Search results for: semantic filtering

Authors: Atena Tazikeh Lemeski, Ismail Ozdamar

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This study presents a detailed analysis of harmonic distortion in a grid-connected photovoltaic (PV) system using real-time data captured from a solar power plant. Harmonics introduced by inverters in PV systems can degrade power quality and lead to increased Total Harmonic Distortion (THD), which poses challenges such as transformer overheating, increased power losses, and potential grid instability. This research addresses these issues by applying Fast Fourier Transform (FFT) to identify significant harmonic components and employing notch filters to target specific frequencies, particularly the 3rd harmonic (150 Hz), which was identified as the largest contributor to THD. Initial analysis of the unfiltered voltage signal revealed a THD of 21.15%, with prominent harmonic peaks at 150 Hz, 250 Hz and 350 Hz, corresponding to the 3rd, 5th, and 7th harmonics, respectively. After implementing the notch filters, the THD was reduced to 5.72%, demonstrating the effectiveness of this approach in mitigating harmonic distortion without affecting the fundamental frequency. This paper provides practical insights into the application of real-time filtering techniques in PV systems and their role in improving overall grid stability and power quality. The results indicate that targeted harmonic mitigation is crucial for the sustainable integration of renewable energy sources into modern electrical grids.

Keywords: grid-connected photovoltaic systems, fast Fourier transform, harmonic filtering, inverter-induced harmonics

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Authors: Hazel P. Atilano

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In this age of AI-assisted teaching and learning, there seems to be a dearth of research literature on the linguistic analysis of English as a Second Language (ESL) student-generated paraphrased academic texts. This study sought to examine the lexico-semantic, morphosyntactic features of paraphrased academic texts generated by ESL students. Employing a descriptive qualitative design, specifically linguistic analysis, the study involved a total of 85 students from senior high school, college, and graduate school enrolled in research courses. Data collection consisted of a 60-minute real-time, on-site paraphrasing practice exercise using excerpts from discipline-specific literature reviews of 150 to 200 words. A focus group discussion (FGD) was conducted to probe into the challenges experienced by the participants. The writing exercise yielded a total of 516 paraphrase pairs. A total of 176 paraphrase units (PUs) and 340 non-paraphrase pairs (NPPs) were detected. Findings from the linguistic analysis of PUs reveal that the modifications made to the original texts are predominantly syntax-based (Diathesis Alterations and Coordination Changes) and a combination of Miscellaneous Changes (Change of Order, Change of Format, and Addition/Deletion). Results of the analysis of paraphrase extremes (PE) show that Identical Structures resulting from the use of synonymous substitutions, with no significant change in the structural features of the original, is the most frequently occurring instance of PE. The analysis of paraphrase errors reveals that synonymous substitutions resulting in identical structures are the most frequently occurring error that leads to PE. Another type of paraphrasing error involves semantic and content loss resulting from the deletion or addition of meaning-altering content. Three major themes emerged from the FGD: (1) The Challenge of Preserving Semantic Content and Fidelity; (2) The Best Words in the Best Order: Grappling with the Lexico-semantic and Morphosyntactic Demands of Paraphrasing; and (3) Contending with Limited Vocabulary, Poor Comprehension, and Lack of Practice. A pedagogical paradigm was designed based on the major findings of the study for a sustainable instructional intervention.

Keywords: academic text, lexico-semantic analysis, linguistic analysis, morphosyntactic analysis, paraphrasing

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Authors: Nilson Barbieri, Bruno Matos Martins, Gabriel de Sant'Anna Vitor Barbieri

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In more complex systems, such as automotive gearbox, a rigorous treatment of the data is necessary because there are several moving parts (gears, bearings, shafts, etc.), and in this way, there are several possible sources of errors and also noise. The basic objective of this work is the detection of damage in automotive gearbox. The detection methods used are the wavelet method, the bispectrum; advanced filtering techniques (selective filtering) of vibrational signals and mathematical morphology. Gearbox vibration tests were performed (gearboxes in good condition and with defects) of a production line of a large vehicle assembler. The vibration signals are obtained using five accelerometers in different positions of the sample. The results obtained using the kurtosis, bispectrum, wavelet and mathematical morphology showed that it is possible to identify the existence of defects in automotive gearboxes.

Keywords: automotive gearbox, mathematical morphology, wavelet, bispectrum

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Authors: Samad Sajjadi

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Machine translation is a new subject increasingly being used by academic writers, especially students and researchers whose native language is not English. There are numerous studies conducted on machine translation, but few investigations have assessed the accuracy of machine translation from Persian to English at lexical, semantic, and syntactic levels. Using Groves and Mundt’s (2015) Model of error taxonomy, the current study evaluated Persian-to-English translations produced by two famous online translators, Google Translate and Bing Translator. A total of 240 texts were randomly selected from different academic fields (law, literature, medicine, and mass media), and 60 texts were considered for each domain. All texts were rendered by the two translation systems and then by four human translators. All statistical analyses were applied using SPSS. The results indicated that Google translations were more accurate than the translations produced by the Bing Translator, especially in the domains of medicine (lexis: 186 vs. 225; semantic: 44 vs. 48; syntactic: 148 vs. 264 errors) and mass media (lexis: 118 vs. 149; semantic: 25 vs. 32; syntactic: 110 vs. 220 errors), respectively. Nonetheless, both machines are reasonably accurate in Persian-to-English translation of lexicons and syntactic structures, particularly from mass media and medical texts.

Keywords: machine translations, accuracy, human translation, efficiency

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Authors: Abanob Nady Wasef Moawed

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In more complicated systems, inclusive of automotive gearboxes, a rigorous remedy of the data is essential because there are several transferring elements (gears, bearings, shafts, and many others.), and in this way, there are numerous viable sources of mistakes and also noise. The fundamental goal of these elements are the detection of damage in car gearbox. The detection strategies used are the wavelet technique, the bispectrum, advanced filtering techniques (selective filtering) of vibrational alerts and mathematical morphology. Gearbox vibration assessments were achieved (gearboxes in proper circumstance and with defects) of a manufacturing line of a huge car assembler. The vibration indicators have acquired the use of five accelerometers in distinct positions of the sample. The effects acquired using the kurtosis, bispectrum, wavelet and mathematical morphology confirmed that it's far possible to identify the lifestyles of defects in automobile gearboxes.

Keywords: 3D-shaped electronics, electronic components, thermoforming, component positioningautomotive gearbox, mathematical morphology, wavelet, bispectrum

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Authors: Yasemin Filiz-Kuruel, Emine Koseoglu

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This study aims to evaluate Haskoy district of Beyoglu town of Istanbul. Haskoy is located in Halic region, between Kasimpasa district and Kagithane district. After the conquest of Istanbul, Fatih Sultan Mehmet (the Conqueror) set up his tent here. Therefore, the area gets its name as Haskoy, 'imperial village' that means a village which is special for Sultan. Today, there are shipyard and ateliers in variable sizes in Haskoy. In this study, the legibility of Haskoy streets is investigated comparatively. As a research method, semantic differential scale is used. The photos of the streets, which contain specific criteria, are chosen. The questionnaire is directed to first and third grade architecture students. The spatial evaluation of Haskoy streets is done through the survey.

Keywords: Haskoy, legibility, semantic differential scale, urban streets

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Authors: Lican Wang, Rongqian Chen, Yancheng You, Ruofan Qiu

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In the applications of jet engine, open-jet wind tunnel and airframe, there wildly exists a shear layer formed by the velocity and temperature gradients between jet flow and surrounded medium. The presence of shear layer will refract and reflect the sound path that consequently influences the measurement results in far-field. To investigate and evaluate the shear layer effect, a gradient term suppression and filtering method is adopted to simulate sound propagation through a steady sheared flow in three dimensions. Two typical configurations are considered: one is an incompressible and cold jet flow in wind tunnel and the other is a compressible and hot jet flow in turbofan engine. A numerically linear microphone array is used to localize the position of given sound source. The localization error is presented and linearly fitted.

Keywords: aeroacoustic, linearized Euler equation, acoustic propagation, source localization

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Authors: Hamza Nejib, Okba Taouali

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This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS

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Authors: Benjamin Anderson-Sackaney, Amr Abdel-Dayem

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This paper presents a comprehensive survey of recent source camera identification (SCI) systems. Then, the performance of various sensor pattern noise (SPN) estimators was experimentally assessed, under common photo response non-uniformity (PRNU) frameworks. The experiments used 1350 natural and 900 flat-field images, captured by 18 individual cameras. 12 different experiments, grouped into three sets, were conducted. The results were analyzed using the receiver operator characteristic (ROC) curves. The experimental results demonstrated that combining the basic SPN estimator with a wavelet-based filtering scheme provides promising results. However, the phase SPN estimator fits better with both patch-based (BM3D) and anisotropic diffusion (AD) filtering schemes.

Keywords: sensor pattern noise, source camera identification, photo response non-uniformity, anisotropic diffusion, peak to correlation energy ratio

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Authors: Lutful Karim, Mohammed S. Al-kahtani

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Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

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Authors: G. Leena Giri, Archana Mathur, S. H. Manjula, K. R. Venugopal, L. M. Patnaik

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Keyword search in XML documents is based on the notion of lowest common ancestors in the labelled trees model of XML documents and has recently gained a lot of research interest in the database community. In this paper, we propose the Modified Active (MA) algorithm which is an improvement over the active clustering algorithm by taking into consideration the entity aspect of the nodes to find the level of the node pertaining to a particular keyword input by the user. A portion of the bibliography database is used to experimentally evaluate the modified active algorithm and results show that it performs better than the active algorithm. Our modification improves the response time of the system and thereby increases the efficiency of the system.

Keywords: keyword matching patterns, MA algorithm, semantic search, knowledge management

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Authors: Vera Lucia Teixeira da Silva, Sandra Regina Buttros Gattolin de Paula

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The present study focuses on academic literacy and addresses the impact of semantic-discursive resources on the constitution of genres that are produced in such context. The research considers the development of writing in the academic context in Portuguese. Researches that address academic literacy and the characteristics of the texts produced in this context are rare, mainly with focus on the development of writing, considering three variables: the constitution of the writer, the perception of the reader/interlocutor and the organization of the informational text flow. The research aims to map the semantic-discursive resources of the written register in texts of several genres and produced by students in the first semester of the undergraduate course in Letters. The hypothesis raised is that writing in the academic environment is not a recurrent literacy practice for these learners and can be explained by the ontogenetic and phylogenetic nature of language development. Qualitative in nature, the present research has as empirical data texts produced in a half-yearly course of Reading and Textual Production; these data result from the proposition of four different writing proposals, in a total of 600 texts. The corpus is analyzed based on semantic-discursive resources, seeking to contemplate relevant aspects of language (grammar, discourse and social context) that reveal the choices made in the reader/writer interrelationship and the organizational flow of the Text. Among the semantic-discursive resources, the analysis includes three resources, including (a) appraisal and negotiation to understand the attitudes negotiated (roles of the participants of the discourse and their relationship with the other); (b) ideation to explain the construction of the experience (activities performed and participants); and (c) periodicity to outline the flow of information in the organization of the text according to the genre it instantiates. The results indicate the organizational difficulties of the flow of the text information. Cartography contributes to the understanding of the way writers use language in an effort to present themselves, evaluate someone else’s work, and communicate with readers.

Keywords: academic writing, Portuguese mother tongue, semantic-discursive resources, academic context

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Authors: Reji Thankachan, Varsha PS

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Both image capturing devices and human visual systems are nonlinear. Hence nonlinear filtering methods outperforms its linear counterpart in many applications. Linear methods are unable to remove impulsive noise in images by preserving its edges and fine details. In addition, linear algorithms are unable to remove signal dependent or multiplicative noise in images. This paper presents an approach to denoise and smoothen the Bipolar impulse noised images and videos using improved Kuwahara filter. It involves a 2 stage algorithm which includes a noise detection followed by filtering. Numerous simulation demonstrate that proposed method outperforms the existing method by eliminating the painting like flattening effect along the local feature direction while preserving edge with improvement in PSNR and MSE.

Keywords: bipolar impulse noise, Kuwahara, PSNR MSE, PDF

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Authors: Bowen Ding, Yihao Kuang

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In recent years, large-scale pre-trained language models have achieved state-of-the-art performance on a variety of natural language processing tasks. The word vectors produced by these language models can be viewed as dense encoded presentations of natural language that in text form. However, it is unknown how much linguistic information is encoded and how. In this paper, we construct several corresponding probing tasks for multiple linguistic information to clarify the encoding capabilities of different language models and performed a visual display. We firstly obtain word presentations in vector form from different language models, including BERT, ELMo, RoBERTa and GPT. Classifiers with a small scale of parameters and unsupervised tasks are then applied on these word vectors to discriminate their capability to encode corresponding linguistic information. The constructed probe tasks contain both semantic and syntactic aspects. The semantic aspect includes the ability of the model to understand semantic entities such as numbers, time, and characters, and the grammatical aspect includes the ability of the language model to understand grammatical structures such as dependency relationships and reference relationships. We also compare encoding capabilities of different layers in the same language model to infer how linguistic information is encoded in the model.

Keywords: language models, probing task, text presentation, linguistic information

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Authors: M. Hijazi, A. Zeki, A. Ismail

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An enormous amount of valuable human knowledge is preserved in documents. The rapid growth in the number of machine-readable documents for public or private access requires the use of automatic text classification. Text classification can be defined as assigning or structuring documents into a defined set of classes known in advance. Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information written in the Arabic language on the web. This paper presents a review on the published researches of Arabic Text Classification using classical data representation, Bag of words (BoW), and using conceptual data representation based on semantic resources such as Arabic WordNet and Wikipedia.

Keywords: Arabic text classification, Arabic WordNet, bag of words, conceptual representation, semantic relations

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Authors: Chun-Min Lin, Yuh-Jen Wu, Ching-Ting Chung

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The formulation of tourism policies requires objective academic research and evidence as support, especially research from local academia. Taiwan is a small island, and its economic growth relies heavily on tourism revenue. Taiwanese government has been devoting to the promotion of the tourism industry over the past few decades. Scientific research outcomes by Taiwanese scholars may and will help lay the foundations for drafting future tourism policy by the government. In this study, a total of 120 full journal articles published between 2008 and 2016 from the Journal of Tourism and Leisure Studies (JTSL) were examined to explore the scientific research trend of tourism study in Taiwan. JTSL is one of the most important Taiwanese journals in the tourism discipline which focuses on tourism-related issues and uses traditional Chinese as the study language. The method of co-word analysis from bibliometrics approaches was employed for semantic analysis in this study. When analyzing Chinese words and phrases, word segmentation analysis is a crucial step. It must be carried out initially and precisely in order to obtain meaningful word or word chunks for further frequency calculation. A word segmentation system basing on N-gram algorithm was developed in this study to conduct semantic analysis, and 100 groups of meaningful phrases with the highest recurrent rates were located. Subsequently, co-word analysis was employed for semantic classification. The results showed that the themes of tourism research in Taiwan in recent years cover the scope of tourism education, environmental protection, hotel management, information technology, and senior tourism. The results can give insight on the related issues and serve as a reference for tourism-related policy making and follow-up research.

Keywords: bibliometrics, co-word analysis, word segmentation, tourism research, policy

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Authors: Yating Mu

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Young children acquire native language with amazing rapidity. After noticing this interesting phenomenon, lots of linguistics, as well as psychologists, devote themselves to exploring the best explanations. Thus researches on first language acquisition emerged. Early lexical development is an important branch of children’s FLA (first language acquisition). Verb, the most significant class of lexicon, the most grammatically complex syntactic category or word type, is not only the core of exploring syntactic structures of language but also plays a key role in analyzing semantic features. Obviously, early verb development must have great impacts on children’s early lexical acquisition. Most scholars conclude that verbs, in general, are very difficult to learn because the problem in verb learning might be more about mapping a specific verb onto an action or event than about learning the underlying relational concepts that the verb or relational term encodes. However, the previous researches on early verb development mainly focus on the argument about whether there is a noun-bias or verb-bias in children’s early productive vocabulary. There are few researches on general characteristics of children’s early verbs concerning both semantic and syntactic aspects, not mentioning a general survey on Chinese-speaking children’s verb acquisition. Therefore, the author attempts to examine the general conditions and characteristics of Chinese-speaking children’s early productive verbs, based on data from a longitudinal study on three Chinese-speaking children. In order to present an overall picture of Chinese verb development, both semantic and syntactic aspects will be focused in the present study. As for semantic analysis, a classification method is adopted first. Verb category is a sophisticated class in Mandarin, so it is quite necessary to divide it into small sub-types, thus making the research much easier. By making a reasonable classification of eight verb classes on basis of semantic features, the research aims at finding out whether there exist any universal rules in Chinese-speaking children’s verb development. With regard to the syntactic aspect of verb category, a debate between nativist account and usage-based approach has lasted for quite a long time. By analyzing the longitudinal Mandarin data, the author attempts to find out whether the usage-based theory can fully explain characteristics in Chinese verb development. To sum up, this thesis attempts to apply the descriptive research method to investigate the acquisition and the usage of Chinese-speaking children’s early verbs, on purpose of providing a new perspective in investigating semantic and syntactic features of early verb acquisition.

Keywords: Chinese-speaking children, early verb acquisition, verb classes, verb grammatical structures

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Authors: Amhimmid Mohammed Saffour, Abdelkader Salama

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Filtering is a part of image enhancement techniques, it is used to enhance certain details such as edges in the image that are relevant to the application. Additionally, filtering can even be used to eliminate unwanted components of noise. Medical images typically contain salt and pepper noise and Poisson noise. This noise appears to the presence of minute grey scale variations within the image. In this paper, different filters techniques namely (Median, Wiener, Rank order3, Rank order5, and Average) were applied on CT medical images (Brain and chest). We using all these filters to remove salt and pepper noise from these images. This type of noise consists of random pixels being set to black or white. Peak Signal to Noise Ratio (PSNR), Mean Square Error r(MSE) and Histogram were used to evaluated the quality of filtered images. The results, which we have achieved shows that, these filters, are more useful and they prove to be helpful for general medical practitioners to analyze the symptoms of the patients with no difficulty.

Keywords: CT imaging, median filter, adaptive filter and average filter, MATLAB

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Authors: Deepa Vijay, Chellammal Surianarayanan, Gopinath Ganapathy

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Requirements of a software project serve as a guideline for the entire project team which enable the team towards producing the right outcome. As requirements are the key in deciding the success of the project, it should be specified in an unambiguous manner. Also, the requirements should be complete and consistent. It should be interpreted in the same way by the entire software project team as the customer interprets. Specifying requirements in textual manner is common in software development. This leads to poor understanding of the requirements which results in more errors and degraded quality. There are some literatures which focus on semantic way of specifying functional requirement which ensure the consistency and completeness of requirements. Alternately in the work, a method is proposed to map the syntactic requirements with corresponding semantics in the form of ontologies. This improves the understanding of requirements, prevents errors and improves quality.

Keywords: functional requirement, ontology, requirements management, semantics

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Authors: Nicole Merkle, Stefan Zander

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Ambient Assisted Living (AAL) describes a technological and methodological stack of (e.g. formal model-theoretic semantics, rule-based reasoning and machine learning), different aspects regarding the behavior, activities and characteristics of humans. Hence, a semantic representation of the user environment and its relevant elements are required in order to allow assistive agents to recognize situations and deduce appropriate actions. Furthermore, the user and his/her characteristics (e.g. physical, cognitive, preferences) need to be represented with a high degree of expressiveness in order to allow software agents a precise evaluation of the users’ context models. The correct interpretation of these context models highly depends on temporal, spatial circumstances as well as individual user preferences. In most AAL approaches, model representations of real world situations represent the current state of a universe of discourse at a given point in time by neglecting transitions between a set of states. However, the AAL domain currently lacks sufficient approaches that contemplate on the dynamic adaptions of context-related representations. Semantic representations of relevant real-world excerpts (e.g. user activities) help cognitive, rule-based agents to reason and make decisions in order to help users in appropriate tasks and situations. Furthermore, rules and reasoning on semantic models are not sufficient for handling uncertainty and fuzzy situations. A certain situation can require different (re-)actions in order to achieve the best results with respect to the user and his/her needs. But what is the best result? To answer this question, we need to consider that every smart agent requires to achieve an objective, but this objective is mostly defined by domain experts who can also fail in their estimation of what is desired by the user and what not. Hence, a smart agent has to be able to learn from context history data and estimate or predict what is most likely in certain contexts. Furthermore, different agents with contrary objectives can cause collisions as their actions influence the user’s context and constituting conditions in unintended or uncontrolled ways. We present an approach for dynamically updating a semantic model with respect to the current user context that allows flexibility of the software agents and enhances their conformance in order to improve the user experience. The presented approach adapts rules by learning sensor evidence and user actions using probabilistic reasoning approaches, based on given expert knowledge. The semantic domain model consists basically of device-, service- and user profile representations. In this paper, we present how this semantic domain model can be used in order to compute the probability of matching rules and actions. We apply this probability estimation to compare the current domain model representation with the computed one in order to adapt the formal semantic representation. Our approach aims at minimizing the likelihood of unintended interferences in order to eliminate conflicts and unpredictable side-effects by updating pre-defined expert knowledge according to the most probable context representation. This enables agents to adapt to dynamic changes in the environment which enhances the provision of adequate assistance and affects positively the user satisfaction.

Keywords: ambient intelligence, machine learning, semantic web, software agents

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Authors: Hsien-Tsen Wang

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Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution.

Keywords: business intelligence, artificial intelligence, semantic web, big data, cloud computing

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Authors: Saad Bakkali

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Image processing is a powerful tool for the enhancement of edges in images used in the interpretation of geophysical potential field data. Arial and terrestrial gravimetric surveys were carried out in the region of Tangier-Tetuan. From the observed and measured data of gravity Bouguer gravity anomalies map was prepared. This paper reports the results and interpretations of the transformed maps of Bouguer gravity anomaly of the Tangier-Tetuan area using image processing. Filtering analysis based on classical image process was applied. Operator image process like logarithmic and gamma correction are used. This paper also present the results obtained from this image processing analysis of the enhancement edges of the Bouguer gravity anomaly map of the Tangier-Tetuan zone.

Keywords: bouguer, tangier, filtering, gamma correction, logarithmic enhancement edges

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Authors: Pooja Khanal, Huaming Zhang

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Labeling of issues/bugs, also known as bug classification, plays a vital role in software engineering. Some known labels/classes of bugs are 'User Interface', 'Security', and 'API'. Most of the time, when a reporter reports a bug, they try to assign some predefined label to it. Those issues are reported for a project, and each project is a repository in GitHub/GitLab, which contains multiple issues. There are many software project repositories -ranging from individual projects to commercial projects. The labels assigned for different repositories may be dependent on various factors like human instinct, generalization of labels, label assignment policy followed by the reporter, etc. While the reporter of the issue may instinctively give that issue a label, another person reporting the same issue may label it differently. This way, it is not known mathematically if a label in one repository is similar or different to the label in another repository. Hence, the primary goal of this research is to find the semantic differences between bug labeling of different repositories via machine learning. Independent optimal classifiers for individual repositories are built first using the text features from the reported issues. The optimal classifiers may include a combination of multiple classifiers stacked together. Then, those classifiers are used to cross-test other repositories which leads the result to be deduced mathematically. The produce of this ongoing research includes a formalized open-source GitHub issues database that is used to deduce the similarity of the labels pertaining to the different repositories.

Keywords: bug classification, bug labels, GitHub issues, semantic differences

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Authors: Hyojin Lim, Cuong Nguyen Khac, Ho-Youl Jung

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In this paper, we propose moving object detection method which is helpful for driver to safely take his/her car out of parking lot. When moving objects such as motorbikes, pedestrians, the other cars and some obstacles are detected at the rear-side of host vehicle, the proposed algorithm can provide to driver warning. We assume that the host vehicle is just before departure. Gaussian Mixture Model (GMM) based background subtraction is basically applied. Pre-processing such as smoothing and post-processing as morphological filtering are added.We examine “which color space has better performance for detection of moving objects?” Three color spaces including RGB, YCbCr, and Y are applied and compared, in terms of detection rate. Through simulation, we prove that RGB space is more suitable for moving object detection based on background subtraction.

Keywords: gaussian mixture model, background subtraction, moving object detection, color space, morphological filtering

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Authors: M. Samadzadeh Mahabadi, J. Shanbehzadeh

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This paper presents a hybrid digital image-watermarking scheme, which is robust against varieties of attacks and geometric distortions. The image content is represented by important feature points obtained by an image-texture-based adaptive Harris corner detector. These feature points are extracted from LL2 of 2-D discrete wavelet transform which are obtained by using the Harris-Laplacian detector. We calculate the Fourier transform of circular regions around these points. The amplitude of this transform is rotation invariant. The experimental results demonstrate the robustness of the proposed method against the geometric distortions and various common image processing operations such as JPEG compression, colour reduction, Gaussian filtering, median filtering, and rotation.

Keywords: digital watermarking, geometric distortions, geometrical attack, Harris Laplace, important feature points, rotation, scale invariant feature

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Authors: Matthew Baucum

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With the increasing popularity of fMRI as an experimental method, psychology and neuroscience can greatly benefit from advanced techniques for summarizing and synthesizing large amounts of data from brain imaging studies. One promising avenue is automated meta-analyses, in which natural language processing methods are used to identify the brain regions consistently associated with certain semantic concepts (e.g. “social”, “reward’) across large corpora of studies. This study builds on this approach by demonstrating how, in fMRI meta-analyses, individual voxels can be treated as vectors in a semantic space and evaluated for their “proximity” to terms of interest. In this technique, a low-dimensional semantic space is built from brain imaging study texts, allowing words in each text to be represented as vectors (where words that frequently appear together are near each other in the semantic space). Consequently, each voxel in a brain mask can be represented as a normalized vector sum of all of the words in the studies that showed activation in that voxel. The entire brain mask can then be visualized in terms of each voxel’s proximity to a given term of interest (e.g., “vision”, “decision making”) or collection of terms (e.g., “theory of mind”, “social”, “agent”), as measured by the cosine similarity between the voxel’s vector and the term vector (or the average of multiple term vectors). Analysis can also proceed in the opposite direction, allowing word cloud visualizations of the nearest semantic neighbors for a given brain region. This approach allows for continuous, fine-grained metrics of voxel-term associations, and relies on state-of-the-art “open vocabulary” methods that go beyond mere word-counts. An analysis of over 11,000 neuroimaging studies from an existing meta-analytic fMRI database demonstrates that this technique can be used to recover known neural bases for multiple psychological functions, suggesting this method’s utility for efficient, high-level meta-analyses of localized brain function. While automated text analytic methods are no replacement for deliberate, manual meta-analyses, they seem to show promise for the efficient aggregation of large bodies of scientific knowledge, at least on a relatively general level.

Keywords: FMRI, machine learning, meta-analysis, text analysis

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Authors: Ahd Aljarf, Saad Amin

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Images are important source of information used as evidence during any investigation process. Their clarity and accuracy is essential and of the utmost importance for any investigation. Images are vulnerable to losing blocks and having noise added to them either after alteration or when the image was taken initially, therefore, having a high performance image processing system and it is implementation is very important in a forensic point of view. This paper focuses on improving the quality of the forensic images. For different reasons packets that store data can be affected, harmed or even lost because of noise. For example, sending the image through a wireless channel can cause loss of bits. These types of errors might give difficulties generally for the visual display quality of the forensic images. Two of the images problems: noise and losing blocks are covered. However, information which gets transmitted through any way of communication may suffer alteration from its original state or even lose important data due to the channel noise. Therefore, a developed system is introduced to improve the quality and clarity of the forensic images.

Keywords: image filtering, image reconstruction, image processing, forensic images

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Authors: Shen Jian, Hu Jie, Ma Jin, Peng Ying Hong, Fang Yi, Liu Wen Hai

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Biologically inspired design inspires inventions and new technologies in the field of engineering by mimicking functions, principles, and structures in the biological domain. To deal with the obstacles of cross-domain knowledge acquisition in the existing biologically inspired design process, functional semantic clustering based on functional feature semantic correlation and environmental constraint clustering composition based on environmental characteristic constraining adaptability are proposed. A knowledge cell clustering algorithm and the corresponding prototype system is developed. Finally, the effectiveness of the method is verified by the visual prosthetic device design.

Keywords: knowledge clustering, knowledge acquisition, knowledge based engineering, knowledge cell, biologically inspired design

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Authors: Hyun-Woo Cho

Abstract:

It is quite important to utilize right time and intelligent production monitoring and diagnosis of industrial processes in terms of quality and safety issues. When compared with monitoring task, fault diagnosis represents the task of finding process variables responsible causing a specific fault in the process. It can be helpful to process operators who should investigate and eliminate root causes more effectively and efficiently. This work focused on the active use of combining a nonlinear statistical technique with a preprocessing method in order to implement practical real-time fault identification schemes for data-rich cases. To compare its performance to existing identification schemes, a case study on a benchmark process was performed in several scenarios. The results showed that the proposed fault identification scheme produced more reliable diagnosis results than linear methods. In addition, the use of the filtering step improved the identification results for the complicated processes with massive data sets.

Keywords: diagnosis, filtering, nonlinear statistical techniques, process monitoring

Procedia PDF Downloads 243

Authors: C. B. Abhilash, Kavi Mahesh

Abstract:

Human beings are the most advanced species on earth; it's all because of the ability to communicate and share information via human language. In today's world, a huge amount of data is available on the web in text format. This has also resulted in the generation of big data in structured and unstructured formats. In general, the data is in the textual form, which is highly unstructured. To get insights and actionable content from this data, we need to incorporate the concepts of text mining and natural language processing. In our study, we mainly focus on Interesting data through which interesting facts are generated for the knowledge base. The approach is to derive the analytics from the text via the application of natural language processing. Using semantic web Resource framework descriptors (RDF), we generate the triple from the given data and derive the interesting patterns. The methodology also illustrates data integration using the RDF for reliable, interesting patterns.

Keywords: RDF, interestingness, knowledge base, semantic data

Procedia PDF Downloads 162