Large-scale access scheduling in wireless mesh networks using social centrality

Wireless mesh networking is an economic and convenient way to provide last mile Internet access through ad hoc peer-to-peer communication links. However, without systematic network configuration and channel resource management, these networks suffer from scalability, performance degradation and service disruption issues due to overwhelming co-channel interference, unscrupulous channel utilization and inherent network mobility. The IEEE 802.11 DCF and EDCA mechanisms based on CSMA/CA are the most widely used random channel access mechanisms, but unfortunately these cannot effectively eliminate hidden terminal and exposed terminal problems in multi-hop scenarios. Social network analysis techniques proposed for economic and social studies have recently been shown to be a successful approach for characterizing information propagation in multi-hop wireless networks. We propose a set of efficient resource allocation algorithms and channel access scheduling protocols based on Latin squares and social centrality metrics for wireless mesh networks (WMNs) with multi-radio multi-channel (MRMC) communication capabilities, called LaSo$LaSo$, which can coexist with IEEE 802.11 DCF and be effectively applied in large scale WMNs. Based on interference information provided by the interference graph, LaSo$LaSo$ uses nodal degree centrality to form cliques for intra-cluster communication, and betweenness centrality to choose bridge nodes to form cliques for inter-cluster communication in WMNs, and then applies Latin squares to map the clique-based clustering structure to radios and channels for wireless communication purposes. Afterwards, LaSo$LaSo$ again applies Latin squares to schedule the channel access amongst nodes within each cluster in a collision-free manner. We evaluate LaSo$LaSo$ using simulations, and results show that LaSo$LaSo$ achieves much better performance than existing IEEE 802.11 standards and other multi-channel access control protocols.     

Visual analysis of social networks in space and time using smartphone logs

We designed and applied interactive visualisation techniques for investigating how social networks are embedded in time and space, using data collected from smartphone logs. Our interest in spatial aspects of social networks is that they may reveal associations between participants missed by simply making contact through smartphone devices. Four linked and co-ordinated views of spatial, temporal, individual and social network aspects of the data, along with demographic and attitudinal variables, helped add context to the behaviours we observed. Using these techniques, we were able to characterise spatial and temporal aspects of participants’ social networks and suggest explanations for some of them. This provides some validation of our techniques.Unexpected deficiencies in the data that became apparent prompted us to evaluate the dataset in more detail. Contrary to what we expected, we found significant gaps in participant records, particularly in terms of location, a poorly connected sample of participants and asymmetries in reciprocal call logs. Although the data captured are of high quality, deficiencies such as these remain and are likely to have a significant impact on interpretations relating to spatial aspects of the social network. We argue that appropriately-designed interactive visualisation techniques–afforded by our flexible prototyping approach–are effective in identifying and characterising data inconsistencies. Such deficiencies are likely to exist in other similar datasets, and although the visual approaches we discuss for identifying data problems may not be scalable, the categories of problems we identify may be used to inform attempts to systematically account for errors in larger smartphone datasets.     

Designing business logistics networks using model-based reasoning and heuristic-based searching

This article describes an intelligent system called LogNet that provides advice on how to design business logistics networks. It implements its capabilities by utilizing model-based reasoning techniques. In addition, it utilizes heuristic-based searching to guide an end-user toward more effective network designs.The network design problem studied in this article addresses the warehouse facility location problem: how many warehouses are needed in a network and to which customer markets should they be assigned? LogNet enables end-users to incrementally create and test a logistics network design by using a graphical user interface. LogNet employs model-based reasoning procedures that analyze the structure of the current network design in order to offer recommendations on how to consolidate or decentralize a network. The overall goal of LogNet is to provide a flexible user interface that is capable of supporting this design task.     

Designing business logistics networks using model-based reasoning and heuristic-based searching

This article describes an intelligent system called LogNet that provides advice on how to design business logistics networks. It implements its capabilities by utilizing model-based reasoning techniques. In addition, it utilizes heuristic-based searching to guide an end-user toward more effective network designs.

The network design problem studied in this article addresses the warehouse facility location problem: how many warehouses are needed in a network and to which customer markets should they be assigned? LogNet enables end-users to incrementally create and test a logistics network design by using a graphical user interface. LogNet employs model-based reasoning procedures that analyze the structure of the current network design in order to offer recommendations on how to consolidate or decentralize a network. The overall goal of LogNet is to provide a flexible user interface that is capable of supporting this design task.     

Visual reasoning instructional software system

Visual reasoning is an essential skill for an engineer to possess, particularly as computer-aided design tools become more prevalent. In this paper, we describe an innovative interactive multimedia application that provides a student with the tools to build a strong foundation in visual reasoning. The system, called the Visual Reasoning Tutor, exploits the missing view problem as a mechanism to develop the visual reasoning abilities of students. The Visual Reasoning Tutor provides a student with interactive, graphical operations to construct 3-D geometric objects, varying levels of intelligent critiques throughout the solution process, and a graphical user interface which supports multimedia capabilities aimed to enhance the learning process. This paper presents an overview of the system components, a complete sample exercise, and testing results of several prototypes that have been used in the classroom.     

Planning for tourism routes using social networks

Traveling recommendation systems have become very popular applications for organizing and planning tourist trips. Among other challenges, these applications are faced with the task of maintaining updated information about popular tourist destinations, as well as providing useful tourist guides that meet the users preferences. In this work we present the PlanTour, a system that creates personalized tourist plans using the human-generated information gathered from the minube¹ traveling social network. The system follows an automated planning approach to generate a multiple-day plan with the most relevant points of interest of the city/region being visited. Particularly, the system collects information of users and points of interest from minube, groups these points with clustering techniques to split the problem into per-day sub-problems. Then, it uses an off-the-shelf domain-independent automated planner that finds good quality tourist plans. Unlike other tourist recommender systems, the PlanTour planner is able to organize relevant points of interest taking into account user’s expected drives, and user scores from a real social network. The paper also highlights how to use human provided recommendations to guide the search for solutions of combinatorial tasks. The resulting intelligent system opens new possibilities of combining human-generated knowledge with efficient automated techniques when solving hard computational tasks. From an engineering perspective we advocate for the use of declarative representations of problem solving tasks that have been shown to improve modeling and maintenance of intelligent systems.     

Personality assessment using multiple online social networks

Personality plays an important role in various aspects of our daily life. It is being used in many application scenarios such as i) personalized marketing and advertisement of commercial products, ii) designing personalized ambient environments, iii) personalized avatars in virtual world, and iv) by psychologists to treat various mental and personality disorders. Traditional methods of personality assessment require a long questionnaire to be completed, which is time consuming. On the other hand, several works have been published that seek to acquire various personality traits by analyzing Internet usage statistics. Researchers have used Facebook, Twitter, YouTube, and various other websites to collect usage statistics. However, we are still far from a successful outcome. This paper uses a range of divergent features of Facebook and LinkedIn social networks, both separately and collectively, in order to achieve better results. In this work, the big five personality trait model is used to analyze the five traits: openness to experience, conscientiousness, extroversion, agreeableness, and neuroticism. The experimental results show that the accuracy of personality detection improves with the use of complementary features of multiple social networks (Facebook and LinkedIn, in our case) for openness, conscientiousness, agreeableness, and neuroticism. However, for extroversion we found that the use of only LinkedIn features provides better results than the use of only Facebook features or both Facebook and LinkedIn features.     

Uncertain reasoning about agents' beliefs and reasoning

Reasoning about mental states and processes is important in varioussubareas of the legal domain. A trial lawyer might need to reason andthe beliefs, reasoning and other mental states and processes of membersof a jury; a police officer might need to reason about the conjecturedbeliefs and reasoning of perpetrators; a judge may need to consider adefendant's mental states and processes for the purposes of sentencing;and so on. Further, the mental states in question may themselves beabout the mental states and processes of other people. Therefore, if AIsystems are to assist with reasoning tasks in law, they may need to beable to reason about mental states and processes. Such reasoning isriddled with uncertainty, and this is true in particular in the legaldomain. The article discusses how various different types ofuncertainty arise, and shows how they greatly complicate the task ofreasoning about mental states and processes. The article concentrates onthe special case of states of belief and processes of reasoning, andsketches an implemented, prototype computer program (ATT-Meta) thatcopes with the various types of uncertainty in reasoning about beliefsand reasoning. In particular, the article outlines the system'sfacilities for handling conflict between different lines of argument,especially when these lie within the reasoning of different people. Thesystem's approach is illustrated by application to a real-life muggingexample.     

Visual analysis of large heterogeneous social networks by semantic and structural abstraction

Social network analysis is an active area of study beyond sociology. It uncovers the invisible relationships between actors in a network and provides understanding of social processes and behaviors. It has become an important technique in a variety of application areas such as the Web, organizational studies, and homeland security. This paper presents a visual analytics tool, OntoVis, for understanding large, heterogeneous social networks, in which nodes and links could represent different concepts and relations, respectively. These concepts and relations are related through an ontology (also known as a schema). OntoVis is named such because it uses information in the ontology associated with a social network to semantically prune a large, heterogeneous network. In addition to semantic abstraction, OntoVis also allows users to do structural abstraction and importance filtering to make large networks manageable and to facilitate analytic reasoning. All these unique capabilities of OntoVis are illustrated with several case studies     

Genetic clustering of social networks using random walks

In the era of globalization, traditional theories and models of social systems are shifting their focus from isolation and independence to networks and connectedness. Analyzing these new complex social models is a growing, and computationally demanding area of research. In this study, we investigate the integration of genetic algorithms (GAs) with a random-walk-based distance measure to find subgroups in social networks. We test our approach by synthetically generating realistic social network data sets. Our clustering experiments using random-walk-based distances reveal exceptionally accurate results compared with the experiments using Euclidean distances.     

Arnetminer: expertise oriented search using social networks

Expertise Oriented Search (EOS) aims at providing comprehensive expertise analysis on data from distributed sources. It is useful in many application domains, for example, finding experts on a given topic, detecting the confliction of interest between researchers, and assigning reviewers to proposals. In this paper, we present the design and implementation of our expertise oriented search system, Arnetminer (). Arnetminer has gathered and integrated information about a half-million computer science researchers from the Web, including their profiles and publications. Moreover, Arnetminer constructs a social network among these researchers through their co-authorship, and utilizes this network information as well as the individual profiles to facilitate expertise oriented search tasks. In particular, the co-authorship information is used both in ranking the expertise of individual researchers for a given topic and in searching for associations between researchers. We have conducted initial experiments on Arnetminer. Our results demonstrate that the proposed relevancy propagation expert finding method outperforms the method that only uses person local information, and the proposed two-stage association search on a large-scale social network is order of magnitude faster than the baseline method.     

MEBC: social network immunization via motif-based edge-betweenness centrality

Knowledge and Information Systems - Immunization of social networks has attracted increasing attention over the last decade. Various algorithms have been proposed based on the topological structure...     

模糊推理的统计敏感性分析

在设计模糊逻辑系统时，如何实现其对输入噪声的鲁棒性是一个首要的问题，相应地如何很好地分析其对输入噪声的鲁棒性（也称敏感性分析）也就成了一个重要问题．使用统计的方法，对常见的模糊推理方法进行了敏感性分析．首先以均值与方差为基础，提出了2个模糊集的ε-统计相等的概念；随后导出了常见的模糊推理方法的统计敏感性，这包括链接模糊推理与多规则模糊推理．与前人相关工作不同的是，更着重于模糊推理的方差分析，这一方法从数理统计的角度来看能更好地揭示模糊推理本质的敏感性．     

On point-duration networks for temporal reasoning

We present here a point-duration network formalism which extends the point algebra model to include additional variables that represent durations between points of time. Thereafter the new qualitative model is enlarged for allowing unary metric constraints on points and durations, subsuming in this way several point-based approaches to temporal reasoning. We deal with some reasoning tasks within the new models and we show that the main problem, deciding consistency, is NP-complete. However, tractable special cases are identified and we show efficient algorithms for checking consistency, finding a solution and obtaining the minimal network.     

Two-stage adaptive robot position/force control using fuzzy reasoning and neural networks

Many studies have been performed on the position/force control of robot manipulators. Since the desired position and force required to realize certain tasks are usually designated in the operational space, the controller should adapt itself to an environment and generate the control force vector in the operational space. On the other hand, the friction of each joint of a robot manipulator is a serious problem since it impedes control accuracy. Therefore, the friction should be effectively compensated for in order to realize precise control of robot manipulators. Recently, soft computing techniques (fuzzy reasoning, neural networks and genetic algorithms) have been playing an important role in the control of robots. Applying the fuzzy-neuro approach (a combination of fuzzy reasoning and neural networks), learning/adaptation ability and human knowledge can be incorporated into a robot controller. In this paper, we propose a two-stage adaptive robot manipulator position/force control method in which the uncertain/unknown dynamic of the environment is compensated for in the task space and the joint friction is effectively compensated for in the joint space using soft computing techniques. The effectiveness of the proposed control method was evaluated by experiments.     

Overlapping community detection in social networks using coalitional games

Community detection is a significant research problem in various fields such as computer science, sociology and biology. The singular characteristic of communities in social networks is the multimembership of a node resulting in overlapping communities. But dealing with the problem of overlapping community detection is computationally expensive. The evolution of communities in social networks happens due to the self-interest of the nodes. The nodes of the social network acts as self-interested players, who wish to maximize their benefit through interactions in due course of community formation. Game theory provides a systematic framework tox capture the interactions between these selfish players in the form of games. In this paper, we propose a Community Detection Game (CDG) that works under the cooperative game framework. We develop a greedy community detection algorithm that employs Shapley value mechanism and majority voting mechanism in order to disclose the underlying community structure of the given network. Extensive experimental evaluation on synthetic and real-world network datasets demonstrates the effectiveness of CDG algorithm over the state-of-the-art algorithms.     

An integrated semantics for reasoning about SysML design models using refinement

SysML is a variant of UML for systems design. Several formalisations of SysML (and UML) are available. Our work is distinctive in two ways: a semantics for refinement and for a representative collection of elements from the UML4SysML profile (blocks, state machines, activities, and interactions) used in combination. We provide a means to analyse and refine design models specified using SysML. This facilitates the discovery of problems earlier in the system development lifecycle, reducing time, and costs of production. Here, we describe our semantics, which is defined using a state-rich process algebra and implemented in a tool for automatic generation of formal models. We also show how the semantics can be used for refinement-based analysis and development. Our case study is a leadership-election protocol, a critical component of an industrial application. Our major contribution is a framework for reasoning using refinement about systems specified by collections of SysML diagrams.     

Nonmonotonic reasoning,nonmonotonic logics and reasoning about change

In this paper we introduce nonmonotonic reasoning and the attempts at formalizing it using nonmonotonic logics. We examine and compare the best known of these. Despite the difference in motivation and technical construction there are strong similarities between these logics which are confirmed when they are finally shown to have a common basis. Finally we consider using nonmonotonic logics to represent reasoning about change.