targets

Real-time detection of time-critical ground targets by wideband radar is a vital task for missiles and unmanned aerial vehicles. It is challenging to give consideration to efficiency and performance with low signal-to-noise ratio, non-uniform clutter distribution, radar cross section fluctuation, multiple-target overlapping, and interfering objects. This paper presents a complete method including integration, detection and tracking. First, an improved non-coherent integration algorithm based on velocity prediction via Keystone transform is designed to achieve fast energy focusing of weak targets. Then, an anti-false-alarm CCA-CFAR based on clutter energy balance process is used to extract reliable observations. Finally, a real-time multi-target tracking and error correction algorithm based on the estimation of the number of targets is proposed, which realizes real-time target tracking and elimination of false alarms, and reduces the missed detection caused by the phenomenon of radar cross section fluctuation and multiple-target overlapping. Experimental results based on practical data show that the proposed method effectively reduces false alarms and missed detections, and achieves real-time, continuous and accurate detections under complex condition.
A Two-level Concurrent Address Translation Cache of High Performance Interconnect Network

Most of users are accustomed to utilize the virtual address in their parallel programs running at the exascale computer systems. Therefore the virtual and physical address translation mechanism is necessary and crucial to bridge the hardware interface and software application. We proposed a novel two-level concurrent address translation Cache (TLC) for high performance interconnect network–TH Express-2. The TLC is composed of L1 Cache (L1C) and main eDRAM-based Cache (MEC). A fast and small L1 Cache implemented by high-speed SRAM is adopted. The MEC employs the large capacity eDRAM (embedded Dynamic Random Access Memory) macros to meet the high hit ratio requirement. To avoid the stall incurring by refresh collision, a novel eDRAM stall-hidden refreshing algorithm is proposed. Many tests have been conducted on the real chip implementing TLC. The results show that the MEC has high hit ratio and L1C has considerable hit ratio while running the well-known benchmarks. Owing to the L1 Cache involved, the total runtime of TLC is reduced about 14%, only at the cost of 1.2% area occupied.
A Measurement Study on Gray Channel-based Deepfake Detection

Deepfake detection techniques have been widely studied to resolve security issues. However, existing techniques mainly focused on RGB channel-based analysis, which still shows incomplete detection accuracy. In this paper, we validate the performance of Gray channel-based deepfake detection. To compare RGB channel-based analysis and Gray channel-based analysis in deepfake detection, we quantitatively measured the performance by using popular CNN models, deepfake datasets, and evaluation indicators. Our experimental results confirm that Gray channel-based deepfake detection outperforms RGB channel-based deepfake detection in terms of accuracy and analysis time.
Disciplined Teams vs. Agile Teams

Software development has been considered a socio-technical activity over the past decades. Particularly, in the case of software engineering, the necessity to communicate effectively with stakeholders and team members has been progressively emphasized. Human resources play a critical role in the success of software projects. Many techniques, methods, tools, models, and methodologies have been proposed, applied, and improved in order to help and ease the management of the software development process. Regardless of the software development methodology adopted, delivering a quality product in a predictable, efficient, and responsive manner is the objective for every team. Disciplined and Agile teams have different characteristics, but also share common aspects when working to accomplish their goals. The main motivation of this chapter is to present the differences and similarities of both teams in the context of software development.
Gender Classification Using Discrete Cosine Transform and multi classifiers

Abstract- Gender detection is one of the fundamental tasks of facial analysis. Most of the presented research focuses on facial images obtained under controlled conditions. Nevertheless, real-world applications necessitate gender categorization of real faces, which is much more difficult due to the large variations in appearance in unlimited scenarios. In this paper, we proposed discrete cosine transform (DCT) to represent the face features, and DCT has the advantage of highly efficient computational algorithms. In designing the proposed gender detection system, linear discrimination analysis (LDA), random forest (RF), and support vector machine (SVM) classifiers are used for the classification process. Experiments were conducted using the challenging ORL, FEI, and JAFFE databases to ascertain the usefulness of the approaches. The proposed approach has a significant improvement in the classification rate compared to other methods used which obtained 100% in terms of accuracy in the (FEI+JAFFE) and (ORL+JAFFE) databases.
Detection Emotion using Artificial Intelligence based on Comment in Online Learning

Many schools use online learning as media learning that all material of media such as videos, text, or audio can be given feedback from students. The teacher wants to know about students' emotions, such as happy, disappointed, or sad after they accessed the media, and teachers get an evaluation of quality from their media. This research built an application mobile for the detection of emotion from column comments in media online. The application uses artificial intelligence to classify text from feedback and to determine the emotion of students. The application runs on a mobile device. The algorithm in the application is k-Nearest Neighbor for the text mining function in this research. The data of testing in this research take comments in Y ouTube channels and online learning such as SIPEJAR. The testing result is that the average accuracy is 0,697 with a recall of 0.5595 and an average precision of 0,4421. Moreover, the accuracy for this mobile application is 70% for the detection of emotion-based comments in online learning.
Efficient balanced focal loss function for manipulated images detection

Convolutional neural networks based segmentation methods of falsified images are confronted with the imbalance class problem; Imbalanced classes generally refer to a classification problem where the observations ratio of a class to all observations is very low. This class imbalance clearly increases the difficulty of learning and introduces strongly biased predictions in favour of high precision but low recall.In this article, we propose the balance factor based a focal loss function to solve this data imbalance problem. The experimental results show that the proposed method presents the best performance in terms of Accuracy, MCC and F-Measure on the CASIA-v1 and CASIA-v2 database.
Local and Global Interpretability Using Mutual Information in Explainable Artificial Intelligence

Numerous studies have exploited the potential of Artificial Intelligence (AI) and Machine Learning (ML) models to develop intelligent systems in diverse domains for complex tasks, such as analysing data, extracting features, prediction, recommendation etc. However, presently these systems embrace acceptability issues from the end-users. The models deployed at the back of the systems mostly analyse the correlations or dependencies between the input and output to uncover the important characteristics of the input features, but they lack explainability and interpretability that causing the acceptability issues of intelligent systems and raising the research domain of eXplainable Artificial Intelligence (XAI). In this study, to overcome these shortcomings, a hybrid XAI approach is developed to explain an AI/ML model’s inference mechanism as well as the final outcome. The overall approach comprises of 1) a convolutional encoder that extracts deep features from the data and computes their relevancy with features extracted using domain knowledge, 2) a model for classifying data points using the features from autoencoder, and 3) a process of explaining the model’s working procedure and decisions using mutual information to provide global and local interpretability. To demonstrate and validate the proposed approach, experimentation was performed using an electroencephalography dataset from road safety to classify drivers’ in-vehicle mental workload. The outcome of the experiment was found to be promising that produced a Support Vector Machine classifier for mental workload with approximately 89% performance accuracy. Moreover, the proposed approach can also provide an explanation for the classifier model’s behaviour and decisions with the combined illustration of Shapely values and mutual information.
Research of sub-aperture stitching algorithm for error correction in aspheric surface testing

In the testing of optical surfaces with large aperture or large aperture angle, sub-aperture stitching technology has been developed for a long time, while most testing analysis is for the rotationally symmetrical surface. In view of a cylindrical surface sub-aperture stitching algorithm based on discrete phase data, by using Legendre polynomials to fit the wave surface, numerical simulation and analysis for several main influencing factors have been carried out. For instance, a bicubic interpolation method has been used for non-numerical point in a discrete phase and fuzzy points in overlapping areas during the process of stitching sub-apertures, on account of its accuracy and calculation time. By considering alignment error, random noise, position error, especially rotation error in non-rotationally symmetrical cylindrical surface testing, we stimulate 9 sub-aperture phase data from a measured phase data to analyze the correction coefficient which determines the percentage of error correction in stitching algorithm. While the correction coefficient is approaching 1, the stitching result is most likely overcorrected. In order to achieve the best error correction, the correction coefficient should be set at around 0.6. Furthermore, combined with chaos particle swarm optimization, we reduce the possibility of the algorithm falling into the local optimum, and improve the accuracy of stitching.
Experimental research on the influence of glass spacing on digital image correlation in measuring optical path

To study the influence of glass spacing on digital image correlation measurement in the optical path, an experimental method of displacement stepping for digital image correlation measurement was designed under nine working conditions (without glass, single glass, and double glass with different spacings of 20 mm, 70 mm, 120 mm, 170 mm, 220 mm, 270 mm, 320 mm respectively). The results of digital image correlation method and dial gauge method were compared, and the measurement error and standard deviation of digital image correlation measurement under different working conditions were analyzed. The experimental results show that the variation trend of displacement measured by DIC under nine working conditions is consistent with the step change of the specimen displacement, but there exist obvious errors between the nine conditions. Based on the results of dial gauge measurement, DIC measurement results are larger, and the maximum deviation is less than 3 μm. Under the condition of without glass, the relative error of displacement measured by DIC was the smallest, with an average error of 2.92%. Under the condition of double glass spacing of 170 mm, the relative error was the largest, with an average error of 11.37%. The standard deviation of double glass condition with a certain spacing was less than that of single glass condition. In general, when the double glass spacing is within a certain range, the measurement results are more accurate and less discrete than those of single glass, and the thermal protection performance is also better.
Adaptive Bayesian estimation of conditional discrete-continuous distributions with an application to stock market trading activity

We consider Bayesian nonparametric estimation of conditional discrete-continuous distributions. Our model is based on a mixture of normal distributions with covariate dependent mixing probabilities. We use continuous latent variables for modeling the discrete part of the distribution. The marginal distribution of covariates is not modeled. Under anisotropic smoothness conditions on the data generating conditional distribution and a possibly increasing number of the support points for the discrete part of the distribution, we show that the posterior in our model contracts at frequentist adaptive optimal rates up to a log factor. Our results also imply an upper bound on the posterior contraction rate for predictive distributions when the data follow an ergodic Markov process and our model is used for modeling the Markov transition distribution. The proposed model performs well in an application to stock market trading activity.
Path Optimization for Cooperative Mapping Using Multiple Robots with Limited Sensing Capabilities

This study addresses the problem of path optimization for conducting a mapping mission using a multi-robot system with limited sensing capability, which aims to ensure efficient mapping with emphasis on the cooperative aspect of the mission. To achieve the cooperative mapping, a new path planning algorithm is proposed which can take advantage of the multi-robot system while dealing with the lack of observability due to the nature of bearing-only or range-only sensing. The Fisher information matrix is used to estimate the mapping uncertainty affected by the robots’ geometric configuration. Also, a simple method to predict the convergence rate of the uncertainty over a short time horizon is presented for efficient path planning. The performance of the proposed algorithm is shown through simulations and compared with other path planning algorithms.
Make U-Net Greater: An Easy-to-Embed Approach to Improve Segmentation Performance Using Hypergraph

Cardiac anatomy segmentation is essential for cardiomyopathy clinical diagnosis and treatment planning.Thus, accurate delineation of target volumes at risk in cardiac anatomy is important.However, manual delineation is a time-consuming and labor-intensive process for cardiologists and has been shown to lead to significant inter-and intra-practitioner variability.Thus, computer-aided or fully automatic segmentation methods are required.They can significantly economize on manpower and improve treatment efficiency.Recently, deep convolutional neural network (CNN) based methods have achieved remarkable successes in various kinds of vision tasks, such as classification, segmentation and object detection.Semantic segmentation can be considered as a pixel-wise task, it requires high-level abstract semantics information while maintaining spatial detail contexts.Long-range context information plays a crucial role in this scenario.However, the traditional convolution kernel only provides the local and small size of the receptive field.To address the problem, we propose a plug-and-play module aggregating both local and global information (aka LGIA module) to capture the high-order relationship between nodes that are far apart.We incorporate both local and global correlations into hypergraph which is able to capture high-order relationships between nodes via the concept of a hyperedge connecting a subset of nodes.The local correlation considers neighborhood nodes that are spatially adjacent and similar in the same CNN feature maps of magnetic resonance (MR) image; and the global correlation is searched from a batch of CNN feature maps of MR images in feature space.The influence of these two correlations on semantic segmentation is complementary.We validated our LGIA module on various CNN segmentation models with the cardiac MR images dataset.Experimental results demonstrate that our approach outperformed several baseline models.
Safety Situation Prediction Method for Power Energy System Based on AFSA-ELM Algorithm

Aiming at the problems of low prediction accuracy and slow convergence speed of the existing power system network security situation prediction models. In this paper, a prediction method of artificial fish swarms algorithm-Extreme Learning Machine Algorithm (AFSA-ELM)is proposed. Firstly, the parameters of hidden layer nodes and corresponding nodes are randomly selected in the algorithm to achieve the purpose of unsupervised learning. Secondly, aiming at the problem that Extreme Learning Machine (ELM) is easy to fall into local optimum, a confidence interval strategy based on artificial fish swarms algorithm (AFSA) is proposed to limit the weight of input data and hidden layer, which can guide the result of falling into local optimum to global optimum. Through the experimental analysis, it is verified that the model has a good evaluation and prediction effect, and can predict the real-time network situation completely and accurately.
A Fine-grained Sparse Neural Network Accelerator for Image Classification

The ever-increasing demand sets higher standards for the computational accuracy and network complexity of neural networks, which undoubtedly brings challenges to its further development, including more complex calculations, huge amounts of data, and limitations on processor memory bandwidth, etc. The Artificial Intelligence (AI) Accelerator can accelerate the calculation of the model, which can increase the speed of the model in the inference process. We design a sparse network accelerator for image classification in this paper. The main contributions of this work are summarized as follows: (1) We designed a fixed-scale sparse convolutional neural network accelerator, which can increase the forward reasoning speed by 1.96 times on the Resnet50 network based on NVDLA. (2) A training method for this sparse network is proposed, which can achieve flops pruned of 48.2% with only 1.4% reduction of accuracy rate for Resnet50 on ILSVRC 2012.
Application of information retrieval thesaurus for the improvement of information retrieval technologies in specific data domain

The accumulation of Big Data and its constant growth determine the relevance and significance of the investigations in the field of basic domain description generation. The rapid development of network technologies and Internet contribute to the significant increase in available information resources and information about particular domain, which is often difficult for understanding. In order to increase the relevance of online searching, you should have the resource which gives a good impression of this subject. Such description can be represented by the created thesaurus of the investigated domain which is a tool for lexical system analysis and construction, terminological data classification and storage, information processing in search engines. Due to the thesaurus, a unified search image of the document and a search image of the user request is formed, contributing to the increase of the search processes efficiency in automated information systems.
Self-Balancing Online Dataset for Incremental Driving Intelligence

Autonomous driving with imitation learning is vulnerable to the quality of an expert dataset. Typical driving involves situations or online data that are biased toward specific scenarios such as lane following or stop. This property causes an imbalance in the driving dataset, and it is highly likely to deteriorate the performance of autonomous driving with imitation learning. In this paper, we propose a dataset self-balancing system with biased online data and an imbalanced dataset. By estimating the probability distribution of a dataset, we compute the probability and novelty of online data and then filter only qualified novel data. In addition, using the computed probability distribution, we determine the data that are non-informative in the current dataset and then exchange them with novel online data. At last, by retraining the driving neural network with high-entropy data batches, our method achieves incremental driving intelligence. We demonstrated the effectiveness of our method through open-loop evaluation and ablation studies in a CARLA simulator; the results show that our proposed system effectively balances the dataset with 100 scenarios and decreases test loss over time.
Investigating the relationship between linguistic changes in L2 writers’ paraphrasing, paraphrasing performance and L2 proficiency

Abstract While the typology of paraphrasing revolves around linguistic changes of paraphrasing, little is known about the importance of different types of linguistic changes and their relationship between paraphrasing performance and L2 proficiency. Empirical enquiry has focused on L2 writers’ inappropriate paraphrasing performance against the norm of L1 writer, which is problematic in that L2 and L1 writers displayed considerable variation in paraphrasing. The present study drew upon 202 Chinese EFL writers’ written responses in a paraphrasing test to look into the discrete linguistic transformations in paraphrasing and examine how the frequency of different linguistic changes in paraphrasing relates to their paraphrasing performance and L2 proficiency. Correlation analysis was run to analyze the relationship between the frequency of linguistic changes and paraphrasing performance. Multivariate analysis of variance analysis was conducted to examine how the frequency of linguistic changes relates to L2 proficiency. The findings revealed that Conceptual Transformation had the highest significant correlation with paraphrasing scores, followed by Lexical Transformation and then Syntactic Transformation . The frequency of Synonym Substitution , Morphology , Multiple Word Units , Phrase/Clause Shift , Active/Passive Shift and Conceptual Transformation increased as L2 writers’ proficiency levels increased. Implications are drawn from the findings for paraphrasing instruction and assessment, research in paraphrasing and L2 writers’ academic writing practice.
Study on Learning Path Selection of English Writing Based on Big Data Technology

Advent of the era of big data for many fields have produced great impact, education field also have started to use big data such as modern science and technology to promote teaching reform, especially in the aspect of English teaching, The continuous development of modern network technology and its deep integration with the field of education have brought great changes to contemporary English teaching, which has exerted a certain influence on teachers, students, English teaching methods and learning environment. As an important link in English teaching, English writing is directly related to the internalization of students’ theoretical knowledge of English and the improvement of their ability to apply the subject. The improvement of students’ English writing ability requires not only the accumulation of vocabulary and the change of teaching methods, but also the innovation of English writing learning paths with the help of big data technology. Taking college students as an example, this paper first makes a brief analysis of the current situation and problems of English writing teaching for college students, and on this basis, focuses on the study of the learning path of English writing by combining big data technology.