Reviewerly/exHarmony huggingface dataset:
An Efficient Cloud-Edge Aware Strategy for Task Deployment and Load Balancing

Distributed computing is rapidly evolving, and a slew of new cloud providers are popping up. Cost efficiency and asset cost increase have emerged as two of the most pressing concerns that cloud providers must address in order to remain profitable. The topic of benefit expansion under unified cloud conditions has been investigated in order to increase the level of multiplexing. A new financial framework, from the standpoint of a solely operating cloud provider, improved asset distribution components to address the benefit amplification issue. It has been suggested that custom-made affirmation control components be deployed within a Profit the board system to reduce asset expenses. Existing in-memory stockpiling on group reflections, such as appropriated shared memory, key worth stores, data sets, and Piccolo, give an interface based on fine-grained variable state adjustments (e.g., cells in a table). It&#x0027;s been tinkered with in attempt to forecast where it&#x0027;ll end up in comparison to its colleagues. The final heap of the complete lattice is obtained by adding the heaps of each group. In the Smart Grid, the suggested load pred<sup>1</sup>iction technique offers two major advantages. Client practices are learned, which enhances prediction precision while needing little computational resources. CCRF may successfully show one client&#x0027;s heap anticipating problem while also identifying critical factors that characterize its energy utilization plan. Replicating information between workstations or logging refreshes across machines are two of the most common ways to respond to internal failure with this interface. Where the supplier supports multiple estimating plans in various commercial centers, a bartering-based unique valuing instrument is appropriate for selling the server farm&#x0027;s excess capacity. An acceptance of the proposed dynamic estimating system as a support structure within a valuation. Asset designation based on after procedures is a financially wise asset designation. Cloud Cost Effectiveness: As the cost of the product falls and the value of the product increases, multi-tenure implications, pay-as-you-go estimation as a cost-effective framework, planning and asset designation: Use characteristics are exploited, and client experience/satisfaction is explicitly considered.
A Cyber Deception Defense Method Based on Signal Game to Deal with Network Intrusion

Increasingly, cyber security personnel are using cyber deception defense techniques to deal with network intrusions. However, traditional cyber deception techniques (such as honeypots and honeynets) are easily detected by attackers, thus leading to failure. Therefore, we propose a cyber deception defense method based on the signal game to improve the effectiveness of the defense. More specifically, first, we propose a moving target defense (MTD) enhanced cyber deception defense mechanism. Then, on the basis of the in-depth analysis of network attack and defense scenarios, a signal game model is constructed to describe the network attack and defense process, and a multistage attack and defense game equilibrium solution method is designed to guide the selection of the optimal deception defense strategy. Meanwhile, considering the actual network attack and defense, we quantify the game revenue based on a probabilistic model. The experimental results show that the defense method proposed in this paper could guide the defender to implement the optimal defense strategy and achieve a better defense effect.
Deep pancreas segmentation through quantification of pancreatic uncertainty on abdominal CT images

Accurate segmentation of the pancreas on abdominal CT images is a prerequisite step for understanding the shape of the pancreas in pancreatic cancer diagnosis, surgery, and treatment planning. However, pancreas segmentation is very challenging due to the characteristics of the pancreas about the high within and between patient variability and the poor contrast with surrounding organs. In addition, the uncertain area arising from variability in the location and morphology of the pancreas will lead to over-segmentation or under-segmentation. Therefore, the purpose of this study is to improve the performance of pancreas segmentation by increasing the level of confidence through multi-scale prediction network (MP-Net) for areas with high uncertainty due to the characteristics of the pancreas. First, the pancreas is localized using U-Net based 2D segmentation networks on the three-orthogonal planes and combined through a majority voting. Second, the localized pancreas is segmented using a 2D MP-Net considering pancreatic uncertainty from multi-scale prediction results. The average F1-score, recall, and precision of the proposed pancreas segmentation method were 78.60%, 78.44%, and 79.72%, respectively. Our deep pancreas segmentation can be used to reduce intra- and inter-patient variations for understanding the shape of the pancreas, which is helpful in diagnosing cancer, surgery, and planning treatment.
The paradox of the virtual Iñárritu's Carne y Arena between innovative spect-actor and traditional fruition

This article analyses the relationships between text and participant in the mixed-reality film/installation Carne y Arena by the Mexican director Alejandro Inarritu (2017), which I experienced in 2018 at Prada Foundation in Milan, Italy. The film is divided into three sections: the first theatrical, the second based on virtual reality (VR) and the third concerning TV. Theoretically, the article draws on Boal's spect-actor and on theories analyzing the position of the audience between the opposite poles of passivity and activity; and on conceptualizations of how the digital media have increased participants' activity. Starting from all of this, the article applies rhetorical narratology analysis to the three parts of Inarritu's film to find out what kind of narrative experience the film/installation offers the participant. The results show that the second part of the installation mostly surprises and excite the participant. In contrast, the materiality of the theatrical part and the informational value of the TV part talk to the participant more completely and concretely. In the end, Boal's spect-actor is more present when the participant deals with old and traditional forms of communication than when they are only apparently immersed in the VR experience. Thus, to bridge its gap, VR needs help from old languages such as theatre and TV. This is the paradox of the virtual mentioned in the title.
Hybrid Deep Learning Models for Mapping Surface No2 Across China: One Complicated Model, Many Simple Models, or Many Complicated Models?

While deep learning is an emerging trend in modeling spatiotemporal dynamics of air pollution, designing a high-performance architecture with various neural network components is difficult. This study aims to identify a suitable deep learning architecture for deriving spatiotemporal distributions of surface NO 2 across China. Based on satellite retrievals and geographic variables, we compared the performance of a series of deep learning models and a benchmark model (i.e., random forest) in predicting daily NO 2 during 2016-2017 on a 0.1° grid. The deep learning models tested were hybrids of full connection layers (FC), autoencoders (AE), residual networks (RES), and convolution layers (CNN). Their ensemble forms were also comparatively evaluated. The integrated results of the sample-, cell-, and date-based holdout tests show that the ensemble FC-AE-RES-CNN model ( R 2 =0.71; RMSE=9.9 μg/m 3 ) outperformed the random forest ( R 2 =0.67; RMSE=10.8 μg/m 3 ), but none of the single-form networks did ( R 2 ranged from 0.56 to 0.61, and RMSE ranged from 11.6 to 12.2 μg/m 3 ). The superior performance of the ensemble FC-AE-RES-CNN model could be attributed to its lower bias and lower variance, which benefited from the sophisticated model structure and the ensemble strategy. Consequently, the ensemble FC-AE-RES-CNN model predictions demonstrated a spatiotemporal distribution of surface NO 2 with higher fidelity. In light of the comprehensive comparisons, we expect that ensemble deep learning models will be substituting for traditional machine learning models in environmental spatiotemporal mapping applications.
Improved Search Algorithm of Digital Speckle Pattern Based on PSO and IC-GN

Digital speckle correlation method has not only been widely used in a variety of photometric mechanical scenarios, but also integrated with multiple disciplines. In the future, it will even be inextricably linked to the Internet of Things, autonomous driving, deep learning and other fields. For a given hardware condition, it is of great significance to improve the efficiency of integer-pixel search and increase the accuracy and efficiency of the sub-pixel algorithm. In this paper, we propose an improved digital speckle correlation method, which consists of an integer-pixel search algorithm and a sub-pixel search algorithm. With respect to the integer-pixel search, aiming to address the two problems of uniqueness of maximum value and parameter setting of PSO-W algorithm, the algorithm PSO-1 is proposed, and the results of comparison experiments show that it has higher search efficiency. In terms of sub-pixels, based on IC-GN algorithm with the highest accuracy at present, the IV-ICGN algorithm is proposed, and the simulation experiment results show that the proposed algorithm has higher accuracy and higher efficiency than the comparison algorithm.
A Critical Insight into Marathi Speech recognition and techniques

Artificial intelligence and processing experts have been fascinated by the recognition of human speech for a long time. The most essential and critical mode of human communication is speech. Several studies on Marathi speech recognition and strategies are discussed. The current speech recognition system for Marathi was discussed in this paper. The subject of signal processing in Speech recognition systems is discussed, with a focus on the most widely used approaches. Speech analysis, feature extraction, modelling and matching techniques are the four core speech recognition procedures.
Market segmentation in the emoji era

A better understanding and use of unstructured data can improve customer loyalty and relationships for most organizations.
ADIM-MVSNet: Adaptive Depth Interval Multi-View Stereo Network for 3D Reconstruction

research-article Share on ADIM-MVSNet: Adaptive Depth Interval Multi-View Stereo Network for 3D Reconstruction Authors: Wei Cheng School of Information Science and Engineering, Yunnan University, China School of Information Science and Engineering, Yunnan University, ChinaView Profile , Zhengyao Bai School of Information Science and Engineering, Yunnan University, China School of Information Science and Engineering, Yunnan University, ChinaView Profile , Junjie Li School of Information Science and Engineering, Yunnan University, China School of Information Science and Engineering, Yunnan University, ChinaView Profile , Huijie Liu School of Information Science and Engineering, Yunnan University, China School of Information Science and Engineering, Yunnan University, ChinaView Profile , Lifang Yang School of Information Science and Engineering, Yunnan University, China School of Information Science and Engineering, Yunnan University, ChinaView Profile Authors Info & Claims ICIGP 2022: 2022 the 5th International Conference on Image and Graphics Processing (ICIGP)January 2022 Pages 281–287https://doi.org/10.1145/3512388.3512429Online:28 March 2022Publication History 0citation40DownloadsMetricsTotal Citations0Total Downloads40Last 12 Months40Last 6 weeks7 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Blockchain Network based Decentralized Applications for Healthcare Sector

Distributed computing is a well-known concept in the contemporary era. Every big technological company/firm runs their applications on servers distributed across the world. It compliments multiple benefits, including yet not limited to rapid response times to clients and better reliability. But, distributed the server may be, or it is still under the control of a centralized entity, i.e., the company spearheading the servers. Decentralized applications, abbreviated as DApps, aims to counteract this drawback. The server-side code of DApps are stored and executed upon a decentralized peer-to-peer network, like an Ethereum blockchain. This way, DApps maintain all the advantages of a distributed computing system while presenting several added benefits.
A spotlight on circular states

Circular Rydberg states provide an ideal resource for large-scale quantum computing and simulation. These circular states can be controlled using coherent optical pulses, providing a route to programmable quantum hardware.
Role of biochar in the removal of organic and inorganic contaminants from wastewater

We all know how the water resources are continuously being deteriorated by the number of organic and inorganic materials like dyes, metals, pesticides and byproducts from factories. Discharge of these pollutants has a negative impact on our ecosystem. To remove these pollutants there are several technologies, but we need a technology that is both economic and efficient. Here comes biochar (carbonized biomass or charcoal formed by the pyrolysis process of biomass) to rescue us from our problem. Biochar is eco-friendly and readily available which adds to its attention at present and in the future.
A Dance Somersault Pose Recognition Model Using Multifeature Fusion Algorithm

With the continuous progress of society, dance art has gradually entered the lives of ordinary people, and people’s appreciation level and artistic attention have gradually improved. How to improve the level of dance art has become an urgent problem to be resolved. In order to solve the problems encountered in dance tumbling pose recognition, this paper proposes a dance tumbling pose recognition model based on multifeature fusion algorithm. According to the characteristics of dance movements, an effective feature extraction method is studied. By combining the key information of bones, the fusion features of the relative position of human joints, the angle of joints, and the ratio of limb length are selected to classify the movements in the dance scene. Through the residual block automatic motion detection method, a multifeature fusion module is used to fuse multiple features, so as to improve the estimation of complex pose by the algorithm and finally complete the dance action identify. The test and analysis results on the data set show that the algorithm can identify the dance somersault pose and can effectively improve the accuracy of dance movement recognition, with strong recognition performance. It is convenient for people to recognize dance tumble posture in the video, thereby realizing the action correction function for dancers.
Fuzzy Risk Assessment of Tunnels’ Undercrossing Existing Structures

Numerical calculation is usually employed to analyse the safety risks of tunnel construction under existing structures. However, due to the fuzziness and randomness of the construction environment, the simulation results fluctuate significantly. Given the fuzziness and randomness encountered during the safety risk assessment of tunnels undercrossing existing structures, this study analyses and summarises the previous experiences and selects a relevant algorithm of the cloud model to solve the uncertainty problem during the assessment. The study proposes three forms of cloud models to evaluate the risks of tunnels undercrossing existing structures and performs a comparative analysis between them. Finally, the effectiveness and feasibility of the cloud model calculation method are verified through a case study. The research shows that (1) the accuracy of the one-dimensional and two-dimensional cloud models was high, and the difference was small, indicating that the cloud model method used to determine the risk level of tunnels underpassing existing structures was reasonably effective, (2) since the soft ‘and’ two dimensions operated computation, the distribution probability was dispersed, making the soft ‘and’ computation more affected by the index, resulting in lower accuracy than the one-dimensional and two-dimensional cloud models, and (3) compared with numerical calculation, the cloud model calculation was more convenient, reflecting the relationship between the indices and the relationship between the indices and the complex whole. The evaluation results used the comprehensive evaluation probability to determine the risk level so that the evaluation results were reliable.
Readability Enhancement in Handwritten English Documents by Eliminating Noisy Characters Through Convolutional Neural Network

There is always a need to convert handwritten documents such as manuscripts, notes, and journals to digital text format for electronic storage, maintenance, and also for editing the textual content using text-processing applications. In addition to that, searching a specific detail in a digital document is far easier when compared to handwritten documents. When stricken-out characters are present in handwritten text, the OCR engine may produce garbage output. Moreover, the stricken-out text are, in general, not intended to be included as part of the final recognized output string. The main objective of this work is to detect and eliminate such stricken-out text, so that the recognized text output is valid. The existing solutions do not seem to be promising when it comes to detecting stricken-out text at character levels.
Nonlinearity of nonbalanced and nearly bent boolean functions based on Galois ring

Designing of nonlinear confusion component of block cipher is one of the most important and inevitable research problem. Nowadays mostly heuristic search schemes were utilized for the construction of these confusion component. To construct, a cryptographically secure confusion component several algebraic structures were utilized. The thirst for new algebraic structure for the construction of these nonlinear confusion component has always been a point of interest. In this communication, we have utilized a maximal cyclic subgroup of unit of Galois ring. The offered algorithm is more general as compared to Galois field. The class of Boolean functions over Galois ring fall in mixed category which are not completely balanced. Boolean functions having higher nonlinearity and others cryptographic aspects added an inevitable significance in the construction of modern block ciphers. The primary idea of this article is to structure non-balanced Boolean functions on n variables, where n is an even integer, sustaining strict avalanche criterion (SAC) and bit independent criterion (BIC). By comparing SAC with available cryptographic Boolean functions, the constructed multivalued Boolean function acquire highest nonlinearity which does not follow the existing nonlinearity bound of Boolean functions. These newly proposed S-boxes consists of n basic Boolean functions which satisfy the balancedness and non-balancedness criterion. Therefore, these S-box structure lies within a less balanced and more bent Boolean function categories.
Accounting information system design using MS Excel A case study at the National Home Furniture Manufacturing Company

Management depends primarily on information when exercising its various functions in managing the organization.Especially because of the rapid changes in which contemporary organizations operate and that require management to obtain accurate and reliable information.Specifically, the accounting information that is supposed to not accept the error, as it relates to the accounting operations that took place in the organization, in addition to that it is related to the financial aspects of profits and losses, which mainly concern the administration.From here, the idea of the study focuses on the importance of having a computerized accounting information system on which the management relies in general, and individuals working in the accounting function in particular in obtaining accounting information by relying on MS Excel software, Therefore, the problem of the study stipulated the possibility of designing a computerized accounting information system in the organization under study, especially in the digital age?Accordingly, the study came in its importance to move towards building a theoretical framework, and another field that reflects the importance of designing a computerized accounting information system, and in a manner commensurate with the nature of the research organization.The study adopted the case study method, and the National Company for Home Furniture Industry in Nineveh Province was chosen as the field of application.The researchers adopted the method of interviews, observing and reviewing some records and documents related to the topic
From Data to Action: Toward a Framework for Collective Personal Informatics in Dementia Care

Individuals increasingly have access to and use a variety of self-tracking technologies in their daily lives, most notably to assist with personal health self-management. As important as self-tracking is for individuals, some argue it should be examined beyond self because it generates data streams and information that impact and are impacted by many others. The collaborative and data-intensive nature of using self-tracking technology for health management motivates this proposed work, which uses a theory-driven approach grounded in personal informatics and collaborative information behavior to empirically examine tracking practices in dementia care. I anticipate that the resulting collective personal informatics framework and design implications will be generalizable and applicable to a broader context of personal health management.
Efficient multiclass classification with duet

In the upcoming era of edge computing, the capability to perform fast training and classification at the edge is an increasing need due to limited connectivity, hardware resources, privacy concerns, profitability, and more.