We consider the task of classifying trajectories of boat activities as a proxy for assessing maritime threats. Previous approaches have considered entropy-based metrics for clustering boat activity into three broad categories: random walk, following, and chasing. Here, we comprehensively assess the accuracy of neural network-based approaches as alternatives to entropy-based clustering. We train four neural network models and compare them to shallow learning using synthetic data. We also investigate the accuracy of models as time steps increase and with and without rotated data. To improve test-time robustness, we normalize trajectories and perform rotation-based data augmentation. Our results show that deep networks can achieve a test-set accuracy of up to 100% on a full trajectory, with graceful degradation as the number of time steps decreases, outperforming entropy-based clustering.

Over the past several years, due to the progression toward data-driven scientific disciplines, the field of Big Data has gained significant importance. These developments pose certain challenges in the area of efficient, effective, and secure management and transmission of digital information. This paper presents and evaluates a novel Distributed Ledger Technology (DLT) system, Fibereum, in a variety of use-cases, including a DLT-based system for Big Data exchange, as well as the fungible and non-fungible exchange of artwork, goods, commodities, and digital currency. Fibereum’s innovations include the application of non-linear data structures and a new concept of Lazy Verification. We demonstrate the benefits of these novel features for DLT system applications’ cost performance and their added resilience towards cyber-attacks via the consideration of several use cases.

resented here is a model objectivizing real estate prices so that prices across time could be compared to understand historical price trends and also to assist in a property evaluation or appraisal, as well as for the analysis of comparables in estimating a reasonable offer for a property on the market. Given a timespan of interest, a locale (e.g., a particular zipcode, a city, a county, a state), a category of properties of interest (e.g., condos), an objective historical trend in values can be computed by first evaluating the ratios between the transactions’ realized prices and objective governmental assessment of the properties at some fixed point of time; then, for each period (a month) averaging the ratios of all transaction in that period; then, comparing said averages (or medians) between different periods.

Over the past few years the need for early-warning maritime threat detection systems has dramatically increased. Our research aims to address this need by tackling three main problems: 1) classify boat activities into three categories: random walk, following, and chasing, 2) real-time classification of boat path trajectories, and 3) designing a novel perception-based framework for activity detection in the maritime context. We propose the implementation of an entropy-based detection algorithm, trained using synthetic data. We assess the viability of the proposed framework based on accuracy and the number of time steps required prior to identification. The synthetic data generated has the potential to spur other research efforts in the field of maritime detection.

Methods of surface reconstruction from 3D point clouds have received much attention in recent years due to their vast array of applications and the increasing supply of accurate 3D data. Providing smoothness, local modification, and robustness to noise, the B-spline surface fitting is one of the most popular of such methods. However, a problem encountered when using B-spline surface reconstruction is the representation of sharp features: corners and edges tend to be smoothed out. We propose an approach to sharp feature preservation which relies on curvature analysis of the B-spline surface. B-spline patches that have high curvature and are surrounded by patches with low curvature are identified as those representing sharp features. The location of sharp features is then determined through interpolation from low-curvature patches surrounding the identified patches. Finally, these features are preserved through repeated addition of points to the point cloud. We evaluate our sharp feature preservation algorithm at varying levels of noise, demonstrating its high accuracy at low noise and moderate robustness as the noise increases.

In this paper, we propose to apply recent advances in deep learning to design and train algorithms to localize, identify, and track small maritime objects under varying conditions (e.g., a snowstorm, high glare, night), and in computing-with-words to identify threatening activities where lack of training data precludes the use of deep learning. The recent rise of maritime piracy and attacks on transportation ships has cost the global economy several billion dollars. To counter the threat, researchers have proposed agent-driven modeling to capture the dynamics of the maritime transportation system, and to score the potential of a range of piracy countermeasures. Combining information from onboard sensors and cameras with intelligence from external sources for early piracy threat detection has shown promising results but lacks real-time updates for situational context. Such systems can benefit from early warnings, such as “a boat is approaching the ship and accelerating,” “a boat is circling the ship,” or “two boats are diverging close to the ship.” Existing onboard cameras capture these activities, but there are no automated processing procedures of this type of patterns to inform the early warning system. Visual data feed is used by crew only after they have been alerted of a possible attack. Camera sensors are inexpensive but transforming the incoming video data streams into actionable items still requires expensive human processing.

Intuitionistic fuzzy sets are useful for modeling uncertain data of realistic problems. In this paper, we generalize and expand the utility of complex intuitionistic fuzzy sets using the space of quaternion numbers. The proposed representation can capture composite features and convey multi-dimensional fuzzy information via the functions of real membership, imaginary membership, real non-membership, and imaginary non-membership. We analyze the order relations and logic operations of the complex intuitionistic fuzzy set theory and introduce new operations based on quaternion numbers. We also present two quaternion distance measures in algebraic and polar forms and analyze their properties. We apply the quaternion representations and measures to decision-making models. The proposed model is experimentally validated in medical diagnosis, which is an emerging application for tackling patient’s symptoms and attributes of diseases.

Virtual reality (VR) has emerged as a promising technological intervention for anxiety disorders. However, there are no existing standards and best practices to evaluate the effectiveness of environments to achieve their intervention goals. The purpose of this study was to develop a VR intervention for student veterans with social anxiety disorder and test feasibility utilizing a three-stage development model. The development of a therapeutic VR environment may benefit from an interdisciplinary collaboration of researchers from various fields of study. Utilizing three stages of prototyping with two virtual reality platforms, fully immersive video (n = 6) and three-dimensional (3-D) immersive virtual reality (n = 8), the research team designed an intervention for student veterans with social anxiety disorder, testing bio-reactivity of participants. Results of prototyping include user feedback validating increased stress levels and increased bio-reactivity specifically in galvanic skin response and heart rate elevation. Implications include the use of 360 degrees video for prototyping 3-D virtual reality interventions.

In this research, we have studied the applicability of the Collatz Conjecture to pseudo-random number generators (PRNG). The research was motivated by the simplicity of the Collatz function, which makes it attractive as a potential PRNG. We have experimented with several candidate PRNGs based on the trajectory property of the Collatz function and the Collatz graph. The NIST Test Suite (SP 800-22) was used to evaluate the statistical randomness of the output of our PRNGs. In addition, we utilized a method to rank each PRNG by quality of random output. The test results have demonstrated that two of our PRNGs pass the NIST Test Suite, and that there is no significant statistical difference between the outputs of our PRNGs to that of the Mersenne Twister, the built-in PRNG in Python 3.7. To the best of our knowledge, our algorithms are the first to successfully utilize properties of the Collatz function to generate random numbers. Additionally, we have proved that one of our PRNGs generates uniformly distributed output with a period of 232. Finally, we have found that two of our PRNGs perform on par with the Mersenne Twister algorithm. Because our algorithms pass the NIST Test Suite, they are suitable for usage in certain cryptographic applications as well as simulations.

In this paper we explore the possibility of using computation with words (CWW) systems and CWW-based human-computer interface (HCI) and interaction to enable efficient computation and HCI. The application selected to demonstrate the problems and potential solutions is in the context of autonomous driving. The specific problem addressed is of a machine instructed by human word commands to execute the task of parking two manned or unmanned cars in a two-car garage using CWW. We divide the interaction process into two steps: (1) feasibility verification and (2) execution. In order to fulfill the task, we begin with verifications of feasibility in terms of assessing whether the garage is unoccupied, checking general ballpark dimensions, inspecting irregular shapes, and classifying the cars that need to be parked, in terms of size, types of vehicles, ranges of acceptable tolerances needed if the cars are manned or not, and means of collision avoidance. The execution of the autonomous driving part is directed by sensory non-numeric fuzzy information that indicates distances from walls or obstacles. The execution algorithm uses a sequence of driving instructions aimed at using the available space in a simple and efficient way without resorting to elaborate numerical calculations, such as making sure that the car is within 2 inches of the wall. The system and its usability are qualitatively analyzed. The analysis shows that the approach has a potential for reducing computational complexity and improving system usability.

In this paper we detail a new approach for lossless integer compression that can be used to extend and improve several existing dynamic lossless data compression methods. The new methodology, referred to as Delta-Huffman, uses the Elias Delta code as a uniquely decodable representation of the infinite alphabet of unbounded integers and utilizes this representation to enable the application of dynamic Huffman coding on the Delta encoded set of integers. The method can be extended to combinations of other integer encoding techniques with additional dynamic symbol coding algorithms.

The Fuzzy Set Theory has been applied in various problems in numerous fields. In particular, the concepts of t-norms and t-conorms serve a significant role in shaping the theory and its applications. The notion of Complex Fuzzy Sets extends the Fuzzy Set Theory and provides several advantages over the classical theory, especially in terms of the capability to concisely, efficiently, and accurately represent complex relations between fuzzy set components. Some of the areas where complex fuzzy sets have been successfully applied are the areas of time series analysis and multi-criteria decision making problems. The notions of complex t-norms and t-conorms have not been fully developed so far. In this paper, we present the complex fuzzy set forms of t-norms and t-conorms and detail their properties. Additionally, we provide two numerical examples of applying the complex t-norm and t-conorm to multi-criteria decision making in the context of medicine- related problems using medical datasets.

Researchers have observed that multistage clustering can accelerate convergence and improve clustering quality. A two-stage and two-phase fuzzy C-means (FCM) algorithms have been reported. A pyramid multistage approach, however, has not been applied to FCM. This paper describes pyramid FCM clustering, where in the first stage the FCM uses an arbitrary partition matrix applied to a low resolution input sample. Next, the resultant partition matrix is used to seed the following, higher resolution stage where the sample size is doubled. The process of seeding higher resolution FCM using the results of lower resolution FCM, continues until the entire data is clustered. The utility and validity of the traditional FCM, two-stage FCM, two-phase FCM, and pyramid FCM are tested through fuzzy clustering of synthetic data and natural color images. The pyramid FCM outperforms the two-stage and two-phase multistage variants and obtains a speedup of ~3X, while maintaining the same or slightly better clustering quality than the traditional FCM. The two-stage and twophase multistage variants achieve a speedup of about 2X with slight degradation in performance. Furthermore, all the multistage variants can be used to identify several local optimum solutions at the same time the traditional C-means identifies one solution.

Complex numbers can capture compound features and convey multifaceted information; thereby providing means for solving complicated problems. In this paper, we combine the degree of membership and a degree of non-membership of members of Intuitionistic Fuzzy Sets via complex numbers to characterize these fuzzy sets. This approach enables extending several concepts such as classical fuzzy sets, Pythagorean fuzzy sets, and complex fuzzy sets. We discuss complex numbers-based set theoretic operations such as union, intersection, and complement. We define the No-Man-Zone (NMZ) set and establish the relation of NMZ characterization with complex numbers. Further, we introduce athematic complex numbers-based operations of intuitionistic fuzzy sets. We show that the square of the absolute of an intuitionistic fuzzy set becomes a Pythagorean fuzzy set. The polar form of an intuitionistic fuzzy set is reduced to a complex fuzzy set.

Quantitative software engineering is one of the most important paradigms for software development. That is, Requirements, Analysis, Design, Coding, and Testing. One of the challenges associated with quantitative software engineering is the fact that many of the quantifiable parameters are concomitant with uncertainty. Part of the uncertainty is due to the fact that a significant portion of the software engineering process involves human beings presenting rational, yet difficult to quantify, behavior. Due to this fact, soft computing approaches, specifically fuzzy logic based reasoning, present significant opportunities for constructing sound quantitative software engineering models. This work presents a new and innovative approach for fuzzy logic based quantitative software engineering procedures. We present a complex fuzzy logic based inference system used to account for the intricate relations between software engineering constraints such as quality, software features, and development effort. The new model concentrates on the requirements specifications part of the software engineering process. Moreover, the new model significantly improves the expressive power and inference capability of the soft computing component in the soft computing based quantitative software engineering.

This book presents a comprehensive report on the evolution of Fuzzy Logic since its formulation in Lotfi Zadehs seminal paper on fuzzy sets, published in 1965. In addition, it features a stimulating sampling from the broad field of research and development inspired by Zadehs paper. The chapters, written by pioneers and prominent scholars in the field, show how fuzzy sets have been successfully applied to artificial intelligence, control theory, inference, and reasoning. The book also reports on theoretical issues; features recent applications of Fuzzy Logic in the fields of neural networks, clustering, data mining and software testing; and highlights an important paradigm shift caused by Fuzzy Logic in the area of uncertainty management. Conceived by the editors as an academic celebration of the fifty years anniversary of the 1965 paper, this work is a must-have for students and researchers willing to get an inspiring picture of the potentialities, limitations, achievements and accomplishments of Fuzzy Logic-based systems.

A complex fuzzy class is characterized by a pure complex fuzzy grade of membership. Pure complex fuzzy classes are paramount in providing rich semantics for cases where the fuzzy data is periodic with a fuzzy period. Often, however, the available data is contaminated by noise, opposing expert opinions, ambiguity, and false information. This opens the door for using intuitionistic fuzzy sets theory: representing the false information via a degree of non-membership. Several researchers have identified the benefits of integrating the two concepts of complex fuzzy sets and intuitionistic fuzzy sets. Nevertheless, complex fuzzy sets allow for only one component of the degree of membership to be fuzzy. In this paper, we introduce the concept of complex intuitionistic fuzzy classes, which are characterized by pure complex intuitionistic fuzzy grade of membership. We define the basic terms and operations on complex intuitionistic fuzzy classes and provide a motivating example of relevant application.

Fuzzy Logic, introduced by Zadeh along with his introduction of fuzzy sets, is a continuous multi-valued logic system. Hence, it is a generalization of the classical logic and the classical discrete multi-valued logic (e.g. Lukasiewicz' three/many-valued logic). Throughout the years Zadeh and other researches have introduced extensions to the theory of fuzzy setts and fuzzy logic. Notable extensions include linguistic variables, type-2 fuzzy sets, complex fuzzy numbers, and Z-numbers. Another important extension to the theory, namely the concepts of complex fuzzy logic and complex fuzzy sets, has been investigated by Kandel et al. This extension provides the basis for control and inference systems relating to complex phenomena that cannot be readily formalized via type-1 or type-2 fuzzy sets. Hence, in recent years, several researchers have used the new formalism, often in the context of hybrid neuro-fuzzy systems, to develop advanced complex fuzzy logic-based inference applications. In this chapter we reintroduce the concept of complex fuzzy sets and complex fuzzy logic and survey the current state of complex fuzzy logic, complex fuzzy sets theory, and related applications.

The affinity of threads with cores in current chipmultiprocessor systems has a substantial impact on the execution time, latency, and power consumption of multi-threaded workloads. Finding an optimal mapping configuration of threads is a significant challenge as it requires detailed knowledge of each thread’s demands for shared system resources. This paper describes a software-based strategy that makes judicious thread migration decisions founded on careful inspection of dynamic resource utilization. The main novelty of the system reported in this paper is the extensive utilization of hardware performance counters to develop a set of synthesized metrics that capture resource contention among co-running threads. Experimental results with a set of contemporary parallel workloads, show that the system can achieve significant improvements in power consumption and performance over the default scheduling heuristics implemented in the Linux kernel. Keywords–energy efficiency; thread scheduling; workload characterization.