ppxscal/embedding_network huggingface dataset:
Succinct Representations of Arbitrary Graphs
Authors: Arash Farzan, J. Ian Munro
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

We consider the problem of encoding a graph with n vertices and m edges compactly supporting adjacency, neighborhood and degree queries in constant time in the logn-bit word RAM model. The adjacency query asks whether there is an edge between two vertices, the neighborhood query reports the neighbors of a given vertex in constant time per neighbor, and the degree query reports the number of incident edges to a given vertex.We study the problem in the context of succinctness, where the goal is to achieve the optimal space requirement as a function of n and m, to within lower order terms. We prove a lower bound in the cell probe model that it is impossible to achieve the information-theory lower bound within lower order terms unless the graph is too sparse (namely m = o(n ¿ ) for any constant ¿> 0) or too dense (namely m = ¿(n 2 ¿ ¿ ) for any constant ¿> 0).Furthermore, we present a succinct encoding for graphs for all values of n,m supporting queries in constant time. The space requirement of the representation is always within a multiplicative 1 + ¿ factor of the information-theory lower bound for any arbitrarily small constant ¿> 0. This is the best achievable space bound according to our lower bound where it applies. The space requirement of the representation achieves the information-theory lower bound tightly within lower order terms when the graph is sparse (m = o(n ¿ ) for any constant ¿> 0).
Working Angle Recognition of Screw Compressor Valve
Authors: Qun-Feng Niu, Li Wang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

A novel working angle recognition system of screw compressor valve using wavelets and Probabilistic Neural Networks for engineering application is proposed. According to tested valve vibration signal energy changing characteristic under different working angle of valve flake, the paper obtains vibration signal power distribution on different scales using continuous wavelet transform, that is, scale-wavelet power spectrum of vibration signal. Then, according to different wavelet power distribution on scales, a Probabilistic Neural Networks model is applied to classify corresponding working angle of valve flake using scale-wavelet power spectrum as feature parameters. The experimental results show that the methods of using continuous wavelets transform and extracting each scale-wavelet power spectrum as feature vectors can depict vibration signal changing rules along with valve flake angle of compressor changing correctly. The Probabilistic Neural Networks model applied can recognize different working angles and realize monitoring of valve working condition well. The methods provide a new effective tool for screw compressor valve states monitoring and fault diagnosis.
Kansei Processing Agent for Personalizing Retrieval
Authors: Sunkyoung Baek, Myunggwon Hwang, Pankoo Kim
Venue: Proceedings of the 11th international conference on User Modeling (2007)

In the present, methods of creating and processing a profile are insufficient for achieving personalization information retrieval that reflects the subjective Kansei preference of users. To rectify this insufficiency, we have created a Kansei information processing agent. Our study proposes a Kansei agent for the creation, accumulation and renewal of profiles in personalized retrieval and explores possible contributions to the development of a Kansei-based recommendation system and personalizing service.
A multilayered neuro-fuzzy classifier with self-organizing properties
Authors: N. E. Mitrakis, J. B. Theocharis, V. Petridis
Venue: Fuzzy Sets and Systems (2008)

A novel self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) is suggested in this paper which is composed of small-scale interconnected fuzzy neuron classifiers (FNCs) arranged in layers. The model provides a different perspective for generating a new class of hierarchical classifiers with multilevel classifiers combination. At each layer, parent FNCs are combined to construct a descendant FNC at the next layer with enhanced classification qualities. The generic FNCs exhibit an original structure including four modules: (1) The fuser aggregates the decision support outputs of the parent FNCs using a fusion operator. (2) The data splitting module divides data into correctly classified patterns with high degree of confidence which are handled by the fuser and ambiguous ones. (3) The last two modules are realized by fuzzy rule-based systems and implement a neuro-fuzzy classifier within each FNC, used to improve the classification accuracy of ambiguous patterns. The former performs feature transformations to an intermediate output space while the latter provides the decision supports of non-confident patterns. Unlike traditional classifiers, the classification mapping is accomplished in SONeFMUC by performing successive decision fusions and feature transformations. SONeFMUC structure is determined sequentially via a self-constructing learning algorithm. Structure learning inherently implements feature selection, considering the most informative attributes as model inputs. To improve classification accuracy, the models obtained after structure learning are optimized using a parameter learning scheme based on genetic algorithms. The effectiveness of our approach is tested on a set of benchmark classification problems. Experimental results show that the proposed neuro-fuzzy classifier is favorably compared to other well-known classification techniques of the literature.
A Contextual Acceptance Model of Mobile Commerce Based on TAM
Authors: Zhengchuan Xu, Chenghong Zhang, Hong Ling
Venue: Proceedings of the 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008) (2008)

Users may use a mobile service in various contexts. It is critical to study how users' perceptions and intentions are affected by context. We discuss the definition and structure of context, then propose a contextual acceptance model of mobile commerce based on TAM. We apply this model to the case of a GPS-based taxi-dispatching system. Preliminary data analysis illustrates that context has significant impact on uses' behavior.
The AUTOSAR Way of Model-Based Engineering of Automotive Systems
Authors: Heiko Dörr
Venue: Proceedings of the 4th international conference on Graph Transformations (2008)

The international development partnership AUTOSAR strives for reuse and exchange of SW components along various dimensions: between OEM and supplier, between vehicle platforms, between electronic networks, and between individual electronic control units (ECUs). To enable the exchange an abstraction layer --- the AUTOSAR runtime environment (RTE) --- has been defined as a set of services provided to applications. Typical services are communication via buses, memory access, or diagnostic support. The services are implemented by several stacks of basic SW-modules being highly configurable to support the large variety of ECUs. By configuration, the same SW module can be used for those ECUs with a given micro-controller abstraction.To exploit the variability in a structured approach, AUTOSAR defined a methodology which heavily makes use of so-called templates capturing all information of an electronic vehicle network. The data-structures are defined by a meta-model allowing for a consistent definition of the templates.The development approach adopted by AUTOSAR is related to model-driven development. At the first step in the methodology, an overall system description is defined showing the logical architecture of the SW system. Each SW component is described by an instance of the SW-template. When the SW system shall be applied to an electronic network, the dedicated HW resources must be described as well. So, the properties and capabilities of ECUs and buses are captured by an ECU resource template. During the next step, a mapping of the logical to the physical architecture has to be found. This task is of high importance, since it determines the overall system performance. Once the mapping is established, configuration files for individual ECUs of the network are generated. Based on the configuration the appropriate SW-components can be loaded onto the ECU and the required communication channels are established such that the SW application can be successfully executed.Currently, there is no strong tool-support for mapping besides plain editors. Rule-based transformations may be applied to define and realize (semi-) automatic mapping strategies for the different areas in the system and ECU resource template.
Scalable Specification and Reasoning: Challenges for Program Logic
Authors: Peter W. O'Hearn
Venue: Verified Software: Theories, Tools, Experiments: First IFIP TC 2/WG 2.3 Conference, VSTTE 2005, Zurich, Switzerland, October 10-13, 2005, Revised Selected Papers and Discussions (2005)

If program verification tools are ever to be used widely, it is essential that they work in a modular fashion. Otherwise, verification will not scale. This paper discusses the scientific challenges that this poses for research in program logic. Some recent work on separation logic is described, and test problems that would be useful in measuring advances on modular reasoning are suggested.
New Closed-Form Bounds on the Partition Function
Authors: Krishnamurthy Dvijotham, Soumen Chakrabarti, Subhasis Chaudhuri
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

Estimating the partition function is a key computation in graphical models that is required for important tasks like parameter estimation, model selection and structure learning. However, this computation is intractable in general. Thus, developing efficient and accurate approximations is of considerable interest. Variational methods express the computation of the partition function as an optimization problem (solving which intractable in general) and then relax the optimization problem in various ways to obtain approximations to the partition function. Two popular algorithms belonging to this framework are loopy belief propagation (LBP) and tree-reweighted belief propagation (TRW-BP). Both these algorithms are so-called message passing algorithms that work by updating local beliefs about probabilities at graph nodes by passing messages between the nodes in the graph until convergence is achieved. TRW-BP is guaranteed to give an upper bound on the partition function. However, neither algorithm is guaranteed to converge in general. Although convergent alternatives to TRW-BP have been proposed, they have no guarantees on the number of iterations required for convergence. Thus, these algorithms could be prohibitively expensive for applications requiring repeated inference on large graphs (like training large CRFs). In order to overcome this problem, Sutton et al. propose the piecewise approximation (PW) to the partition function. PW tends to be loose, but can be computed very fast, because it has a closed-form expression. Sutton et al. apply it successfully to common NLP tasks. In this paper, for a special class of potentials (that contain associative potentials), we prove that both LBP and TRW-BP converge in a single iteration. Using this fact, we obtain closed-form expressions for the TRW and LBP approximations to the partition function. In recent work, Wainwright et al. prove that LBP gives a lower bound on the partition function for binary attractive MRFs. We thus also get closed-form lower bounds for attractive associative MRFs. This enables us to obtain bounds on the error between the true partition function and these approximations for attractive associative MRFs. We also construct examples which show that TRW and LBP can give arbitrarily bad approximations to the marginal probabilities. Using the closed-form bounds for these special cases, we also develop novel closed-form upper bounds for arbitrary MRFs and closed-form lower bounds for associative binary MRFs. We also present experimental results showing that the new upper bounds are almost always tighter than the piecewise bound. The experiments also show that the novel lover bounds beat popular existing bounds like the mean-field bound on densely connected graphs.
Intelligent Functional Model for Costs Minimization in Hybrid Manufacturing Systems
Authors: Algirdas Bargelis, Guenter Hoehne, Aurimas Česnulevičius
Venue: Informatica (2004)

The paper deals with the intelligent functional model for optimizing the product design and its manufacturing process in hybrid manufacturing systems consisting of people, machines and computers. The knowledge-based framework of an intelligent functional model has been developed. It furnishes the possibility for a product designer and manufacturer to find an optimal production plan in the early stage of the product design. The mathematical model formalization is provided. A consecutive optimization scheme has been applied for selecting an optimal alternative of a product design and its production plan. The proposed model is being implemented both in industry and university education process.
Analysis and Interpretation of Visual Hierarchical Heavy Hitters of Binary Relations
Authors: Arturas Mazeika, Michael H. Böhlen, Daniel Trivellato
Venue: Proceedings of the 12th East European conference on Advances in Databases and Information Systems (2008)

The emerging field of visual analytics changes the way we model, gather, and analyze data. Current data analysis approaches suggest to gather as much data as possible and then focus on goal and process oriented data analysis techniques. Visual analytics changes this approach and the methodology to interpret the results becomes the key issue.This paper contributes with a method to interpret visual hierarchical heavy hitters (VHHHs). We show how to analyze data on the general level and how to examine specific areas of the data. We identify five common patterns that build the interpretation alphabet of VHHHs. We demonstrate our method on three different real world datasets and show the effectiveness of our approach.
Energy-Efficient Fixed-Priority Scheduling for Periodic Real-Time Tasks with Multi-priority Subtasks
Authors: Zhigang Gao, Zhaohui Wu, Man Lin
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

With the rapid development of embedded systems, battery life becomes a critical restriction factor. Dynamic voltage scaling (DVS) has been proven to be an effective method for reducing energy consumption of processors. This paper proposes an energy-saving algorithm under a task model (the MSPR model) where a task consists of multiple subtasks with different fixed priorities. This algorithm includes two parts. The first part is a static algorithm, which exploits the relationship among tasks to set the slowdown factors of subtasks. The second part is an algorithm that dynamically reclaims and reuses the slack time of precedent subtasks during the execution of tasks. To the best of our knowledge, this is the first work for energy-efficient scheduling under the complex periodic real-time task model where a task consists of multiple subtasks with different fixed priorities. Experimental results show this method can reduce energy consumption by 20%-80%, while guaranteeing the real-time requirements of systems.
A Semantic Language for Querying Anonymous Web Sources
Authors: François Pinet, Michel Schneider
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

A great deal of work has been carried out in recent years to facilitate access to data and information available on the Web. Proposals converge in two additional areas which consist in providing the sources with semantic annotations and in designing languages and tools that are capable of using these annotations. However, a large number of sources have not yet been annotated suitably. Besides, languages and existing tools do not allow the user to formulate "blind" queries without knowing the sources. To overcome these two limitations, in this paper we propose a flexible query language which allows a user to query sources in an anonymous way without knowing their existence and their structure. Queries can be solved by a system which in advance discovers potential sources and memorizes their schemas. We clarify how such a system can function.
Computer game engines for developing first-person virtual environments
Authors: David Trenholme, Shamus P. Smith
Venue: Virtual Reality (2008)

Building realistic virtual environments is a complex, expensive and time consuming process. Although virtual environment development toolkits are available, many only provide a subset of the tools needed to build complete virtual worlds. One alternative is the reuse of computer game technology. The current generation of computer games present realistic virtual worlds featuring user friendly interaction and the simulation of real world phenomena. Using computer games as the basis for virtual environment development has a number of advantages. Computer games are robust and extensively tested, both for usability and performance, work on off-the-shelf systems and can be easily disseminated, for example via online communities. Additionally, a number of computer game developers provide tools, documentation and source code, either with the game itself or separately available, so that end-users can create new content. This short report overviews several currently available game engines that are suitable for prototyping virtual environments.
Power Efficient Relaying MAC Protocol for Rate Adaptive Wireless LANs
Authors: Jaeeun Na, Yeonkwon Jeong, Joongsoo Ma
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

To exploit multi-rate capability as well as improve performance in wireless networks, many relaying mechanisms are proposed on IEEE 802.11 media access control (MAC) layer. However, no effort has been invested to exploit the multi-rate capability for power saving mechanism in MAC. In this paper, we propose a Power Efficient Relaying MAC Protocol, called PERP, to achieve both performance improvement and power saving by leveraging the multi-rate capability. In proposed relaying scheme, if a node can support low rate node's packet at higher rate and has sufficient power, after voluntarily helping data transmission at higher rate, all nodes go into sleep mode as quickly as possible to reduce power consumption. Simulation results show that the PERP improves throughput by 30~50% as well as reduces power consumption and transmission delay by 10~55% than the legacy mechanism.
Verifying Dynamic Pointer-Manipulating Threads
Authors: Thomas Noll, Stefan Rieger
Venue: Proceedings of the 15th international symposium on Formal Methods (2008)

We present a novel approach to the verification of concurrent pointer-manipulating programs with dynamic thread creation and memory allocation as well as destructive updates operating on arbitrary (possibly cyclic) singly-linked data structures. Correctness properties of such programs are expressed by combining a simple pointer logic for specifying heap properties with linear-time (LTL) operators for reasoning about system executions. To automatically solve the corresponding model-checking problem, which is undecidable in general, we abstract from non-interrupted sublists in the heap, resulting in a finite-state representation of the data space. We also show that the control flow of a concurrent program with unbounded thread creation can be characterized by a Petri net, making LTL model checking decidable (though not feasible in practice). In a second abstraction step we also derive a finite-state representation of the control flow, which then allows us to employ standard LTL model checking techniques.
A Multi-objective Genetic Algorithm Based Approach to the Optimization of Oligonucleotide Microarray Production Process
Authors: Filippo Menolascina, Vitoantonio Bevilacqua, Caterina Ciminelli, Mario Nicola Armenise, Giuseppe Mastronardi
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

Microarrays are becoming more and more utilized in the experimental platform in molecular biology. Although rapidly becoming affordable, these micro devices still have quite high production cost which limits their commercial appeal. Here we present a novel multiobjective evolutionary approach to the optimization of the production process of microarray devices mainly aimed at lowering the number of fabrication steps. In order to allow the reader to better understand what we describe we report herein a detailed description of a real-world study case carried out on the most recent microarray platforms of the market leader in this field. A comparative analysis of the most widely used approaches, main potentialities and drawbacks of the proposed approach are presented.
Weighted Kernel Regression for Predicting Changing Dependencies
Authors: Steven Busuttil, Yuri Kalnishkan
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Consider the online regression problem where the dependence of the outcome y t on the signal x t changes with time. Standard regression techniques, like Ridge Regression, do not perform well in tasks of this type. We propose two methods to handle this problem: WeCKAAR, a simple modification of an existing regression technique, and KAARCh, an application of the Aggregating Algorithm. Empirical results on artificial data show that in this setting, KAARCh is superior to WeCKAAR and standard regression techniques. On options implied volatility data, the performance of both KAARCh and WeCKAAR is comparable to that of the proprietary technique currently being used at the Russian Trading System Stock Exchange (RTSSE).
Fast Digital TRNG Based on Metastable Ring Oscillator
Authors: Ihor Vasyltsov, Eduard Hambardzumyan, Young-Sik Kim, Bohdan Karpinskyy
Venue: Proceeding sof the 10th international workshop on Cryptographic Hardware and Embedded Systems (2008)

In this paper, a new true random number generator (TRNG), based entirely on digital components is proposed. The design has been implemented using a fast random number generation method, which is dependent on a new type of ring oscillator with the ability to be set in metastable mode. Earlier methods of random number generation involved employment of jitter, whereas the proposed method leverages the metastability phenomenon in digital circuits and applies it to a ring oscillator. The new entropy employment method allows an increase in the TRNG throughput by significantly reducing the required entropy accumulating time. Samples obtained from simulation of TRNG design have been evaluated using AIS.31 and FIPS 140-1/2 statistical tests. The results of these tests have proven the high quality of generated data. Corners analysis of the TRNG design was also performed to estimate the robustness to technology process and environment variations. Investigated in FPGA technology, phase distribution highlighted the advantages of the proposed method over traditional architectures.