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This paper describes a latent space understandable network: Self- excited Generative Adversarial Network (Self- ExGAN), a novel self- excited structure based on adversarial learning. Compared with the conventional generative adversarial networks, SelfExGAN consists of three components, which are en- coder (E), generator (G), and discriminator (D). Different from other papers which directly apply reconstruction...
Class imbalance exists in many applications of bioinformatics and biomedicine, while dimension reduction in the feature space is often needed when building prediction models on a dataset. When the above two issues need to be considered simultaneously for skewed/imbalanced datasets, practitioners and researchers in machine learning may raise the following question: should feature selection be conducted...
The hashtag recommendation problem addresses recommending (suggesting) one or more hashtags to explicitly tag a post made on a given social network platform, based upon the content and context of the post. In this work, we propose a novel methodology for hashtag recommendation for microblog posts, specifically Twitter. The methodology, EmTaggeR, is built upon a training-testing framework that builds...
As an SNS, Twitter is popular because users can post their emotions as a short message easily. Emotional tweets may influence user relationships. In our previous study, we found that positive users construct mutual relationships in Twitter. Keyword matching with emotional word dictionaries was used to detect positive users. The problem of keyword matching is the limitation of word number. To solve...
Multi-objective optimization plays an important role when one has fitness functions that are somehow conflicting with each other. Also, parameter-dependent machine learning techniques can benefit from such optimization tools. In this paper, we propose a multi-objective-based strategy approach to build compact though representative training sets for Optimum-Path Forest (OPF) learning purposes. Although...
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification performance. A novel Chinese spam classification method based on weighted distributed feature is proposed by combining the features of TF-IDF weighted algorithm with the distributed text-based...
Automatic grading systems, such as WebWork, are becoming much more widely used as they relieve the instructor from needing to grade student work, provide students with automatic feedback, and can allow for immediate resubmission. They have also been shown to improve the effectiveness of teaching and learning. In this paper, we apply Item Response Theory (IRT) to a large WebWork Calculus homework dataset...
MU-MIMO beamforming offers great potential for the AP with multiple antennas to serve multiple receivers concurrently. A key factor to implement the MU-MIMO system is the CSI feedback mechanism. However, it might be exploited by malicious attackers to threaten the transmission security of benign clients. To better explore the attacks through false CSI feedback in MU-MIMO systems, this paper proposes...
Mislabeled examples are difficult to avoid while building large scale datasets. In this paper we discuss an efficient approach for finding those mislabeled examples. Our approach involves selecting a small number of potentially mislabeled examples for review by an expert. We demonstrate the utility of our method by finding some mislabeled examples in one large scale dataset (ImageNet). We found 92...
There has been a phenomenal increase in the utility of text classification (TC) in applications like targeted advertisement and sentiment analysis. Most applications demand that the model be efficient and robust, yet produce accurate categorizations. This is quite challenging as their is a dearth of labelled training data because it requires assigning labels after reading the whole document. Secondly,...
E-learning is the application of IT and Internet in education to make it easier, spacious, and more efficient. Advantages of e-learning are recognized, but its impact on learning achievement and knowledge transferring are not confirmed clearly. Learning is considered the skills of students and knowledge gained through experience in the training process. Learning achievement has been defined as students'...
Breast cancer (BC) is a deadly disease, killing millions of people every year. Developing automated malignant BC detection system applied on patient's imagery can help dealing with this problem more efficiently, making diagnosis more scalable and less prone to errors. Not less importantly, such kind of research can be extended to other types of cancer, making even more impact to help saving lives...
Beyond providing alternatives to build rubrics, a group of researchers in Tecnológico de Costa Rica found the need to implement best practices in the definition and validation of evaluation rubrics, to encourage the creation of a Higher Education bank of assessment instruments. To achieve this goal, in the Tecnológico de Costa Rica, a teacher training process was established, which includes cooperative-collaborative...
We present work-in-progress reflecting on the initial year of a distinctive summer Research Experiences for Undergraduates (REU) program. Our REU model combines fundamental research in computational sensing with a scholarly context that connects computer science with computational liberal arts. Students are intellectually stimulated to make sense of people's behaviors and cognitive processes with...
The term gamification is a relatively new concept, but the use of games for solving a variety of problems is not a new phenomenon. Gamification is used in many industries such as marketing, politics, health, eniviroment. In education, gamification) is used as a tool to strengthen e-learning systems, motivate students to learn more effectively and engage more in the learning process. Moreover, games...
During the last decade, several Internet of Things (IoT) applications has been developed to facilitate machine-to-human and machine-to-machine communication with the physical world by integrating both digital and physical entities through the internet. However, multiple important challenges need to be addressed in order to take the full advantage of these applications. One of the most important of...
Background: Telecentre implementation in developing countries such as Ghana, has been inundated with failure. Various methods have been proposed to mitigate these failures, considering the substantial amounts invested. Various methods have been proposed. Recent work has shown that case-based reasoning (CBR) can be used to predict sustainability of a telecentre. Unfortunately, the factors that play...
The aim of this paper is to compare two different types of filter for the diving video processing. The two filters are a boolean filter and a fuzzy filter. These filters are applied for improving the diving video analysis aimed to introduce quantitative tools and diving performance measurement and therefore to improve training. The aim of the filter is to identify the athlete in the video to further...
As a special kind of software, spreadsheets have been evolving during their life cycle. Understanding spreadsheet evolution can help facilitate spreadsheet design, maintenance and fault detection. However, understanding spreadsheet evolution is challenging in practice. There are many factors that hinder spreadsheet evolution comprehension, such as, lack of version information, complicated structure...
Generative Adversarial Networks (GANs) are efficient frameworks for estimating generative model via adversarial process. However, GAN has known for suffering from training instability. Wasserstein GAN (WGAN) improves the training stability significantly but also brings an additional Lipschitz requirement for the critic network. To enforce the Lipschitz constraint, instead of weight clipping strategy,...
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