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We develop T2API, a context-sensitive, graph-based statisticaltranslation approach that takes as input an English description of aprogramming task and synthesizes the corresponding API code templatefor the task. We train T2API to statistically learn the alignmentsbetween English and APIs and determine the relevant API elements. Thetraining is done on StackOverflow, which is a bilingual corpus onwhich...
The study of compound-target binding profiles has been a central theme in cheminformatics. For data repositories that only provide positive binding profiles, a popular assumption is all unreported profiles are negative. In this paper, we caution audience not to take such assumption for granted. Under a problem setting where binding profiles are used as features to train predictive models, we present...
The MOOCs (Massive Open Online Courses) represent a category in the frame of TEL (Technology Enhanced Learning) particularly fashionable today since they allow the largest number of learners to access specific teachings. However, the principle of proposing very sequential and linear pedagogical paths is not attractive enough. In fact, the low success rate shows that it is necessary to maintain the...
Performance assessment of human teaming in complex, real-world contexts is a fundamental challenge for research and training communities alike. We highlight a unique partnership between the cybersecurity training and research communities with the common goal of capturing human team performance. Whether in the context of a training assessment or a research endeavor; both are two sides of the same coin...
Despite the growing demand by Brazilian companies for well-qualified professionals in Information Technology (IT), the Brazilian educational system has not been able to meet this demand in a satisfactory way, especially the quality of vocational training question. One way to address this training problem has been the creation of specialized postgraduate courses in IT area focused on students already...
We propose several new concepts for providing enhanced explanations of classifier decisions in linguistic (human readable) form. These are intended to help operators to better understand the decision process and support them during sample annotation to improve their certainty and consistency in successive labeling cycles. This is expected to lead to better, more consistent data sets (streams) for...
People often under or over-estimate their performance on interactive learning experiences with virtual agents, especially when it comes to interpersonal skills such as empathy. To generate more accurate self-assessment of performance, we propose the use of in-action reflective learning opportunities during interactive learning experiences with virtual agents. We conducted a user study in which third-year...
Ordinal input variables are common in many supervised and unsupervised machine learning problems. We focus on ordinal classification problems, where the target variable is also categorical and ordinal. In order to represent categorical input variables for measuring distances or applying continuous mapping functions, they have to be transformed to numeric values. This paper evaluates five different...
Integrating technology in pedagogy is a step for ICT capacity building for higher education to meet its current demands. Therefore, the integration of eLearning systems has been problematic, albeit huge investments in ICT infrastructure. This study investigates teacher adoption of a new upgraded eLearning platform being integrated at University of Rwanda. A six-constructs model related to technology...
Digital intercultural training tools play an important role in helping people to mediate cultural misunderstandings. In recent years, these tools were made to teach about specific cultures, but there has been little attention for the design of a tool to teach about differences across a wide range of cultures. In this work, we take the first steps to create a digital self-contained culture-general...
Smart home is one of the most important applications of ubiquitous computing. In this work, we propose an infrastructure of Vietnamese Smart homes as well as a training framework for activity recognition and forecast. In this framework, active learning technique is applied and a new mining algorithm is proposed. In addition to activity recognition, a forecast mechanism is also added into the smart...
Multi-document summarization has gained popularity in many real world applications because significant information can be obtained within a short time. Extractive summarization aims to generate a summary of a document or a set of documents by ranking sentences, whose performance relies heavily on the quality of sentence features. However, almost all previous algorithms require hand-crafted features...
In order to perform well, an engineer has not only to solve a task but also cope with the environment. This is very important in military operations, where underperforming might have fatal consequences. As a military academy, we have developed ten coping strategies that we use to increase students' ability to perform in military situations. These strategies have been introduced in the education, but...
The influence of the missing values in the classification of incomplete pattern mainly depends on the context. In this paper, we present a fast classification method for incomplete pattern based on the fusion of belief functions where the missing values are selectively (adaptively) estimated. At first, it is assumed that the missing information is not crucial for the classification, and the object...
Cyber criminals use phishing emails in high-volume and spear phishing emails in low volume to achieve their malicious objectives. Hereby they inflict financial, reputational, and emotional damages on individuals and organizations. These (spear) phishing attacks get steadily more sophisticated as cyber criminals use social engineering tricks that combine psychological and technical deceptions to make...
We recently demonstrated that the gaze sequences produced by infants during an habituation event predict their looking times to both the habituation and (one of two) posthabituation test events [1]. Specifically, we trained a simple recurrent network (SRN) to predict infants' habituation gaze sequences. Sequences that were easier for the SRN to learn were associated with shorter looking times at the...
The sequential covering strategy has been and still is a very common way to develop rule learning algorithms. This strategy follows a greedy procedure to learn rules, where, after each step one rule is obtained. Recently, we proposed a new sequential covering strategy that allowed the review of previously learned knowledge during the learning process itself. This review of knowledge allowed the algorithm...
We have created and already experienced with a high degree of success a training system specially designed to tackle the problems of computer illiteracy derived from the lack of proper educational resources. We have developed video tutorials that allow our beneficiaries to gain confidence in the use of computers and understand how computers can help them in their daily lives. The success of the system...
We propose a new unsupervised method to identify Named Entities (NE) in resource-poor languages. The idea is to transfer the knowledge of NEs from a resource-rich language to a resource-poor one by using a bilingual parallel corpus of this language pair. After extracting all NE pair candidates and filtering these candidates (includes lexical and contextual filters) to obtain a high precision seed...
Police officers are often confronted with different unexpected and untrained scenarios and have to respond adequately. To prepare officers for situations that cannot be trained in reality because of high costs, danger, time or effort involved, virtual training seems to be the obvious choice. This paper explicates a theory-driven design process of a virtual training environment and its application...
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