Search results for: assembly feature

Authors: Lan Du, Yan Wang, Hui Dai

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Synthetic aperture radar (SAR) image change detection has recently become a challenging problem owing to the existence of speckle noises. In this paper, an unsupervised distribution-free change detection for SAR image based on scale-invariant feature transform (SIFT) keypoints and segmentation is proposed. Firstly, the noise-robust SIFT keypoints which reveal the blob-like structures in an image are extracted in the log-ratio image to reduce the detection range. Then, different from the traditional change detection which directly obtains the change-detection map from the difference image, segmentation is made around the extracted keypoints in the two original multitemporal SAR images to obtain accurate changed region. At last, the change-detection map is generated by comparing the two segmentations. Experimental results on the real SAR image dataset demonstrate the effectiveness of the proposed method.

Keywords: change detection, Synthetic Aperture Radar (SAR), Scale-Invariant Feature Transformation (SIFT), segmentation

Procedia PDF Downloads 360

Authors: Yang Cui, Yong Qiang Chen

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An appropriate project delivery system (PDS) is crucial to the success of a construction project. Case-based reasoning (CBR) is a useful support for PDS selection. However, the traditional CBR approach represents cases as attribute-value vectors without taking relations among attributes into consideration, and could not calculate the similarity when the structures of cases are not strictly same. Therefore, this paper solves this problem by adopting the relational case-based reasoning (RCBR) approach for PDS selection, considering both the structural similarity and feature similarity. To develop the feature terms of the construction projects, the criteria and factors governing PDS selection process are first identified. Then, feature terms for the construction projects are developed. Finally, the mechanism of similarity calculation and a case study indicate how RCBR works for PDS selection. The adoption of RCBR in PDS selection expands the scope of application of traditional CBR method and improves the accuracy of the PDS selection system.

Keywords: relational cased-based reasoning, case-based reasoning, project delivery system, PDS selection

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Authors: Yanting Cao, Kazumitsu Nawata

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As smartphones upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described as more refined, complex, and detailed. In this context, we analyzed a set of experimental data obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model becomes extremely challenging. After a series of feature selection and parameters adjustment, a well-performed SVM classifier has been trained.

Keywords: smart sensors, human activity recognition, artificial intelligence, SVM

Procedia PDF Downloads 124

Authors: M. N. Zelenin, V. S. Mikhailov, R. P. Zhivotovsky

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It is known that residual welding deformations give negative effect to processability and operational quality of welded structures, complicating their assembly and reducing strength. Therefore, selection of optimal technology, ensuring minimum welding deformations, is one of the main goals in developing a technology for manufacturing of welded structures. Through years, JSC SSTC has been developing a theory for estimation of welding deformations and practical activities for reducing and compensating such deformations during welding process. During long time a methodology was used, based on analytic dependence. This methodology allowed defining volumetric changes of metal due to welding heating and subsequent cooling. However, dependences for definition of structures deformations, arising as a result of volumetric changes of metal in the weld area, allowed performing calculations only for simple structures, such as units, flat sections and sections with small curvature. In case of complex 3D structures, estimations on the base of analytic dependences gave significant errors. To eliminate this shortage, it was suggested to use finite elements method for resolving of deformation problem. Here, one shall first calculate volumes of longitudinal and transversal shortenings of welding joints using method of analytic dependences and further, with obtained shortenings, calculate forces, which action is equivalent to the action of active welding stresses. Further, a finite-elements model of the structure is developed and equivalent forces are added to this model. Having results of calculations, an optimal sequence of assembly and welding is selected and special measures to reduce and compensate welding deformations are developed and taken.

Keywords: residual welding deformations, longitudinal and transverse shortenings of welding joints, method of analytic dependences, finite elements method

Procedia PDF Downloads 387

Authors: Bamidele Samson Alobalorun, Ifedotun Roseline Idowu

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Biometrics technologies apply the human body parts for their unique and reliable identification based on physiological traits. The iris recognition system is a biometric–based method for identification. The human iris has some discriminating characteristics which provide efficiency to the method. In order to achieve this efficiency, there is a need for feature extraction of the distinct features from the human iris in order to generate accurate authentication of persons. In this study, an approach for an iris recognition system using 2D Gabor for feature extraction is applied to iris templates. The 2D Gabor filter formulated the patterns that were used for training and equally sent to the hamming distance matching technique for recognition. A comparison of results is presented using two iris image subjects of different matching indices of 1,2,3,4,5 filter based on the CASIA iris image database. By comparing the two subject results, the actual computational time of the developed models, which is measured in terms of training and average testing time in processing the hamming distance classifier, is found with best recognition accuracy of 96.11% after capturing the iris localization or segmentation using the Daughman’s Integro-differential, the normalization is confined to the Daugman’s rubber sheet model.

Keywords: Daugman rubber sheet, feature extraction, Hamming distance, iris recognition system, 2D Gabor wavelet transform

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Authors: Shreya Asthana

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Testers know that the feature they tested on stage is working perfectly in production only after release went live. Sometimes something breaks in production and testers get to know through the end user’s bug raised. The panic mode starts when your staging test results do not reflect current production behavior. And you started doubting your testing skills when finally the user reported a bug to you. Testers can deploy their confidence on release day by testing on production. Once you start doing testing in production, you will see test result accuracy because it will be running on real time data and execution will be a little faster as compared to staging one due to elimination of bad data. Feature flagging, canary releases, and data cleanup can help to achieve this technique of testing. By this paper it will be easier to understand the steps to achieve production testing before making your feature live, and to modify IT company’s testing procedure, so testers can provide the bug free experience to the end users. This study is beneficial because too many people think that testing should be done in staging but not in production and now this is high time to pull out people from their old mindset of testing into a new testing world. At the end of the day, it all just matters if the features are working in production or not.

Keywords: bug free production, new testing mindset, testing strategy, testing approach

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Authors: Vito M. Manghisi, Michele Gattullo, Alessandro Evangelista, Enricoandrea Laviola

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In this work, we compared two solutions for displaying a demo version of an Augmented Reality (AR) manual when the real product is not available, opting to replace it with its computer-aided design (CAD) model. AR has been proved to be effective in maintenance and assembly operations by many studies in the literature. However, most of them present solutions for existing products, usually converting old, printed manuals into AR manuals. In this case, authoring consists of defining how to convey existing instructions through AR. It is not a simple choice, and demo versions are created to test the design goodness. However, this becomes impossible when the product is not physically available, as for new products. A solution could be creating an entirely virtual environment with the product and the instructions. However, in this way, user interaction is completely different from that in the real application, then it would be hard testing the usability of the AR manual. This work aims to propose and compare two different solutions for the displaying of a demo version of an AR manual to support authoring in case of a product that is not physically available. We used as a case study that of an innovative semi-hermetic compressor that has not yet been produced. The applications were developed for a handheld device, using Unity 3D. The main issue was how to show the compressor and attach instructions on it. In one approach, we used Vuforia natural feature tracking to attach a CAD model of the compressor to a 2D image that is a drawing in scale 1:1 of the top-view of the CAD model. In this way, during the AR manual demonstration, the 3D model of the compressor is displayed on the user's device in place of the real compressor, and all the virtual instructions are attached to it. In the other approach, we first created a support application that shows the CAD model of the compressor on a marker. Then, we registered a video of this application, moving around the marker, obtaining a video that shows the CAD model from every point of view. For the AR manual, we used the Vuforia model target (360° option) to track the CAD model of the compressor, as it was the real compressor. Then, during the demonstration, the video is shown on a fixed large screen, and instructions are displayed attached to it in the AR manual. The first solution presents the main drawback to keeping the printed image with everyone working on the authoring of the AR manual, but allows to show the product in a real scale and interaction during the demonstration is very simple. The second one does not need a printed marker during the demonstration but a screen. Still, the compressor model is resized, and interaction is awkward since the user has to play the video on the screen to rotate the compressor. The two solutions were evaluated together with the company, and the preferred was the first one due to a more natural interaction.

Keywords: augmented reality, human computer interaction, operating instructions, maintenance, assembly

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Authors: B. Kishore Kumar, Pogula Rakesh, T. Kishore Kumar

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The music analysis is a part of the audio content analysis used to analyze the music by using the different features of audio signal. In music analysis, the first step is to divide the music signal to different sections based on the feature profiles of the music signal. In this paper, we present a music segmentation technique that will effectively segmentize the signal and thresholding technique to remove silence from the percussive sounds produced by percussive instruments, which uses two features of music, namely signal energy and spectral centroid. The proposed method impose thresholds on both the features which will vary depends on the music signal. Depends on the threshold, silence part is removed and the segmentation is done. The effectiveness of the proposed method is analyzed using MATLAB.

Keywords: percussive sounds, spectral centroid, spectral energy, silence removal, feature extraction

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Authors: Chin-Chen Chang, I-Ta Lee, Tsung-Ta Ke, Wen-Kai Tai

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Common methods for resizing image size include scaling and cropping. However, these two approaches have some quality problems for reduced images. In this paper, we propose an image resizing algorithm by separating the main objects and the background. First, we extract two feature maps, namely, an enhanced visual saliency map and an improved gradient map from an input image. After that, we integrate these two feature maps to an importance map. Finally, we generate the target image using the importance map. The proposed approach can obtain desired results for a wide range of images.

Keywords: energy map, visual saliency, gradient map, seam carving

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Authors: Sung Un Kim, Ivana Martinović, Snježana Stanarević Katavić

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As more and more youth use mobile devices, such as tablets and smartphones, for information seeking in their everyday lives, the purpose of this study is to understand the behaviors of youth seeking health information on mobile devices. The specific objective of this study is to examine 1) for what health issues youth use mobile devices, 2) for what reasons youth use mobile devices to obtain health information, 3) in what ways youth use mobile devices for health information, and 4) the features of health applications that youth find useful. The researchers devised a questionnaire for this study. Four hundred eight students from two high schools, located in Osijek, Croatia, participated by answering the questionnaire (281 girls and 127 boys). The collected data were analyzed using descriptive statistics and content analysis. The results show that among all participants, about 85 percent (n = 344) reported having used mobile devices for health information. The most frequent health topic for which they had been using mobile devices is physical activity (n = 273), followed by eating issues and nutrition (n = 224), mental health (n = 160), sexual health (n = 157), alcohol, drugs, and tobacco (n = 125), safety (n = 96) and particular diseases (n = 62). They use mobile devices to obtain health information due to the ease of use (n = 342), the ease of sharing health information (n = 281), portability (n = 215), timeliness (n = 162), and the ease of tracking/recording/monitoring health status (n = 147). Of those who have used mobile devices for health information, three-quarters (n = 261) use mobile devices to search health information, while 32.8% (n =113) use applications and 31.7% (n =109) browse information. Those who have used applications for health information (n = 113) consider the alert feature (n=107) as the most useful, followed by the tracking/recording/monitoring feature (n =92), the customized information feature (n = 86), the video feature (n = 58), and the sharing feature (n =39). It is notable that although health applications have been actively developed and studied, a majority of the participants search for or browse information on mobile devices, instead of using applications. The researchers will discuss reasons that some of them did not use mobile devices to obtain health information, students’ concerns about using health applications, and features that they wish to have in health applications.

Keywords: Croatia, health information, information seeking behaviors, mobile devices, youth

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Authors: Elif Kalkanli, Constantinos Soutis

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The use of textile preform in the advanced fields including aerospace, automotive and marine has exponentially grown in recent years. These preforms offer excellent advantages such as being lightweight and low-cost, and also, their suitability for creating different fiber architectures with different materials whilst improved mechanical properties in certain aspects. In this study, a novel honeycomb core is developed by a 3Dweaving process. The assembly of the layers is achieved thanks to innovative weaving design. Polyester yarn is selected for the 3D woven honeycomb core (3DWHC). The core is used to manufacture a sandwich panel with 2x2 twill glass fiber composite face sheets. These 3DWHC sandwich panels will be tested in three-point bending. The in-plane and out-of-plane (through-the-thickness) mechanical response of the core will be examined as a function of cell size in addition to the flexural response of the sandwich panel. The failure mechanisms of the core and the sandwich skins will be reported in addition to flexural strength and stiffness. Possible engineering applications will be identified.

Keywords: 3D woven, assembly, failure modes, honeycomb sandwich panel

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Authors: Andreas Lind, Aitor Iriondo Pascual, Dan Hogberg, Lars Hanson

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Factory setup lifecycles are most often described and prepared in CAD environments; the preparation is based on experience and inputs from several cross-disciplinary processes. Early in the factory setup preparation, a so-called block layout is created. The intention is to describe a high-level view of the intended factory setup and to claim area reservations and allocations. Factory areas are then blocked, i.e., targeted to be used for specific intended resources and processes, later redefined with detailed factory setup layouts. Each detailed layout is based on the block layout and inputs from cross-disciplinary preparation processes, such as manufacturing sequence, productivity, workers’ workplace requirements, and resource setup preparation. However, this activity is often not carried out with all variables considered simultaneously, which might entail a risk of sub-optimizing the detailed layout based on manual decisions. Therefore, this work aims to realize a digital method for assembly manufacturing layout planning where productivity, area utilization, and ergonomics can be considered simultaneously in a cross-disciplinary manner. The purpose of the digital method is to support engineers in finding optimized designs of detailed layouts for assembly manufacturing factories, thereby facilitating better decisions regarding setups of future factories. Input datasets are company-specific descriptions of required dimensions for specific area reservations, such as defined dimensions of a worker’s workplace, material façades, aisles, and the sequence to realize the product assembly manufacturing process. To test and iteratively develop the digital method, a demonstrator has been developed with an adaptation of existing software that simulates and proposes optimized designs of detailed layouts. Since the method is to consider productivity, ergonomics, area utilization, and constraints from the automatically generated block layout, a multi-objective optimization approach is utilized. In the demonstrator, the input data are sent to the simulation software industrial path solutions (IPS). Based on the input and Lua scripts, the IPS software generates a block layout in compliance with the company’s defined dimensions of area reservations. Communication is then established between the IPS and the software EPP (Ergonomics in Productivity Platform), including intended resource descriptions, assembly manufacturing process, and manikin (digital human) resources. Using multi-objective optimization approaches, the EPP software then calculates layout proposals that are sent iteratively and simulated and rendered in IPS, following the rules and regulations defined in the block layout as well as productivity and ergonomics constraints and objectives. The software demonstrator is promising. The software can handle several parameters to optimize the detailed layout simultaneously and can put forward several proposals. It can optimize multiple parameters or weight the parameters to fine-tune the optimal result of the detailed layout. The intention of the demonstrator is to make the preparation between cross-disciplinary silos transparent and achieve a common preparation of the assembly manufacturing factory setup, thereby facilitating better decisions.

Keywords: factory setup, multi-objective, optimization, simulation

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Authors: Yu Hung Chiang, Hei Chia Wang

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Nowadays, tourism has become a part of life. Before reserving hotels, customers need some information, which the most important source is online reviews, about hotels to help them make decisions. Due to the dramatic growing of online reviews, it is impossible for tourists to read all reviews manually. Therefore, designing an automatic review analysis system, which summarizes reviews, is necessary for them. The main purpose of the system is to understand the opinion of reviews, which may be positive or negative. In other words, the system would analyze whether the customers who visited the hotel like it or not. Using sentiment analysis methods will help the system achieve the purpose. In sentiment analysis methods, the targets of opinion (here they are called the feature) should be recognized to clarify the polarity of the opinion because polarity of the opinion may be ambiguous. Hence, the study proposes an unsupervised method using Part-Of-Speech pattern and multi-lexicons sentiment analysis to summarize all reviews. We expect this method can help customers search what they want information as well as make decisions efficiently.

Keywords: text mining, sentiment analysis, product feature extraction, multi-lexicons

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Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

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Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

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Authors: Tajinder Kaur, Anu Bala

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An image retrieval system is a computer system for browsing, searching, and retrieving images from a large database of digital images a new algorithm meant for content-based image retrieval (CBIR) is presented in this paper. The proposed method combines the color and texture features which are extracted the global and local information of the image. The local texture feature is extracted by using local binary patterns (LBP), which are evaluated by taking into consideration of local difference between the center pixel and its neighbors. For the global color feature, the color histogram (CH) is used which is calculated by RGB (red, green, and blue) spaces separately. In this paper, the combination of color and texture features are proposed for content-based image retrieval. The performance of the proposed method is tested on Corel 1000 database which is the natural database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and CH.

Keywords: color, texture, feature extraction, local binary patterns, image retrieval

Procedia PDF Downloads 336

Authors: Konysbek Aksaule

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The purpose of this work is to explore new and effective ways of teaching English to students who are studying a foreign language since the timeliness of the problem disclosed in this article is due to the high level of English proficiency that potential specialists must have due to high competition in the context of global globalization. The article presents an analysis of the feasibility and effectiveness of using an authentic feature film in teaching English to students. The methodological basis of the study includes an assessment of the level of students' proficiency in a foreign language, the stage of evaluating the film, and the method of selecting the film for certain categories of students. The study also contains a list of practical tasks that can be applied in the process of viewing and perception of an original feature film in a foreign language, and which are aimed at developing language skills such as speaking and listening. The results of this study proved that teaching English to students through watching an original film is one of the most effective methods because it improves speech perception, speech reproduction ability, and also expands the vocabulary of students and makes their speech fluent. In addition, learning English through watching foreign films has a huge impact on the cultural views and knowledge of students about the country of the language being studied and the world in general. Thus, this study demonstrates the high potential of using authentic feature film in English lessons for pedagogical science and methods of teaching English in general.

Keywords: university, education, students, foreign language, feature film

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Sandisiwe Mangali, Graeme Bradley

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Genome is complete set of the organism's hereditary information encoded as either deoxyribonucleic acid or ribonucleic acid in most viruses. The three different types of genomes are nuclear, mitochondrial and the plastid genome and their sequences which are uncovered by genome sequencing are known as an archive for all genetic information and enable researchers to understand the composition of a genome, regulation of gene expression and also provide information on how the whole genome works. These sequences enable researchers to explore the population structure, genetic variations, and recent demographic events in threatened species. Particularly, genome sequencing refers to a process of figuring out the exact arrangement of the basic nucleotide bases of a genome and the process through which all the afore-mentioned genomes are sequenced is referred to as whole or complete genome sequencing. Gelidium pristoides is South African endemic Rhodophyta species which has been harvested in the Eastern Cape since the 1950s for its high economic value which is one motivation for its sequencing. Its endemism further motivates its sequencing for conservation biology as endemic species are more vulnerable to anthropogenic activities endangering a species. As sequencing, mapping and annotating the Gelidium pristoides genome is the aim of this study. To accomplish this aim, the genomic DNA was extracted and quantified using the Nucleospin Plank Kit, Qubit 2.0 and Nanodrop. Thereafter, the Ion Plus Fragment Library was used for preparation of a 600bp library which was then sequenced through the Ion S5 sequencing platform for two runs. The produced reads were then quality-controlled and assembled through the SPAdes assembler with default parameters and the genome assembly was quality assessed through the QUAST software. From this assembly, the plastid and the mitochondrial genomes were then sampled out using Gelidiales organellar genomes as search queries and ordered according to them using the Geneious software. The Qubit and the Nanodrop instruments revealed an A260/A280 and A230/A260 values of 1.81 and 1.52 respectively. A total of 30792074 reads were obtained and produced a total of 94140 contigs with resulted into a sequence length of 217.06 Mbp with N50 value of 3072 bp and GC content of 41.72%. A total length of 179281bp and 25734 bp was obtained for plastid and mitochondrial respectively. Genomic data allows a clear understanding of the genomic constituent of an organism and is valuable as foundation information for studies of individual genes and resolving the evolutionary relationships between organisms including Rhodophytes and other seaweeds.

Keywords: Gelidium pristoides, genome, genome sequencing and assembly, Ion S5 sequencing platform

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Authors: Dim En Nyaung, Thin Lai Lai Thein

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Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective.

Keywords: opinion mining, opinion summarization, sentiment analysis, text mining

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Authors: Meenal Badki

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We address the issue of active feature determination, where the objective is to determine the set of examples on which additional data (such as lab tests) needs to be gathered, given a large number of examples with some features (such as demographics) and some examples with all the features (such as the complete Electronic Health Record). We note that certain features may be more costly, unique, or laborious to gather. Our proposal is a general active learning approach that is independent of classifiers and similarity metrics. It allows us to identify examples that differ from the full data set and obtain all the features for the examples that match. Our comprehensive evaluation shows the efficacy of this approach, which is driven by four authentic clinical tasks.

Keywords: feature determination, classification, active learning, sample-efficiency

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Usman Qamar, Younus Javed

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Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: rough-sets, classification, feature selection, entropy, outliers, frequent itemset mining

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Authors: Woei-Shyan Lee, Shuo-Ling Chang

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The nanoindentation behaviour and phase transformation of annealed single-crystal silicon wafers are examined. The silicon specimens are annealed at temperatures of 250, 350 and 450ºC, respectively, for 15 minutes and are then indented to maximum loads of 30, 50 and 70 mN. The phase changes induced in the indented specimens are observed using transmission electron microscopy (TEM) and micro-Raman scattering spectroscopy (RSS). For all annealing temperatures, an elbow feature is observed in the unloading curve following indentation to a maximum load of 30 mN. Under higher loads of 50 mN and 70 mN, respectively, the elbow feature is replaced by a pop-out event. The elbow feature reveals a complete amorphous phase transformation within the indented zone, whereas the pop-out event indicates the formation of Si XII and Si III phases. The experimental results show that the formation of these crystalline silicon phases increases with an increasing annealing temperature and indentation load. The hardness and Young’s modulus both decrease as the annealing temperature and indentation load are increased.

Keywords: nanoindentation, silicon, phase transformation, amorphous, annealing

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Authors: Aleksandra Chiniaeva

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The following paper describes the activity of national and international parliamentary assemblies of the European region in protection and promotion of human rights. It may be said that parliamentarians have a “double mandate” — as members of the international assembly and of their respective national parliaments. In other words, parliamentarization at both international and national level provides a situation for parliamentarians, where they link people, national governments and international organizations. The paper is aimed towards demonstrating that the activity of the main international parliamentary assemblies of the European region have a real positive impact on the human rights situation in the European region. In addition, the paper describes the assemblies that include protection of human rights in their Agenda as one of the main subjects: the EP, the PACE, the OSCE PA and the IPA CIS. Co-operation activities such as joint election observation; participation in inter-parliamentary associations, such as the IPU; conclusion agreements allow assemblies to provide observation of human right situation in the states that are not members of the particular organization and as consequence make their impact broader.

Keywords: human rights, international parliamentary assembly, IPU, EP, PACE, OSCE, international election observation

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Authors: Rohin Rameswarapu, Sameer Valsangkar

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Background: Globally, musculoskeletal disorders are the largest single cause of work-related illnesses accounting for over 33% of all newly reported occupational illnesses. Risk factors for MSD need to be delineated to suggest means for amelioration. Material and methods: In this current cross-sectional study, the prevalence of MSDs among workers in an electrical company assembly line, the socio-demographic and job characteristics associated with MSD were obtained through a semi-structured questionnaire. A quantitative assessment of the physical risk factors through the Rapid Upper Limb Assessment (RULA) tool, and measurement of psychosocial risk factors through a Likert scale was obtained. Statistical analysis was conducted using Epi-info software and descriptive and inferential statistics including chi-square and unpaired t test were obtained. Results: A total of 263 workers consented and participated in the study. Among these workers, 200 (76%) suffered from MSD. Most of the workers were aged between 18–27 years and majority of the workers were women with 198 (75.2%) of the 263 workers being women. A chi square test was significant for association between male gender and MSD with a P value of 0.007. Among the MSD positive group, 4 (2%) had a grand score of 5, 10 (5%) had a grand score of 6 and 186 (93%) had a grand score of 7 on RULA. There were significant differences between the non-MSD and MSD group on five out of the seven psychosocial domains, namely job demand, job monotony, co-worker support, decision control and family and environment domains. Discussion: The current cross-sectional study demonstrates a high prevalence of MSD among assembly line works with inherent physical and psychosocial risk factors and recommends that not only physical risk factors, addressing psychosocial risk factors through proper ergonomic means is also essential to the well-being of the employee.

Keywords: musculoskeletal disorders, India, occupational health, Rapid Upper Limb Assessment (RULA)

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Authors: Pengfei Meng, Shuangcheng Jia, Qian Li

Abstract:

We proposed a real-time high-precision semantic segmentation network based on a multi-resolution feature fusion module, the auxiliary feature extracting module, upsampling module, and atrous spatial pyramid pooling (ASPP) module. We designed a feature fusion structure, which is integrated with sufficient features of different resolutions. We also studied the effect of side-branch structure on the network and made discoveries. Based on the discoveries about the side-branch of the network structure, we used a side-branch auxiliary feature extraction layer in the network to improve the effectiveness of the network. We also designed upsampling module, which has better results than the original upsampling module. In addition, we also re-considered the locations and number of atrous spatial pyramid pooling (ASPP) modules and modified the network structure according to the experimental results to further improve the effectiveness of the network. The network presented in this paper takes the backbone network of Bisenetv2 as a basic network, based on which we constructed a network structure on which we made improvements. We named this network deep multiple-resolution bilateral networks for real-time, referred to as DMBR-Net. After experimental testing, our proposed DMBR-Net network achieved 81.2% mIoU at 119FPS on the Cityscapes validation dataset, 80.7% mIoU at 109FPS on the CamVid test dataset, 29.9% mIoU at 78FPS on the COCOStuff test dataset. Compared with all lightweight real-time semantic segmentation networks, our network achieves the highest accuracy at an appropriate speed.

Keywords: multi-resolution feature fusion, atrous convolutional, bilateral networks, pyramid pooling

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Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

Abstract:

Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

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Authors: Jianghao Hu, Hongyu Wang

Abstract:

The effective and efficient human pose estimation algorithm is an important task for real-time human pose estimation on mobile devices. This paper proposes a light-weight human key points detection algorithm, Light-Weight Network for Real-Time Pose Estimation (LWPE). LWPE uses light-weight backbone network and depthwise separable convolutions to reduce parameters and lower latency. LWPE uses the feature pyramid network (FPN) to fuse the high-resolution, semantically weak features with the low-resolution, semantically strong features. In the meantime, with multi-scale prediction, the predicted result by the low-resolution feature map is stacked to the adjacent higher-resolution feature map to intermediately monitor the network and continuously refine the results. At the last step, the key point coordinates predicted in the highest-resolution are used as the final output of the network. For the key-points that are difficult to predict, LWPE adopts the online hard key points mining strategy to focus on the key points that hard predicting. The proposed algorithm achieves excellent performance in the single-person dataset selected in the AI (artificial intelligence) challenge dataset. The algorithm maintains high-precision performance even though the model only contains 3.9M parameters, and it can run at 225 frames per second (FPS) on the generic graphics processing unit (GPU).

Keywords: depthwise separable convolutions, feature pyramid network, human pose estimation, light-weight backbone

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Authors: Saroj Bijarnia, Preety Singh

Abstract:

We propose a simple and effective biometrics system based on face verification across aging using a new variant of texture feature, Pyramid Binary Pattern. This employs Local Binary Pattern along with its hierarchical information. Dimension reduction of generated texture feature vector is done using Principal Component Analysis. Support Vector Machine is used for classification. Our proposed method achieves an accuracy of 92:24% and can be used in an automated age-invariant face verification system.

Keywords: biometrics, age invariant, verification, support vector machine

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Authors: Aymen Bouzid, Jihen Ben Smida

Abstract:

In this paper, we propose to prove the importance of the application of blind source separation methods on remote sensing data in order to index multispectral images. The proposed method starts with Gabor Filtering and the application of a Blind Source Separation to get a more effective representation of the information contained on the observation images. After that, a feature vector is extracted from each image in order to index them. Experimental results show the superior performance of this approach.

Keywords: blind source separation, content based image retrieval, feature extraction multispectral, satellite images

Procedia PDF Downloads 379