Reviewerly/exHarmony huggingface dataset:
Examination of Water Temperature Interpolation Method for Prediction

In aquaculture, fishery workers and researchers strong require to visualize water temperature. This is because knowing the water temperature makes it possible to take measures against fish diseases and predict the occurrence of red tide. In collaboration with fishery researchers, we constructed a multi-depth sensor network consisting of 17 water temperature observation devices over the Uwa Sea in Ehime Prefecture. In addition, we developed a Web system that can instantly visualize the water temperature measured on this network and provide it to fishery workers in the Uwa Sea. However, the water temperature information provided by this Web system is only current or past information, and fishery workers are requiring the provision of near-future water temperature forecasts for about 3 days. Therefore, in this research, as a new function of this Web system, we will implement a function to predict the water temperature in the near future from the past water temperature information and provide the forecast information. This paper examines the steps to be solved for the prediction of seawater temperature, which is the current problem in this system. Moreover, among the steps, this paper reports on the solution method and its evaluation experiment for improving the accuracy of water temperature interpolation.
Expressive And Compressive Gan Inversion Network

This paper presents a novel encoding framework for the generative adversarial network (GAN), named compressive GAN inverter (Comp-GI), which can effectively compress, restore, and edit the attributes of real-world images. In particular, Comp-GI first transforms the input image into the latent variable space of GAN through an encoder network and then manipulates the latent code vector to enable various semantic image editing operations. StyleGAN [1] and its improved version StyleGANv2 [2] are adopted as the default GANs for image generation. Experiments on benchmark datasets FFHQ demonstrate that the proposed Comp-GI achieves better restoration performance with finer details and fewer artifacts than existing state-of-the-art GAN-inversion methods.
Optical Processing for Artificial Neural Vision

Convolutional neural networks have become established as the primary mechanisms for image processing over the past decade. While general purpose optical neural networks remain a long term project, in the near term optical prefilters act as the first layers of electronic deep convolutional networks and enable 10-100x reduction in system power per reconstructed voxel.
Semantic-Compensated and Attention-Guided Network for Scene Text Detection

Recently, scene text detection based on deep learning has witnessed rapid development. However, most previous methods suffer from (i) semantic information diluted, (ii) false positives in their detections. To alleviate the above dilemmas, we propose an end-to-end trainable text detector named Semantic-compensated and Attention-guided Network (SANet). It contains a Semantic Compensation Module (SCM) and a Text Attention Module (TAM). Specifically, SCM aims to alleviate the dilution of semantic information via compensating the semantic information to the features at each level in a top-down pathway. Furthermore, TAM is employed to encode the strong supervised information into convolutional features, where the text-related features are significantly enhanced by increasing the response gap between text and background. The experimental results on three benchmark datasets, ICDAR2015, MSRA-TD500, and ICDAR2017-MLT prove the effectiveness of our method.
Free Electrons Can Induce Quantum Correlations Between Two Separate Photonic Cavities

We find that free electrons passing through two independent photonic cavities induce quantum correlations between them, creating nonzero quantum mutual information and second-order coherence. The concept is general and applicable for example in electron-phonon interactions.
Resolution-Invariant Person Reid Based On Feature Transformation And Self-Weighted Attention

Person Re-identification (ReID) is a critical computer vision task which aims to match the same person in images or video sequences. Most current works focus on settings where the resolution of images is kept the same. However, the resolution is a crucial factor in person ReID, especially when the cameras are at different distances from the person or the camera’s models are different from each other. In this paper, we propose a novel two-stream network with a lightweight resolution association ReID feature transformation (RAFT) module and a self-weighted attention (SWA) ReID module to evaluate features under different resolutions. RAFT transforms the low resolution features to corresponding high resolution features. SWA evaluates both features to get weight factors for the person ReID. Both modules are jointly trained to get a resolution-invariant representation. Extensive experiments on five benchmark datasets show the effectiveness of our method. For instance, we achieve Rank-1 accuracy of 43.3% and 83.2% on CAVIAR and MLR-CUHK03, outperforming the state-of-the-art.
Financial Risk Estimation in Conditions of Stochastic Uncertainties

The problem of modeling and forecasting possible financial loss in the form of market risk using stochastic measurements is considered. The sequence of operations directed towards risk estimation includes data preparing to model building with selected filters: exponential smoothing, optimal Kalman filter and probabilistic Bayesian filter. A short review of the possibilities for data filtering is proposed, and then some of them are selected for specific practical application to process financial data in the form of prices for selected stock instrument. After preprocessing the data is used for constructing forecasting models for the financial process itself and dynamic of its conditional variance. In the first case regression models with polynomial trend are hired, and to describe dynamic of conditional variance GARCH and EGARCH models are constructed. Further on the results of variance prediction are used for computing possible market loss hiring VaR approach. Adequacy analysis of the models constructed and back testing of risk estimates performed indicate that there is improvement of quality of the final results.
Research Based on Multiple Integrated methods for Prediction on Resource Capacity

This paper constructs a method by combining the multiple prediction model on IT resource capacity. By using the result of this integrated forecasting model based on the historical data, we could forecast our IT resource utilities like the usage of CPU, hard disk, memory in few days or weeks. It could help optimize capacity to manage the limited resource to keep stable and reliable forecasting system. Moreover, this new method could promote the precision of prediction result comparing with the base single model. Therefore, this conclusion could be used in resource capacity management to guide the reasonable allocation of resource expansion and shrinkage.
Multiclass and Multifunction Subordinate Clauses in Arabic

The clause is a unit lower than the sentence in the hierarchical rank scale of units though, like the sentence, it has a subject and a predicate. Some sentences may contain only one clause and  are, therefore, called simple sentences, while others that include more than one clause are called non-simple sentences (compound, complex). The current study focuses on the second type and specifically the  complex sentence which typically contains two clauses one of which is the main clause while the other  is  subordinate since it functions in the structure of the main clause to realize one of four functions, namely, subject(S), object (O), complement(C) or adverbial (A). Subordinate clauses  (SCs)can be classified into two categories: nominal SCs and adverbial SCs. The former can function as S, O or C whereas the latter can function as A only. However , some SCs belong to different classes and realize different functions .The study tries to shed light on complex sentences in Arabic since they have not been investigated before.In fact,grammarians of Arabic do not acknowledge the existence of complex sentences in Arabic at all. The study ,therefore,  tries to prove that complex sentences and ultimately SCs exist in Arabic just like other live languages. It aims at listing the categories of SCs in Arabic and the functions that can be achieved by each of these categories.
Two new alternatives for a general method of construction of PRNGs based on the Residue Number System

This article presents two new alternatives for the construction of Pseudo-Random Number Generators (PRNGs) based on the Residue Number System (RNS). The procedure follows a previously proposed methodology. To assess the statistical quality of the proposed generators, the series are studied by applying different tests that measure randomness of the proposed Number Generators such as: basic statistical studies, Diehard test, statistical complexity measurements, zipping complexity and Hurst exponent. The new proposed PRNGs successfully passed all the tests applied to measure their characteristics.
Encoder-Decoder based Neural Network for Perspective Estimation

Article Encoder-Decoder based Neural Network for Perspective Estimation Share on Authors: Yutong Wang Sungkyunkwan University and Northwestern Polytechnical University, Republic of Korea Sungkyunkwan University and Northwestern Polytechnical University, Republic of KoreaView Profile , Qi Zhang Northwestern Polytechnical University, China Northwestern Polytechnical University, ChinaView Profile , Joongkyu Kim Sungkyunkwan University, Republic of Korea Sungkyunkwan University, Republic of KoreaView Profile , Huifang Li Northwestern Polytechnical University, China Northwestern Polytechnical University, ChinaView Profile Authors Info & Claims IPMV 2021: 2021 3rd International Conference on Image Processing and Machine Vision (IPMV)May 2021 Pages 42–46https://doi.org/10.1145/3469951.3469967Online:21 August 2021Publication History 0citation24DownloadsMetricsTotal Citations0Total Downloads24Last 12 Months24Last 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
VVS: Action Recognition With Virtual View Synthesis

Action recognition research is usually in the single-view setting. But human action is not single-view based in many cases. A lot of simple action is composed of both body movements from the third-person view, and vision guidance from the first-person view. Therefore, linking two viewpoints of data is critical for action recognition algorithms. Currently, the scale of aligned multi-view dataset is small, which limits the advancement in this direction of research. To alleviate the data limitation, we present the novel Virtual View Synthesis (VVS) module. Instead of training and testing on small scale multi-view data, VVS is first pre-trained on multi-view data to generalize the multi-view “supervisory attention”. Then it is incorporated into single-view action recognition model to transfer the ability of how to better observe the existing view based on experience from another view. Extensive experiments demonstrate that VVS can improve strong baselines on several single-view action recognition benchmarks.
Welcoming Researchers to Participate as Reviewer for Global Journal of Technology and Optimization

Firstly, we would like to extend our warm gratitude to the Reviewers of this Open Access Journal. Global Journal of Technology and Optimization has collaborated with more than 60 eminent Reviewers and is successfully publishing quality articles in the field of technology, mathematics, and optimization, etc. We are using an Editorial Tracking System for quality in the peer-review process which is an online manuscript submission, review, and tracking system. Review processing is performed by the editorial board members of the Journal or outside experts; at least two independent reviewers’ approval followed by editor approval is required for acceptance of any citable manuscript. Authors may submit manuscripts and track their progress through the system, hopefully to publication.
Empowering Locksmith Crafts via Mobile Augmented Reality

The advancements in terms of networking, image resolution, computer vision (CV) and mobile cloud computing performances are transforming Mobile Augmented Reality (MAR) into a technology which may be put to good use in a variety of everyday contexts. To make this point, we here consider an artisanal craft rooted back into the past, key locksmithing, and show how today MAR capabilities may simplify and ameliorate the performances of such ancient trade. To this aim, we introduce the requirements posed by such craft and present a MAR-based workflow which may be implemented to support and speed its execution. This could also impact the everyday lives of ordinary citizens, since it pose the bases of remote locksmithing activities.
Human Carving: A Parsing-Based Framework For 3d Human Reconstruction

Human-centric computer vision tasks often benefit from each other. In this paper, we propose a novel framework called Human Carving to explore the relationships between human parsing and multi-view 3D human reconstruction, which is the first method to consider the two related tasks. It consists of three modules: 1) Pose-aware Multi-view Human Parsing, 2) Semantic Visual Hull Carving and 3) Hierarchical Human Model Fitting. Taking the sparse multi-view images as input, the framework automatically generates a Part-Aware Visual Hull (PAVH) of human body parts and then estimates the human shape and pose simultaneously. Experimental results on real scenes demonstrate the effectiveness of our framework.
Towards a Cloud-WSDL Metamodel: A New Extension of WSDL for Cloud Service Description

Several approaches have been proposed to describe services in a rich and generic manner (such as WSDL, OWL-S, WSMO, and SAWSDL). However, current approaches remain inappropriate for cloud computing since: 1) they lack in a way or another semantic or business aspect, 2) they cannot fully cope with non-functional properties and cloud characteristics, 3) they are unable to cover all kinds of services (such as SaaS, PaaS, IaaS). Despite the existence of several attempts which have tried to extent existing studies, the problem remains open. In this paper, we propose Cloud-WSDL, a new description model aligned with WSDL language, the most popular language, to make it more suitable for describing cloud services. The idea is to enhance WSDL description with our ontological Generic Cloud Service Description called GCSD to cope with many aspects (technical, operational, business, semantic and contextual) to ensure a high interoperability between services belonging to multiple heterogeneous clouds, and to support all the kinds of cloud services (SaaS, PaaS, and IaaS).
Implementation of School Information Systems Using the CodeIgniter Framework

The development of Information and Communication Technology is growing rapidly. The need to obtain information quickly and easily encourages people to create better information and communication technologies. The development of this technology has affected all sectors, including in the world of education. Education in Indonesia shows a low quality because some schools still do not take advantage of technological developments. Therefore, schools in Indonesia must start updating school management to create efficient and more advanced management, including the use of the school information system. School Information System is an information system that helps in managing school information. The method used is a literal study by looking at and comparing existing research. The expected result of this research is a website-based school information system.
Building a Website Based Gemstone Authenticity Certificate Information System

In practice, the Gems Identification Laboratory (GIL) process of recording gemstone authenticity certificates is still manually and data only stored on hard disk, making it easier to create fake certificate. because the certificate can be printed by anyone, and the certificate code cannot be verified by the gemstone buyer or user, when the user buys gemstones outside the area from the Lab location. User must also send a short message to the admin to verify the authenticity of the certificate document printed by the Lab. The method used in this study uses observational data collection. The system was tested using XAMPP software. The results of this study are a website based gemstone authenticity certificate information system that can make it easier for users to check certificates anywhere by using a code that will be traced on the website without having to come to the Lab. Lab admins can also input and save certificate data in the admin menu.
An Enriched Emoji Picker to Improve Accessibility in Mobile Communications

We present an emoji picker designed to enrich emojis selection on mobile devices using audio cues. The aim is to make emojis selection more intuitive by better identify their meanings. Unlike the typical emoji input components currently in use (known as “pickers”), in our component each emotion-related item is represented by both an emoji and a non-verbal vocal cue, and it is displayed according to a two-dimensional model suggesting the pleasantness and intensity of the emotion itself. The component was embedded in an Android app in order to exploit touchscreen interaction together with audio cues to ease the selection process by using more than one channel (visual and auditory). Since the component adds non-visual information that drives the emoji selection, it may be particularly useful for users with visual impairments. In order to investigate the feasibility of the approach and the acceptability/usability of the emoji picker component, a preliminary remote evaluation test involving both sighted and visually impaired users was performed. Analysis of the data collected through the evaluation test shows that all the participants, whether sighted or visually impaired, rated the usability of our picker as good, and also evaluated positively the model adopted to add semantic value to emojis.
The Research Status of Formulaic Language Identification

Formulaic language, also known as formulaic sequences, is essentially a special kind of multiword strings. The words in the string usually have a fixed or semi-fixed combination form that falls in between phrase and sentence in the language. Formulaic language identification is to identifying schematic string patterns in corpora. According to statistics from different researchers, formulaic language is very common in both spoken and written language. Therefore, its research is helpful to the development of machine translation as well as second language acquisition. In order to better sort out the research hotspots of formula identification, this article took 143 papers included in the core database of web of science and 139 papers in CNKI in the past ten years as the research objects, adopted the method of bibliometric to conduct the following research. By using bibliometric online analysis platform, knowledge graph is drawn from the distribution of research forces of countries, institutions, authors and journals, and keyword co-occurrence, so as to deeply analyze the technical topics of formulaic language identification. The research found that the research strength of formula identification is mainly concentrated in universities at home and abroad. The identification methods have changed from initial manual labeling to simple frequency-based identification. At present, the application of computer technology identification has become a new research hotspot.
Building Virtual High-Performance Computing Clusters with Docker: An Application Study at the University of Economics Ho Chi Minh City

The need for high-performance computing in science and technology is becoming a challenging issue in recent years. Building a high-performance computing system by utilizing the existing hardware and software resources is a low-cost solution. Virtualization technology is proposed to solve this problem. It has brought convenience and efficiency as it can run on various operating systems. It can be used for implementing many computational algorithms simultaneously on the same hardware system including parallel processing and/or cluster processing systems. It can be expanded for computation and storage if the resources are still available. Virtualization also can combine the existing hardware and software resources to solve the problem of mobilizing multiple resources. Docker virtualization technology is considered a powerful virtualization technology, offering a new virtualization solution, instead of creating independent virtual machines with different virtual hardware and operating systems. Because this technology allows applications can be repackaged into individual data units and run together on the operating system kernel, sharing the resources of the mobilizing hardware platforms is the strength of Docker. The paper will focus on analyzing the superiority of using hardware virtualization technology, thereby proposing to build a high-performance virtualization system by using Docker technology with utilizing the available hardware platform at the University of Economics Ho Chi Minh City (UEH).
Forecasting Of Criminality Problems Using Double Exponential Smoothing Method

Crime is a form of social action that violates legal norms relating to acts of seizing property rights of others, disturbing public order and peace, and killing one or a group of people. This has always been a concern for residents in various places in the Ngoro sub-district, therefore this information system was created to help police officers to find out where crimes have occurred. This information sfystem was created to predict the area in Ngoro sub-district using the Double Exponential Smoothing method. So that this system can predict which areas in the next month there will be no crime, and can assist the public in reporting the occurrence of criminal acts without having to go to the police station first. The Double Exponential Smoothing method was chosen by the author because this method can be used. The data used is data on theft of crime from 2017 – 2019. The results of forecasting in one village in Ngoro sub-district such as Manduro are 0.07426431198 if rounded up to 0.1 which is categorized as low crime and has a MAPE value of 7.94%. Based on the MAPE value of the forecasting results, it can be concluded that a good constant is between 0.1 – 0.3.
Implementation of Marine Fire Fighting VR Platform Based on Probuilder Modular

Abstract In this paper, the multi person cooperative ship fire training system based on VR technology provides an effective training method for ship fire safety training. Through the existing ship fire protection process, the simulation scheme is proposed, and the corresponding process is developed on the basis of the simulation scheme. According to the functional requirements of ship fire simulation module, using VR technology, the 3D model is established with the help of the rapid modeling kit of Probuilder, the typical training scene is built after importing unity3d, the script is written with C # language, and the online VR fire training system is realized with the help of network communication framework, so that the trainees can carry out cooperative fire drill in VR interactive way, and realize the actual ship firefighting simulation training of anti-jamming process. The development of multi person cooperative ship fire training system will be of great significance to improve the efficiency of ship fire training, reduce the cost of ship fire drill and ensure the personal safety of trainees.