When and Where: Predicting Human Movements Based on Social Spatial-Temporal Events

Predicting both the time and the location of human movements is valuable but challenging for a variety of applications. To address this problem, we propose an approach considering both the periodicity and the sociality of human movements. We first define a new concept, Social Spatial-Temporal Event (SSTE), to represent social interactions among people. For the time prediction, we characterise the temporal dynamics of SSTEs with an ARMA (AutoRegressive Moving Average) model. To dynamically capture the SSTE kinetics, we propose a Kalman Filter based learning algorithm to learn and incrementally update the ARMA model as a new observation becomes available. For the location prediction, we propose a ranking model where the periodicity and the sociality of human movements are simultaneously taken into consideration for improving the prediction accuracy. Extensive experiments conducted on real data sets validate our proposed approach

Image-Based Search Engine For Art Exhibition Gallery (ImBa SEA Exhibition/Gallery)

The image-based search engine for art exhibition/gallery (ImBa SEA) is like any search engine but will provide a better functionality. The ImBa SEA is specially design for art gallery where lies thousands of arts displayed on the exhibition. With the help of the ImBa SEA, the user can upload image that they have snapped into the system to retrieve the information about the art itself. Some features are very difficult to describe with text, some special textures and complex shapes cannot be clearly represented by alphanumeric inputs. The arts need to be digitalized and stored in the gallery’s database. Based on the existing way of accessing information for art gallery, instead of directly using the image as a ‘keyword’ to retrieve information, each art have its own name. The name is stored in the art gallery database, with the relevant information. However, this method takes some time because some arts have the similar name or a long name. This decreases the accuracy of the search engine to retrieve information of the art wish to be accessed. With ImBa SEA, the user directly uses the image to retrieve the information on the given art. Each art will have its own unique id to ease the process of retrieving information. The image is uploaded into the search engine, and then it will process the image to look for similarity of the image with the images stored in the database. Once the similar image is found in the database, the unique id of the art is used to retrieve the information of the image. Basically,ImBa SEA compares the two images pixel by pixel to look for similarities. This technique will surely provide better efficiency and accuracy on information retrieval. This paper will discuss everything from the problem faced, and scope concentrated to achieve the objectives of the project which are discussed in Chapter 1. The system or technologies related or similar with the ImBa SEA are discussed in Chapter 2: Literature Review. Also, the research methodology of the project is discussed later in Chapter 3 together with the development methodology. The basic structure is also discussed in this paper to give a clear view on how the system works. The future plans on the ImBa SEA are discussed at the end of this paper

Modeling network traffic on a global network-centric system with artificial neural networks

This dissertation proposes a new methodology for modeling and predicting network traffic. It features an adaptive architecture based on artificial neural networks and is especially suited for large-scale, global, network-centric systems. Accurate characterization and prediction of network traffic is essential for network resource sizing and real-time network traffic management. As networks continue to increase in size and complexity, the task has become increasingly difficult and current methodology is not sufficiently adaptable or scaleable. Current methods model network traffic with express mathematical equations which are not easily maintained or adjusted. The accuracy of these models is based on detailed characterization of the traffic stream which is measured at points along the network where the data is often subject to constant variation and rapid evolution. The main contribution of this dissertation is development of a methodology that allows utilization of artificial neural networks with increased capability for adaptation and scalability. Application on an operating global, broadband network, the Connexion by Boeingʼ network, was evaluated to establish feasibility. A simulation model was constructed and testing was conducted with operational scenarios to demonstrate applicability on the case study network and to evaluate improvements in accuracy over existing methods --Abstract, page iii

Measurement of the B0 s → μ+μ− decay properties and search for the B0 → μ+μ− decay in proton-proton collisions at √s = 13 TeV

CMS Collaboration: et al.Measurements are presented of the B0s → μ+μ− branching fraction and effective lifetime, as well asresults of a search for the B0 → μ+μ− decay in proton-proton collisions at √s = 13 TeV at the LHC. The analysis is based on data collected with the CMS detector in 2016–2018 corresponding to an integrated luminosity of 140 fb−1. The branching fraction of the B0s → μ+μ− decay and the effective B0s meson lifetime are the most precise single measurements to date. No evidence for the B0 → μ+μ− decay has been found. All results are found to be consistent with the standard model predictions and previous measurements.Individuals have received support from the Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 884104, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS and FWO (Belgium) under the “Excellence of Science – EOS” – be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; The Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Hellenic Foundation for Research and Innovation (HFRI), Project Number 2288 (Greece); the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy – EXC 2121 “Quantum Universe” – 390833306, and under project number 400140256 - GRK2497; the Hungarian Academy of Sciences, the New National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary); the Council of Science and Industrial Research, India; the Latvian Council of Science; the Ministry of Education and Science, project no. 2022/WK/14, and the National Science Center, contracts Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552 (Poland); the Fundação para a Ciência e a Tecnologia, grant CEECIND/01334/2018 (Portugal); the National Priorities Research Program by Qatar National Research Fund; MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”, and the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and Programa Severo Ochoa del Principado de Asturias (Spain); the Chulalongkorn Academic into Its 2nd Century Project Advancement Project, and the National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B05F650021 (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; the Welch Foundation, contract C-1845; and the Weston Havens Foundation (USA).Peer reviewe

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested