Safe Sharing for Sensitive Data

This workshop focused on the question of when and how human subjects\u27 data can be safely shared. It introduced the basics of data anonymization and discussed how to tell if a dataset has been de-identified. Case studies of successful anonymization and some spectacular failures were share

Autonomous Driving Vehicles and Control System Design

Autonomous driving vehicles and the control system design have been undergoing rapid changes in the last decade and affecting the concept and behaviour of human traffic. However, the control system design for autonomous driving vehicles is still a great challenge since the real vehicles are subject to enormous dynamic constraints depending on the vehicle physical limitations, environmental constraints and surrounding obstacles. This paper presents a new scheme of nonlinear model predictive control subject to softened constraints for autonomous driving vehicles. When some vehicle dynamic limitations can be converted to softened constraints, the model predictive control optimizer can be easier to find out the optimal control action. This helps to improve the system stability and the application for further intelligent control in the future. Simulation results show that the new controller can drive the vehicle tracking well on different trajectories amid dynamic constraints on states, outputs and inputs

PI controller optimization by artificial gorilla troops for liquid level control

In this paper a novel metaheuristic method, artificial gorilla troops optimizer, is used in order to optimize classical proportional-integral controller for liquid level system, that has wide application in many industries. In optimization process nonlinear model of the system is used. Obtained results are provided. It is shown that optimized controller represents superior solution compared to classical controller

Di-ANFIS: an integrated blockchain–IoT–big data-enabled framework for evaluating service supply chain performance

Service supply chain management is a complex process because of its intangibility, high diversity of services, trustless settings, and uncertain conditions. However, the traditional evaluating models mostly consider the historical performance data and fail to predict and diagnose the problems’ root. This paper proposes a distributed, trustworthy, tamper-proof, and learning framework for evaluating service supply chain performance based on Blockchain and Adaptive Network-based Fuzzy Inference Systems (ANFIS) techniques, named Di-ANFIS. The main objectives of this research are: 1) presenting hierarchical criteria of service supply chain performance to cope with the diagnosis of the problems’ root; 2) proposing a smart learning model to deal with the uncertainty conditions by a combination of neural network and fuzzy logic, 3) and introducing a distributed Blockchain-based framework due to the dependence of ANFIS on big data and the lack of trust and security in the supply chain. Furthermore, the proposed six-layer conceptual framework consists of the data layer, connection layer, Blockchain layer, smart layer, ANFIS layer, and application layer. This architecture creates a performance management system using the Internet of Things (IoT), smart contracts, and ANFIS based on the Blockchain platform. The Di-ANFIS model provides a performance evaluation system without needing a third party and a reliable intermediary that provides an agile and diagnostic model in a smart and learning process. It also saves computing time and speeds up information flow.Service supply chain management is a complex process because of its intangibility, high diversity of services, trustless settings, and uncertain conditions. However, the traditional evaluating models mostly consider the historical performance data and fail to predict and diagnose the problems’ root. This paper proposes a distributed, trustworthy, tamper-proof, and learning framework for evaluating service supply chain performance based on Blockchain and Adaptive Network-based Fuzzy Inference Systems (ANFIS) techniques, named Di-ANFIS. The main objectives of this research are: 1) presenting hierarchical criteria of service supply chain performance to cope with the diagnosis of the problems’ root; 2) proposing a smart learning model to deal with the uncertainty conditions by a combination of neural network and fuzzy logic, 3) and introducing a distributed Blockchain-based framework due to the dependence of ANFIS on big data and the lack of trust and security in the supply chain. Furthermore, the proposed six-layer conceptual framework consists of the data layer, connection layer, Blockchain layer, smart layer, ANFIS layer, and application layer. This architecture creates a performance management system using the Internet of Things (IoT), smart contracts, and ANFIS based on the Blockchain platform. The Di-ANFIS model provides a performance evaluation system without needing a third party and a reliable intermediary that provides an agile and diagnostic model in a smart and learning process. It also saves computing time and speeds up information flow

Fixed Cluster Based Cluster Head Selection Algorithm in Vehicular Adhoc Network

The emergence of Vehicular Adhoc Networks (VANETs) is expected support variety of applications for driver assistance, traffic efficiency and road safety. For proper transmission of messages in VANET, one of the proposed solutions is dividing the network into clusters and then selecting a cluster head (CH) in each cluster. This can decrease the communication overhead between road side units (RSUs) and other components of VANETs, because instead of every node communicating with RSU, only CH communicates with RSU and relays relevant messages. In clustering, an important step is the selection of CH. In this thesis, we implemented vehicle to vehicle (V2V), cluster head to road side unit and road side unit to trusted authority authentication for the clustered network. We also presented a heuristic algorithm for selecting a suitable vehicle as the cluster head in a cluster. For the selection of head vehicle, we used weighted fitness values based on three parameters; trust value, position from the cluster boundary and absolute relative average speed. Simulation results indicate that the proposed approach can lead to improvements in terms of QoS metrics like delay, throughput and packet delivery ratio

Railway Engineering: Timetable Planning and Control, Artificial Intelligence and Externalities

This chapter is a case study of the dissemination of railway engineering research in Latin America developed by a railway engineering research group. The leader of the group is a female researcher. The authors aim to inspire to other women researchers in Latin American and Caribbean (LAC) countries who are trying to develop research in IT areas, many times facing serious difficulties, incomprehension, and great challenges. This chapter is divided in set sections like introduction, background, development of railway engineering research. This third section is divided into subsections like timetable planning and trains control, characterization of Panama metro line 1, dwelling times, fuzzy logic, artificial intelligence, social-economics railway externalities, and environmental railway externalities. The fourth section presents the results of the relationship between research activity and teaching of railway engineering obtained in this case study. Finally, the authors present a brief vision about future and emerging regional trends about railway engineering projects.This chapter is a case study of the dissemination of railway engineering research in Latin America developed by a railway engineering research group. The leader of the group is a female researcher. The authors aim to inspire to other women researchers in Latin American and Caribbean (LAC) countries who are trying to develop research in IT areas, many times facing serious difficulties, incomprehension, and great challenges. This chapter is divided in set sections like introduction, background, development of railway engineering research. This third section is divided into subsections like timetable planning and trains control, characterization of Panama metro line 1, dwelling times, fuzzy logic, artificial intelligence, social-economics railway externalities, and environmental railway externalities. The fourth section presents the results of the relationship between research activity and teaching of railway engineering obtained in this case study. Finally, the authors present a brief vision about future and emerging regional trends about railway engineering projects

Programmable Insight: A Computational Methodology to Explore Online News Use of Frames

abstract: The Internet is a major source of online news content. Online news is a form of large-scale narrative text with rich, complex contents that embed deep meanings (facts, strategic communication frames, and biases) for shaping and transitioning standards, values, attitudes, and beliefs of the masses. Currently, this body of narrative text remains untapped due—in large part—to human limitations. The human ability to comprehend rich text and extract hidden meanings is far superior to known computational algorithms but remains unscalable. In this research, computational treatment is given to online news framing for exposing a deeper level of expressivity coined “double subjectivity” as characterized by its cumulative amplification effects. A visual language is offered for extracting spatial and temporal dynamics of double subjectivity that may give insight into social influence about critical issues, such as environmental, economic, or political discourse. This research offers benefits of 1) scalability for processing hidden meanings in big data and 2) visibility of the entire network dynamics over time and space to give users insight into the current status and future trends of mass communication.Dissertation/ThesisDoctoral Dissertation Computer Science 201

ADAPTIVE, MULTI-OBJECTIVE JOB SHOP SCHEDULING USING GENETIC ALGORITHMS

This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed