Reviewerly/exHarmony huggingface dataset:
Assessment of Speckle Pattern for Use in Digital Image Correlation Analysis Using Simulated Displacement Field

Local stresses and strains near the crack tip control fatigue crack growth process. Calculation of elastic–plastic strains and stresses at the crack tip requires solving the nonlinear boundary value problem of a cracked body. Analytical solutions of such problems for varied structural geometry are rarely attainable. A popular method of structural integrity assessment involves stress field characterization ahead of the crack tip. Stress Intensity FactorStress intensity factor (SIF) calculation using full-field displacement from digital image correlationDigital image correlation (DIC) results is a popular experimental method. Reliable estimation of displacement field from successive images of a specimen under deformation relies largely on intensity profile and its gradient on the specimen, known as speckle patternsSpeckle pattern . Application of DIC in integrity assessment of structural components of infrastructure and power plant systems should begin with speckle pattern characterization. Speckle patternSpeckle pattern characterization can be carried out using two images without any motion or images with rigid-body motions (rigid-body translation and rotation) or images obtained using constant strain tests. The goal of speckle pattern characterization is to quantitatively compare the imposed motion with the recovered motion from DIC and estimation of minimum displacement resolution. Although characterizing speckle pattern using the methods suggested above is relatively straightforward, reproducing representative speckles for multiple cases is a challenge. To overcome this challenge, a speckle pattern assessment procedure, which uses simulated displacement field, is proposed in the present study. A rigid transformation and constant strain condition are simulated and the effectiveness of speckle patternSpeckle pattern in capturing simulated displacement from DIC analysis is observed. Error distribution in the estimation of displacement is plotted. For quantitative evaluation of error in length, image calibration is also carried out.
Detecting Global Periodic Correlated Clusters in Event Series based on Parameter Space Transform

Periodicities are omnipresent: In nature in the cycles of predator and prey populations, reoccurring patterns regarding our power consumption over the days, or the presence of flu diseases over the year. With regards to the importance of periodicities we ask: Is there a way to detect periodic correlated clusters which are hidden in event series? We propose as a work in progress a method for detecting sinusoidal periodic correlated clusters on event series which relies on parameter space transformation. Our contributions are: Providing the first non-linear correlation clustering algorithm for detecting periodic correlated clusters. Further our method provides an explicit model giving domain experts information on parameters such as amplitude, frequency, phase-shift and vertical-shift of the detected clusters. Beyond that we approach the issue of determining an adequate frequency and phase-shift of the detected correlations given a frequency and phase-shift boundary.
Research on multi-dimensional target time series prediction model for power grid infrastructure projects

Abstract In order to adapt to the changes in the external power industry environment, prevent external audit supervision risks, and deepen the internal lean management level, we take the investment statistics of power grid infrastructure projects as the entry point, scientifically regulates investment statistics, and focuses on solving the distortion of investment statistics and the problem of the department not cooperating. By constructing the multi-dimensional target timing sequence prediction curve of investment statistics, the paper provides reference for the reported value of investment statistics, improves the authenticity and reliability of investment statistics, and at the same time, deepens the investment statistics management, providing solid support for the lean management and control target of power grid companies.
Multimedia Service Data Preprocessing and Feature Extraction

In this chapter, we focus on the multimedia service data preprocessing and feature extraction, which are core steps in the multimedia QoE evaluation. Specifically, multimedia service data collection and preprocessing is firstly introduced. We briefly describe three representative datasets: (1) IPTV service dataset collected by operator; (2) OTT service dataset collected from students in Nanjing University of Posts and Telecommunications; (3) dataset crawled across the web. Then, we highlight our works about feature extractions for user-related IFs, containing five aspects: viewing time ratio calculation, user interest inference, user type classification, user behavior analysis, and user emotion parsing from their comments in danmaku.
Object contour based on improving region growing method and context border condition

Summary Image diagnosis is a very significant medical specialty. Thereby, the doctor will see the form and function of the structure inside the body. The recorded images will be an important basis to help doctors find the disease and treatment more effectively. However, the quality of medical images is a vital factor. Improving the quality of medical images and object contour are the major challenges, because each tiny detail of medical images can contain much information. Object contour in good medical image quality is hard work and it is more difficult in low‐quality medical image. The organs in the image are not clear, which is difficult for the doctor to distinguish the border of the organs in the image. In this paper, we proposed a method for object contour based on improving region growing in bandelet domain in low‐quality medical images. The method includes two stages: increasing the quality of medical image in bandelet domain and object contour by improving region growing combined with context border condition in medical images in the first stage. The result of the proposed method is better than the other methods.
Reform and Innovation of College English Teaching Mode Based on “Internet+”

“Internet+” has become a new form in the field of higher education, therefore, the reform of college English teaching has a new direction and a reliable carrier. With the advantage of “Internet+”, college English teaching can actively construct new teaching mechanism and talent training mechanism to improve the quality of college English teaching. Based on the author’s study and practical experience, this paper first analyzed the problems in college English teaching under the background of the “Internet+”, and then put forward the innovative strategies of the college English teaching model based on “Internet+”.
Developing evacuation scenario for ecotourism based on hazard assesment in borobudur area

Abstract Borobudur area is identical to the existence of Borobudur Temple which is an international tourist destination, besides that this area actually also has other potential to be developed as a tourist destination, especially tourism which elevates geological and geomorphological uniqueness in the Borobudur Purba Lake region. Based on previous studies identified objects that can be recommended as ecotourism include Springs around Pawon Temple, Former Swamp between Bumisegoro Village and Sabrangrawa Village, Saltwater Eyes in Kaliduren Village, and Elo and Progo River Meetings, some of these objects are then packaged through design spatial planning in order to become an ecotourism that can attract tourists. In addition, on the other hand there is the potential for ecotourism, there is a disaster threat in the area, there are variations in disasters in the ecotourism area, namely landslides and floods, therefore the purpose of this study is 1) Identifying the types of disasters in the Borobudur Ecotourism area 2) Creating a SOP design for disaster management in the Borobudur Ecotourism area, to answer the objectives of the study, this study uses descriptive methods with GIS analysis with buffering methods. From this study produced a SOP for disaster management, for the first object, namely The spring around the Pawon Temple is recommended for the first evacuation route to the wide field in front of Candi Pawon, for the third object, namely the Salty Springs in the Kaliduren Village in handling disasters in this area, it is recommended that the first evacuation route to the bridge above the Springs object be located much higher than the disaster-prone area, for the next object, the Salty Well in Ngentak Village, it is recommended that the first evacuation route go to an open field around the object area, then for the last object, namely the meeting of the Elo and Progo rivers, it is recommended that the first evacuation route to be crossed is towards the upper cliff which is on the courtyard of the residents’ house near the object. The SOP for each object has been determined by the network analysis method. Therefore, the most effective pathway for the evacuation maintenance in Borobudur Purba area is obtained.
Changing Data Policies in China: Implications for Enabling FAIR Data

As fundamental resources of research activities, data is vitally important for scientific progress and general social society. Thus, open data practices are becoming more prevalent and the adoption of the FAIR (Findable, Accessible, Interoperable, and Reusable) principles is fostering open access data from four general perspectives. This paper firstly analyzes the general benefits and necessities of open data and the FAIR principles. Then, data policies in China are described from four views. Subsequently, challenges and opportunities for data usability across disciplinary boundaries and levels of expertise are described. Finally, categories are presented for how data policies in China enable FAIR data in terms of the four views of Chinese data policies. Above all, FAIR data is a good beginning, and for FAIR open data, we still need more efforts on intrinsic data culture, trustworthiness, sustainability, and multilateral cooperation among various stakeholders, as well as consistent and effective approaches for adopting data policies.
Adaptive Lossy Image Compression Based on Singular Value Decomposition

Image compression techniques aim to reduce redundant information in order to allow data storage and transmission in an efficient way.In this work, we propose and analyze a lossy image compression method based on the singular value decomposition using an optimal choice of eigenvalues and an adaptive mechanism for block partitioning.Experiments are conducted on several images to demonstrate the effectiveness of the proposed compression method in comparison with the direct application of the singular value decomposition.
Minimizing Bias in Estimation of Mutual Information from Data Streams

Mutual information is a measure for both linear and non-linear associations between variables. There exist several estimators of mutual information for static data. In the dynamic case, one needs to apply these estimators to samples of points from data streams. The sampling should be such that more detailed information on the recent past is available. We formulate a list of natural requirements an estimator of mutual information on data streams should fulfill, and we propose two approaches which do meet all of them. Finally, we compare our algorithms to an existing method both theoretically and experimentally. Our findings include that our approaches are faster and have lower bias and better memory complexity.
Values of rational functions in small subgroups of finite fields and the identity testing problem from powers

Motivated by some algorithmic problems, we give lower bounds on the size of the multiplicative groups containing rational function images of low-dimensional affine subspaces of a finite field [Formula: see text] considered as a linear space over a subfield [Formula: see text]. We apply this to the recently introduced algorithmic problem of identity testing of “hidden” polynomials [Formula: see text] and [Formula: see text] over a high degree extension of a finite field, given oracle access to [Formula: see text] and [Formula: see text].
Updating consensus in case of adding or removing new elements to the profile

Data integration is one of the trending topics in the modern computer science. It is not an uncommon task to deliver a unified perspective on a set of heterogenous data that would serve as a consensus of participating elements. Many computationally expensive solutions can be found in the literature . Moreover, one cannot determine how potential changes applied to inputs of these methods impact their results. In this paper we present a framework of managing evolving data and handling the entailments of the unforeseen alterations of inputs in terms of performing sound data integration in an acceptable time. We base our work on the consensus theory and provide theoretical foundations, an experimental evaluation and a statistical analysis of obtained results.
Succinct Representations of Finite Groups

The Cayley table representation of a group uses $$O(n^2)$$ words for a group of order n and answers multiplication queries in time O(1) in word RAM model. It is interesting to ask if there is a $$o(n^2)$$ space representation of groups that still has O(1) query-time. We show that for any $$\delta $$, $$\frac{1}{\log n} \le \delta \le 1$$, there is an $$\mathcal {O}(\frac{n^{1 +\delta }}{\delta })$$ space representation for groups of order n with $$\mathcal {O}(\frac{1}{\delta })$$ query-time. We also show that for Dedekind groups, simple groups and several group classes defined in terms of semidirect product, there are linear space representation to answer multiplication queries in logarithmic time. Farzan and Munro (ISSAC'06) defined a model for group representation and gave a succinct data structure for abelian groups with constant query-time. They asked if their result can be extended to categorically larger group classes. We show we can construct data structures in their model to represent Hamiltonian groups and extensions of abelian groups by cyclic groups to answer multiplication queries in constant time.
Polymedia Design for Network Metadata Monitoring

The design of a realtime monitor for an organization’s digital network can produce several significant design challenges, both from the technical and human operational perspectives. One challenge is how to capture network data with minimal impact on the network itself. Also, from an operational perspective, sounds need to perform en suite over long periods of time while producing only minimal listener fatigue. This chapter describes two related network data sonification projects which resulted in a set of audiovisual “concert” compositions (Corpo Real), an immersive installation, and a perceptual monitoring tool (Netson). This tool uses both sonification and visualization to present monitoring humans with features of data flow that allow them to experience selectable operational network characteristics. In doing so, it can be used to assist in the peripheral monitoring of a network for improved operational performance.
Evaluating Deep Learning Feature Towards Ranking of Immunocytochemistry Images

The analysis (figuring out that how well antibodies are linked to the Nucleus of interest) of Immunocytochemistry (ICC) images is mostly done manually. In this paper, we propose an automatic classification process to rank the images according to adherence of antibodies to the nucleus. To have a robust computerized diagnosis system, it is necessary to design a classifier with meaningful features. In this work, we evaluate two feature extractions based on deep neural network and engineered feature extraction. We proposed the first machining learning approach for ranking of Immunocytochemistry (ICC) images, while some of the automation works have been proposed for Immunohistochemistry (IHC) images. We tested our method on 300 microscopic images collected by ourselves and reached a reasonable classification ranking accuracy of 59.4% using all features.
A Representation Theorem for Change through Composition of Activities

The expanding use of information systems in industrial and commercial settings has increased the need for interoperation between software systems. In particular, many social, industrial, and business information systems require a common basis for a seamless exchange of complex process information. This is, however, inhibited, because different systems may use distinct terminologies or assume different meanings for the same terms. A common solution to this problem is to develop logical theories that act as an intermediate language between different parties. In this article, we characterize a class of activities that can act as intermediate languages between different parties in those cases. We show that for each domain with finite number of elements there exists a class of activities, we called canonical activities, such that all possible changes within the domain can be represented as a sequence of occurrences of those activities. We use an algebraic structure for representing change and characterizing canonical activities, which enables us to abstract away domain-dependent properties of processes and activities, and demonstrate general properties of formalisms required for semantic integration of dynamic information systems.
Temporal feature analysis in brain-inspired neural systems

The brain identifies potentially salient features within continuous information streams, but the underlying mechanisms are poorly understood. I will show two biologically inspired neural network models that perform such analyses. The seemingly different models suggest a common principle, which we term self-consistent mismatch detection, for temporal feature analyses. A network of two-compartment neuron model implementing this principle conducts a surprisingly wide variety of temporal feature analysis. The model is also potentially useful in neural engineering.
Research on Overseas Communication Mechanism of Moyan Novels Based on Network Cloud Computing

Platform services in cloud computing services can provide a more reasonable platform for cloud computing in the application process. The IT resources and infrastructure needed during the operation are provided to users as a service. In this service, users only need to use this platform to test, create and manage their own services. The virtual computer laboratory can not only improve the phenomenon of uneven resource allocation, but also improve the utilization of system resources. All the things described in Mo Yan’s works and the colorful artistic descriptions presented in his works, as well as the real China displayed to the greatest extent, contain the unique outlook on life, history and life of the Chinese people. It is for this reason that foreign translators and scholars are attracted to carry out translation publicity. Nowadays, as one of the most influential Chinese writers in the world, the development of the Internet has promoted the overseas promotion of Mo Yan’s works.
A 2D Observation Model-Based Algorithm for Blind Single Image Super-Resolution Reconstruction

In essence, image super-resolution refers to the transformation from small size image to large size image, that is, the increase of pixel density of image can provide more detailed information. It's well-known that 1D super-resolution model can not be written directly into the form of 2D model, because the matrix dimension of high-solution image and low-solution image does not agree. The proposed 2D-based blind super-resolution algorithm combining with sparse representation model and TV term. The proposed method is to reduce the complexity of the operation by decomposing the blur matrix and the sampling matrix in the horizontal (row) and vertical (column) directions. The experimental results show that the proposed method can better protect the edge and provide more texture structure.
On systematic hyperparameter analysis through the example of subspace clustering

In publications where a clustering method is described, the chosen hyperparameters are in many cases to our current observation empirically determined. In this work in progress we discuss and propose one approach on how hyperparameters can be systematically explored and their effects regarding the data set analyzed. We further introduce in the context of hyperparameter analysis a modified definition of the resilience term, which refers here to a subset of data points which persists to be in the same cluster over different hyperparameter settings. In order to analyze relations among different hyperparameters we further introduce the concept of dynamic intersection computing.
Spectrum Analysis of Filtering Technologies in Management Networks and Wireless Systems

Filtering technology has been widely applied in signal processing and object tracking for decades, and most recently it has become a useful instrument for time series analysis. In this article, we introduce several popular technologies developed for analysis of the dynamic state space model, including Kalman and particle filtering, Markov chain Monte Carlo algorithms, as well as the sequential Bayesian learning method. Their applications in fields of interest are also discussed. Filtering technologies have great superiority in solving the problems arising from management and communications, making them deserving of further exploration.
Machine Perception MU—Picture Classes

Perceptual abilities enable us to go beyond the data that are in the image, since we can achieve reliable identification from a small subset of the predicted correspondences and then use our knowledge to infer many properties of the scene that may not be directly supported by visual data or solve other complex perceptual problems. Without constraining influence of prior expectations, many perceptual problems would be under constrained to the extent that they could never be solved. This emphasis on world knowledge parallels developments in most other areas of machine perception, in which large amounts of problem-specific knowledge are increasingly being used both to constrain solutions and to speed the process of obtaining the solution. In machine perception MU, the knowledge that is needed during solving perceptual problems is supplied by the contextual information that is coded in the categorical structure of the shape classes, the 3D object classes, the picture classes, and the perceptual and ontological categories of visual objects.
A Subjective Trust Management System in Multi-Cloud Environment

Managing trust in cloud environment is the biggest obstacles in its adoption and increase in popularity among the clients. Since the cloud service users have to share all their confidential data with the cloud service providers, hence, it is obvious that they will choose service providers that are reliable. This article describes a solution for the problem of evaluating an unbiased subjective trust. We have described a method that divides the users chosen for feedback into two groups. These groups are interviewed by the trust service providers. These are the people who provide the trustworthiness and reliability of the cloud service providers. These trust ratings are provided based on two things. First the feedback from different cloud service users. These users are divided into two groups. Firstly, the users who have used the service in their past. And second, the users who are currently using the services. Next measure is how well that cloud service provider adheres to the service level agreement.