Reviewerly/exHarmony huggingface dataset:
Comparing Performances of Native Host and Virtualization on ESXi hypervisor

This paper presents the performance of the VMWare ESXi hypervisor through the filesystem performance testing procedures. To measure the performances we used the Filebench benchmark tool. As a guest operating system, we have chosen Linux Ubuntu 18.04. All the tests are performed under the same conditions. The tests were first conducted on a native operating system, and then also on one, two, and three virtual machines. Finally, we compared the results of performances between the native operating system and ESXi hypervisor environment. We expected the lower performances when using the system with ESXi and that the performance drop will not be significant in the case when running only one virtual machine.
Formal Definition of the Term “Semantics” as a Foundation for Semantic Interoperability in the Industrial Internet of Things

Semantic interoperability is seen as the key to realize the ideas of the Industrial Internet of Things (IIoT). In order to equip technical systems with such a capability, a precise definition of the term "semantics" is needed. Complex IIoT devices can only be developed properly on a formal foundation. Existing approaches that intend to specify the term "semantics" are often more intuitively motivated. These include, for example, the knowledge pyramid or the Levels of Conceptual Interoperability Model (LCIM). The paper provides a formal definition of the term "semantics" and relates these existing approaches critically to the proposed definition.
Adaptive covariate acquisition for minimizing total cost of classification

Abstract In some applications, acquiring covariates comes at a cost which is not negligible. For example in the medical domain, in order to classify whether a patient has diabetes or not, measuring glucose tolerance can be expensive. Assuming that the cost of each covariate, and the cost of misclassification can be specified by the user, our goal is to minimize the (expected) total cost of classification, i.e. the cost of misclassification plus the cost of the acquired covariates. We formalize this optimization goal using the (conditional) Bayes risk and describe the optimal solution using a recursive procedure. Since the procedure is computationally infeasible, we consequently introduce two assumptions: (1) the optimal classifier can be represented by a generalized additive model, (2) the optimal sets of covariates are limited to a sequence of sets of increasing size. We show that under these two assumptions, a computationally efficient solution exists. Furthermore, on several medical datasets, we show that the proposed method achieves in most situations the lowest total costs when compared to various previous methods. Finally, we weaken the requirement on the user to specify all misclassification costs by allowing the user to specify the minimally acceptable recall (target recall). Our experiments confirm that the proposed method achieves the target recall while minimizing the false discovery rate and the covariate acquisition costs better than previous methods.
Eliminating the Web Noise by Text Categorization and Optimization Algorithm

In this research, it had been proposed a novel hybrid framework Optimized K-NearestNeighbor (KNN) and FireFly (FF) algorithms to eliminate local noises from web pages. Firstly, the Optimized-KNN is used to classify the contents in the web pages, and second, the FF is used for optimization and noise reduction. The performance metrics for proposed KNN-FF such as Text categorization, Noise classification, and Accuracy show better results while comparing it with the existing NWDL-STC and DOM-SST-ANN techniques.
Distributed Diffusion Unscented Kalman Filtering Algorithm with Application to Object Tracking

In this paper, a distributed diffusion unscented Kalman fltering algorithm based on covariance intersection strategy (DDUKF-CI) is proposed for object tracking. By virtue of the pseudo measurement matrix, the standard unscented Kalman fltering (UKF) is transformed to the information form that can be fused by the diffusion strategy. Then, intermediate information from neighbors are fused based on the diffusion framework to attain better estimation performance. Considering the unknown correlations in sensor networks, covariance intersection (CI) strategy is combined with the diffusion algorithm. Moreover, it is proved that the estimation error of the proposed DDUKF-CI is exponentially bounded in mean square using the stochastic stability theory. Finally, the performances of the proposed algorithm and the weighted average consensus unscented Kalman fltering (CUKF) are compared in a target tracking problem with a sensor network.
Semantic Parsing for Cognitive Framing in Specialized Texts

The paper presents the first steps in the field of cognitive framing of specialized texts by means of semantic parsing. This work is undertaken as part of a long-term project on Special Knowledge Mediation by means of Automated Ontological and Metaphorical Modelling, which resulted in the development of the programs for automated terms identification, classification, and ontological representation (TSBuilder, TSGraph). The current proposal is aimed at automatic tagging terms (nouns) in Computer security texts, assigning them semantic roles in the framework of Ch. Filmore's theory of valency and semantic roles. The paper presents an overview of existing annotation tools, like FrameNet, SEMAFOR, Framat, etc., analyzes their applicability for specialized texts annotation and their compliance with the demands of modern cognitive linguistics. We outline the measures we take for incorporating these tools in our project and specify the technical part of the parser we develop for this purpose.
Enterprise Financial Shared Service Platform Based on Big Data

Abstract With the advent of the era of big data, the market economy continues to develop in depth, and the social division of labor is becoming more and more refined. The financial sharing platform service center can realize the transformation of financial management functions from financial accounting to management accounting according to the strategic needs of enterprises. Prevent financial risks in a timely manner, provide strong endogenous vitality and back-up support for corporate management, and promote the development of corporate economies of scale. The purpose of this paper is to study the corporate financial shared service platform based on big data. This article compares and analyzes the difference between the traditional “financial management mode” and the “shared service mode”, and at the same time proposes the impact of information technology on the financial sharing platform services in the era of big data, and then conducts the construction process and operation management status of the company’s financial sharing platform service center Describe, analyze and discuss its implementation results, existing problems, opportunities and challenges at this stage, and propose measures to be improved in response to existing problems, and put forward suggestions for optimization of the company’s financial sharing center based on the background of the big data era. The company was selected as a case study to explore its motivations for implementing the financial sharing platform service, to explain its construction plan in detail, and to analyze the benefits from different aspects. Research shows that more than 80% of the company’s accounting business vouchers are automatically generated by the business-driven system. Compared with traditional accounting methods, both the timeliness and accuracy of accounting treatment have undergone a qualitative leap.
Linguistic Features and Patterns of Texting: Results of a Case Study at an Indian University

The main objective of the present study is to explore the linguistic features which characterize English used by texters. It also aims to investigate if texting follows any specific pattern. As this study aimed to investigate the linguistic features of texting and their impact on the structure of Standard English, the data of the study were collected from a sample of ninety students. The morphosyntactic elements, phonological elements and code mixing elements were linguistically analyzed separately, providing some examples from the data collected for the study. Although punctuation has been discussed under both morphosyntax and phonology, its significant presence in the data prompted us to look into the pragma-semantic elements in the use of punctuation by the texters. Five sent emails, five sent SMS and five sent Facebook chats from each of the respondents were collected to test the linguistic features of texting. SMS are more deviated from Standard English than Facebook chat texts in some cases such as deletion of subject and deletion of punctuation. It is found out that email texts were less deviated from Standard English than SMS and Facebook chat texts. It was found out that the respondents deviated from the Standard English in all the aspects which were examined, i.e. in morphosyntactic and phonological structures, which indicated that it could be a threat to Standard English. This study proved that texting followed some patterns in some cases but it was randomly used in other cases and it is difficult to control and find fixed patterns followed.
Regulation of Speed for Driver Assistanceand Removal of Haze using Imageprocessing Algorithm

Abstract The clarity of the outdoor scene images very bad due to the environmental condition result in hazy image. The process of removing the fog from that image is called dehazing. The dehazing process is very difficult in the moving image or video image. The proposed method should improve this problem for the smooth driving of the vehicles by controlling the speed by comparing the image and the scene of video taken during driving. The proposed method should remove the haze so that it will increase the visibility of the image and multi scene. The proposed method can improve the colour changes in the image which is caused by airlight. The single frame is used for the enhancement of the foggy image remove the fog using the transmission map which contains multilevel. We make use of boundary constraints and global airlight estimation is taken into account. The method is fast and noise-free that generally arise in such enhancement techniques.
A Segmentation Algorithm of Image Semantic Sequence Data Based on Graph Convolution Network

Image semantic data have multilevel feature information. In the actual segmentation, the existing segmentation algorithms have some limitations, resulting in the fact that the final segmentation accuracy is too small. To solve this problem, a segmentation algorithm of image semantic sequence data based on graph convolution network is constructed. The graph convolution network is used to construct the image search process. The semantic sequence data are extracted. After the qualified data points are accumulated, the gradient amplitude forms complete rotation field and no scatter field in the diffusion process, which enhances the application scope of the algorithm, controls the accuracy of the segmentation algorithm, and completes the construction of the data segmentation algorithm. After the experimental dataset is prepared and the semantic segmentation direction is defined, we compare our method with four methods. The results show that the segmentation algorithm designed in this paper has the highest accuracy.
Highly Accurate Real time human Counter with Minimum Computation Cost

During the ongoing Covid-19 Pandemic when we need to operate any public facility like museum, shopping mall, restaurants, or public dealing organizations, we not only need to keep the operations going but also have to ensure precautionary measures to ascertain their safety. As per all SOPs (Standard Operating Procedures) it is advisable to restrict number of visitors inside these enclosed spaces which are most likely to be weather controlled. Automatic safety compliance thus becomes imperative in such situations. Even though absolute compliance and alert signalling will require scrutiny and cross-checking at several levels, a beginning towards automation of compliance monitoring seems mandatory in the neo-normal era. Hence In this project we have designed a low cost rapidly implementable design to monitor the number of visitors inside the self-contained hall. The system will give signal once the maximum permissible visitor population is reached at a given time. Monitoring the optimal population and the density and enforcing visitor to wear mask even within the space manually is tantamount to imposing health hazards to the person who will physically have to monitor and it may as well render the visitors vulnerable. Here we have used Artificial Intelligence based model person detection and tracking. Real time tracking with accuracy is still an important area in computer vision. There are some commercial solution available for the problem but all of them either implemented considering ideal situation or need huge cost and infrastructure. But as a part of museums community we are passing through a financial crises as due to pandemic we closed for visitors. Hence neither we can afford costly system nor a system designed with ideal condition. This motivates us to develop a new system according to our criteria. Here we have modified the available solution for implementation in real world environment using very minimum hardware infrastructure requirements to work on real time with maximum possible efficiency. This system is not only useful for COVID-19 Situation but also its use can be extended beyond the boundary of museums for visitor density monitoring system for large public establishment with minimum computation cost.
Terminal-Edge-Cloud Collaboration: An Enabling Technology for Robust Multimedia Streaming

To reconcile the conflict between ceaselessly growing mobile data demands and the network capacity bottleneck, we exploit the terminal-edge-cloud collaboration to design a streaming distribution framework, SD-TEC, with the major objective to avoid streaming interruptions caused by inter-cluster handovers and corresponding user defections. First, the merge-and-split rule in the coalition game is employed for virtualized passive optical network clustering to structurally reduce the inter-cluster handover frequency. Second, the terminal-edge collaboration leverages device-to-device communications to sustain streaming services when inter-cluster handovers inevitably occur, reducing the time of possible streaming interruptions and improving the quality of experience of multimedia services. Lastly, the edge-cloud collaboration proactively caches streaming contents to alleviate the traffic congestion of peak hours and considers user priorities and buffer queue underflow/overflow to manage both fronthaul and backhaul resources. Simulation results validate the efficiency of our proposed SD-TEC in reducing the traffic congestion and streaming interruptions caused by inter-cluster handovers.
Symbolic regression of uncertainty-resilient inferential sensors for fault diagnostics

An algorithm is presented for the design of inferential sensors for fault diagnostics in thermal management systems. The algorithm uses input and output sensed system information to improve the detection and isolation of a fault by generating inferential sensors that augment the measured information to: (i) reduce the evidence of uncertainty in the inferred variables, and thus decrease false alarm and nondetection rates; and (ii) provide distinguishable responses to faults, and thus reduce reduce the rate of misdiagnoses. The novelty of the algorithm is its use of genetic programming to evolve explainable inferential sensors that maximize information criteria specific to fault diagnostics. The chosen criteria: (i) least squares regression; and (ii) Ds -optimality (calculated from the Fisher Information Matrix), leverage symbolic mathematics and automatic differentiation to obtain parametric sensitivities of the measured outputs and inferential sensors. The algorithm is included in a standard work for fault diagnostics, where its effectiveness is assessed through k-NN classification and illustrated in an application to an aircraft cross-flow plate-fin heat exchanger.
Graph Contrastive Learning with Local and Global Mutual Information Maximization

Graph is a common data structure in social networks, citation networks, bio-protein molecules and so on. Recent years, Graph Neural Networks(GNNs) have attracted more and more research attention because of its superior performance on some graph learning tasks. Training of GNNs needs large amount of labeled data, which casts shadows on the usability and expansibility of GNNs. Inspired by recent unsupervised research on computer vision and natural language processing, we propose a novel unsupervised graph representation learning model together with several graph data augmentation methods (drop edge, blur, mask features) and a local and global graph mutual information maximization strategy. By maximize two types of mutual information between original graph and augmented graph, the model is forced to learn some useful prior domain knowledge. We conduct experiments on both node classification and graph classification tasks and show the superior performance of the proposed model over state-of-the-art baselines.
MATHEMATICAL MODELING AS A TOOL FOR INCREASING THE MATHEMATICAL LITERACY OF STUDENTS

Today, the problem of developing the functional literacy of schoolchildren is relevant at the community level. In this regard, new requirements are imposed on the level of professional training of future mathematics teachers. The authors consider the issue of preparing future mathematics teachers to develop students' mathematical literacy. The works of scientists on the development of functional literacy of schoolchildren were studied and the concepts of "model", "mathematical modeling" were explained. In addition, the specifics of the method of mathematical modeling in the process of teaching mathematics in secondary school have been clarified, and the pedagogical conditions for using the modeling method have been determined. It is shown that mathematical modeling is not only a tool for solving applied problems, but also a way of developing intellectual skills that a student must master.
A Survey of Recent Trends in Two-Stage Object Detection Methods

Object detection deals with locating an object in an image or a video and identifying its class label. In this regard, integration of object detection techniques with deep learning has revolutionized the area of computer vision. Furthermore, the ability of a deep neural network to directly learn feature representation from images has significantly improved object detection models. The present survey analyzes and systematically provides a comprehensive overview of typical two-stage object detection methods with deep learning and summarizes the most popular benchmark dataset for object detection.
Chinese Wall or Swiss Cheese? Keyword filtering in the Great Firewall of China

The Great Firewall of China (GFW) prevents Chinese citizens from accessing online content deemed objectionable by the Chinese government. One way it does this is to search for forbidden keywords in unencrypted packet streams. When it detects them, it terminates the offending stream by injecting TCP RST packets, and blocks further traffic between the same two hosts for a few minutes.
Mobile Application for flood disaster in Jakarta

Jakarta as the Indonesian capital city has extreme average rainfall in 2020, based on data from the DKI Jakarta Representative Central Bureau of Statistics and the meteorology, climatology, and geophysics agency. From year to year, the city of Jakarta has not been spared from floods which have seriously disrupted the activities of the people of Jakarta. Flood disasters greatly disrupt people's economic activities and cause various other problems including public health problems, one of which is causing infectious diseases. In such a case, this research work proposes a mobile application for flood disaster monitoring that will help the Jakarta government to maintain the flood as real flood information in Jakarta. The Jakarta city government and the residents of the city of Jakarta can work hand in hand using mobile application technology to combat this flood problem and enable the Jakarta city government to quickly overcome any reported flood problems. The proposed mobile application was modeled with a use case and class diagrams.
A Survey on Third Party Auditor in Cloud Computing

The cloud computing is used to compute the resources that provide a means of processing the deposition or acquisition of data safely through the network. Even though the customers are feeling convenient from cloud computing, providing security of the confidential data to the end customers would still not make them believe to use the cloud resources. The data sector needs policies to be set and secured technologies are needed to be accessed as they could be misused by vulnerabilities. These vulnerabilities increase the number of attacks which impact on system security. There is a huge challenge behind the success of cloud computing potential for ensuring the security. The cloud computing involves the process of providing data security that plays an important role in the process of data integrity. The data integrity provides the best security in the field of cloud computing, thereby provides confidentially for more data focusing. This research reviews the cloud services to encrypt the data from the cloud providers using different types of encryption algorithm. This research reviews about the encryption schemes used for data integrity and security and also discuss about the problems faced during the research. Finally, the solutions to overcome the problems is also suggested and thereby concludes a suitable technique which would be used in future study.
Reconsideration of multi-stage deep network for human pose estimation

Estimating human pose is a difficult task due to the high flexibility of joints and possible occlusion. In this paper, the proposed architecture efficiently predicts human pose and solves the preceding problem. The proposed framework has three main consecutive parts: (1) Deep Convolutional Neural Network (DCNN) based feature extraction, (2) feature refinement, and (3) the fusion of detection and context information. During the feature extraction phase, we have proposed a fusion of two DCNN modules, which have been inspired by VGG-19 and Inception-v4 deep learning architectures. In the feature refinement, a cascaded feature integration technique has been proposed over the stacked hourglass, to make the system efficiently locate the challenging joints. At last, a fusion of context information with the detected prediction is performed, which makes the system accurate towards occlusion. In this way, poses with difficult joint coordinates can be reliably estimated even in the presence of occlusion or severe distracting factors. The successful testing of the proposed method has been done on popular MPII and LSP datasets. Based on the experimental results and the analysis of the selected datasets, it is found that the proposed framework is more accurate compared to other state-of-the-art methods in terms of the PCK metric.