Search results for: spatialdistribution of labels

Authors: Tetsuya Furukawa, Masahiro Kuzunishi

Abstract:

Collected data must be organized to be utilized efficiently, and hierarchical classification of data is efficient approach to organize data. When data is classified to multiple categories or annotated with a set of labels, users request multi-labeled data by giving a set of labels. There are several interpretations of the data expressed by a set of labels. This paper discusses which data is expressed by a set of labels by introducing orders for sets of labels and shows that there are four types of orders, which are characterized by whether the labels of expressed data includes every label of the given set of labels within the range of the set. Desirable properties of the orders, data is also expressed by the higher set of labels and different sets of labels express different data, are discussed for the orders.

Keywords: Classification Hierarchies, Multi-labeled Data, Multiple Classificaiton, Orders of Sets of Labels

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Authors: Sarka Velcovska

Abstract:

The paper deals with quality labels used in the food products market, especially with labels of quality, labels of origin, and labels of organic farming. The aim of the paper is to identify perception of these labels by consumers in the Czech Republic. The first part refers to the definition and specification of food quality labels that are relevant in the Czech Republic. The second part includes the discussion of marketing research results. Data were collected with personal questioning method. Empirical findings on 150 respondents are related to consumer awareness and perception of national and European food quality labels used in the Czech Republic, attitudes to purchases of labelled products, and interest in information regarding the labels. Statistical methods, in the concrete Pearson´s chi-square test of independence, coefficient of contingency, and coefficient of association are used to determinate if significant differences do exist among selected demographic categories of Czech consumers.

Keywords: Food quality labels, quality labels awareness, quality labels perception, marketing research.

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Authors: Masahiro Kuzunishi, Tetsuya Furukawa, Ke Lu

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Classifying data hierarchically is an efficient approach to analyze data. Data is usually classified into multiple categories, or annotated with a set of labels. To analyze multi-labeled data, such data must be specified by giving a set of labels as a semantic range. There are some certain purposes to analyze data. This paper shows which multi-labeled data should be the target to be analyzed for those purposes, and discusses the role of a label against a set of labels by investigating the change when a label is added to the set of labels. These discussions give the methods for the advanced analysis of multi-labeled data, which are based on the role of a label against a semantic range.

Keywords: Classification Hierarchies, Data Analysis, Multilabeled Data, Orders of Sets of Labels

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Authors: Dimitrios Zarpalas, Anastasios Zafeiropoulos, Petros Daras, Nicos Maglaveras

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Segmentation techniques based on Active Contour Models have been strongly benefited from the use of prior information during their evolution. Shape prior information is captured from a training set and is introduced in the optimization procedure to restrict the evolution into allowable shapes. In this way, the evolution converges onto regions even with weak boundaries. Although significant effort has been devoted on different ways of capturing and analyzing prior information, very little thought has been devoted on the way of combining image information with prior information. This paper focuses on a more natural way of incorporating the prior information in the level set framework. For proof of concept the method is applied on hippocampus segmentation in T1-MR images. Hippocampus segmentation is a very challenging task, due to the multivariate surrounding region and the missing boundary with the neighboring amygdala, whose intensities are identical. The proposed method, mimics the human segmentation way and thus shows enhancements in the segmentation accuracy.

Keywords: Medical imaging & processing, Brain MRI segmentation, hippocampus segmentation, hippocampus-amygdala missingboundary, weak boundary segmentation, region based segmentation, prior information, local weighting scheme in level sets, spatialdistribution of labels, gradient distribution on boundary.

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Authors: Noboru Abe, Yoshinori Amai, Toshinori Nakatake, Sumio Masuda, Kazuaki Yamaguchi

Abstract:

We consider the problem of placing labels of the points on a plane. For each point, its position, the size of its label and a priority are given. Moreover, several candidates of its label positions are prespecified, and each of such label positions is assigned a priority. The objective of our problem is to maximize the total sum of priorities of placed labels and their points. By refining a labeling algorithm that can use these priorities, we propose a new heuristic algorithm which is more suitable for treating the assigned priorities.

Keywords: Map labeling, greedy algorithm, heuristic algorithm, priority.

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Authors: Fabio Fabris, Alex A. Freitas

Abstract:

Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.

Keywords: Algorithm recommendation, meta-learning, bioinformatics, hierarchical classification.

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Authors: Brent McKenzie, Victoria Taylor

Abstract:

The use of QR (Quick Response Codes) codes for customer scanning with mobile phones is a rapidly growing trend. The QR code can provide the consumer with product information, user guides, product use, competitive pricing, etc. One sector for QR use has been in retail, through the use of Electronic Shelf Labeling (henceforth, ESL). In Europe, the use of ESL for pricing has been in practice for a number of years but continues to lag in acceptance in North America. Stated concerns include costs as a key constraint, but there is also evidence that consumer acceptance represents a limitation as well. The purpose of this study is to present the findings of a consumer based study to gage the impact on their use in the retail food sector.

Keywords: Electronic shelf labels (ESL), consumer insights, retail food sector.

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Authors: H. V. Chen

Abstract:

Assume that we have m identical graphs where the graphs consists of paths with k vertices where k is a positive integer. In this paper, we discuss certain labelling of the m graphs called c-Erdösian for some positive integers c. We regard labellings of the vertices of the graphs by positive integers, which induce the edge labels for the paths as the sum of the two incident vertex labels. They have the property that each vertex label and edge label appears only once in the set of positive integers {c, . . . , c+6m- 1}. Here, we show how to construct certain c-Erdösian of m paths with 2 and 3 vertices by using Skolem sequences.

Keywords: c-Erdösian, Skolem sequences, magic labelling

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Authors: Dipti Patra, Mridula J

Abstract:

In this paper, we propose a new image segmentation approach for colour textured images. The proposed method for image segmentation consists of two stages. In the first stage, textural features using gray level co-occurrence matrix(GLCM) are computed for regions of interest (ROI) considered for each class. ROI acts as ground truth for the classes. Ohta model (I1, I2, I3) is the colour model used for segmentation. Statistical mean feature at certain inter pixel distance (IPD) of I2 component was considered to be the optimized textural feature for further segmentation. In the second stage, the feature matrix obtained is assumed to be the degraded version of the image labels and modeled as Markov Random Field (MRF) model to model the unknown image labels. The labels are estimated through maximum a posteriori (MAP) estimation criterion using ICM algorithm. The performance of the proposed approach is compared with that of the existing schemes, JSEG and another scheme which uses GLCM and MRF in RGB colour space. The proposed method is found to be outperforming the existing ones in terms of segmentation accuracy with acceptable rate of convergence. The results are validated with synthetic and real textured images.

Keywords: Texture Image Segmentation, Gray Level Cooccurrence Matrix, Markov Random Field Model, Ohta colour space, ICM algorithm.

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Authors: Shigeaki Sakurai, Ryohei Orihara

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This paper proposes an improvement method of classification efficiency in a classification model. The model is used in a risk search system and extracts specific labels from articles posted at bulletin board sites. The system can analyze the important discussions composed of the articles. The improvement method introduces ensemble learning methods that use multiple classification models. Also, it introduces expressions related to the specific labels into generation of word vectors. The paper applies the improvement method to articles collected from three bulletin board sites selected by users and verifies the effectiveness of the improvement method.

Keywords: Text mining, Risk search system, Corporate reputation, Bulletin board site, Ensemble learning

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Authors: Ziad Abdallah, Mohamad Oueidat, Ali El-Zaart

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Image Multi-label Classification (IMC) assigns a label or a set of labels to an image. The big demand for image annotation and archiving in the web attracts the researchers to develop many algorithms for this application domain. The existing techniques for IMC have two drawbacks: The description of the elementary characteristics from the image and the correlation between labels are not taken into account. In this paper, we present an algorithm (MIML-HOGLPP), which simultaneously handles these limitations. The algorithm uses the histogram of gradients as feature descriptor. It applies the Label Priority Power-set as multi-label transformation to solve the problem of label correlation. The experiment shows that the results of MIML-HOGLPP are better in terms of some of the evaluation metrics comparing with the two existing techniques.

Keywords: Data mining, information retrieval system, multi-label, problem transformation, histogram of gradients.

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Authors: Nabil M. Hewahi, Motaz K. Saad

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In large datasets, identifying exceptional or rare cases with respect to a group of similar cases is considered very significant problem. The traditional problem (Outlier Mining) is to find exception or rare cases in a dataset irrespective of the class label of these cases, they are considered rare events with respect to the whole dataset. In this research, we pose the problem that is Class Outliers Mining and a method to find out those outliers. The general definition of this problem is “given a set of observations with class labels, find those that arouse suspicions, taking into account the class labels". We introduce a novel definition of Outlier that is Class Outlier, and propose the Class Outlier Factor (COF) which measures the degree of being a Class Outlier for a data object. Our work includes a proposal of a new algorithm towards mining of the Class Outliers, presenting experimental results applied on various domains of real world datasets and finally a comparison study with other related methods is performed.

Keywords: Class Outliers, Distance-Based Approach, Outliers Mining.

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: Virtual Reality, effective computing, effective VR, emotion-based effective physiological database.

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Authors: Christian Arcos, Marley Vellasco, Abraham Alcaim

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In this paper, we present a wavelet coefficients masking based on Local Binary Patterns (WLBP) approach to enhance the temporal spectra of the wavelet coefficients for speech enhancement. This technique exploits the wavelet denoising scheme, which splits the degraded speech into pyramidal subband components and extracts frequency information without losing temporal information. Speech enhancement in each high-frequency subband is performed by binary labels through the local binary pattern masking that encodes the ratio between the original value of each coefficient and the values of the neighbour coefficients. This approach enhances the high-frequency spectra of the wavelet transform instead of eliminating them through a threshold. A comparative analysis is carried out with conventional speech enhancement algorithms, demonstrating that the proposed technique achieves significant improvements in terms of PESQ, an international recommendation of objective measure for estimating subjective speech quality. Informal listening tests also show that the proposed method in an acoustic context improves the quality of speech, avoiding the annoying musical noise present in other speech enhancement techniques. Experimental results obtained with a DNN based speech recognizer in noisy environments corroborate the superiority of the proposed scheme in the robust speech recognition scenario.

Keywords: Binary labels, local binary patterns, mask, wavelet coefficients, speech enhancement, speech recognition.

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Authors: R.N.Pise, S.A.Kulkarni, R.V.Pawar

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MultiProtocol Label Switching (MPLS) is an emerging technology that aims to address many of the existing issues associated with packet forwarding in today-s Internetworking environment. It provides a method of forwarding packets at a high rate of speed by combining the speed and performance of Layer 2 with the scalability and IP intelligence of Layer 3. In a traditional IP (Internet Protocol) routing network, a router analyzes the destination IP address contained in the packet header. The router independently determines the next hop for the packet using the destination IP address and the interior gateway protocol. This process is repeated at each hop to deliver the packet to its final destination. In contrast, in the MPLS forwarding paradigm routers on the edge of the network (label edge routers) attach labels to packets based on the forwarding Equivalence class (FEC). Packets are then forwarded through the MPLS domain, based on their associated FECs , through swapping the labels by routers in the core of the network called label switch routers. The act of simply swapping the label instead of referencing the IP header of the packet in the routing table at each hop provides a more efficient manner of forwarding packets, which in turn allows the opportunity for traffic to be forwarded at tremendous speeds and to have granular control over the path taken by a packet. This paper deals with the process of MPLS forwarding mechanism, implementation of MPLS datapath , and test results showing the performance comparison of MPLS and IP routing. The discussion will focus primarily on MPLS IP packet networks – by far the most common application of MPLS today.

Keywords: Forwarding equivalence class, incoming label map, label, next hop label forwarding entry.

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Authors: Ammar Ayad Issa Al-Rifaie, Zuhreya Muazu, Maysam Ali Abdulwahid, Dermot Gleeson

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The aim of this audit was to examine the efficiency of alcohol history documentation and screening for hazardous drinkers at the Medical Admission Unit (MAU) of Northern General Hospital (NGH), Sheffield, to identify any potential for enhancing clinical practice. Data were collected from medical clerking sheets, ICE system and directly from 82 patients by three junior medical doctors using both CAGE questionnaire and AUDIT-C tool for newly admitted patients to MAU in NGH, in the period between January and March 2015. Alcohol consumption was documented in around two-third of the patient sample and this was documented fairly accurately by health care professionals. Some used subjective words such as 'social drinking' in the alcohol units’ section of the history. CAGE questionnaire was applied to only four patients and none of the patients had documented advice, education or referral to an alcohol liaison team. AUDIT-C tool had identified 30.4%, while CAGE 10.9%, of patients admitted to the NGH MAU as hazardous drinkers. The amount of alcohol the patient consumes positively correlated with the score of AUDIT-C (Pearson correlation 0.83). Re-audit is planned to be carried out after integrating AUDIT-C tool as labels in the notes and presenting a brief teaching session to junior doctors. Alcohol misuse screening is not adequately undertaken and no appropriate action is being offered to hazardous drinkers. CAGE questionnaire is poorly applied to patients and when satisfactory and adequately used has low sensitivity to detect hazardous drinkers in comparison with AUDIT-C tool. Re-audit of alcohol screening practice after introducing AUDIT-C tool in clerking sheets (as labels) is required to compare the findings and conclude the audit cycle.

Keywords: Alcohol screening, AUDIT-C, CAGE, Hazardous drinking.

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Authors: Eleonora Tudora, Adriana Alexandru

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The radio frequency identification (RFID) is a technology for automatic identification of items, particularly in supply chain, but it is becoming increasingly important for industrial applications. Unlike barcode technology that detects the optical signals reflected from barcode labels, RFID uses radio waves to transmit the information from an RFID tag affixed to the physical object. In contrast to today most often use of this technology in warehouse inventory and supply chain, the focus of this paper is an overview of the structure of RFID systems used by RFID technology and it also presents a solution based on the application of RFID for brand authentication, traceability and tracking, by implementing a production management system and extending its use to traders.

Keywords: RFID, RFID Tag, Electronic Product Code (EPC), EPC network, Object Naming Service (ONS), Authentication, Traceability.

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Authors: Farag Azzedin, Ahmad Ridha, Ali Rizvi

Abstract:

Trust management is one of the drawbacks in Peer-to-Peer (P2P) system. Lack of centralized control makes it difficult to control the behavior of the peers. Reputation system is one approach to provide trust assessment in P2P system. In this paper, we use fuzzy logic to model trust in a P2P environment. Our trust model combines first-hand (direct experience) and second-hand (reputation)information to allow peers to represent and reason with uncertainty regarding other peers' trustworthiness. Fuzzy logic can help in handling the imprecise nature and uncertainty of trust. Linguistic labels are used to enable peers assign a trust level intuitively. Our fuzzy trust model is flexible such that inference rules are used to weight first-hand and second-hand accordingly.

Keywords: P2P Systems; Trust, Reputation, Fuzzy Logic.

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Authors: M. Nemissi, H. Seridi, H. Akdag

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This paper presents and evaluates a new classification method that aims to improve classifiers performances and speed up their training process. The proposed approach, called labeled classification, seeks to improve convergence of the BP (Back propagation) algorithm through the addition of an extra feature (labels) to all training examples. To classify every new example, tests will be carried out each label. The simplicity of implementation is the main advantage of this approach because no modifications are required in the training algorithms. Therefore, it can be used with others techniques of acceleration and stabilization. In this work, two models of the labeled classification are proposed: the LMLP (Labeled Multi Layered Perceptron) and the LNFC (Labeled Neuro Fuzzy Classifier). These models are tested using Iris, wine, texture and human thigh databases to evaluate their performances.

Keywords: Artificial neural networks, Fusion of neural networkfuzzysystems, Learning theory, Pattern recognition.

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Authors: Katrin Zaiß, Tim Schlüter, Stefan Conrad

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Ontology Matching is a task needed in various applica-tions, for example for comparison or merging purposes. In literature,many algorithms solving the matching problem can be found, butmost of them do not consider instances at all. Mappings are deter-mined by calculating the string-similarity of labels, by recognizinglinguistic word relations (synonyms, subsumptions etc.) or by ana-lyzing the (graph) structure. Due to the facts that instances are oftenmodeled within the ontology and that the set of instances describesthe meaning of the concepts better than their meta information,instances should definitely be incorporated into the matching process.In this paper several novel instance-based matching algorithms arepresented which enhance the quality of matching results obtainedwith common concept-based methods. Different kinds of formalismsare use to classify concepts on account of their instances and finallyto compare the concepts directly.KeywordsInstances, Ontology Matching, Semantic Web

Keywords: Instances, Ontology Matching, Semantic Web

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Authors: S. Soltan, A. Zarnani, R. AliMohammadzadeh, M. Rahgozar

Abstract:

XML has become a popular standard for information exchange via web. Each XML document can be presented as a rooted, ordered, labeled tree. The Node label shows the exact position of a node in the original document. Region and Dewey encoding are two famous methods of labeling trees. In this paper, we propose a new insert friendly labeling method named IFDewey based on recently proposed scheme, called Extended Dewey. In Extended Dewey many labels must be modified when a new node is inserted into the XML tree. Our method eliminates this problem by reserving even numbers for future insertion. Numbers generated by Extended Dewey may be even or odd. IFDewey modifies Extended Dewey so that only odd numbers are generated and even numbers can then be used for a much easier insertion of nodes.

Keywords: XML, tree labeling, query processing.

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Authors: Kristina Georgoulakis

Abstract:

A new blind symbol by symbol equalizer is proposed. The operation of the proposed equalizer is based on the geometric properties of the two dimensional data constellation. An unsupervised clustering technique is used to locate the clusters formed by the received data. The symmetric properties of the clusters labels are subsequently utilized in order to label the clusters. Following this step, the received data are compared to clusters and decisions are made on a symbol by symbol basis, by assigning to each data the label of the nearest cluster. The operation of the equalizer is investigated both in linear and nonlinear channels. The performance of the proposed equalizer is compared to the performance of a CMAbased blind equalizer.

Keywords: Blind equalization, channel equalization, cluster based equalisers

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Authors: M.Sushanth Babu, P.Krishna, U.Venu, M.Ranjith

Abstract:

In this paper, we evaluate the choice of suitable quantization characteristics for both the decoder messages and the received samples in Low Density Parity Check (LDPC) coded systems using M-QAM (Quadrature Amplitude Modulation) schemes. The analysis involves the demapper block that provides initial likelihood values for the decoder, by relating its quantization strategy of the decoder. A mapping strategy refers to the grouping of bits within a codeword, where each m-bit group is used to select a 2m-ary signal in accordance with the signal labels. Further we evaluate the system with mapping strategies like Consecutive-Bit (CB) and Bit-Reliability (BR). A new demapper version, based on approximate expressions, is also presented to yield a low complexity hardware implementation.

Keywords: Low Density parity Check, Mapping, Demapping, Quantization, Quadrature Amplitude Modulation

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Authors: Sorin Valcan, Mihail Găianu

Abstract:

Labeling is a very costly and time consuming process which aims to generate datasets for training neural networks in several functionalities and projects. For driver monitoring system projects, the need of labeled images has a significant impact on the budget and distribution of effort. This paper presents the modifications done to a ground truth data generation algorithm for 2D eyes location on infrared images with drivers in order to improve the quality of the data and performance of the trained neural networks. The algorithm restrictions become tougher which makes it more accurate but also less constant. The resulting dataset becomes smaller and shall not be altered by any kind of manual labels adjustment before being used in the neural networks training process. These changes resulted in a much better performance of the trained neural networks.

Keywords: Labeling automation, infrared camera, driver monitoring, eye detection, Convolutional Neural Networks.

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Authors: P.Cordero, R.Poler, R.Sanchis

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The environmental impacts caused by the current production and consumption models, together with the impact that the current economic crisis, bring necessary changes in the European industry toward new business models based on sustainability issues that could allow them to innovate and improve their competitiveness. This paper analyzes the key environmental issues and the current and future market trends in one of the most important industrial sectors in Spain, the furniture sector. It also proposes new decision support tools -diagnostic kit, roadmap and guidelines- to guide companies to implement sustainability criteria into their organizations, including eco-design strategies and other economical and social strategies in accordance with the sustainability definition, and other available tools such as eco-labels, environmental management systems, etc., and to use and combine them to obtain the results the company expects to help improve its competitiveness.

Keywords: Furniture sector, eco-design, sustainability, economical crisis, market trends, roadmap

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Authors: Stefanie Rukavina, Sascha Gruss, Steffen Walter, Holger Hoffmann, Harald C. Traue

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OPEN_EmoRec_II is an open multimodal corpus with experimentally induced emotions. In the first half of the experiment, emotions were induced with standardized picture material and in the second half during a human-computer interaction (HCI), realized with a wizard-of-oz design. The induced emotions are based on the dimensional theory of emotions (valence, arousal and dominance). These emotional sequences - recorded with multimodal data (facial reactions, speech, audio and physiological reactions) during a naturalistic-like HCI-environment one can improve classification methods on a multimodal level. This database is the result of an HCI-experiment, for which 30 subjects in total agreed to a publication of their data including the video material for research purposes*. The now available open corpus contains sensory signal of: video, audio, physiology (SCL, respiration, BVP, EMG Corrugator supercilii, EMG Zygomaticus Major) and facial reactions annotations.

Keywords: Open multimodal emotion corpus, annotated labels.

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Authors: Stefanie Rukavina, Sascha Gruss, Steffen Walter, Holger Hoffmann, Harald C. Traue

Abstract:

OPEN_EmoRec_II is an open multimodal corpus with experimentally induced emotions. In the first half of the experiment, emotions were induced with standardized picture material and in the second half during a human-computer interaction (HCI), realized with a wizard-of-oz design. The induced emotions are based on the dimensional theory of emotions (valence, arousal and dominance). These emotional sequences - recorded with multimodal data (facial reactions, speech, audio and physiological reactions) during a naturalistic-like HCI-environment one can improve classification methods on a multimodal level. This database is the result of an HCI-experiment, for which 30 subjects in total agreed to a publication of their data including the video material for research purposes*. The now available open corpus contains sensory signal of: video, audio, physiology (SCL, respiration, BVP, EMG Corrugator supercilii, EMG Zygomaticus Major) and facial reactions annotations.

Keywords: Open multimodal emotion corpus, annotated labels.

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Authors: Ágoston Winkler, Sándor Juhász

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The localization of software products is essential for reaching the users of the international market. An important task for this is the translation of the user interface into local national languages. As graphical interfaces are usually optimized for the size of the texts in the original language, after the translation certain user controls (e.g. text labels and buttons in dialogs) may grow in such a manner that they slip above each other. This not only causes an unpleasant appearance but also makes the use of the program more difficult (or even impossible) which implies that the arrangement of the controls must be corrected subsequently. The correction should preserve the original structure of the interface (e.g. the relation of logically coherent controls), furthermore, it is important to keep the nicely proportioned design: the formation of large empty areas should be avoided. This paper describes an algorithm that automatically rearranges the controls of a graphical user interface based on the principles above. The algorithm has been implemented and integrated into a translation support system and reached results pleasant for the human eye in most test cases.

Keywords: Graphical user interface, GUI, natural languages, software localization, translation support systems.

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Authors: Ijeoma Chinyere Ukonu

Abstract:

This research seeks to investigate how the globalisation of fast food has affected students’ food choice. A mixed method approach was used in this research; basically involving quantitative and qualitative methods. The quantitative method uses a self-completion questionnaire to randomly sample one hundred and four students; while the qualitative method uses a semi structured interview technique to survey four students on their knowledge and choice to consume fast food. A cross tabulation of variables and the Kruskal Wallis nonparametric test were used to analyse the quantitative data; while the qualitative data was analysed through deduction of themes, and trends from the interview transcribe. The findings revealed that globalisation has amplified the evolution of fast food, popularising it among students. Its global presence has affected students’ food choice and preference. Price, convenience, taste, and peer influence are some of the major factors affecting students’ choice of fast food. Though, students are familiar with the health effect of fast food and the significance of using food information labels for healthy choice making, their preference of fast food is more than homemade food.

Keywords: Fast food, food choice, globalisation, students.

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Authors: A. Ouanane, A. Serir

Abstract:

In this paper, a novel algorithm based on Ridgelet Transform and support vector machine is proposed for human action recognition. The Ridgelet transform is a directional multi-resolution transform and it is more suitable for describing the human action by performing its directional information to form spatial features vectors. The dynamic transition between the spatial features is carried out using both the Principal Component Analysis and clustering algorithm K-means. First, the Principal Component Analysis is used to reduce the dimensionality of the obtained vectors. Then, the kmeans algorithm is then used to perform the obtained vectors to form the spatio-temporal pattern, called set-of-labels, according to given periodicity of human action. Finally, a Support Machine classifier is used to discriminate between the different human actions. Different tests are conducted on popular Datasets, such as Weizmann and KTH. The obtained results show that the proposed method provides more significant accuracy rate and it drives more robustness in very challenging situations such as lighting changes, scaling and dynamic environment

Keywords: Human action, Ridgelet Transform, PCA, K-means, SVM.

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