Reviewerly/exHarmony huggingface dataset:
Speckle deflectometry based on digital image correlation

Digital image correlation (DIC) is a widely used non-contact measurement method for the deformation of a diffused object surfaces in the experimental solid mechanics. In specular surface figure measurement, the rapidly developing deflectometry using sinusoidal fringes can achieve a sub-micron level accuracy. The fringe pattern used in the phase shift algorithm is sensitive to noise, but the speckle image used in the DIC technique is robust against noise. In this paper, three kinds of speckle deflectometry methods, which are speckle pattern defletometry(SPD), speckle pattern shifting deflectometry(SPSD) and dynamic speckle deflectometry(DSD), are introduced to measure the figure of specular surface. The principles of the three methods are introduced in detail, and the experimental results are given in order to illustrate the measurement accuracy.
Dual-CNN fusion panoptic segmentation based on edge optimization

As a newly proposed research topic in recent years, panoptic segmentation is the combination of semantic segmentation and instance segmentation. The difficulties of panoptic segmentation include not only the common problems in traditional segmentation tasks, such as small object segmentation and low edge segmentation accuracy, but also the two-way fusion and the determination of conflicts. Considering the defect of edge segmentation, we use the edge optimization module SegFix based on edge detection and direction prediction for edge optimization, which reaches higher accuracy and shortens the calculation time by 3 times compared with Dense CRF. Based on the dual CNN fusion, we select EfficientNet as the baseline, which is more efficient, with depthwise separable convolutions and Mask R-CNN to achieve two-way segmentation. In addition, we use SegFix in multiple panoptic segmentation models to verify its versatility in panoptic segmentation. Finally, our PQ on the Cityscapes validation set reaches 65.5, which achieves the state-of-the-art result with all other panoptic segmentation models under the same experimental conditions, and we confirm that the edge optimization algorithm we use is universal for panoptic segmentation, and its consequence is better than other edge optimization algorithms.
Epipolar plane images based light-field angular super-resolution network

Light field (LF) imaging has proven to be a promising technique in computer vision field. However, there is a tradeoff between spatial and angular resolution of LF images, which limits the application of LF cameras. Super-resolution (SR) of the angular domain is proposed to improve the angular resolution of LF images. However, most of the SR frameworks cannot adapt to LF datasets with multi-size disparities, especially the large disparities. In this paper, we proposed a learning-based SR framework named EASRnet. The EASRnet consists of three parts - Disparity adaptation, Feature extraction, and Feature restoration parts, and achieves angular SR tasks by using residual blocks and a structure with branches to reconstruct high-frequency details of up-sampled epipolar plane images (EPI). It employs an additional blur layer to accommodate LF datasets with different disparities. The experimental results show that the proposed approach can reconstruct novel view images with satisfactory accuracy.
Radar gesture recognition based on lightweight convolutional neural network

In order to effectively overcome the limitations of traditional gesture recognition technology, a method of gesture recognition using millimeter wave radar is proposed. First, according to the introduction of the millimeter-wave radar system and the description of the echo model, the millimeter-wave radar is used to collect the measured data; then the average cancellation method is used to suppress the clutter of the measured data, and the joint time-frequency analysis technology is used for effective feature extraction; The extracted features are used as the model input, and a lightweight convolutional neural network model is improved. Its recognition rate is over 96%, and it has good recognition performance.
Gesture recognition with feature fusion using FMCW radar

We propose a feature fusion method for gesture recognition based on Frequency Modulated Continuous Wave (FMCW) radar. First, we estimate the radial information distance, Doppler and tangential information azimuth, elevation of the gesture by signal processing to construct the multi-dimensional feature data set; Then, for feature extraction and accurate classification, we design feature fusion scheme and build multi-dimensional feature convolutional neural network. Experimental results show that our proposed scheme with gesture multi-dimensional feature as input can solve the problem of insufficient feature representation in traditional Range-Doppler (RD) domain gesture recognition methods, and the recognition accuracy is improved by 4%~8% compared with the case without feature fusion.
A multi-images variational Bayesian super-resolution reconstruction method based dual sparse priors

Aiming at solving the problem of prior constraints on variational bayesian super-resolution reconstruction method, we propose a novel prior model to overcome the under-constraint of non-edge regions of image due to total variation prior, so the generation and spread of noise are further suppressed. We combine the weighted total variation model and L1 norm model, achieving a variational bayesian super-resolution reconstruction method based dual sparse priors. The super-resolution results of the simulation data and real data demonstrate that our algorithm is more effective and stable than the same type of other methods.
In-situ testing of front and rear surfaces of transparent element

Phase Measuring Deflectometry (PMD) with high dynamic range and high robustness is a more powerful method to achieve the integration of optical components fabrication and testing, which would avoid the secondary positioning error caused by the removal of optical components in off-line status. However, for surface shape measurement of transparent element, the captured fringe patterns are superposition of the patterns reflected from the front and rear surfaces, leading to the failure of the traditional phase-shifting algorithm to extract the true phase distribution. In order to separate the superposed fringe patterns, the limitations of the existing methods such as UV deflectometry and polarized light deflectometry, which require special light source and polarizer filter, are expensive instrument and complex processes. Moreover, only the front surface shape can be reconstructed at a time, which lowers the efficiency of the measurement. To achieve in-situ measurement of the front and rear surface shapes of the transparent element simultaneously, the combination of PMD and power spectrum estimation is used to separate parasitic fringe patterns. And the front and rear surface shapes are reconstructed using ray tracing and non-linear optimization. The feasibility of the proposed method is demonstrated by numerical simulation. In the experiment, the transparent element with a thickness of about 10 mm is detected and the front and rear surface shapes are reconstructed. The front and rear surface shapes differences of the transparent element with a diameter of 77.8 mm are 173 nm in RMS and 212 nm in RMS, respectively.
A lightweight convolutional network for infrared object tracking

As an important application of computer vision, visual tracking has being a fundamental topic. Compared with visible image, infrared image has the characteristic of low resolution, blurred contour and single color feature. Thus, it is still a challenge for infrared object tracking. Further, it is difficult to balance the real-time performance and accuracy. This paper proposed a tracker based on the SiamRPN tracker with a deeper and lightweight MobileNet V2 structure as the backbone network. During network training, the weights are updated by the scheme of the model-independent metalearning method. The computed model only passes a few gradient descents on the first frame can obtains the most suitable for the current frame. In the end, the tracker is tested on various datasets. Experimental results show that the tracker can achieve a balance between tracking accuracy and inference speed, which is crucial for deployment on mobile devices.
Harmonic frequency estimation method in multi-surface interferometry

The surface morphologies and thickness variation are the basic characteristics of a transparent parallel plate with multiple surfaces. Measurements of these profiles are greatly significant for the evaluation of surface quality. The measurement accuracy of phase-shifting interferometry can reach the nanometer level, which is an effective method for high-precision measurements of ultra-smooth surfaces. However, each measured surface will give its own harmonic to the captured interferograms, resulting in the information of harmonics mixed. Thus the surface topology information can not be detected directly, which can cause problems of the wavefront reconstruction of the measured surface. To solve this problem, separating the mixed harmonics is necessary. The fundamental difference in harmonics lies in their frequency. Therefore, determining these differences of harmonics by frequency is a preliminary method of phase demodulation.
A novel semantic representation approach for web documents using deep entity linking and multidocument support

Summary Representing semantic web information in the form of existing formats like extensible mark‐up language (XML), resource description framework (RDF) or web ontology language (OWL) is limited by the inability to maintain intradocument relationships between different semantic entities. Moreover, these standards are built for generic data representation and thus do not have inherent support for the semantic description of the document and, consequently, do not support features like multidocument linkages and context‐sensitivity. In order to remove these drawbacks, a semantic web‐specific document representation approach, namely, deep semantic XML (DS‐XML) using deep linking and machine learning, is presented in this paper. The DS‐XML approach facilitates the use of semantics with the help of a novel document structure that focuses on defining any web document in a semantic form. It includes ontologies and linkage between different vocabularies, which assists in achieving semantic interoperability. In order to make this approach future proof, a novel deep linking approach is integrated at the document level, which allows DS‐XML to understand more about the contents of the document and represent information in an aggregated manner. The deep linking layer is combined with the machine learning‐based classification layer that allows the document information to be represented in an interactive manner. The proposed DS‐XML approach is tested on different document retrieval applications, including a few Internet of Things (IoT) applications, and the representation performance is found to be superior to the existing semantic approach (XML, RDF and OWL).
Evaluation of Smart City Healthcare Features (SCHF) through Machine Learning

Internet of Things (IoT) approaches are allowing new creativities all over the world in smart cities. There is not any specific tool or criteria for calculate of worth for enable smart city for Healthcare area. There are many key emphasis as facts are on dealing with issues faced by urban cummunities sustainable but my work is moving around only the healthcare sector to prediction of it implementation valid criteria. My work is move around the Evaluation of Smart City Healthcare Features (SCHF) that is a Machine Learning(ML) methodology is core concept to successful implementation of the IoT-based wireless devices networks for this tenacity since there is huge amount of dataset to be handled &#x0026; implemented. All over this paper, I have been take 17 city of Pakistan for evaluate its healthcare features as results that how AI-based IoT and ML devices with applications are applied in the healthcare sector. This work will be a model study for empathetic the role of the IoT in Health sector in smart cities.
IELTS translation education corpus construction based on bilingual non-parallel data model

With the rapid development of intelligent technology, IELTS translation education has gradually explored a brand-new education model. In order to solve the problems of small scale, slow speed and incomplete domain of the traditional bilingual parallel corpus machine translation, we construct an IELTS translation education corpus based on bilingual non-parallel data model, which can be used to train Moses, an IELTS translation education machine translation model, for better aids of translation education. In the process of construction, parallel sentence pairs are extracted from non-parallel corpus by using the translation retrieval framework represented by word graph, and a translation retrieval model based on bilingual non-parallel data is constructed. The experimental results of training Moses translation model with elementary IELTS translation corpus show that the bilingual non-parallel data model constructed in this paper has good translation retrieval performance. Compared with existing algorithms, the BLEU value extracted from parallel sentence pairs is increased by 2.58. Therefore, the proposed algorithm and corpus will do favor for the machine translation of IELTS. In addition, the retrieval method based on the structure of translation option word graph proposed in this paper is time-efficient and has better performance and efficiency in assisting IELTS translation education.
Opening Sentences in Academic Writing

Traditionally, education in computer science focuses on stakeholders like teachers, undergraduate students, and employers. However researchers also educate themselves about recent results and new subject matters. An important vehicle in this informal, self-education process is reading peer-reviewed academic papers---papers that are also used in the curriculum of graduate-level research courses. Technical writing skills are important in this domain, as well as engaging the reader with interesting text. This paper is a study of academic writing. We study in depth the first sentence used by researchers in opening their academic papers and how this sentence operates to draw the reader in. We use a corpus of 379 papers from a top-tier cybersecurity conference and use qualitative analysis (coding from grounded theory) to create a taxonomy of 5 general types and 14 sub-types of opening sentences. In this paper, we define and illustrate each type through examples, and reflect on what we learned about writing after examining all of these sentences.
Impact of New Construction Activities of Min-Zhe Wooden Arch Bridge in the Conservation of Its Traditional Building Craftsmanship

This paper focuses on discussing the new construction activities of such bridges and their impacts on Min-Zhe Bridge’s Traditional Building Craftsmanship (TBC for short). A total number of fifty-six such bridges were investigated including their implementing mechanism, designs, structures and functions. Based on the investigation, it discussed the advantages and challenges of these activities in terms of the conservation of the TBC, the relevant woodworking masters and the core communities.
Role of WSDL Metrics in the Detection of Web Service Anti-Patterns

Many IT businesses now employ service-oriented architecture (SOA) to develop their systems. A service-based system (SBS) can be updated to accommodate new user needs, just like many other complicated structures. Continuously improving service-based systems to fulfill customer requests will lower software development quality, resulting in Anti-patterns in web services. Anti-patterns are frequently picked as a viable solution to a problem. However, they come with more drawbacks than advantages. Anti-patterns sabotage software systems’ long-term viability and perception.
A Report on the First International Workshop on Program Equivalence (PEQ 2022)

Program equivalence [1] is the problem of proving that two programs are equal under some definition of equivalence, e.g., input-output equivalence. The field draws researchers from formal verification, semantics and logics. Program equivalence is arguably one of the most interesting and at the same time important problems in formal verification. It is a cross-cutting topic that has attracted the interest of several research communities: denotational semantics, deductive software verification, bounded model checking, specification inference, High level synthesis, software evolution and regression testing, etc. The goal of the workshop is to stimulate an exchange of ideas to forge a community working on Program Equivalence.
Identifying and Extracting Hierarchical Information from Business PDF Documents

Portable Document Format (PDF) is a popular choice for a secure communication and persistence of business information and is a universally accepted format by businesses choosing to become digital. PDF provides multiple ways to make the information visually appealing and readable, and device independent rendering. To achieve this, PDF stores metadata with individual text characters, graphic components and other layout elements. Such atomic component wise meta-data makes machine processing of information in the PDF format very challenging; the challenge is further extended due to the difficulty of stitching together the original semantics from the componentized information. We propose a generic approach for extracting the hierarchy of the document structure while separating the content from header and footer, and extracting metadata associated with checkboxes to annotate the business information contained in PDF for tasks like mining specifications and rules from the document. Our prototype is able to process real-life, large PDF documents each running into roughly 400 pages, with nearly 95% of the extraction requiring no human intervention.
Musical Instrument Performance in Augmented Virtuality

In this paper, we present the preliminary study results about musical instrument performance in Augmented Virtuality (AV). In our proposed approach, musician can prerecord their performance using any camera device. Then, through the processing pipeline that we have proposed, the musicians are able to play musical instruments in entirely virtual worlds. According to the 3D virtual scene set up for Virtual Reality (VR) glasses, the audience can watch the performance in virtual reality where the musician is playing music in the real world. In the end, we rendered the musician’s performance into 360 VR video to enhance the immersiveness of the user experiences.
Commit-Checker: A human-centric approach for adopting bug inducing commit detection using machine learning models

short-paper Share on Commit-Checker: A human-centric approach for adopting bug inducing commit detection using machine learning models Authors: Naz Zarreen Zarreen Oishie Computer Science, University of Saskatchewan, Canada Computer Science, University of Saskatchewan, CanadaView Profile , Banani Roy Computer Science, University of Saskatchewan, Canada Computer Science, University of Saskatchewan, CanadaView Profile Authors Info & Claims ISEC 2022: 15th Innovations in Software Engineering ConferenceFebruary 2022 Article No.: 36Pages 1–3https://doi.org/10.1145/3511430.3511463Online:24 February 2022Publication History 0citation33DownloadsMetricsTotal Citations0Total Downloads33Last 12 Months33Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Development of an English Teaching Robot for Japanese Students

research-article Share on Development of an English Teaching Robot for Japanese Students Authors: Bin Zhang Department of Mechanical Engineering, Kanagawa University, Japan Department of Mechanical Engineering, Kanagawa University, JapanSearch about this author , Taichi Hirano Department of Mechanical Engineering, Kanagawa University, Japan Department of Mechanical Engineering, Kanagawa University, JapanSearch about this author , Hun-ok Lim Department of Mechanical Engineering, Kanagawa University, Japan Department of Mechanical Engineering, Kanagawa University, JapanSearch about this author Authors Info & Claims ICDSP '22: Proceedings of the 6th International Conference on Digital Signal ProcessingFebruary 2022 Pages 43–47https://doi.org/10.1145/3529570.3529578Online:29 June 2022Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Classifying Toggles-smells and Investigating Development Effort

Companies are moving towards rapid release to deliver features as quickly as possible using Feature Toggles. Feature toggle is a variable that controls the state of a feature allowing unfinished code into the trunk. However, Maintaining the feature toggles needs a great effort, otherwise, it may lead to technical debt. Toggles may turn into code smells since they can be used in many ways if there is no standard of usage. We are calling such standard-less use of feature-toggles as “Toggle Smell’’. We classify different uses of toggle smells, and then we measure how much effort the code files are consuming to develop features, and maintain the toggles in each component. Our quantitative analysis on the Chromium open-source project finds that there are 3.1K toggles in 38 components and of the six different types of toggle usage, we classify three different toggle smells. The other types of usage will be analyzed in a future work. Three classification models predict the development effort in files as “High’’, “Medium’’, and “Low’’ with a similar accuracy of 95.x%.
Explainable lexical entailment with semantic graphs

Abstract We present novel methods for detecting lexical entailment in a fully rule-based and explainable fashion, by automatic construction of semantic graphs, in any language for which a crowd-sourced dictionary with sufficient coverage and a dependency parser of sufficient accuracy are available. We experiment and evaluate on both the Semeval-2020 lexical entailment task (Glavaš et al . (2020). Proceedings of the Fourteenth Workshop on Semantic Evaluation , pp. 24–35) and the SherLIiC lexical inference dataset of typed predicates (Schmitt and Schütze (2019). Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pp. 902–914). Combined with top-performing systems, our method achieves improvements over the previous state-of-the-art on both benchmarks. As a standalone system, it offers a fully interpretable model of lexical entailment that makes detailed error analysis possible, uncovering future directions for improving both the semantic parsing method and the inference process on semantic graphs. We release all components of our system as open source software.