Reviewerly/exHarmony huggingface dataset:
Remote Monitoring System of Mechanical Manufacturing Equipment based on Digital Image Processing

Precision instrument, machinery manufacturing equipment needs to use a monitoring system to monitor production details in real time during its production and processing. However, the traditional remote monitoring system has a weak ability to process monitoring images, resulting in a low peak signal-to-noise ratio of the system. The surveillance image is blurry. Therefore, based on digital image processing, a new remote monitoring system for machinery manufacturing equipment is designed. In hardware, the front-end of voltage signal acquisition is redesigned, and the communication circuit is optimized. In software, digital image processing technology is used to sharpen the image fuzzy contour, correct the image texture, and enhance the fuzzy details of the monitoring image; the remote positioning and monitoring function of the system is set to realize the remote monitoring of mechanical manufacturing equipment. Experimental results: Compared with the monitoring system under the traditional design, the peak signal-to-noise ratio of the remote monitoring system designed this time is much higher than that of the traditional monitoring system. Visible digital image processing technology can help the surveillance system to enhance the fuzzy details of the image.
Background Modeling with Freely Moving Camera

Detecting moving objects with a moving camera is a challenge task due to camera motion, parallax effect, registration error and etc. Currently, a lot of techniques detect moving objects by establishing a panoramic background model for the video sequence captured by moving camera. In this process, motion compensation is usually used to registrar the pixel between current frame and panoramic background. However, most of methods don't consider the registration error (caused by inadequate motion model, incorrect parameter estimation and computation error etc.) and usually update background model directly in the location after registration. Since it is difficult to realize the motion compensation with sufficient pixel accuracy, the pixel-wise decision for foreground or background through background model will fail. In this paper, we use the spatial distribution of Gaussians (SDG) model to reduce the registration error caused by motion compensation. According to this model, we only assume the alignment between current frame and background image is approximate, we will find the real correspond location for each current frame pixel in the neighborhood of the predicted location in background image. Based on this model we proposed a new algorithm to model panoramic background for video sequences captured by the freely moving camera. The proposed method and the original method are evaluated and compared based on six video sequences taken by PTZ camera and handheld camera. Compared with traditional method, the proposed method is works well both in detection accuracy and the effect of background modeling.
An Adaptive Technique for Estimating Traffic Intensities for a Network in Random Markovian Routing

We propose an efficient method for estimating a vector of parameters that characterize the node-tonode traffic intensities in a network. The estimation procedure is free from any initial estimate which is often difficult to choose for iterative procedures that occur in maximum likelihood estimation or method of moments. The procedure discussed here updates the information from a continuous chain of data until a stopping variable decides the final sample size and the estimate. More importantly, it considers the cost of logistics in sampling. We discuss important desirable properties of the estimation-procedure under the assumption of Poisson distribution. Data analyses reveal a highly accurate estimation and demonstrate a fast convergence
Impact of coseismic off-fault damage on the overall energy budget.

&amp;lt;p&amp;gt;In the brittle part of the crust, deformation is usually perceived to be the result of displacement along fault planes, whose behaviors are controlled by their frictional properties. However, fault zones not only consist of a narrow fault core where slip occurs, they are also surrounded by a complex structure which is of key importance in the mechanics of faulting, hence in determining the overall energy budget. Indeed, as pointed out by the numerous field, geophysical, mechanical and laboratory observations, if the behavior of fault zones is intrinsically linked to the properties of the main sliding plane, it also depends on those of the surrounding medium.&amp;lt;strong&amp;gt; &amp;amp;#160;&amp;lt;/strong&amp;gt;In parallel, fault displacements may induce a substantial change in the physical properties of the surrounding medium. As a consequence, to improve our understanding of active fault zones, fault slip and the evolving physical properties must be studied as a unique system of stress accommodation and no longer as two distinct entities. To tackle this problem, we have developed a micromechanics-based constitutive model, thermodynamically argued, that can determine the inelastic behavior at macroscopic scale that arises from structural rearrangements at microscale. It is therefore the compulsory tool to emulate the strong coupling between the bulk and the fault that prevails during earthquakes. With this code, we can reproduce the strain rate sensitive, non-linear stress-strain relationship that leads to off-fault damage as a seismic event is propagating. We explore different scenarii and we show that there is a unique off-fault damage pattern associated with supershear transition of an earthquake rupture, that is also observed in the field.&amp;amp;#160; We define, in return, the impact of damage on the propagation of the earthquake in itself and the generated waves. We conclude by assessing the kinetic energy, the dissipated energy and the radiated energy to define how energy is consumed within crustal systems during seismic events.&amp;lt;/p&amp;gt;
7.2: Invited Paper: Design and Simulation of Pressure‐Sensing Foldable Displays that Enable Novel User Interfaces

SID Symposium Digest of Technical PapersVolume 52, Issue S1 p. 44-44 Technical Sessions: Session 7: Touch and Interactive Display (Touch and Interactive Display)Free to Read 7.2: Invited Paper: Design and Simulation of Pressure-Sensing Foldable Displays that Enable Novel User Interfaces Tolis Voutsas, Tolis Voutsas Peratech Holdco Ltd., Old Repeater Station, Brompton-on-Swale, North Yorkshire, UK Corresponding AuthorSearch for more papers by this authorSarah Dempsey, Sarah Dempsey Peratech Holdco Ltd., Old Repeater Station, Brompton-on-Swale, North Yorkshire, UKSearch for more papers by this authorTim Wiles, Tim Wiles Peratech Holdco Ltd., Old Repeater Station, Brompton-on-Swale, North Yorkshire, UKSearch for more papers by this author Tolis Voutsas, Tolis Voutsas Peratech Holdco Ltd., Old Repeater Station, Brompton-on-Swale, North Yorkshire, UK Corresponding AuthorSearch for more papers by this authorSarah Dempsey, Sarah Dempsey Peratech Holdco Ltd., Old Repeater Station, Brompton-on-Swale, North Yorkshire, UKSearch for more papers by this authorTim Wiles, Tim Wiles Peratech Holdco Ltd., Old Repeater Station, Brompton-on-Swale, North Yorkshire, UKSearch for more papers by this author First published: 24 February 2021 https://doi.org/10.1002/sdtp.14368 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume52, IssueS1International Conference on Display Technology (ICDT 2020)February 2021Pages 44-44 RelatedInformation
Revisiting My Self‐Quarantine Experience through a Data‐Driven Approach

One constructive possibility was that as a designer, I could authentically capture my personal self‐quarantine experience to identify user pain points, and improve the school's quarantine procedures to help more people in the future. Undeterred by an unplanned quarantine, a student uses technology and questions the experience from four perspectives in hopes of improving it for others.
Approximating the clusters' prior distribution in Bayesian nonparametric models

In Bayesian nonparametrics, knowledge of the prior distribution induced on the number of clusters is key for prior specification and calibration. However, evaluating this prior is infamously difficult even for moderate sample size. We evaluate several statistical approximations to the prior distribution on the number of clusters for Gibbs-type processes, a class including the Pitman-Yor process and the normalized generalized gamma process. We introduce a new approximation based on the predictive distribution of Gibbs-type process, which compares favourably with the existing methods. We thoroughly discuss the limitations of these various approximations by comparing them against an exact implementation of the prior distribution of the number of clusters.
Distributionally Robust Weighted $k$-Nearest Neighbors

Learning a robust classifier from a few samples remains a key challenge in machine learning. A major thrust of research has been focused on developing $k$-nearest neighbor ($k$-NN) based algorithms combined with metric learning that captures similarities between samples. When the samples are limited, robustness is especially crucial to ensure the generalization capability of the classifier. In this paper, we study a minimax distributionally robust formulation of weighted $k$-nearest neighbors, which aims to find the optimal weighted $k$-NN classifiers that hedge against feature uncertainties. We develop an algorithm, \texttt{Dr.k-NN}, that efficiently solves this functional optimization problem and features in assigning minimax optimal weights to training samples when performing classification. These weights are class-dependent, and are determined by the similarities of sample features under the least favorable scenarios. When the size of the uncertainty set is properly tuned, the robust classifier has a smaller Lipschitz norm than the vanilla $k$-NN, and thus improves the generalization capability. We also couple our framework with neural-network-based feature embedding. We demonstrate the competitive performance of our algorithm compared to the state-of-the-art in the few-training-sample setting with various real-data experiments.
A Cognitive Architecture for Object Recognition in Video

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. This talk describes our efforts to abstract from the animal visual system the computational principles to explain images in video. We develop a hierarchical, distributed architecture of dynamical systems that self-organizes to explain the input imagery using an empirical Bayes criterion with sparseness constraints and dual state estimation. The interpretation of the images is mediated through causes that flow top down and change the priors for the bottom up processing. We will present preliminary results in several data sets.
Lie-Sensor: A Live Emotion Verifier or a Licensor for Chat Applications using Emotional Intelligence

Veracity is an essential key in research and development of innovative products. Live Emotion analysis and verification nullify deceit made to complainers on live chat, corroborate messages of both ends in messaging apps and promote an honest conversation between users. The main concept behind this emotion artificial intelligent verifier is to license or decline message accountability by comparing variegated emotions of chat app users recognized through facial expressions and text prediction. In this paper, a proposed emotion intelligent live detector acts as an honest arbiter who distributes facial emotions into labels namely, Happiness, Sadness, Surprise, and Hate. Further, it separately predicts a label of messages through text classification. Finally, it compares both labels and declares the message as a fraud or a bonafide. For emotion detection, we deployed Convolutional Neural Network (CNN) using a miniXception model and for text prediction, we selected Support Vector Machine (SVM) natural language processing probability classifier due to receiving the best accuracy on training dataset after applying Support Vector Machine (SVM), Random Forest Classifier, Naive Bayes Classifier, and Logistic regression.
Memory-efficient Speech Recognition on Smart Devices

Recurrent transducer models have emerged as a promising solution for speech recognition on the current and next generation smart devices. The transducer models provide competitive accuracy within a reasonable memory footprint alleviating the memory capacity constraints in these devices. However, these models access parameters from off-chip memory for every input time step which adversely effects device battery life and limits their usability on low-power devices. We address transducer model's memory access concerns by optimizing their model architecture and designing novel recurrent cell designs. We demonstrate that i) model's energy cost is dominated by accessing model weights from off-chip memory, ii) transducer model architecture is pivotal in determining the number of accesses to off-chip memory and just model size is not a good proxy, iii) our transducer model optimizations and novel recurrent cell reduces off-chip memory accesses by 4.5x and model size by 2x with minimal accuracy impact.
Penilaian Standar Mutu Pada Aplikasi Tiket Bioskop dengan ISO 27001 dan Fishbone Analisis

Cinema ticket application services make it easy to purchase movie tickets. Various applications, especially in the business sector, certainly require information security that has been guaranteed to be applied and international information security certificates in ISO 27001 quality standards. Therefore, the requirements for obtaining information security certificates in international quality ISO 27001 need to be analyzed first. It requires a lot of preparedness that must be requested in meeting ISO 27001 quality standards. The purpose of this study is to analyze the application of cinema tickets for ISO 27001 eligibility. Analytical research methods used include clauses, checklists, Likert scales, maturity levels, and fishbone diagrams. The authors' references were based on the writer's team's analysis that the authors made, including graphs, charts, clause scores, checklist scores, Likert scales, maturity levels, and fishbone diagrams. Based on the author's analysis results, the cinema ticket application system does not meet ISO 27001 standards, so much needs to be improved in the system. Likert scale scores are still 19%, while to meet ISO 27001 standards, Likert scale scores must reach at least 60%. The overall problem with assessing this analysis, which results in cinema tickets being inappropriate or not passing ISO 27001 quality standards, is the lack of Likert scale scores and fishbone diagrams. This analytical research hypothesis can be the basis for future research that the application can meet the feasibility test of ISO 27001 quality standards.
SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification

Visible-Infrared person re-identification (VI-ReID) is a challenging matching problem due to large modality varitions between visible and infrared images. Existing approaches usually bridge the modality gap with only feature-level constraints, ignoring pixel-level variations. Some methods employ GAN to generate style-consistent images, but it destroys the structure information and incurs a considerable level of noise. In this paper, we explicitly consider these challenges and formulate a novel spectrum-aware feature augementation network named SFANet for cross-modality matching problem. Specifically, we put forward to employ grayscale-spectrum images to fully replace RGB images for feature learning. Learning with the grayscale-spectrum images, our model can apparently reduce modality discrepancy and detect inner structure relations across the different modalities, making it robust to color variations. In feature-level, we improve the conventional two-stream network through balancing the number of specific and sharable convolutional blocks, which preserve the spatial structure information of features. Additionally, a bi-directional tri-constrained top-push ranking loss (BTTR) is embedded in the proposed network to improve the discriminability, which efficiently further boosts the matching accuracy. Meanwhile, we further introduce an effective dual-linear with batch normalization ID embedding method to model the identity-specific information and assits BTTR loss in magnitude stabilizing. On SYSU-MM01 and RegDB datasets, we conducted extensively experiments to demonstrate that our proposed framework contributes indispensably and achieves a very competitive VI-ReID performance.
2급 문화예술교육사 교육과정에 따른 대학 사진학과 교직전공 교과현황 조사 연구

The interest in the Arts & Culture Education has resulted in the increasing demand in the Arts & Culture Education and also, required to train professional personnel in the Arts & Culture Education as well. The purpose of this study was to examine Arts & Culture Education Instructor System and its revision and then, look into the pictures of Arts & Culture Education Instructor Curriculum and teaching competency subject.These subjects aimed at improving qualification not only as an artist but also educationist to cultivate the ability as the educationist. In this paper, the current status of Dept. of Photography in all four-year college throughout the country and the teaching competency subject were compared with the Arts & Culture Education Instructor subject and also, new initiative to train Arts & Culture Education Instructor in the photography was proposed.
Informational entropy refinement as a stochastic mechanism for sequential decision-making in humans

While perceptual decision making in humans is often considered to be governed by evidence accumulator models (like drift-diffusion), mechanisms driving harder situations where prospection of future scenarios is necessary remain largely unknown. Here, experimental and computational evidence is given in favour of a mechanism in which prospection of possible future payoffs associated to each available choice could be used, through the internal estimation of the corresponding Shannon's entropy $S$. So, the decision would be triggered as soon as $S$ reaches a threshold which ensures that a choice is reliable enough. We illustrate this idea using a task in which subjects have to navigate sequentially through a maze on the computer screen while avoiding trajectory overlaps, forcing them to use memory and prospection skills that we indirectly capture through eye-tracking. Comparison of the experimental data to that from virtual (ideal) subjects allows us to verify that the performances observed, as well as the distribution of decision-making times, of humans are compatible with the aforementioned mechanism.
Numerical Simulation of Strength Limit of Surrounding Rock in Geotechnical Engineering Based on Machine Learning

When the traditional method numerically simulates the strength of the surrounding rock, the error of obtaining the strength parameters is large, which leads to a large range of the numerical value of the strength simulation. Therefore, a numerical simulation method based on machine learning for the strength limit of surrounding rock in geotechnical engineering is proposed. Evaluate the volume joints and structural surfaces of the surrounding rock, collect the surrounding rock environment information and geotechnical characteristics, perform mechanical simulation on it, use machine learning technology to optimize the strength parameter algorithm, obtain the optimal stress parameter, and realize the strength numerical simulation. The experimental results show that the variation range of numerical simulation method of surrounding rock strength is less than that of two traditional methods, which improves the accuracy of numerical simulation.
LibGuides: ScholComm Landing: Overview

A landing page with paths to diverse areas across the Scholarly Communication ecosystem An introduction to CSU Fullerton's Institutional Repository
Software Functional Complexity Using a New Set of Criteria

With the rapid technology advances, there is an emerging consensus that the size and complexity of software designs are increasing so rapidly that they proportionally affect the magnitude of administrative and development efforts. An important consideration is how to estimate software complexity. This subject continues to be a research topic in the literature. The software design process researched here uses the Unified Modeling Language (UML) diagrams and the database design for extracting pertinent information. The Entity Relationship (ER) model of Peter Chen (of MIT) is a conceptual method of describing the data in a relational structure. An Entity Relationship Diagram (ERD) and an Entity Relationship Schema (ERS) represents the ER model, containing the entities, attributes, primary and foreign keys, and the relationships between the entities. Extending this ERS modeling construct, this paper uses an additional enhanced schema, called the Object Relationship Schema (ORS), which, together with the existing ERS, creates an enhanced view of the requirements and the design of the database. In addition, functional dependency, security, computational complexity, use cases, component structure and interpretations are considered for estimating functional complexity of modern software systems which is very valuable in higher education for new workforce development.
نحو تحسين أداء نموذج التميمي للوسم النحوي الآلي للغة العربية

One of the most important tasks in natural language processing is POS tagging. There are many works, projects and efforts made in this field, but they are not up to the required linguistic level due to many problems that arise at the level of implementation or performance. This paper aims to improve the performance of the initial CRF model of the POS tagging with the basic Tamimi tags based on a hand-tagged corpus with twelve grammatical tags (noun - adjective - verb - pronoun - adverb - interjection - practical - punctuation - abbreviation - foreign word - symbol - number), by the model retraining method and adding 46,811 tokens throughout the Arabic eras, its regions, and domains. The performance has developed as the initial model achieved an accuracy score of 91.58%, and the enhanced model achieved a score of 93.97%