ppxscal/embedding_network huggingface dataset:
The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI
Authors: John Johnston
Venue:  (2008)

In The Allure of Machinic Life, John Johnston examines new forms of nascent life that emerge through technical interactions within human-constructed environments--"machinic life"--in the sciences of cybernetics, artificial life, and artificial intelligence. With the development of such research initiatives as the evolution of digital organisms, computer immune systems, artificial protocells, evolutionary robotics, and swarm systems, Johnston argues, machinic life has achieved a complexity and autonomy worthy of study in its own right. Drawing on the publications of scientists as well as a range of work in contemporary philosophy and cultural theory, but always with the primary focus on the "objects at hand"--the machines, programs, and processes that constitute machinic life--Johnston shows how they come about, how they operate, and how they are already changing. This understanding is a necessary first step, he further argues, that must precede speculation about the meaning and cultural implications of these new forms of life. Developing the concept of the "computational assemblage" (a machine and its associated discourse) as a framework to identify both resemblances and differences in form and function, Johnston offers a conceptual history of each of the three sciences. He considers the new theory of machines proposed by cybernetics from several perspectives, including Lacanian psychoanalysis and "machinic philosophy." He examines the history of the new science of artificial life and its relation to theories of evolution, emergence, and complex adaptive systems (as illustrated by a series of experiments carried out on various software platforms). He describes the history of artificial intelligence as a series of unfolding conceptual conflicts--decodings and recodings--leading to a "new AI" that is strongly influenced by artificial life. Finally, in examining the role played by neuroscience in several contemporary research initiatives, he shows how further success in the building of intelligent machines will most likely result from progress in our understanding of how the human brain actually works.
Neural Network Ensembles for Classification Problems Using Multiobjective Genetic Algorithms
Authors: David Lahoz, Pedro Mateo
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

In this work a Multiobjective Genetic Algorithm is developed in order to obtain an appropriate ensemble of neural networks. The algorithm does not use any back-propagation method. Furthermore, it considers directly the classification error instead of the mean square error. To obtain the multiobjective environment, the training pattern set is divided into subsets such that each one has its own error function and then, all the error functions are considered simultaneously. The proposed algorithm is found to be competitive with other current methods in the literature.
Countering Persistent Kernel Rootkits through Systematic Hook Discovery
Authors: Zhi Wang, Xuxian Jiang, Weidong Cui, Xinyuan Wang
Venue: Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection (2008)

Kernel rootkits, as one of the most elusive types of malware, pose significant challenges for investigation and defense. Among the most notable are persistent kernel rootkits, a special type of kernel rootkits that implant persistent kernel hooks to tamper with the kernel execution to hide their presence. To defend against them, an effective approach is to first identify those kernel hooks and then protect them from being manipulated by these rootkits. In this paper, we focus on the first step by proposing a systematic approach to identify those kernel hooks. Our approach is based on two key observations: First, rootkits by design will attempt to hide its presence from all running rootkit-detection software including various system utility programs (e.g., ps and ls). Second, to manipulate OS kernel control-flows, persistent kernel rootkits by their nature will implant kernel hooks on the corresponding kernel-side execution paths invoked by the security programs. In other words, for any persistent kernel rootkit, either it is detectable by a security program or it has to tamper with one of the kernel hooks on the corresponding kernel-side execution path(s) of the security program. As a result, given an authentic security program, we only need to monitor and analyze its kernel-side execution paths to identify the related set of kernel hooks that could be potentially hijacked for evasion. We have built a proof-of-concept system called HookMap and evaluated it with a number of Linux utility programs such as ls, ps, and netstat in RedHat Fedora Core 5. Our system found that there exist 35 kernel hooks in the kernel-side execution path of ls that can be potentially hijacked for manipulation (e.g., for hiding files). Similarly, there are 85 kernel hooks for ps and 51 kernel hooks for netstat, which can be respectively hooked for hiding processes and network activities. A manual analysis of eight real-world rootkits shows that our identified kernel hooks cover all those used in them.
Fault Tolerance for Small-World Cellular Neural Networks
Authors: Kautsuyoshi Matsumoto, Minoru Uehara, Hideki Mori
Venue: Proceedings of the 2nd international conference on Network-Based Information Systems (2008)

In this paper, we propose a mechanism for fault tolerance for Small-World Cellular Neural Networks (SWCNN). Small-world networks exist in the range between regular and random networks. SWCNN is a Cellular Neural Network (CNN) that has small-world network structure and has better performance than CNN. SWCNN needs to be fault tolerant because it has higher levels of error propagation than CNN.
Saliency-Based Image Quality Assessment Criterion
Authors: Qi Ma, Liming Zhang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Image quality assessment (IQA) is a critical issue in image processing applications, but traditional criteria based on the differences between reference and distorted images do not correlate well with perceived quality. MSSIM and VIF criteria proposed recently are regarded as excellent models in comparison with others, but they only consider local features and ignore some global concepts. In this paper, we propose an improved method that adds global saliency features to the criteria of MSSIM and VIF. For the sake of reducing the computational complexity, we propose a simpler and faster method to extract the saliency map. Experimental results for a set of intuitive examples as well as validation on a database of 779 images with different distortion types demonstrate that the improved IQA criteria can get a better performance than their original forms.
Using Redundancies to Find Errors
Authors: Yichen Xie, Dawson Engler
Venue: IEEE Transactions on Software Engineering (2003)

Programmers generally attempt to perform useful work. If they performed an action, it was because they believed it served some purpose. Redundant operations violate this belief. However, in the past, redundant operations have been typically regarded as minor cosmetic problems rather than serious errors. This paper demonstrates that, in fact, many redundancies are as serious as traditional hard errors (such as race conditions or null pointer dereferences). We experimentally test this idea by writing and applying five redundancy checkers to a number of large open source projects, finding many errors. We then show that, even when redundancies are harmless, they strongly correlate with the presence of traditional hard errors. Finally, we show how flagging redundant operations gives a way to detect mistakes and omissions in specifications. For example, a locking specification that binds shared variables to their protecting locks can use redundancies to detect missing bindings by flagging critical sections that include no shared state.
Parallel Single-Thread Strategies in Scheduling
Authors: Wojciech Bożejko, Jarosław Pempera, Adam Smutnicki
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

This paper, as well as coupled paper [2], deal with various aspects of scheduling algorithms dedicated for processing in parallel computing environments. In this paper, for the exemplary problem, namely the flow-shop scheduling problem with makespan criterion, there are proposed original methods for parallel analysis of a solution as well as a group of concentrated and/or distributed solutions, recommended for the use in metaheuristic approaches with single-thread trajectory. Such methods examine in parallel consecutive local sub-areas of the solution space, or a set of distributed solutions called population, located along the single trajectory passed through the space. Supplementary multi-thread search techniques applied in metaheuristics have been discussed in complementary our paper [2].
Bayesian Filtering for Location Estimation
Authors: Dieter Fox, Jeffrey Hightower, Lin Liao, Dirk Schulz, Gaetano Borriello
Venue: IEEE Pervasive Computing (2003)

Location awareness is important to many pervasive computing applications. Unfortunately, no location sensor takes perfect measurements or works well in all situations. So, it is crucial to represent uncertainty in sensed location information and combine information from different types of sensors. Bayesian-filter techniques provide a powerful statistical tool to help manage measurement uncertainty and perform multisensor fusion and identity estimation. In this article, the authors survey Bayes filter implementations and show their application to real-world location estimation tasks common in pervasive computing.
Dynamic Normal Forms and Dynamic Characteristic Polynomial
Authors: Gudmund Skovbjerg Frandsen, Piotr Sankowski
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

We present the first fully dynamic algorithm for computing the characteristic polynomial of a matrix. In the generic symmetric case our algorithm supports rank-one updates in O(n 2 logn) randomized time and queries in constant time, whereas in the general case the algorithm works in O(n 2 k logn) randomized time, where k is the number of invariant factors of the matrix. The algorithm is based on the first dynamic algorithm for computing normal forms of a matrix such as the Frobenius normal form or the tridiagonal symmetric form. The algorithm can be extended to solve the matrix eigenproblem with relative error 2-b in additional O(n log2 n logb) time. Furthermore, it can be used to dynamically maintain the singular value decomposition (SVD) of a generic matrix. Together with the algorithm the hardness of the problem is studied. For the symmetric case we present an &Omega;(n 2) lower bound for rank-one updates and an &Omega;(n) lower bound for element updates.
The effect of lecture support media on software skills learning
Authors: Scott A. Meier
Venue:  (2007)

The purpose of this study was to compare two types of lecture support both designed to teach computer skills within music classroom. One type of lecture support was computer-based and the other was paper-based. This comparison utilized two sets of data. The first set consisted of a test of prior knowledge and posttest. The second set was comprised of data from attitudinal and demographic surveys. The sample (N = 61), when divided by age, contained 41 high school age participants and 20 college-age participants. The participants were divided into six groups. There were two college age groups, and four high school age groups. Three of the groups (n = 30) received paper-based lecture support media and the other three (n = 31) received computer-based lecture support media. Comparisons were made using posttest scores with a test of prior knowledge as a covariant. Statistical testing (ANCOVA) demonstrated a significant difference in posttest scores in several comparisons. The participants who experienced the computer-based multimedia as lecture support significantly outperformed those receiving paper-based multimedia both in composite posttest scores and in near transfer scores. Also, the college age participants scored significantly higher than the high school age participants. There were significant correlations between years of study and posttest scores; and, high school age participants scored significantly better when they worked alone at the computer. This study has served the purpose of demonstrating that computer-based multimedia, when used to train music software skills, is a viable instructional tool, and in this experimental situation produced significantly better results than paper-based media. Results demonstrate that educators can create their own computer-based multimedia that may produce equal or better learning outcomes than paper-based media. 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 approximation of solution of nonhomogeneous backward heat conduction problem in bounded region
Authors: Xiao-Li Feng, Zhi Qian, Chu-Li Fu
Venue: Mathematics and Computers in Simulation (2008)

In this paper we consider a numerical approximation of solution of nonhomogeneous backward heat conduction problem (BHCP) in bounded region based on Tikhonov regularization method. Error estimate at t=0 for this method is provided. According to the error estimate, a selection of regularization parameter is given. Meanwhile, a numerical implementation is described and the numerical results show that our algorithm is effective.
Finding the boundaries of compound documents on the web
Authors: William Y. Arms
Venue:  (2008)

In both paper and digital worlds information resources generally consist of individual parts combined into a whole. A book is an aggregation of physical pages, and a web document is often published as several HTML pages. While in the paper world the connection between the aggregated resource and its parts is often obvious, in the digital world this connection is less clear and may depend on the context. Given a set of information units, the problem is to decide how-many aggregated resources there are, and which information units are parts of which resource. The ability to automatically identify aggregate resources is useful in many applications. On the web, example applications include web and intranet search, user navigation, automated collection generation, and information extraction. It is also useful in biological applications, digital libraries, and P2P systems. The focus of this dissertation is developing algorithms for automatically identifying such aggregate resources. While the main scope of the dissertation is the problem of identifying such resources, or compound documents (cDocs), on the web, the proposed techniques can be extended to other domains. The problem is difficult because the structure of cDocs typically varies and is not necessarily inherent in the web site, but may be imposed by a specific application or context. To address these complexities, this dissertation describes a machine learning based approach to recognizing cDocs. Given example cDocs provided by an agent, the approach first infers the agent's criteria of a cDoc, and then identifies other cDocs according to these criteria. Two specific approaches implementing the above framework are described. The first approach, Weighted Graph Clustering, learns the correct clustering of a web site based on analysis of the features of individual web pages and their immediate neighbors. The second approach, Generalized Pattern Matching, is based on graph mining techniques and analyses the web site as a whole. The approaches are evaluated on real web sites from educational, commercial, and news domains. The strengths and weaknesses of each approach are discussed and a combined hybrid approach is described. Experiments demonstrate significant improvements in performance over baseline heuristic approaches.
Geometrical Multiscale Noise Resistant Method of Edge Detection
Authors: Agnieszka Lisowska
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

In the paper the multiscale geometrical noise resistant method of edge detection based on second order wedgelets has been presented. Unlike the other known methods the proposed one can detect arc edges as segments of second degree curves instead of a set of straight lines. Such curve edges are parameterized by only one additional parameter reflecting the curvature of the edge. That approach allows for more compact representation of edges in an image also better reflecting the image geometry than the other well known methods. Thanks to that the new method can be used as the first step in high performance object recognition techniques. The experiments confirmed high effectiveness of the method in edge detection including noisy images.
Sharp and unsharp observables on σ-MV algebras---A comparison with the Hilbert space approach
Authors: S. Pulmannová
Venue: Fuzzy Sets and Systems (2008)

We prove that any observable with a standard value space on a probability MV algebra M can be extended from a mapping defined on measurable subsets with values in M to a mapping defined on bounded real valued measurable functions with values in the set of sharp observables associated with M. Our method of proof is quite different from the method of the proof of analogous result for POV-observables on a Hilbert space. We show that for POV-observables with commuting range both approaches give the same results.
Model-Based Reinforcement Learning in a Complex Domain
Authors: Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu
Venue: RoboCup 2007: Robot Soccer World Cup XI (2008)

Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the environment. Model-free algorithms perform updates solely bas ed on observed experiences. By contrast, model-based algorithms learn a model of the environment that effectively simulates its dynamics. The model may be used to simulate experiences or to plan into the future, potentially expediting the learning process. This paper presents a model-based reinforcement learning approach for Keepaway, a complex, continuous, stochastic, multiagent subtask of RoboCup simulated soccer. First, we propose the design of an environmental model that is partly learned based on the agent's experiences. This model is then coupled with the reinforcement learning algorithm to learn an action selection policy. We evaluate our method through empirical comparisons with model-free approaches that have been previously applied successfully to this task. Results demonstrate significant gains in the learning speed and asymptotic performance of our method. We also show that the learned model can be used effectively as part of a planning-based approach with a hand-coded policy.
ODDI: Ontology-Driven Data Integration
Authors: Paolo Ceravolo, Zhan Cui, Ernesto Damiani, Alex Gusmini, Marcello Leida
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

Data Integration systems are used to integrate heterogeneous data sources in a single view. Recent works on Business Intelligence do highlight the need of on-time, trustable and sound data access systems. This require for method based on a semi-automatic procedure that can provide reliable results. A crucial factor for any semi automatic algorithm is based on the matching operators implemented. Different categories of matching operators carry different semantics. For this reason combining them in a single algorithm is a non trivial process that have to take into account a variety of options.This paper proposes a solution based on a categorization of marching operators that allow to group similar attributes on a semantic rich form. The validation of the system have demonstrate how the aggregation of matching operators is not a trivial problem because traditional aggregators produce a compensation effect on operators that can have very different informative values. For this reason this work is now evolving thought the implementation of aggregators based on logic theories, able to distinguish different properties of matching operators.
Sc-expressions in object-oriented languages
Authors: Qing Zhao
Venue:  (2007)

In recent years, parallel computing and object-orientation have played increasingly central roles in information processing and data management. It seems clear that serious consideration should be given to adding support for parallelism in high-level object-oriented programming (OOP) languages, By reviewing both the existing parallelizing compilers and parallel programming languages, we demonstrated the necessity of developing high-level parallel object-oriented programming languages. Synchronization is an essential part of parallel processing. However, the synchronization constructs currently used in parallel programming languages are at too low a level to be compatible with high-level language constructs. More importantly, they are not particularly designed for object-oriented languages. As the main contribution of this thesis, we have designed SC-expressions for specifying synchronization constraints in parallel and OO environments. An SC-expression is a new kind of class member, distinct from properties (data members) and methods (attached function members). SC-expressions specify synchronization constraints for invoking and executing methods in the host object. SC-expressions are designed for ease of use providing programmers the ability to specify synchronization with high-level constructs without worrying about low-level details. In this thesis, we introduce process systems, process traces, P-expressions and their extensions to describe the relationship between concurrent processes based on the "period" view of time. These concepts build the foundations in defining the SC-expression and its languages. The languages of SC-expressions provide us with not only a theoretical model for the semantics of the synchronization constraints in parallel environment but also a systematic approach for implementing SC-expressions based on automata theory. 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
Facial Gender Classification Using Shape from Shading and Weighted Principal Geodesic Analysis
Authors: Jing Wu, W. A. Smith, Edwin R. Hancock
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

In this paper, we investigate gender classification based on 2.5D facial surface normals (facial needle-maps) which can be recovered from 2D intensity images using a non-lambertian Shape-from-shading (SFS) method. We also describe a weighted principal geodesic analysis (WPGA) method to extract features from facial surface normals. By incorporating the weight matrix into principal geodesic analysis (PGA), we control the obtained principal variance axes to be in the direction of the variance on gender information. For classification, an a posteriori probability based method is adopted. Experimental results confirms that using WPGA increases the gender discriminating power in the leading eigenvectors, and also demonstrates the feasibility of gender classification based on facial shape information.