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A hybrid supervised semi-supervised graph-based model to predict one-day ahead movement of global stock markets and commodity prices
, Article Expert Systems with Applications ; Volume 105 , 2018 , Pages 159-173 ; 09574174 (ISSN) ; Haratizadeh, S ; Bagheri Shouraki, S ; Sharif University of Technology
Abstract
Market prediction has been an important machine learning research topic in recent decades. A neglected issue in prediction is having a model that can simultaneously pay attention to the interaction of global markets along historical data of the target markets being predicted. As a solution, we present a hybrid supervised semi-supervised model called HyS3 for direction of movement prediction. The graph-based semi-supervised part of HyS3 models the markets global interactions through a network designed with a novel continuous Kruskal-based graph construction algorithm called ConKruG. The supervised part of the model injects results extracted from each market's historical data to the network...
Generating summaries for methods of event-driven programs: An Android case study
, Article Journal of Systems and Software ; Volume 170 , 2020 ; Izadi, M ; Heydarnoori, A ; Sharif University of Technology
Elsevier Inc
2020
Abstract
The lack of proper documentation makes program comprehension a cumbersome process for developers. Source code summarization is one of the existing solutions to this problem. Many approaches have been proposed to summarize source code in recent years. A prevalent weakness of these solutions is that they do not pay much attention to interactions among elements of software. An element is simply a callable code snippet such as a method or even a clickable button. As a result, these approaches cannot be applied to event-driven programs, such as Android applications, because they have specific features such as numerous interactions between their elements. To tackle this problem, we propose a novel...
Improved K2 algorithm for Bayesian network structure learning
, Article Engineering Applications of Artificial Intelligence ; Volume 91 , 2020 ; Beigy, H ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
In this paper, we study the problem of learning the structure of Bayesian networks from data, which takes a dataset and outputs a directed acyclic graph. This problem is known to be NP-hard. Almost most of the existing algorithms for structure learning can be classified into three categories: constraint-based, score-based, and hybrid methods. The K2 algorithm, as a score-based algorithm, takes a random order of variables as input and its efficiency is strongly dependent on this ordering. Incorrect order of variables can lead to learning an incorrect structure. Therefore, the main challenge of this algorithm is strongly dependency of output quality on the initial order of variables. The main...
LESS-MICS: A low energy standby-sparing scheme for mixed-criticality systems
, Article IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ; Volume 39, Issue 12 , 2020 , Pages 4601-4610 ; Hessabi, S ; Ershadi, G ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Multicore platforms are becoming the dominant trend in mixed-criticality systems (MCSs). Multicores provide great opportunities to realize task-level redundancy for reliability enhancement. However, they may experience limited utility in battery-powered mixed-criticality embedded systems. Hence, joint energy and reliability management is a crucial issue in designing MCSs. In this article, we propose the low energy standby-sparing mechanism in mixed-criticality system (LESS-MICS) scheme, which uses the inherent redundancy of multicores to apply the standby-sparing technique for fault-tolerance. Also, by using the inherent redundancy, the LESS-MICS scheme proposes the Parallelism and Reduction...