ppxscal/embedding_network huggingface dataset:
Towards view-invariant gait modeling: Computing view-normalized body part trajectories
Authors: Frédéric Jean, Alexandra Branzan Albu, Robert Bergevin
Venue: Pattern Recognition (2009)

This paper proposes an approach to compute view-normalized body part trajectories of pedestrians walking on potentially non-linear paths. The proposed approach finds applications in gait modeling, gait biometrics, and in medical gait analysis. Our approach uses the 2D trajectories of both feet and the head extracted from the tracked silhouettes. On that basis, it computes the apparent walking (sagittal) planes for each detected gait half-cycle. A homography transformation is then computed for each walking plane to make it appear as if walking was observed from a fronto-parallel view. Finally, each homography is applied to head and feet trajectories over each corresponding gait half-cycle. View normalization makes head and feet trajectories appear as if seen from a fronto-parallel viewpoint, which is assumed to be optimal for gait modeling purposes. The proposed approach is fully automatic as it requires neither manual initialization nor camera calibration. An extensive experimental evaluation of the proposed approach confirms the validity of the normalization process.
Somniloquy: augmenting network interfaces to reduce PC energy usage
Authors: Yuvraj Agarwal, Steve Hodges, Ranveer Chandra, James Scott, Paramvir Bahl, Rajesh Gupta
Venue: Proceedings of the 6th USENIX symposium on Networked systems design and implementation (2009)

Reducing the energy consumption of PCs is becoming increasingly important with rising energy costs and environmental concerns. Sleep states such as S3 (suspend to RAM) save energy, but are often not appropriate because ongoing networking tasks, such as accepting remote desktop logins or performing background file transfers, must be supported. In this paper we present Somniloquy, an architecture that augments network interfaces to allow PCs in S3 to be responsive to network traffic. We show that many applications, such as remote desktop and VoIP, can be supported without application-specific code in the augmented network interface by using application-level wakeup triggers. A further class of applications, such as instant messaging and peer-to-peer file sharing, can be supported with modest processing and memory resources in the network interface. Experiments using our prototype Somniloquy implementation, a USB-based network interface, demonstrates energy savings of 60% to 80% in most commonly occuring scenarios. This translates to significant cost savings for PC users.
Automatically characterizing resource quality for educational digital libraries
Authors: Steven Bethard, Philipp Wetzer, Kirsten Butcher, James H. Martin, Tamara Sumner
Venue: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009)

With the rise of community-generated web content, the need for automatic characterization of resource quality has grown, particularly in the realm of educational digital libraries. We demonstrate how identifying concrete factors of quality for web-based educational resources can make machine learning approaches to automating quality characterization tractable. Using data from several previous studies of quality, we gathered a set of key dimensions and indicators of quality that were commonly identified by educators. We then performed a mixed-method study of digital library curation experts, showing that our characterization of quality captured the subjective processes used by the experts when assessing resource quality for classroom use. Using key indicators of quality selected from a statistical analysis of our expert study data, we developed a set of annotation guidelines and annotated a corpus of 1000 digital resources for the presence or absence of these key quality indicators. Agreement among annotators was high, and initial machine learning models trained from this corpus were able to identify some indicators of quality with as much as an 18% improvement over the baseline.
Software architecture evaluation methods based on cost benefit analysis and quantitative decision making
Authors: Jihyun Lee, Sungwon Kang, Chang-Ki Kim
Venue: Empirical Software Engineering (2009)

Since many parts of the architecture evaluation steps of the Cost Benefit Analysis Method (CBAM) depend on the stakeholders' empirical knowledge and intuition, it is very important that such an architecture evaluation method be able to faithfully reflect the knowledge of the experts in determining Architectural Strategy (AS). However, because CBAM requires the stakeholders to make a consensus or vote for collecting data for decision making, it is difficult to accurately reflect the stakeholders' knowledge in the process. In order to overcome this limitation of CBAM, we propose the two new CBAM-based methods for software architecture evaluation, which respectively adopt the Analytic Hierarchy Process (AHP) and the Analytic Network Process (ANP). Since AHP and ANP use pair-wise comparison they are suitable for a cost and benefit analysis technique since its purpose is not to calculate correct values of benefit and cost but to decide AS with highest return on investment. For that, we first define a generic process of CBAM and develop variations from the generic process by applying AHP and ANP to obtain what we call the CBAM+AHP and CBAM+ANP methods. These new methods not only reflect the knowledge of experts more accurately but also reduce misjudgments. A case study comparison of CBAM and the two new methods is conducted using an industry software project. Because the cost benefit analysis process that we present is generic, new cost benefit analysis techniques with capabilities and characteristics different from the three methods we examine here can be derived by adopting various different constituent techniques.
Streaming algorithms for network flow data analysis
Authors: Mitsunori Ogihara
Venue:  (2008)

The algorithmic field of Data Streaming was first proposed over a decade ago by Alon, Matias, and Szegedy. Since then, considerable work has been done in the streaming model of computation. The streaming model is precisely the one we want to use to perform real-time computation on network data because it allows only single pass access to the data and guarantees low memory consumption and processor usage per packet. However, the ideas from the streaming context have been slow to catch on in the networking literature. In this dissertation we will seek to catalyze this change by demonstrating both theoretically and empirically that the ideas from streaming algorithms will have a considerable impact on the performance of algorithms for online network analysis. In particular, we present novel algorithms for maintaining counts of large number of items and for computing the entropy of network traffic to demonstrate that streaming algorithms can solve these problems remarkably well in practice.
Optimal power allocation in server farms
Authors: Anshul Gandhi, Mor Harchol-Balter, Rajarshi Das, Charles Lefurgy
Venue: Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems (2009)

Server farms today consume more than 1.5% of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available power. An important question which arises in this context is how to distribute available power among servers in a server farm so as to get maximum performance. By giving more power to a server, one can get higher server frequency (speed). Hence it is commonly believed that, for a given power budget, performance can be maximized by operating servers at their highest power levels. However, it is also conceivable that one might prefer to run servers at their lowest power levels, which allows more servers to be turned on for a given power budget. To fully understand the effect of power allocation on performance in a server farm with a fixed power budget, we introduce a queueing theoretic model, which allows us to predict the optimal power allocation in a variety of scenarios. Results are verified via extensive experiments on an IBM BladeCenter. We find that the optimal power allocation varies for different scenarios. In particular, it is not always optimal to run servers at their maximum power levels. There are scenarios where it might be optimal to run servers at their lowest power levels or at some intermediate power levels. Our analysis shows that the optimal power allocation is non-obvious and depends on many factors such as the power-to-frequency relationship in the processors, the arrival rate of jobs, the maximum server frequency, the lowest attainable server frequency and the server farm configuration. Furthermore, our theoretical model allows us to explore more general settings than we can implement, including arbitrarily large server farms and different power-to-frequency curves. Importantly, we show that the optimal power allocation can significantly improve server farm performance, by a factor of typically 1.4 and as much as a factor of 5 in some cases.
Analyzing the Coherence and Cohesion in Human Tutorial Dialogues when Learning with Hypermedia
Authors: Roger Azevedo, Moongee Jeon
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

We examined the coherence and cohesion from 38 think-aloud transcriptions from a human tutorial dialogue study examining the role of tutoring on college students' learning about the circulatory system with hypermedia. The corpus we examined had a total of 800 pages. We used Coh-Metrix, a web-based tool designed to examine text and discourse, to evaluate the coherence and cohesion of the text produced between a human tutor and low-domain knowledge college students during 38 tutoring sessions. Our findings demonstrated that there were significant differences in the tutorial dialogues of the high-jump students (i.e., those who showed conceptual gains in their pretest-posttest mental models of the circulatory system) versus the medium-jumpers or no-jumper semantic/conceptual overlap, the negative additive connectives incidence scores, the number of turns, and the average words per sentence. However, the tutorial dialogues of the high jump students substantially shared the syntactic and linguistic similarities with the tutorial dialogues of the medium or no jump students in the standard readability formulas, the co-referential cohesion (argument and stem overlaps), and the incidence scores of all the connectives. We argue that the semantic/conceptual overlap of the tutorial dialogues primarily promoted the high “jumps” or improvement in students' mental model and deep learning while interacting with a tutor. Our findings have implications for the design to intelligent tutorial dialogue hypermedia systems aimed at fostering learners' understanding of complex and challenging science topics.
Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters
Authors: Marcos Dias de Assuncao, Alexandre di Costanzo, Rajkumar Buyya
Venue: Proceedings of the 18th ACM international symposium on High performance distributed computing (2009)

In this paper, we investigate the benefits that organisations can reap by using "Cloud Computing" providers to augment the computing capacity of their local infrastructure. We evaluate the cost of six scheduling strategies used by an organisation that operates a cluster managed by virtual machine technology and seeks to utilise resources from a remote Infrastructure as a Service (IaaS) provider to reduce the response time of its user requests. Requests for virtual machines are submitted to the organisation's cluster, but additional virtual machines are instantiated in the remote provider and added to the local cluster when there are insufficient resources to serve the users' requests. Naïve scheduling strategies can have a great impact on the amount paid by the organisation for using the remote resources, potentially increasing the overall cost with the use of IaaS. Therefore, in this work we investigate six scheduling strategies that consider the use of resources from the "Cloud", to understand how these strategies achieve a balance between performance and usage cost, and how much they improve the requests' response times.
Exploring trusted platform module capabilities: a theoretical and experimental study
Authors: Stephen R. Tate
Venue:  (2008)

Trusted platform modules (TPMs) are hardware modules that are bound to a computer's motherboard, that are being included in many desktops and laptops. Augmenting computers with these hardware modules adds powerful functionality in distributed settings, allowing us to reason about the security of these systems in new ways.In this dissertation, I study the functionality of TPMs from a theoretical as well as an experimental perspective. On the theoretical front, I leverage various features of TPMs to construct applications like random oracles that are impossible to implement in a standard model of computation. Apart from random oracles, I construct a new cryptographic primitive which is basically a non-interactive form of the standard cryptographic primitive of oblivious transfer. I apply this new primitive to secure mobile agent computations, where interaction between various entities is typically required to ensure security. I prove these constructions are secure using standard cryptographic techniques and assumptions.To test the practicability of these constructions and their applications, I performed an experimental study, both on an actual TPM and a software TPM simulator which has been enhanced to make it reflect timings from a real TPM. This allowed me to benchmark the performance of the applications and test the feasibility of the proposed extensions to standard TPMs. My tests also show that these constructions are practical.
Using Multinets for Learner Modelling
Authors: Mathieu Hibou, Jean-Marc Labat
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

In this paper we shall introduce a new conceptual model for learner modelling based on multinets, which are Bayesian networks mixture. This conceptuel model makes it possible to take into account in a single student model different Bayesian networks, with the same nodes but different structures. We also present experiemental results obtained with real students data.
Analysis and Intuition in Strategic Decision Making: The Case of California
Authors: Zita Zoltay Paprika
Venue: Proceeding of the 2008 conference on Collaborative Decision Making: Perspectives and Challenges (2008)

This paper reports the results of a study that attempts to assess the effects of decision making circumstances focusing mainly on the approaches applied and the managerial skills and capabilities the decision makers built on during concrete strategic decision making procedures. The study was conducted in California between September 2005 and June 2006 and it was sponsored by a Fulbright Research Scholarship Grant. The continuation of the research is sponsored by the OTKA 68799 Research Grant.
Efficient and provably secure password-based group key agreement protocol
Authors: Ming-Hui Zheng, Hui-Hua Zhou, Jun Li, Guo-Hua Cui
Venue: Computer Standards Interfaces (2009)

This paper considers the issue on authenticated group key agreement protocol among n users broadcasting communication over an insecure public network. Many authenticated group Diffie-Hellman key agreement protocols have been proposed to meet the challenges. However, existing protocols are either limited by the use of public key infrastructure or by their scalability, requiring O(n) rounds. To overcome these disadvantages, we propose an efficient password-based group key agreement protocol resistant to the dictionary attacks by adding password-authentication services to a non-authenticated multi-party key agreement protocol proposed by Horng. The proposed protocol is very efficient since it only requires constant rounds to agree upon a session key, and each user broadcasts a constant number of messages and only requires four exponentiations. Under the Decisional Diffie-Hellman assumption, we will show the proposed protocol is provably secure in both the ideal-cipher model and the random-oracle model.
Lag-Dependent Regularization for MLPs Applied to Financial Time Series Forecasting Tasks
Authors: Andrew Skabar
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

The application of multilayer perceptrons to forecasting the future value of some time series based on past (or lagged) values of the time series usually requires very careful selection of the number of lags to be used as inputs, and this must usually be determined empirically. This paper proposes a regularization technique by which the influence that a lag has in determining the forecast value decreases exponentially with the lag, and is consistent with the intuitive notion that recent values should have more influence than less recent values in predicting future values. This means that in principle an infinite number of dimensions could be used. Empirical results show that the regularization technique yields superior performance on out-of-sample data compared with approaches that use a fixed number of inputs without lag-dependent regularization.
Feature string-based intelligent information retrieval from Tamil document images
Authors: S. Abirami, D. Manjula
Venue: International Journal of Computer Applications in Technology (2009)

Information Retrieval (IR) in document images has become a growing and challenging problem due to its rising popularity. This paper proposes a simple and effective method to extract the text and perform intelligent IR from Tamil Document Images without Optical Character Recognition (OCR). This methodology generates a feature string for every word image by extracting its features. This relies on their basic characteristics or shapes of letters instead of recognising the letters like OCR. The strength of this technique lies in extracting the text based on their basic features such as lines and black and white disposition rates in characters which is almost same for the characters across various font sizes and font faces. As an offline process, document images are preprocessed and text extraction process extracts the features from the word images based on their shapes and they are stored in temporary files. During online retrieval, textual keyword is obtained from the user and its primitive string is framed. Based on the primitive string, IR is performed and the resultant images are provided to the user. This technique could be easily adopted in large digital libraries for IR.
Modeling Habituation in the Cnidarian Hydra
Authors: Malin Aktius, Mats Nordahl, Tom Ziemke
Venue: Proceeding of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008 (2008)

In the design of behavior-based control architectures for robots it is common to use biology as inspiration, and often the observed functionalities of insect behaviors are used as templates. While several robot behaviors have been successfully implemented using this approach, relatively little has been done when it comes to building models of animal behavior using quantitative empirical data. The work reported here uses a system identification approach to model constituent behaviors of a simple biological organism, the hydra. This paper reports on the evolutionary optimization of a behavior module that is based on the hydra's response to mechanical stimuli, which shows habituation. Two model representation schemes were investigated: a recurrent neural network, and a model based on cascaded leaky integrators. Both models were structurally and parametrically optimized by means of an evolutionary algorithm, and it was found that the leaky integrator model performed better on unseen data.
Large human communication networks: patterns and a utility-driven generator
Authors: Nan Du, Christos Faloutsos, Bai Wang, Leman Akoglu
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Given a real, and weighted person-to-person network which changes over time, what can we say about the cliques that it contains? Do the incidents of communication, or weights on the edges of a clique follow any pattern? Real, and in-person social networks have many more triangles than chance would dictate. As it turns out, there are many more cliques than one would expect, in surprising patterns. In this paper, we study massive real-world social networks formed by direct contacts among people through various personal communication services, such as Phone-Call, SMS, IM etc. The contributions are the following: (a) we discover surprising patterns with the cliques, (b) we report power-laws of the weights on the edges of cliques, (c) our real networks follow these patterns such that we can trust them to spot outliers and finally, (d) we propose the first utility-driven graph generator for weighted time-evolving networks, which match the observed patterns. Our study focused on three large datasets, each of which is a different type of communication service, with over one million records, and spans several months of activity.
Network-aware support for mobile distributed teams
Authors: Rick van der Kleij, Alexis de Jong, Guido te Brake, Tjerk de Greef
Venue: Computers in Human Behavior (2009)

An experiment evaluated network-aware support to increase understanding of the factors that are important for successful teamwork in mobile geographically dispersed teams of first responders. Participants performed a simulated search and rescue team task and were equipped with a digitized map and real-time situation updates on the location of other participants in a simulated disaster area. The connection to a server, however, was made deliberately error-prone, leading to occasional losses of network connections. Consequently, participants were not provided with real-time situation updates. To deal with this problem we equipped team members with a network-aware application that signaled network loss to them and adapted the graphical representation of the location of fellow team members accordingly to the quality of location information present. The experiment revealed that presenting complete and reliable geospatial information improves teamwork. Teams connected to a server over a fast and reliable link showed superior performance over teams with no network connection whatsoever to a server. The present study failed, however, to demonstrate the added value of network-aware support when teams had to collaborate in the presence of an unreliable communications infrastructure. Although participants indicated a slight preference for the network-aware application over a condition without support signaling network loss, no differences were observed in team process and outcome measures.
Evidence transmutations: gathering admissible evidence using belief revision
Authors: Mary-Anne Williams
Venue: Proceedings of the 12th International Conference on Artificial Intelligence and Law (2009)

In this paper we explore the potential of using extended belief change operators for modeling the evolution of legal evidence. We introduce a new representation, an evidence structure that can be used to support rich change operators called transmutations. Evidence structures and transmutations support the iterated nature of evidence gathering and acquisition. Importantly reliability is maintained during transmutations and modified where necessary using the Principle of Minimal Change. We establish that this process possesses desirable properties; in particular we construct transmutations that do not assume logical omniscience and that satisfy the widely accepted AGM rationality postulates for revision and contraction but at the same time preserve a key value of the evidence, its reliability.