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Author attribution has grown into an area that is more challenging from the past decade. It has become an inevitable task in many sectors like forensic analysis, law, journalism and many more as it helps to detect the author in every documentation. Here unigram/bigram features along with latent semantic features from word space were taken and the similarity of a particular document was tested using...
A machine translation system converts text from a natural language to other while abiding to the syntax and semantics of the latter. The area of interest here is a Rule Based machine translation system that translates text from English to Malayalam using transfer approach. The system is designed to translate sentences from cricket domain related articles. The purpose behind making the system domain...
E-Commerce products often come with rich and tree-structured content information describing the attributes. To well utilize the content information, this study proposed a fuzzy content matching-based recommendation approach to assist e-Commerce customers to choose their truly interested items. In this paper, users' ratings and preferences are represented using fuzzy numbers to remain uncertainties...
In this paper we investigate the use of fuzzy rule-based classifiers for multi-label classification. This classification task deals with problems where more than one label could be assigned simultaneously to a given instance. We concentrate on problem transformation methods, which use different strategies to transform a multi-label problem into a different single-label classification problems. This...
Scene classification for high-resolution remotely sensed imagery have been widely investigated in recent years. However, there is few public, widely accepted and large scale dataset for benchmarking different methods. This paper presents a new and large dataset consisting of 5000 high-resolution remote sensing images which is manually labeled in 20 semantic classes for scene classification. Each class...
In developing the human-machine technology, it is essentially important to infer human mind state. A machine learning approach is promising to this need. However, the machine-learning approach essentially requires training data, ideally supervised training data, which may not be readily available. An idea is to overcome this shortcoming is to take the so-called subjective rate measure. Take the problem...
This study considers the problem of in-depth document analysis. We propose a new document analysis method, named Multi-Dimensional Linear Discriminant Analysis (MDL-DA), which enables us to formulate an efficient class specific semantic representation of local information from a document with respect to term associations and spatial distributions. MDL-DA works by firstly partitioning each document...
People often need help when filling out paper forms, because they do not fully understand the meaning of form fields. Commonly adopted solutions are referring to the form filling instructions or consulting other people. However, they are either inefficient or inconvenient. In this paper, we propose a situation-aware and interactive system, named Interact Form, to help people fill out paper forms....
This paper reports and discusses on a project about designing a digital tool to support Chinese undergraduate students in reflecting on their English language (L2) learning experience. The tool namely ACTIVE was developed primarily based on a classification framework called A-S-E-R and Latent Semantic Analysis. It can automatically classify reflective L2 learning skills into four elements with each...
Large amounts of available training data and increasing computing power have led to the recent success of deep convolutional neural networks (CNN) on a large number of applications. In this paper, we propose an effective semantic pixel labelling using CNN features, hand-crafted features and Conditional Random Fields (CRFs). Both CNN and hand-crafted features are applied to dense image patches to produce...
In the current social, technological and economic context, customers make their decisions based mostly on the opinion of other consumers. On the other side, companies need quick feedback from their customers in order to adapt to their needs in real time. The effective connection between these two aspects relies on opinion mining tools, which automatically process consumers' reviews and opinions about...
Video-based coaching systems have seen increasing adoption in various applications including dance, sports, and surgery training. Most existing systems are either passive (for data capture only) or barely active (with limited automated feedback to a trainee). In this paper, we present a video-based skill coaching system for simulation-based surgical training by exploring a newly proposed problem of...
With the explosive growth of video data, content-based video analysis and management technologies such as indexing, browsing and retrieval have drawn much attention. Video shot boundary detection (SBD) is usually the first and important step for those technologies. Great efforts have been made to improve the accuracy of SBD algorithms. However, most works are based on signal rather than interpretable...
Comment Analysis for food recipe preferences is to identify user comments on the food recipes to the positive or the negative comments. The proposed method is suitable for analyzing comments or opinions about food recipes by counting the polarity words of food domain. The benefit of this research is to help users to choose the preferred recipes from different food recipes. To analyze food recipes,...
Scalability is one of the main challenges of social media analyses such as sentiment analysis. Micro logs as emerging opinion sharing platforms require new approaches that are more scalable and accurate. In this paper, we propose, implement, and evaluate SSSA, a Semantic Scoring Sentiment Analysis service, which matches the demand of scalability and efficiency of a sentiment analysis system for a...
Semantic annotation of Web Services can facilitate the automated service discovery and composition. At present, however, many solutions suffer from redundant annotations or imprecise derived annotations. The fundamental task to address the issue is to find parameters that have same semantics in a large number of Web Services. This paper proposes a classification based approach for identifying parameters...
We propose a method to extract user attributes from the pictures posted in social media feeds, specifically gender information. While traditional approaches rely on text analysis or exploit visual information only from the user profile picture or colors, we propose to look at the distribution of semantics in the pictures coming from the whole feed of a person to estimate gender. In order to compute...
Location is one of the most valuable and extensively used information in mobile context-aware systems. Its understanding may vary from geolocation that uses GPS infrastructure to locate objects on Earth, up to microlocation, which aims at locating users and objects inside closed areas. Although geolocation can be considered as a mature field, there is an ongoing research in the area of microlocation...
Articulated hand pose recovery in egocentric vision is useful for in-air interaction with the wearable devices, such as the Google glasses. Despite the progress obtained with the depth camera, this task is still challenging with ordinary RGB cameras. In this paper we demonstrate the possibility to recover both the articulated hand pose and its distance from the camera with a single RGB camera in egocentric...
The natural language processing became one of the most important fields of artificial intelligence because is related to the area of human-computer interaction using human languages (natural language generation, question answering, machine translation, etc.) or speech understanding (language modeling).To model the relations between words it is necessary to find the syntactic and semantic relations...
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