Kohei Arai Supriya Kapoor Rahul Bhatia Editors Intelligent ......The series “Advances in...

Advances in Intelligent Systems and Computing 857

Kohei AraiSupriya KapoorRahul Bhatia Editors

Intelligent ComputingProceedings of the 2018 Computing Conference, Volume 2

Advances in Intelligent Systems and Computing

Volume 857

Series editor

Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Polande-mail: [email protected]

The series “Advances in Intelligent Systems and Computing” contains publications on theory,applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually alldisciplines such as engineering, natural sciences, computer and information science, ICT, economics,business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all theareas of modern intelligent systems and computing such as: computational intelligence, soft computingincluding neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms,social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds andsociety, cognitive science and systems, Perception and Vision, DNA and immune based systems,self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centriccomputing, recommender systems, intelligent control, robotics and mechatronics includinghuman-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligentdata analysis, knowledge management, intelligent agents, intelligent decision making and support,intelligent network security, trustmanagement, interactive entertainment,Web intelligence andmultimedia.

The publications within “Advances in Intelligent Systems and Computing” are primarily proceedingsof important conferences, symposia and congresses. They cover significant recent developments in thefield, both of a foundational and applicable character. An important characteristic feature of the series isthe short publication time and world-wide distribution. This permits a rapid and broad dissemination ofresearch results.

Advisory Board

Chairman

Nikhil R. Pal, Indian Statistical Institute, Kolkata, Indiae-mail: [email protected]

Members

Rafael Bello Perez, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cubae-mail: [email protected]

Emilio S. Corchado, University of Salamanca, Salamanca, Spaine-mail: [email protected]

Hani Hagras, University of Essex, Colchester, UKe-mail: [email protected]

László T. Kóczy, Széchenyi István University, Győr, Hungarye-mail: [email protected]

Vladik Kreinovich, University of Texas at El Paso, El Paso, USAe-mail: [email protected]

Chin-Teng Lin, National Chiao Tung University, Hsinchu, Taiwane-mail: [email protected]

Jie Lu, University of Technology, Sydney, Australiae-mail: [email protected]

Patricia Melin, Tijuana Institute of Technology, Tijuana, Mexicoe-mail: [email protected]

Nadia Nedjah, State University of Rio de Janeiro, Rio de Janeiro, Brazile-mail: [email protected]

Ngoc Thanh Nguyen, Wroclaw University of Technology, Wroclaw, Polande-mail: [email protected]

Jun Wang, The Chinese University of Hong Kong, Shatin, Hong Konge-mail: [email protected]

More information about this series at http://www.springer.com/series/11156

http://www.springer.com/series/11156

Kohei Arai • Supriya KapoorRahul BhatiaEditors

Intelligent ComputingProceedings of the 2018 ComputingConference, Volume 2

123

EditorsKohei AraiFaculty of Science and Engineering,Department of Information Science

Saga UniversityHonjo, Saga, Japan

Supriya KapoorThe Science and Information (SAI)Organization

Bradford, West Yorkshire, UK

Rahul BhatiaThe Science and Information (SAI)Organization

Bradford, West Yorkshire, UK

ISSN 2194-5357 ISSN 2194-5365 (electronic)Advances in Intelligent Systems and ComputingISBN 978-3-030-01176-5 ISBN 978-3-030-01177-2 (eBook)https://doi.org/10.1007/978-3-030-01177-2

Library of Congress Control Number: 2018956173

© Springer Nature Switzerland AG 2019This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made. The publisher remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

Statistical Learning of Lattice Option Pricing and Traders’ BehaviorUsing Ising Spin Model for Asymmetric Information Transitions . . . . . 1Prabir Sen and Nang Laik Ma

Deep Time Series Neural Networks and Fluorescence Data StreamNoise Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18James Obert and Matthew Ferguson

Controlled Under-Sampling with Majority Voting Ensemble Learningfor Class Imbalance Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Riyaz Sikora and Sahil Raina

Optimisation of Hadoop MapReduce Configuration ParameterSettings Using Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40Ali Khaleel and H. S. Al-Raweshidy

Fast Interpolation and Fourier Transformin High-Dimensional Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Michael Hecht and Ivo F. Sbalzarini

A Reconfigurable Architecture for Implementing Locally ConnectedNeural Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76J. E. G-H-Cater, C. T. Clarke, B. W. Metcalfe, and P. R. Wilson

Minimum Spanning Tree Problem with Single-Valued TrapezoidalNeutrosophic Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Said Broumi, Mohamed Talea, Assia Bakali, Florentin Smarandache,and Santanu Kumar Patro

Hybrid Evolutionary Algorithm Based on PSOGA for ANFISDesigning in Prediction of No-Deposition Bed Load SedimentTransport in Sewer Pipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Bahram Gharabaghi, Hossein Bonakdari, and Isa Ebtehaj

v

Extreme Learning Machines in Predicting the Velocity Distributionin Compound Narrow Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119Hossein Bonakdari, Bahram Gharabaghi, and Isa Ebtehaj

Rice Classification Using Scale Conjugate Gradient (SCG)Backpropagation Model and Inception V3 Model . . . . . . . . . . . . . . . . . 129Zahida Parveen, Yumnah Hasan, Anzar Alam, Hafsa Abbas,and Muhammad Umair Arif

A Machine Learning Approach to Analyze and Reduce Featuresto a Significant Number for Employee’s Turn OverPrediction Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142Mirza Mohtashim Alam, Karishma Mohiuddin, Md. Kabirul Islam,Mehedi Hassan, Md. Arshad-Ul Hoque,and Shaikh Muhammad Allayear

Legal Document Retrieval Using Document Vector Embeddingsand Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Amal Shehan Perera,Vindula Jayawardana, Dimuthu Lakmal, and Madhavi Perera

Deep Learning Based Classification System for Identifying WeedsUsing High-Resolution UAV Imagery . . . . . . . . . . . . . . . . . . . . . . . . . . . 176M. Dian Bah, Eric Dericquebourg, Adel Hafiane, and Raphael Canals

Recognition of Heart Murmur Based on Machine Learningand Visual Based Analysis of Phonocardiography . . . . . . . . . . . . . . . . . 188Magd Ahmed Kotb, Hesham Nabih Elmahdy, Fatma El Zahraa Mostafa,Christine William Shaker, Mohamed Ahmed Refaey,and Khaled Waleed Younis Rjoob

Activity Recognition from Multi-modal Sensor Data Using a DeepConvolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203Aboozar Taherkhani, Georgina Cosma, Ali A. Alani, and T. M. McGinnity

Quality Scale for Rubric Based Evaluation in Capstone Projectof Computer Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219Zeeshan Haider Malik, Sabur Butt, and Hanzla Sajid

Transfer of Empirical Engineering Knowledge Under TechnologicalParadigm Shift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234Xinyu Li, Zuhua Jiang, Yeqin Guan, and Geng Li

Role of Digital Fluency and Spatial Ability in Student Experienceof Online Learning Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251Tatiana Tchoubar, Thomas R. Sexton, and Lori L. Scarlatos

vi Contents

Identifying the Underlying Factors of Students’ Readinessfor E-Learning in Studying English as a Foreign Languagein Saudi Arabia: Students’ and Teachers’ Perspectives . . . . . . . . . . . . . 265Ibrahim M. Mutambik, John Lee, and Yvonne Foley

Using Cyber Competitions to Build a Cyber Security Talent Pipelineand Skilled Workforce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280R. Cherinka

Practical Security for Electronic Examinations on Students’ Devices . . . 290Bastian Küppers, Marius Politze, Richard Zameitat, Florian Kerber,and Ulrik Schroeder

Automating the Configuration Management and Assessmentof Practical Outcomes in Computer Networking Laboratories . . . . . . . . 307Neville Palmer, Warren Earle, and Jomo Batola

Designing the University’s Creative Environment:Structural-Functional Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Alexander O. Karpov

Birds Control in Farmland Using Swarm of UAVs: A BehaviouralModel Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333Chika O. Yinka-Banjo, Wahab A. Owolabi, and Andrew O. Akala

A Review of Path Smoothness Approaches for Non-holonomicMobile Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346Iram Noreen, Amna Khan, and Zulfiqar Habib

Design and Implementation of Bluetooth Controlled Painting Robotfor Auto Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359Bilal Ahmad, Ayesha Iqbal, Roshaan Saqib, Mohammad Mustafa Mirza,and Atta ul Mohsin Lali

A Survey on Trust in Autonomous Systems . . . . . . . . . . . . . . . . . . . . . . 368Shervin Shahrdar, Luiza Menezes, and Mehrdad Nojoumian

An Energy Efficient Coverage Path Planning Approach forMobile Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387Amna Khan, Iram Noreen, and Zulfiqar Habib

SOAP-Based Web Service for Localization of Multi-robot Systemin Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398Tlijani Hayet and Jilani Knani

Metrics for Real-Time Solutions Design . . . . . . . . . . . . . . . . . . . . . . . . . 411Khaldia Laredj, Mostefa Belarbi, and Abou Elhassan Benyamina

Contents vii

GGSE-Website Usability Evaluation Framework . . . . . . . . . . . . . . . . . . 426Aiman Khan Nazir, Iqra Zafar, Asma Shaheen, Bilal Maqbool,and Usman Qamar

Measuring Application Stability Using Software StabilityAssessment Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437Bassey Asuquo Ekanem and Evans Woherem

Automated Scenario-Based Evaluation of Embedded Softwareand System Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452Thomas Kuhn, Pablo Oliveira Antonino, and Andreas Morgenstern

Development Approaches for Mobile Applications: ComparativeAnalysis of Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 470Lisandro Delia, Pablo Thomas, Leonardo Corbalan, Juan Fernandez Sosa,Alfonso Cuitiño, Germán Cáseres, and Patricia Pesado

Using Linked Data Resources to Generate Web Pages Basedon a BBC Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485Leila Zemmouchi-Ghomari, Rania Sefsaf, and Kahina Azni

Trade-off Analysis Among Elicitation Techniques Using SimpleAdditive Weighting Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498Asif Ullah, Shah Nazir, and Sara Shahzad

Risk Management in Software Engineering: What Still Needsto Be Done . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515Tauqeer Hussain

A Survey of Quality of Service (QoS) Protocols and Software-DefinedNetworks (SDN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527Ronak Al-Haddad and Erika Sanchez Velazquez

Ambiguity Function Analysis of Frequency Diverse ArrayRadar Receivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 546Jianbin Gao, Kwame Opuni-Boachie Obour Agyekum,Emmanuel Boateng Sifah, Qi Xia, and Edward Agyemang-Duah

WebNSM: A Novel Scalable WebRTC Signalling Mechanismfor One-to-Many Bi-directional Video Conferencing . . . . . . . . . . . . . . . 558Naktal Moaid Edan, Ali Al-Sherbaz, and Scott Turner

Developing an Asynchronous Technique to Evaluate the Performanceof SDN HP Aruba Switch and OVS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569Ameer Mosa Al-Sadi, Ali Al-Sherbaz, James Xue, and Scott Turner

viii Contents

Energy Aware Cluster-Head Selection for Improving NetworkLife Time in Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . 581Faheem Khan, Toor Gul, Shujaat Ali, Abdur Rashid, Dilawar Shah,and Samiullah Khan

Small Cells Solution for Enhanced Traffic Handlingin LTE-A Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594Raid Sakat, Raed Saadoon, and Maysam Abbod

Differential Cooperative E-health System over AsymmetricRayleigh-Weibull Fading Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607Sara AlMaeeni

Calculating Minimum Rotation Angle to Find Marked GeographicalLocations Using Built-in Mobile Sensors . . . . . . . . . . . . . . . . . . . . . . . . 617Naween Fonseka and Cassim Farook

Performance Evaluation of Sending Location Update Packetto a Locator Identity Split Mapping Infrastructure . . . . . . . . . . . . . . . . 626Avinash Mungur, Atish Foolchand, and Avishan Gopaul

Power Allocation Scheme Using PSO for Amplify and ForwardCooperative Relaying Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 636Kamarul Ariffin Bin Noordin, Mhd Nour Hindia, Faizan Qamar,and Kaharudin Dimyati

Performance Evaluation of Resources Management in WebRTC for aScalable Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 648Naktal Moaid Edan, Ali Al-Sherbaz, and Scott Turner

Barriers to Adopting Interoperability Standards for Cyber ThreatIntelligence Sharing: An Exploratory Study . . . . . . . . . . . . . . . . . . . . . . 666Nicole Gong

qCB-MAC: QoS Aware Cluster-Based MAC Protocol for VANETs . . . 685A. F. M. Shahen Shah, Haci Ilhan, and Ufuk Tureli

Error Reconciliation with Turbo Codes for Secret Key Generationin Vehicular Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696Dhouha Kbaier Ben Ismail, Petros Karadimas, Gregory Epiphaniou,and Haider M. Al-Khateeb

Enumeration and Applications of Spanning Trees in Book Networkswith Common Path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705Raihana Mokhlissi, Dounia Lotfi, Joyati Debnath,and Mohamed El Marraki

Contents ix

Digital Chunk Processing with Orthogonal GFDM Doubles WirelessChannel Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719Mohammad R. Kadhum, Triantafyllos Kanakis, Ali Al-Sherbaz,and Robin Crockett

Model of Maturity of Communication Processes in Human CapitalManagement in an Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 732Andrzej Sołtysik

PD Controller for Resilient Packet Ring Networks . . . . . . . . . . . . . . . . . 751Fahd Alharbi

Visual Meaningful Encryption Scheme Using IntertwinningLogistic Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764Saadullah Farooq Abbasi, Jawad Ahmad, Jan Sher Khan,Muazzam A. Khan, and Shehzad Amin Sheikh

Behavior of Organizational Agents on ManagingInformation Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 774Mark van der Pas and Rita Walczuch

Expanded Algorithm for Inertial Navigation . . . . . . . . . . . . . . . . . . . . . 789P. M. Aksonenko, V. V. Avrutov, Yu. F. Lazarev, P. Henaff,and L. Ciarletta

In-Body Antenna for Wireless Capsule Endoscopy at MICS Band . . . . 801Md. Rasedul Islam, Raja Rashidul Hasan, Md. Anamul Haque,Shamim Ahmad, Khondaker Abdul Mazed, and Md. Rafikul Islam

Classification and Analyses of Tendencies of Prior Published ResearchStudies on Unmanned Aerial Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . 811Il-Kyu Ha

Interference and Channel Quality Based Channel Assignmentfor Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823Manish Wadhwa and Komalpreet Kaur

Super Adaptive Routing Protocol for Mobile Ad Hoc Networks . . . . . . 834Firas Sabah Al-Turaihi and Hamed S. Al-Raweshidy

Multi-hop Hierarchical Routing Based on the Node Health Statusin Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849A. Anhar and R. Nilavalan

TCP-MAC Cross Layer Integration for Xor Network Coding . . . . . . . . 860Khaled Alferaidi and Robert Piechocki

Rotating Signal Point Shape Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 876Janak Sodha

x Contents

Measuring the Effectiveness of TCP Technique for Event SequenceTest Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 881Johanna Ahmad, Salmi Baharom, Abdul Azim Abd Ghani,Hazura Zulzalil, and Jamilah Din

Ternary Computing to Strengthen Cybersecurity . . . . . . . . . . . . . . . . . 898Bertrand Cambou and Donald Telesca

A Novel Spatial Domain Semi-fragile Steganography Algorithmand Authentication Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 920Nikolaos G. Bakaoukas and Anastasios G. Bakaoukas

Towards Personalised, DNA Signature Derived Music via the ShortTandem Repeats (STR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 951Tirthankar Paul, Seppo Vainio, and Juha Roning

SEAMS: A Symmetric Encryption Algorithm Modification Systemto Resist Power Based Side Channel Attacks . . . . . . . . . . . . . . . . . . . . . 965K. P. A. P. Pathirana, L. R. M. O. Lankarathne,N. H. A. D. A. Hangawaththa, K. Y. Abeywardena,and N. Kuruwitaarachchi

A Light Weight Cryptographic Solution for 6LoWPANProtocol Stack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 977Sushil Khairnar, Gaurav Bansod, and Vijay Dahiphale

Forensics Data Recovery of Skype Communicationfrom Physical Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 995Ahmad Ghafarian and Charlie Wood

Looking Through Your Smartphone Screen to Steal Your PinUsing a 3D Camera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1010Diksha Shukla, Vir V. Phoha, and Saurav Prakash

Cyber Physical Security Protection in Online AuthenticationMechanisms for Banking Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1021Ahmed Yousuf Jama, Tansal Güçlüoğlu, and Maheyzah Md Siraj

The Impact of Social Networks on Students’ Electronic Privacy inSaudi Arabia Society . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1032Nabih T. Abdelmajeed

Comparison of Different Types of ANNs for Identificationof Vulnerable Web Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1042Mahmoud O. Elish

Contents xi

E-Secure: An Automated Behavior Based Malware Detection Systemfor Corporate E-Mail Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1056K. Thebeyanthan, M. Achsuthan, S. Ashok, P. Vaikunthan,A. N. Senaratne, and K. Y. Abeywardena

An Intelligent Path Towards Fast and Accurate Attribution . . . . . . . . . 1072Jim Q. Chen

Survey of Automated Vulnerability Detection and Exploit GenerationTechniques in Cyber Reasoning Systems . . . . . . . . . . . . . . . . . . . . . . . . 1083Teresa Nicole Brooks

Managing Privacy Through Key Performance Indicators WhenPhotos and Videos Are Shared via Social Media . . . . . . . . . . . . . . . . . . 1103Srinivas Madhisetty and Mary-Anne Williams

Incentivizing Blockchain Miners to Avoid Dishonest Mining Strategiesby a Reputation-Based Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1118M. Nojoumian, A. Golchubian, L. Njilla, K. Kwiat, and C. Kamhoua

A BI Solution to Identify Vulnerabilities and Detect Real-TimeCyber-Attacks for an Academic CSIRT . . . . . . . . . . . . . . . . . . . . . . . . . 1135Francsico Reyes, Walter Fuertes, Freddy Tapia, Theofilos Toulkeridis,Hernán Aules, and Ernesto Pérez

Security Metrics for Ethical Hacking . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154Reem Al-Shiha and Sharifa Alghowinem

Spanning Tree Protocol for Preventing Loops and Saving Energyin Software Defined Networks Along with Its Vulnerabilityand Threat Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1166Aleena Rehman, Farhan A. Siddiqui, Jibran R. Khan,and Muhammad Saeed

Simulations for Deep Random Secrecy Protocol . . . . . . . . . . . . . . . . . . . 1181Thibault de Valroger

PREDECI Model: An Implementation Guide . . . . . . . . . . . . . . . . . . . . . 1196Fernando Molina-Granja, Glen D. Rodríguez Rafael, Washington Luna,Raúl Lozada-Yanez, Fabián Vásconez, Juan Santillan-Lima,Katherine Guerrero, and Cristian Rocha

SAHCE: Strong Authentication Within the HybridCloud Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1212Belbergui Chaimaa, Elkamoun Najib, and Hilal Rachid

A Blockchain-Based Decentralized System for Proper Handlingof Temporary Employment Contracts . . . . . . . . . . . . . . . . . . . . . . . . . . 1231Andrea Pinna and Simona Ibba

xii Contents

Poster: Password Input Method Using Simple Device . . . . . . . . . . . . . . 1244Reina Momose, Manabu Okamoto, and Miyu Shibata

Detection of Prolonged Stress in Smart Office . . . . . . . . . . . . . . . . . . . . 1253Elena Vildjiounaite, Ville Huotari, Johanna Kallio, Vesa Kyllönen,Satu-Marja Mäkelä, and Georgy Gimel’farb

An Efficient NTRU-Based Authentication Protocolin IoT Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1262SeongHa Jeong, KiSung Park, YoHan Park, and YoungHo Park

Pain Evaluation Using Analgesia Nociception Index DuringSurgical Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1269Jiann-Shing Shieh, Bhekumuzi Mathunjwa, Muammar Sadrawi,and Maysam F. Abbod

Automated Inner Limiting Membrane Segmentation in OCT RetinalImages for Glaucoma Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1278Aneeqa Ramzan, M. Usman Akram, Javeria Ramzan,Qurat-ul-Ain Mubarak, Anum Abdul Salam, and Ubaid Ullah Yasin

Kernel Matrix Regularization via Shrinkage Estimation . . . . . . . . . . . . 1292Tomer Lancewicki

Single Image Based Random-Value Impulse Noise LevelEstimation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1306Long Bao, Karen Panetta, and Sos Agaian

Hybrid Vehicular Network System for the Malfunctioned VehiclesOver Highways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1319Weam Gahsim Mergani, Abo-Obyida Mohammed Alahssen,L. M. Ahmed, and M. F. L. Abdullah

International Cyber Attackers Eyeing Eastern India:Odisha - A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1328Bhaswati Sahoo, Rabindra Narayan Behera, and Sanghamitra Mohanty

Computer Graphics Based Approach as an Aid to Analyze Mechanicsof the Replaced Knee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1340Ahmed Imran

Efficient Set-Bit Driven Shift-Add Binary Multiplier . . . . . . . . . . . . . . . 1346Alvernon Walker and Evelyn Sowells-Boone

Estimation of Stress Condition of Children by NasalSkin Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1351Madoka Suzuki and Tomoaki Ohtsuki

Private Collaborative Business Benchmarking in the Cloud . . . . . . . . . . 1359Somayeh Sobati-Moghadam and Amjad Fayoumi

Contents xiii

Mobile Phone Operations Just by Sight and Its Applications . . . . . . . . . 1366Kohei Arai

Music4D: Audio Solution for Virtual Reality . . . . . . . . . . . . . . . . . . . . . 1375Shu-Nung Yao and Cheng-Yuan Yu

c3d.io: Enabling STEAM (Science, Technology, Engineering, Arts,Mathematics) Education with Virtual Reality . . . . . . . . . . . . . . . . . . . . 1380Jason Madar

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1387

xiv Contents

Statistical Learning of Lattice Option Pricingand Traders’ Behavior Using Ising Spin Model

for Asymmetric Information Transitions

Prabir Sen1(&) and Nang Laik Ma2

1 STATGRAF Research, Vancouver, [email protected]

2 School of Business, Singapore University of Social Sciences,Singapore, Singapore

[email protected]

Abstract. Financial fluctuations are one type of complex problem to determinethe market behavior. The study of such fluctuations and statistical (machine)learning methods to predict the option price changes has been done by manyresearchers in the past. With the advancement in technology, one can capture thecomplexities in the financial systems and use of deep statistical (machine)learning, and apply unique set of rules and principles to these multi-layeredcomplex networks. This paper provides a framework for lattice option pricing todetermine the state for choice-sets, as one such unique set, in complex financialnetworks. This is largely based on human intelligence that learns features ofeach individual stock, and their trade-off, pay-off, preferential attachment andstrategic options in the decision-making process. This paper also focuses oncases where both price and demand fluctuates stochastically and where bothbuyers and sellers have asymmetric information with limited time for high-quality decisions at their disposal to encourage or deter behavioral change. Thesituation draws on statistical mechanics and Ising-spin approaches to derivecomputational methods that infer and explain patterns and themes from high-dimensional data to “manage the probable” as well as “lead the possibilities” formulti-stage optimal control in dynamic systems.

Keywords: Data science � Machine learning � Lattice option pricingSignal processing � Cognitive decisions � Statistical physics � Ising-spin modelIntelligent systems � Network science � Statistical mechanics

1 Introduction

Trading is very common in financial industry. Trader will send important information,as a signal to another party who would be buyer or seller or the IT system or platform.The question that we are interested in this business scenario is the interactive structureof a market accounts for informational content, if any, of these potential signals(Spence 1973) [1], the endogenous market process whereby the trader, for example,requires (and transmits) information about potential partners (as buyer or seller), whichultimately determines the implicit speculation involved in selecting portfolios,

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allocating information to people and triggering people for further information in themarket? As additional new partners join the market, this study assumes repeated cyclesaround the loop to “manage the probable”. This study also modifies trader’s conditionalprobabilistic outcomes, as end-reward schedules, are adjusted to partners’ behaviorwith respect to signal choice, signal change and post-change to new signals, as theybecome available to the trader.

It is useful to introduce a distinction between attributes that collectively constitutethe state of the partner’s present; some are fixed, while others are alterable, but irre-versible. It is these aspects of the mind or process, as cognitive state, that correspondsto thinking and feeling, comprising a conglomerate of mental representations andpropositional attitudes (Simon 1973) [2]. At any point in time when interacting with anindividual partner, the trader’s subjective assessment of the speculation which he or sheconfronts is defined by these states or conditions, based on probability distributions.This viewpoint regards signals and states as drivers in probability distributions thatdefine a trader’s eventual outcome or pay-offs.

At this point it is perhaps clear that there are actions of the trader that “lead thepossibilities”, for a pre-defined outcome or a series of outcomes. These actions, attri-butes of the dynamics in a complex financial network, are a mathematical function thattakes the trajectory or history of the financial system as an argument and the metrics asresults. As new market information becomes available to the trader through selectionand subsequent observations on partner capabilities, as they represent signals and state,the trader’s conditional probabilistic outcomes are adjusted, and a new round starts.The fee schedule of each subsequent new entrant in this network generally differs fromthat of the previous partners in the network.

It is desirable to find the expected response, to each action, and impact thereof inthe market over time. It could be something said or written as a reply to a possibleaction: something that is expected as a response to an action. To avoid studying thecomplex financial system in continuous state of action and response, it is useful to lookat this as a non-transitory configuration of the responsive system, where the market isgenerating - either through each “satisficing” decision or a series of small (quantumlike) decisions towards a big decision – an empirical distribution of partner capabilitiesthrough observable action-response or signals (in cognitive states). The trader’s pay-offor reward, is an intended outcome, of this relationship for information or knowledge,so yt � yp, where the trader’s outcome, yt, and the partner’s outcome, yp, is acceptableto both party. A reward is as an appetitive stimulus given to a human or some otherentities in recognition of their service, effort or achievement n in order to alter theirbehavior. Again, the information or knowledge level conveys the partners’ intent ofinvesting in information or knowledge in the financial network (Barabasi 2002) [3]. Ifthey do not invest they might incur lower fees but the loss would exceed the gain fromnot making the knowledge-based decisions. This implies that there could be prereq-uisites that convey no information and hence it is not useful to make any decision basedon that information.

In recent years, due to the advancement in data science and advanced analyticstechniques as well as the massive amount of data available, the vision of better pre-diction to ‘manage the probable’ and prescription to “lead the possibilities” is gaining

2 P. Sen and N. L. Ma

momentum. In the next few sections, we discuss our “slicing” approach of training datafor machines to learn probabilities and their densities: by breaking down the deepfinancial network into several layers for a data model, where individual systems –

trader, buyer and seller – interact to take multiple quanta-like small decisions towards abig decision that naturally generalizes the model (Watts and Strogatz 1998) [4]. Wefurther slice the data for each machine to learn features and trade-offs, for one asset vis-à-vis another that likely attracts buyers and sellers to the trader. This multi-layered,multi-dimensional learning model is designed to determine probabilities and volatilities– using the Ising-spin model in asymmetric information transition algorithms – forfinding short paths with high probability and low errors for each option. We further usea multi-layered, multi-dimensional optimization model for efficient learning andsteepest descent to execute the model in a decentralized financial system for dynamicdecisions (Scholtes and Tessone 2011) [5]. Later, we also present some computationalresults and performance of the model. Finally, we discuss the limitations of the modeland propose some challenges and opportunities that emerge when using quantum-likemachine learning for strategic decisions with predictable and quantifiable prescriptiveproperties.

2 Quantum Neural Data

Individual training data, including asset features, trading (exchange) location, pre-order, order, delivery, volatility, etc., were structured in a way to provide an under-standing of the membrane properties of different features of a particular type of partneror individual. These features of a neuron (partner or individual) in the neural-networkstructure show different (labeled) compartments, together with a canonical represen-tation. Therefore, the search for membrane properties (set to data search), instead ofinferring for deep-layer properties, gives an overview of the membrane propertiesfound in all compartments in the neuron while connectivity shows the membraneproperties and synaptic connections to all compartments in this neuron and inferencesprovides interactions collected at a particular time for all properties of all compartmentsin this neuron. The search enables the user to observe the combination of membraneproperties that mediates the integrated activity of a given compartment; compare it tocombined property in different compartments; identify a series of inferences that areestablished in a property in a compartment of this neuron type; and learn the differencesusing the evidence from a particular property as a signal.

A cognitive and predictive system (Sen 2015) [6] is operable to estimate a humancognitive state based on activities and trajectories using multiple sources of dynamicbehavioral data and advancing analyses techniques in real-time to draw inferences forindividually-signaled adaptive decisions. Entities may respond to these inferences withpersonalized services at a location and at a moment in time that is relevant to theindividual. Inferences for each individual machine may be aggregated to form a col-lection of dynamic decisions relevant to an individual, and the collection of dynamicdecisions may also be used to draw the inferences for the individual. Representation ofvarious canonical forms of cognitive states requires the diversity of their dendritic trees,as in neural-network structure. In a single dendrite represented as an equivalent

Statistical Learning of Lattice Option Pricing 3

cylinder (e) consists of a chain of three decision compartments, (p) for proximal,(m) for middle, and (d) for distal, with respect to the cell body. The approach (Shepherd1968) [7] is representing these patterns as equivalent dendrites, with corresponding p,m and d compartments.

A lattice fractal is a graph, which corresponds to a fractal, with self-similarities, butmost of them have no translation invariance (Chen 1992) [8]. They are generallydistributed compositional semantics of multi-dimensional objects. Chen’s model showsthat the Ising model on the lattice Sierpinski carpet exhibits the phase transition in anydimension, but has no transition phase (because of the character of the finitely ramifiedfractal) on the lattice Sierpinski gasket. When missing data are present, the model usesexpectation-maximization (EM) algorithm to estimate parameter in Bayesian Networkswhen there is training data (Friedman 1997) [9] and in-record linkage when there is notraining data (unsupervised learning). The model further uses EM and Monte CarloMarkov Chain (MCMC) methods to estimate the error rate automatically in some of therecord linkage situations (Larsen and Rubin 2001) [10].

3 Measuring Trade-offs

Transforming signals in complex financial networks begins with understanding theflow of information between the trader and partner machines. Trader systems try toidentify signals in the individual machines which are relatively more attractive andmeasure the extent of their (dis)satisfaction so as to prioritize areas for improvement.Individual machines and their interactions with the financial network have two fun-damental properties: the small world (get “satisfied” or “dissatisfied” from one or twonetwork interactions) and the scale-free properties (“make it viral” in other networkinteractions). In small world network, one can reach a given node from another onewith the smallest number of links between the nodes, in a very small number of steps.Many naturally occurring networks are of this nature. It is mathematically expressed bythe slow (logarithmic) increase of the average diameter of the network, l; with the totalnumber of nodes N, l < lnN; where l is the shortest distance between two nodes anddefines the distance metric in complex networks (Erdos and Renyi 1960) [11] equiv-

alently, obtains N � el=lo , where lo is a characteristic length.Secondly, these complex networks arises with the discovery that the probability

distribution of the number of links per node, P(k) (also known as the degree distri-bution), can be represented by a power-law (‘scale-free’) with a degree exponent c thatis usually in the range 2\c\3 (Bollobas 1985) [12].

P kð Þ � k�c

These discoveries have been confirmed in many empirical studies of diverse net-works. Multi-layered multi-dimensional networks (Newman 2003) [13], as depicted inFig. 1, explicitly incorporate multiple service channels of connectivity and constitutethe natural environment to describe systems interconnected through different categoriesof connections: each partner channel (relationship, activity, category) is represented by


a layer and the same node or entity may have different kinds of interactions (differentset of neighbors in each layer) for different set of decisions. Partner (dis)satisfactionmeasurement efforts generate accurate, actionable insights based on three guidingprinciples:

(a) Individuals signal what matter most, without their being asked directly: Explicitlyasking people which they considered as most important to improve in the eco-nomic delivery, for example, the time required to respond a request versus thedirection and guidance from a staff, is unlikely to produce accurate results.Unfortunately, most people will say every aspect is equally important. So indi-vidual machines are trained to learn how to rate each feature (for example, theoverall trading process of settlement) or trade-offs between the features. Thismethod provides insights about partners’ needs and priorities.

(b) Identify natural break points in service-quality: It is unrealistic to achieve zerowait times and one-click transactions across all services as it will increase theoperational cost exponentially. Thus, traders need to find a balance betweendelivering high-quality and responsive services verses managing resources effi-ciently and effectively by using metrics to determine “statisficing’ economiclevels. One way for machines to learn is to identify break points – the “last-mile”journey of experience in trading – at which delays or shortfalls cause (dis)satis-faction. Machines are able to identify the trade-offs between trader and partnersatisfaction for both of these channels in real time, which will in turn raise overallpartner satisfaction (Faloutsos et al. 1999) [14].

(c) Combine pay-off for internal data with partner data and external public data touncover hidden painpoints: Combining trader’s internal information with opera-tional data of partners – usage data, volume, delay, annual reports, social media

Fig. 1. Multi-layered relationships in the financial network


data, public review, market trends, etc. – can produce additional insights onmachines’ cognitive state implicitly or explicitly. This will help to identify thecorrelation between problematic machines and the key drivers of their (dis)sat-isfaction. The trader system requires a structured model, as in Fig. 2, to foreseethe unintended consequences and “manage the probable” for a collective decision-making process.

In addition, the trader system also requires a deeper understanding of suchunderlying latent variables to identify how individual machines interact in differentfinancial networks. These, so called interaction mechanisms, are paramount in under-standing the emergent nature of collective decisions, as they often lead to commonfeatures and same patterns within the network structure. This also hypothesizes whatother outcomes there could be, should the interactions change. And that shines light onthe unintended consequences at some future time. Very little study has been found onthe connectivity of interconnected nodes which have different length. But manyexamples in finance exhibits the importance of individual and collective trade-offsbehavior, for example, between helpful responses and long wait-time in banking net-works and links between clusters of similar subject payment sites of exhibit similarbehavior (Berlingerio et al. 2011) [15]. In such structured systems, virtual connectionsbetween participating machines are created in a globally consistent way to construct apre-determined topology. While this allows for the development of highly efficientalgorithms for distributed search, routing or information dissemination, the fact that thisnetwork topology has to be maintained at all times in the face of highly dynamicconstituents is a major challenge.

If the expected activities, for example, for a group of partners indicate that a largenumber of machines are exercising a particular choice, the forecasting module maymodify the machine’s target choices to encourage (e.g. frequency of trade) or deter

Fig. 2. These steps in decision making give machines the essential elements of a structuredprocess model.


behavior (e.g. disinvestment). This model also estimates the cognitive state of amachine, which may include their experience for an activity. A stochastic sub-processmay be executed to determine expectations the probability of a machine (n) being in anunobserved state r at time t. A state-choice sub-process to determine the cognitive statefor each choice associated with a choice set attribute. The method applied that train onesupport vector machine (SVM) per individual choice basket b. . .Bð Þ and to computethe individual partworths generated from Gaussian distribution by regularizing with theaggregated partworths. A choice from the choice set (j…J) with feature variables areshown as X and a choice for one over another is expressed as 1. . .:ið Þ of X, that wasselected by a machine – the trader or partner – is determined and the maximumlikelihood probability for that choice may be used to determine the expected cognitivestate defined as:

Pnr jð Þ ¼ 1

B jð ÞYKi¼1

Xji�1i

The expected cognitive states for each machine’s decision on a choice-set areaccumulated based on the last activity performed for machines 1 to N. A relationalactivity model is used to accumulate the cognitive states. A relational clique is aconstruct of a clique over all activities at various states on a trajectory, which may be atravel path of one or more machines. Each clique C is associated with a potentialfunction that maps a tuple (values of decisions or aggregations). Together they provide(a) activity-based decision, (b) state and (c) actions of consecutive activities asexpressed by the following Equation [6].

Xy0

Yc � c

av0c � c

;c v0c� �

Since each machine is modeled as a quantum candidate, for example, eachmachine’s last decision is modeled as a function of one or more of time, state, transitionand constraints. A single machine’s cognitive state, therefore, is determined and then Zis defined, where Z is the probability statistical distribution of finding the machine inany particular cognitive state associated with a decision U, machines N and state-density V. Z is proportional to the degeneracy of the accumulated cognitive states (of Ras in Relational activity model). The grand sum is the sum of the exponential, whichmay be determined by expanding the exponential in Taylor series, over all possiblecombinations of U, V and N. The single-particle density of states is investigated for afinancial system of localized assets (electrons) with Coulomb interaction in a randompotential situated on a fractal lattice of the Vicsek type with a fractal dimension of twoembedded in three-dimensional Euclidean space. The study found that the density isdetermined by the geometric fractal dimension of the lattice instead of the spectraldimension, which usually determines the density.


4 Use of Ising: Spin Model for Stochastic Volatility

Next we consider a price model of auctions for an asset in an exchange (stock) market.Assume that each trader can trade the asset several times at each day t 2 {1, 2, …, T},but at most one unit number of the asset at each time. Let (t) denote the daily closingprice of tth trading day. And let Kn be a subset of (2), where.

Kn ¼ f y; xf g 2 S2ð Þ : �3n � y� 3n;�3n � x� 3ng

and Ct 0ð Þ be a random open cluster on Kn. Suppose that this asset consists of jKnj (n islarge enough) investors, who are located in Kn lattice. And (0) is a random set of theselected traders who receive the information. At the beginning of trading in each day,suppose that the investors receive some news. We define a random variable ft for theseinvestors, suppose that these investors taking buying positions ft ¼ 1ð Þ selling positionsft ¼ �1ð Þ, or neutral positions ft ¼ 0ð Þ with probability q1; q�1 or 1 � ðq1 þ q�1Þðq1; q2 [ 0; q1 þ q2 � 1Þ, respectively. Then these investors send bullish, bearish orneutral signal to themarket. However, the volatility of the underlying asset is a function ofan exogenous stochastic process, typically assumed to be mean-reverting. Assuming thatonly discrete past asset data is available, we adapt an interacting particle stochasticMonteCarlo algorithm (Moral et al. 2001) [16] to estimate the stochastic volatility (SV), andconstruct a multinomial tree which samples volatilities from the SV filter’s empiricalmeasure approximation at time 0. The study applied the construction of a multivariategeneralization of the Dirichlet-multinomial distribution (DMD), instead of binomial ortrinomial tree, as approximations to continuous-time short-rate models (Kalwani 1980)[17]. The proposed model of DMD to determine the distribution f(X) of the proportion ofthe partners exposed none (F1 = 0), one (F2 = 1), two (F3 = 2)…mn (Fmn+1 = mn) of then position of m asset. This model can be expressed in the general form:

f XjA; nð Þ ¼Z 1

0

Z 1�p0

0. . .

Z 1�Pm�2

i¼0pi

0f XjP; nð ÞD PjAð Þdpm�1. . .. . .dp1dp0

with Ai ¼ pi: Sð Þ and S ¼ Pmi¼0

Ai.

The P vector contains the probabilities of m + 1 occurrence (p0; p1. . .pm ¼ P), andis the multinomial probability vector. The random variable (pi) satisfy the followingconstraint:

Xmi¼0

pi ¼ 1 and 0� pi � 1

Such machine probabilities are Dirichlet distributed, and expressed as

D PjAð Þ ¼ C A0 þA1 þ . . .Amð ÞC A0ð ÞC A1ð Þ. . .C Amð Þ p

A0�10 pA1�1

1 . . .pAm�1m


where the A vector {A0, A1, …, Am} is the Dirichlet parameter vector. When themultivariate Dirichlet and multinomial distribution are compounded, the form of theproposed model is given as:

f XjA; nð Þ ¼ n!x0!x1!. . .xm!

� �C

Pmi¼o

Ai

� � Qmi¼o

C xi þAið Þ� ��

Qmi¼o

C Aið Þ�

C nþPmi¼o

Ai

� ��

where X vector {x0, x1…xm} specifies the expected return from each asset of m asset atn level position opportunities. As with arbitrage-free pricing scheme, we also introduceone or more Martingale (a.k.a. risk-neutral) probability measures, usually denoted byQ, which is defined as the SV model with l replaced by r, the risk-free rate. The risk-neutral price of an option is its discounted expected future value under such amartingale measure Q. This means that, given the present state of the asset at the timeof pricing, the price must be determined by using the model for the asset under Q.

For simplicity, the study assumes that each partner can be in either one of two spinstates, one state is designated by A, # or +1, and the other by B, " or –1. The totalnumber of spins (partner states) is B, the number of +1 spins is N or NA and the numberof –1 spin is by B – N. The intensity of action (magnetization), I, may be convenientlydefined as the net number of –1 spins. The intensity of action per spin, I, is then

I ¼ IB ¼ 1� 2

NB ¼ B � 2q

where q ¼ NjB. Then the canonical ensemble is:

Qm B; I; Tð Þ ¼ jm Tð ÞBXXi¼1

e�Ei=kT

where Ei is the sum of nearest-neighbor (pair) trade for the ith configuration and jmrepresents the non-configurational trade (partition) function of each of the B partners ofthe system.

To derive buyer(a)-trader(b)-seller(c) configurations at exchange (d), the system atexchange (e) limits itself to the simple cubic lattice and allowing nearest-neighborinteractions that are not only between abh i bch i cdh i and deh i, but also bdh i (trader andexchange) in a three-dimension Ising model as nearest-neighbor interactions in a self-similar way does affect critical behavior (Gefen et al. 1980) [18]. We derive the model as:

K y; xð Þ ¼ 1þ xð Þ34

:1þ yð Þ2

Xr� 0

ur yð Þurð1þ yÞ2r


The consensus time is characterized in terms of a certain measure of segregationthat depends only on large-scale linking patterns among groups, and (with highprobability) not on idiosyncratic details of network realizations, as depicted in Fig. 3.This measures the extent to which machines of an economic (dis)satisfaction group arebiased toward forming links with other, similar types.

In terms of modeling their performance and robustness, most unstructuredapproaches to the information of such interactions through economic (dis)satisfactionrely, either explicitly or implicitly. In order to be able to appreciate the analogiesbetween the information of large, dynamic networked systems, statistical mechanicsand thermodynamics, briefly recalled one of the basic models of random graph theory.This model defines a probability space that contains all possible graphs or networks Gwith n nodes. Assuming that edges between pairs of nodes are being generated by astochastic process with uniform probability p, the G(n,p) model assigns each networkG = (V, E) with n ¼ Vj j nodes and m ¼ Ej j edges the same probability

PG n� pð Þ ¼ pm:pn n�1ð Þ=2�m

This simple stochastic model for networks has been used in the modeling of avariety of structural features of real-world networks. This mapping predicts that thecommon epithets used to characterize competitive systems, such as “winner takes all,”“fit get rich” (FGR), or “first-mover advantage,” emerge naturally as thermodynami-cally and topologically distinct phases of the underlying complex evolving network

Fig. 3. Features and attributes of assets at different buying and selling process.


(Bianconi and Barabasi 2001) [19]. To demonstrate the existence of a state transitionfrom the FGR phase, the pay-off distribution follows:

g �ð Þ ¼ C 2h

where h is a free parameter and the pay-offs are chosen from � 2 ð0; �maxÞ; withnormalization C ¼ hþ 1= �hþ 1

max

� �: For this class of distributions the cognitive state (a

Bose condensation) is:

hþ 1

b �maxð Þhþ 1

Z b �maxb �min tð Þ dx

xh

ex � 1\1

As the classical G(n, p) model can be viewed alternatively as an adiabatic ensemblewith a fixed Poissonian degree distribution, since power-laws (or at least heavy tailed)degree distributions have been observed for a number of real-world networks (Berg andLassig 2002) [20]. In order to formalize a useful representation of a graph in terms ofits adjacency matrix A where each element is aij ¼ 1 aij ¼ 0

� �if the nodes i and j are

connected (respectively, disconnected). Then, the spectrum of such a network is givenby the set of n eigenvalues of its adjacency matrix. Then, the spectrum of such anetwork is given by the set of n eigenvalues of its adjacency matrix. For the G(n, p)model, it is possible to characterize the spectrum of the networks in the limit ofdiverging network sizes. In this regime, if there is a giant cluster that spans the com-plete financial network, the probability p kð Þ of finding an eigenvalue ka a in thespectrum follows the so-called semi-circle law (Cohen et al. 2001) [21].

This recognized that different kinds of transitions appear when studying evolu-tionary processes in networks, as depicted in Fig. 4. When the different nodes exhibitcommon dynamics at the global level, it can be said that a synchronized state hasemerged. This also shows we need large enough grid to have sharp predictions. Theidea of this model is to start with the estimated volatility distribution Yn

0 :¼ UnK at the

present time, represented by its weighted particles Yn ¼ Yj; pj� �

: j ¼ 1; 2; . . .:n �

andwith the logarithm of the price of the asset today x0 = log S0, and then to takeadvantage of the same particle filtering scheme to generate future asset prices andvolatility values: that is to say, we used stochastic volatility filtering in a dynamic wayfor pricing. Once we find the value at the expiration X(x0)N = XT we can compute thevalue of the option at the expiration, and then we discount back to the present valueusing the risk-free rate. This represents one replication of a Monte Carlo method: tocompute an estimate of the option price we generate many replications (typically of theorder n″ = 106) then compute the average of the values obtained. This average is ourestimate for the price of the option today. The convergence, and the convergence speed,of order (n′′)−1/2, is of course guaranteed by a standard argument based on the centrallimit theorem.

Since option prices given by our dynamic model is not as close to those given in themarket— static model’s prices—, there seems little reason to improve the efficiency ofour Monte Carlo method for the dynamic model. Interestingly, this also means that


there exists a relationship between trader and partners in the financial network (in termsof its eigenvalues) and the dynamic processes are happening at the level of nodes.

Dynamic replication processes also take place in a discrete space. (Farbod 2009)[22] uses the discrete logarithmic mapped in a graph for such process, it is equivalent tostochastic search, and transport phenomena within the network. This study introducesthe basic idea of the replica method by computing the spectrum of random matrices. Tobe confident, the study explores the case of real symmetric matrices of large orderN having random elements Jij that are independently distributed with zero mean andvariance J2 ¼ N�1.

The resolvent R of the N � N matrix J is defined as follows:

R eð Þ ¼ eI� j� ��1

where I is the N � N identity matrix and e is a complex number. This also formulatesin computing the trace of R eð Þ:

T r R eð Þ� ¼ Xk

1e� ek

Fig. 4. The same mechanisms of learning models that lead to the improvements in transitionswith larger lattices for bother partners (M) and Traders (E) – a myopia of tendency to ignore thelarger picture.


in the large N limit. One can argue that, decision-making is not invariant to the physicalenvironment in which a decision is made. In addition, a partner with access to deeper(quantum) information resources may be able to do strictly better than a partner withaccess only to classical information resources.

In this respect, our findings are somewhat akin to those in computer science thathave established the superiority of quantum over classical algorithms for certainproblems. In the nutshell the function R eð Þ is a real analytic function with a cut on thereal line and the discontinuity on the cut is the density of states. Now we can write theaverage of the logarithm of the determinant in the following way (Dotsenko 2005) [23]:

log det eI� j� � ¼ �2 lim

n!0

ddn

1

detðeI� j

� �n=2

Operatively one computes the quantity on the r.h.s for integer (positive) values ofthe replica number n, then one performs an analytical continuation of the result to realvalues of n and eventually takes the limit n ! 0. Such replication in decision-makingin complex systems is based on multi-layered, multi-dimensional-optimization problemwith several criteria to identify the sensitive preferences of a decision maker.

The key idea of quantum decisions (QD), decentralized deeper (quantum) signals inthe decision-making, as, is to provide the simplest generalization of the underlyingindividualized multiple decisions towards a single decision, so as to account for thecomplex dynamics of the many non-local hidden variables that may be involved in thecognitive trade-offs and decision making processes of the brain (Yukalov and Sornette2009) [24]. In such decision-making, involving signals with unknown states of nature,actions of trade-offs, responses to actions, and rewards in subconscious play the role ofhidden variables. However, the learning model represented the neural network as aquantum-like object for which several mechanisms have been suggested (Lockwood1989) [25]. It also meant a genuine quantum nature of some elements in the psycho-logical processes in decision-making (Cheon and Takahashi 2010) [26]. However, torigorously test the existence or the absence of genuinely quantum effects, the study, inFig. 5, considered a decision-making experiment with incomplete information, analo-gous to the Harsanyi type quantum game (Cheon and Iqbal 2008) [27] in whichinequalities (Bell 1964) [28] were tested. In the case of uncertainty, when the outcomesare unknown, partners may lack a clear reason for choosing an option and consequentlythey could abstain and make an irrational choice. When partners confronting uncer-tainty— paralyzing them against acting— are presented with a detailed explanation ofthe possible outcomes, they changed their mind and decided to act, thus reducing thedisjunction effect (Tversky and Shaffir 1992) [29].

Thus, by providing the partner with additional explanations, it is possible toinfluence their state or conditions. This gives the partner a drive to either alter theirexisting cognitions, or to reframe their interpretation of a situation, through a re-orientation of their local frame. Rather than positing a collapse of the partner’s state towhichever axis represents their decision, this model updates their decision by shifting ittowards the axis representing the decision.


5 Multi-layered, Multi-dimension Optimization

This learning model is optimized using a parallelizable Branch-and-Fix Combinatorial(BFC) algorithm. For overall process flow, we want to minimize the overall time spentat each stage of learning, which is a function of trading rate, buy-sell rate and numberof trades assigned to derive an outcome, as expressed below:

min f ðt; ait; lit;Xit þ gðtþ c; ai

0tþ c; l

i0tþ c; Y

i0tþ cÞþ h(tþ k; ai

00tþ k; l

i00tþ k; Z

i00tþ kÞ

where ti – set of time period {1, 2, …, T} at stage i; Dit – total partners buy-sell at stage

i at time t; Di0tþ c – total partners buy-sell at stage i0 at time t + c; Di00

tþ k – total partnersbuy-sell at stage i00 at time t + k; ait- buy-sell rate at stage i at time t; ai

0tþ c – buy-sell rate

at stage i0 at time t + c; ai00tþ k – buy-sell rate at stage i00 at time t + k; lit – trade rate at

stage i at time t; li0tþ c – trade rate at stage i0 at time t + c; li

00tþ k – trade rate at stage i00 at

time t + k; Cit – delay cost at stage i at time t; Ci0

tþ c – delay cost at stage i0 at time t + c;Ci00tþ k – delay cost at stage i00 at time t + k; Rt – Available trades at time t; R0

tþ c –

Available trades at time t + c; R00tþ k – Available trades at time t + k. The model

includes

Xit ¼

1 if trades are assigned at stage i at time t0 otherwise

�

Fig. 5. Results show big fluctuations near the critical point as the behavior of (Partner) M vs(Trader) E is very different.


Yi0tþ c ¼ 1 if trades are assigned at stage i0 at time t + c

0 otherwise

�

Zi00tþ k ¼ 1 if trades are assigned at stage i00 at time t + k

0 otherwise

�

In order to obtain a set of Pareto optimal solutions efficiently, the learning modeluses an improved algorithm with multiple-choice constraints to arrive at a new solutionmethod. To introduce the uncertainty in the parameters, the model uses a scenarioanalysis approach. A scenario consists of a realization of all random variables in allstages, that is, a path through the scenario tree. The scenario tree, in real-life, is veryfrequently asymmetric. Thus, the learning model for the trader’s interaction with apartner under the first-encounter scenario is defined as:

MinXT

t¼1CitX

it

Subject toPT

t¼1 ait �

PTt¼1 l

itX

it ;PI

i¼1 Xit �Rt;

PIi¼1

PTt¼1 l

itX

it �

PIi¼1

PTt¼1 D

it.

The trader’s subsequent interaction with another partner in the relational activity isthe second-encounter scenario, which is conditional to the first partner in the firstscenario, is defined as:

MinXT 0

t¼tþ cCi0tþ cY

i0tþ c

Subject toPT 0

t¼tþ c ai0tþ c �

PT 0t¼tþ c l

i0tþ cY

i0tþ c;

PI 0i¼i0 Y

i0tþ c �R

0tþ c;

PI 0i¼i0PT 0

t¼tþ c li0tþ cY

i0tþ c �

PI 0i¼i0

PT 0t¼tþ c D

i0tþ c.

The trader’s subsequent interaction with another partner in the relational activity isthe third-encounter scenario, which is conditional to the first partner in the first scenarioand second partner in the second-encounter, is defined as:

MinXT 00

t¼tþ kCi00tþ kZ

i00tþ k

Subject toPT 00

t¼tþ k ai00tþ k �

PT 00t¼tþ k l

i00tþ k Zi00

tþ k;PI 00

i¼i00 Yi00tþ k �R00

tþ k;PT 00

t¼tþ k

Yi00tþ k ¼ 1;

PI 00i¼i00

PT 00t¼tþ k l

i00tþ kZ

i00tþ k �

PI 00i¼i00

PT 00t¼tþ k D

i00tþ k.

In order to reduce the dimensions of the model, the explicit representation of thenon-anticipativity constraints is not desirable for all pairs of scenarios. The learningmodel takes astonishingly small computing time, a fraction of the time required by theproposed algorithm for solving a large-scale real-world problem.

6 Conclusion

Since the training data-sets and statistical (machine) learning methods that derive latticeoption pricing model require a larger grid for sharper predictions and, therefore, requirequantum-like decisions in small world networks, as a foundation, to “manage the


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