bluuebunny/crossref_metadata_2025 huggingface dataset:
Rethinking iBGP routing
Authors: Oprescu Iuniana M. , Meulle Mickael , Uhlig Steve , Pelsser Cristel , Maennel Olaf , Owezarski Philippe
Published: October 2012

The Internet is organized as a collection of administrative domains, known as Autonomous Systems (ASes). These ASes interact through the Border Gateway Protocol (BGP) that allows them to share reachability information. Adjacent routers in distinct ASes use external BGP (eBGP), whereas in a given AS routes are propagated over internal BGP (iBGP) sessions between any pair of routers. In large ASes where a logical full-mesh is not possible, confederations or route reflectors (RRs) are used. However, these somewhat scalable alternatives have introduced their own set of unpredictable effects (persistent routing oscillations and forwarding loops causing an increase of the convergence time) addressed in the literature [1].

The solution we propose to these issues consists of a structured routing overlay holding a comprehensive view of the routes. We describe the design of a distributed entity that performs BGP route pre-computation for its clients inside a large backbone network and propagates the paths to the routers. Compared to the current iBGP routing, the advantage of the overlay approach is the separation between the responsibility of the control plane (route storage and best path computation) and the forwarding of the packets.

One of the major improvements we bring is the divided routing table tackling the scalability concerns and allowing for parallel computation of paths.
Fived
Authors: Capelis D. J. , Long Darrell D.E.
Published: October 2012

Security functions such as access control, encryption and authentication are typically left up to applications on the modern Internet. There is no unified system to implement these critical features. The access control that does exist on the network doesn't integrate well with user authentication systems, so access control decisions are based on the network location of a computer rather than the privilege level of its user. Just about every layer of the Internet provides optional encryption, yet most data on the Internet continues to be sent in the clear. Application developers routinely make mistakes in security critical code leading to bugs that manifest in worms, malware or provide a doorway for actively malicious attackers. We propose a unified session layer that integrates trustworthiness features into the core of the network. This would reverse the fortunes of security on the Internet and lead us toward a safer, more secure global network.
Vehicular wifi access and rate adaptation
Authors: Joshi Ajinkya Uday , Kulkarni Purushottam
Published: October 2012

Vehicular WiFi access is distinct in two respects, (i) continuous mobility of clients and (ii) possibility of predictable link quality. As part of this study, we aim to comprehensively evaluate existing rate adaptation algorithms in real environments. Further, if required, we aim to develop a simple, low-overhead rate adaptation algorithm suited for vehicular WiFi access.
Achieving O(1) IP lookup on GPU-based software routers
Authors: Zhao Jin , Zhang Xinya , Wang Xin , Xue Xiangyang
Published: October 2012

IP address lookup is a challenging problem due to the increasing routing table size, and higher line rate. This paper investigates a new way to build an efficient IP lookup scheme using graphics processor units(GPU). Our contribution here is to design a basic architecture for high-performance IP lookup engine with GPU, and to develop efficient algorithms for routing prefix operations such as lookup, deletion, insertion, and modification. In particular, the IP lookup scheme can achieve
O
(1) time complexity. Our experimental results on real-world route traces show promising 6x gains in IP lookup throughput.
Road traffic estimation using in-situ acoustic sensing
Authors: Rajendra C. Viven , Kulkarni Purushottam
Published: October 2012

In this paper, we explore the efficacy of curb-side acoustic sensing to estimate road traffic conditions. We formulated a set of hypotheses which attempted to correlate traffic conditions with the ambient traffic noise. We present the evaluation of our hypotheses under various traffic conditions. Our threshold-based-classification yields 70-90% accuracy in distinguishing congested from free-flowing traffic.
Empowering users against sidejacking attacks
Authors: Riley Ryan D. , Ali Nada Mohammed , Al-Senaidi Kholoud Saleh , Al-Kuwari Aisha Lahdan
Published: October 2012

SideJacking occurs when an attacker intercepts a session cookie and uses it to impersonate a user and gain unauthorized access to a web-based service. To prevent SideJacking, a server should enable HTTPS and configure all session cookies to only be transmitted over a secure link. Many websites do not do this, however, and the user may be unaware. In this work we present a Firefox extension that will allow users to quickly and easily determine whether the server they are visiting is susceptible to SideJacking attacks.
QuagFlow
Authors: Nascimento Marcelo Ribeiro , Rothenberg Christian Esteve , Salvador Marcos Rogério , Magalhães Maurício Ferreira
Published: October 2012

Computing history has shown that open, multi-layer hardware and software stacks encourage innovation and bring costs down. Only recently this trend is meeting the networking world with the availability of entire open source networking stacks being closer than ever. Towards this goal, we are working on QuagFlow, a transparent interplay between the popular Quagga open source routing suite and the low level vendor-independent OpenFlow interface. QuagFlow is a distributed system implemented as a NOX controller application and a series of slave daemons running along the virtual machines hosting the Quagga routing instances.
Experimenting with multipath TCP
Authors: Barré Sébastien , Bonaventure Olivier , Raiciu Costin , Handley Mark
Published: October 2012

It is becoming the norm for small mobile devices to have access to multiple technologies for connecting to the Internet. This gives researchers an increasing interest for solutions allowing to use efficiently several communication mediums. We propose a demonstration of our Multipath TCP implementation for Linux, that allows spreading a single TCP flow across multiple Internet paths, without requiring any change to applications. The demonstration will involve a real Internet communication with MPTCP, with simultaneous use of several paths, as well as a demonstration of MPTCP failover capability.
Multi-hop packet tracking for experimental facilities
Authors: Santos Tacio , Henke Christian , Schmoll Carsten , Zseby Tanja
Published: October 2012

The Internet has become a complex system with increasing numbers of end-systems, applications, protocols and types of networks. Although we have a good understanding of how data is transferred over the network we cannot observe what happens with our data after sending and before receiving it - how packets traverse through the network and with which QoS characteristics remains unknown. Towards this objective we have developed a multi-hop packet tracking system intended to be used in experimental facilities, such as PlanetLab, where we have made our first tests. This paper describes our packet tracking realization and the results from our prototype implementation.
Ripcord
Authors: Heller Brandon , Erickson David , McKeown Nick , Griffith Rean , Ganichev Igor , Whyte Scott , Zarifis Kyriakos , Moon Daekyeong , Shenker Scott , Stuart Stephen
Published: October 2012

In this demo, we present Ripcord, a modular platform for rapidly prototyping scale-out data center networks. Ripcord enables researchers to build and evaluate new network features and topologies, using only commercially available hardware and open-source software. The Ripcord demo will show three examples of custom network functions, operating together, on top of a 160-node cluster. The first is a routing engine that isolates classes of traffic. The second is a dynamic network manager than adjusts links and switch power states to reduce energy. The third is a statistics aggregator that supports network health monitoring and automatic alerts. The demo will be interactive, with a visualization of live parameters for each link and switch, such as bandwidth, drops, and power status, as well a control panel to modify the traffic load. We feel that an interactive demo is the best way to introduce the research community to Ripcord and get their feedback.
LokVaani
Authors: Gabale Vijay , Raman Bhaskaran , Chebrolu Kameswari , Kulkarni Purushottam
Published: October 2012

In this work, we consider the goal of enabling effective voice communication in a TDMA, multi-hop mesh network, using low cost and low power platforms. We consider two primary usage scenarios: (1) enabling a local voice communication within a village-like setting, in developing regions (2) supporting an on-site local communication among a team of users e.g. during emergency response systems.

While there is plentiful literature on the use of TDMA for multi-hop wireless mesh networks, a practical multi-hop TDMA system remains elusive. Our contributions in this regard are three-fold. (1) We demonstrate the working of an 802.15.4-based low-cost, low-power, local communication system (referred as Lo3) using custom made handsets and off-the-shelf platforms. (2) We show the practicability of LiT: a full-fledged TDMA-based multi-hop, multi-channel MAC protocol for real-time applications; especially on resource constrained platforms, (3) We present implementation-based evaluations results of LiT and show that our protocol achieves practical synchronization, and robust operation in the face of wireless packet errors. As the part of the demo, we showcase LokVaani: an interactive voice application for local communication with the help of Lo3 prototype.
Automating root-cause analysis of network anomalies using frequent itemset mining
Authors: Paredes-Oliva Ignasi , Dimitropoulos Xenofontas , Molina Maurizio , Barlet-Ros Pere , Brauckhoff Daniela
Published: October 2012

Finding the root-cause of a network security anomaly is essential for network operators. In our recent work, we introduced a generic technique that uses frequent itemset mining to automatically extract and summarize the traffic flows causing an anomaly. Our evaluation using two different anomaly detectors (including a commercial one) showed that our approach works surprisingly well extracting the anomalous flows in most studied cases using sampled and unsampled NetFlow traces from two networks. In this demonstration, we will showcase an open-source anomaly-extraction system based on our technique, which we integrated with a commercial anomaly detector and use in the NOC of the GÉANT network since late 2009. We will report a number of detected security anomalies and will illustrate how an operator can use our system to automatically extract and summarize anomalous flows.
A framework for low-power IPv6 routing simulation, experimentation, and evaluation
Authors: Tsiftes Nicolas , Eriksson Joakim , Finne Niclas , Österlind Fredrik , Höglund Joel , Dunkels Adam
Published: October 2012

Low-power networked devices, such as sensors and actuators, are becoming a vital part of our everyday infrastructure. Being networked, the continued development of these systems needs involvement from the networking community. We present a framework for simulation, experimentation, and evaluation of routing mechanisms for low-power IPv6 networking. The framework provides a detailed simulation environment for low-power routing mechanisms, and allows the system to be directly uploaded to a physical testbed for experimental measurements.
Evolution of state-dependent risk preferences
Authors: Roos Patrick , Carr J. Ryan , Nau Dana S.
Published: October 2012

Researchers have invested much effort in constructing models of the state-dependent (sometimes risk-averse and sometimes risk-prone) nature of human decision making. An important open question is how state-dependent risk behavior can arise and remain prominent in populations. We believe that one part of the answer is the interplay between risk-taking and sequentiality of choices in populations subject to evolutionary population dynamics. To support this hypothesis, we provide simulation and analytical results for evolutionary lottery games, including results on evolutionary stability. We consider a parameterized class of
imitation dynamics
in which the parameter 0 ≤ α ≤ 1 yields the
replicator dynamic
with α = 1 and the
imitate-the-better
dynamic with α = 0. Our results demonstrate that for every population dynamic in this class except for the replicator dynamic, the interplay between risk-taking and sequentiality of choices allows state-dependent risk behavior to have an evolutionary advantage over expected-value maximization.
Social media as crisis platform
Authors: Goolsby Rebecca
Published: October 2012

Social media provides the means for creating new communities and for reenergizing old communities. Recently, a new kind of quickly formulated, powerful community has formed as existing social media communities, news organizations, and users have converged in social media spaces to respond to sudden tragedies. This article addresses the ad-hoc crisis community, whith uses the social madia as a crisis platform to generate community crisis maps.
Accessible image search for colorblindness
Authors: Wang Meng , Liu Bo , Hua Xian-Sheng
Published: October 2012

This article introduces an intelligent system that accommodates colorblind users in image search. Color plays an important role in the human perception and recognition of images. However, there are about 8% of men and 0.8% of women suffering from colorblindness. We show that the existing image search techniques cannot provide satisfactory results for these users since many images will not be well perceived by them due to the loss of color information. To deal with this difficulty, we introduce a system named Accessible Image Search (AIS) to accommodate these users. Different from the general image search scheme that aims at returning more relevant results, AIS further takes into account the colorblind accessibilities of the returned results, that is, the image qualities in the eyes of colorblind users. The system contains three components: accessibility assessment, accessibility improvement, and color indication. The accessibility assessment component measures the accessibility scores of images, and consequently different reranking methods can be performed to prioritize images with high accessibilities. In the accessibility improvement component, we propose an efficient recoloring algorithm to modify the colors of the images such that they can be better perceived by colorblind users. Color indication aims to indicate the name of the interesting color in an image. We evaluate the introduced system with more than 60 queries and 20 anonymous colorblind users, and the empirical results demonstrate its effectiveness and usefulness.
CORALS
Authors: Benaskeur Abder Rezak , Kabanza Froduald , Beaudry Eric
Published: October 2012

Forces involved in modern conflicts may be exposed to a variety of threats, including coordinated raids of advanced ballistic and cruise missiles. To respond to these, a defending force will rely on a set of combat resources. Determining an efficient allocation and coordinated use of these resources, particularly in the case of multiple simultaneous attacks, is a very complex decision-making process in which a huge amount of data must be dealt with under uncertainty and time pressure. This article presents CORALS (COmbat Resource ALlocation Support), a real-time planner developed to support the command team of a naval force defending against multiple simultaneous threats. In response to such multiple threats, CORALS uses a local planner to generate a set of local plans, one for each threat considered apart, and then combines and coordinates them into a single optimized, conflict-free global plan. The coordination is performed through an iterative process of plan merging and conflict detection and resolution, which acts as a plan repair mechanism. Such an incremental plan repair approach also allows adapting previously generated plans to account for dynamic changes in the tactical situation.
Planning for human-robot teaming in open worlds
Authors: Talamadupula Kartik , Benton J. , Kambhampati Subbarao , Schermerhorn Paul , Scheutz Matthias
Published: October 2012

As the number of applications for human-robot teaming continue to rise, there is an increasing need for planning technologies that can guide robots in such teaming scenarios. In this article, we focus on adapting planning technology to Urban Search And Rescue (USAR) with a human-robot team. We start by showing that several aspects of state-of-the-art planning technology, including temporal planning, partial satisfaction planning, and replanning, can be gainfully adapted to this scenario. We then note that human-robot teaming also throws up an additional critical challenge, namely, enabling existing planners, which work under closed-world assumptions, to cope with the open worlds that are characteristic of teaming problems such as USAR. In response, we discuss the notion of conditional goals, and describe how we represent and handle a specific class of them called open world quantified goals. Finally, we describe how the planner, and its open world extensions, are integrated into a robot control architecture, and provide an empirical evaluation over USAR experimental runs to establish the effectiveness of the planning components.
Human-aware task planning
Authors: Cirillo Marcello , Karlsson Lars , Saffiotti Alessandro
Published: October 2012

Consider a house cleaning robot planning its activities for the day. Assume that the robot expects the human inhabitant to first dress, then have breakfast, and finally go out. Then, it should plan not to clean the bedroom while the human is dressing, and to clean the kitchen after the human has had breakfast. In general, robots operating in inhabited environments, like households and future factory floors, should plan their behavior taking into account the actions that will be performed by the humans sharing the same environment. This would improve human-robot cohabitation, for example, by avoiding undesired situations for the human. Unfortunately, current task planners only consider the robot's actions and unexpected external events in the planning process, and cannot accommodate expectations about the actions of the humans.

In this article, we present a human-aware planner able to address this problem. Our planner supports alternative hypotheses of the human plan, temporal duration for the actions of both the robot and the human, constraints on the interaction between robot and human, partial goal achievement and, most importantly, the possibility to use observations of human actions in the policy generated for the robot. Our planner has been tested both as a stand-alone component and within a full framework for human-robot interaction in a real environment.
Learning travel recommendations from user-generated GPS traces
Authors: Zheng Yu , Xie Xing
Published: October 2012

The advance of GPS-enabled devices allows people to record their location histories with GPS traces, which imply human behaviors and preferences related to travel. In this article, we perform two types of travel recommendations by mining multiple users' GPS traces. The first is a generic one that recommends a user with top interesting locations and travel sequences in a given geospatial region. The second is a personalized recommendation that provides an individual with locations matching her travel preferences. To achieve the first recommendation, we model multiple users' location histories with a tree-based hierarchical graph (
TBHG
). Based on the
TBHG
, we propose a HITS (Hypertext Induced Topic Search)-based model to infer the interest level of a location and a user's travel experience (knowledge). In the personalized recommendation, we first understand the correlation between locations, and then incorporate this correlation into a collaborative filtering (CF)-based model, which predicts a user's interests in an unvisited location based on her locations histories and that of others. We evaluated our system based on a real-world GPS trace dataset collected by 107 users over a period of one year. As a result, our HITS-based inference model outperformed baseline approaches like
rank-by-count
and
rank-by-frequency
. Meanwhile, we achieved a better performance in recommending travel sequences beyond baselines like
rank-by-count
. Regarding the personalized recommendation, our approach is more effective than the weighted Slope One algorithm with a slightly additional computation, and is more efficient than the Pearson correlation-based CF model with the similar effectiveness.
Recognizing pair-activities by causality analysis
Authors: Zhou Yue , Ni Bingbing , Yan Shuicheng , Huang Thomas S.
Published: October 2012

In this article, beyond solo-activity analysis for single object, we study the more complicated pair-activity recognition problem by exploring the relationship between two active objects based on their trajectory clues obtained from video sensor. Our contributions are three-fold. First, we design two sets of features for representing the pair-activities encoded as length-variable trajectory pairs. One set characterizes the strength of causality between two trajectories, for example, the causality ratio and feedback ratio based on the Granger Causality Test (GCT), and another set describes the
style
of causality between two trajectories, for example, the sampled frequency responses of the digital filter with these two trajectories as the input and output discrete signals respectively. These features along with conventional velocity and position features of a trajectory-pair are essentially of multi-modalities, and may be greatly different in scales and importance. To make full use of them, we then develop a novel feature fusing procedure to learn the coefficients for weighting these features by maximizing the discriminating power measured by weighted correlation. Finally, we collected a pair-activity database of five popular categories, each of which consists of about 170 instances. The extensive experiments on this database validate the effectiveness of the designed features for pair-activity representation, and also demonstrate that the proposed feature fusing procedure significantly boosts the pair-activity classification accuracy.
Active learning in multimedia annotation and retrieval
Authors: Wang Meng , Hua Xian-Sheng
Published: October 2012

Active learning is a machine learning technique that selects the most informative samples for labeling and uses them as training data. It has been widely explored in multimedia research community for its capability of reducing human annotation effort. In this article, we provide a survey on the efforts of leveraging active learning in multimedia annotation and retrieval. We mainly focus on two application domains: image/video annotation and content-based image retrieval. We first briefly introduce the principle of active learning and then we analyze the sample selection criteria. We categorize the existing sample selection strategies used in multimedia annotation and retrieval into five criteria:
risk reduction
,
uncertainty
,
diversity
,
density
and
relevance
. We then introduce several classification models used in active learning-based multimedia annotation and retrieval, including semi-supervised learning, multilabel learning and multiple instance learning. We also provide a discussion on several future trends in this research direction. In particular, we discuss cost analysis of human annotation and large-scale interactive multimedia annotation.
Distance metric learning from uncertain side information for automated photo tagging
Authors: Wu Lei , Hoi Steven C.H. , Jin Rong , Zhu Jianke , Yu Nenghai
Published: October 2012

Automated photo tagging is an important technique for many intelligent multimedia information systems, for example, smart photo management system and intelligent digital media library. To attack the challenge, several machine learning techniques have been developed and applied for automated photo tagging. For example, supervised learning techniques have been applied to automated photo tagging by training statistical classifiers from a collection of manually labeled examples. Although the existing approaches work well for small testbeds with relatively small number of annotation words, due to the long-standing challenge of object recognition, they often perform poorly in large-scale problems. Another limitation of the existing approaches is that they require a set of high-quality labeled data, which is not only expensive to collect but also time consuming. In this article, we investigate a social image based annotation scheme by exploiting
implicit
side information that is available for a large number of social photos from the social web sites. The key challenge of our intelligent annotation scheme is how to learn an effective distance metric based on
implicit
side information (visual or textual) of social photos. To this end, we present a novel “Probabilistic Distance Metric Learning” (PDML) framework, which can learn optimized metrics by effectively exploiting the
implicit
side information vastly available on the social web. We apply the proposed technique to photo annotation tasks based on a large social image testbed with over 1 million tagged photos crawled from a social photo sharing portal. Encouraging results show that the proposed technique is effective and promising for social photo based annotation tasks.
Neighboring joint density-based JPEG steganalysis
Authors: Liu Qingzhong , Sung Andrew H. , Qiao Mengyu
Published: October 2012

The threat posed by hackers, spies, terrorists, and criminals, etc. using steganography for stealthy communications and other illegal purposes is a serious concern of cyber security. Several steganographic systems that have been developed and made readily available utilize JPEG images as carriers. Due to the popularity of JPEG images on the Internet, effective steganalysis techniques are called for to counter the threat of JPEG steganography. In this article, we propose a new approach based on feature mining on the discrete cosine transform (DCT) domain and machine learning for steganalysis of JPEG images. First, neighboring joint density features on both intra-block and inter-block are extracted from the DCT coefficient array and the absolute array, respectively; then a support vector machine (SVM) is applied to the features for detection. An evolving neural-fuzzy inference system is employed to predict the hiding amount in JPEG steganograms. We also adopt a feature selection method of support vector machine recursive feature elimination to reduce the number of features. Experimental results show that, in detecting several JPEG-based steganographic systems, our method prominently outperforms the well-known Markov-process based approach.
Prediction in financial markets
Authors: Dhar Vasant
Published: October 2012

Predictive models in regression and classification problems typically have a single model that covers most, if not all, cases in the data. At the opposite end of the spectrum is a collection of models, each of which covers a very small subset of the decision space. These are referred to as “small disjuncts.” The trade-offs between the two types of models have been well documented. Single models, especially linear ones, are easy to interpret and explain. In contrast, small disjuncts do not provides as clean or as simple an interpretation of the data, and have been shown by several researchers to be responsible for a disproportionately large number of errors when applied to out-of-sample data. This research provides a counterpoint, demonstrating that a portfolio of “simple” small disjuncts provides a credible model for financial market prediction, a problem with a high degree of noise. A related novel contribution of this article is a simple method for measuring the “yield” of a learning system, which is the percentage of in-sample performance that the learned model can be expected to realize on out-of-sample data. Curiously, such a measure is missing from the literature on regression learning algorithms. Pragmatically, the results suggest that for problems characterized by a high degree of noise and lack of a stable knowledge base it makes sense to reconstruct the portfolio of small rules periodically.
PLDA+
Authors: Liu Zhiyuan , Zhang Yuzhou , Chang Edward Y. , Sun Maosong
Published: October 2012

Previous methods of distributed Gibbs sampling for LDA run into either memory or communication bottlenecks. To improve scalability, we propose four strategies:
data placement
,
pipeline processing
,
word bundling
, and
priority-based scheduling
. Experiments show that our strategies significantly reduce the unparallelizable communication bottleneck and achieve good load balancing, and hence improve scalability of LDA.
LIBSVM
Authors: Chang Chih-Chung , Lin Chih-Jen
Published: October 2012

LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Submodularity and its applications in optimized information gathering
Authors: Krause Andreas , Guestrin Carlos
Published: October 2012

Where should we place sensors to efficiently monitor natural drinking water resources for contamination? Which blogs should we read to learn about the biggest stories on the Web? These problems share a fundamental challenge: How can we obtain the most useful information about the state of the world, at minimum cost?

Such information gathering, or active learning, problems are typically NP-hard, and were commonly addressed using heuristics without theoretical guarantees about the solution quality. In this article, we describe algorithms which efficiently find provably near-optimal solutions to large, complex information gathering problems. Our algorithms exploit submodularity, an intuitive notion of diminishing returns common to many sensing problems: the more sensors we have already deployed, the less we learn by placing another sensor. In addition to identifying the most informative sensing locations, our algorithms can handle more challenging settings, where sensors need to be able to reliably communicate over lossy links, where mobile robots are used for collecting data, or where solutions need to be robust against adversaries and sensor failures.

We also present results applying our algorithms to several real-world sensing tasks, including environmental monitoring using robotic sensors, activity recognition using a built sensing chair, a sensor placement challenge, and deciding which blogs to read on the Web.
Sustainable biomass power plant location in the Italian Emilia-Romagna region
Authors: Cattafi Massimiliano , Gavanelli Marco , Milano Michela , Cagnoli Paolo
Published: October 2012

Biomass power plants are very promising for reducing carbon oxides emissions, because they provide energy with a carbon-neutral process. Biomass comes from trees and vegetables, so they provide a renewable type of energy. However, biomass plants location, along with their provisioning basins, are heavily regulated by economical aspects, often without careful consideration of their environmental footprint. For example, some Italian biomass plants import from overseas palm-tree oil that is economically convenient. However, the energy consumed for the oil transportation is definitely greater than the energy produced by the palm-tree oil burning. In this way biomass power plants turn out to be environmentally inefficient, even if they produce renewable energy.

We propose an Integer Linear Programming approach for defining the energy and cost-efficient biomass plant location along with the corresponding provisioning basin. In addition, the model enables to evaluate existing plants and their energy and cost efficiency. Our study is based on real data gathered in the Emilia-Romagna region of Italy.

Finally, this optimization tool is just a small part of a wider perspective that is aimed to define decision support tools for the improvement of regional planning and its precise strategic environmental assessment.
Temporal data mining approaches for sustainable chiller management in data centers
Authors: Patnaik Debprakash , Marwah Manish , Sharma Ratnesh K. , Ramakrishnan Naren
Published: October 2012

Practically every large IT organization hosts data centers---a mix of computing elements, storage systems, networking, power, and cooling infrastructure---operated either in-house or outsourced to major vendors. A significant element of modern data centers is their cooling infrastructure, whose efficient and sustainable operation is a key ingredient to the “always-on” capability of data centers. We describe the design and implementation of CAMAS (Chiller Advisory and MAnagement System), a temporal data mining solution to mine and manage chiller installations. CAMAS embodies a set of algorithms for processing multivariate time-series data and characterizes sustainability measures of the patterns mined. We demonstrate three key ingredients of CAMAS---motif mining, association analysis, and dynamic Bayesian network inference---that help bridge the gap between low-level, raw, sensor streams, and the high-level operating regions and features needed for an operator to efficiently manage the data center. The effectiveness of CAMAS is demonstrated by its application to a real-life production data center managed by HP.
PTIME
Authors: Berry Pauline M. , Gervasio Melinda , Peintner Bart , Yorke-Smith Neil
Published: October 2012

In a world of electronic calendars, the prospect of intelligent, personalized time management assistance seems a plausible and desirable application of AI. PTIME (
Personalized Time Management
) is a learning cognitive assistant agent that helps users handle email meeting requests, reserve venues, and schedule events. PTIME is designed to unobtrusively learn scheduling preferences, adapting to its user over time. The agent allows its user to flexibly express requirements for new meetings, as they would to an assistant. It interfaces with commercial enterprise calendaring platforms, and it operates seamlessly with users who do not have PTIME. This article overviews the system design and describes the models and technical advances required to satisfy the competing needs of preference modeling and elicitation, constraint reasoning, and machine learning. We further report on a multifaceted evaluation of the perceived usefulness of the system.
Planning solar array operations on the international space station
Authors: Reddy Sudhakar Y. , Frank Jeremy D. , Iatauro Michael J. , Boyce Matthew E. , Kürklü Elif , Ai-Chang Mitchell , Jónsson Ari K.
Published: October 2012

Flight controllers manage the orientation and modes of eight large solar arrays that power the International Space Station (ISS). The task requires generating plans that balance complex constraints and preferences. These considerations include context-dependent constraints on viable solar array configurations, temporal limits on transitions between configurations, and preferences on which considerations have priority. The Solar Array Constraint Engine (SACE) treats this operations planning problem as a sequence of tractable constrained optimization problems. SACE uses constraint management and automated planning capabilities to reason about the constraints, to find optimal array configurations subject to these constraints and solution preferences, and to automatically generate solar array operations plans. SACE further provides flight controllers with real-time situational awareness and what-if analysis capabilities. SACE is built on the Extensible Universal Remote Operations Planning Architecture (EUROPA) model-based planning system. EUROPA facilitated SACE development by providing model-based planning, built-in constraint reasoning capability, and extensibility. This article formulates the planning problem, explains how EUROPA solves the problem, and provides performance statistics from several planning scenarios. SACE reduces a highly manual process that takes weeks to an automated process that takes tens of minutes.
RECYCLE
Authors: Haigh Karen Zita , Yaman Fusun
Published: October 2012

A
workflow
is a model of a process that systematically describes patterns of activity. Workflows capture a sequence of operations, their enablement conditions, and data flow dependencies among them. It is hard to design a complete and correct workflow from scratch, while it is much easier for humans to demonstrate the solution than to state the solution declaratively.

This article presents RECYCLE, our approach to learning workflow models from example demonstration traces. RECYCLE captures control flow, data flow, and enablement conditions of an underlying workflow process. Unlike prior work from workflow mining and AI planning literature, (1) RECYCLE can learn from a single demonstration trace with loops, (2) RECYCLE learns both loop and conditional branch structure, and (3) RECYCLE handles data flow among actions.

In this article, we describe the phases of RECYCLE's learning algorithm: substructure analysis and node abstraction. To ground the discussion, we present a simplified flight reservation system with some of the important characteristics of the real domains we worked with. We present some results from a patient transport domain.
Scalable Affiliation Recommendation using Auxiliary Networks
Authors: Vasuki Vishvas , Natarajan Nagarajan , Lu Zhengdong , Savas Berkant , Dhillon Inderjit
Published: October 2012

Social network analysis has attracted increasing attention in recent years. In many social networks, besides friendship links among users, the phenomenon of users associating themselves with groups or communities is common. Thus, two networks exist simultaneously: the friendship network among users, and the affiliation network between users and groups. In this article, we tackle the affiliation recommendation problem, where the task is to predict or suggest new affiliations between users and communities, given the current state of the friendship and affiliation networks. More generally, affiliations need not be community affiliations---they can be a user’s taste, so affiliation recommendation algorithms have applications beyond community recommendation. In this article, we show that information from the friendship network can indeed be fruitfully exploited in making affiliation recommendations. Using a simple way of combining these networks, we suggest two models of user-community affinity for the purpose of making affiliation recommendations: one based on graph proximity, and another using latent factors to model users and communities. We explore the affiliation recommendation algorithms suggested by these models and evaluate these algorithms on two real-world networks, Orkut and Youtube. In doing so, we motivate and propose a way of evaluating recommenders, by measuring how good the top 50 recommendations are for the average user, and demonstrate the importance of choosing the right evaluation strategy. The algorithms suggested by the graph proximity model turn out to be the most effective. We also introduce scalable versions of these algorithms, and demonstrate their effectiveness. This use of link prediction techniques for the purpose of affiliation recommendation is, to our knowledge, novel.
Who is Doing What and When
Authors: Zhao Shiwan , Zhou Michelle X. , Zhang Xiatian , Yuan Quan , Zheng Wentao , Fu Rongyao
Published: October 2012

Content-centric social Web sites, such as discussion forums and blog sites, have flourished during the past several years. These sites often contain overwhelming amounts of information that are also being updated rapidly. To help users locate their interests at such sites (e.g., interesting blogs to read or discussion forums to join), researchers have developed a number of recommendation technologies. However, it is difficult to make effective recommendations for new users (a.k.a. the cold start problem) due to a lack of user information (e.g., preferences and interests). Furthermore, the complexity of recommendation algorithms often prevents users from comprehending let alone trusting the recommended results. To tackle these above two challenges, we are building a social map-based recommender system called Pharos. A social map summarizes users’ content-related social behavior over time (e.g., reading, writing, and commenting behavior during the past week) as a set of latent communities. For a given time interval, each community is characterized by the theme of the content being discussed and the key people involved. By discovering, ranking, and displaying the most popular latent communities at different time intervals, Pharos creates a time-sensitive, visual social map of a Web site. This enables new users to obtain a quick overview of the site, alleviating the cold start problem. Furthermore, we use the social map as a context to help explain Pharos-recommended content and people. Users can also interactively explore the social map to locate the content in which they are interested or people that are not being explicitly recommended, compensating for the imperfections in the recommendation algorithms. We have developed several Pharos applications, one of which is deployed within our company. Our preliminary evaluation of the deployed application shows the usefulness of Pharos.
Fair Seeding in Knockout Tournaments
Authors: Vu Thuc , Shoham Yoav
Published: October 2012

We investigated the existence of fair seeding in knockout tournaments. We define two fairness criteria, both adapted from the literature: envy-freeness and order preservation. We show how to achieve the first criterion in tournaments whose structure is unconstrained, and prove an impossibility result for balanced tournaments. For the second criterion we have a similar result for unconstrained tournaments, but not for the balanced case. We provide instead a heuristic algorithm which we show through experiments to be efficient and effective. This suggests that the criterion is achievable also in balanced tournaments. However, we prove that it again becomes impossible to achieve when we add a weak condition guarding against the phenomenon of tournament dropout.
Geographic Information Systems and Spatial Agent-Based Model Simulations for Sustainable Development
Authors: Cioffi-Revilla Claudio , Rogers J. Daniel , Hailegiorgis Atesmachew
Published: October 2012

In recent years the interdisciplinary field of Computational Social Science has developed theory and methodologies for building spatial Agent-Based Social Simulation (ABSS) models of human societies that are situated in ecosystems with land cover and climate. This article explains the needs and demand for Geographic Information Systems (GIS) in these types of agent-based models, with an emphasis on models applied to Eastern Africa and Inner Asia and relevance for understanding and analyzing development issues. The models are implemented with the MASON (Multi-Agent Simulator Of Networks and Neighborhoods) system, an open-source simulation environment in the Java language and suitable for developing ABSS models with GIS for representing spatial features.
Understanding, Manipulating and Searching Hand-Drawn Concept Maps
Authors: Jiang Yingying , Tian Feng , Zhang Xiaolong (Luke) , Dai Guozhong , Wang Hongan
Published: October 2012

Concept maps are an important tool to organize, represent, and share knowledge. Building a concept map involves creating text-based concepts and specifying their relationships with line-based links. Current concept map tools usually impose specific task structures for text and link construction, and may increase cognitive burden to generate and interact with concept maps. While pen-based devices (e.g., tablet PCs) offer users more freedom in drawing concept maps with a pen or stylus more naturally, the support for hand-drawn concept map creation and manipulation is still limited, largely due to the lack of methods to recognize the components and structures of hand-drawn concept maps. This article proposes a method to understand hand-drawn concept maps. Our algorithm can extract node blocks, or concept blocks, and link blocks of a hand-drawn concept map by combining dynamic programming and graph partitioning, recognize the text content of each concept node, and build a concept-map structure by relating concepts and links. We also design an algorithm for concept map retrieval based on hand-drawn queries. With our algorithms, we introduce structure-based intelligent manipulation techniques and ink-based retrieval techniques to support the management and modification of hand-drawn concept maps. Results from our evaluation study show high structure recognition accuracy in real time of our method, and good usability of intelligent manipulation and retrieval techniques.
Cross-Lingual Adaptation Using Structural Correspondence Learning
Authors: Prettenhofer Peter , Stein Benno
Published: October 2012

Cross-lingual adaptation is a special case of domain adaptation and refers to the transfer of classification knowledge between two languages. In this article we describe an extension of Structural Correspondence Learning (SCL), a recently proposed algorithm for domain adaptation, for cross-lingual adaptation in the context of text classification. The proposed method uses unlabeled documents from both languages, along with a word translation oracle, to induce a cross-lingual representation that enables the transfer of classification knowledge from the source to the target language. The main advantages of this method over existing methods are resource efficiency and task specificity.

We conduct experiments in the area of cross-language topic and sentiment classification involving English as source language and German, French, and Japanese as target languages. The results show a significant improvement of the proposed method over a machine translation baseline, reducing the relative error due to cross-lingual adaptation by an average of 30% (topic classification) and 59% (sentiment classification). We further report on empirical analyses that reveal insights into the use of unlabeled data, the sensitivity with respect to important hyperparameters, and the nature of the induced cross-lingual word correspondences.
Two-Word Collocation Extraction Using Monolingual Word Alignment Method
Authors: Liu Zhanyi , Wang Haifeng , Wu Hua , Li Sheng
Published: October 2012

Statistical bilingual word alignment has been well studied in the field of machine translation. This article adapts the bilingual word alignment algorithm into a monolingual scenario to extract collocations from monolingual corpus, based on the fact that the words in a collocation tend to co-occur in similar contexts as in bilingual word alignment. First, the monolingual corpus is replicated to generate a parallel corpus, in which each sentence pair consists of two identical sentences. Next, the monolingual word alignment algorithm is employed to align potentially collocated words. Finally, the aligned word pairs are ranked according to the alignment scores and candidates with higher scores are extracted as collocations. We conducted experiments on Chinese and English corpora respectively. Compared to previous approaches that use association measures to extract collocations from co-occurrence word pairs within a given window, our method achieves higher precision and recall. According to human evaluation, our method achieves precisions of 62% on a Chinese corpus and 64% on an English corpus. In particular, we can extract collocations with longer spans, achieving a higher precision of 83% on the long-span (> 6 words) Chinese collocations.