Reviewerly/exHarmony huggingface dataset:
Comparing Similarity Learning with Taxonomies and One-Mode Projection in Context of the FEATURE-TAK Framework

This paper describes the learning of new similarity values for existing measures within the framework FEATURE-TAK. Maintenance of similarity measures is not easy, especially when having a semi-automated approach to relieve the knowledge engineer. Based on the extension of the vocabulary, the newly added values have to be integrated into the similarity measures with an initial similarity value to be useful. We describe the extension of the similarity measures with automated taxonomy extension and one-mode projections and present a comprehensive evaluation and comparison between the different approaches to highlight the advantages and short comings.
Using session types for reasoning about boundedness in the $$\pi $$-calculus

The $$\pi $$ -calculus is a well-established theoretical framework for describing mobile and parallel computation using name passing, and a central notion here is that of name binding. Unfortunately, non-trivial properties of $$\pi $$ -calculus processes such as termination and bisimilarity are undecidable as a consequence of the fact that the calculus is Turing-powerful. The classes of depth-bounded and name-bounded processes are classes of $$\pi $$ -calculus processes that impose constraints on how name binding is used in a process. A consequence of this is that some of the important decision problems that are undecidable for the full calculus now become decidable. However, membership of these classes of processes is undecidable, so it is difficult to make use of the positive decidability results in practice. In this paper we use binary session types to devise two type systems that give a sound and decidable characterization of each of these two properties. If a process is well-typed in our first system, it is depth-bounded. If a process is well-typed in our second, more restrictive type system, it will also be name-bounded.
Knowledge Graph Provision for Heterogeneous Service Network

Cloud computing and service computing are merging together rapidly. This process brings large challenges to create knowledge graphs for service data. Our aim is to develop a knowledge graph for service analysis, and recommendation to boost API economy. In this paper, knowledge extraction and knowledge discovery are shown for the knowledge graph.
Study on the State and Influencing Factors of Family Art Education for Children Aged 0–3 in China

In art education, families with children aged 0–3 have nonutilitarian objectives, show some tendencies on some art forms in the selection of education content, heavily rely on family-in activity, emphasize on emotional attitude and learning quality in evaluation process, arrange time and schedule reasonably among more than 80% families, and give children abundant emotional support. However, some families go with crowds when developing art education objectives. Most families don’t involve in some art forms. The use of public education resources is less in the organization of art education. Nearly 20% of families do not provide art education activities to infants and young children. Environmental support is not sufficient for young kids in the family. Parental education, family monthly income, and family location significantly affect the overall state, evaluation, and environment of art education for children aged 0–3. The overall state of art education is also affected by the father and mother’s occupation. Education evaluation is also influenced by the father’s occupation. The education environment is also affected by the father’s and mother’s occupation, and the number of kids in one family. The content of education is significantly influenced by the location of the family and the gender of the child. Education time and schedule are significantly affected by parental education and the location of the family.
Optimization driven actor-critic neural network for sentiment analysis in social media

Purpose Sentiment analysis is the subfield of data mining, which is profusely used for studying the opinions of the users by analyzing their suggestions on the Web platform. It plays an important role in the daily decision-making process, and every decision has a great impact on daily life. Various techniques including machine learning algorithms have been proposed for sentiment analysis, but still, they are inefficient for extracting the sentiment features from the given text. Although the improvement in sentiment analysis approaches, there are several problems, which make the analysis inefficient and inaccurate. This paper aims to develop the sentiment analysis scheme on movie reviews by proposing a novel classifier. Design/methodology/approach For the analysis, the movie reviews are collected and subjected to pre-processing. From the pre-processed review, a total of nine sentiment related features are extracted and provided to the proposed exponential-salp swarm algorithm based actor-critic neural network (ESSA-ACNN) classifier for the sentiment classification. The ESSA algorithm is developed by integrating the exponentially weighted moving average (EWMA) and SSA for selecting the optimal weight of ACNN. Finally, the proposed classifier classifies the reviews into positive or negative class. Findings The performance of the ESSA-ACNN classifier is analyzed by considering the reviews present in the movie review database. From, the simulation results, it is evident that the proposed ESSA-ACNN classifier has improved performance than the existing works by having the performance of 0.7417, 0.8807 and 0.8119, for sensitivity, specificity and accuracy, respectively. Originality/value The proposed classifier can be applicable for real-world problems, such as business, political activities and so on.
Privacy Protection Oriented Video Data Hiding Method

With the advent of the era of big data, enterprise big data, government big data, and network users big data have played great value in various industries. But at the same time, any behavior of people may be tracked and recorded. People's demand for privacy protection is also growing. In this paper, a HEVC reversible video data hiding algorithm combined with encryption technology is proposed for video big data to realize privacy protection. A monoalphabetic substitution cipher algorithm is used for encryption, then the encrypt data is embedded into the proposed multivariate array of the 4 × 4 luminance DST blocks to avert the distortion drift. With the inverse operation of multivariate array in decoder, the embedded video is perfectly reconstructed as the original encoded video. It is proved analytically and shown experimentally that the proposed algorithm can achieve high embedding capacity, security and low visual distortion. Performance comparisons with other existing schemes are provided to demonstrate the superiority of the proposed scheme.
RECOGNIZING HANDWRITTEN MATHEMATICAL EXPRESSIONS

This paper surveys the techniques to recognise handwritten mathematical characters in different expressions and to see the availability of systems that eventually understand and solve them.Humans are accustomed to writing mathematical expressions containing integrals, fractions, exponents or indices by hand.Entering each such expression into a computer is uncomfortable and tedious.Inspired by recent success in Neural Network Modelling, we discuss a proposed model that gives high accuracy in recognizing mathematical symbols and digits in different mathematical expressions using Neural Network Modelling for identification of offline handwritten expressions.
Sub-Linear Time Aggregation in Probabilistic Population Protocol Model

A passively mobile system is an abstract notion of mobile ad-hoc networks. It is a collection of agents with computing devices. Agents move in a region, but the algorithm cannot control their physical behavior (i.e., how they move). The population protocol model is one of the promising models in which the computation proceeds by the pairwise communication between two agents. The communicating agents update their states by a specified transition function (algorithm). In this paper, we consider a general form of the aggregation problem with a base station. The base station is a special agent having the computational power more powerful than others. In the aggregation problem, the base station has to sum up for inputs distributed to other agents. We propose an algorithm that solves the aggregation problem in sub-linear parallel time using a relatively small number of states per agent. More precisely, our algorithm solves the aggregation problem with input domain X in O(√n log2 n) parallel time and O(|X|2) states per agent (except for the base station) with high probability.
An Adaptive Iteration Algorithm Used for Speckle Reduction Diffusion in Ultrasound Imaging

In this paper, we propose an adaptive iteration algorithm used for speckle reduction diffusion. In order to reduce the iteration times and improve the result image, we propose to use relative peak signal to noise ratio as a criterion of step changing during iteration. To evaluate the performance of the proposed algorithm, we use synthetically introduced speckle images for speckle suppression. The simulation results show that our method not only preserve image details effectively while filtering, but also reduce the iteration times.
Visual Analysis Scenarios for Oceanographic Buoy Data

The analysis of oceanographic data is a difficult task due to the large volume and multidimensionality of the data. Often is it necessary to aggregate other datasets to establish relationships and more complex analysis in order to answer environmental questions. Thus, this work aims to present different information visualization techniques (InfoVis) for oceanographic data concerning the most frequently used techniques, such as 2D and 3D Charts, Surface Map, Choropleth Map, and Line Map. For this purpose, a dataset of oceanographic buoys from the National Oceanic and Atmospheric Administration (NOAA) covering a period of 02 years (from 2015 to 2016) was used. This set of data was treated and classified according to the seasonal periods (winter, spring, summer, and autumn). The Rose Wind technique revealed that the wind speed average was between 10-12 m/s. The Circle Parking technique showed a predominance of wind and waves from NE. In 2015, highest waves were recorded, mainly during the winter. The obtained results can be considered a useful tool for the assessment of the wind and waves relationship and help to coastal management strategies.
Cyber-physical Control System of a Hybrid Power Plant

A cyber-physical system has been designed to diagnose, monitor and control a hybrid power plant. Algorithms for the functioning of the system hardware and human–machine interface for mobile devices based on Android have been developed. The system has been tested to verify the adequacy and quality of software and hardware.
Review dan Analisa Faktor-Faktor Yang Mempengaruhi Kecepatan Akses Halaman Website

The change in the behavior of internet users from using computers or laptops to mobile internet users makes changes in the way the browser and also the web pages display information. Internet users generally want a quick access time when visiting a website page to get the desired information. In the research conducted in the writing of this journal, the researchers wanted to show and explain the several important factors that&#x0D; influence the speed of access from a website page, as well as analyzing based on technical factors. Where the main discussion in this study will focus more on the evaluation of technical factors starting from the programming side (server side programming and client side programming) and also the design of the user interface using web pages using minify CSS along with the use of AJAX technology. The results to be achieved from this study are to identify how much influence the technical factors mentioned above have on the speed of visitor access to a web page, apart from other technical factors such as internet network speed, devices and areas where users can access website page.
Modeling Human Perception for Image Aesthetic Assessme

Deep convolution network (CNN) has shown promising progress in image aesthetic assessment. However, the properties of the network have become the bottleneck in this task. The parameter-sharing mechanism in CNN does not consider the difference between foreground and background, which is crucial for human to judge images and decide their aesthetic scores. In this paper, we proposed a Visual Perception Networks (VP-Net) to support learning from attractive region representation. We find out attractive region by introducing an unsupervised region search algorithm and conduct adaptive aggregation. Experimental results on a large scale aesthetic dataset (AVA) demonstrate the effectiveness of our approach.
Data Science Institutes and Data Centers

Optimum ways of addressing large data volumes across a variety of disciplines have led to the formation of national and institutional Data Science Institutes and Centers. The objectives and functions of such institutes and centers are summarized. In reflecting the driver of national priority, they are able to attract academic support within their institutions to bring together interdisciplinary expertise to address a wide variety of datasets from disciplines such as astronomy, bioinformatics, engineering, science, medicine, social science, and the humanities. All are generating increasing volumes of data, often in real time, and require efficient and effective solutions. The opportunities and challenges of data science are presented. The processes of knowledge discovery in data science often require new methods and software, new organizational arrangements, and new skills in order to be effective. Data science centers and institutes provide a focus for the development and implementation of such new structures and arrangements for the development of appropriate facilities, with academic leadership and professional support. These are summarized and reviewed.
Crowd-Sourced Collection of Task-Oriented Human-Human Dialogues in a Multi-domain Scenario

There is a lack of high-quality corpora for the purposes of training task-oriented, end-to-end dialogue systems. This paper describes a dialogue collection process which used crowd-sourcing and a Wizard-of-Oz set-up to collect written human-human dialogues for a task-oriented, multi-domain scenario. The context is a tourism agency, where users try to select the more desired hotel, restaurant, museum or shop. To respond to users, wizards were assisted by an exploratory system supporting Preference-enriched Faceted Search. An important aspect was the translation of user intent to a number of actions (hard or soft-constraints) by wizards. The main goal was to collect dialogues as realistic as possible between a user and an operator, suitable for training end-to-end dialogue systems. This work describes the experiences made, the options and the decisions taken to minimize the human effort and budget, along with the tools used and developed, and describes in detail the resulting dialogue collection.
PDH : Probabilistic Deep Hashing Based on Map Estimation of Hamming Distance

With the growth of image on the web, research on hashing which enables high-speed image retrieval has been actively studied. In recent years, various hashing methods based on deep neural networks have been proposed and achieved higher precision than the other hashing methods. In these methods, multiple losses for hash codes and the parameters of neural networks are defined. They generate hash codes that minimize the weighted sum of the losses. Therefore, an expert has to tune the weights for the losses heuristically, and the probabilistic optimality of the loss function cannot be explained. In order to generate explainable hash codes without weight tuning, we theoretically derive a single loss function with no hyperparameters for the hash code from the probability distribution of the images. By generating hash codes that minimize this loss function, highly accurate image retrieval with probabilistic optimality is performed. We evaluate the performance of hashing using MNIST, CIFAR-10, SVHN and show that the proposed method outperforms the state-of-the-art hashing methods.
Online Education and Quality of Life: Universidad de Palermo as a Model of Innovation in Latin America

We are living in a time full of challenges as the Fourth Industrial Revolution unfolds at an unprecedented pace, in an interconnected world which has changed the way we work, the way we communicate, and education can’t be an exception. The boundaries between the physical and digital worlds are becoming more and more blurred, and the convergence of online and face-to-face interaction is becoming ever more evident, with machine learning and artificial intelligence applied to everyday life. Technology is changing the world, and universities must not only adapt to this reality but also innovate and take advantage of it, in order to offer more and better quality education to an increasingly diverse audience, furthering and spreading knowledge. Online education means conducting a course (or more) partially or entirely through the internet. It is also referred to as eLearning (electronic learning). It is a form of distance education (Ko S, Rossen S, Teaching online. A practical guide. Routledge, Chicago, 2008). Students that opt for online learning state that it enhances their quality of life. This is not only the case of students participating in distance learning, but also of those taking blended courses (the majority of students). Many combine on campus and online studies based on their professional and personal schedules. They acquire different abilities in each of these modalities and achieve a better balance among their multiple responsibilities (academic, family, professional and social commitments). This chapter aims to discuss three important issues: the historical context of the education paradigm shift in which this experience is embedded, the educational model for online courses and degree programs at Universidad de Palermo, and a proposal to teach quality of life within the framework of this pedagogical model.
Basics of Cloud Management

Having mastered the technological, migration, and monitoring aspects of cloud computing the reader is now in a position to look at how the cloud computing environment is managed. The objective of this chapter is to introduce the basics of cloud managementBasics of cloud management . By the end of the chapter the reader should have the necessary insights into cloud management functionalities and how they are managed by Cloud Management Platform (CMP) tools.
Architectural Approach to Ontological Maintenance of Solving the Project Tasks in Conceptual Designing a Software Intensive System

The paper focuses on developing a complex means of ontological maintenance (OM) in designing the software intensive systems (SIS). The essential feature of an offered version of the OM is accessible to such means via interfaces based on architectural views, any of which is realized in the form of the block-and-line scheme reflecting the states of possible applying the OM. Thus, any of such views is applied for developing the OM and its using via the certain interface presented in the understandable form. The prototype version of the OM was developed in the toolkit WIQA (Working In Questions and Answers), which provides the solution of design tasks at the conceptual stage of the development of the SIS. In the described case, this toolkit is applied for solving the task of developing the OM as the complex embedded to the WIQA. In its turn, the complex is intended for the ontological maintenance of solving the project task in conceptual designing of any SIS.
Contemplation of Cloud Services Quality by exerting Coadunate-Coalesce Cryptography and Yajat method

Cloud computing is network consisting of configurable multifarious resources and predominant-level services which can be rapidly exerted with minimal management effort over the Internet. Cloud computing endows lot of services and consumers avail services from cloud agilely bestowed through internet. Some consumers without any knowledge about quality peculiarities, consumers may merely avail cloud service and feel unhappy, disenchant by using cloud service. As it is extremely imperative for consumers to recognize about cloud service quality before availing the service, this paper bestows about contemplation of quality for the services existing in cloud. This paper induces Yajat method for cogitation about cloud services quality to forthcoming patrons. In Yajat method quality ranking fixin based on different quality peculiarities is calculated for every service in cloud. Security quandary will occur while congregating quality assessment testimony by deeming multifarious quality peculiarities from consumers for Yajat method. To solve security quandary coadunate cryptography and Coalesce cryptography method is induced in this paper. Firstly by exerting Coadunate and coalesce cryptography, quality assessment testimony about cloud is collected securely and bestowed to Yajat method as input. Secondly by availing proctors in Yajat method, quality ranking fixin are calculated and bestowed to forthcoming consumers. The output of yajat method is bestowed to forthcoming consumers by proctors of cloud. Thus this paper intends forthcoming consumers by scrutinizing the output of yajat method, effortlessly choose to avail cloud service with the quality they aspire and feel elated, blissful by using cloud service.
Dataset Weighting via Intrinsic Data Characteristics for Pairwise Statistical Comparisons in Classification

In supervised learning, some data characteristics (e.g. presence of errors, overlapping degree, etc.) may negatively influence classifier performance. Many methods are designed to overcome the undesirable effects of the aforementioned issues. When comparing one of those techniques with existing ones, a proper selection of datasets must be made, based on how well each dataset reflects the characteristic being specifically addressed by the proposed algorithm. In this setting, statistical tests are necessary to check the significance of the differences found in the comparison of different methods. Wilcoxon's signed-ranks test is one of the most well-known statistical tests for pairwise comparisons between classifiers. However, it gives the same importance to every dataset, disregarding how representative each of them is in relation to the concrete issue addressed by the methods compared. This research proposes a hybrid approach which combines techniques of measurement for data characterization with statistical tests for decision making in data mining. Thus, each dataset is weighted according to its representativeness of the property of interest before using Wilcoxon's test. Our proposal has been successfully compared with the standard Wilcoxon's test in two scenarios related to the noisy data problem. As a result, this approach stands out properties of the algorithms easier, which may otherwise remain hidden if data characteristics are not considered in the comparison.