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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Elisa Indriasari Haryono Soeparno Ford Lumban Gaol Tokuro Matsuo Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Professor at Advanced Institute of Industrial Technology Japan

The issues that relate with the automatics and intelligent tracing of the transaction and banking customer become the main issues nowadays. Whereas most of transaction that already moved to digital and online transaction that needed more intelligent features on the Predictive Analyst. The rise of predictive analytics (PA) is one of the biggest disruptions in financial services. In the last years, PA methods grew sharply to give the baseline of data driven decision-making process for forecasting future business situation. PA uses various algorithms to discover different patterns in the big data environment that might create more value for businesses including in financial institutions. The potential implementation of data science in financial industry is still to be explored. In this paper, we summarize work done on PA at financial industry. We also explore the potential application of PA and investigating how data science and big data is going to be used in financial institutions in the future.

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Automatic Turning ON/OFF Bike Indicator Using Offline GPS Navigation System

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IOP Conference Series: Materials science and Engineering 2021年第1期1055卷

作者： S Usha M Karthik R Lalitha M. Jothibasu T Krishnamoorthy Associate Professor Department of Electrical and Electronics Engineering Kongu Engineering College Perundurai-638 060 Erode Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Assistant Professor (Senior Grade) Department of Electronics and Communication Engineering PSG Institute of Technology and Applied Research Coimbatore UG Scholar Department of EEE Kongu Engineering College Perundurai-638060 Erode

In the present modern world, most of the people are using the roadways for transportation purposes. The utilization of roadways increases by one side, on the other side the road accidents are also escalating. The current assessment shows that the reasons for the road accidents are not obeying the traffic rules, inappropriate usage of indicators while turning right or left while driving the vehicles and so on. In peak hours and in traffic times people are speeding up with their vehicle and forgot to use their indicator. This leads to an accident while sudden turning and braking without a proper indication. In order to overcome these types of difficulties, an automatic turning on/off bike indicators using offline GPS navigation system is proposed. The proposed system works in an offline mode and does not need any internet connectivity. By turning on the supply to the board, it will generate the hotspot and it will be connected with Wi-Fi in Mobile Phone. Then the destination is fixed in the mobile application. When turning is available in the way to destination before 100 meters of that turning, indicator will be automatically turned on and after that indicator will be automatically turned off. Thus the proposed work would help the people during the rush hours and also during the emergency situations to avoid accidents.

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A new approach to identify similar users based on customer reviews

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IOP Conference Series: Materials science and Engineering 2020年第1期804卷

作者： Dimple Chehal Parul Gupta Payal Gulati Research Scholar Department of Computer Engineering J.C. Bose University of Science and Technology YMCA Faridabad Assistant Professor Department of Computer Engineering J.C. Bose University of Science and Technology YMCA Faridabad

Recommender System suggests items of interest to users based on their preferences. These preferences are gauged through various sources such as purchase history, ratings, reviews and browsing behaviour. Collaborative filtering and content based filtering are the two widely used techniques that help in generating recommendations to the target user(s) by identifying similar users to target user or similar items to items of interest. Through this paper a new method to identify similar users based on the similarity of reviews has been proposed.

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Deep 2.5D vehicle classification with sparse SfM depth prior for automated toll systems

arXiv

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arXiv 2018年

作者： Waltner, Georg Maurer, Michael Holzmann, Thomas Ruprecht, Patrick Opitz, Michael Possegger, Horst Fraundorfer, Friedrich Bischof, Horst Institute of Computer Graphics and Vision Faculty of Computer Science Graz University of Technology Graz8010 Austria

Automated toll systems rely on proper classification of the passing vehicles. This is especially difficult when the images used for classification only cover parts of the vehicle. To obtain information about the whole vehicle. we reconstruct the vehicle as 3D object and exploit this additional information within a Convolutional Neural Network (CNN). However, when using deep networks for 3D object classification, large amounts of dense 3D models are required for good accuracy, which are often neither available nor feasible to process due to memory requirements. Therefore, in our method we reproject the 3D object onto the image plane using the reconstructed points, lines or both. We utilize this sparse depth prior within an auxiliary network branch that acts as a regularizer during training. We show that this auxiliary regularizer helps to improve accuracy compared to 2D classification on a real-world dataset. Furthermore due to the design of the network, at test time only the 2D camera images are required for classification which enables the usage in portable computer vision systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Classification (of information)

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PRS-Net: Planar reflective symmetry detection net for 3D models

arXiv

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arXiv 2019年

作者： Gao, Lin Zhang, Ling-Xiao Meng, Hsien-Yu Ren, Yi-Hui Lai, Yu-Kun Kobbelt, Leif Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Maryland College ParkMD United States Visual Computing Group School of Computer Science and Informatics Cardiff University Wales United Kingdom Institute for Computer Graphics and Multimedia RWTH Aachen University Aachen Germany

In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this paper, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

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A cutting-edge approach to generate random bit sequence with confound chaotic maps

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AIP Conference Proceedings 2021年第1期2387卷

作者： Sathya K. Premalatha J. Vani Rajasekar Sathiyananthan P. TharunPrasath S. Ragul S. 1Assistant Professor Department of Computer Science Engineering Kongu Engineering College Perundurai India 2Professor Department of Information Technology Kongu Engineering College Perundurai India 3UG Scholar Dept of CT-UG Kongu Engineering College Perundurai India

A random number sequence is a sequence of numbers that exhibit statistical randomness. In other words, all numbers in the sequence have an equal chance and it’s hard to predict the next number in the sequence. They are most commonly used in cryptography to strengthen the security. Same key can be used by sender and receiver to generate the same set of random numbers. The pseudo-random number generation is an important task in computer science with various security features. There are 2 types of random number generators; they are True Random Number Generators (TRNG) and Pseudo Random Number Generators (PRNG). In recent years, PRNGs are implemented in hardware or software using chaotic maps. Some of the chaotic maps used in PRNG are Logistic map, Lorenz map, Henon map, Skew Tent map, Saw tooth map, etc. The Tinker Bell map is a discrete-time chaotic dynamical system with four system parameters. It is a most commonly studied examples of dynamical systems that exhibit chaotic behaviour. Chaotic maps possess several drawbacks such as the discontinuity of ranges, non-uniform distribution, periodicity in chaotic range, and small key space. In this PRNG, the numbers are generating in an unpredictable manner. To improve the uniform distribution and periodicity in random number generation, combination of Logistic and Tinker bell map is proposed. The Logistic map is a polynomial mapping. The Tinker bell map is known to have shorter periods. It is notable for having chaotic solutions for certain parameter values and initial conditions. In the proposed work, the Tinker bell map perturbs Logistic map to increase the chaotic behaviour of Logistic map. Output of the Logistic map is taken as Pseudo random bit sequence or Pseudo random number. NIST statistical test suite is used to verify the randomness of the generated random sequence.

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Modified linear congruential generator to secure random number generation

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AIP Conference Proceedings 2021年第1期2387卷

作者： Sathya K. Premalatha J. Vani Rajasekar Madhan Kumar M. Deepak M. Manoj S. R. 1Assistant Professor Department of Computer Science Engineering Kongu Engineering College Perundurai India 2Professor Department of Information Technology Kongu Engineering College Perundurai India 3UG Scholar Dept of CT-UG Kongu Engineering College Perundurai India

Random numbers are important tools for generating secret keys, encrypting messages, or masking the content of certain protocols with a random sequence that can be deterministically generated. The nonattendance of demand about the self-decisive numbers conveyed can make genuine harm cryptographic shows, impelling weaknesses to be mishandled by the aggressors. In this paper, another pseudo - optional number generator calculating that utilizes dynamic design clock changed over to Epoch Timestamp as PRNG seed was made. The figuring utilizes a Linear Congruential Generator (LCG) tally that makes a get-together of pseudo - randomized numbers that performs numerical tasks to change numbers that has all the stores of being disconnected to the Seed. Multiplication result shows that the new PRNG calculation doesn’t convey emphasized self-emphatic numbers subject to the rehash of highlight, a decent pointer that the key for flighty numbers is guaranteed about. Mathematical evaluation utilizing NIST Test Suite results focused to optional movements made flighty numbers has a completely conventional of 0.342 P-Value. For a P-Value≥0.001, a social event would be viewed as abstract with an affirmation of 99.9%. This shows that strength and weirdness were developed. Accordingly, It is astoundingly deterministic in nature and has a benevolence of Pseudo-Random Numbers. It is hence a pleasant wellspring of a social event key age for encryption, equal in the assertion plans and other cryptographic assessment restricts that improve and secure information from a security assault.

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The impact of generative artificial intelligence on socioeconomic inequalities and policy making

arXiv

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arXiv 2023年

作者： Capraro, Valerio Lentsch, Austin Acemoglu, Daron Akgun, Selin Akhmedova, Aisel Bilancini, Ennio Bonnefon, Jean-François Brañas-Garza, Pablo Butera, Luigi Douglas, Karen M. Everett, Jim A.C. Gigerenzer, Gerd Greenhow, Christine Hashimoto, Daniel A. Holt-Lunstad, Julianne Jetten, Jolanda Johnson, Simon Kunz, Werner H. Longoni, Chiara Lunn, Pete Natale, Simone Paluch, Stefanie Rahwan, Iyad Selwyn, Neil Singh, Vivek Suri, Siddharth Sutcliffe, Jennifer Tomlinson, Joe van der Linden, Sander Van Lange, Paul A.M. Wall, Friederike Van Bavel, Jay J. Viale, Riccardo Department of Psychology University of Milan-Bicocca Italy Department of Economics MIT United States Institute Professor and Department of Economics MIT United States College of Education Michigan State University United States IMT School of Advanced Studies Lucca Italy Toulouse School of Economics France Department of Economics Loyola Andalucia University Spain Department of Economics Copenhagen Business School Denmark School of Psychology University of Kent United Kingdom Max Planck Institute for Human Development Berlin Germany Penn Computer Assisted Surgery and Outcomes Laboratory Department of Surgery Perelman School of Medicine University of Pennsylvania United States Department of Computer and Information Science School of Engineering and Applied Science University of Pennsylvania United States Department of Psychology and Neuroscience Brigham Young University United States School of Psychology University of Queensland Australia MIT Sloan School of Management United States Department of Marketing University of Massachusetts Boston United States Department of Marketing Bocconi University Italy Economic & Social Research Institute Dublin Ireland Department of Humanities University of Turin Italy Department of Service and Technology Marketing Aarhus University Denmark Center for Humans and Machines Max Planck Institute for Human Development Berlin Germany Faculty of Education Monash University Australia Microsoft Research United States York Law School United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Experimental and Applied Psychology Vrije Universiteit Amsterdam Netherlands Department of Management Control and Strategic Management University of Klagenfurt Austria Department of Psychology Center for Neural Science New York University United States Norwegian School of Economics Bergen Norway Department of Economics University of Milan-Bicocca Italy

Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access, but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI. © 2023, CC BY.

关键词： Artificial intelligence

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Review on various authentication schemes and attacks on connected vehicles

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IOP Conference Series: Materials science and Engineering 2020年第1期993卷

作者： M. S. Bennet Praba J S Femilda Josephin Assistant Professor Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India. Associate Professor Department of Software Engineering SRM Institute of Science and Technology Kattankulathur Chennai – 603 203 India.

Connected cars are comfortable to drivers. But it also threat to humans. Connected vehicles share data like street safety, traffic analysis, road administration information like café, hospital, etc utilizing intruded on web availability and it is time delicate. Connected vehicles allow drivers and passengers to connect to the outside world while on the road. Security is the primary challenge for automakers and other stakeholders, because connected vehicles rely on wireless and cellular communication interfaces. Information should be protected from intruders. The communications between connected vehicles should be a trusted one. Safety is the primary focus of connected cars and the intruders should be identified and the intruders and falsified nodes can be identified by authentication schemes. In VANET vehicles react as indicated by the data got from the opposite end, so it is very vital that the data spreading in the framework is valid and produced by a genuine client. But there are many pitfalls in various authentication schemes. Privacy of the connected vehicles should be while sharing information and also information shared between connected vehicles should be an authenticated one. So still it a major challenge in connected vehicles due to the special characteristics like mobility, time sensitive, etc. This survey discussed the various authentication schemes and the various attacks related to that. Based on the survey, future directions of the secured trusted communications of connected vehicles have been discussed. The aim of this survey is to identify secured authentication techniques for a trusted communication.

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Mathematical model for implementing Non Linear Time Series Forecasting using Deep Learning

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IOP Conference Series: Materials science and Engineering 2020年第2期981卷

作者： Imran Qureshi Burhanuddin Mohammad Mohammed Abdul Habeeb Mohammed Ali Shaik Lecturer IT Department University of Technology and Applied Science-AlMusanna Oman Assistant Professor Department of Computer Science & Engineering SR University Warangal Telangana India

In this paper we propose a novel algorithm for implementing the mathematical model using the Non Linear Time Series Forecasting using the methods of deep learning which has to avoid noise and tends to transforms the space to next level in a iterative manner by swapping the down-trend to up-trend or up-trend to down-trend based on the iterative set values by computing the transformations. The proposed model must handle huge dimensional data which is in a dynamic nature of proposed model and then we need to compare the results attained from the proposed model with the complex ARIMA model and identified various trial and error methods for predicting the future price without guidance. For obtaining the results we used the petroleum dataset of India and attained the results which prove that our proposed mathematical model is far better than the famous ARIMA model while handling non linear time series applications.

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