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We performed an experimental study (n=48) of the effects of context congruency on human perceptions of robotic facial expressions across cultures (Western and East Asian individuals). We found that context congruency had a significant effect on human perceptions, and that this effect varied by the emotional valence of the context and facial expression. Moreover, these effects occurred regardless of...
Recent years have brought a symbolic growth in the volume of research in Sentiment Analysis, mostly on highly subjective text types like movie or product reviews. The main difference these texts have with news articles is that their target is apparently defined and unique across the text. Thence while dealing with news articles, we performed three subtasks namely identifying the target; separation...
Customer reviews are important sources of opinions of customers. The reviews contain some sentence pattern and some sort of patterns will be useful to extract the opinions of the customers easily. We propose a method to classify customer reviews using estimation expression patterns by support vector machine. We confirm an effectiveness of the proposed method by experimental results.
Studies about human interaction have shown that subtle changes in movement performance and body posture may improve people's acceptance in social groups. The same applies also to robots. However, most of the work has been done on faces and bio-inspired or humanoid robots, while still few works have focused on generic robot body movement to produce interesting interaction settings, which include emotion...
In this paper, we propose a method for conversation summarization. For the method, we combine two approaches, a scoring method and a machine learning technique (SVMs). First we compare important utterance extraction by the scoring method and SVMs. In the machine learning technique, we introduce verbal features, such as relations between utterances and anaphora features, and nonverbal features. Next...
In this paper we propose a twitter sentiment analytics that mines for opinion polarity about a given topic. Most of current semantic sentiment analytics depends on polarity lexicons. However, many key tone words are frequently bipolar. In this paper we demonstrate a technique which can accommodate the bipolarity of tone words by context sensitive tone lexicon learning mechanism where the context is...
In this paper, we present a novel self-maintaining, domain-independent, and context-sensitive Sentiment Lexicon (SL) which finds and maps opinion words and phrases to a fuzzy sentiment score ranging from strong negative to strong positive. We show that our automatically built SL has advantage over other already existing lexicons in various aspects, namely, reducing the number of word false-matches...
We explore a novel avenue for generating absorbing boundary conditions for wave problems. The key part of our approach is Trefftz approximations of the solution, i.e. approximations by functions satisfying locally the underlying wave equation. Trefftz functions include outgoing waves only (and possibly evanescent waves), but no incoming waves. We show how this idea can be applied in three different...
Analyzing and researching microblog text and microblog context, we propose a Chinese microblog's semantic expansion model based on specific context. Firstly, on the basis of analysis of microblog type, microblogs are classified into six categories: information-publish microblogs, journaling microblogs, sharing microblogs, forwarding microblogs, commentary microblogs and interactive microblogs. For...
Context provides additional information in detection and tracking and several works proposed online trained trackers that make use of the context. However, the context is usually considered during tracking as items with motion patterns significantly correlated with the target. We propose a new approach that exploits context in tracking-by-detection and makes use of persistent false positive detections...
One of the biggest challenges faced by law enforcement entities in the present digital era, is fighting against online Child Sexual Abuse (CSA), due in particular to the massive amount of data that they receive for analysis. Pattern recognition system can provide an aid, e.g., to ease the identification of both the perpetrator and the victim of the crime. In particular, ancillary cues related the...
Context information has been widely studied for recognizing collective activities. Most existing works assume that all individuals in a single image share the same activity label. However, in many cases, multiple activities can be coexisted and serve as the context for each other in real-world scenarios. Based on this observation, we propose a novel approach to model both the intra-class and inter-class...
We propose in this paper a framework for the segmentation and classification of document streams. The framework is composed of two modules: segmentation and verification. The two modules use an incremental classifier which learns progressively along the stream. In the segmentation module a relationship between two consecutive pages is classified as either: continuity or rupture. Rupture is synonymous...
Unlike the traditional recommender systems, that make recommendations only by using the relation between user and item, a context-aware recommender system makes recommendations by incorporating available contextual information into the recommendation process as explicit additional categories of data to improve the recommendation process. In this paper, we propose to use contextual information from...
In the task of action recognition, object and scene can provide rich source of contextual information for analyzing human actions, as human actions often occur under particular scene settings with certain related objects. Therefore, we try to utilize the contextual object and scene for improving the performance of action recognition. Specifically, a latent structural SVM is introduced to build the...
In this work we consider a machine learning setting where data are represented as graphs. First, we derive a kernel function which evaluates the similarity between graphs, while capturing pair-wise constraints between graph nodes. Second, we apply it to the problem of classifying collective activities: on this respect we first represent groups of people located in a spatial neighborhood as graphs,...
In this paper, we present a novel signature matching method based on supervised topic models. Shape Context features are extracted from signature shape contours which capture the local variations in signature properties. We then use the concept of topic models to learn the shape context features which correspond to individual authors. The approach consists of three primary steps. First, K-means is...
The number of applications available since the late 2010's, and the number of smartphone user sharply increasing. However, not all applications are not useful or helpful. In other words, to obtain satisfactory results in the search can be difficult means. Users to find what they want to search for a many times. To solve this problem, previous studies have proposed the use of recommender systems. Most...
In this study we evaluate the potential of local binary descriptors for automatic sorting in an industrial context. This problem is different from that of retrieval for human handling as we need to identify the one correct class, rather than finding all the similar classes. We have looked at classes of objects that need to be identified by their cover or label, rather than their shape. Challenges...
In this paper, we show how pedestrian detection accuracy and efficiency can be improved for static surveillance cameras using scene context and temporal non-maximal suppression. First, using the geometry of the scene, we derive the relationship between height in the image with respect to height in the real-world. This relationship is used to learn the range of scales to evaluate for potential detections...
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