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Representation learning algorithm in medical area maps high dimensional real world medical concepts to low dimensional vector space, encodes rich medical knowledge, and has brought improvement to various machine learning applications in medical area. However, previous representation learning models in medical area failed to consider the multi-sense characteristic of medical concept. Moreover, the...
Enterprise Modeling Languages (EMLs) are generally perceived as conceptual modeling languages having a formal syntax and informal semantics. The non-formality of semantics is mainly caused by the materiality of the addressed domain (enterprises and its related aspects) and the resulting personal interpretation of syntactical constructs. However, EMLs may also explicitly define invariant interpretations...
Meta-heuristics have emerged as an efficient way to solve NP-hard problems even without the guaranteed of optimal values. The main issue of meta-heuristics is that they are built using domain-specific knowledge. Therefore, they require a great effort to be adapted to a new domain. The concept of Hyper-heuristic was proposed to solve this problem. Hyper-heuristics are search methods that aim to solve...
The transition from a requirements document to a formal specification in Event-B is usually manual and ad-hoc. In order to bridge this gap, we propose a method based on Behavior-Driven Development, an agile approach, and that uses a structured natural language conformant to the formalism of the Semantics of Business Vocabulary and Business Rules (SBVR) standard. This method will successively refine...
Sequence to sequence (seq2seq) prediction is a key to many tasks of machine learning. Personal computer software sequence, as one of these tasks, was regarded as stochastic and unpredictable in the past. However, the deep neural networks (DNNs) have achieved excellent performance recently in sequence to sequence tasks, especially in the field of natural language process (NLP) such as language model,...
Recurrent neural networks are represented as non-linear models of dynamic systems. This kind of neural networks is divided into two groups, which are globally and locally recurrent neural networks. Some types are distinguished among globally recurrent networks. The major approximation properties and features of every distinguished type are emphasized. The represented analysis is useful for choosing...
Regression-based tasks have been the forerunner regarding the application of machine learning tools in the context of data mining. Problems related to price and stock prediction, selling estimation, and weather forecasting are commonly used as benchmarking for the comparison of regression techniques, just to name a few. Neural Networks, Decision Trees and Support Vector Machines are the most widely...
Word embedding in the NLP area has attracted increasing attention in recent years. The continuous bag-of-words model (CBOW) and the continuous Skip-gram model (Skip-gram) have been developed to learn distributed representations of words from a large amount of unlabeled text data. In this paper, we explore the idea of integrating extra knowledge to the CBOW and Skip-gram models and applying the new...
Identifying important nodes in complex networks is a fundamental problem in network analysis. Although a plethora of measures has been proposed to identify important nodes in static (i.e., time-invariant) networks, there is a lack of tools in the context of temporal networks (i.e., networks whose connectivity dynamically changes over time). The aim of this paper is to propose a system-theoretic approach...
Due to the popularity of context-aware computingand the rapid growth of the smart phone devices, modeling anindividual's phone call response behavior may assist them intheir daily activities for managing call interruptions. A key stepof such modeling is to discovering call response behavioral rulesbased on multi-dimensional contexts related to individual'sbehavior. Currently, researchers use classification...
It is critical for automatic chat-bots to gain the ability of conversation comprehension, which is the essence to provide context-aware responses to conduct smooth dialogues with human beings. As the basis of this task, conversation modeling will notably benefit from the background knowledge, since such knowledge indeed implicates semantic hints that help to further clarify the relationships between...
This paper considers the event-triggered leader-follower tracking control for interconnected systems. Unlike the large body of existing work, here we consider systems which are physically coupled with uncertain norm-bounded coupling between the subsystems. We propose a model based event-triggered tracking control strategy and an event triggering rule with a time-dependent threshold which guarantees...
We consider a special class of monotone systems for which the system equations are also convex in both the state and the input. For such systems we study optimal infinite horizon operation with respect to an objective function that is also monotone and convex. The main results state that, under some technical assumptions, these systems are optimally operated at steady state, i.e. there does not exist...
The past ten years has seen increasing calls to makesecurity research more "scientific".On the surface, most agree that this is desirable, given universal recognition of "science" as a positive force. However, we find that there is little clarity on what "scientific" means inthe context of computer security research, or consensus onwhat a "Science of Security"...
While the term "vulnerability" is widespread in the protection of the critical infrastructures, there is still a gap between its meanings according to the different perspectives from which the security problem is viewed. Cyber and physical notions of vulnerability are different notwithstanding the scientific community has underlined the importance to deal with these two aspects in a unified...
Despite the growth and commercial potential of instant messaging service, little is known about what factors create users' satisfaction with a particular service application. In this context, the major contribution is to examine the sources of users' satisfaction with WeChat official account. For this purpose, combining Information System Success theory, Theory of Reasoned Action and Technology Acceptance...
In order to understand information transmission, it is highly important to find a hidden structure of a social network such as rumors in daily life, and leaders of the underground organizations. The information above is difficult to be forecasted because such information can only be obtained in fragments and cannot be achieved through observation itself based on the data. This research aims to clarify...
This study empirically tests the model of the Technology Threats Avoidance Theory (TTAT) in a healthcare context to investigate health information technology (HIT) avoidance behaviors when used in unsecure environment. Testing the model in this new context explained avoidance behaviors towards HIT in a holistic way. It is found that the HIT avoidance is predicted by levels of perceived threat, avoidance...
The application of information retrieval techniques to search tasks in software engineering is made difficult by the lexical gap between search queries, usually expressed in natural language (e.g. English), and retrieved documents, usually expressed in code (e.g. programming languages). This is often the case in bug and feature location, community question answering, or more generally the communication...
The “Sequentially Drilled” Joint Congruence (SeDJoCo) transformation is a set of matrix transformation equations, which coincide with the Likelihood Equations for semi-blind source separation, when each source is modeled as a zero-mean Gaussian process with a known (and distinct) temporal covariance matrix. Therefore, with such a model a solution of SeDJoCo can lead to the Maximum Likelihood (ML)...
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