518 research outputs found
PSEUDO-CAML PERFORMANCE OF THE ASEAN-5’S INNOVATIVE BANKING USING THE PARALLELISM OF MARX, SCHUMPETER, AND CHRISTENSEN INNOVATION CONCEPTS
Routine banking transactions have been practically simplified since the advent of financial technology. The application of ChatPay, AliPay, PayPal, ApplePay, etc. demonstrated a massive use of mobile phones on hand. The use of chat bots and AI replacing the function of bank tellers. Given the cybernetic innovation, the author asks the question, “what is really happening in the business world today?” The three gentlemen, Karl Marx, Joseph Schumpeter and Clayton Christensen theorized in 1867, 1942 and 2013, respectively, about wealth annihilation, creative destruction and disruptive innovation. By employing a descriptive method using secondary data from the BIS, AFIN, Asean fintech reports and scientific literature, including that of non-parametric statistics for interpretation, the study sought to answer three main questions; first, the interpretation of their theories; second, the effects of disruptive innovation, and third, the association of pseudo-CAML and the related FPIs. The study recommended that the Asean banking should continue with the sustainable banking innovations that would combat the development of fintech transactions in addition to its active Asean integration framework
Sawja: Static Analysis Workshop for Java
Static analysis is a powerful technique for automatic verification of
programs but raises major engineering challenges when developing a full-fledged
analyzer for a realistic language such as Java. This paper describes the Sawja
library: a static analysis framework fully compliant with Java 6 which provides
OCaml modules for efficiently manipulating Java bytecode programs. We present
the main features of the library, including (i) efficient functional
data-structures for representing program with implicit sharing and lazy
parsing, (ii) an intermediate stack-less representation, and (iii) fast
computation and manipulation of complete programs
The Price of Safety in an Active Network
Security is a major challenge for "Active Networking," accessible programmability creates numerous opportunities for mischief. The point at which programmability is exposed, e.g., through the loading and execution of code in network elements, must therefore be carefully crafted to ensure security. The SwitchWare active networking research project has studied the architectural implications of various tradeoffs between performance and security. Namespace protection and type safety were achieved with a module loader for active networks, ALIEN, which carefully delineated boundaries for privilege and dynamic updates. ALIEN supports two extensions, the Secure Active Network Environment (SANE), and the Resource Controlled Active Network Environment (RCANE). SANE extends ALIEN's node protection model into a distributed setting, and uses a secure bootstrap to guarantee integrity of the namespace protection system. RCANE provides resource isolation between active network node users, including separate heaps and robust time-division multiplexing of the node. The SANE and RCANE systems show that convincing active network security can be achieved. This paper contributes a measurement-based analysis of the costs of such security with an analysis of each system based on both execution traces and end-to-end behavior
Skeletons for parallel image processing: an overview of the SKiPPER project
International audienceThis paper is a general overview of the SKIPPER project, run at Blaise Pascal University between 1996 and 2002. The main goal of the SKIPPER project was to demonstrate the appli- cability of skeleton-based parallel programming techniques to the fast prototyping of reactive vision applications. This project has produced several versions of a full-fledged integrated pa- rallel programming environment (PPE). These PPEs have been used to implement realistic vi- sion applications, such as road following or vehicle tracking for assisted driving, on embedded parallel platforms embarked on semi-autonomous vehicles. All versions of SKIPPER share a common front-end and repertoire of skeletons--presented in previous papers--but differ in the techniques used for implementing skeletons. This paper focuses on these implementation issues, by making a comparative survey, according to a set of four criteria (efficiency, expres- sivity, portability, predictability), of these implementation techniques. It also gives an account of the lessons we have learned, both when dealing with these implementation issues and when using the resulting tools for prototyping vision applications
AutoML in heavily constrained applications
Optimizing a machine learning pipeline for a task at hand requires careful configuration of various hyperparameters, typically supported by an AutoML system that optimizes the hyperparameters for the given training dataset. Yet, depending on the AutoML system’s own second-order meta-configuration, the performance of the AutoML process can vary significantly. Current AutoML systems cannot automatically adapt their own configuration to a specific use case. Further, they cannot compile user-defined application constraints on the effectiveness and efficiency of the pipeline and its generation. In this paper, we propose Caml, which uses meta-learning to automatically adapt its own AutoML parameters, such as the search strategy, the validation strategy, and the search space, for a task at hand. The dynamic AutoML strategy of Caml takes user-defined constraints into account and obtains constraint-satisfying pipelines with high predictive performance
AutoML in Heavily Constrained Applications
Optimizing a machine learning pipeline for a task at hand requires careful
configuration of various hyperparameters, typically supported by an AutoML
system that optimizes the hyperparameters for the given training dataset. Yet,
depending on the AutoML system's own second-order meta-configuration, the
performance of the AutoML process can vary significantly. Current AutoML
systems cannot automatically adapt their own configuration to a specific use
case. Further, they cannot compile user-defined application constraints on the
effectiveness and efficiency of the pipeline and its generation. In this paper,
we propose Caml, which uses meta-learning to automatically adapt its own AutoML
parameters, such as the search strategy, the validation strategy, and the
search space, for a task at hand. The dynamic AutoML strategy of Caml takes
user-defined constraints into account and obtains constraint-satisfying
pipelines with high predictive performance
Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study
Medical coding is the task of assigning medical codes to clinical free-text
documentation. Healthcare professionals manually assign such codes to track
patient diagnoses and treatments. Automated medical coding can considerably
alleviate this administrative burden. In this paper, we reproduce, compare, and
analyze state-of-the-art automated medical coding machine learning models. We
show that several models underperform due to weak configurations, poorly
sampled train-test splits, and insufficient evaluation. In previous work, the
macro F1 score has been calculated sub-optimally, and our correction doubles
it. We contribute a revised model comparison using stratified sampling and
identical experimental setups, including hyperparameters and decision boundary
tuning. We analyze prediction errors to validate and falsify assumptions of
previous works. The analysis confirms that all models struggle with rare codes,
while long documents only have a negligible impact. Finally, we present the
first comprehensive results on the newly released MIMIC-IV dataset using the
reproduced models. We release our code, model parameters, and new MIMIC-III and
MIMIC-IV training and evaluation pipelines to accommodate fair future
comparisons.Comment: 11 pages, 6 figures, to be published in Proceedings of the 46th
International ACM SIGIR Conference on Research and Development in Information
Retrieval (SIGIR '23), July 23--27, 2023, Taipei, Taiwa
Turning down the lamp: Software specialisation for the cloud
© USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2010.All right reserved. The wide availability of cloud computing offers an unprecedented opportunity to rethink how we construct applications. The cloud is currently mostly used to package up existing software stacks and operating systems (e.g. LAMP) for scaling out websites. We instead view the cloud as a stable hardware platform, and present a programming framework which permits applications to be constructed to run directly on top of it without intervening software layers. Our prototype (dubbed Mirage) is unashamedly academic; it extends the Objective Caml language with storage extensions and a custom run-time to emit binaries that execute as a guest operating system under Xen. Mirage applications exhibit significant performance speedups for I/O and memory handling versus the same code running under Linux/Xen. Our results can be generalised to offer insight into improving more commonly used languages such as PHP, Python and Ruby, and we discuss lessons learnt and future directions
Special Libraries, November 1912
Volume 3, Issue 9https://scholarworks.sjsu.edu/sla_sl_1912/1008/thumbnail.jp
- …