World Academy of Science, Engineering and Technology

Authors: Frederic Schenker, Jonathan J. Hendriks, Gianluca Nicchiotti

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In a product industrialization perspective, the end-product shall always be at the peak of technological advancement and developed in the shortest time possible. Thus, the constant growth of complexity and a shorter time-to-market calls for important changes on both the technical and business level. Undeniably, the common understanding of the system is beclouded by its complexity which leads to the communication gap between the engineers and the sale department. This communication link is therefore important to maintain and increase the information exchange between departments to ensure a punctual and flawless delivery to the end customer. This evolution brings engineers to reason with more hindsight and plan ahead. In this sense, they use new viewpoints to represent the data and to express the model deliverables in an understandable way that the different stakeholder may identify their needs and ideas. This article focuses on the usage of Model-Based System Engineering (MBSE) in a perspective of system industrialization and reconnect the engineering with the sales team. The modeling method used and presented in this paper concentrates on displaying as closely as possible the needs of the customer. Firstly, by providing a technical solution to the sales team to help them elaborate commercial offers without omitting technicalities. Secondly, the model simulates between a vast number of possibilities across a wide range of components. It becomes a dynamic tool for powerful analysis and optimizations. Thus, the model is no longer a technical tool for the engineers, but a way to maintain and solidify the communication between departments using different views of the model. The MBSE contribution to cost optimization during New Product Introduction (NPI) activities is made explicit through the illustration of a case study describing the support provided by system models to architectural choices during the industrialization of a novel optical sensor.

Keywords: analytical model, architecture comparison, MBSE, product industrialization, SysML, system thinking

Procedia PDF Downloads 160

Authors: Bing Li

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The primitive code-level design patterns (PDP) are the rudimentary programming elements to develop any distributed systems in the generic distributed programming environment, GreatFree. The PDP works with the primitive distributed application programming interfaces (PDA), the distributed modeling, and the distributed concurrency for scaling-up. They not only hide developers from underlying technical details but also support sufficient adaptability to a variety of distributed computing environments. Programming with them, the simplest distributed system, the lightweight messaging two-node client/server (TNCS) system, is constructed rapidly with straightforward and repeatable behaviors, copy-paste-replace (CPR). As any distributed systems are made up of the simplest ones, those PDAs, as well as the PDP, are generic for distributed programming.

Keywords: primitive APIs, primitive code-level design patterns, generic distributed programming, distributed systems, highly patterned development environment, messaging

Procedia PDF Downloads 191

Authors: Gaelle Candel, David Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

Procedia PDF Downloads 143

Authors: Kamyar Fakhr, Roozbeh Salmani

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Alphonse Bertillon developed the first biometric security system in the 1800s. Today, many governments and giant companies are considering or have procured biometrically enabled security schemes. Iris is a kaleidoscope of patterns and colors. Each individual holds a set of irises more unique than their thumbprint. Every single day, giant companies like Google and Apple are experimenting with reliable biometric systems. Now, after almost 200 years of improvements, face ID does not work with masks, it gives access to fake 3D images, and there is no global usage of biometric recognition systems as national identity (ID) card. The goal of this paper is to demonstrate the advantages of iris recognition overall biometric recognition systems. It make two extensions: first, we illustrate how a very large amount of internet fraud and cyber abuse is happening due to bugs in face recognition systems and in a very large dataset of 3.4M people; second, we discuss how establishing a secure global network of iris recognition devices connected to authoritative convolutional neural networks could be the safest solution to this dilemma. Another aim of this study is to provide a system that will prevent system infiltration caused by cyber-attacks and will block all wireframes to the data until the main user ceases the procedure.

Keywords: biometric system, convolutional neural network, cyber-attack, secure

Procedia PDF Downloads 218

Authors: Tanveer Hussain, Khan Muhammad, Amin Ullah, Mi Young Lee, Sung Wook Baik

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The Big Data generated from distributed vision sensors installed on large scale in smart cities create hurdles in its efficient and beneficial exploration for browsing, retrieval, and indexing. This paper presents a three-folded framework for effective video summarization of such data and provide a compact and representative format of Big Video Data. In the first fold, the paper acquires input video data from the installed cameras and collect clues such as type and count of objects and clarity of the view from a chunk of pre-defined number of frames of each view. The decision of representative view selection for a particular interval is based on fuzzy inference system, acquiring a precise and human resembling decision, reinforced by the known clues as a part of the second fold. In the third fold, the paper forwards the selected view frames to the summary generation mechanism that is supported by a saliency-aware convolution neural network (CNN) model. The new trend of fuzzy rules for view selection followed by CNN architecture for saliency computation makes the multi-view video summarization (MVS) framework a suitable candidate for real-world practice in smart cities.

Keywords: big video data analysis, fuzzy logic, multi-view video summarization, saliency detection

Procedia PDF Downloads 188

Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

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In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

Procedia PDF Downloads 197

Authors: Christina A. van Hal, Xiaoqian Jiang, Luyao Chen, Yan Chu, Robert D. Jolly, Yaobin Lin, Jitian Zhao, Kang Lin Hsieh

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The COVID-19 Literature Summary App was created for the primary purpose of enabling academicians and clinicians to quickly sort through the vast array of recent coronavirus publications by topics of interest. Multiple methods of summarizing and sorting the manuscripts were created. A summary page introduces the application function and capabilities, while an interactive map provides daily updates on infection, death, and recovery rates. A page with a pivot table allows publication sorting by topic, with an interactive data table that allows sorting topics by columns, as wells as the capability to view abstracts. Additionally, publications may be sorted by the medical topics they cover. We used the CORD-19 database to compile lists of publications. The data table can sort binary variables, allowing the user to pick desired publication topics, such as papers that describe COVID-19 symptoms. The application is primarily designed for use by researchers but can be used by anybody who wants a faster and more efficient means of locating papers of interest.

Keywords: COVID-19, literature summary, information retrieval, Snorkel

Procedia PDF Downloads 152

Authors: Rania Alshikhe, Vinita Jindal

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Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from traveling vehicles, such as taxis through installed global positioning system (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.

Keywords: GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE

Procedia PDF Downloads 157

Authors: S. S. Bendib, L. W. Mouss, S. Kalla

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This paper proposes a self-organization-based approach for real-time systems design. The addressed issue is the mapping of an application onto an architecture of heterogeneous processors while optimizing both makespan and reliability. Since this problem is NP-hard, a heuristic algorithm is used to obtain efficiently approximate solutions. The proposed approach takes into consideration the quality as well as the diversity of solutions. Indeed, an alternate treatment of the two objectives allows to produce solutions of good quality while a self-organization approach based on the neighborhood structure is used to reorganize solutions and consequently to enhance their diversity. Produced solutions make different compromises between the makespan and the reliability giving the user the possibility to select the solution suited to his (her) needs.

Keywords: embedded real-time systems design, makespan, reliability, self-organization, compromises

Procedia PDF Downloads 134

Authors: Liudmyla Koliechkina, Olena Dvirna

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The statement of the multi-objective optimization problem on combinatorial configurations is formulated, and the approach to its solution is proposed. The problem is of interest as a combinatorial optimization one with many criteria, which is a model of many applied tasks. The approach to solving the multi-objective optimization problem on combinatorial configurations consists of two stages; the first is the reduction of the multi-objective problem to the single criterion based on existing multi-objective optimization methods, the second stage solves the directly replaced single criterion combinatorial optimization problem by the horizontal combinatorial method. This approach provides the optimal solution to the multi-objective optimization problem on combinatorial configurations, taking into account additional restrictions for a finite number of steps.

Keywords: discrete set, linear combinatorial optimization, multi-objective optimization, Pareto solutions, partial permutation set, structural graph

Procedia PDF Downloads 167

Authors: Vanessa Vasconcelos, Mariza A. S. Bigonha

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In order to facilitate both processes, this paper presents HaskellFL, a tool that uses fault localization techniques to locate a logical error in Haskell code. The Haskell subset used in this work is sufficiently expressive for those studying functional programming to get immediate help debugging their code and to answer questions about key concepts associated with the functional paradigm. HaskellFL was tested against functional programming assignments submitted by students enrolled at the functional programming class at the Federal University of Minas Gerais and against exercises from the Exercism Haskell track that are publicly available on GitHub. Furthermore, the EXAM score was chosen to evaluate the tool’s effectiveness, and results showed that HaskellFL reduced the effort needed to locate an error for all tested scenarios. Results also showed that the Ochiai method was more effective than Tarantula.

Keywords: debug, fault localization, functional programming, Haskell

Procedia PDF Downloads 298

Authors: Kiri Trier, Theresa Treffers

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In the course of continuous volatility like production stops due to the COVID-19 pandemic, video-on-demand player monopolizing the film industry, filmmakers are stuck in traditional, linear content development processes. The industry has to become more agile in order to react quickly and easily to changes. Since content development in agile project management is scientifically–empirically not at all recorded, and a lack beyond the software development in terms of agile methods consists, we examined if the agile manifesto values and principles from the software development can be adapted to the film industry to enable agility and digitalization of content development in the industry. We conducted an online questionnaire with 184 German filmmakers (producers, authors, directors, actors, film financiers) for a first cross-sectional assessment for adaptability of the agile manifesto from the software development to the film industry, factor analysis was used to validate the construct. Our results show that it is crucial to digitalize traditional content development to agile content development end-to-end, with tools, lean processes, new collaboration structures, and holacracy to prepare for any volatility. Overall, we examined the first construct for an agile manifesto for the film industry with four values related to nine own principles. Our findings help to get a better understanding of the agile manifesto beyond the software development as a guideline for implementing agility in the film industry.

Keywords: agile manifesto, agile project management, agility, film industry

Procedia PDF Downloads 198

Authors: Expedito Pinto De Paula Junior

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Current systems such as artificial satellites, airplanes, automobiles, turbines, power systems and air traffic controls are becoming increasingly more complex and/or highly integrated as defined in SAE-ARP-4754A (Society Automotive Engineering - Certification considerations for highly-integrated or complex aircraft systems standard). Among other processes, the development of such systems requires careful Change and Configuration Management (CCM) to establish and maintain product integrity. Understand the maturity of CCM process based in historical approach is crucial for better implementation in hardware and software lifecycle. The sense of work organization, in all fields of development is directly related to the order and interrelation of the parties, changes in time, and record of these changes. Generally, is observed that engineers, administrators and managers invest more time in technical activities than in organization of work. More these professionals are focused in solving complex problems with a purely technical bias. CCM process is fundamental for development, production and operation of new products specially in the safety critical systems. The objective of this paper is open a discussion about the historical revision based in standards focus of CCM around the world in order to understand and reflect the importance across the years, the contribution of this process for technology evolution, to understand the mature of organizations in the system lifecycle project and the benefits of CCM to avoid errors and mistakes during the Lifecycle Product.

Keywords: changes, configuration management, historical, revision

Procedia PDF Downloads 201

Authors: Jamshed Ahmad, Abdul Wahid Khan, Javed Ali Khan

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Managing software quality requirements change management is a difficult task in the field of software engineering. Avoiding incoming changes result in user dissatisfaction while accommodating to many requirement changes may delay product delivery. Poor requirements management is solely considered the primary cause of the software failure. It becomes more challenging in global software outsourcing. Addressing success factors in quality requirement change management is desired today due to the frequent change requests from the end-users. In this research study, success factors are recognized and scrutinized with the help of a systematic literature review (SLR). In total, 16 success factors were identified, which significantly impacted software quality requirement change management. The findings show that Proper Requirement Change Management, Rapid Delivery, Quality Software Product, Access to Market, Project Management, Skills and Methodologies, Low Cost/Effort Estimation, Clear Plan and Road Map, Agile Processes, Low Labor Cost, User Satisfaction, Communication/Close Coordination, Proper Scheduling and Time Constraints, Frequent Technological Changes, Robust Model, Geographical distribution/Cultural differences are the key factors that influence software quality requirement change. The recognized success factors and validated with the help of various research methods, i.e., case studies, interviews, surveys and experiments. These factors are then scrutinized in continents, database, company size and period of time. Based on these findings, requirement change will be implemented in a better way.

Keywords: global software development, requirement engineering, systematic literature review, success factors

Procedia PDF Downloads 197

Authors: Hamsini Pulugurtha, V.S. Lakshmi Jagadmaba Paluri

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Distributed denial of service attack (DDoS) is one altogether the top-rated cyber threats presently. It runs down the victim server resources like a system of measurement and buffer size by obstructing the server to supply resources to legitimate shoppers. Throughout this text, we tend to tend to propose a mathematical model of DDoS attack; we discuss its relevancy to the choices like inter-arrival time or rate of arrival of the assault customers accessing the server. We tend to tend to further analyze the attack model in context to the exhausting system of measurement and buffer size of the victim server. The projected technique uses an associate in nursing unattended learning technique, self-organizing map, to make the clusters of identical choices. Lastly, the abstract applies mathematical correlation and so the standard likelihood distribution on the clusters and analyses their behaviors to look at a DDoS attack. These systems not exclusively interconnect very little devices exchanging personal data, but to boot essential infrastructures news standing of nuclear facilities. Although this interconnection brings many edges and blessings, it to boot creates new vulnerabilities and threats which might be conversant in mount attacks. In such sophisticated interconnected systems, the power to look at attacks as early as accomplishable is of paramount importance.

Keywords: application attack, bandwidth, buffer correlation, DDoS distribution flooding intrusion layer, normal prevention probability size

Procedia PDF Downloads 224

Authors: Siffat Ullah Khan, Rahmat Ullah Khan, Rafiq Ahmad Khan, Habibullah Khan

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Green IT or green computing has emerged as a fast growing business paradigm in recent years in order to develop energy-efficient Software and peripheral devices. With the constant evolution of technology and the world critical environmental status, all private and public information technology (IT) businesses are moving towards sustainability. We identified, through systematic literature review and questionnaire survey, 9 motivators, in total, faced by vendors in IT-Outsourcing relationship. Amongst these motivators 7 were ranked as critical motivators. We also identified 21, in total, practices for addressing these critical motivators. Based on these inputs we have developed Green IT-Outsourcing Assurance Model (GITAM) for IT-Outsourcing vendors. The model comprises four different levels. i.e. Initial, White, Green and Grey. Each level comprises different critical motivators and their relevant practices. We conclude that our model, GITAM, will assist IT-Outsourcing vendors in gauging their level in order to manage IT-Outsourcing activities in a green and sustainable fashion to assist the environment and to reduce the carbon emission. The model will assist vendors in improving their current level by suggesting various practices. The model will contribute to the body of knowledge in the field of Green IT.

Keywords: Green IT-outsourcing Assurance Model (GITAM), Systematic Literature Review, Empirical Study, Case Study

Procedia PDF Downloads 252

Authors: H. Apaza, L. Chévez, H. Loro

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Plastic and microplastic pollution in human food chain is a big problem for human health that requires more elaborated techniques that can identify their presences in different kinds of food. Hyperspectral imaging technique is an optical technique than can detect the presence of different elements in an image and can be used to detect plastics and microplastics in a scene. To do this statistical techniques are required that need to be evaluated and compared in order to find the more efficient ones. In this work, two problems related to the presence of plastics are addressed, the first is to detect and identify pieces of plastic immersed in almond seeds, and the second problem is to detect and quantify microplastic in almond flour. To do this we make use of the analysis hyperspectral images taken in the range of 900 to 1700 nm using 4 unmixing techniques of hyperspectral imaging which are: least squares unmixing (LSU), non-negatively constrained least squares unmixing (NCLSU), fully constrained least squares unmixing (FCLSU), and scaled constrained least squares unmixing (SCLSU). NCLSU, FCLSU, SCLSU techniques manage to find the region where the plastic is found and also manage to quantify the amount of microplastic contained in the almond flour. The SCLSU technique estimated a 13.03% abundance of microplastics and 86.97% of almond flour compared to 16.66% of microplastics and 83.33% abundance of almond flour prepared for the experiment. Results show the feasibility of applying near-infrared hyperspectral image analysis for the detection of plastic contaminants in food.

Keywords: food, plastic, microplastic, NIR hyperspectral imaging, unmixing

Procedia PDF Downloads 129

Authors: Hualter Barbosa, Bruno Bonifacio

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The process of globalization and new business has transformed the dynamics of software development. To meet the new demands, the software industry has adapted new methodologies that can shorten development cycles to ensure greater competitiveness. Given this scenario, Global Software Development (GSD) has become a strategic element for new products' success. However, the reliability, opportunity, and perceived value can be influenced substantially with the automation of steps in the development process activities. In this sense, the development of new technologies can help developers and managers to improve the quality of development. This paper presents a report on improving one of the release process activities of Sidia's mobile product area using software technology. The objective is to present the improvement of the CLCATCH tool developed based on experimental studies and qualitative analysis on the points of improvement for the release process in Android update projects for Samsung mobile devices. The results show improvement for the new version and approach of the tool, with points that can facilitate new features of the proposed technology.

Keywords: Android updated, empirical studies, GSD, process improvement

Procedia PDF Downloads 142

Authors: Benjamin Okike

Abstract:

There is a need to hide information along the superhighway. Today, information relating to the survival of individuals, organizations, or government agencies is transmitted from one point to another. Adversaries are always on the watch along the superhighway to intercept any information that would enable them to inflict psychological ‘injuries’ to their victims. But with information encryption, this can be prevented completely or at worst reduced to the barest minimum. There is no doubt that so many encryption techniques have been proposed, and some of them are already being implemented. However, adversaries always discover loopholes on them to perpetuate their evil plans. In this work, we propose the enhanced data encryption standard (EDES) that would deploy randomly generated numbers as an encryption method. Each time encryption is to be carried out, a new set of random numbers would be generated, thereby making it almost impossible for cryptanalysts to decrypt any information encrypted with this newly proposed method.

Keywords: encryption, enhanced data encryption, encryption techniques, information security

Procedia PDF Downloads 150

Authors: Nauman Idrees

Abstract:

Modeling and simulation during the conceptual design phase are the most effective means of system testing resulting in time and cost savings as compared to the testing of hardware prototypes, which are mostly not available during the conceptual design phase. This paper uses the model-based design (MBD) method in designing the fly-by-wire flight controls system of a fighter aircraft using Simulink. The process begins with system definition and layout where modeling requirements and system components were identified, followed by hierarchical system layout to identify the sequence of operation and interfaces of system with external environment as well as the internal interface between the components. In the second step, each component within the system architecture was modeled along with its physical and functional behavior. Finally, all modeled components were combined to form the fly-by-wire flight controls system of a fighter aircraft as per system architecture developed. The system model developed using this method can be simulated using any simulation software to ensure that desired requirements are met even without the development of a physical prototype resulting in time and cost savings.

Keywords: fly-by-wire, flight controls system, model based design, Simulink

Procedia PDF Downloads 117

Authors: Ebrahim Shayesteh, Nikolaos Styliaras, Alin George Raducu, Ozan Sahin, Daniel Pombo VáZquez, Jonas Funkquist, Sotirios Thanopoulos

Abstract:

One of the important requirements in many code generation projects is defining some of the model parameters tunable. This helps to update the model parameters without performing the code generation again. This paper studies the concept of embedded code generation by MATLAB/Simulink coder targeting the TwinCAT Simulink system. The generated runtime modules are then tested and deployed to the TwinCAT 3 engineering environment. However, defining the parameters tunable in MATLAB/Simulink code generation targeting TwinCAT is not very straightforward. This paper focuses on this subject and reviews some of the techniques tested here to make the parameters tunable in generated runtime modules. Three techniques are proposed for this purpose, including normal tunable parameters, callback functions, and mask subsystems. Moreover, some test Simulink models are developed and used to evaluate the results of proposed approaches. A brief summary of the study results is presented in the following. First of all, the parameters defined tunable and used in defining the values of other Simulink elements (e.g., gain value of a gain block) could be changed after the code generation and this value updating will affect the values of all elements defined based on the values of the tunable parameter. For instance, if parameter K=1 is defined as a tunable parameter in the code generation process and this parameter is used to gain a gain block in Simulink, the gain value for the gain block is equal to 1 in the gain block TwinCAT environment after the code generation. But, the value of K can be changed to a new value (e.g., K=2) in TwinCAT (without doing any new code generation in MATLAB). Then, the gain value of the gain block will change to 2. Secondly, adding a callback function in the form of “pre-load function,” “post-load function,” “start function,” and will not help to make the parameters tunable without performing a new code generation. This means that any MATLAB files should be run before performing the code generation. The parameters defined/calculated in this file will be used as fixed values in the generated code. Thus, adding these files as callback functions to the Simulink model will not make these parameters flexible since the MATLAB files will not be attached to the generated code. Therefore, to change the parameters defined/calculated in these files, the code generation should be done again. However, adding these files as callback functions forces MATLAB to run them before the code generation, and there is no need to define the parameters mentioned in these files separately. Finally, using a tunable parameter in defining/calculating the values of other parameters through the mask is an efficient method to change the value of the latter parameters after the code generation. For instance, if tunable parameter K is used in calculating the value of two other parameters K1 and K2 and, after the code generation, the value of K is updated in TwinCAT environment, the value of parameters K1 and K2 will also be updated (without any new code generation).

Keywords: code generation, MATLAB, tunable parameters, TwinCAT

Procedia PDF Downloads 227

Authors: Arjun Kumar Rath, Titus Dhanasingh

Abstract:

Embedded product development requires software to test hardware functionality during development and finding issues during manufacturing in larger quantities. As the components are getting integrated, the devices are tested for their full functionality using advanced software tools. Benchmarking tools are used to measure and compare the performance of product features. At present, these tests are based on a variety of methods involving varying hardware and software platforms. Typically, these tests are custom built for every product and remain unusable for other variants. A majority of the tests goes undocumented, not updated, unusable when the product is released. To bridge this gap, a solution accelerator in the form of a framework can address these issues for running all these tests from one place, using an off-the-shelf tests library in a continuous integration environment. There are many open-source test frameworks or tools (fuego. LAVA, AutoTest, KernelCI, etc.) designed for testing embedded system devices, with each one having several unique good features, but one single tool and framework may not satisfy all of the testing needs for embedded systems, thus an extensible framework with the multitude of tools. Embedded product testing includes board bring-up testing, test during manufacturing, firmware testing, application testing, and assembly testing. Traditional test methods include developing test libraries and support components for every new hardware platform that belongs to the same domain with identical hardware architecture. This approach will have drawbacks like non-reusability where platform-specific libraries cannot be reused, need to maintain source infrastructure for individual hardware platforms, and most importantly, time is taken to re-develop test cases for new hardware platforms. These limitations create challenges like environment set up for testing, scalability, and maintenance. A desirable strategy is certainly one that is focused on maximizing reusability, continuous integration, and leveraging artifacts across the complete development cycle during phases of testing and across family of products. To get over the stated challenges with the conventional method and offers benefits of embedded testing, an embedded test framework (ETF), a solution accelerator, is designed, which can be deployed in embedded system-related products with minimal customizations and maintenance to accelerate the hardware testing. Embedded test framework supports testing different hardwares including microprocessor and microcontroller. It offers benefits such as (1) Time-to-Market: Accelerates board brings up time with prepacked test suites supporting all necessary peripherals which can speed up the design and development stage(board bring up, manufacturing and device driver) (2) Reusability-framework components isolated from the platform-specific HW initialization and configuration makes the adaptability of test cases across various platform quick and simple (3) Effective build and test infrastructure with multiple test interface options and preintegrated with FUEGO framework (4) Continuos integration - pre-integrated with Jenkins which enabled continuous testing and automated software update feature. Applying the embedded test framework accelerator throughout the design and development phase enables to development of the well-tested systems before functional verification and improves time to market to a large extent.

Keywords: board diagnostics software, embedded system, hardware testing, test frameworks

Procedia PDF Downloads 145

Authors: Aleksander Mendoza

Abstract:

Glushkov's construction has many interesting properties, and they become even more evident when applied to transducers. This article strives to show the vast range of possible extensions and optimisations for this algorithm. Special flavour of regular expressions is introduced, which can be efficiently converted to e-free functional subsequential weighted finite state transducers. Produced automata are very compact, as they contain only one state for each symbol (from input alphabet) of original expression and only one transition for each range of symbols, no matter how large. Such compactified ranges of transitions allow for efficient binary search lookup during automaton evaluation. All the methods and algorithms presented here were used to implement open-source compiler of regular expressions for multitape transducers.

Keywords: weighted automata, transducers, Glushkov, follow automata, regular expressions

Procedia PDF Downloads 162

Authors: Jairson Barbosa Rodrigues, Paulo Romero Martins Maciel, Germano Crispim Vasconcelos

Abstract:

This paper presents an investigation of the performance impacts regarding the variation of five factors (input data size, node number, cores, memory, and disks) when applying a distributed implementation of Naïve Bayes for text classification of a large Corpus on the Spark big data processing framework. Problem: The algorithm's performance depends on multiple factors, and knowing before-hand the effects of each factor becomes especially critical as hardware is priced by time slice in cloud environments. Objectives: To explain the functional relationship between factors and performance and to develop linear predictor models for time and cost. Methods: the solid statistical principles of Design of Experiments (DoE), particularly the randomized two-level fractional factorial design with replications. This research involved 48 real clusters with different hardware arrangements. The metrics were analyzed using linear models for screening, ranking, and measurement of each factor's impact. Results: Our findings include prediction models and show some non-intuitive results about the small influence of cores and the neutrality of memory and disks on total execution time, and the non-significant impact of data input scale on costs, although notably impacts the execution time.

Keywords: big data, design of experiments, distributed machine learning, natural language processing, spark

Procedia PDF Downloads 120

Authors: Songwu Shen, Garrett Drysdale, Veerendranath Mannepalli, Qihua Dai, Yuan Wang, Yuli Chen, David Qian, Utkarsh Kakaiya

Abstract:

Hardware accelerator has been a promising solution to reduce the cost of cloud data centers. This paper investigates the QoS enhancement of the acceleration of an important datacenter workload: the webserver (or proxy) that faces high computational consumption originated from secure sockets layer (SSL) or transport layer security (TLS) procession in the cloud environment. Our study reveals that for the accelerator maintenance cases—need to upgrade driver/firmware or hardware reset due to hardware hang; we still can provide cryptography services by switching to software during maintenance phase and then switching back to accelerator after maintenance. The switching is seamless to server application such as Nginx that runs inside a VM on top of the server. To achieve this high availability goal, we propose a comprehensive fallback solution based on Intel® QuickAssist Technology (QAT). This approach introduces an architecture that involves the collaboration between physical function (PF) and virtual function (VF), and collaboration among VF, OpenSSL, and web application Nginx. The evaluation shows that our solution could provide high reliability, availability, and scalability (RAS) of hardware cryptography service in a 7x24x365 manner in the cloud environment.

Keywords: accelerator, cryptography service, RAS, secure sockets layer/transport layer security, SSL/TLS, virtualization fallback architecture

Procedia PDF Downloads 158

Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

Abstract:

The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

Procedia PDF Downloads 111

Authors: Akaash Tawade, Manan Khattar, Lucas Spangher, Costas J. Spanos

Abstract:

Many grids are increasing the share of renewable energy in their generation mix, which is causing the energy generation to become less controllable. Buildings, which consume nearly 33% of all energy, are a key target for demand response: i.e., mechanisms for demand to meet supply. Understanding the behavior of office workers is a start towards developing demand response for one sector of building technology. The literature notes that dynamic computational modeling can be predictive of individual action, especially given that occupant behavior is traditionally abstracted from demand forecasting. Recent work founded on Social Cognitive Theory (SCT) has provided a promising conceptual basis for modeling behavior, personal states, and environment using control theoretic principles. Here, an adapted linear dynamical system of latent states and exogenous inputs is proposed to simulate energy demand amongst office workers engaged in a social energy shifting game. The energy shifting competition is implemented in an office in Singapore that is connected to a minigrid of buildings with a consistent 'price signal.' This signal is translated into a 'points signal' by a reinforcement learning (RL) algorithm to influence participant energy use. The dynamic model functions at the intersection of the points signals, baseline energy consumption trends, and SCT behavioral inputs to simulate future outcomes. This study endeavors to analyze how the dynamic model trains an RL agent and, subsequently, the degree of accuracy to which load deferability can be simulated. The results offer a generalizable behavioral model for energy competitions that provides the framework for further research on transfer learning for RL, and more broadly— transactive control.

Keywords: energy demand forecasting, social cognitive behavioral modeling, social game, transfer learning

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Authors: Ali Alshumrani, Nathan Clarke, Bogdan Ghite, Stavros Shiaeles

Abstract:

Digital forensics has become an essential tool in the investigation of cyber and computer-assisted crime. Arguably, given the prevalence of technology and the subsequent digital footprints that exist, it could have a significant role across almost all crimes. However, the variety of technology platforms (such as computers, mobiles, Closed-Circuit Television (CCTV), Internet of Things (IoT), databases, drones, cloud computing services), heterogeneity and volume of data, forensic tool capability, and the investigative cost make investigations both technically challenging and prohibitively expensive. Forensic tools also tend to be siloed into specific technologies, e.g., File System Forensic Analysis Tools (FS-FAT) and Network Forensic Analysis Tools (N-FAT), and a good deal of data sources has little to no specialist forensic tools. Increasingly it also becomes essential to compare and correlate evidence across data sources and to do so in an efficient and effective manner enabling an investigator to answer high-level questions of the data in a timely manner without having to trawl through data and perform the correlation manually. This paper proposes a Unified Forensic Analysis Tool (U-FAT), which aims to establish a common language for electronic information and permit multi-source forensic analysis. Core to this approach is the identification and development of forensic analyses that automate complex data correlations, enabling investigators to investigate cases more efficiently. The paper presents a systematic analysis of major crime categories and identifies what forensic analyses could be used. For example, in a child abduction, an investigation team might have evidence from a range of sources including computing devices (mobile phone, PC), CCTV (potentially a large number), ISP records, and mobile network cell tower data, in addition to third party databases such as the National Sex Offender registry and tax records, with the desire to auto-correlate and across sources and visualize in a cognitively effective manner. U-FAT provides a holistic, flexible, and extensible approach to providing digital forensics in technology, application, and data-agnostic manner, providing powerful and automated forensic analysis.

Keywords: digital forensics, evidence correlation, heterogeneous data, forensics tool

Procedia PDF Downloads 196

Authors: I. D. Arroyo

Abstract:

This article seeks to integrate different concepts from contemporary software engineering with an agile development approach. We seek to clarify some definitions and uses, we make a difference between the Systems Development Life Cycle (SDLC) and the methodologies, we differentiate the types of frameworks such as methodological, philosophical and behavioral, standards and documentation. We define relationships based on the documentation of the development process through formal and ad hoc models, and we define the usefulness of using DevOps and Agile Modeling as integrative methodologies of principles and best practices.

Keywords: methodologies, modeling languages, agile modeling, UML

Procedia PDF Downloads 185

Authors: Hailyie Tekleselase

Abstract:

Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators.

Keywords: artificial intelligence, machine learning, deep learning, cyber security, big data

Procedia PDF Downloads 126