The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Essay assessment within e-learning need to be conducted manually by human expert. This process takes time and costly. Hence, automatic essay scoring is needed. Since the scoring system will be integrated to the e-learning, we need a computationally lightweight method that still does not rule out the accuracy of the assessment. In this paper, we propose an automatic scoring system for essay examination...
Intrusion Detection Systems (IDS) are security tools that generate alerts when detecting a malicious activity. The main drawback of IDS is the high number of generated alerts. We propose an approach that integrates the knowledge of several security experts to improve IDS results and reduce the alerts number. The experts' knowledge are expressed in IFO (Instantiated First Order) logic. A new logical...
In recent years, the use of intelligent platform and the artificial intelligence methods in fault treatment are increasing rapidly with the development of intelligent equipment. In this paper, a novel diagnosis method based on cosine similarity and fuzzy semantic inference for intelligent IETM (Interactive Electronic Technical Manual) platform is investigated. The proposed diagnosis method is composed...
Addressing the problem of information overload, automatic multi-document summarization (MDS) has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of MDS systems. In this paper, we proposed a novel unsupervised pattern-enhanced topic model (PETMSum) for the MDS task. PETMSum combining pattern...
A historically important tradition in exegesis, rooted in a number of scriptural passages, considers the Qur'an to be a self-similar text. This claim, while being sharply debated in literature, has never been independently tested. This paper proposes a strategy to measure self-similarity in classical Arabic texts, based on Leven-shtein distance, within the Self-Similar Qur'an (SSQ) project. The significance...
Research is proposed for improving the human-interpretability of topic models; specifically, for topic models of small sequential documents. Experiments are proposed for evaluating the usefulness of topic modeling. The proposed experiments will model the topics of a diverse set of social media content and attempt to correlate the presence of topics related to terror attacks with actual attacks; additionally,...
Sketch-based image retrieval (SBIR) has become a prominent research topic in recent years due to the proliferation of touch screens. The problem is however very challenging for that photos and sketches are inherently modeled in different modalities. Photos are accurate (colored and textured) depictions of the real-world, whereas sketches are highly abstract (black and white) renderings often drawn...
Semantic computing is one of the important and indispensable approaches to analyze various kinds of environmental phenomena and its changes in the real world. In this paper, we present “A Seawater-Quality Analysis Semantic-Space in Hawaii-Islands with Multi-Dimensional World Map System” to realize a global and environmental analysis for ocean environment with the multi-dimensional world map system...
A large number of images are available on online photo-sharing services along with rich meta-data, including tags, groups, and locations, etc. For associating two domains of different modalities, e.g. images and tags, Canonical Correlation Analysis (CCA) and its extended methods are used widely. We employ a more flexible graph embedding method called Cross-Domain Matching Correlation Analysis (CDMCA),...
In this paper we propose an online multi-task learning algorithm for video concept detection. In particular, we extend the Efficient Lifelong Learning Algorithm (ELLA) in the following ways: a) we solve the objective function of ELLA using quadratic programming instead of solving the Lasso problem, b) we add a new label-based constraint that considers concept correlations, c) we use linear SVMs as...
Convolutional Neural Networks (CNNs), which have nowadays dominated image analysis tasks, constitute feed-forward methods that model increasingly complex data structures and patterns along the subsequent hidden layers of the network. However, the common practice of using the activation features from the last network layer inevitably leads to a visual recognition bottleneck. This is due to the fact...
The recent decade has witnessed remarkable developments of SIFT-based approaches for image retrieval. However, such approaches are inherently insufficient in handling the semantic gap and large viewpoint changes, leading to inferior performance. To address these limitations, this paper extends SIFT-based match kernels by integrating the match functions for SIFT and CNN features. Specifically, a thresholded...
This paper presents a novel approach to detecting crowd groups and learning semantic regions with a Gestalt laws-based similarity. Different from the existing approaches based on optical flows or complete trajectories, our model adopts tracklets as the original input, because they carry more detailed information. Though those tracklets do not appear in the same duration, they are more robust to noise...
Over the recent years, there has been a growing interest in developing new research evaluation methods that could go beyond the traditional citation-based metrics. This interest is motivated on one side by the wider availability or even emergence of new information evidencing research performance, such as article downloads, views and Twitter mentions, and on the other side by the continued frustrations...
A number of computational techniques have been proposed that aim to detect mimicry in online conversations. In this paper, we investigate how well these reflect the prevailing cognitive science model, i.e. the Interactive Alignment Model. We evaluate Local Linguistic Alignment, word vectors, and Language Style Matching and show that these measures tend to show the features we expect to see in the...
Among most existing methods of multi-lingual text clustering, the dictionary-based approaches did not effectively deal with ambiguity and polysemy problem, the approaches based on machine translation inevitably introduced noise, Cross-lingual latent semantic indexing (CL-LSI) does not fully take into account bilingual semantic relationship. The paper proposes a new method based on Bilingual Semantic...
Patent retrieval is important for technology survey and knowledge protection. Its aim is to search as many patent documents relevant to the patent document query as possible, which is considered as a recall-oriented task. However, existing methods suffer from the term mismatch problem caused by the frequent use of many non-standard technical terminologies in patents. To address the issue, we present...
Recent developments in social media and cloud storage lead to an exponential growth in the amount of multimedia data, which increases the complexity of managing, storing, indexing, and retrieving information from such big data. Many current content-based concept detection approaches lag from successfully bridging the semantic gap. To solve this problem, a multi-stage random forest framework is proposed...
Word clouds are popular graphical representations used to effectively summarize the core content of a document, or of a set of documents. In particular, in a semantic word cloud the positions of the words also reflect their semantic correlation. Several algorithms have been proposed in the literature to compute semantic word clouds, both in a static and in a dynamic setting. However, so far, solutions...
Mining user reviews to discover what the user likes and dislikes is vital to understanding user behaviors. Topic modeling techniques have been extensively used to discover meaningful topics for user reviews and to discover user behaviors. Extracted topics may be a mixture of different concepts and hence very likely to be less coherent and unclear, especially when extracting a relatively small number...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.