68 research outputs found
Benchmarking Symbolic Execution Using Constraint Problems -- Initial Results
Symbolic execution is a powerful technique for bug finding and program
testing. It is successful in finding bugs in real-world code. The core
reasoning techniques use constraint solving, path exploration, and search,
which are also the same techniques used in solving combinatorial problems,
e.g., finite-domain constraint satisfaction problems (CSPs). We propose CSP
instances as more challenging benchmarks to evaluate the effectiveness of the
core techniques in symbolic execution. We transform CSP benchmarks into C
programs suitable for testing the reasoning capabilities of symbolic execution
tools. From a single CSP P, we transform P depending on transformation choice
into different C programs. Preliminary testing with the KLEE, Tracer-X, and
LLBMC tools show substantial runtime differences from transformation and solver
choice. Our C benchmarks are effective in showing the limitations of existing
symbolic execution tools. The motivation for this work is we believe that
benchmarks of this form can spur the development and engineering of improved
core reasoning in symbolic execution engines
RecXplainer: Post-Hoc Attribute-Based Explanations for Recommender Systems
Recommender systems are ubiquitous in most of our interactions in the current
digital world. Whether shopping for clothes, scrolling YouTube for exciting
videos, or searching for restaurants in a new city, the recommender systems at
the back-end power these services. Most large-scale recommender systems are
huge models trained on extensive datasets and are black-boxes to both their
developers and end-users. Prior research has shown that providing
recommendations along with their reason enhances trust, scrutability, and
persuasiveness of the recommender systems. Recent literature in explainability
has been inundated with works proposing several algorithms to this end. Most of
these works provide item-style explanations, i.e., `We recommend item A because
you bought item B.' We propose a novel approach, RecXplainer, to generate more
fine-grained explanations based on the user's preference over the attributes of
the recommended items. We perform experiments using real-world datasets and
demonstrate the efficacy of RecXplainer in capturing users' preferences and
using them to explain recommendations. We also propose ten new evaluation
metrics and compare RecXplainer to six baseline methods.Comment: Awarded the Best Student Paper at TEA Workshop at NeurIPS 2022. 13
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The blue plug : a power consumption regulation system
Our goal is to develop a system that reduces peak grid loads by 60%, which saves consumers and businesses money as well as protecting the environment by eliminating the use of fast start up generators. We accomplish this by implementing Arduinos --which are versatile microcontrollers-- equipped with networking technology that allows communication between houses in a neighborhood. Through this communication, we can schedule when appliances are used to avoid everyone using their appliances at the same time and we can eliminate the need for such exorbitant energy usage during peak hours. Our work implements ongoing research that is looking into cost-effective and environmentally friendly grid management
P2P Computing Concepts and Their Discussions
Peer-to-peer is a technology concept applied at different levels of the systems architecture. Its main characteristics are direct interaction and data exchange between peer systems. It is the basis for decentralized distributed computing. The concept is widely deployed in different contexts and no formal definition exists. This paper gives an overview of the different areas peer-to-peer technology is used and introduces the main characteristics of peer-to-peer systems. It also discusses the issues and problems encountered when deploying peer-to-peer technology
A Comparative Analysis of Machine Learning Models for Banking News Extraction by Multiclass Classification With Imbalanced Datasets of Financial News: Challenges and Solutions
Online portals provide an enormous amount of news articles every day. Over the years, numerous studies have concluded that news events have a significant impact on forecasting and interpreting the movement of stock prices. The creation of a framework for storing news-articles and collecting information for specific domains is an important and untested problem for the Indian stock market. When online news portals produce financial news articles about many subjects simultaneously, finding news articles that are important to the specific domain is nontrivial. A critical component of the aforementioned system should, therefore, include one module for extracting and storing news articles, and another module for classifying these text documents into a specific domain(s). In the current study, we have performed extensive experiments to classify the financial news articles into the predefined four classes Banking, Non-Banking, Governmental, and Global. The idea of multi-class classification was to extract the Banking news and its most correlated news articles from the pool of financial news articles scraped from various web news portals. The news articles divided into the mentioned classes were imbalanced. Imbalance data is a big difficulty with most classifier learning algorithms. However, as recent works suggest, class imbalances are not in themselves a problem, and degradation in performance is often correlated with certain variables relevant to data distribution, such as the existence in noisy and ambiguous instances in the adjacent class boundaries. A variety of solutions to addressing data imbalances have been proposed recently, over-sampling, down-sampling, and ensemble approach. We have presented the various challenges that occur with data imbalances in multiclass classification and solutions in dealing with these challenges. The paper has also shown a comparison of the performances of various machine learning models with imbalanced data and data balances using sampling and ensemble techniques. From the result, it’s clear that the performance of Random Forest classifier with data balances using the over-sampling technique SMOTE is best in terms of precision, recall, F-1, and accuracy. From the ensemble classifiers, the Balanced Bagging classifier has shown similar results as of the Random Forest classifier with SMOTE. Random forest classifier's accuracy, however, was 100% and it was 99% with the Balanced Bagging classifier
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