Search results for: perceptual features

Authors: D. F. Carigma, E. Cruzado, M. J. Hagos, K. Perater, D. Ramos, R. Navarro, R. Galingan

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Korean culture disseminates rapidly across the globe. The young generation is highly engaged in Korean trends, such as Korean pop music, dramas or movies, fashion, food, and beauty standards. With the use of media, the effects of Korean trends may have resulted in the effects of media on people and society, such as addiction, perceptual influence, psychological effect, time consumption, and impulsive spending. The study aimed to determine whether there is a relationship between variable factors affecting the student's academic performance. The proponents used a quantitative approach in the 388 participants at the Technological Institute of the Philippines. This study shows that the Korean trends and the effect of media on people and society correlated to its variable factors. Moreover, this study may help future research in colleges and universities in the Philippines about how the students in higher education who engage in Korean trends affect their behavior and academic performance.

Keywords: academic performance, addiction, effect of media on people and society, Korean trend, perceptual influence, psychological effect

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Authors: Toni Maristela C. Estabillo, Michaela V. Matienzo, Mikaela L. Sabangan, Rosette M. Tienzo, Justine L. Bahinting

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This study is about blind watermarking on images with different categories and properties using two algorithms namely, Discrete Wavelet Transform and Patchwork Algorithm. A program is created to perform watermark embedding, extraction and evaluation. The evaluation is based on three watermarking criteria namely: image quality degradation, perceptual transparency and security. Image quality is measured by comparing the original properties with the processed one. Perceptual transparency is measured by a visual inspection on a survey. Security is measured by implementing geometrical and non-geometrical attacks through a pass or fail testing. Values used to measure the following criteria are mostly based on Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). The results are based on statistical methods used to interpret and collect data such as averaging, z Test and survey. The study concluded that the combined DWT and Patchwork algorithms were less efficient and less capable of watermarking than DWT algorithm only.

Keywords: blind watermarking, discrete wavelet transform algorithm, patchwork algorithm, digital watermark

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Authors: Michael Zimba

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A novel set of features for copy-move image forgery, CMIF, detection method is proposed. The proposed set presents a new approach which relies on electrostatic field theory, EFT. Solely for the purpose of reducing the dimension of a suspicious image, firstly performs discrete wavelet transform, DWT, of the suspicious image and extracts only the approximation subband. The extracted subband is then bijectively mapped onto a virtual electrostatic field where concepts of EFT are utilised to extract robust features. The extracted features are shown to be invariant to additive noise, JPEG compression, and affine transformation. The proposed features can also be used in general object matching.

Keywords: virtual electrostatic field, features, affine transformation, copy-move image forgery

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Authors: Min L. Stewart, Patrick Johnston

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Predictive coding has been proposed as a general explanatory framework for understanding the neural mechanisms of perception. As such, an underweighting of perceptual priors has been hypothesised to underpin a range of differences in inferential and sensory processing in autism spectrum disorders. However, empirical evidence to support this has not been well established. The present study uses an electroencephalography paradigm involving changes of facial identity and person category (actors etc.) to explore how levels of autistic traits (AT) affect predictive coding at multiple stages in the visual processing hierarchy. The study uses a rapid serial presentation of faces, with hierarchically structured sequences involving both periodic and aperiodic repetitions of different stimulus attributes (i.e., person identity and person category) in order to induce contextual expectations relating to these attributes. It investigates two main predictions: (1) significantly larger and late neural responses to change of expected visual sequences in high-relative to low-AT, and (2) significantly reduced neural responses to violations of contextually induced expectation in high- relative to low-AT. Preliminary frequency analysis data comparing high and low-AT show greater and later event-related-potentials (ERPs) in occipitotemporal areas and prefrontal areas in high-AT than in low-AT for periodic changes of facial identity and person category but smaller ERPs over the same areas in response to aperiodic changes of identity and category. The research advances our understanding of how abnormalities in predictive coding might underpin aberrant perceptual experience in autism spectrum. This is the first stage of a research project that will inform clinical practitioners in developing better diagnostic tests and interventions for people with autism.

Keywords: hierarchical visual processing, face processing, perceptual hierarchy, prediction error, predictive coding

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Authors: John P. O’Sullivan, Thomas M. P. Ratouis, Michael J. O’Sullivan

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As the use of geothermal energy grows internationally more effort is required to monitor and protect areas with rare and important geothermal surface features. A number of approaches are presented for developing and calibrating numerical geothermal reservoir models that are capable of accurately representing geothermal surface features. The approaches are discussed in the context of cases studies of the Rotorua geothermal system and the Orakei-korako geothermal system, both of which contain important surface features. The results show that models are able to match the available field data accurately and hence can be used as valuable tools for predicting the future response of the systems to changes in use.

Keywords: geothermal reservoir models, surface features, monitoring, TOUGH2

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Authors: Kyi Pyar Zaw, Zin Mar Kyu

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Character recognition is the process of converting a text image file into editable and searchable text file. Feature Extraction is the heart of any character recognition system. The character recognition rate may be low or high depending on the extracted features. In the proposed paper, 25 features for one character are used in character recognition. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation and vertical cropping method is used for character segmentation. In the Feature extraction step, features are extracted in two ways. The first way is that the 8 features are extracted from the entire input character using eight direction chain code frequency extraction. The second way is that the input character is divided into 16 blocks. For each block, although 8 feature values are obtained through eight-direction chain code frequency extraction method, we define the sum of these 8 feature values as a feature for one block. Therefore, 16 features are extracted from that 16 blocks in the second way. We use the number of holes feature to cluster the similar characters. We can recognize the almost Myanmar common characters with various font sizes by using these features. All these 25 features are used in both training part and testing part. In the classification step, the characters are classified by matching the all features of input character with already trained features of characters.

Keywords: chain code frequency, character recognition, feature extraction, features matching, segmentation

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Authors: Issa Qabaja, Fadi Thabtah

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Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set.

Keywords: data mining, email classification, phishing, online security

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Ronak Khosravi, Mahmood Abbasi Layegh, Siamak Haghipour, Avin Esmaili

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In this paper, a study on classification of musical woodwind instruments using a small set of features selected from a broad range of extracted ones by the sequential forward selection method was carried out. Firstly, we extract 42 features for each record in the music database of 402 sound files belonging to five different groups of Flutes (end blown and internal duct), Single –reed, Double –reed (exposed and capped), Triple reed and Quadruple reed. Then, the sequential forward selection method is adopted to choose the best feature set in order to achieve very high classification accuracy. Two different classification techniques of support vector machines and relevance vector machines have been tested out and an accuracy of up to 96% can be achieved by using 21 time, frequency and coefficient features and relevance vector machine with the Gaussian kernel function.

Keywords: coefficient features, relevance vector machines, spectral features, support vector machines, temporal features

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Authors: Bashayer Almalki, Mayar Bajrai, Dana Mirah, Kholood Alghamdi, Hala Sanyour

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This research aims to investigate the features of an AI chess app that are most preferred by users. A questionnaire was used as the methodology to gather responses from a varied group of participants. The questionnaire consisted of several questions related to the features of the AI chess app. The responses were analyzed using descriptive statistics and factor analysis. The findings indicate that the most preferred features of an AI chess app are the ability to play against the computer, the option to adjust the difficulty level, and the availability of tutorials and puzzles. The results of this research could be useful for developers of AI chess apps to enhance the user experience and satisfaction.

Keywords: chess, game, application, computics

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Authors: Jiyoung Bae, Gyoomi Kim

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This study explores a range of linguistic features used in Korean college students’ argumentative writings for the purpose of developing a model that identifies variables which predict writing proficiencies. This study investigated the latent variable structure of L2 linguistic features, including syntactic complexity, the lexical complexity, and fluency. One hundred forty-six university students in Korea participated in this study. The results of the study’s confirmatory factor analysis (CFA) showed that indicators of linguistic features from this study-provided a foundation for re-categorizing indicators found in extant research on L2 Korean writers depending on each latent variable of linguistic features. The CFA models indicated one measurement model of L2 syntactic complexity and L2 learners’ writing proficiency; these two latent factors were correlated with each other. Based on the overall findings of the study, integrated linguistic features of L2 writings suggested some pedagogical implications in L2 writing instructions.

Keywords: linguistic features, syntactic complexity, lexical complexity, fluency

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Authors: Essam Al Daoud

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Gradient boosting methods have been proven to be a very important strategy. Many successful machine learning solutions were developed using the XGBoost and its derivatives. The aim of this study is to investigate and compare the efficiency of three gradient methods. Home credit dataset is used in this work which contains 219 features and 356251 records. However, new features are generated and several techniques are used to rank and select the best features. The implementation indicates that the LightGBM is faster and more accurate than CatBoost and XGBoost using variant number of features and records.

Keywords: gradient boosting, XGBoost, LightGBM, CatBoost, home credit

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Authors: Nguyen Van Anh Le, Mafuyu Kitahara

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In several languages, it is widely acknowledged that vowels are longer before voiced consonants than before voiceless ones such as English. However, in Mandarin Chinese, Vietnamese, Japanese, and Korean, the distribution of voiced-voiceless stop contrasts and long-short vowel differences are vastly different from English. The purpose of this study is to determine whether these targeted learners' L2 English production and perception change in terms of vowel duration as a function of stop voicing. The production measurements in the database of Asian learners revealed a distinct effect than the one observed in native speakers. There was no evident vowel lengthening patterns. The results of the perceptual experiment with 24 participants indicated that individuals tended to prefer voiceless stops when preceding vowels were shortened, but there was no statistically significant difference between intermediate, upper-intermediate, and advanced-level learners. However, learners demonstrated distinct perceptual patterns for various vowels and stops. The findings have valuable implications for L2 English speech acquisition. Keywords: voiced/voiceless stops, preceding vowel duration, voiced/voiceless perception, L2 English, L1 Mandarin Chinese, L1 Vietnamese, L1 Japanese, L1 Korean

Keywords: voiced/voiceless stops, preceding vowel duration, voiced/voiceless perception, L2 english

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Authors: Yasmeen Bassas, Sandra Kuebler, Allen Riddell

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Native language identification is one of the growing subfields in natural language processing (NLP). The task of native language identification (NLI) is mainly concerned with predicting the native language of an author’s writing in a second language. In this paper, we investigate the performance of two types of features; content-based features vs. content independent features, when they are evaluated on a different corpus (using social media data “Reddit”). In this NLI task, the predefined models are trained on one corpus (TOEFL), and then the trained models are evaluated on different data using an external corpus (Reddit). Three classifiers are used in this task; the baseline, linear SVM, and logistic regression. Results show that content-based features are more accurate and robust than content independent ones when tested within the corpus and across corpus.

Keywords: NLI, NLP, content-based features, content independent features, social media corpus, ML

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Authors: Maria Bonolo Mathevula

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Background: Children are important members of communities playing major role in the future of any given country (Pera, Fails, Gelsomini, &Garzotto, 2018). Visual Perceptual Skills (VPSs) in children are important health aspect of early childhood development through the Foundation Phases in school. Subsequently, children should undergo visual screening before commencement of schooling for early diagnosis ofVPSs anomalies because the primary role of VPSs is to capacitate children with academic performance in general. Aim : The aim of this study was to determine the anomalies of visual VPSs amongst school children in Foundation Phase. The study’s objectives were to determine the prevalence of VPSs anomalies amongst school children in Foundation Phase; Determine the relationship between children’s academic and VPSs anomalies; and to investigate the relationship between VPSs anomalies and refractive error. Methodology: This study was a mixed method whereby triangulated qualitative (interviews) and quantitative (questionnaire and clinical data) was used. This was, therefore, descriptive by nature. The study’s target population was school children in Foundation Phase. The study followed purposive sampling method. School children in Foundation Phase were purposively sampled to form part of this study provided their parents have given a signed the consent. Data was collected by the use of standardized interviews; questionnaire; clinical data card, and TVPS standard data card. Results: Although the study is still ongoing, the preliminary study outcome based on data collected from one of the Foundation Phases have suggested the following:While VPSs anomalies is not prevalent, it, however, have indirect relationship with children’s academic performance in Foundation phase; Notably, VPSs anomalies and refractive error are directly related since majority of children with refractive error, specifically compound hyperopic astigmatism, failed most subtests of TVPS standard tests. Conclusion: Based on the study’s preliminary findings, it was clear that optometrists still have a lot to do in as far as researching on VPSs is concerned. Furthermore, the researcher recommends that optometrist, as the primary healthcare professionals, should also conduct the school-readiness pre-assessment on children before commencement of their grades in Foundation phase.

Keywords: foundation phase, visual perceptual skills, school children, refractive error

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Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour

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Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.

Keywords: decision tree, feature selection, intrusion detection system, support vector machine

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Authors: Georgios P. Georgiou

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Infants are able to discriminate a number of sound contrasts in most languages. However, this ability is not available in adults who might face difficulties in discriminating accurately second language sound contrasts as they filter second language speech through the phonological categories of their native language. For example, Spanish speakers often struggle to perceive the difference between the English /ε/ and /æ/ because both vowels do not exist in their native language; so they assimilate these vowels to the closest phonological category of their first language. The present study aims to uncover the perceptual patterns of Arabic adult speakers in regard to the vowels of their second language (Greek). Still, there is not any study that investigates the perception of Greek vowels by Arabic speakers and, thus, the present study would contribute to the enrichment of the literature with cross-linguistic research in new languages. To the purpose of the present study, 15 native speakers of Egyptian Arabic who permanently live in Cyprus and have adequate knowledge of Greek as a second language passed through vowel assimilation and vowel contrast discrimination tests (AXB) in their second language. The perceptual stimuli included non-sense words that contained vowels in both stressed and unstressed positions. The second language listeners’ patterns were analyzed through the Perceptual Assimilation Model which makes testable hypotheses about the assimilation of second language sounds to the speakers’ native phonological categories and the discrimination accuracy over second language sound contrasts. The results indicated that second language listeners assimilated pairs of Greek vowels in a single phonological category of their native language resulting in a Category Goodness difference assimilation type for the Greek stressed /i/-/e/ and the Greek stressed-unstressed /o/-/u/ vowel contrasts. On the contrary, the members of the Greek unstressed /i/-/e/ vowel contrast were assimilated to two different categories resulting in a Two Category assimilation type. Furthermore, they could discriminate the Greek stressed /i/-/e/ and the Greek stressed-unstressed /o/-/u/ contrasts only in a moderate degree while the Greek unstressed /i/-/e/ contrast could be discriminated in an excellent degree. Two main implications emerge from the results. First, there is a strong influence of the listeners’ native language on the perception of the second language vowels. In Egyptian Arabic, contiguous vowel categories such as [i]-[e] and [u]-[o] do not have phonemic difference but they are subject to allophonic variation; by contrast, the vowel contrasts /i/-/e/ and /o/-/u/ are phonemic in Greek. Second, the role of stress is significant for second language perception since stressed vs. unstressed vowel contrasts were perceived in a different manner by the Greek listeners.

Keywords: Arabic, bilingual, Greek, vowel perception

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Authors: Huangmei Liu, Si Liu, Jia’nan Liu

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The present experiment investigated and compared the effectiveness of two kinds of methods of attention control: Task distraction and visual enhancement. In the study, the effectiveness of task distractions to explicit features and of visual enhancement to implicit features of the same group of Chinese characters were compared based on their effect on the participants’ reaction time, subjective confidence rating, and verbal report. We found support that the visual enhancement on implicit features did overcome the contrary effect of training distraction and led to awareness of those implicit features, at least to some extent.

Keywords: task distraction, visual enhancement, attention, awareness, learning

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Authors: Tamil Chelvi Vadivelu, Nurazean Maarop, Rasimah Che Yusoff, Farhana Aini Saludin

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Implementing a remote healthcare system needs to consider many security features. Therefore, before any deployment of the remote healthcare system, a feasibility study from the security perspective is crucial. Remote healthcare system using WBAN technology has been used in other countries for medical purposes but in Malaysia, such projects are still not yet implemented. This study was conducted qualitatively. The interview results involving five healthcare practitioners are further elaborated. The study has addressed four important security features in order to incorporate remote healthcare system using WBAN in Malaysian government hospitals.

Keywords: remote healthcare, IT security, security features, wireless sensor application

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Authors: Xie Weifan, Wang Zhongde

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Everyday landscape in the countryside has long played an important role as a cultural representation of the countryside and a link between the countryside and social relations. In the transformation of modernization, the daily landscape in the countryside needs to change with the transformation of daily life in countryside therefore, interpreting the daily landscape in the countryside and understanding the basic characteristics and value perception of the daily landscape from the villagers' perspective can help to understand the daily landscape in the countryside and its conservation and development. Taking Lizi Village in Qianjiang District, Chongqing Municipality, China, as a case study, we collected important daily landscapes in villagers' perceptions through in-depth interviews, categorized them into personal living space, public affairs space, and public activity space, and analyzed the characteristics of the spatial distribution of daily landscapes. The perceptual characteristics of the villagers' perceptions are analyzed and divided into four major types, namely, physical environment perception, atmosphere and culture perception, emotional feelings, and behavioral preferences, and their perceptual characteristics are analyzed respectively to understand the important characteristics of the villagers' perceptions of the daily landscapes. Finally, it is proposed that the protection and development of daily landscape in villages need to improve the mechanism of discovering and evaluating daily landscape, encourage residents to participate in the construction of daily landscape, protect the high-value daily landscape, and promote the innovative development of daily landscape.

Keywords: rural landscape, everyday landscape, landscape perception, conservation and development

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Authors: Vishwa Joshi

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The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

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Authors: Ola A. Younis, Said Ghoul

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Systems running these days are huge, complex and exist in many versions. Controlling these versions and tracking their changes became a very hard process as some versions are created using meaningless names or specifications. Many versions of a system are created with no clear difference between them. This leads to mismatching between a user’s request and the version he gets. In this paper, we present a system versions meta-modeling approach that produces versions based on system’s features. This model reduced the number of steps needed to configure a release and gave each version its unique specifications. This approach is applicable for systems that use features in its specification.

Keywords: features, meta-modeling, semantic modeling, SPL, VCS, versioning

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Authors: Navab Karimi, Tohid Alizadeh

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An intelligent vision-based system was designed to measure the quality and purity of raisins. A machine vision setup was utilized to capture the images of bulk raisins in ranges of 5-50% mixed pure-impure berries. The textural features of bulk raisins were extracted using Grey-level Histograms, Co-occurrence Matrix, and Local Binary Pattern (a total of 108 features). Genetic Algorithm and neural network regression were used for selecting and ranking the best features (21 features). As a result, the GLCM features set was found to have the highest accuracy (92.4%) among the other sets. Followingly, multiple feature combinations of the previous stage were fed into the second regression (linear regression) to increase accuracy, wherein a combination of 16 features was found to be the optimum. Finally, a Support Vector Machine (SVM) classifier was used to differentiate the mixtures, producing the best efficiency and accuracy of 96.2% and 97.35%, respectively.

Keywords: sun-dried organic raisin, genetic algorithm, feature extraction, ann regression, linear regression, support vector machine, south azerbaijan.

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Authors: Stylianos Kampakis

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This paper presents a new algorithm for neural networks called “Input Dropout with Aggressive Re-weighting” (IDAR) aimed specifically at datasets with many useless features. IDAR combines two techniques (dropout of input neurons and aggressive re weighting) in order to eliminate the influence of noisy features. The technique can be seen as a generalization of dropout. The algorithm is tested on two different benchmark data sets: a noisy version of the iris dataset and the MADELON data set. Its performance is compared against three other popular techniques for dealing with useless features: L2 regularization, LASSO and random forests. The results demonstrate that IDAR can be an effective technique for handling data sets with many useless features.

Keywords: neural networks, feature selection, regularization, aggressive reweighting

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Authors: Lia Marchi, Ernesto Antonini

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The impact of industrial sites on people’s living environment both involves detrimental effects on the ecosystem and perceptual-aesthetic interferences with the scenery. These, in turn, affect the economic and social value of the landscape, as well as the wellbeing of workers and local communities. Given the diffusion of the phenomenon and the relevance of its effects, it emerges the need for a joint approach to assess and thus mitigate the impact of factories on the landscape –being this latest assumed as the result of the action and interaction of natural and human factors. However, the impact assessment tools suitable for the purpose are quite heterogeneous and mostly monodisciplinary. On the one hand, green building rating systems (GBRSs) are increasingly used to evaluate the performance of manufacturing sites, mainly by quantitative indicators focused on environmental issues. On the other hand, methods to detect the visual and social impact of factories on the landscape are gradually emerging in the literature, but they generally adopt only qualitative gauges. The research addresses the integration of the environmental impact assessment and the perceptual-aesthetic interferences of factories on the landscape. The GBRSs model is assumed as a reference since it is adequate to simultaneously investigate different topics which affect sustainability, returning a global score. A critical analysis of GBRSs relevant to industrial facilities has led to select the U.S. GBC LEED protocol as the most suitable to the scope. A revision of LEED v4 Building Design+Construction has then been provided by including specific indicators to measure the interferences of manufacturing sites with the perceptual-aesthetic and social aspects of the territory. To this end, a new impact category was defined, namely ‘PA - Perceptual-aesthetic aspects’, comprising eight new credits which are specifically designed to assess how much the buildings are in harmony with their surroundings: these investigate, for example the morphological and chromatic harmonization of the facility with the scenery or the site receptiveness and attractiveness. The credits weighting table was consequently revised, according to the LEED points allocation system. As all LEED credits, each new PA credit is thoroughly described in a sheet setting its aim, requirements, and the available options to gauge the interference and get a score. Lastly, each credit is related to mitigation tactics, which are drawn from a catalogue of exemplary case studies, it also developed by the research. The result is a modified LEED scheme which includes compatibility with the landscape within the sustainability assessment of the industrial sites. The whole system consists of 10 evaluation categories, which contain in total 62 credits. Lastly, a test of the tool on an Italian factory was performed, allowing the comparison of three mitigation scenarios with increasing compatibility level. The study proposes a holistic and viable approach to the environmental impact assessment of factories by a tool which integrates the multiple involved aspects within a worldwide recognized rating protocol.

Keywords: environmental impact, GBRS, landscape, LEED, sustainable factory

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Authors: Kritika Kulhari, Aparna Sahu

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Test anxiety results from concerns about failure in examinations or evaluative situations. Attentional biases are known to pronounce the symptomatic expression of test anxiety. In recent times, the inattentional blindness (IB) paradigm has shown promise as an attention bias modification treatment (ABMT) for anxiety by overcoming practice and expectancy effects which preexisting paradigms fail to counter. The IB paradigm assesses the inability of an individual to attend to a stimulus that appears suddenly while indulging in a perceptual discrimination task. The present study incorporated an IB task with three critical items (book, face, and triangle) appearing randomly in the perceptual discrimination task. Attentional biases were assessed as detection and identification of the critical item. The sample (N = 50) consisted of low test anxiety (LTA) and high test anxiety (HTA) groups based on the reactions to tests scale scores. Test threat manipulation was done with pre- and post-test assessment of test anxiety using the State Test Anxiety Inventory. A mixed factorial design with gender, test anxiety, presence or absence of test threat, and critical items was conducted to assess their effects on attentional biases. Results showed only a significant main effect for test anxiety on detection with higher accuracy of detection of the critical item for the LTA group. The study presents promising results in the realm of ABMT for test anxiety.

Keywords: attentional bias, attentional bias modification treatment, inattentional blindness, test anxiety

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Authors: Awais Ahmad Khan, Emad Abouel Nasr, H. M. A. Hussein, Abdulrahman Al-Ahmari

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Sheet-metal parts have been widely applied in electronics, communication and mechanical industries in recent decades; but the advancement in sheet-metal part design and manufacturing is still behind in comparison with the increasing importance of sheet-metal parts in modern industry. This paper presents a methodology for automatic extraction of some common 2D internal sheet metal features. The features used in this study are taken from Unipunch ™ catalogue. The extraction process starts with the data extraction from STEP file using an object oriented approach and with the application of suitable algorithms and rules, all features contained in the catalogue are automatically extracted. Since the extracted features include geometry and engineering information, they will be effective for downstream application such as feature rebuilding and process planning.

Keywords: feature extraction, internal features, punch shapes, sheet metal

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Authors: Hajer Rahali, Zied Hajaiej, Noureddine Ellouze

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The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC called Modified Function Cepstral Coefficients (MODFCC) were tested and compared against the original MFCC and RASTA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases.

Keywords: auditory filter, impulsive noise, MFCC, prosodic features, RASTA filter

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Authors: Michal Krčál

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Since their introduction in 1990’s, Enterprise Information Portals (EIPs) were investigated from different perspectives (e.g. project management, technology acceptance, IS success). However, no systematic literature review was produced to systematize both the research efforts and the technology itself. This paper reports first results of an extent systematic literature review study focused on research of EIPs and its categorization, specifically it reports a conceptual model of EIP features. The previous attempt to categorize EIP features was published in 2002. For the purpose of the literature review, content of 89 articles was analyzed in order to identify and categorize features of EIPs. The methodology of the literature review was as follows. Firstly, search queries in major indexing databases (Web of Science and SCOPUS) were used. The results of queries were analyzed according to their usability for the goal of the study. Then, full-texts were coded in Atlas.ti according to previously established coding scheme. The codes were categorized and the conceptual model of EIP features was created.

Keywords: enterprise information portal, content analysis, features, systematic literature review

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Authors: Hamed Qazanfari, Hamid Hassanpour, Kazem Qazanfari

Abstract:

In this paper, a method is provided for content-based image retrieval. Content-based image retrieval system searches query an image based on its visual content in an image database to retrieve similar images. In this paper, with the aim of simulating the human visual system sensitivity to image's edges and color features, the concept of color difference histogram (CDH) is used. CDH includes the perceptually color difference between two neighboring pixels with regard to colors and edge orientations. Since the HSV color space is close to the human visual system, the CDH is calculated in this color space. In addition, to improve the color features, the color histogram in HSV color space is also used as a feature. Among the extracted features, efficient features are selected using entropy and correlation criteria. The final features extract the content of images most efficiently. The proposed method has been evaluated on three standard databases Corel 5k, Corel 10k and UKBench. Experimental results show that the accuracy of the proposed image retrieval method is significantly improved compared to the recently developed methods.

Keywords: content-based image retrieval, color difference histogram, efficient features selection, entropy, correlation

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