ppxscal/embedding_network huggingface dataset:
Review of "Software Security: Building Security In by Gary McGraw," Addison-Wesley Professional, 2006, $49.99, ISBN: 0321356705.
Authors: Radu State
Venue: Queue (2006)

Gary McGraw, Addison-Wesley Professional, 2006, $49.99, ISBN: 0321356705 Over the years, I have read several books covering software security from a system or programming language perspective. While most of them provided excellent overviews, I was hoping eventually to see a holistic approach. Software Security is just the kind of book I had in mind and is one of the best introductions to software security that I have seen.
An Advanced Java Card System Architecture for Smart Card Based on Large RAM Memory
Authors: Yoon-Sim Yang, Won-Ho Choi, Min-Sik Jin, Cheul-Jun Hwang, Min-Soo Jung
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02 (2006)

The paper proposes and designs architecture of Java Card System(JCS), which has been emulated by ARM32bit, SAMSUNG MicroController for smart card. The proposed architecture can directly execute Java Card application into the RAM. JCS first backups the previous data about Java objects into the transaction buffer, then writes new data into EEPROM, during the downloading and execution of Java Card application called an applet. Java Card technology supports both persistent object stored in EEPROM and transient objects stored in RAM because of the high limitation of its memory resources. In this paper, we made a suggestion on how to improve the speed of the execution by using the improved data store mechanism and memory structure. We moved Java Objects stored heap area in EEPROM into RAM. It makes Java Card much faster and is similar with the execution of application and the memory management in Java System of PC environment.
Concerning the cohesive nature of CSCL communities
Authors: Earl Woodruff
Venue: Proceedings of the 1999 conference on Computer support for collaborative learning (1999)

This paper examines the concept of community relative to CSCL networks. Starting from the point of view that a learning community is a group of individuals who engage in discourse for the purpose of advancing the knowledge of a collective, I suggest that these communities are held together by four cohesion factors; namely, function, identity, discursive participation, and shared values. An examination of learning communities suggests that students' knowledge building should improve if CSCL designs attend to, and support, each of the cohesive factors.
The Research on the Application Strategies of Information and Communication Technologies to Promote the Knowledge Transfer in Regional Innovation System
Authors: Jie Xue, Zhengang Zhang
Venue: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (2006)

This paper does research on the application of the information and communication technologies to the knowledge transfer process in regional innovation system. Firstly, we analyze the characteristics of the knowledge in regional innovation system that involves the analysis of the nature of knowledge, the classification of the knowledge, the knowledge transfer process and the SECI model of knowledge transformation in regional innovation system. Secondly, the paper investigates the benefits derived from the application of information and communication technologies to regional innovation system. At last, we bring forward the ideas to apply the information and communication technologies to knowledge transfer process in regional innovation system and put forward the implementing strategies accordingly, which include the application of the information and communication technologies to the SECI model and the construction of the integrated regional knowledge management system.
2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection
Authors: Laurence Devillers, Laurence Vidrascu, Lori Lamel
Venue: Neural Networks (2005)

Since the early studies of human behavior, emotion has attracted the interest of researchers in many disciplines of Neurosciences and Psychology. More recently, it is a growing field of research in computer science and machine learning. We are exploring how the expression of emotion is perceived by listeners and how to represent and automatically detect a subject's emotional state in speech. In contrast with most previous studies, conducted on artificial data with archetypal emotions, this paper addresses some of the challenges faced when studying real-life non-basic emotions. We present a new annotation scheme allowing the annotation of emotion mixtures. Our studies of real-life spoken dialogs from two call center services reveal the presence of many blended emotions, dependent on the dialog context. Several classification methods (SVM, decision trees) are compared to identify relevant emotional states from prosodic, disfluency and lexical cues extracted from the real-life spoken human-human interactions.
Using Future Search Conference for e-Learning Strategy Formulation in Higher Education
Authors: Imran A. Zualkernan, Leland Blank, Jamal Abdalla, Abdul-Rahman Al-Ali, Hasan Al-Nashash, Hany El Kadi, Rana Ahmed, Ghassan Qadah
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

Much is understood about success factors, issues, and lessons learned in implementing e-Learning in the context of higher education. A consensus is emerging that suggests that each institution is unique and hence requires a highly customized e-Learning strategy. This paper presents a strategy formulation process that takes a community building view towards strategy formulation. In specific, the process uses the future search conference and the Cademenos model of growth of e-Learning within higher education. An application and evaluation of the strategy formulation process at an American University is presented.
TEEMSS2: technology enhanced elementary math and science - year 1 report
Authors: Shari Jackson Metcalf
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

Technology Enhanced Elementary Math and Science (TEEMSS2), has the goal of bringing the power of information and communication technology to science education in grades 3--8, by creating and disseminating valuable, proven, and easily implemented technology-based science learning materials and associated teacher professional development. The project has selected age-appropriate, standards-based content for which technology offers real advantages. The learning strategy is based on student investigations of real phenomena using sensors and of virtual environments based on computer models. The new materials take advantage of computers, sensors, handhelds, and electronic networking to more effectively teach students and give them deeper insights into the process of science inquiry. This paper reports on the year 1 formative evaluation of TEEMSS2, which showed both quantitative and qualitative evidence of student learning when teachers implemented the materials in their classrooms.
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
Authors: Christian Siagian, Laurent Itti
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

We describe and validate a simple context-based scene recognition algorithm for mobile robotics applications. The system can differentiate outdoor scenes from various sites on a college campus using a multiscale set of early-visual features, which capture the "gist” of the scene into a low-dimensional signature vector. Distinct from previous approaches, the algorithm presents the advantage of being biologically plausible and of having low-computational complexity, sharing its low-level features with a model for visual attention that may operate concurrently on a robot. We compare classification accuracy using scenes filmed at three outdoor sites on campus (13,965 to 34,711 frames per site). Dividing each site into nine segments, we obtain segment classification rates between 84.21 percent and 88.62 percent. Combining scenes from all sites (75,073 frames in total) yields 86.45 percent correct classification, demonstrating the generalization and scalability of the approach.
Towards Autonomic Distribution of Existing Object Oriented Programs
Authors: Debzani Deb, M. Muztaba Fuad, Michael J. Oudshoorn
Venue: Proceedings of the International Conference on Autonomic and Autonomous Systems (2006)

By harnessing computational power of distributed heterogeneous resources, it is possible to build a large scale integrated system so that a centralized program is partitioned and distributed to those resources in a way that results in both efficient execution of the program and maximized resource utilization. However, building such a system is a staggering challenge because of the associated complexities and required user intervention. This paper proposes an autonomic distributed architecture that statically analyzes the existing Java application, partitions it to self-managed components that handles the complexities related to distribution and coordination without user involvement. An efficient static analysis mechanism is implemented that identifies run time program instances and their dependencies in terms of a graph. It is observed that such a view of the program is essential towards self optimization and self management.
Application of abductive ILP to learning metabolic network inhibition from temporal data
Authors: Alireza Tamaddoni-Nezhad, Raphael Chaleil, Antonis Kakas, Stephen Muggleton
Venue: Machine Learning (2006)

In this paper we use a logic-based representation and a combination of Abduction and Induction to model inhibition in metabolic networks. In general, the integration of abduction and induction is required when the following two conditions hold. Firstly, the given background knowledge is incomplete. Secondly, the problem must require the learning of general rules in the circumstance in which the hypothesis language is disjoint from the observation language. Both these conditions hold in the application considered in this paper. Inhibition is very important from the therapeutic point of view since many substances designed to be used as drugs can have an inhibitory effect on other enzymes. Any system able to predict the inhibitory effect of substances on the metabolic network would therefore be very useful in assessing the potential harmful side-effects of drugs. In modelling the phenomenon of inhibition in metabolic networks, background knowledge is used which describes the network topology and functional classes of inhibitors and enzymes. This background knowledge, which represents the present state of understanding, is incomplete. In order to overcome this incompleteness hypotheses are considered which consist of a mixture of specific inhibitions of enzymes (ground facts) together with general (non-ground) rules which predict classes of enzymes likely to be inhibited by the toxin. The foreground examples are derived from in vivo experiments involving NMR analysis of time-varying metabolite concentrations in rat urine following injections of toxins. The model's performance is evaluated on training and test sets randomly generated from a real metabolic network. It is shown that even in the case where the hypotheses are restricted to be ground, the predictive accuracy increases with the number of training examples and in all cases exceeds the default (majority class). Experimental results also suggest that when sufficient training data is provided, non-ground hypotheses show a better predictive accuracy than ground hypotheses. The model is also evaluated in terms of the biological insight that it provides.
Hierarchical power management in disruption tolerant networks with traffic-aware optimization
Authors: Hyewon Jun, Mostafa H. Ammar, Mark D. Corner, Ellen W. Zegura
Venue: Proceedings of the 2006 SIGCOMM workshop on Challenged networks (2006)

Recent efforts in Disruption Tolerant Networks (DTNs) have shown that mobility can be a powerful means for delivering messages in highly-challenged environments. DTNs are wireless mobile networks that are particularly useful in sparse environments where the density of nodes is insufficient to support direct end-to-end communication. Unfortunately, many mobility scenarios depend on untethered devices with limited energy supplies. Without careful management depleted energy supplies will degrade network connectivity and counteract the robustness gained by mobility. A primary concern is the energy consumed by wireless communication, and in particular the energy consumed in searching for other nodes to communicate with. In this architecture, energy can be conserved by using the low-power radio to discover communication opportunities with other nodes and waking the high-power radio to undertake the data transmission. We develop a generalized power management framework for controlling the wakeup intervals of the two radios. In addition, we show how to incorporate knowledge of the traffic load, and we devise approximation algorithms to control the sleep/wake-up cycling to provide maximum energy conservation while discovering enough communication opportunities to handle that load. We evaluate our schemes through simulation and compare them against single radio architectures, and against algorithms that do not incorporate information about the load. Our results show that the generalized power management mechanism achieves better energy efficiency in discovering communication opportunities than mechanisms relying on only one radio. Also, when traffic load can be predicted, our approximation algorithm could reduce energy consumption from 73% to 93% compared with the case without power management. Finally, our results show that the statistical information about traffic load and network connectivity allows us to devise an efficient algorithm even for the one-radio architecture, so energy can be saved almost equivalently to the two radio architecture.
A Multi-stage Graph Decomposition Algorithm for Distributed Constraint Optimisation
Authors: Terence H. -W. Law, Adrian R. Pearce
Venue: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology (2006)

In this paper, we propose a novel approach to solving the distributed constraint optimisation problem (DCOP) that guarantees completeness, while having linear communication complexity. The key to performance advantages, in terms of both computation and communication, derives from the application of the repeatedly-half principle to manage complexity by a combination of problem distribution through graph decomposition and multi-stage solution quality propagation. Experimental result shows that our new algorithm is faster than a recent competitive distributed algorithm for solving MaxSAT graph colouring problems. It also indicates the potential for the decomposition approach over a centralised method based on the same search strategy, and is consistent with recent results on domain propagation and structural decomposition in the CSP literature.
Experimental analysis of privacy loss in DCOP algorithms
Authors: Rachel Greenstadt, Jonathan P. Pearce, Emma Bowring, Milind Tambe
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. Unfortunately, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking despite the fact that agent privacy is a key motivation for applying DCOPs in many applications. Recently, Maheswaran et al. [3, 4] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that early DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. Do state-of-the art DCOP algorithms suffer from a similar shortcoming? This paper answers that question by investigating the most efficient DCOP algorithms, including both DPOP and ADOPT.
Scheduling cluster tools using filtered beam search and recipe comparison
Authors: Simon Oechsner, Oliver Rose
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Cluster tools have gained a lot of importance in today's semiconductor manufacturing. A cluster tool basically consists of several processing chambers in a mainframe, several load locks to insert wafer lots and a robot arm to move them. This means that these tools are able to work on more than one lot at the same time. Since the lot combination processed together can have an influence on the cycle times of these lots, scheduling is needed to ensure that the overall cycle times are kept low. In a previous work, we presented a method based on filtered beam search using slowdown factors as evaluation methods. Here, we will present another evaluation method based on recipe comparison that produces even better results. We will also show results of a beam width parameter study.
Adaptive Clustering for Multiple Evolving Streams
Authors: Bi-Ru Dai Jen-Wei Huang Mi-Yen Yeh Ming-Syan Chen
Venue: IEEE Transactions on Knowledge and Data Engineering (2006)

In the data stream environment, the patterns generated at different time instances are different due to data evolution. As time progresses, the behavior and members of clusters usually change. Hence, clustering continuous data streams allows us to observe the changes of group behavior. In order to support flexible clustering requirements, we devise in this paper a Clustering on Demand framework, abbreviated as COD framework, to dynamically cluster multiple data streams. While providing a general framework of clustering on multiple data streams, the COD framework has two advantageous features, namely, one data scan for online statistics collection and compact multiresolution approximations, which are designed to address, respectively, the time and the space constraints in a data stream environment. The COD framework consists of two phases, i.e., the online maintenance phase and the offline clustering phase. The online maintenance phase provides an efficient mechanism to maintain summary hierarchies of data streams with multiple resolutions in time linear in both the number of streams and the number of data points in each stream. On the other hand, an adaptive clustering algorithm is devised for the offline phase to retrieve approximations of desired substreams from summary hierarchies according to clustering queries. We propose two summarization techniques, based on wavelet and regression analyses, to construct the summary hierarchies. The regression-based summary hierarchy approximates the data stream more precisely and provides better clustering results, at the cost of slightly longer time than and twice the storage space as the wavelet-based one. An adaptive version of COD framework is designed to make a selection between a wavelet-based model and a regression-based model for building the summary hierarchy. By the adaptive COD, we can obtain clustering results with almost the same quality as the regression-based COD while using much less storage space for the summary hierarchy. As shown in the complexity analyses and also validated by our empirical studies, the COD framework performs very efficiently in the data stream environment while producing clustering results of very high quality.
Distance Functions Association for Content-Based Image Retrieval using Multiple Comparison Criteria
Authors: Ives R. V. Pola, Agma J. M. Traina, Caetano Traina Jr.
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

The comparison operators available in traditional Database Management Systems (DBMS) are not adequate to handle complex data such as images, rather comparing them using similarity operators is the option of choice. Similarity operators need a way to measure the similarity between pairs of objects. Although there are many interesting works dealing with similarity queries and functions to measure similarity, they all rely on a single similarity function that must be applicable over the whole dataset. However, images from medical exams often require several ways to measure similarity, depending on many factors, such as the particular pathological condition being searched, or the existence of specific clinical condition revealed in the images compared. Therefore, the ability to handle several ways to compare images by similarity is important in medical software handling images. This work develop a technique to allow several similarity functions to be combined when indexing a large set of images, allowing queries to probe the dataset regarding distinct comparison criteria. This technique also allows a flexible way to pose queries supporting fast retrieval of the answers.
Supporting mobile access to digital video archives without user queries
Authors: Cathal Gurrin, Lars Brenna, Dmitrii Zagorodnov, Hyowon Lee, Alan F. Smeaton, Dag Johansen
Venue: Proceedings of the 8th conference on Human-computer interaction with mobile devices and services (2006)

In this paper we present a technique for supporting mobile access to digital video archives without requiring explicit user queries. The idea is to infer the interests and needs of users from their WWW browsing history and represent those needs as persistent queries to the archive. An experiment, which we present here, suggests that this technique is effective for recommending video content to users on mobile devices. We also describe how to apply these findings to a mobile interface for a digital video archive.
Solving the single server semi-Markov queue with matrix exponential kernel matrices for interarrivals and services
Authors: Nail Akar, Khosrow Sohraby
Venue: Proceedings of the 1st international conference on Performance evaluation methodolgies and tools (2006)

Markov renewal processes with semi-Markov kernel matrices that have matrix-exponential representations form a superset of the well-known phase-type renewal process, Markovian arrival process, and the recently introduced rational arrival process. In this paper, we study the steady-state waiting time distribution in an infinite capacity single server queue with the auto-correlation in interarrival and service times modeled with this general Markov renewal process. Our method relies on the algebraic equivalence between this waiting time distribution and the output of a feedback control system certain parameters of which are to be determined through the solution of a well known numerical linear algebra problem, namely the SDC (Spectral-Divide-and-Conquer) problem. We provide an algorithmic solution to the SDC problem and in turn obtain a simple matrix exponential representation for the waiting time distribution using the ordered Schur decomposition that is known to have numerically stable and efficient implementations in various computing platforms.