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Corporate finance is the backbone of the development, its classical classification is huge and complex. People tend to have more impact on the factors that affect the analysis.,it is subjective and not comprehensive. In this paper, the financial system of the China Company to carry out the logical structure of the index,in view of the complex problem of coupling factors, the algorithm of nonlinear...
Tackling the challenges posed by Social Networkingcontent and addressing its casual nature, n-gram graphstechnique provides a language-independent supervised approach for text mining. Adopting this data analysis model, this paper provides an extended study of sentiment analysis, using a multilingual and multi-topic environment, employing and combining different classification algorithms, and attempting...
Sentiment analysis is a technology with great practical value, it can solve the phenomenon of network comment information disorderly to a certain extent, and accurate positioning of user information required. Currently for Chinese sentiment analysis research is relatively small, including a variety of supervised learning method of classification result and the text feature representation methods and...
It is widely recognized that software process improvement (SPI) engineers need to be trained by software engineering groups to perform quality SPI activities. However, such engineers are required to have a wide range of skills, and therefore it is difficult to properly determine the scope and goal of training courses. To solve such problems, the group companies of Mitsubishi Electric Corporation have...
This paper solves the task of complex objects approximation with a discrete output based on information approach to modeling. We propose a model of fuzzy rules and the inference algorithm on the rules, and describe the neuro-fuzzy model for generation of a knowledge base. The approximation of known data sets and comparison of the results with those of other authors is performed. Examples of knowledge...
Military Simulation and Training (MS&T) Master Plan of Defense Technology Institution (DTI) was intended to build the next generation training simulators. The systems must be integrated and inter-operated under disparate distributed virtual environments. Dealing with single or multi-architecture for simulation environment, it is essential that the best practice system engineering must be employed...
Fast and accurate performance and power prediction is a key challenge in co-development of hardware and software. Traditional analytical or simulation-based approaches are often too inaccurate or slow. In this work, we propose LACross, a novel learning-based, analytical cross-platform prediction framework that provides fast and accurate estimation of time-varying software performance and power consumption...
With the extensive growth of social media services, many users express their feelings and opinions through news articles, blogs and tweets/microblogs. To discover the connections between emotions evoked in a user by varied-scale documents effectively, the paper is concerned with the problem of sentiment analysis over online news. Different from previous models which treat training documents uniformly,...
We present an approach to brand-related Twitter sentiment analysis using feature engineering and the Dynamic Architecture for Artificial Neural Networks (DAN2). The approach addresses challenges associated with the unique characteristics of the Twitter language, and the recall of mild sentiment expressions that are of interest to brand management practitioners. We demonstrate the effectiveness of...
In this paper, we present a framework for automatically analyzing activities and interactions, and recognizing traffic states from surveillance video. Activities and interactions are firstly learned by Hierarchical Dirichlet Process (HDP) models based on low-level visual features. Based on the learning results, a Gaussian Process (GP) classifier is trained to classify the traffic states in online...
With the development of Internet based services, the requirement of keeping keep their vitality and the user viscosity has become an important challenge. Better understanding of users behaviour is an effective way to improve the services lifecycle management. As such analysis of users experience from web log, questionnaire and some other ways have been attached much importance. From previous studies...
With the explosive growth of online multi-media data, methodologies of retrieving documents from heterogeneous modalities are indispensable to facilitate information acquisition in real applications. Most of existing research efforts are focused on building correlation learning models on hand-crafted features for visual and textual modalities. However, they lack the ability to capture the meaningful...
Discrete-event simulation (DES) has been used since the late 1950s. In contrast, agent-based simulation (ABS) is much newer but has been the “hottest” topic in simulation since 2005, despite a lack of agreement on what is an agent or ABS. We carefully define DES and ABS, and discuss their similarities and differences. We argue that emergence is not a fundamental tenet of ABS, as is often suggested...
In this paper, we study the problem of learning from label proportions in which label information of data is provided in bag level. In this kind of problem, training data is grouped into various bags and only the proportions of positive instances is known. Inspired by proportion-SVM, we propose a new classification model based on twin SVM, which is also in a large-margin framework and only needs to...
In this paper, we address the problem of learning object class models from weakly labeled training images, where labels of object classes are only provided at image level. Such weakly supervised object learning can be considered as a Multiple Instance Learning (MIL) problem. We observed that object instances of a common category are visually similar and when characterized as high-dimensional feature...
Many advanced EMG-based upper-limb prosthesis control methods require model training in which subjects produce supervised forces/movements. Since unilateral limb-absent subjects cannot produce forces/movements on their affected side, one technique (mirrored bi-lateral training) relates forces/motions produced on the sound side to EMG on the affected side. However, the efforts made by the phantom limb...
A procedure of the object set typing system of the parameter analytical field and the analyzed object identification profile formation is presented. The expediency of neural network technologies application for arbitrary objects bisubject qualimetric evaluation is substantiated. As an example, we consider the results of modeling the solution of fixing the main employees types and identifying experts,...
Social simulation often concerns the behaviour of humans interacting within some system. Simulation applications are increasingly requiring more realistic and complex human modelling, than reactive rules. We suggest that the established Belief Desire Intention (BDI) approach to modelling cognitive agents, can usefully be applied to modelling humans in social simulations. Traditional social science...
Most marine engine accidents are caused by human errors, such as misperception, misjudgment and misoperation. It is essential to comprehensively analyze human errors occurred during marine engine plant operation, and to investigate more effective measures to prevent the similar errors. The Engine-room Resource Management training has become more important for marine engineers working in a complex...
As a systems-based technique for accident analysis, Accimap involves the construction of a multi-layered diagram in which the various causes of an accident are arranged according to their causal remoteness from the outcome. Though there is systematic research and development for this technique, Accimap is inadequate in terms of the contributory factors identified. This paper presents a modified Accimap...
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