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About this book

The LNCS journal Transactions on Computational Science reflects recent developments in the field of Computational Science, conceiving the field not as a mere ancillary science but rather as an innovative approach supporting many other scientific disciplines. The journal focuses on original high-quality research in the realm of computational science in parallel and distributed environments, encompassing the facilitating theoretical foundations and the applications of large-scale computations and massive data processing. It addresses researchers and practitioners in areas ranging from aerospace to biochemistry, from electronics to geosciences, from mathematics to software architecture, presenting verifiable computational methods, findings, and solutions, and enabling industrial users to apply techniques of leading-edge, large-scale, high performance computational methods.

This, the 35th issue of the Transactions on Computational Science, focusses on signal processing and security in distributed systems. The topics covered include classification of visual attention levels using microsaccades; analysis of textual content using Eyegaze; automatic car-accident detection and passenger counting; face recognition; secure data fusion in IoT; business compliance using goal models; and microfluidic executions.

Table of Contents


Classification of Visual Attention Level During Target Gazing Using Microsaccades

In the previous researches on microsaccades, a typical and basic experimental method involves the following: a dot is displayed as a visual target in the middle or near the middle of a monitor, and eye movements of the subject are measured, and then the number of microsaccades is extracted from the measured eye movements. However, it is difficult to determine whether or not the subject is paying visual attention while gazing at the target, and the degree of visual attention paid by the subject. This paper proposes a system that uses microsaccades to classify visual attention levels during visual target gazing. In our experiment, ten subjects performed three tasks requiring different levels of visual attention. Microsaccades were measured and the number of microsaccades was extracted for each task. Statistical analysis showed that the number of microsaccades differed among the tasks. Our results suggest that visual attention level can be classified by the number of microsaccades.
Soichiro Yokoo, Nobuyuki Nishiuchi, Kimihiro Yamanaka

Multiscale Analysis of Textual Content Using Eyegaze

Reading of a textual content involves a complex coordination between various parts of brain responsible for visual inputs, language processing, cognitive functions and motor response. In addition, psychological factors like attention and perception play a major role in understanding of the content. Many of these factors get reflected in the behaviour of eye movement, as the content is read. In this paper, we present an approach for analysing a textual content in various scales using eyegaze. The scales include (i) individual fixation characteristics, (ii) saccades and fixations within a line (iii) overall difficulty score of the content. An affordable infrared eye tracking device is used to capture the gaze characteristics in an unobtrusive manner. Two types (easy and difficult) of textual contents are designed for the experiment which are benchmarked using standard readability indices. The fixation characteristics include fixation duration, change in drift direction within a fixation and spatial area of a fixation. Using Analysis of Variance (ANOVA), the former two are found to be statistically significant in distinguishing the two types of contents. Within a line, the spatial distance between fixations and the number of switching between saccades and fixations characterize the flow during a reading where the later is found to be statistically significant. A mixture of two partial sigmoid is used as a mapping function to compute the difficulty score of a content from the significant features. For a given content, the variation of these scores among individual readers, enables us to get deeper insights into their cognitive and psychological aspects.
Aniruddha Sinha, Rikayan Chaki, Bikram Kumar De, Rajlakshmi Guha, Sanjoy Kumar Saha, Anupam Basu

In-Car eCall Device for Automatic Accident Detection, Passengers Counting and Alarming

The European eSafety initiative aims to improve the safety and efficiency of road transport. The main element of eSafety is the pan European eCall project - an in-vehicle system which idea is to inform reliably and automatically about road collisions and even very serious accidents. As estimated by the European Commission, the implemented system will reduce services’ response time by 40%. This would probably save 2,500 people a year. In 2015 the European Parliament adopted the legislation that from the end of March 2018 all new cars sold in EU should be equipped with the eCall system. The limitation of this idea is that only a small part of cars driven in UE are sold yearly (about 3.7% cars in 2015). This paper presents the details of concept of an on-board eCall device which can be installed at the owners’ request in used vehicles. Proposed system will be able to detect a road accident, indicate the number of vehicle’s occupants and send those information to dedicated emergency services via duplex communication channel. This paper presents (1) the basis of the system, (2) the details on accident detection algorithms and hardware used experimentally and (3) state of the art and chosen approach for human detection in vehicle environment.
Anna Lupinska-Dubicka, Marek Tabedzki, Marcin Adamski, Mariusz Rybnik, Miroslaw Omieljanowicz, Maciej Szymkowski, Marek Gruszewski, Adam Klimowicz, Grzegorz Rubin, Khalid Saeed

Volumetric Density of Triangulated Range Images for Face Recognition

In this paper, a volumetric space representation of 3D range face image has been established for developing a robust 3D face recognition system. A volumetric space has been created on some distinct triangular regions of the 3D range face image. Further, we have constructed 3D voxels corresponding to those regions for developing voxelization-based 3D face classification system. The proposed 3D face recognition system has mainly three parts. At first, seven significant landmarks are detected on the face. Secondly, any three individual landmarks are used to create a triangular region; in this way, six distinct triangular areas have been generated, where the nose tip is a common landmark to all the triangles. Next, assume a plane at the nose tip level for representing the volumetric space. The total density volume and some statistical features are considered for the experiment. From the volumetric space, construct 3D voxel representation.
Further, geometrical features from 3D voxel are used for the experiment. Three popular 3D face databases: Frav3D, Bosphorus, and Gavabdb are used as the input of the system. On these databases, the system acquires 94.28%, 95.3%, and 90.83% recognition rates using kNN and 95.59%, 96.37%, and 92.51% recognition rates using SVM classifier. Using geometric features with SVM classifier, the system acquires 92.09%, 93.67%, and 89.7% recognition rates.
Koushik Dutta, Debotosh Bhattacharjee, Mita Nasipuri

Combining Merkle Hash Tree and Chaotic Cryptography for Secure Data Fusion in IoT

With the wide applicability of sensors in our daily lives, security has become one of the primary concerns in an Internet of Things (IoT) environment. Particularly, user’s privacy and unauthorized access to sensitive information needs to be kept in mind while designing security algorithms. This paper puts forward a security protocol that integrates authentication of the deployed IoT devices and encryption of the generated data. We have modified the well-known Merkle Hash Tree to adapt to an IoT environment for authenticating the devices and utilized the concepts of Chaos theory for developing the encryption algorithm. The use of chaos in cryptography are known to satisfy the basic requirements of the cryptosystem such as, high sensitivity, high computational speed and high security. In addition, we have proposed a chaotic map named Quadratic Sinusoidal Map which exhibits better array of chaotic regime when compared to the traditional quadratic map. The security analysis demonstrate that the proposed protocol is simple having low computational requirements, has strong security capabilities and highly resilient to security attacks.
Nashreen Nesa, Indrajit Banerjee

A Deployment Framework for Ensuring Business Compliance Using Goal Models

Based on initial research to transform a sequence agnostic goal model into a finite state model (FSM) and then checking them against temporal properties (in CTL), researchers have come up with guidelines for generating compliant finite state models altogether. The proposed guidelines provide a formal approach to prune a non-compliant FSM (generated by the Semantic Implosion Algorithm) and generate FSM-alternatives that satisfy the given temporal property. This paper is an extension of the previous work that implements the proposed guidelines and builds a deployment interface called \(i^*\)ToNuSMV 3.0. The working of the framework is demonstrated with the help of some use cases. In the end, a comparative study of the performance between the previous and current versions of the Semantic Implosion Algorithm (SIA) with respect to the size of the solution space and the execution times, respectively, has also been presented.
Novarun Deb, Mandira Roy, Surochita Pal, Ankita Bhaumick, Nabendu Chaki

A Methodology for Root-Causing In-field Attacks on Microfluidic Executions

Recent research on security and trustworthiness of micro-fluidic biochips has exposed several backdoors in their established design flows that can lead to compromises in assay results. This is a serious concern, considering the fact that these biochips are now extensively used for clinical diagnostics in healthcare. In this paper, we propose a novel scheme for root-causing assay manipulation attacks for actuations on digital microfluidic biochips that manifest as errors after execution. In particular, we show how the presence of a functionally correct reaction sequence graph has a significant advantage in the micro-fluidic context for debugging errors resulting out of such attacks. Such a sequence graph is the basis from which the actuation sequence to be implemented on a target Lab-on-chip is synthesized. In this paper, we investigate the possibility of using this sequence graph as a reference model for debugging erroneous reaction executions with respect to the desired output concentration. Our debugging method consists of program slicing with respect to the observable error in the golden implementation. During slicing, we also perform a step-by-step comparison between the slices of the erroneous output with other erroneous and error-free outputs. The reaction steps are then compared to accurately locate the root cause of a given error. In this paper, we consider two different types of assay descriptions, namely (a) unconditional assays, which have a fixed execution path, and (b) conditional assays that alter the execution at runtime depending on the outputs of sensor observations. Experimental results on the Polymerase Chain Reaction (PCR) and Linear Dilution Tree (LDT) and its conditional variant show that our method is able to pinpoint the errors.
Pushpita Roy, Ansuman Banerjee, Bhargab B. Bhattacharya


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