ppxscal/embedding_network huggingface dataset:
Unsupervised discovery of activity primitives from multivariate sensor data
Authors: Thad Starner
Venue:  (2008)

This research addresses the problem of temporal pattern discovery in real-valued, multivariate sensor data. Several algorithms were developed, and subsequent evaluation demonstrates that they can efficiently and accurately discover unknown recurring patterns in time series data taken from many different domains. Different data representations and motif models were investigated in order to design an algorithm with an improved balance between run-time and detection accuracy. The different data representations are used to quickly filter large data sets in order to detect potential patterns that form the basis of a more detailed analysis. The representations include global discretization, which can be efficiently analyzed using a suffix tree, local discretization with a corresponding random projection algorithm for locating similar pairs of subsequences, and a density-based detection method that operates on the original, real-valued data. In addition, a new variation of the multivariate motif discovery problem is proposed in which each pattern may span only a subset of the input features. An algorithm that can efficiently discover such &ldquo;subdimensional&rdquo; patterns was developed and evaluated. The discovery algorithms are evaluated by measuring the detection accuracy of discovered patterns relative to a set of expected patterns for each data set. The data sets used for evaluation are drawn from a variety of domains including speech, on-body inertial sensors, music, American Sign Language video, and GPS tracks.
Passivity Analysis of a General Form of Recurrent Neural Network with Multiple Delays
Authors: Jinhua Huang, Jiqing Liu
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

In this paper, by using some analytic techniques, several sufficient conditions are given to ensure the passivity of a general form of recurrent neural network with multiple delays. The passivity conditions are presented in terms of a negative semi-definite matrix declared. They are easily verifiable and easier to check computing with some conditions in terms of complicated linear matrix inequality.
Delay tolerant bulk data transfers on the internet
Authors: Nikolaos Laoutaris, Georgios Smaragdakis, Pablo Rodriguez, Ravi Sundaram
Venue: Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems (2009)

Many emerging scientific and industrial applications require transferring multiple Tbytes of data on a daily basis. Examples include pushing scientific data from particle accelerators/colliders to laboratories around the world, synchronizing data-centers across continents, and replicating collections of high definition videos from events taking place at different time-zones. A key property of all above applications is their ability to tolerate delivery delays ranging from a few hours to a few days. Such Delay Tolerant Bulk (DTB) data are currently being serviced mostly by the postal system using hard drives and DVDs, or by expensive dedicated networks. In this work we propose transmitting such data through commercial ISPs by taking advantage of already-paid-for off-peak bandwidth resulting from diurnal traffic patterns and percentile pricing. We show that between sender-receiver pairs with small time-zone difference, simple source scheduling policies are able to take advantage of most of the existing off-peak capacity. When the time-zone difference increases, taking advantage of the full capacity requires performing store-and-forward through intermediate storage nodes. We present an extensive evaluation of the two options based on traffic data from 200+ links of a large transit provider with PoPs at three continents. Our results indicate that there exists huge potential for performing multi Tbyte transfers on a daily basis at little or no additional cost.
How to Build a Robot Army: Tips on Defending Planet Earth Against Alien Invaders, Ninjas, and Zombies
Authors: Daniel H. Wilson
Venue:  (2007)

Defend yourselfand your planetagainst the onslaught of evil at your door. It goes without saying that robots kill. They hunt, swarm, and fire lasers from their eyes. They even beat humans at chess. So who better to stand with us when the real villains arrive? Movies instruct us that, whether we like it or not, we will one day be under siege by pirates, ninjas, zombies, aliens, and Godzilla. Also great white sharks. Andlets face itwere not prepared. But with the advice contained in this brilliantly illustrated, ingenious book, you can build your own robot army to fend off hordes of bloodthirsty foes. From common-sense injunctions (never approach an unfamiliar robot in a militarized zone) to tactical pointers (low-power radar beats cameras for detecting mummies in a fog-shrouded crypt) to engineering advice (passive-dynamic exoskeleton suits will increase sprint speeds but not leg strength), this book contains all the wisdom youll need to fend off the coming apocalypse. Witty, informative, and utterly original, How to Build a Robot Army is the ideal book for readers of any age. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Numerical Simulation of the Dynamics of a Periodically Forced Spherical Particle in a Quiescent Newtonian Fluid at Low Reynolds Numbers
Authors: Tumkur Ramaswamy Ramamohan, Inapura Siddagangaiah Shivakumara, Krishnamurthy Madhukar
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

In this paper we present the results of a numerical simulation of the dynamics of a periodically forced spherical particle in a quiescent Newtonian fluid at low Reynolds number. We describe the simulation and tests performed to validate our simulation. We have obtained results which are physically reasonable and hence we have confidence in our results. We include the effects of both convective and unsteady inertia on the dynamics at low Reynolds numbers. The inclusion of inertia results in additional linear and nonlinear terms in the equations representing a fading memory of the entire history of the motion. The nonlinearity though small in the parametric regime of interest, gives rise to some interesting features in the solution of the problem.
Transparent Electronics, 1st edition
Authors: John F. Wager, Douglas A. Keszler, Rick E. Presley
Venue:  (2007)

This monograph is essentially the first roadmap for transparent electronics. It will define and assess what and where the field is, where it is going, and what needs to happen to get it there. Although the central focus of this monograph involves transparent electronics, many of the materials, devices, circuits, and process integration strategies discussed will be of great interest to researchers working in other emerging fields, including printed electronics, large-area electronics, low-cost electronics, and disposable electronics.
Pareto Paths with SHARC
Authors: Daniel Delling, Dorothea Wagner
Venue: Proceedings of the 8th International Symposium on Experimental Algorithms (2009)

Up to now, research on speed-up techniques for Dijkstra's algorithm focused on single-criteria scenarios. The goal was to find the quickest route within a transportation network. However, the quickest route is often not the best one. A user might be willing to accept slightly longer travel times if the cost of the journey is less. A common approach to cope with such a situation is to find Pareto-optimal (concerning other metrics than travel times) routes. Such routes have the property that each route is better than any other route with respect to at least one metric under consideration, e.g., travel costs or number of train changes. In this work, we study multi-criteria search in road networks. On the one hand, we focus on the problem of limiting the number of Pareto paths. On the other hand, we present a multi-criteria variant of our recent SHARC algorithm.
Discovering objects in images and videos
Authors: Tsuhan Chen
Venue:  (2008)

This thesis presents a novel way of scene analysis in images and videos. Traditional scene analysis using object detection involves a lot of human labor for labeling the images, and also has the difficulty of handling a large number of objects categories. Our approach to scene analysis is unsupervised in nature. Given a video, we want to &ldquo;discover&rdquo; the objects of interest. No single labeled image is used to pre-train or initialize the system. Still, the system is able to discover the objects of interest. It works on a wide variety of videos and it can discover objects belonging to a large set of different categories. It works in crowded scenes with distracting background pattern and motion. It works in partial occlusions and total removal. The probabilistic framework consists of an appearance model and a motion model. The appearance model exploits the consistency of object parts in appearance across frames. The motion model exploits the motion continuity across frames. Together, they provide appearance and location estimates of the objects of interest. This framework provides a basis for higher level video content analysis tasks.
Educational Game with Blogs: Collaborating to Motivate L2 Undergraduates Critical Thinking Disposition
Authors: Chiann-Ru Song
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

This study outlined the first step in developing online collaborative educational game that allow second language (L2) undergraduates improve their writing abilities by considering various CTD. Findings reveal that L2 undergraduates showed the various CTD characteristics: “Analyticity and Truth seeking”, “Systematicity and Inquisitive”, and “Open-mindedness”. By knowing the CTD, an interface designer would be able to take into account students' different levels of CTD in developing the L2 online collaborative educational game.
Diagnosing Highly Configurable Products: Troubleshooting Support for Airbus Final Assembly Line
Authors: Andreas Junghanns, Mugur Tatar
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

In this paper we describe the successful introduction of the model-based technology in a troubleshooting support tool for aircraft wiring under industrial conditions at Airbus, where strict cost/benefit analyzes decide over success or failure of a tool. Aircraft built at Airbus Deutschland GmbH are highly customized products: virtually no two aircraft are identical. Troubleshooting support cannot be built in the traditional way of compiling static decision trees once and for all aircraft. Rather, diagnosis dialogs must be computed on demand for each individual aircraft. Our solution is based on dynamically generating the diagnosis dialogs from on-line generated electrical signal nets. Generating diagnosis dialogs on demand is a considerable technical challenge. We have solved this with a combination of intelligent document extraction services and model-based diagnosis technologies. However, the paper focuses less on technical issues and more on the non-technological aspects that highlight the challenges of deploying well established intelligent technologies in industry. For example, while existing engineering data is correctly interpretable by “fault tolerant and flexible” humans, it remains a challenging input for algorithms. Furthermore, some psychological aspects remain a challenge to the acceptance of the proposed tool.
Auto-tuning and fuzzy PID temperature controllers for hollow metal block
Authors: Shiuh-Jer Huang, Yi-Ho Lo
Venue: Proceedings of the 7th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics (2008)

Temperature is an important production control parameter in chemical and semiconductor industry. Generally, the temperature control system has nonlinear time-delay, slow response speed and one-way control input characteristics. It is difficult to accurately estimate the dynamic model and design a general purpose temperature controller for achieving good control performance. Here model-free auto-tuning and fuzzy PID control strategies are employed to design a general temperature controller for different plants. The experimental results show that these control schemes can obtain reasonable control performance. The steady state error of the step input response is less than 0.2 °C with small overshoot. The time response behavior is better than that of the model-based IMC-PID due to the modeling error.
A study of MAC schemes for wireless sensor networks powered by ambient energy harvesting
Authors: Zhi Ang Eu, Winston K. G. Seah, Hwee-Pink Tan
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

Energy consumption is a perennial issue in the design of wireless sensor networks which typically rely on portable sources like batteries for power. Recent advances in ambient energy harvesting technology have made it a potential alternative source of energy for powering wireless sensor networks. In this paper, we study the performance of four different medium access control (MAC) protocols based on CSMA and polling techniques for wireless sensor networks which are powered by ambient energy harvesting. First, we define our model for describing the ambient energy harvesting process. Next, we derive the main performance metrics which are the per-node throughput of each sensor node (R) and the network throughput (S) which is the rate of sensor data received by the sink. We validate the analytical models using simulations, and the results show that neither CSMA-based nor polling protocols always gives the best performance results. These results aim to provide insights to the design of algorithms and protocols for wireless sensor networks that rely on ambient energy harvesting for power which is different from the typical assumption of limited power as in the case of batteries.
Towards a Scalable and Robust DHT
Authors: Baruch Awerbuch, Christian Scheideler
Venue: Theory of Computing Systems (2009)

The problem of scalable and robust distributed data storage has recently attracted a lot of attention. A common approach in the area of peer-to-peer systems has been to use a distributed hash table (or DHT). DHTs are based on the concept of virtual space. Peers and data items are mapped to points in that space, and local-control rules are used to decide, based on these virtual locations, how to interconnect the peers and how to map the data to the peers. DHTs are known to be highly scalable and easy to update as peers enter and leave the system. It is relatively easy to extend the DHT concept so that a constant fraction of faulty peers can be handled without any problems, but handling adversarial peers is very challenging. The biggest threats appear to be join-leave attacks (i.e., adaptive join-leave behavior by the adversarial peers) and attacks on the data management level (i.e., adaptive insert and lookup attacks by the adversarial peers) against which no provably robust mechanisms are known so far. Join-leave attacks, for example, may be used to isolate honest peers in the system, and attacks on the data management level may be used to create a high load-imbalance, seriously degrading the correctness and scalability of the system. We show, on a high level, that both of these threats can be handled in a scalable manner, even if a constant fraction of the peers in the system is adversarial, demonstrating that open systems for scalable distributed data storage that are robust against even massive adversarial behavior are feasible.
A Hybrid MPSO-BP-RBFN Model for Reservoir Lateral Prediction
Authors: Shiwei Yu, Kejun Zhu, Xiufu Guo, Jing Wang
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

The degree of success of many oil and gas drilling, completion, and production activities depends on the accuracy of the models used in the reservoir lateral prediction and description. In this paper, a hybrid MPSO-BP-RBFN model for predicting reservoir from seismic attributes is proposed. The model in which every particle consists of binary and real parts is able to simultaneously search for optimal network topology (the number of hidden nodes) and parameters, as it proceeds. The model has been used to reservoir lateral prediction of a reservoir zone and proved the model's applicability.
Emergence of Names and Compositionality
Authors: Jaak Henno
Venue: Proceeding of the 2007 conference on Information Modelling and Knowledge Bases XVIII (2007)

Here is presented an algebraic model of emergence of natural language, which defines language as the limit of a communication process in a society of agents. The heterogeneous model consists of semantic algebra Obj created by the set of attributes (elementary perceptions) and syntactic algebra L. Objects are compositional, determined by their attributes and sub-objects. Every agent a maintains its own language La⊆L, which is developed in the communication process using a meaning morphism ma:La→Obj, the speech morphism spa:Obj→La and a naming function na:Obj→Na, where Na is a set of names (subjective attributes, Na∩La=Ø). The model is investigated using computer modelling (language game). In the communication process agents add new words to their languages (at the beginning all La=Ø) and improve their meaning and speech functions using inference and disambiguation of semantics when objects are presented in different contexts. At the first stage of language creation agents use grounded messages (the message's object is included, e.g. pointed at), but on later stages they can also use ungrounded messages. Communication allows errors and several random features. If in the process of sending-receiving messages agents understanding of each other improves, i.e. error rate in recreating message's object decreases, then at the limit they create common language. It is shown, how compositionality (structure) in semantic domain creates elementary compositionality (structured denotations) also in the language. Compositionality of denotations follows from a very simple algorithm of agent's behaviour and does not require any pre-defined word categories or syntactic rules.
Half-day tutorial: describing, building, and using the public administration genome (PAG)
Authors: John W. Dickey
Venue: Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government (2009)

This is an outline for a half-day tutorial program to introduce attendees to a new concept for a comprehensive support system intended for public administrators, theorists, consultants, students, and inquiring citizens alike. The system, called "COMPASS", is a product of the overarching Public Administration Genome Project (PAGP). The effort here will be directed towards describing, building, and making use of the PAGP and COMPASS.
Y-lib: a user level library to increase the performance of MPI-IO in a lustre file system environment
Authors: Phillip M. Dickens, Jeremy Logan
Venue: Proceedings of the 18th ACM international symposium on High performance distributed computing (2009)

It is widely known that MPI-IO performs poorly in a Lustre file system environment, although the reasons for such performance are currently not well understood. The research presented in this paper strongly supports our hypothesis that MPI-IO performs poorly in this environment because of the fundamental assumptions upon which most parallel I/O optimizations are based. In particular, it is almost universally believed that parallel I/O performance is optimized when aggregator processes perform large, contiguous I/O operations in parallel. Our research shows that this approach generally provides the worst performance in a Lustre environment, and that the best performance is often obtained when the aggregator processes perform a large number of small, non-contiguous I/O operations. In this paper, we first demonstrate and explain these non-intuitive results. We then present a user-level library, termed Y-lib, which redistributes data in a way that conforms much more closely with the Lustre storage architecture than does the data redistribution pattern employed by MPI-IO. We then provide experimental results showing that Y-lib can increase performance between 300% and 1000% depending on the number of aggregator processes and file size. Finally, we cause MPI-IO itself to use our data redistribution scheme, and show that doing so results in an increase in performance of a similar magnitude when compared to the current MPI-IO data redistribution algorithms.
Yet a Faster Algorithm for Building the Hasse Diagram of a Concept Lattice
Authors: Jaume Baixeries, Laszlo Szathmary, Petko Valtchev, Robert Godin
Venue: Proceedings of the 7th International Conference on Formal Concept Analysis (2009)

Formal concept analysis (FCA) is increasingly applied to data mining problems, essentially as a formal framework for mining reduced representations (bases) of target pattern families. Yet most of the FCA-based miners, closed pattern miners, would only extract the patterns themselves out of a dataset, whereas the generality order among patterns would be required for many bases. As a contribution to the topic of the (precedence) order computation on top of the set of closed patterns, we present a novel method that borrows its overall incremental approach from two algorithms in the literature. The claimed innovation consists of splitting the update of the precedence links into a large number of lower-cover list computations (as opposed to a single upper-cover list computation) that unfold simultaneously. The resulting method shows a good improvement with respect to its counterpart both on its theoretical complexity and on its practical performance. It is therefore a good starting point for the design of efficient and scalable precedence miners.
Rethinking Chinese word segmentation: tokenization, character classification, or wordbreak identification
Authors: Chu-Ren Huang, Petr Šimon, Shu-Kai Hsieh, Laurent Prévot
Venue: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (2007)

This paper addresses two remaining challenges in Chinese word segmentation. The challenge in HLT is to find a robust segmentation method that requires no prior lexical knowledge and no extensive training to adapt to new types of data. The challenge in modelling human cognition and acquisition it to segment words efficiently without using knowledge of wordhood. We propose a radical method of word segmentation to meet both challenges. The most critical concept that we introduce is that Chinese word segmentation is the classification of a string of character-boundaries (CB's) into either word-boundaries (WB's) and non-word-boundaries. In Chinese, CB's are delimited and distributed in between two characters. Hence we can use the distributional properties of CB among the background character strings to predict which CB's are WB's.
The Framework for Business Rule Based Software Modeling: an Approach for Data Analysis Models Integration
Authors: Olegas Vasilecas, Aidas Smaizys
Venue: Proceeding of the 2007 conference on Databases and Information Systems IV: Selected Papers from the Seventh International Baltic Conference DB&IS'2006 (2007)

This paper discusses a possibility to take business rules out of the software system code and proposes a method based on presented framework for use of such business rules for dynamical software system integrated model transformations into the final software system code or system requirements at the same time tracking relations between them through the entire business rule based information system engineering process.