ppxscal/embedding_network huggingface dataset:
Fast factorization by similarity in formal concept analysis of data with fuzzy attributes
Authors: Radim Belohlavek, Jiří Dvořák, Jan Outrata
Venue: Journal of Computer and System Sciences (2007)

We present a method of fast factorization in formal concept analysis (FCA) of data with fuzzy attributes. The output of FCA consists of a partially ordered collection of clusters extracted from a data table describing objects and their attributes. The collection is called a concept lattice. Factorization by similarity enables us to obtain, instead of a possibly large concept lattice, its factor lattice. The elements of the factor lattice are maximal blocks of clusters which are pairwise similar to degree exceeding a user-specified threshold. The factor lattice thus represents an approximate version of the original concept lattice. We describe a fuzzy closure operator the fixed points of which are just clusters which uniquely determine the blocks of clusters of the factor lattice. This enables us to compute the factor lattice directly from the data without the need to compute the whole concept lattice. We present theoretical solution and examples demonstrating the speed-up of our method.
A Contraction Method to Decide MSO Theories of Deterministic Trees
Authors: Angelo Montanari, Gabriele Puppis
Venue: Proceedings of the 22nd Annual IEEE Symposium on Logic in Computer Science (2007)

In this paper we generalize the contraction method, originally proposed by Elgot and Rabin and later extended by Carton and Thomas, from labeled linear orderings to colored deterministic trees. The method we propose rests on a suitable notion of indistinguishability of trees with respect to tree automata that allows us to reduce a number of instances of the acceptance problem for tree automata to decidable instances involving regular trees. We prove that such a method works effectively for a large class of trees, which is closed under noticeable operations and includes all the deterministic trees of the Caucal hierarchy obtained via unfoldings and inverse finite mappings as well as several trees outside such a hierarchy.
An 8 kbit/s ACELP coder with improved background noise performance
Authors: R. Hagen
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01 (1999)

This paper describes an 8 kbit/s ACELP speech coder with high performance for both speech and non-speech signals such as background noise. While the traditional waveform matching LPAS structure employed in many existing speech coders provides high quality for speech signals, it has significant performance limitations for, for example, background noise. The coder presented here employs a novel adaptive gain coding technique using energy matching in combination with a traditional waveform matching criterion providing high quality for both speech and background noise. The coder has a basic structure similar to that of the 7.4 kbit/s D-AMPS EFR coder, with a 10 th order LPC, high resolution adaptive codebook and a 4 pulse algebraic codebook. The performance for speech signals is equivalent to or better than that of state-of-the-art 8 kbit/s coders, while for background noise conditions the performance is significantly improved.
Resolution enhancement of compressed low resolution video
Authors: J. Mateos
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04 (2000)

We propose an iterative algorithm for the estimation of high resolution frames from a low resolution compressed video sequence. The algorithm exploits the existing correlation between the high and low resolution frames and the information provided by the encoder to obtain a high resolution frame. The performance of the algorithm is demonstrated experimentally.
Playing games with approximation algorithms
Authors: Sham M. Kakade, Adam Tauman Kalai, Katrina Ligett
Venue: Proceedings of the thirty-ninth annual ACM symposium on Theory of computing (2007)

In an online linear optimization problem, on each period t, an online algorithm chooses st ∈ S from a fixed (possibly infinite) set S of feasible decisions. Nature (who may be adversarial) chooses a weight vector wt ∈ R, and the algorithm incurs cost c(st,wt), where c is a fixed cost function that is linear in the weight vector. In the full-information setting, the vector wt is then revealed to the algorithm, and in the bandit setting, only the cost experienced, c(st,wt), is revealed. The goal of the online algorithm is to perform nearly as well as the best fixed s ∈ S in hindsight. Many repeated decision-making problems with weights fit naturally into this framework, such as online shortest-path, online TSP, online clustering, and online weighted set cover. Previously, it was shown how to convert any efficient exact offline optimization algorithm for such a problem into an efficient online bandit algorithm in both the full-information and the bandit settings, with average cost nearly as good as that of the best fixed s ∈ S in hindsight. However, in the case where the offline algorithm is an approximation algorithm with ratio α > 1, the previous approach only worked for special types of approximation algorithms. We show how to convert any offline approximation algorithm for a linear optimization problem into a corresponding online approximation algorithm, with a polynomial blowup in runtime. If the offline algorithm has an α-approximation guarantee, then the expected cost of the online algorithm on any sequence is not much larger than α times that of the best s ∈ S, where the best is chosen with the benefit of hindsight. Our main innovation is combining Zinkevich's algorithm for convex optimization with a geometric transformation that can be applied to any approximation algorithm. Standard techniques generalize the above result to the bandit setting, except that a "Barycentric Spanner" for the problem is also (provably) necessary as input.Our algorithm can also be viewed as a method for playing largerepeated games, where one can only compute approximate best-responses, rather than best-responses.
A 3D Notice Board Metaphor for Visualizing,Categorizing, and Sharing Learning Objects
Authors: ASM Mahfujur Rahman, Abdulmotaleb El Saddik
Venue: Proceedings of the Second International Conference on Systems (2007)

This paper illustrates a new metaphor for publishing and sharing Learning Objects Metadata (LOM) across P2P search environments. The metaphor allows the learners to publish a group of Learning Objects (LO) with certain tags. The shared information is augmented by the presentation and organization style. The style communicates both the individual user's perception and insights about a collection of LOM information. The prototype focuses not only on sharing the LOMs under certain user mapped tags but it also addresses the sharing of a user's view and perception about a set of multimedia resources.
Bit error probabilities of MDPSK over correlated nonselective Rayleigh fading channel with diversity reception
Authors: Songhua Zhang, Jiayi Wang, P. Ho
Venue: Proceedings of the The 8th International Conference on Communication Systems - Volume 01 (2002)

We analyze the bit error probability (BEP) of differential phase shift keying (DPSK) with postdetection maximal ratio combining (MRC) in Rayleigh fading channels. The system accommodates temporal fading correlation in each channel as well as equal fading correlation and noise correlation between different diversity branches. Exact BEP results for 2DPSK and 4DPSK and a very good approximation for 8DPSK are derived in closed-form. The results are new and unify some previously published BEP results for MDPSK with diversity reception.
Linguistic mapping in LSF space for low-bit rate coding
Authors: J. J. Parry
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02 (1999)

In this paper we investigate the spectral density of line spectral frequency (LSF) content in languages. The results show that the phonetic variation of languages is reflected in the LSF space. This leads to an alternative approach to the design of LSF quantisers. A trained LSF codebook, like the phonetic inventory of a language, is a static description of spectral behaviour of speech. As clear relationships exist between phonetic segments and LSFs, the structure of an LSF codebook can be analysed in terms of the phonetic segments. The new approach incorporates phonetic information into the structure of LSF codebooks through combining individual phonetic codebooks. The investigation leads to the conclusion that phonetic information can be usefully employed in codebook training in terms of perceptual performance and bit-rate reductions.
A study of context inference for Web-based information systems
Authors: Sangjun Kim, Euiho Suh, Keedong Yoo
Venue: Electronic Commerce Research and Applications (2007)

Recently, context-awareness has been a hot topic in the ubiquitous computing field. Numerous methods for capturing, representing and inferring context have been developed and relevant projects have been performed. Existing research has tried to determine user's contextual information physically by using stereo type cameras, RFID, smart devices, etc. These are heavily focusing on external context such as location, temperature, light, etc. However, cognitive elements of the context are important and need more study. This paper confines its research domain to the Web-based information system (IS) and, first, proposes two algorithms, the context inference algorithm and the service recommendation algorithm, for inferring cognitive context in the IS domain. Second, this paper demonstrates cognitive context-awareness on the Web-based information systems through implementing prototype deploying the proposed algorithms. The proposed system deploying the context inference and service recommendation algorithm can help the IS user to work with an IS conveniently and enable an existing IS to deliver ubiquitous service. In this fashion, we show the direction of existing IS and, ultimately, shows the typical services of a ubiquitous computing-based IS.
Decision Support as Knowledge Creation: An Information System Design Theory
Authors: David M. Steiger, Natalie M. Steiger
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

The primary purpose of decision support systems (DSS) is to improve the quality of decisions. This paper suggests that improvement in decision quality is a function of the creation of new data and information applicable to the decision domain and the understanding and learning of that new knowledge by the decision maker; i.e., the purpose of decision support is knowledge creation. We propose an information system design theory for DSS as knowledge creation based on Nonaka's knowledge spiral, including multiple kernel theories. These kernel theories and the associated process design methods are drawn from five distinct literatures: cognitive science, the theory of learning, artificial intelligence, knowledge management, and decision theory.
Copyright protection of images using robust digital signatures
Authors: N. Nikolaidis
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04 (1996)

A method for copyright protection of digital images is presented. Copyright protection is achieved by embedding an "invisible" signal, known as digital signature, in the digital image. Signature casting is performed in the spatial domain by slightly modifying the intensity level of randomly selected image pixels. Signature detection is done by comparing the mean intensity value of the marked pixels against that of the not marked pixels. Statistical hypothesis testing is used for this purpose. The signature can be designed in such a way that it is resistant to JPEG compression and lowpass filtering. This is done by minimizing the energy content of the signature signal in higher frequencies. Experiments on real image data verify the effectiveness of the method.
3D registration using a new implementation of the ICP algorithm based on a comprehensive lookup matrix: Application to medical imaging
Authors: Ahmad Almhdie, Christophe Léger, Mohamed Deriche, Roger Lédée
Venue: Pattern Recognition Letters (2007)

The iterative closest point (ICP) algorithm is an efficient algorithm for robust rigid registration of 3D data. Results provided by the algorithm are highly dependent upon the step of finding corresponding pairs between the two sets of 3D data before registration. In this paper, a look up matrix is introduced in the point matching step to enhance the overall ICP performance. Convergence properties and robustness are evaluated in the presence of Gaussian and impulsive noise, and under different data set sizes. The new algorithm has been evaluated on 3D medical data. It has been applied successfully to register closed surfaces acquired using different medical imaging modalities.
The application of readily available software packages in communication and radar system simulations
Authors: P. Yum
Venue: Proceedings of the 26th Annual Frontiers in Education - Volume 01 (1996)

This paper summarizes the application of software packages such as Mathcad and MATLAB in system simulations of communication and radar systems. With the help of the software packages, little time was spent on computer programming; nearly all the energy was concentrated on the analyses of the fundamental theory behind the simulations.
Voter verified ballots: the backbone of democracy
Authors: Alexa Linton
Venue: ACM SIGCAS Computers and Society (2006)

Americans' right to vote validates the American government as a true democracy. Congress passed the Help America Vote Act (HAVA) in the fall of 2002 to make elections more secure after the debacle in Florida in the 2000 presidential election. While HAVA was intended to make elections more secure and reliable, it has in fact had the opposite effect, even threatening to undermine the very foundations of democracy. HAVA, as commonly interpreted, has forced states to purchase electronic voting machines, most of which, including the ones just purchased by Bucks County, Pennsylvania, provide absolutely no voter verifiable paper ballot. This eliminates any possibility to recount or audit the vote. Paperless electronic voting machines are not only expensive but can also be easily rigged to fix an election. They have weak security systems that can easily be tampered with, proprietary software that is kept secret by the manufacturer, and are already notorious for losing or miscounting votes. These machines should not be used because of their high cost, vulnerability to hacking and fraud, unreliability, and most of all because of their lack of a voter verifiable receipt.
The Community Stack: Concept and Prototype
Authors: Tuan Nguyen, Seng W. Loke, Torab Torabi
Venue: Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02 (2007)

Location awareness and mobility are main factors in pervasive computing. There is a lot of work focusing on develop location aware applications. Generalizing from location awareness is the notion of mobile virtual communities which users enter or leave, analogous to pushing or popping items on or off a stack. The multi-agent paradigm is suitable for modeling distributed systems and can help reduce the complexity of system design. In this paper, we introduce a prototype for the community stack concept that is based on the multi-agent paradigm.
Nearly Optimal Preconditioned Methods for Hermitian Eigenproblems under Limited Memory. Part I: Seeking One Eigenvalue
Authors: Andreas Stathopoulos
Venue: SIAM Journal on Scientific Computing (2007)

Large, sparse, Hermitian eigenvalue problems are still some of the most computationally challenging tasks. Despite the need for a robust, nearly optimal preconditioned iterative method that can operate under severe memory limitations, no such method has surfaced as a clear winner. In this research we approach the eigenproblem from the nonlinear perspective, which helps us develop two nearly optimal methods. The first extends the recent Jacobi-Davidson conjugate gradient (JDCG) method to JDQMR, improving robustness and efficiency. The second method, generalized-Davidson+1 (GD+1), utilizes the locally optimal conjugate gradient recurrence as a restarting technique to achieve almost optimal convergence. We describe both methods within a unifying framework and provide theoretical justification for their near optimality. A choice between the most efficient of the two can be made at runtime. Our extensive experiments confirm the robustness, the near optimality, and the efficiency of our multimethod over other state-of-the-art methods.
Prediction of retail sales of footwear using feedforward and recurrent neural networks
Authors: Prasun Das, Subhasis Chaudhury
Venue: Neural Computing and Applications (2007)

Fluctuation of sales over time is one of the major problems faced by most of the industries. To alleviate this problem management tries to base their plans on forecast of sales pattern, which are mostly adhoc and rarely provides solid foundation for the plans. This study makes an attempt to solve this problem by taking a neural network approach, at the process of sales of footwear, and arriving at an optimum neural network model. The algorithms used for developing such model through neural network are both feedforward and recurrent Elman network. The data used in this work are the weekly sales of footwear and the information about the seasonality of sales process. While solving the problem, the focus is on forecasting of weekly retail sales as per the requirement of management. This work would reduce the uncertainty existing in the short-term/middle term planning of sales and distribution logistics of footwear over different time horizons across the entire supply chain of footwear business.
Exploring permutation inconsistency in blind separation of speech signals in a reverberant environment
Authors: M. Z. Ikram
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02 (2000)

We study and explore the limitations of methods for blind separation of a mixture of multiple speakers in a real reverberant environment. To support our results, we analyze a frequency-domain method, which achieves blind source separation (BSS) by transforming the time-domain convolutive problem to multiple short-term problems in the frequency domain. We show that treating the problem independently at different frequency bins introduces a "permutation inconsistency" problem, which becomes worse as the length of room impulse response increases. Our studies prove that the ideas proposed in the existing literature are not capable of effectively handling this problem and a need exists for its satisfactory solution. We speculate that time-domain BSS techniques may also suffer from an equivalent permutation inconsistency problem when long un-mixing filters are used.
An Unsupervised and Fully-Automated Image Analysis Method for cDNA Microarrays
Authors: E. Zacharia, D. Maroulis
Venue: Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems (2007)

Microarray gene expression image analysis is a labor-intensive task and requires human intervention since microarray images are contaminated with noise and artifacts while spots are often poorly contrasted and ill-defined. The analysis is divided into two main stages: Gridding and Spot-Segmentation. In this paper, an original, unsupervised and fully-automated approach to gridding and spot-segmenting microarray images, which is based on two genetic algorithms, is presented. The first genetic algorithm determines the optimal grid while the second one determines, in parallel, the boundaries of multiple spots. Experiments on 16-bit microarray images show that the proposed method is effective and achieves more accurate gridding and spot-segmentation results in comparison with existing methods.
SOC Implementation of HMM Based Speaker Independent Isolated Digit Recognition System
Authors: V. Amudha, B. Venkataramani, R. Vinoth kumar, S. Ravishankar
Venue: Proceedings of the 20th International Conference on VLSI Design held jointly with 6th International Conference: Embedded Systems (2007)

In this paper, the design and implementation results of a system on a chip (SOC) based speech recognition system are presented. The hidden markov model (HMM) is used for the speech recognition. In order to implement this in SOC, the various tasks required are optimally partitioned between hardware and software. The SOC, housed in Altera UP3 kit, consists of both Altera Nios II soft core processor and custom hardware blocks for computationally intensive blocks such as Viterbi decoder. The preprocessing and training of HMM are implemented in software (using C program). The Viterbi decoding is implemented in hardware for real time recognition. It is also implemented in software for verification and comparison. It is observed that the hardware implementation of viterbi block is 80 times faster than the software approach using C program. The speech recognition system is trained for digits 0-9 uttered by three male speakers. It is tested with trained speakers utterances and three new untrained speakers utterances. An over all recognition accuracy of 94.8% is achieved. Extension of this work for larger vocabulary size is under progress.
Availability and Performance of the Adaptive Voting Replication
Authors: Johannes Osrael, Lorenz Froihofer, Norbert Chlaupek, Karl M. Goeschka
Venue: Proceedings of the The Second International Conference on Availability, Reliability and Security (2007)

Replication is used to enhance availability and performance in distributed systems. Replica consistency and data integrity (constraint consistency) are correctness criteria for data-centric distributed systems. If consistency needs to be ensured all time, such systems soon become (partially) unavailable if node and link failures occur. However, some applications exist (e.g., in control engineering) where consistency can be temporarily relaxed during degradation in order to achieve higher availability. Recently we proposed Adaptive Voting (AV), a novel replication protocol based on traditional quorum consensus (voting) that allows the configuration of this trade-off. AV allows non-critical operations (that cannot violate critical constraints) even if no quorum exists. Since this might impose replica conflicts and data integrity violations, different reconciliation policies are needed to re-establish correctness at repair time. The contribution of this paper is two-fold: First, we present an availability analysis of AV. Second, we present performance measurements of AV from our .NET-based proof-of-concept implementation. The availability analysis shows that AV provides better availability than traditional voting if (i) some data integrity constraints of the system are relaxable and (ii) reconciliation time is shorter than degradation time. The performance results indicate that a read-one/writeall configuration of AV is fastest for write-operations that involve checking of inter-object constraints and thus implicitly include read operations.