Reviewerly/exHarmony huggingface dataset:
DyRAC: Cost-aware Resource Assignment and Provider Selection for Dynamic Cloud Workloads

A primary concern for cloud users is how to minimize the total cost of ownership of cloud services. This is not trivial to achieve due to workload dynamics. Users need to select the number, size, type of VMs, and the provider to host their services based on available offerings. To avoid the complexity of re-configuring a cloud service, related work commonly approaches cost minimization as a packing problem that minimizes the resources allocated to services. However, this approach does not consider two problem dimensions that can further reduce cost: (1) provider selection and (2) VM sizing. In this paper, we explore a more direct approach to cost minimization by adjusting the type, number, size of VM instances, and the provider of a cloud service (i.e. a service deployment) at runtime. Our goal is to identify the limits in service cost reduction by online re-deployment of cloud services. For this purpose, we design DyRAC, an adaptive resource assignment mechanism for cloud services that, given the resource demands of a cloud service, estimates the most cost-efficient deployment. Our evaluation implements four different resource assignment policies to provide insight into how our approach works, using VM configurations of actual offerings from main providers (AWS, GCP, Azure). Our experiments show that DyRAC reduces cost by up to 33% compared to typical strategies.
Content Based Medical Image Retrieval Using Multilevel Hybrid Clustering Segmentation with Feed Forward Neural Network

Content-Based Image Retrieval (CBIR) is another yet broadly recognized method for distinguishing images from monstrous and unannotated image databases. With the improvement of network and mixed media headways ending up being increasingly famous, customers are not content with the regular information retrieval progresses. So nowadays, Content-Based Image Retrieval (CBIR) is the perfect and fast recovery source. Lately, various strategies have been created to improve CBIR execution. Data clustering is an overlooked method of hiding formatting extraction from large data blocks. With large data sets, there is a possibility of high dimensionality Models are a challenging domain with both massive numerical accuracy and efficiency for multidimensional data sets. The calibration and rich information dataset contain the problem of recovery and handling of medical images. Every day, more medical images were converted to digital format. Therefore, this work has applied these data to manage and file a novel approach, the “Clustering (MHC) Approach Using Content-Based Medical Image Retrieval Hybrid.” This work is implemented as four levels. With each level, the effectiveness of job retention is improved. Compared to some of the existing works that are being done in the analysis of this work’s literature, the results of this work are compared. The classification and learning features are used to retrieve medical images in a database. The proposed recovery system performs better than the traditional approach; with precision, recall, F -measure, and accuracy of proposed method are 97.29%, 95.023%, 4.36%, and 98.55% respectively. The recommended approach is most appropriate for recuperating clinical images for various parts of the body.
Optimal guessing in ‘Guess Who’

Are you Richard? Are you Anne? We look at the strategic problem in the children’s guessing game Guess Who , which is a form of zero-sum symmetric game with perfect information. We discuss some preliminary strategic insights and formally derive an optimal strategy and win-probabilities for the game. We discuss the first-mover advantage in the game and other strategic aspects coming out of the optimal strategy. While the paper is based on the popular children’s game, our analysis generalises the actual game by allowing any initial game state with an arbitrarily large number of starting characters. With the aid of these mathematical results you can now comprehensively thrash your young children and be a terrible parent!
Design and Implementation of Chinese Journal Data Analysis System

Journal industry is developing rapidly and the amount of journal paper data continues to increase. Journals have become an important way for researchers to publish research results and consult literature. As far as the platform of China National Knowledge Infrastructure and database exchange service center is concerned, there are nearly 1800 kinds of core journals and more than 5000 papers are updated every day. How to conduct research and analysis on such huge journal paper data? In this paper, a Chinese Journal Data Analysis System is designed by using machine learning algorithm, apriori algorithm and time-series keyword co-occurrence method. This system is to perform statistical analysis, text similarity analysis, hot spot analysis and hot spot prediction on the abstract, authors, keywords, funds, publication date and, etc. of the China National Knowledge Infrastructure's chinese core journal paper data. The experiments prove that the analysis system has high accuracy and good stability. Meanwhile, the design and implementation of the system provide guidance for the development of data analysis systems.
Four‐dimensional collision detection and behaviour based on the physics‐based calculation

Abstract In the field of 4‐D space visualization, the information of 4‐D space is obtained by projecting 4‐D data onto 3‐D space. Most of the previous research has been aimed at the recognition of 4‐D space, whereas the target of the recognition has been limited to the geometrical information of 4‐D objects in 4‐D space or static spatial information without dynamics. Our research aims to develop a visualization system for providing the human experience of the physics‐based environment in 4‐D space. In this research, we mainly focus on collision detection and the behaviour of 4‐D objects in 4‐D space in order to construct the physics‐based environment of 4‐D space. Our contribution in this paper is the development of a collision detection algorithm for 4‐D objects and a calculation method for physics based behaviour of 4‐D objects. Our proposed collision detection algorithm is based on the intersection test of tetrahedrons in 4‐D space, so that 4‐D objects in our system is represented as tetrahedral meshes. The tetrahedron‐based collision detection algorithm is performed by a combination of half‐space tests with the use of 5‐D homogeneous processing to enhance the calculation accuracy of the collision detection. Our proposed method calculates the behaviour of the 4‐D objects after the collision by solving the motion equation based on the principle of physics. Consequently, the visualization system with the proposed algorithm allows us to observe the physics‐based environment in 4‐D space.
Multiculturalism of the modern city as a necessary condition for the modification of music-pedagogical education

The article substantiates the need to integrate pedagogical communities in the context of multiculturalism as a social reality of contemporary urban life. The authors note the unity of the existence of such a phenomenon in the multicultural megacities of the polyethnic world in general, and in Russia and China, in particular. Nevertheless, training future teachers to multicultural music education is usually carried out locally based on the students’ country of residence, which today requires concerted efforts on the part of teachers-musicians of different countries, including Russia and China, of which each has its own view on the solution to this issue and a wealth of experience in the formation and development of students’ respect for different musical cultures within the country, in general, and metropolises, in particular. In this regard, the research aims to study the theoretical justification and testing of technologies for students to master the multicultural component of music education in the Russian Federation and China in the comparative context. To achieve this goal, the authors used theoretical and empirical methods. The scientific novelty of the research is based on a comparative analysis of the methodological foundations, the essence and content of the multicultural component of the music programs of the Russian Federation and China, as well as identification of common and characteristic features of pedagogical approaches, general and specific methods of multicultural education in the Russian Federation and China. The reliability and validity of the research results are proven by relying on the fundamental provisions of science, as well as consideration of the research problem from the standpoint of philosophical, general scientific, and specific scientific analysis.
A Proposed Framework for Building Semantic Search Engine with Map-Reduce

Every daily moment, people share billion of information worldwide via the Internet, which makes in the other side large database stores for that web-based information. With the massive databases, Search Engines (SE) are increasing. They act as filters to allow users to find the information they are interested in easily and quickly. Semantic search engines try to understand what a user is asking in a query by placing it in context by analyzing the query’s terms and language. This analysis is conducted against tightly pre-compiled pools of knowledge, potentially including knowledge about the user. Most researchers did their best to convert such a Keyword-based search engine to a Semantic one, but without giving any attention to the increasing of the generated indices. This paper proposes a framework for building a semantic search engine using MapReduce for speeding up indexing and retrieving big ontological data.
Multi-objective Lung Image Detection Method Based on Self-regulating Sparse Representation

There are more and more lung diseases, and the detection of lung images is an important research topic. The intensity and difficulty of manual detection are large, and sometimes there is a misjudgment. Based on the research of sparse expression theory, this paper improves the existing sparse expression algorithm, applies it to target tracking to realize multi-target tracking algorithm based on self-regulating sparse expression. A multi-objective lung image detection method based on self-regulating sparse expression is proposed for the judgment of nodules and tumors. Experiments show that the recognition rate of nodules in lung images can reach more than 95.5%, which improves the accuracy and real-time of target tracking, and will improve the means and level of assistant diagnosis and treatment in medicine.
Generalized Bounded Linear Logic and its Categorical Semantics

Abstract We introduce a generalization of Girard et al.’s called (and its affine variant ). It is designed to capture the core mechanism of dependency in , while it is also able to separate complexity aspects of . The main feature of is to adopt a multi-object pseudo-semiring as a grading system of the !-modality. We analyze the complexity of cut-elimination in , and give a translation from with constraints to with positivity axiom. We then introduce indexed linear exponential comonads ( ILEC for short) as a categorical structure for interpreting the $${!}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>!</mml:mo> </mml:math> -modality of . We give an elementary example of ILEC using folding product, and a technique to modify ILECs with symmetric monoidal comonads. We then consider a semantics of using the folding product on the category of assemblies of a BCI-algebra, and relate the semantics with the realizability category studied by Hofmann, Scott and Dal Lago.
Generation of Pseudo Random Sequences based on Chaotic Cubic Map and Cyclic Elliptic Curves over GF (2<sup>8</sup>) for Cryptographic Applications

Cyclic elliptic curves (CEC) and chaotic sequences are used for cryptographic application from many decades. In this paper, authors propose a hybrid model to generate pseudo random key sequences for symmetric ciphers. Hybrid model is developed using CEC points over GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ) and a one-dimensional cubic map. In the proposed system first, binary chaotic sequence and CEC points are given to AES SBOX to get two random sequences separately. Byte wise parity is computed for both the sequences. Based on the parity the sequences are either left or right circular shifted by a byte and two sequences are combined using the XOR operation to get the random binary sequence. The randomness of the generated sequences is analysed using various randomness test suites like NIST SP 800-22 Rev.1a and Correlation. It has been observed that all the sequences pass the randomness tests. These sequences are used to encrypt an image using additive stream cipher algorithm. The security analysis of the cipher image is conducted. The results obtained for the proposed system indicates that it is secure against cryptographic attacks.
Controlled Gaussian process dynamical models with application to robotic cloth manipulation

Abstract Over the last years, significant advances have been made in robotic manipulation, but still, the handling of non-rigid objects, such as cloth garments, is an open problem. Physical interaction with non-rigid objects is uncertain and complex to model. Thus, extracting useful information from sample data can considerably improve modeling performance. However, the training of such models is a challenging task due to the high-dimensionality of the state representation. In this paper, we propose Controlled Gaussian Process Dynamical Models (CGPDMs) for learning high-dimensional, nonlinear dynamics by embedding them in a low-dimensional manifold. A CGPDM is constituted by a low-dimensional latent space, with an associated dynamics where external control variables can act and a mapping to the observation space. The parameters of both maps are marginalized out by considering Gaussian Process priors. Hence, a CGPDM projects a high-dimensional state space into a smaller dimension latent space, in which it is feasible to learn the system dynamics from training data. The modeling capacity of CGPDM has been tested in both a simulated and a real scenario, where it proved to be capable of generalizing over a wide range of movements and confidently predicting the cloth motions obtained by previously unseen sequences of control actions.
Reverse Clustering—The Essence and The Interpretations

We cast the paradigm of reverse clustering against the broader background in methodological and theoretical perspectives. After some comments on clustering in general, the relation to the wider concept of reverse engineering is considered. Then, we refer to the notion of explainable AI, or more broadly, comprehensive and explainable data analysis and show the connection with the reverse clustering. Regarding the more strictly methodological issues, we address the associations and differences with respect to such domains as transfer learning, domain adaptation, learning from cluster examples, adaptive clustering and so on. Following this, such narrower fields are commented upon as selection of attributes or features, choice of distance definition, as well as comparison or similarity of partitions. Finally, we provide a perspective on the possible interpretations of the reverse clustering problem as clearly distinct from the classification or classifier identification.
WITHDRAWN: Machine-learning approach to analyze on-the-job data via cloud services

Nowadays, most industries want to boost their business by using artificial intelligence. And besides, companies can use Artificial Intelligence and Data Analytics to understand their target audience better and automate those goods according to consumer demand. In return, all of these variables increase a company’s profitability that gives it an edge over its competitors. For the most instances, income is the bottom line. Yet corporations had to spend a great deal of money in artificial intelligence for a long time in the past to make this profit. AI machines were costly, programmers who knew AI were expensive and even rarer when useful data were not found! It is not so much a problem for big multinational organizations, but for small and medium-sized firms, it was very challenging. Yet it was all much easier for us to get to know and improve cloud services. Today, businesses can access a third-party AI platform and start to reap the advantages of artificial intelligence and data analysis with a much smaller initial investment, according to their customer requirements. It is the strength as a function of Artificial Intelligence! AIaaS provides the solution to a large number of SMEs who do not want to build their artificial intelligence systems, to test them, and introduce them from scratch. They can concentrate on their core business without becoming an expert on data and apprenticeships and obtain the value addition from artificial intelligence. We also have higher income, thus lowering their investment risk with AIaaS.
Secure, Efficient and Dynamic Data Search using Searchable Symmetric Encryption

Fog computing, which works complementary to cloud computing, is being developed to overcome the issue of cloud computing such as latency in the cases where the data is to be retrieved immediately. But, along with solving the problem of latency, fog computing brings along with it different set of security issues than cloud computing. The storage and processing capabilities of fog computing is limited and hence, the security issues must be solved with these constrained resources. One of the problems faced when the data is stored outside the internal network is loss of confidentiality. For this, the data must be encrypted. But, whenever document needs to be searched, all the related documents must be decrypted first and later the required document is to be fetched. Within this time frame, the document data can be accessed by an unauthorized person. So, in this paper, a searchable symmetric encryption scheme is proposed wherein the authorized members of an organization can search over the encrypted data and retrieve the required document in order to preserve the security and privacy of the data. Also, the searching complexity of the algorithm is much less so that it suitable to fog computing environment.
A Primer on Conditional Text based Image Generation through Generative Models

Synthesis of Images from text descriptions has emerged as an interesting albeit a challenging task in the domain of Image Synthesis. Many promising advances have been made in the direction of text-based image generation in the recent years, with the emergence of Multi-modal Generative Adversarial Networks. In this paper, we discuss the various approaches which utilise Conditional-GANs to accomplish the task of generating photo-realistic images based on their text descriptions and compare their architectures and performance on various benchmark datasets. The performance of these approaches are evaluated using various well-known metrics.
Natural Resource Exports, Foreign Aid and Terrorism

Linkages between foreign aid, terrorism and natural resource (fuel and iron ore) exports are investigated in this study. The focus is on 78 developing countries with data for the period 1984 to 2008. The generalised method of moment is employed as empirical strategy. Three main foreign aid variables are used for the analysis, namely: bilateral aid, multilateral aid and total aid. The corresponding terrorism variables employed are: domestic terrorism, transnational terrorism, unclear terrorism and total terrorism. The following findings are established. First, the criteria informing the validity of specifications corresponding to iron ore exports do not hold. Second, there is evidence of convergence in fuel exports. Third, whereas the unconditional impacts of aid dynamics are not significant, the unconditional impacts of terrorism dynamics are consistently positive on fuel exports. Fourth, the interaction between terrorism and aid dynamics consistently display negative signs, with corresponding modifying aid thresholds within respective ranges. Unexpected signs are elicited and policy implications discussed. Given the unexpected results, an extended analysis is performed in which net effects are computed. These net effects are constitutive of the unconditional effect from terrorism and the conditional impacts from the interaction between foreign aid and terrorism dynamics. Based on the extended analysis, bilateral aid and total aid modulate terrorism dynamics to induce net positive effects on fuel exports while multilateral aid moderates terrorism dynamics to engender negative net effects on fuel exports. The research improves extant knowledge on nexuses between resources, terrorism and foreign aid.
Probabilistic Semantic Web Reasoning and Learning, by Zese Riccardo

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Information Retrieval System Based on Query Translation Approach for Cross-Languages

This article presents the performance evaluation of information retrieval system for English and Hindi cross-Indian languages. As India is a multilingual country, different varieties of data material in varieties of languages are increasing day by day on Internet database. And hence, multilingual information retrieval is big issue in country like India. Usually, people used their native dialect to represent their needs and communication, and hence, language diversity produces a great barricade. In this paper, a new research design of cross-lingual information retrieval system is proposed. Here, a new research design has been implemented for information retrieval based on English and Hindi cross-languages by applying an approach of query translation. English language is considered for the query language, and content is stored in the database in Hindi Language. Here, query translation from English language to Hindi language is done by using dictionary-based approach. Hindi WordNet is used for the query translation. Query expansion and query preprocessing methods are implemented to improve the performance of the new research design and accordingly get the good precision.
Increasing robustness of deep neural network models against adversarial attacks

Abstract In Autonomous driving detecting correct object is important, further studies proved that by adding small pattern above object can also lead intentional fooling of network. Small intentional changes in the input can significantly distort output of a deep neural network model. This makes the machine learned model vulnerable to these small changes in images. Hence, these models have wide scope of failure. If we are able to tackle these intentional attacks it will help to make system more robust. In this project, we have combined multiple techniques used for defending against adversarial attacks. First technique is Adversarial training which include modifying training dataset, second technique is pre-processing input data before applying it to deep learned model and Third technique randomly selects image pre-processing technique. Third method is aimed to distract attacker who know the method used in pre-processing by randomly selecting from multiple methods in image pre-processing. We will measure robustness of deep learned model in terms of Accuracy on Designed system and previous deep learned model. Test samples and adversarial images generated from dataset will be used for testing on deep learned model. Among all methods which we have combined, Adversarial training proved best method to defend against white box attack. If we would have used strong defences in Random selection of image transformation then the system could have performed much better. However, Random selection have done its work of confusing the attacker by selecting random transformations.