Bücher der Reihe SpringerBriefs in Computer Science

This book presents the fundamentals of mobile visual computing in iOS development and provides directions for developers and researchers interested in developing iOS applications with image processing and computer vision capabilities.

This book sheds light on the challenges facing social media in combating malicious accounts, and aims to introduce current practices to address the challenges.

This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring.This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-basedurban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale.This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics benefit from this book as well.

This book discusses how machine learning and the Internet of Things (IoT) are playing a part in smart control of underwater environments, known as Internet of Underwater Things (IoUT).

For a better understanding of the past and the current research, it also discusses the components of financial opinions one-by-one with the related works and highlights some possible research avenues, providing a research agenda with both micro- and macro-views toward financial opinions.

As computer systems evolve, the volume of data to be processed increases significantly, either as a consequence of the expanding amount of available information, or due to the possibility of performing highly complex operations that were not feasible in the past. Nevertheless, tasks that depend on the manipulation of large amounts of information are still performed at large computational cost, i.e., either the processing time will be large, or they will require intensive use of computer resources. In this scenario, the efficient use of available computational resources is paramount, and creates a demand for systems that can optimize the use of resources in relation to the amount of data to be processed. This problem becomes increasingly critical when the volume of information to be processed is variable, i.e., there is a seasonal variation of demand. Such demand variations are caused by a variety of factors, such as an unanticipated burst of client requests, a time-critical simulation, or high volumes of simultaneous video uploads, e.g. as a consequence of a public contest. In these cases, there are moments when the demand is very low (resources are almost idle) while, conversely, at other moments, the processing demand exceeds the resources capacity. Moreover, from an economical perspective, seasonal demands do not justify a massive investment in infrastructure, just to provide enough computing power for peak situations. In this light, the ability to build adaptive systems, capable of using on demand resources provided by Cloud Computing infrastructures is very attractive.

Recognition of the importance of power and energy in the field of high performance computing (HPC) has never been greater. Research has been conducted in a number of areas related to power and energy, but little existing research has focused on large-scale HPC. Part of the reason is the lack of measurement capability currently available on small or large platforms. Typically, research is conducted using coarse methods of measurement such as inserting a power meter between the power source and the platform, or fine grained measurements using custom instrumented boards (with obvious limitations in scale). To analyze real scientific computing applications at large scale, an in situ measurement capability is necessary that scales to the size of the platform.In response to this challenge, the unique power measurement capabilities of the Cray XT architecture were exploited to gain an understanding of power and energy use and the effects of tuning both CPU and network bandwidth. Modifications were made at the operating system level to deterministically halt cores when idle. Additionally, capabilities were added to alter operating P-state. At the application level, an understanding of the power requirements of a range of important DOE/NNSA production scientific computing applications running at large scale (thousands of nodes) is gained by simultaneously collecting current and voltage measurements on the hosting nodes. The effects of both CPU and network bandwidth tuning are examined and energy savings opportunities of up to 39% with little or no impact on run-time performance is demonstrated. Capturing scale effects was key. This research provides strong evidence that next generation large-scale platforms should not only approach CPU frequency scaling differently, as we will demonstrate, but could also benefit from the capability to tune other platform components, such as the network, to achieve more energy efficient performance.

This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website.

A powerful new image presentation technique has evolved over the last twenty years, and its value demonstrated through its support of many and varied common tasks. Conceptually, Rapid Serial Visual Presentation (RSVP) is basically simple, exemplified in the physical world by the rapid riffling of the pages of a book in order to locate a known image. Advances in computation and graphics processing allow RSVP to be applied flexibly and effectively to a huge variety of common tasks such as window shopping, video fast-forward and rewind, TV channel selection and product browsing. At its heart is a remarkable feature of the human visual processing system known as pre-attentive processing, one which supports the recognition of a known image within as little as one hundred milliseconds and without conscious cognitive effort.Knowledge of pre-attentive processing, together with extensive empirical evidence concerning RSVP, has allowed the authors to provide useful guidance to interaction designers wishing to explore the relevance of RSVP to an application, guidance which is supported by a variety of illustrative examples.

The amount and the complexity of the data gathered by current enterprises are increasing at an exponential rate. Consequently, the analysis of Big Data is nowadays a central challenge in Computer Science, especially for complex data. For example, given a satellite image database containing tens of Terabytes, how can we find regions aiming at identifying native rainforests, deforestation or reforestation? Can it be made automatically? Based on the work discussed in this book, the answers to both questions are a sound "e;yes"e;, and the results can be obtained in just minutes. In fact, results that used to require days or weeks of hard work from human specialists can now be obtained in minutes with high precision. Data Mining in Large Sets of Complex Data discusses new algorithms that take steps forward from traditional data mining (especially for clustering) by considering large, complex datasets. Usually, other works focus in one aspect, either data size or complexity. This work considers both: it enables mining complex data from high impact applications, such as breast cancer diagnosis, region classification in satellite images, assistance to climate change forecast, recommendation systems for the Web and social networks; the data are large in the Terabyte-scale, not in Giga as usual; and very accurate results are found in just minutes. Thus, it provides a crucial and well timed contribution for allowing the creation of real time applications that deal with Big Data of high complexity in which mining on the fly can make an immeasurable difference, such as supporting cancer diagnosis or detecting deforestation.

Research has deeply investigated several issues related to the use of integrity constraints on relational databases. In particular, a great deal of attention has been devoted to the problem of extracting "e;reliable"e; information from databases containing pieces of information inconsistent with regard to some integrity constraints. In this manuscript, the problem of extracting consistent information from relational databases violating integrity constraints on numerical data is addressed. Aggregate constraints defined as linear inequalities on aggregate-sum queries on input data are considered. The notion of repair as consistent set of updates at attribute-value level is exploited, and the characterization of several data-complexity issues related to repairing data and computing consistent query answers is provided. Moreover, a method for computing "e;reasonable"e; repairs of inconsistent numerical databases isintroduced, for a restricted but expressive class of aggregate constraints. An extension of this method for dealing with the data repairing problem in the presence of weak aggregate constraints which are expected to be satisfied, but not required to, is presented. Furthermore, a technique for computing consistent answers of aggregate queries in the presence of a wide form of aggregate constraints is provided. Finally, extensions of the framework as well as several open problems are discussed.

In this work, we provide a treatment of the relationship between two models that have been widely used in the implementation of autonomous agents: the Belief DesireIntention (BDI) model and Markov Decision Processes (MDPs). We start with an informal description of the relationship, identifying the common features of the two approaches and the differences between them. Then we hone our understanding of these differences through an empirical analysis of the performance of both models on the TileWorld testbed. This allows us to show that even though the MDP model displays consistently better behavior than the BDI model for small worlds, this is not the case when the world becomes large and the MDP model cannot be solved exactly. Finally we present a theoretical analysis of the relationship between the two approaches, identifying mappings that allow us to extract a set of intentions from a policy (a solution to an MDP), and to extract a policy from a set of intentions.

These days, construction companies are beginning to be concerned about a potential labor shortage by demographic changes and an aging construction work force. Also, an improvement in construction safety could not only reduce accidents but also decrease the cost of the construction, and is therefore one of the imperative goals of the construction industry. These challenges correspond to the potential for Automation and Robotics in Construction as one of solutions. Almost half of construction work is said to be material handling and materials used for construction are heavy and bulky for humans. To date, various types of robots have been developed for glazed panel construction. Through the case studies on construction, to which the robots were applied, however, we identified difficulties to be overcome. In this study, a human-robot cooperative system is deduced as one approach to surmount these difficulties; then, considerations on interactions among the operator, robot and environment are applied to design of the system controller. The human-robot cooperative system can cope with various and construction environments through real-time interaction with a human, robot and construction environment simultaneously. The physical power of a robot system helps a human to handle heavy construction materials with a relatively scaled-down load. Also, a human can feel and respond to the force reflected from robot end effecter acting with working environment. Through the experiments and mock-up tests with a prototype robot, we observe the characteristics of the power assist and the force reflection, the merits of the human-robot cooperation system. To apply human-robot cooperative system at real construction sites, Glazed Ceiling Panel Construction Robot is developed for the first time. This robot is distinguished from other glazed panel construction robots because of the methods of lifting the panel to high installation positions and installing the fragile and bulk panel with robot force control. After applying to real construction sites, evaluation on the productivity of the developed robot was done by comparing and analyzing with the existing installation methods.

The research in content-based indexing and retrieval of visual information such as images and video has become one of the most populated directions in the vast area of information technologies. Social networks such as YouTube, Facebook, FileMobile, and DailyMotion host and supply facilities for accessing a tremendous amount of professional and user generated data. The areas of societal activity, such as, video protection and security, also generate thousands and thousands of terabytes of visual content. This book presents the most recent results and important trends in visual information indexing and retrieval. It is intended for young researchers, as well as, professionals looking for an algorithmic solution to a problem.

Cooperative transmission aims to improve the reliability of wireless mobile communications through the use of diversity provided by additional relays assisting in the transmission between the source and destination nodes. This is possible as the rationale behind spatio-temporal processing can be easily mapped onto networked systems. Autonomic Cooperative Networking studies the further evolution of this phenomenon by first involving the network layer routines and then additionally incorporating the notion of autonomic system design.

This book describes a computational framework for real-time detection of psychological signals related to Post-Traumatic Stress Disorder (PTSD) in online text-based posts, including blogs and web forums. Further, it explores how emerging computational techniques such as sentiment mining can be used in real-time to identify posts that contain PTSD-related signals, flag those posts, and bring them to the attention of psychologists, thus providing an automated flag and referral capability. The use of sentiment extraction technologies allows automatic in-depth analysis of opinions and emotions expressed by individuals in their online posts. By training these automated systems with input from academic and clinical experts, the systems can be refined so that the accuracy of their detection of possible PTSD signals is comparable to that of psychologists reading the same online posts. While a portion of the literature on this and related topics explores the correlation between text patterns in archived documents and PTSD, no literature to date describes a system performing real-time analysis. Our system allows analysts to quickly identify, review, and validate online posts which have been flagged as exhibiting signs or symptoms of PTSD and enables follow-up, thus allowing for the presentation of treatment options to the authors of those posts. We describe the ontology of PTSD-related terms (i.e., terms which signal PTSD and related conditions) that need to be tracked, the algorithms used for extraction of the intensity of these signals, and the training process used to fine-tune sentiment analysis algorithms. We then present the results of processing a validation data set, different from the training set, comparing the algorithmic output with opinions of clinical psychologists, and explain how the concept can be extended to detect signals of other psychological conditions. We present a sample system architecture and implementation which can be used to engage users and their families, either anonymously or eponymously, and use the sentiment extraction algorithms as an early screening tool to alert clinicians to participants who may require close monitoring or follow-up. Finally, we describe a user test conducted with users recruited from the Veteran population and present the results of the analyses on the data.

Machine learning and nonparametric function estimation procedures can be effectively used in forecasting. One important and current application is used to make forecasts of "e;future dangerousness"e; to inform criminal justice decision. Examples include the decision to release an individual on parole, determination of the parole conditions, bail recommendations, and sentencing. Since the 1920s, "e;risk assessments"e; of various kinds have been used in parole hearings, but the current availability of large administrative data bases, inexpensive computing power, and developments in statistics and computer science have increased their accuracy and applicability. In this book, these developments are considered with particular emphasis on the statistical and computer science tools, under the rubric of supervised learning, that can dramatically improve these kinds of forecasts in criminal justice settings. The intended audience is researchers in the social sciences and data analysts in criminal justice agencies.

Network Robustness under Large-Scale Attacks provides the analysis of network robustness under attacks, with a focus on large-scale correlated physical attacks. The book begins with a thorough overview of the latest research and techniques to analyze the network responses to different types of attacks over various network topologies and connection models. It then introduces a new large-scale physical attack model coined as area attack, under which a new network robustness measure is introduced and applied to study the network responses. With this book, readers will learn the necessary tools to evaluate how a complex network responds to random and possibly correlated attacks.

Automated Configuration has long been the subject of intensive research, especially in Artificial Intelligence. It is a pervasive problem to be solved, and it is a good test of various knowledge representation and reasoning techniques. The problem shows up in applications such as various electrical circuit design, utility computing and even concurrent engineering. Automated Configuration Problem Solving defines the ubiquitous problem, illustrates the various solution techniques, and includes a survey using these techniques from the mid-70's until the mid-90's. During this time, various general approaches were developed, and then become more specialized. This book covers the development of the general problem solving techniques for automated configuration, which are based on both published academic work and patents.

Driven by the increasing demand for capacity and Quality of Service in wireless cellular networks and motivated by the distributed antenna system, the authors proposed a cooperative communication architecture-Group Cell architecture, which was initially brought forward in 2001. Years later, Coordinated Multiple-Point Transmission and Reception (CoMP) for LTE-Advanced was put forward in April 2008, as a tool to improve the coverage of cells having high data rates, the cell-edge throughput and/or to increase system throughput. This book mainly focuses on the Group Cell architecture with multi-cell generalized coordination, Contrast Analysis between Group Cell architecture and CoMP, Capacity Analysis, Slide Handover Strategy, Power Allocation schemes of Group Cell architecture to mitigate the inter-cell interference and maximize system capacity and the trial network implementation and performance evaluations of Group Cell architecture.

This brief investigates distributed medium access control (MAC) with QoS provisioning for both single- and multi-hop wireless networks including wireless local area networks (WLANs), wireless ad hoc networks, and wireless mesh networks. For WLANs, an efficient MAC scheme and a call admission control algorithm are presented to provide guaranteed QoS for voice traffic and, at the same time, increase the voice capacity significantly compared with the current WLAN standard. In addition, a novel token-based scheduling scheme is proposed to provide great flexibility and facility to the network service provider for service class management.Also proposed is a novel busy-tone based distributed MAC scheme for wireless ad hoc networks and a collision-free MAC scheme for wireless mesh networks, respectively, taking the different network characteristics into consideration. The proposed schemes enhance the QoS provisioning capability to real-time traffic and, at the same time, significantly improve the system throughput and fairness performance for data traffic, as compared with the most popular IEEE 802.11 MAC scheme.

Performance Evaluation Software: Moving Object Detection and Tracking in Videos introduces a software approach for the real-time evaluation and performance comparison of the methods specializing in moving object detection and/or tracking (D&T) in video processing. Digital video content analysis is an important item for multimedia content-based indexing (MCBI), content-based video retrieval (CBVR) and visual surveillance systems. There are some frequently-used generic algorithms for video object D&T in the literature, such as Background Subtraction (BS), Continuously Adaptive Mean-shift (CMS), Optical Flow (OF), etc. An important problem for performance evaluation is the absence of any stable and flexible software for comparison of different algorithms. In this frame, we have designed and implemented the software for comparing and evaluating the well-known video object D&T algorithms on the same platform. This software is able to compare them with the same metrics in real-time and on the same platform. It also works as an automatic and/or semi-automatic test environment in real-time, which uses the image and video processing essentials, e.g. morphological operations and filters, and ground-truth (GT) XML data files, charting/plotting capabilities, etc. Along with the comprehensive literature survey of the abovementioned video object D&T algorithms, this book also covers the technical details of our performance benchmark software as well as a case study on people D&T for the functionality of the software.

This book surveys the outstanding work of physical-layer (PHY) security, including the recent achievements of confidentiality and authentication for wireless communication systems by channel identification. A practical approach to building unconditional confidentiality for Wireless Communication security by feedback and error correcting code is introduced and a framework of PHY security based on space time block code (STBC) MIMO system is demonstrated. Also discussed is a scheme which combines cryptographic techniques implemented in the higher layer with the physical layer security approach using redundant antennas of MIMO systems to provide stronger security for wireless networks. The channel responses between communication peers have been explored as a form of fingerprint with spatial and temporal uniqueness. Finally, the book develops a new lightweight method of channel identification for Sybil attack and node clone detection in wireless sensor networks (WSNs).

This brief focuses on radio resource allocation in a heterogeneous wireless medium. It presents radio resource allocation algorithms with decentralized implementation, which support both single-network and multi-homing services. The brief provides a set of cooperative networking algorithms, which rely on the concepts of short-term call traffic load prediction, network cooperation, convex optimization, and decomposition theory. In the proposed solutions, mobile terminals play an active role in the resource allocation operation, instead of their traditional role as passive service recipients in the networking environment.

This brief guides the reader through three basic Internet resource pricing models using an Internet cost analysis. Addressing the evolution of service types, it presents several corresponding mechanisms which can ensure pricing implementation and resource allocation. The authors discuss utility optimization of network pricing methods in economics and underline two classes of pricing methods including system optimization and entities' strategic optimization. The brief closes with two examples of the newly proposed pricing strategy helping to solve the profit distribution problem brought by P2P free-riding and improve the pricing efficiency with the introduction of the price discrimination. The Internet resource pricing strategy is not only the key factor of Internet resource allocation efficiency, but also the determinant of the profit. The methods and models discussed in Internet Resource Pricing Models increase the efficiency of existing pricing strategies to ensure a sound and sustainable development of the Internet. The brief will help researchers and professionals working with this key factor of Internet resource allocation.

This brief considers the various stakeholders in today's mobile device ecosystem, and analyzes why widely-deployed hardware security primitives on mobile device platforms are inaccessible to application developers and end-users. Existing proposals are also evaluated for leveraging such primitives, and proves that they can indeed strengthen the security properties available to applications and users, without reducing the properties currently enjoyed by OEMs and network carriers. Finally, this brief makes recommendations for future research that may yield practical and deployable results.

This brief provides readers a complete and self-contained resource for information about DDoS attacks and how to defend against them. It presents the latest developments in this increasingly crucial field along with background context and survey material. The book also supplies an overview of DDoS attack issues, DDoS attack detection methods, DDoS attack source traceback, and details on how hackers organize DDoS attacks. The author concludes with future directions of the field, including the impact of DDoS attacks on cloud computing and cloud technology. The concise yet comprehensive nature of this brief makes it an ideal reference for researchers and professionals studying DDoS attacks. It is also a useful resource for graduate students interested in cyberterrorism and networking.

This book presents several novel approaches to model the interaction between the attacker and the defender and assess the security of Vehicular Ad Hoc Networks (VANETs). The first security assessment approach is based on the attack tree security assessment model, which leverages tree based methods to analyze the risk of the system and identify the possible attacking strategies the adversaries may launch. To further capture the interaction between the attacker and the defender, the authors propose to utilize the attack-defense tree model to express the potential countermeasures which could mitigate the system. By considering rational participants that aim to maximize their payoff function, the brief describes a game-theoretic analysis approach to investigate the possible strategies that the security administrator and the attacker could adopt. A phased attack-defense game allows the reader to model the interactions between the attacker and defender for VANET security assessment. The brief offers a variety of methods for assessing the security of wireless networks. Professionals and researchers working on the defense of VANETs will find this material valuable.