ppxscal/embedding_network huggingface dataset:
Mining social networks for personalized email prioritization
Authors: Shinjae Yoo, Yiming Yang, Frank Lin, Il-Chul Moon
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Email is one of the most prevalent communication tools today, and solving the email overload problem is pressingly urgent. A good way to alleviate email overload is to automatically prioritize received messages according to the priorities of each user. However, research on statistical learning methods for fully personalized email prioritization (PEP) has been sparse due to privacy issues, since people are reluctant to share personal messages and importance judgments with the research community. It is therefore important to develop and evaluate PEP methods under the assumption that only limited training examples can be available, and that the system can only have the personal email data of each user during the training and testing of the model for that user. This paper presents the first study (to the best of our knowledge) under such an assumption. Specifically, we focus on analysis of personal social networks to capture user groups and to obtain rich features that represent the social roles from the viewpoint of a particular user. We also developed a novel semi-supervised (transductive) learning algorithm that propagates importance labels from training examples to test examples through message and user nodes in a personal email network. These methods together enable us to obtain an enriched vector representation of each new email message, which consists of both standard features of an email message (such as words in the title or body, sender and receiver IDs, etc.) and the induced social features from the sender and receivers of the message. Using the enriched vector representation as the input in SVM classifiers to predict the importance level for each test message, we obtained significant performance improvement over the baseline system (without induced social features) in our experiments on a multi-user data collection. We obtained significant performance improvement over the baseline system (without induced social features) in our experiments on a multi-user data collection: the relative error reduction in MAE was 31% in micro-averaging, and 14% in macro-averaging.
Performance improvements of a Kohonen self organizing classification algorithm on sparse data sets
Authors: Francesco Maiorana
Venue: Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems (2008)

This paper presents a variation of a Kohonen self organizing feature map. From the proposed algorithm possible performance improvements are investigated in terms of time and space complexity taking advantage from a sparse input data set. The proposed variation has been tested on different datasets coming from case studies in the field of bioinformatics. The improvements make the application of the algorithms feasible to massive document collections. The application of the proposed improvements for grid implementations could be beneficial to reduce the computing element demand.
Efficient and accurate in-network processing for monitoring applications in wireless sensor networks
Authors: Cyrus Shahabi
Venue:  (2008)

In-network processing has long been considered a fundamental mechanism to improve energy efficiency in various monitoring applications in wireless sensor networks. This thesis proposes in-network processing algorithms which leverage the spatial and temporal correlations in sensor data and the geometric properties in the network topologies to enable monitoring applications to efficiently produce accurate results. We demonstrate this by designing and implementing three application-specific in-network processing techniques. First, we investigate the efficient and approximate in-network aggregation algorithm called Clustered AGgregation (CAG) for environmental monitoring. CAG reduces the number of transmissions by leveraging both spatial and temporal correlations of sensor data to perform in-network aggregation. The CAG algorithm forms clusters with sensor nodes sensing similar values by exploiting spatial correlation of sensor readings, and efficiently adjust those clusters over time under the data or network dynamics. CAG is a novel algorithm in that it provides approximate results with bounded error performing in-network aggregation. Second, we design autonomous detection, identification, and localization algorithms which reduce false alarms in applications that monitor steam and water pipeline in oilfields. We formulate detection and identification problems in oilfield as a decision making problem based on information with uncertain errors and build a decision tree to capture the salient pressure and flow characteristics of each problem and distinguish them from false alarms. The proposed Steam and WAter Tracking System (SWATS) utilizes multi-modal sensing and multi-sensor collaboration and exploits spatial and temporal patterns of the sensed phenomena. Third, we introduce spatial skyline operation to wireless sensor networks and suggest potential applications which can benefit from this operation. Subsequently, we design and implement three flavors of Distributed Spatial Skyline (DSS) algorithms in wireless sensor networks based on different partitioning strategies: (1) TDSS: Triangulation-based DSS, (2) RDSS: Rendezvous-based DSS, and (3) TRDSS: Triangulation and Rendezvous-based DSS. Our algorithms utilize geometric properties in two layers of the protocol stack: (1) geographic routing techniques such as GPSR, GHT-based routing, and Voronoi-based geographic flooding in the routing layer, and (2) geometric notions such as convex hull and Voronoi diagram to compute spatial skyline in the application layer. The proposed DSS algorithms are novel in that they provide accurate and progressive spatial skyline results with modest delay while performing in-network processing efficiently.
Extracting concept descriptions from the Web: the importance of attributes and values
Authors: Massimo Poesio, Abdulrahman Almuhareb
Venue: Proceeding of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge (2008)

When extracting information about concepts from the Web, the problem is not recall, but precision: trying to identify which properties of a concept are genuinely distinctive. We discuss a series of experiments in empirical ontology using both unsupervised and supervised methods, showing that not all semantic relations we can extract from text are equally useful, and suggesting that attempting to identify concept attributes (parts, qualities, and the like) and their values results in better concept descriptions than those obtained by being less selective.
Virtual Reality for Consensus Building: Case Studies
Authors: Thea Lorentzen, Yoshihiro Kobayashi, Yuji Ito
Venue: Proceedings of the 10th International Symposium on Smart Graphics (2009)

This demonstration presents a real-time virtual reality (VR) system that has primarily been used as a tool for simulating and negotiating transportation-related designs. Visualization creation and presentation methods are briefly described. Case studies include applications to infrastructure, traffic, and environmental planning, as well as evacuation analysis. An emphasis is placed on using VR as a tool for translating technical information to non-experts and, ideally, enabling members of the public to better participate in decision making.
The Capabilities Required by Online Tutors Mediated Through Learning Environment Factors
Authors: C. Paul Newhouse, Doug. Reid
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

The Internet has provided distance education with a new delivery opportunity that tertiary institutions are rapidly deploying. However, little is known of the characteristics of effective human tutors to exploit this opportunity and what capabilities they require for particular online learning environments. This paper reports on an in-depth study examining the online learning milieu in six separate courses that incorporated the use of text-based online technologies at an Australian university. The study required a determination of the environmental factors affecting online tutor capabilities and the relationship between these capabilities and the factors. Thirteen environmental factors emerged from an analysis of the data and a set of critical online tutor capabilities based upon five categories distilled after an exhaustive review of the literature. From this a model of the mediated relationship between these capabilities and environmental factors was created and recommendations were made concerning the required capabilities of online tutors that will assist tertiary institutions in selection and training processes. Finally, the study identified a dissonance between the perceptions of tutors, students and course designers that needs careful consideration and further research.
Sources of asymmetric randomness
Authors: Bruno Apolloni, Simone Bassis
Venue: Proceeding of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008 (2009)

In view of discussing the genuine roots of the connectionist paradigm we toss in this paper the non symmetry features of the involved random phenomena. Reading these features in terms of intentionality with which we drive a learning process far from a simple random walk, we focus on elementary processes where trajectories cannot be decomposed as the sum of a deterministic recursive function plus a symmetric noise. Rather we look at nonlinear compositions of the above ingredients, as a source of genuine non symmetric atomic random actions, like those at the basis of a training process. To this aim we introduce an extended Pareto distribution law with which we analyze some intentional trajectories. With this model we issue some preliminary considerations on elapsed times of training sessions of some families of neural networks.
Approximation of Ontologies in CASL
Authors: Klaus Lüttich
Venue: Proceeding of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006) (2006)

In this paper we present methods to generate a Description Logic (DL) theory from a given First Order Logic (FOL) theory, such that each DL axiom is entailed by the given FOL theory. This is obtained by transforming the given FOL formulas. If this method is applied to an ontology specification in FOL, the resulting DL specification is still grounded on the same semantics but clearly weaker than the FOL specification. The benefit of specification in DL is that efficient reasoning procedures are available as implemented in tools such as Racer, Fact++ or Pellet. Such ontologies in DL could be used for knowledge representation systems and the semantic web where efficient reasoning plays a major role. This method can be used to compile a foundational ontology formalized in FOL, like DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), into DL for use with domain ontologies formalized in DL, or for the development of domain ontologies based on the compiled foundational ontology. These weakening strategies are described using CASL (Common Algebraic Specification Language) and CASL-DL, and will be integrated into HETS (Heterogeneous Tool Set). Furthermore, this paper includes examples from DOLCE.
Energy and delay optimized contention for wireless sensor networks
Authors: Ilker Demirkol, Cem Ersoy
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2009)

In wireless sensor network (WSN) studies, the main objective is minimizing the energy consumption so that the lifetime is maximized under the limited battery capacity constraints. Additionally, in most event-driven WSN applications, the end-to-end delay, and hence, the medium access delay should be minimized. Majority of the WSN MAC protocols are contention-based wherein contention window size setting involves an important trade-off between the collision probability and idle listening durations in contentions where both are aimed to be lowered for efficient network operation. In this paper, the energy optimizing and the delay optimizing contention window sizes are derived as a function of the number of contending nodes. For this purpose, we present separate analyses for the contention delay and for the energy consumed which are verified with detailed simulations. In order to obtain close to optimal performance values in a distributed manner, we propose a method for estimating the number of contending nodes since the individual wireless sensor nodes do not have this information readily. Simulations of an event-driven WSN application verify that the proposed method successfully improve both delay and energy efficiency of the contention-based medium access. The end-to-end network performance is also investigated by employing a geographical routing protocol. Results show that using the heuristic method proposed that use the optimum contention window size analyses presented, the overall network performance can be improved without incurring any overhead to the system.
Secured Information System for Concurrent Engineering Applications
Authors: Florin Hartescu
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

The paper presents a sustainable business model for open source software tools, managing and disseminating documents in heterogeneous software (source code files, database objects, graphical objects, text files etc) for concurrent engineering applications. The paper motivates the utilization of open source models for the maintenance and adaptation of the application or generic software. It describes the representation of the software in Internet computing, the architecture of the open source-based XML repository manager and the most important issues for its implementation. The system uses encryption and other security mechanisms to ensure that only authorized users can access a concurrent engineering application and the data cannot be intercepted.
On Generalizing the AGM Postulates
Authors: Giorgos Flouris, Dimitris Plexousakis, Grigoris Antoniou
Venue: Proceeding of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium (2006)

The AGM theory is the dominating paradigm in the field of belief change but makes some non-elementary assumptions which disallow its application to certain logics. In this paper, we recast the theory by dropping most such assumptions; we determine necessary and sufficient conditions for a logic to support operators which are compatible with our generalized version of the theory and show that our approach is applicable to a broader class of logics than the one considered by AGM. Moreover, we present a new representation theorem for operators satisfying the AGM postulates and investigate why the AGM postulates are incompatible with the foundational model. Finally, we propose a weakening of the recovery postulate which has several intuitively appealing properties.
70-270 & 70-290: MCSE/MCSA Guide to Installing and Managing Microsoft Windows XP Professional and Windows Server 2003, 1st edition
Authors: Ted Simpson, Dan DiNicolo, James Michael Stewart, Ed Tittel
Venue: Networking Course Technology (2008)

This full-color book combines Course Technology's proven Microsoft Windows Server 2003 and Windows XP Professional content in one volume for those who teach these two popular subjects in a single course.
Implementation of dynamic channel switching on IEEE 802.11-based wireless mesh networks
Authors: Gang Wu, Sathyanarayana Singh, Tzi-cker Chiueh
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

Interference makes it difficult for a wireless network to provide robust performance and could sometimes lead to transient failures. Dynamic channel switching (DCS) allows a wireless network interface (NIC) to operate in different frequency channels during different time periods without disrupting network connections that traverse the NIC. DCS enables a wireless mesh network to avoid frequency channels with serious interference in certain parts of its coverage area by switching to more idle channels. Although DCS provides additional radio agility, it significantly increases the complexity of the wireless mesh network's routing protocol. This paper describes the design, implementation and evaluation of a wireless mesh network system called Carlsbad, which supports both DCS and load-balancing/fault-tolerant routing, and successfully runs on low-cost commodity IEEE 802.11-based access points. Performance experiments on the first Carlsbad prototype show that despite the additional overhead it introduces, DCS can indeed improve the overall throughput of an IEEE 802.11-based wireless mesh network, sometimes by a factor of more than 2, for both TCP and UDP connections.
Experiments in room acoustics: modelling of a church sound field and reverberation time measurements
Authors: J. Quartieri, S. D'Ambrosio, C. Guarnaccia, G. Iannone
Venue: WSEAS Transactions on Signal Processing (2009)

In this paper a study of the acoustical response of a new built church is shown. This study is based on the measurement of reverberation time, adopting the noise interrupted method, according to the International Standard. This method allows to evaluate the reverberation time by means of acoustical sound level acquisition and analysis. The reverberation time is one of the principal parameters to be optimized in order to design and/or verify the acoustical behaviour of a room and consequently to guarantee a good people hearing sensation. In a post-opera intervention, the reverberation time can be improved modifying the reflecting surfaces of walls, floor and roof, in order to reduce the energetic contributions of late reflections. This improvement can be achieved by replacing or covering reflecting surfaces with absorbing panels or carpets. The design of an appropriate intervention can be aided by a dedicated simulation software, as it is shown in the last part of the paper.
Looking for the order of a system of arbitrary ordinary differential equations: De investigando ordine systematis &#x00e6;quationibus differentialium vulgarium cujuscunque
Authors: Fran&#x00e7;ois Ollivier
Venue: Applicable Algebra in Engineering, Communication and Computing (2009)

This paper was edited by S. Cohn and C. W. Borchardt from posthumous manuscripts of C. G. J. Jacobi. The various canonical forms that a given system ordinary differential equations may take are considered. Looking for the order of the system, without using a normal form, is reduced to a problem of inequalities: the affectation problem. A new type of formulas, the truncated determinants, is introduced. The non vanishing of this quantity means that the order will be equal to the value H, solution of this inequalities problem, which is obtained by an algorithm similar to Harold Kuhn&#x2019;s Hungarian method.
Proposed XML Standards for Law: MetaLex and LKIF
Authors: Alexander Boer, Radboud Winkels, Fabio Vitali
Venue: Proceeding of the 2007 conference on Legal Knowledge and Information Systems: JURIX 2007: The Twentieth Annual Conference (2007)

Electronic government invariably involves XML and electronic law: legislation is as essential to public administration as the ball is to a ball game. This paper gives an overview of two XML standard proposals dealing with two complementary aspects of electronic legislation --the documents themselves as a carrier, and an institutional reality they represent --in a coherent way: MetaLex XML and the Legal Knowledge Interchange format (LKIF). MetaLex XML is well on its way to becoming formal and de facto standard for legislation in XML. LKIF is yet to be submitted as a proposed standard. LKIF includes some interesting innovations from an AI & Law perspective.
Masters degrees in computing
Authors: Lillian N. Cassel, Gordon Davies, Stephen Seidman
Venue: ACM SIGCSE Bulletin (2009)

The ACM Education Board, the Education Activities Board of the IEEE Computer Society, representatives of the Association for Information Systems and the British Computer Society have joined forces to investigate the state of Masters degree programs in computing. This joint effort intends to document the variety and common features of programs throughout the United States and Europe. This poster is a status report on that project and an opportunity for interested individuals to add information about their programs to this work.
An algorithm based on the generalized D-gap function for equilibrium problems
Authors: Liping Zhang, Soon-Yi Wu
Venue: Journal of Computational and Applied Mathematics (2009)

The equilibrium problem (EP) can be reformulated as an unconstrained minimization problem through the generalized D-gap function. In this paper, we propose an algorithm for minimizing the problem and analyze some convergence properties of the proposed algorithm. Under some reasonable conditions, we show that the iteration sequence generated by the algorithm is globally convergent and converges to a solution to the EP and the generalized D-gap function provides a global error bound for the algorithm.
CP-INSIDE: Embedding Constraint-Based Decision Engines in Business Applications
Authors: Jacob Feldman, Eugene Freuder, James Little
Venue: Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (2009)

The CP-INSIDE project seeks to make constraint programming technology more accessible. In particular, it aims to facilitate the embedding of CP technology in business applications, and its integration with familiar software tools. A major objective of the CP-INSIDE project is to allow business developers to create constraint-based decision engines utilizing the power of CP technology in a vendor-neutral way. CP-INSIDE simplifies the development of specialized decision engines making them independent of the underlying generic CP solvers. There have already been several attempts to unify constraint programming languages (for instance, [1,2,3]) that directly or indirectly contributed to the same objective.
Community Intrusion Detection System Based on Radial Basic Probabilistic Neural Network
Authors: Meijuan Gao, Jingwen Tian, Shiru Zhou
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

A community intrusion detection system based on radial basic probabilistic neural network (RBPNN) is presented in this paper. This system is composed of ARM (Advanced RISC Machines) data acquisition nodes, wireless mesh network and control centre. The sensor is used to collect information in the data acquisition node and processes them by image detection algorithm, and then transmits information to control centre with wireless mesh network. When there is abnormal phenomenon, the system starts the camera and the radial basic probabilistic neural network algorithm is used to recognize the face image. We construct the structure of RBPNN that used for recognition face image, and adopt the K-Nearest Neighbor algorithm and least square method to train the network. With the ability of strong pattern classification and function approach and fast convergence of RBPNN, the recognition method can truly classify the face. This system resolves the defect and improves the intelligence and alleviates worker's working stress.