iosr-JCE   Volume-22 ~ Issue-6 ~Nov. - Dec. 2020

Abstract: A construção civil é a responsável pelo grande consumo de matérias-primas e principalmente por agregados, dessa forma, proporcionando possíveis impactos ambientais. Com isso, diversas pesquisas são realizadas visando o emprego de materiais tidos como inservíveis no mercado construção civil, contribuindo com a preservação de recursos naturais não-renováveis e amenizando o acúmulo de resíduos descartados de forma inadequada. Esse artigo tem como objetivo buscar na literatura formas de substituição de agregados utilizados na construção civil. Foi realizada uma revisão da literatura, em que se buscou artigos publicados de 2015 a 2019, que se tratasse de estudos experimentais.....

[1]. ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS. NBR 7211: Agregados para Concreto – Especificação. Rio de Janeiro, 2019.
[2]. ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS. NBR 9935: Agregados - Terminologia. Rio de Janeiro, 2011.
[3]. CONAMA, Conselho Nacional Do Meio Ambiente. Resolução n º 307, de 05 de julho de 2002: Estabelece diretrizes, critérios e procedimentos para a gestão dos resíduos da construção civil. Diário Oficial da União, Brasília, DF, 17 jul. 2002. Disponível em: http://www.mma.gov.br. Acesso em outubro 2016. [4]. DA SILVA, Bruna Mendes; BELLEI, Poliana. SUBSTITUIÇÃO PARCIAL E TOTAL DO AGREGADO MIÚDO UTILIZANDO RESÍDUO DE REVESTIMENTO CERÂMICO NA ARGAMASSA DE REVESTIMENTO. Anais da Engenharia Civil/2595-1823, v. 1, n. 1, p. 24-41, 2018.
[5]. GRANZOTTO, L. Concreto com adição de borracha: uma alternativa ecologicamente viável. Dissertação – Pós-graduação em engenharia urbana, Universidade Estadual de Maringá, 2010

Abstract:In this paper, The stability of a class of infinite-dimensional nonlinear interconnected vehicle following systems was studied. Under the assumption that the systems are globally Lipschitz, from the
conditions of stability of isolated subsystems decomposed from the interconnected nonlinear systems, by applying vector Lyapunov function method and comparison principle, the sufficient conditions of exponential
string stability for this class of interconnected systems was obtained. The obtained sufficient criterion is delayindependent, thus it is easy to test in practice..

Key Word:nonlinear interconnected systems, string stability, global asymptotic stability, vector Lyapunov function, time delays

[1]. Xie Shaobo, Hu Xiaosong, Liu Teng, et al. Predictive vehicle-following power management for plug-in hybrid electric vehicles.
Energy,2019, 166:701-714.
[2]. Xuan J Y, Lu G, Wang J C. Vehicle Following Control System Based on Micro Smart Car. Information and Control, 2014,43(2):
165170.
[3]. Francisco N, Vicente M. Mixing V2V- and non-V2V-equipped vehicles in car following. Transportation Research Part C,2019,
108:167-181.
[4]. Levine J, Athans M. On the optimal error regulation of a string of moving vehicles. IEEE Transactions on Automatic Control, 1966,
11(11): 355-361.
[5]. Caudill R E, Garrard W L. Vehicle follower longitudinal control for automated transit vehicles. ASME Journal of Dynamic Systems,
Measurements, and Control, 1977, 99(4) 241-248..

Abstract: Incopat patent database as the data source, from the year of patent filings, application type and the legal status, research and development subject, technical structure, operating status, key technologies and development route of Chinese equipment manufacturing industry to conduct a comprehensive analysis of patent information, find the problems existing in the equipment manufacturing industry in our country, is advantageous to the government, enterprises and academia reference countermeasures and Suggestions.

Keywords: China; Equipment manufacturing industry; Patent; Incopat

[1]. Xiang Baoqi. Research on development Opportunities and Strategies of BAT image recognition technology based on patent perspective [D]. University of Electronic Science and Technology,2018.
[2]. Wang Haijun, Cheng Jia, Zou Rishong. Collaborative Innovation of Strategic Emerging Industries from the perspective of patent [J]. Journal of Shenyang University of Technology (Social Science edition), 2015,11(05):452-458
[3]. Zhou Greenlu. Research on Development Countermeasures of China's Manufacturing Industry based on informatization [D]. Shandong University,2006.
[4]. Gao Lin. Research on the Construction Mechanism of Equipment Manufacturing Industry in Baotou [D]. Minzu University of China,2012.
[5]. Shang Xiaohu. Research on Technology Innovation Model of China's Equipment Manufacturing Industry [D]. Shanghai Academy of Social Sciences,2013..

Abstract: After the appliance of different statistical norms to validate the quality of synthesized voices applied to an HTS-based spanish synthesizer, which uses LSP and Cepstral Coefficients parameterizations. The authors firmly concluded both things: LSP parameterization can be as good as the standard Mel-Cepstral parameterization. Nevertheless, both parameterizations are still insufficient to qualify as natural sounding speech synthesis.

Keywords: Speech Synthesis, Voice Parameterization, Line Spectral Pair, Mel-Cepstral Parameterization.

[1]. K. Tokuda, Y. Nankaku, T. Toda, H. Zen, J. Yamagishi, and K. Oura, "Speech Synthesis Based on Hidden Markov Models," Proc. IEEE, vol. 101, no. 5, pp. 1234–1252, May 2013.
[2]. A. Herrera-Camacho and F. D. R. Ávila, "Development of a Mexican Spanish Synthetic Voice Using Synthesizer Modules of Festival Speech and HTSStraight," Int. J. Comput. Electr. Eng., pp. 36–39, 2013.
[3]. C. Franco, F. Del Rio, and A. Herrera, "ATINER Conference Paper Series Speech Synthesis of Central Mexico Spanish using Hidden Markov Models," pp. 1–12, 2016.
[4]. N. Nakatani, K. Yamamoto, and H. Matsumoto, "Mel-LSP Parameterization for HMM-based Speech Synthesis," Eurasip Proc. SPECOM 2006, 2006.
[5]. C. Franco, A. Herrera, and B. Escalante, "Speech Synthesis in Mexican Spanish using LSP as voice parameterization," iiisci.org, 2017.

Abstract: Today, unauthorized access to data, propagation and modification of data bring many problems. Data leakage techniques are used to solve these problems. However, a model that will ensure data privacy by protecting the data of all patients in hospitals has not been fully developed. In our study, it is aimed to provide data confidentiality by labelling the patient's data. The patient's data are called EHD (Electronic Health Data) objects. Thus, each patient is provided with access to the information he/she authorizes. Each patient carries out the security management of their own health data

Keys: data leakageprevention, data access, authorization, data breach, privacy

[1]. Prathaben K., "Data LossPrevention", SansInstituteInfosec Reading Room, 2008.
[2]. Asaf S., Yuval E. AdnLion R., "A Survey of Data LeakageDetectionandPrevention Solutions", 2012.
[3]. Jorge B., Julio C. andJuan E.T., "BypassingInfprmationLeakageProtectionwithtrustedapplications", Computer& Security, pp.557-568, 2012.
[4]. "Data LeakPrevention", Isaca White Paper, 2010.
[5]. PrathabenKanagasingham,"Data LossPrevention", SansInstituteInfoSec Reading Room, 2008.

Abstract: Today, unauthorized access to data, propagation and modification of data bring many problems. Data leakage techniques are used to solve these problems. However, a model that will ensure data privacy by protecting the data of all patients in hospitals has not been fully developed. In our study, education data are defined as objects. Each object is labeled while being passed on to another user. Each object is allowed to be seen and processed by the users authorized by the user it owns. Co-owners of an object are authorized to operate on that object. Thus, each user performs the security management of his / her data in training

Keys: data leakageprevention, data access, authorization, data breach, privacy.

[1]. Prathaben K., "Data LossPrevention", SansInstituteInfosec Reading Room, 2008.
[2]. Asaf S., Yuval E. AdnLion R., "A Survey of Data LeakageDetectionandPrevention Solutions", 2012.
[3]. Jorge B., Julio C. andJuan E.T., "BypassingInfprmationLeakageProtectionwithtrustedapplications", Computer& Security, pp.557-568, 2012.
[4]. "Data LeakPrevention", Isaca White Paper, 2010.
[5]. PrathabenKanagasingham,"Data LossPrevention", SansInstituteInfoSec Reading Room, 2008..

Abstract: The development of network has brought great changes to people's life. Not only has the way of communication and office changed, but also the way of shopping. The depression of physical stores and the rise of shopping websites are the trend of the times. In order to better regulate the network market, it is very important to study the platform website of online shopping. To help enterprises better understand the influence of website quality, we can better promote the healthy and sustainable development of the industry. In this paper, Taobao, one of the largest websites at present, is taken as the research object. Through the investigation, we can understand the perception attitude of Taobao consumers for website quality, and then get the factors that affect consumers' purchasing behavior........

Keywords: Website quality; purchase behavior; Taobao.

[1]. Javenpaa SL, Todd PA. Consumer reactions to electronic shopping on the world wide web. International Journal of Electronic Commerce.1996;1(2):59-88.
[2]. YooB, Donthu N. Developing a Scale to Measure the Perceived Quality of Internet Shipping Sites (SITEQUAL). Quarterly Journal of Electronic Commerce.2001;2(1):31-47.
[3]. Miyoung J, Haemoon O. Mary Gregoire. Conceptualizing Website Quality and its Consequences in the Lodging Industry. Hospitality Management. 2003;22(2):161-175.
[4]. Delone WH, McLean ER. The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems.2003;19(4):9-30.
[5]. Lin HF,The impact of website quality dimensions on customer satisfaction in the B2C e-commerce context. Total Quality Management & Business Excellence. 2007;18(4):363-378.

Abstract: Development of Internet communication technology has changed the world tremendously. Today Urban population use communication technology in daily life for many house hold activities and this trend in upcoming generation is increasing day by day. The concept of modern cities or Smart Cities where almost all the activities of the human being are supported with the use of communication technology, which leads us in designing Smart Cities for effective Intelligent Governance: I-Governance. This paper proposes the day to day life activities dependent on hardware to interact with each other as using Internet of Things and extract meaningful knowledge from Big Data for the better development of population through effective I-Governance. Proposed design of Smart.....

Keywords: Smart City, Internet of Things, BigData, Intelligent – Governance (I-Governance), Intelligent - Services (I-Services), Digital Currency, Mobile wallet

[1]. Annalisa Cocchia in their paper "Smart and Digital City: A Systematic Literature Review" published in R. P. Dameri and C. Rosenthal-Sabroux (eds.), Smart City, Progress in IS
[2]. Hollands, R. G. (2008). Will the real smart city please stand up? City: Analysis of Urban Trend, Culture, Theory, Policy, Action, 12(3), 303–320.
[3]. Nada Elgendy and Ahmed Elragal Department of Business Informatics & Operations, German University in Cairo (GUC), Cairo, Egypt.
[4]. TechAmerica: Demystifying Big Data: A Practical Guide to Transforming the Business of Government. In: TechAmerica Reports, pp. 1–40 (2012)
[5]. Cebr: Data equity, Unlocking the value of big data. in: SAS Reports, pp. 1–44.

Abstract: Cryptography is used to secure and protect data during communication. It is helpful to prevent unauthorized person or group of users from accessing any confidential data. Encryption and decryption are the two essential functionalities of cryptography [4].A message sent over the network is transformed into an unrecognizable encrypted message known as data encryption. At the receiving end, the received message is converted to its original form known as decryption [2].Encryption is a process which transforms the original information into an unrecognizable form. This new form of the message is entirely different from the original message. That's why a hacker is not able to read the data as senders use an encryption algorithm. Encryption is usually done using key algorithms. Decryption is a process of converting encoded/encrypted data in a form that is readable and understood by a human or a computer. This method is performed by un-encrypting the text manually or by using keys used to encrypt the original data.

Keywords: Cryptography; AES algorithm; Encryption and Decryption; Openssl tool; Security; Integrity.

[1]. International journal of Scientific and Engineering Research, Volume 7,Issue 2,February-2016 ISSN 2229-5518
[2]. File Encryption, Decryption Using AES Algorithm in Android Phone
[3]. A novel sensitive image encryption algorithm based on the Zaslavsky chaotic map
[4]. International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 6 Issue 4 April 2007 , Page No. 20915-20919
[5]. W. Stallings, Cryptography and Network Security, 4th Ed, pp. 58-309, Prentice Hall,2005.

Abstract: In this paper, a new framework for clustering interval data has been proposed. In this frame work, each cluster is represented by a representative which is a fuzzy set. First, we define the interval data and its cluster representative as the uniform fuzzy set representation. Next, we consider a data set with single attribute of interval data type for the sake of defining similarity and distance functions for interval data and then propose a way of handling this attribute in the process of clustering. It is assumed that the underlying domain of the attribute is a discrete, totally ordered bounded domain. Also, all intervals are assumed to be closed intervals. We suggest measures to calculate the distance/similarity between two interval data, an interval data and a cluster representative and two cluster representatives of interval data. Using these concepts, interval data clustering may also be implemented with any existing representative based or summary based clustering algorithm.

Keywords: cluster representative, interval data, membership value, spread of a cluster

[1]. G. Cabanes, Y. Bennani, and R. Destenay, A Hardy. A new topological clustering algorithm for interval data, Pattern Recognition, 46 (2013) ELSEVIER. pp. 3030-3039.
[2]. J. P .Goswami, and A. K. Mahanta . Categorical Data Clustering Based on an Alternative Data Representation Technique, International Journal of Computer Applications, (0975-8887), Vol.-72, No. 5, pp. 7-12, May 2013.
[3]. J. P. Goswami and A. K. Mahanta . A Genetic Algorithm Based Ensemble Approach for categorical Data Clustering: 12th IEEE India International Conference INDICON 2015.
[4]. Wei Peng and Tao Li. Interval Data Clustering with Applications. ICTAI 2006, 18th IEEE International Conference pp. 355-362.
[5]. Mika Sato – Ilic. Symbolic Clustering with Interval Valued Data. Procedia Computer Science 6 (2011) pp, 358-363

Abstract: By analyzing the methodology of Self- Supervision Learning(SSL) we highlighted the process which provides summarization of deep perception. The paper demonstrated the advantages of Self-Supervision Learning based on the described database. The study is focused on Human Activity Recognition (HAR) and also investigated the envisaged technique obtainable from distinct databases for instance UTD- MHAD and UCI- HAR. The process is accomplished through an analysis supervised network by performing training based on the distinct data compared rationally. The significant functionality of the Self-Supervision Learning (SSL) subdue the gulf between the supervised and unsupervised learning. In this paper, three prominent techniques were investigated comprehensively which are promulgated as rotational task loss, frame order prediction and shuffle and learning for conducting the study exclusively......

Keywords: Self Supervised Learning, Behavior analysis, Rotation Task Loss, Shuffle and Learn Frame Order Prediction.

[1]. Radu V, Tong C, Bhattacharya S, Lane N, Mascolo C, Marina M, and Kawsar F, Multimodal deep learning for activity and context recognition.Proceedings of the ACM on Interactive, Mobile, Wearable and UbiquitousTechnologies, 2018, 1(4):157.
[2]. Hammerla N, Halloran S, and Ploetz T. Deep, convolutional, and recurrent models for human activity recognition usingwearables.arXiv preprint arXiv:1604.08880, 2016.
[3]. Larsson G, Maire M, and Shakhnarovich G. Learning representations for auto-matic colorization. InEuropean Conference on Computer Vision (ECCV), 2016.
[4]. Donahue J and Simonyan K. Large scale adversarial representation learning, 2019, arXiv preprintarXiv:1907.02544.
[5]. Goyal P, Mahajan D, Gupta A, and Misra I. Scaling and benchmarking self-supervised visual representation learning. 2019, arXiv preprint arXiv:1905.01235.