Stability and Hopf Bifurcation Analysis of a Delayed Innovation Diffusion Model with Intra-Specific Competition

This article is concerned with the diffusion of a sport in a region, and the innovation diffusion model comprising of population classes, viz. nonadopters class, information class and adopters class. A qualitative analysis is carried out to assess the global asymptotic stability of the interior equilibrium for null delay. It has also been proved that the parameter [Formula: see text] (age gaps among sportspersons) in the intra-specific competition between the new players and the senior players can even destabilize the otherwise globally stable interior equilibrium state and the coexistence of all the populations is possible through periodic solutions due to Hopf bifurcation. With the help of normal form theory and center manifold arguments, the stability of bifurcating periodic orbits is determined. Numerical simulations have been executed in support of the analytical findings.

Utilizing Class Information for Deep Network Representation Shaping

Statistical characteristics of deep network representations, such as sparsity and correlation, are known to be relevant to the performance and interpretability of deep learning. When a statistical characteristic is desired, often an adequate regularizer can be designed and applied during the training phase. Typically, such a regularizer aims to manipulate a statistical characteristic over all classes together. For classification tasks, however, it might be advantageous to enforce the desired characteristic per class such that different classes can be better distinguished. Motivated by the idea, we design two class-wise regularizers that explicitly utilize class information: class-wise Covariance Regularizer (cw-CR) and classwise Variance Regularizer (cw-VR). cw-CR targets to reduce the covariance of representations calculated from the same class samples for encouraging feature independence. cw-VR is similar, but variance instead of covariance is targeted to improve feature compactness. For the sake of completeness, their counterparts without using class information, Covariance Regularizer (CR) and Variance Regularizer (VR), are considered together. The four regularizers are conceptually simple and computationally very efficient, and the visualization shows that the regularizers indeed perform distinct representation shaping. In terms of classification performance, significant improvements over the baseline and L1/L2 weight regularization methods were found for 21 out of 22 tasks over popular benchmark datasets. In particular, cw-VR achieved the best performance for 13 tasks including ResNet-32/110.

A Multi-Model and Ai-Based Collegebot Management System (Aicms) for Professional Engineering Colleges

Chatbot is a program which provides human conversation using Artificial Intelligence (AI). Chatbots are designed to work as VIRTUAL ASSISTANTS (VA). They themselves provide a platform for the promotions of the Products and Services online. All Higher Educational Institutes provide the complete information through their internet sites for students, which admits the use of social nets such as Facebook, WhatsApp, and College websites. Total-in-All, in any website, searching functionality is required to search for any information and it includes Social Media Applications like Facebook and WhatsApp regular response are utilized. Therefore, Chatbot is an effective auto-response system, and also an instant messaging platform. In this paper, AICMS an AI-Based CollegeBot management system for professional Engineering college system provide the autoresponse to student queries about the college basic information, class timetables, examination schedules related to academics. Many Queries about the subjects and placements can be inputted to the system. Here the system AICMS is designed with Dialogflow which is supported by the Google API. AI and running as a messenger in the Facebook, which takes the input as the text and voice and it provides the response as text and voice. It gives a quick, accurate response to student and staff queries in an interactive fashion.

PERILAKU PENEMUAN INFORMASI BERDASARKAN TEORI WILSON DI PERPUSTAKAAN UIN SUNAN KALIJAGA PADA MAHASISWA PASCASARJANA ILMU PERPUSTAKAAN DAN INFORMASI

Artikel ini bermaksud menjelaskan perilaku mahasiswa dalam penemuan informasi guna menunjang kebutuhan informasi dalam pengerjaan tugas kuliah pada mahasiswa pascasarjana S2 jurusan ilmu perpustakaan dan informasi kelas A tahun 2017. berdasarkan teori Wilson perilaku penemuan informasi itu dipengaruhi oleh empat faktor: perhatian pasif, pencarian aktif, pencarian pasif, dan pencarian berlanjut. Untuk itu penulis tertarik mengulas artikel yang berjudul perilaku penemuan informasi berdasarkan teori Wilson diperpustakaan UIN Sunan Kalijaga pada mahasiswa pascasarjana S2 jurusan ilmu perpustakaan dan informasi kelas A tahun 2017. jenis penelitian yang digunakan yaitu penelitian kualitatif deskriptif, data yang dkumpulkan melalui studi literature, observasi, dan wawancara. diakhir artikel ini menghasilkan beberapa temuan data yang menarik dari temuan tersebut dapat menggambarkan karakteristik, jenis, atau pola perilaku mahasiswa dalam melakukan penemuan informasi. ABSTRACTThis article intends to explain the behavior of students in the discovery of information to support information needs in working on college assignments for postgraduate students majoring in library science and information class A in 2017. Based on Wilson's theory of information discovery behavior is influenced by four factors: passive attention, active search, search passive, and the search continues. For this reason, the authors are interested in reviewing an article entitled information discovery behavior based on Wilson's theory in the library of UIN Sunan Kalijaga for postgraduate students majoring in library and information science class A in 2017. This type of research is descriptive qualitative research, data collected through literature studies, observation and interview. At the end of this article produced some interesting data findings from these findings can describe the characteristics, types, or patterns of behavior of students in conducting information discovery.