ppxscal/embedding_network huggingface dataset:
Effects of Financial Holding Company Act on bank efficiency and productivity in Taiwan
Authors: Chei-Chang Chiou
Venue: Neurocomputing (2009)

Taiwan's banking industry has experienced greatly structural changes since the implementing of Financial Holding Company Act in July 2001. The paper investigates whether Taiwan's commercial banks establishing or joining in financial holding companies (FHCs) could promote their efficiency and productivity, as well as the determinants of efficiency and productivity changes of commercial banks. The paper applies a data envelopment analysis approach for calculating bank efficiency and a Malmquist total factor productivity index for measuring productivity change of banks. The results show that except for pure technical efficiency, other efficiencies and productivity of commercial banks do not improved because of their establishment of or joining in FHCs; on the contrary, it is just because of their better efficiency that they can firstly establish or join in FHCs. The results also reveal that, in the aspect of determinants of bank efficiency, bank size and overdue ratio have significant negative relations to technical efficiency of establishing or joining in FHCs' banks, while equity-to-total asset ratio and loan-to-deposit ratio have significant positive relations to their technical efficiency; moreover, bank size and overdue ratio have significant negative relations to technical efficiency of banks with no established FHCs, while business diversification and loan-to-deposit ratio have significant negative relations to scale efficiency for these banks. Finally, in the aspect of determinants of bank productivity changes, overdue ratio has a significant negative relation to productivity growth of establishing or joining in FHCs' banks, while business diversification has significant negative relations to both technological growth and productivity growth for banks with established and no established FHCs; furthermore, equity-to-total asset ratio and loan-to-deposit ratio have slightly significant negative relations to technical efficiency growth of banks with no established FHCs, while business diversification has a slightly significant positive contribution to their technical efficiency growth.
New Health Information Systems (HIS) Quality-in-Use Model Based on the GQM Approach and HCI Principles
Authors: Reem Al-Nanih, Hana Al-Nuaim, Olga Ormandjieva
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part IV: Interacting in Various Application Domains (2009)

Human Computer Interaction (HCI) is concerned with the design, evaluation, and implementation of interactive computing systems for human use. HCI is important in Health Information Systems (HIS), because misunderstandings arising because of poorly designed interfaces may lead to medical errors. This paper proposes a Quality-in-Use Model for HIS user interfaces, which identifies HIS-specific quality-in-use goals based on HCI principles, such as Mental Model, Metaphor, Visibility, Affordance, and Feedback. The Goal Question Metric (GQM) method was applied to build the new quality-in-use model applicable to most HIS systems. The resulting quality model is tailored for use in the medical field and reflects the values and viewpoints of the various user groups affected (e.g. doctors and nurses). Its qualitative and quantitative feedback can play a constructive and instructive role in medical institutions such as hospitals, and improve user productivity, satisfaction, and performance.
Hybrid shortest path algorithm for vehicle navigation
Authors: Hsun-Jung Cho, Chien-Lun Lan
Venue: The Journal of Supercomputing (2009)

Vehicle navigation is one of the important applications of the single-source single-target shortest path algorithm. This application frequently involves large scale networks with limited computing power and memory space. In this study, several heuristic concepts, including hierarchical, bidirectional, and A*, are combined and used to develop hybrid algorithms that reduce searching space, improve searching speed, and provide the shortest path that closely resembles the behavior of most road users. The proposed algorithms are demonstrated on a real network consisting 374,520 nodes and 502,485 links. The network is preprocessed and separated into two connected subnetworks. The upper layer of network is constructed with high mobility links, while the lower layer comprises high accessibility links. The proposed hybrid algorithms are implemented on both PC and hand-held platforms. Experiments show a significant acceleration compared to the Dijkstra and A* algorithm. Memory consumption of the hybrid algorithm is also considerably less than traditional algorithms. Results of this study showed the hybrid algorithms have an advantage over the traditional algorithm for vehicle navigation systems.
When Human Coders (and Machines) Disagree on the Meaning of Facial Affect in Spontaneous Videos
Authors: Mohammed E. Hoque, Rana Kaliouby, Rosalind W. Picard
Venue: Proceedings of the 9th International Conference on Intelligent Virtual Agents (2009)

This paper describes the challenges of getting ground truth affective labels for spontaneous video, and presents implications for systems such as virtual agents that have automated facial analysis capabilities. We first present a dataset from an intelligent tutoring application and describe the most prevalent approach to labeling such data. We then present an alternative labeling approach, which closely models how the majority of automated facial analysis systems are designed. We show that while participants, peers and trained judges report high inter-rater agreement on expressions of delight, confusion, flow, frustration, boredom, surprise, and neutral when shown the entire 30 minutes of video for each participant, inter-rater agreement drops below chance when human coders are asked to watch and label short 8 second clips for the same set of labels. We also perform discriminative analysis for facial action units for each affective state represented in the clips. The results emphasize that human coders heavily rely on factors such as familiarity of the person and context of the interaction to correctly infer a person's affective state; without this information, the reliability of humans as well as machines attributing affective labels to spontaneous facial-head movements drops significantly.
FAST: Flash-aware external sorting for mobile database systems
Authors: Hyoungmin Park, Kyuseok Shim
Venue: Journal of Systems and Software (2009)

Recently, flash memory has gained its popularity as storage on wide spectrum of computing devices such as cellular phones, digital cameras, digital audio players and PDAs. The integration of high-density flash memory has been accelerated twice every year for past few years. As flash memory's capacity increases and its price drops, it is expected that flash memory will be more competitive with magnetic disk drives. Therefore, it is desirable to adapt disk-based algorithms to take advantage of the flash memory technology. In this paper, we propose a novel Flash-Aware external SorTing algorithm, FAST, that overcomes the limitation of larger writing cost for flash memory to improve both overall execution time and response time. In FAST, we reduce the write operations with additional read operations. We provide the analysis for both traditional and our flash-aware algorithms by comparing the detailed cost formulas. Experimental results with synthetic and real-life data sets show that FAST can result in faster execution time as well as smaller response time than traditional external sorting algorithms.
A speech-in list-out approach to spoken user interfaces
Authors: Vijay Divi, Clifton Forlines, Jan Van Gemert, Bhiksha Raj, Bent Schmidt-Nielsen, Kent Wittenburg, Joseph Woelfel, Peter Wolf, Fang-Fang Zhang
Venue: Proceedings of HLT-NAACL 2004: Short Papers on XX (2004)

Spoken user interfaces are conventionally either dialogue-based or menu-based. In this paper we propose a third approach, in which the task of invoking responses from the system is treated as one of retrieval from the set of all possible responses. Unlike conventional spoken user interfaces that return a unique response to the user, the proposed interface returns a shortlist of possible responses, from which the user must make the final selection. We refer to such interfaces as Speech-In List-Out or SILO interfaces. Experiments show that SILO interfaces can be very effective, are highly robust to degraded speech recognition performance, and can impose significantly lower cognitive load on the user as compared to menu-based interfaces.
Fractals Based on Harmonic Wavelets
Authors: Carlo Cattani
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part I (2009)

In this paper a simple algorithm based on harmonic wavelets is given for the generation of self similar functions. Due to their self similarity property and scale dependence, harmonic wavelets might offer a good approximation of fractals by a very few instances of the wavelet series and a more direct interpretation of the scale invariance for deterministic localized fractals.
Anti-germ Performance Prediction for Detergents Based on Elman Network on Small Data Sets
Authors: Anqi Cui, Hua Xu, Peifa Jia
Venue: Proceedings of the 5th International Conference on Advanced Data Mining and Applications (2009)

Anti-germ performance test is critical in the production of detergents. However, actual biochemical tests are often costly and time consuming. In this paper, we present a neural network based model to predict the performance. The model made it much faster and cost less than doing actual biochemical tests. We also present preprocessing methods that can reduce data conflicts while keeping the monotonicity on small data sets. This model performs well though the training data sets are small. Its input is the actual value of key ingredients, which is not widely used in solving biochemical problems. The results of experiments are generated on the base of two detergent products for two types of bacteria, and appear reasonable according to natural rules. The prediction results show a high precision and fitting with the monotonicity rule mostly. Experts in biochemical area also give good evaluations to the proposed model.
Using GPCE principles for hardware systems and accelerators: (bridging the gap to HW design)
Authors: Rishiyur S. Nikhil
Venue: Proceedings of the eighth international conference on Generative programming and component engineering (2009)

Moore's Law has precipitated a crisis in the creation of hardware systems (ASICs and FPGAs)-how to design such enormously complex concurrent systems quickly, reliably and affordably? At the same time, portable devices, the energy crisis, and high performance computing present a related challenge-how to move complex and high-performance algorithms from software into hardware (for more speed and/or energy efficiency)? In this talk I will start with a brief technical introduction to BSV, a language that directly addresses these concerns. It uses ideas from Guarded Atomic Actions (cf. Term Rewriting Systems, TLA+, Unity, and EventB) to address complex concurrency with scalability. It borrows from Haskell (types, type classes, higher-order functions) for robustness and powerful program generation (a.k.a. "static elaboration" to HW designers). And it is fully synthesizable (compilable) into high-quality RTL (Verilog/VHDL). I will then describe some of the remarkable projects that BSV has enabled in industry and academia today.
Link Relation Analysis in Digital Image Space for Mobile Retrieval
Authors: Yang Xiaogang, Ma Jian
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02 (2009)

In this paper, we present a method of link relation analysis and build the link relation graph in digital image space based on the similarity of multidimensional photo contexts. One image can be linked to the other relevant images based on the similarity of the contexts associated with the image. By building the link relation graph, we can introduce the traditional link and analysis method of web information retrieval into user digital image collection. In addition, we can use graph theory to mine the relation of multidimensional photo contexts for easy, searchable and manageable photo album based on the link relation graph.
The software architecture for the first challenge on generating instructions in virtual environments
Authors: Alexander Koller, Donna Byron, Justine Cassell, Robert Dale, Johanna Moore, Jon Oberlander, Kristina Striegnitz
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Demonstrations Session (2009)

The GIVE Challenge is a new Internet-based evaluation effort for natural language generation systems. In this paper, we motivate and describe the software infrastructure that we developed to support this challenge.
Language-Based Isolation of Untrusted JavaScript
Authors: Sergio Maffeis, Ankur Taly
Venue: Proceedings of the 2009 22nd IEEE Computer Security Foundations Symposium (2009)

Web sites that incorporate untrusted content may use browser- or language-based methods to keep such content from maliciously altering pages, stealing sensitive information, or causing other harm. We study language-based methods for filtering and rewriting JavaScript code, using Yahoo! ADSafe and Facebook FBJS as motivating examples. We explain the core problems by describing previously unknown vulnerabilities and subtleties, and develop a foundation for improved solutions based on an operational semantics of the full ECMA-262 language. We also discuss how to apply our analysis to address the JavaScript isolation problems we discovered.
Fair bandwidth sharing using Shapley value
Authors: Edilayne M. Salgueiro, Paulo R. F. Cunha, Ricardo J. P. B. Salgueiro, José A. S. Monteiro
Venue: Proceedings of the 2008 Euro American Conference on Telematics and Information Systems (2008)

In the Internet, applications with very distinct requirements share the same network resources. In order to avoid QoS degradation, the resource manager component from QoS mechanism should distribute the available bandwidth in efficient and fair way. Resource manager works as an arbitrator, when applications compete for scarce resources. This paper reviews and discusses some bandwidth mechanisms from the literature, and proposes a fair scheme for the bandwidth distribution under a class-based allocation. The proposed allocation method is based on Cooperative Games Justice Mode. The Shapley value is evaluated to find fair bandwidth allocations. When the demand exceeds the quantity to be allocated, Bankruptcy Games are applied to extend the Shapley value method. Numeric experiments with allocation of simultaneous flows from multi-class applications in a CBQ node showed the efficiency of the proposed method.
Evaluation of Distraction in a Driver-Vehicle-Environment Framework: An Application of Different Data-Mining Techniques
Authors: Fabio Tango, Marco Botta
Venue: Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects (2009)

Distraction during driving task is one of the most serious problems affecting traffic safety, being one of the main causes of accidents. Therefore, a method to diagnose and evaluate Distraction appears to be of paramount importance to study and implement efficient counter-measures. This research aims at illustrating our approach in diagnosis of Distraction status, comparing some of the widely used data-mining techniques; in particular, Fuzzy Logic (with Adaptive-Network-based Fuzzy Inference System) and Artificial Neural Networks. The results are compared to select which method gives the best performances.
Privacy-Preserving Face Recognition
Authors: Zekeriya Erkin, Martin Franz, Jorge Guajardo, Stefan Katzenbeisser, Inald Lagendijk, Tomas Toft
Venue: Proceedings of the 9th International Symposium on Privacy Enhancing Technologies (2009)

Face recognition is increasingly deployed as a means to unobtrusively verify the identity of people. The widespread use of biometrics raises important privacy concerns, in particular if the biometric matching process is performed at a central or untrusted server, and calls for the implementation of Privacy-Enhancing Technologies. In this paper we propose for the first time a strongly privacy-enhanced face recognition system, which allows to efficiently hide both the biometrics and the result from the server that performs the matching operation, by using techniques from secure multiparty computation. We consider a scenario where one party provides a face image, while another party has access to a database of facial templates. Our protocol allows to jointly run the standard Eigenfaces recognition algorithm in such a way that the first party cannot learn from the execution of the protocol more than basic parameters of the database, while the second party does not learn the input image or the result of the recognition process. At the core of our protocol lies an efficient protocol for securely comparing two Pailler-encrypted numbers. We show through extensive experiments that the system can be run efficiently on conventional hardware.
The proteome analyst suite of automated function prediction tools
Authors: B. Poulin, D. Szafron, P. Lu, R. Greiner, D. S. Wishart, R. Eisner, A. Fyshe, B. Pearcy, L. Pireddu
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 4 (2005)

Proteome Analyst (PA) is a publicly available, high-throughput, web-based system for automatically predicting the function and properties of proteins. Biologists can use PA to predict, for example, the Gene Ontology (GO) molecular function and subcellular localization of a protein based on sequence information. Using sequence analysis tools and machine learning, PA gives high accuracy and broad coverage for both molecular function and subcellular localization predictions.
Abstract Interpretation of FIFO Replacement
Authors: Daniel Grund, Jan Reineke
Venue: Proceedings of the 16th International Symposium on Static Analysis (2009)

In hard real-time systems, the execution time of programs must be bounded by static timing analysis. For today's embedded systems featuring caches, static analyses must predict cache hits and misses with high precision to obtain useful bounds. For caches with least-recently-used (LRU) replacement policy, efficient and precise cache analyses exist. However, for other widely-used policies like first-in first-out (FIFO), current cache analyses are much less precise.This paper discusses challenges in FIFO cache analysis and advances the state of the art. We identify a generic framework for cache analysis that couples may- and must-analyses by means of domain cooperation. Our main contribution is a more precise may-analysis for FIFO. It not only increases the number of predicted misses, but also--due to the domain cooperation--the number of predicted hits. We instantiate the framework with a canonical must-analysis and three different may-analyses, including our new one, and compare the resulting three analyses to the collecting semantics. Our evaluation results characterize the progress achieved by our new may-analysis and reveal room for further improvement.
Numerical treatment of the spherically symmetric solutions of a generalized Fisher-Kolmogorov-Petrovsky-Piscounov equation
Authors: J. E. Macías-Díaz, I. E. Medina-Ramírez, A. Puri
Venue: Journal of Computational and Applied Mathematics (2009)

In the present work, the connection of the generalized Fisher-KPP equation to physical and biological fields is noted. Radially symmetric solutions to the generalized Fisher-KPP equation are considered, and analytical results for the positivity and asymptotic stability of solutions to the corresponding time-independent elliptic differential equation are quoted. An energy analysis of the generalized theory is carried out with further physical applications in mind, and a numerical method that consistently approximates the energy of the system and its rate of change is presented. The method is thoroughly tested against analytical and numerical results on the classical Fisher-KPP equation, the Heaviside equation, and the generalized Fisher-KPP equation with logistic nonlinearity and Heaviside initial profile, obtaining as a result that our method is highly stable and accurate, even in the presence of discontinuities. As an application, we establish numerically that, under the presence of suitable initial conditions, there exists a threshold for the relaxation time with the property that solutions to the problems considered are nonnegative if and only if the relaxation time is below a critical value. An analytical prediction is provided for the Heaviside equation, against which we verify the validity of our computational code, and numerical approximations are provided for several generalized Fisher-KPP problems.
Avoiding overfitting with BP-SOM
Authors: Ton Weijters, H. Jaap Van Den Herik, Antal Van Den Bosch, Eric Postma
Venue: Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2 (1997)

Overfitting is a well-known problem in the fields of symbolic and connectionist machine learning. It describes the deterioration of generalisation performance of a trained model. In this paper, we investigate the ability of a novel artificial neural network, BP-SOM, to avoid overfitting. BP-SOM is a hybrid neural network which combines a multi-layered feed-forward network (MFN) with Kohonen's self-organising maps (SOMS). During training, supervised back-propagation learning and unsupervised SOM learning cooperate in finding adequate hidden-layer representations. We show that BP-SOM outperforms standard backpropagation, and also back-propagation with a weight decay when dealing with the problem of overfitting. In addition, we show that BP-SOM succeeds in preserving generalisation performance under hidden-unit pruning, where both other methods fail.
RADAR: a personal assistant that learns to reduce email overload
Authors: Michael Freed, Jaime Carbonell, Geoff Gordon, Jordan Hayes, Brad Myers, Daniel Siewiorek, Stephen Smith, Aaron Steinfeld, Anthony Tomasic
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

Email client software is widely used for personal task management, a purpose for which it was not designed and is poorly suited. Past attempts to remedy the problem have focused on adding task management features to the client UI. RADAR uses an alternative approach modeled on a trusted human assistant who reads mail, identifies task-relevant message content, and helps manage and execute tasks. This paper describes the integration of diverse AI technologies and presents results from human evaluation studies comparing RADAR user performance to unaided COTS tool users and users partnered with a human assistant. As machine learning plays a central role in many system components, we also compare versions of RADAR with and without learning. Our tests show a clear advantage for learning-enabled RADAR over all other test conditions.
Adapting WSJ-trained parsers to the British National Corpus using in-domain self-training
Authors: Jennifer Foster, Joachim Wagner, Djamé Seddah, Josef van Genabith
Venue: Proceedings of the 10th International Conference on Parsing Technologies (2007)

We introduce a set of 1,000 gold standard parse trees for the British National Corpus (BNC) and perform a series of self-training experiments with Charniak and Johnson's reranking parser and BNC sentences. We show that retraining this parser with a combination of one million BNC parse trees (produced by the same parser) and the original WSJ training data yields improvements of 0.4% on WSJ Section 23 and 1.7% on the new BNC gold standard set.