Search results for: efficient features selection

Authors: Linyu Wang, Liguo Qiao, Jianhong Xiang, Hao Xu

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In semantic communication, the existing joint Source Channel coding (JSCC) wireless communication system without pilot has unstable transmission performance and can not effectively capture the global information and location information of images. In this paper, a pilot-free image transmission system of joint source channel based on multi-level semantic information (Multi-level JSCC) is proposed. The transmitter of the system is composed of two networks. The feature extraction network is used to extract the high-level semantic features of the image, compress the information transmitted by the image, and improve the bandwidth utilization. Feature retention network is used to preserve low-level semantic features and image details to improve communication quality. The receiver also is composed of two networks. The received high-level semantic features are fused with the low-level semantic features after feature enhancement network in the same dimension, and then the image dimension is restored through feature recovery network, and the image location information is effectively used for image reconstruction. This paper verifies that the proposed multi-level JSCC algorithm can effectively transmit and recover image information in both AWGN channel and Rayleigh fading channel, and the peak signal-to-noise ratio (PSNR) is improved by 1~2dB compared with other algorithms under the same simulation conditions.

Keywords: deep learning, JSCC, pilot-free picture transmission, multilevel semantic information, robustness

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Authors: Faith G. Ehiemua, Chandra E. Ulinfun

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This work presents two approaches that use competing models to combat criminality in human trafficking. It argues that oath-taking is an approach used to combat and repress crime by natives of African descent. Therefore, certain value choices reflected explicitly or implicitly in its habitual functioning are features of crime control, a model of the criminal process used to repress and prevent crime. By pitting the approaches against each other, the work examines the utility of the purpose of each approach with the aim of assessing moral worthiness. The approaches adopted are descriptive, normative, and theoretical. The findings reveal that oath-taking is effective in human trafficking mainly because Africans believe that the African traditional system is efficient. However, the utilitarian ethical theory applied to the use of oath-taking in human trafficking shows oath-taking as protecting the interest of human traffickers against the general good of society.

Keywords: human rights, human trafficking, oath taking, utilitarianism, victim-centered approach

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Authors: Yang Ye, Hongfei Qiu, Yaqi Li

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The planning and construction of traditional cities are oriented by cars, which leads to the problems of insufficient urban public space, fragmentation, and low utilization efficiency. With the development of driverless technology, the urban structure will change from the traditional single-core grid structure to the multi-core model. In terms of traffic organization, with the release of land for traffic facilities, public space will become more continuous and integrated with traffic space. In the context of driverless technology, urban public reconstruction is characterized by modularization and high efficiency, and its planning and layout features accord with points (service facilities), lines (smart lines), surfaces (activity centers). The public space of driverless urban roads will provide diversified urban public facilities and services. The intensive urban layout makes the commercial public space realize the functions of central activities and style display, respectively, in the interior (building atrium) and the exterior (building periphery). In addition to recreation function, urban green space can also utilize underground parking space to realize efficient dispatching of shared cars. The roads inside the residential community will be integrated into the urban landscape, providing conditions for the community public activity space with changing time sequence and improving the efficiency of space utilization. The intervention of driverless technology will change the thinking of traditional urban construction and turn it into a human-oriented one. As a result, urban public space will be richer, more connected, more efficient, and the urban space justice will be optimized. By summarizing the frontier research, this paper discusses the impact of unmanned driving on cities, especially urban public space, which is beneficial for landscape architects to cope with the future development and changes of the industry and provides a reference for the related research and practice.

Keywords: driverless, urban public space, construction strategy, urban design

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Authors: Radostina Popova

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The paper presents an analysis of the innovation performance of small and medium-sized furniture enterprises in Bulgaria, accounting for over 97% of the companies in the sector. It contains advanced features of innovation in enterprises, specific features of the furniture industry in Bulgaria and analysis of the results of studies on the topic. The results from studies of three successive periods - 2006-2008; 2008-2010; 2010-2012, during which were studied 594 small and medium-sized furniture enterprises. There are commonly used in the EU definitions and indicators (European Commission, OECD, Oslo Manual), which allows for the comparability of results.

Keywords: innovation activity, competitiveness of innovation, furniture enterprises in Bulgaria

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Authors: Baha Hasan, Jan Wikander

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This paper describes a part of the integrating work between assembly design and assembly process planning domains (APP). The work is based, in its first stage, on modelling assembly features to support APP. A multi-layer architecture, based on feature-based modelling, is proposed to establish a dynamic and adaptable link between product design using CAD tools and APP. The proposed approach is based on deriving “specific function” features from the “generic” assembly and form features extracted from the CAD tools. A hierarchal structure from “generic” to “specific” and from “high level geometrical entities” to “low level geometrical entities” is proposed in order to integrate geometrical and assembly data extracted from geometrical and assembly modelers to the required processes and resources in APP. The feature concept, feature-based modelling, and feature recognition techniques are reviewed.

Keywords: assembly feature, assembly process planning, feature, feature-based modelling, form feature, ontology

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Authors: Rady A., Elsheshai A., Elsawy M., Nagui R.

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Background and Aim: Metabolic syndrome affect around 20% of general population , authors have incriminated antipsychotics as serious risk factor that may provoke such derangement. The aim of our study is to assess metabolic syndrome in patients presenting psychotic features (delusions and hallucinations) whether schizophrenia or mood disorder and compare results in terms of drug naïf, on medication and healthy control. Subjects and Methods: The study recruited 40 schizophrenic patients, half of them drug naïf and the other half on antipsychotics, 40 patients with mood disorder with psychotic features, half of them drug naïf and the other half on medication, 20 healthy control. Exclusion criteria were put in order to exclude patients having already endocrine or metabolic disorders that my interfere with results obtain to minimize confusion bias. Metabolic syndrome assessed by measuring parameters including weight, body mass index, waist circumference, triglyceride level, HDL, fasting glucose, fasting insulin and insulin resistance Results: No difference was found when comparing drug naïf to those on medication in both schizophrenic and psychotic mood disorder arms, schizophrenic patients whether on medication or drug naïf should difference with control group for fasting glucose, schizophrenic patients on medication also showed difference in insulin resistance compared to control group. On the other hand, patients with psychotic mood disorder whether drug naïf or on medication showed difference from control group for fasting insulin level. Those on medication also differed from control for insulin resistance Conclusion: Our study didn’t reveal difference in metabolic syndrome among patients with psychotic features whether on medication or drug naïf. Only patients with Psychotic features on medication showed insulin resistance. Schizophrenic patients drug naïf or on medication tend to show higher fasting glucose while psychotic mood disorder whether drug naïf or on medication tend to show higher fasting insulin. This study suggest that presence of psychotic features themselves regardless being on medication or not carries a risk for insulin resistance and metabolic syndrome. Limitation: This study is limited by number of participants and larger numbers in future studies should be included in order to extrapolate results. Cohort longitudinal studies are needed in order to evaluate such hypothesis.

Keywords: schizophrenia, metabolic syndrome, psychosis, insulin, resistance

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Authors: Dagnija Blumberga, Toms Prodanuks, Ivars Veidenbergs, Andra Blumberga

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Gas condensing units with inner tubes heat exchangers represent third generation technology and differ from second generation heat and mass transfer units, which are fulfilled by passive filling material layer. The first one improves heat and mass transfer by increasing cooled contact surface of gas and condensate drops and film formed in inner tubes heat exchanger. This paper presents a selection of significant factors which influence the heat and mass transfer. Experimental planning is based on the research and analysis of main three independent variables; velocity of water and gas as well as density of spraying. Empirical mathematical models show that the coefficient of heat transfer is used as dependent parameter which depends on two independent variables; water and gas velocity. Empirical model is proved by the use of experimental data of two independent gas condensing units in Lithuania and Russia. Experimental data are processed by the use of heat transfer criteria-Kirpichov number. Results allow drawing the graphical nomogram for the calculation of heat and mass transfer conditions in the innovative and energy efficient gas cooling unit.

Keywords: gas condensing unit, filling, inner heat exchanger, package, spraying, tunes

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Authors: Shao Bo Cheng, Yong-Jin Han, Se Young Park, Seong-Bae Park

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Consumer-to-Consumer (C2C) E-commerce has been growing at a very high speed in recent years. Since identical or nearly-same kinds of products compete one another by relying on keyword search in C2C E-commerce, some sellers describe their products with spam keywords that are popular but are not related to their products. Though such products get more chances to be retrieved and selected by consumers than those without spam keywords, the spam keywords mislead the consumers and waste their time. This problem has been reported in many commercial services like e-bay and taobao, but there have been little research to solve this problem. As a solution to this problem, this paper proposes a method to classify whether keywords of a product are spam or not. The proposed method assumes that a keyword for a given product is more reliable if the keyword is observed commonly in specifications of products which are the same or the same kind as the given product. This is because that a hierarchical category of a product in general determined precisely by a seller of the product and so is the specification of the product. Since higher layers of the hierarchical category represent more general kinds of products, a reliable degree is differently determined according to the layers. Hence, reliable degrees from different layers of a hierarchical category become features for keywords and they are used together with features only from specifications for classification of the keywords. Support Vector Machines are adopted as a basic classifier using the features, since it is powerful, and widely used in many classification tasks. In the experiments, the proposed method is evaluated with a golden standard dataset from Yi-han-wang, a Chinese C2C e-commerce, and is compared with a baseline method that does not consider the hierarchical category. The experimental results show that the proposed method outperforms the baseline in F1-measure, which proves that spam keywords are effectively identified by a hierarchical category in C2C e-commerce.

Keywords: spam keyword, e-commerce, keyword features, spam filtering

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Authors: Ju-Hong Lee, Ching-Wei Liao, Kun-Che Lee

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This paper deals with the problem of using antenna sensors for adaptive beamforming in the presence of random steering mismatch. We present an efficient adaptive array beamformer with robustness to deal with the considered problem. The robustness of the proposed beamformer comes from the efficient designation of the steering vector. Using the received array data vector, we construct an appropriate correlation matrix associated with the received array data vector and a correlation matrix associated with signal sources. Then, the eigenvector associated with the largest eigenvalue of the constructed signal correlation matrix is designated as an appropriate estimate of the steering vector. Finally, the adaptive weight vector required for adaptive beamforming is obtained by using the estimated steering vector and the constructed correlation matrix of the array data vector. Simulation results confirm the effectiveness of the proposed method.

Keywords: adaptive beamforming, antenna array, linearly constrained minimum variance, robustness, steering vector

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Authors: Shiva Kumar, G. S. Vijay, Srinivas Pai P., Shrinivasa Rao B. R.

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In the present study RBF neural networks were used for predicting the performance and emission parameters of a biodiesel engine. Engine experiments were carried out in a 4 stroke diesel engine using blends of diesel and Honge methyl ester as the fuel. Performance parameters like BTE, BSEC, Tech and emissions from the engine were measured. These experimental results were used for ANN modeling. RBF center initialization was done by random selection and by using Clustered techniques. Network was trained by using fixed and varying widths for the RBF units. It was observed that RBF results were having a good agreement with the experimental results. Networks trained by using clustering technique gave better results than using random selection of centers in terms of reduced MRE and increased prediction accuracy. The average MRE for the performance parameters was 3.25% with the prediction accuracy of 98% and for emissions it was 10.4% with a prediction accuracy of 80%.

Keywords: radial basis function networks, emissions, performance parameters, fuzzy c means

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Authors: Cigdem Safak Saglam

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Most thermal power plants use steam as working fluid in their power cycle. Therefore, in addition to fuel, water is the other main input for thermal plants. Water and steam must be highly pure in order to protect the systems from corrosion, scaling and biofouling. Pure process water is produced in water treatment plants having many several treatment methods. Treatment plant design is selected depending on raw water source and required water quality. Although working principle of fossil-fuel fired thermal power plants are same, there is no standard design and equipment arrangement valid for all thermal power plant utility systems. Besides that, there are many other technology evaluation and selection criteria for designing the most optimal water systems meeting the requirements such as local conditions, environmental restrictions, electricity and other consumables availability and transport, process water sources and scarcity, land use constraints etc. Aim of this study is explaining the adopted methodology for technology selection for process water preparation and industrial waste water treatment plant in a thermal power plant project located in Tufanbeyli, Adana Province in Turkey. Thermal power plant is fired with indigenous lignite coal extracted from adjacent lignite reserves. This paper addresses all above-mentioned factors affecting the thermal power plant water treatment facilities (demineralization + waste water treatment) design and describes the ultimate design of Tufanbeyli Thermal Power Plant Water Treatment Plant.

Keywords: thermal power plant, lignite coal, pretreatment, demineralization, electrodialysis, recycling, ash dampening

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Authors: Ayat Adnan Atwah, Muhammad A. Khan

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Self-cleaning surfaces are getting significant attention in industrial fields. Especially for textile fabrics, it is observed that self-cleaning textile fabric surfaces are created by manipulating the surface features with the help of coatings and nanoparticles, which are considered costly and far more complicated. However, controlling the fabrication parameters of textile fabrics at the microscopic level by exploring the potential for self-cleaning has not been addressed. This study aimed to establish the context of self-cleaning textile fabrics by controlling the fabrication parameters of the textile fabric at the microscopic level. Therefore, 3D-printed textile fabrics were fabricated using the low-cost fused filament fabrication (FFF) technique. The printing parameters, such as orientation angle (O), layer height (LH), and extruder width (EW), were used to control the microscopic features of the printed fabrics. The combination of three printing parameters was created to provide the best self-cleaning textile fabric surface: (LH) (0.15, 0.13, 0.10 mm) and (EW) (0.5, 0.4, 0.3 mm) along with two different (O) of (45º and 90º). Three different thermoplastic flexible filament materials were used: (TPU 98A), (TPE felaflex), and (TPC flex45). The printing parameters were optimised to get the optimum self-cleaning ability of the printed specimens. Furthermore, the impact of these characteristics on mechanical strength at the fabric-woven structure level was investigated. The study revealed that the printing parameters significantly affect the self-cleaning properties after adjusting the selected combination of layer height, extruder width, and printing orientation. A linear regression model was effectively developed to demonstrate the association between 3D printing parameters (layer height, extruder width, and orientation). According to the experimental results, (TPE felaflex) has a better self-cleaning ability than the other two materials.

Keywords: 3D printing, self-cleaning fabric, microscopic features, printing parameters, fabrication

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Authors: Manendra Singh Parihar, Sandip Ghosh Chowdhury

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Austenitic stainless steels are widely used and give a good combination of properties. When this steel is plastically deformed, a phase transformation of the metastable Face Centred Cubic Austenite to the stable Body Centred Cubic (α’) or to the Hexagonal close packed (ԑ) martensite may occur, leading to the enhancement in the mechanical properties like strength. The work was based on variant selection and corresponding texture analysis for the strain induced martensitic transformation during deformation of the parent austenite FCC phase to form the product HCP and the BCC martensite phases separately, obeying their respective orientation relationships. The automated method for reconstruction of the parent phase orientation using the EBSD data of the product phase orientation is done using the MATLAB and TSL-OIM software. The method of triplets was used which involves the formation of a triplet of neighboring product grains having a common variant and linking them using a misorientation-based criterion. This led to the proper reconstruction of the pre-transformation phase orientation data and thus to its micro structure and texture. The computational speed of current method is better compared to the previously used methods of reconstruction. The reconstruction of austenite from ԑ and α’ martensite was carried out for multiple samples and their IPF images, pole figures, inverse pole figures and ODFs were compared. Similar type of results was observed for all samples. The comparison gives the idea for estimating the correct sequence of the transformation i.e. γ → ε → α’ or γ → α’, during deformation of AISI 304 austenitic stainless steel.

Keywords: variant selection, reconstruction, EBSD, austenitic stainless steel, martensitic transformation

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Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

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Authors: T. Thein, S. Kalyar Myo

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Human uses visual information for understanding the speech contents in noisy conditions or in situations where the audio signal is not available. The primary advantage of visual information is that it is not affected by the acoustic noise and cross talk among speakers. Using visual information from the lip movements can improve the accuracy and robustness of automatic speech recognition. However, a major challenge with most automatic lip reading system is to find a robust and efficient method for extracting the linguistically relevant speech information from a lip image sequence. This is a difficult task due to variation caused by different speakers, illumination, camera setting and the inherent low luminance and chrominance contrast between lip and non-lip region. Several researchers have been developing methods to overcome these problems; the one is lip reading. Moreover, it is well known that visual information about speech through lip reading is very useful for human speech recognition system. Lip reading is the technique of a comprehensive understanding of underlying speech by processing on the movement of lips. Therefore, lip reading system is one of the different supportive technologies for hearing impaired or elderly people, and it is an active research area. The need for lip reading system is ever increasing for every language. This research aims to develop a visual teaching method system for the hearing impaired persons in Myanmar, how to pronounce words precisely by identifying the features of lip movement. The proposed research will work a lip reading system for Myanmar Consonants, one syllable consonants (င (Nga)၊ ည (Nya)၊ မ (Ma)၊ လ (La)၊ ၀ (Wa)၊ သ (Tha)၊ ဟ (Ha)၊ အ (Ah) ) and two syllable consonants ( က(Ka Gyi)၊ ခ (Kha Gway)၊ ဂ (Ga Nge)၊ ဃ (Ga Gyi)၊ စ (Sa Lone)၊ ဆ (Sa Lain)၊ ဇ (Za Gwe) ၊ ဒ (Da Dway)၊ ဏ (Na Gyi)၊ န (Na Nge)၊ ပ (Pa Saug)၊ ဘ (Ba Gone)၊ ရ (Ya Gaug)၊ ဠ (La Gyi) ). In the proposed system, there are three subsystems, the first one is the lip localization system, which localizes the lips in the digital inputs. The next one is the feature extraction system, which extracts features of lip movement suitable for visual speech recognition. And the final one is the classification system. In the proposed research, Two Dimensional Discrete Cosine Transform (2D-DCT) and Linear Discriminant Analysis (LDA) with Active Contour Model (ACM) will be used for lip movement features extraction. Support Vector Machine (SVM) classifier is used for finding class parameter and class number in training set and testing set. Then, experiments will be carried out for the recognition accuracy of Myanmar consonants using the only visual information on lip movements which are useful for visual speech of Myanmar languages. The result will show the effectiveness of the lip movement recognition for Myanmar Consonants. This system will help the hearing impaired persons to use as the language learning application. This system can also be useful for normal hearing persons in noisy environments or conditions where they can find out what was said by other people without hearing voice.

Keywords: feature extraction, lip reading, lip localization, Active Contour Model (ACM), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Two Dimensional Discrete Cosine Transform (2D-DCT)

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Authors: Shao-Tzu Huang, Chen-Chien Hsu, Wei-Yen Wang

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Matching high dimensional features between images is computationally expensive for exhaustive search approaches in computer vision. Although the dimension of the feature can be degraded by simplifying the prior knowledge of homography, matching accuracy may degrade as a tradeoff. In this paper, we present a feature matching method based on k-means algorithm that reduces the matching cost and matches the features between images instead of using a simplified geometric assumption. Experimental results show that the proposed method outperforms the previous linear exhaustive search approaches in terms of the inlier ratio of matched pairs.

Keywords: feature matching, k-means clustering, SIFT, RANSAC

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Authors: B. Falah, N. E. Abakouy

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Due to its high computational cost, mutation testing has been neglected by researchers. Recently, many cost and mutants’ reduction techniques have been developed, improved, and experimented, but few of them has relied the possibility of reducing the cost of mutation testing on the program type of the application under test. This paper is a comparative study between four operators’ selection techniques (mutants sampling, class level operators, method level operators, and all operators’ selection) based on the program code type of each application under test. It aims at finding an alternative approach to reveal the effect of code type on mutation testing score. The result of our experiment shows that the program code type can affect the mutation score and that the programs using polymorphism are best suited to be tested with mutation testing.

Keywords: equivalent mutant, killed mutant, mutation score, mutation testing, program code type, software testing

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: A: Annis Fathima, V. Vaidehi, S. Ajitha

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Face recognition system finds many applications in surveillance and human computer interaction systems. As the applications using face recognition systems are of much importance and demand more accuracy, more robustness in the face recognition system is expected with less computation time. In this paper, a hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis (HGWLDA) is proposed. The normalized input grayscale image is approximated and reduced in dimension to lower the processing overhead for Gabor filters. This image is convolved with bank of Gabor filters with varying scales and orientations. LDA, a subspace analysis techniques are used to reduce the intra-class space and maximize the inter-class space. The techniques used are 2-dimensional Linear Discriminant Analysis (2D-LDA), 2-dimensional bidirectional LDA ((2D)2LDA), Weighted 2-dimensional bidirectional Linear Discriminant Analysis (Wt (2D)2 LDA). LDA reduces the feature dimension by extracting the features with greater variance. k-Nearest Neighbour (k-NN) classifier is used to classify and recognize the test image by comparing its feature with each of the training set features. The HGWLDA approach is robust against illumination conditions as the Gabor features are illumination invariant. This approach also aims at a better recognition rate using less number of features for varying expressions. The performance of the proposed HGWLDA approaches is evaluated using AT&T database, MIT-India face database and faces94 database. It is found that the proposed HGWLDA approach provides better results than the existing Gabor approach.

Keywords: face recognition, Gabor wavelet, LDA, k-NN classifier

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Authors: Seyed Nima Tayarani Bathaie

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In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.

Keywords: face detection algorithm, Haar features, security of ATM

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Authors: P. Aarumugam, N. Krishnamoorthy, K. Gunasekaran

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The mosquitoes with loss of minimum three legs especially the hind legs have the negative impact on the survival hood of mosquitoes. Three days old unfed adult female laboratory strain was selected in each generation against sublethal dosages (0.004%, 0.005%, 0.007% and 0.01%) of deltamethrin upto 40 generations. Impregnated papers with acetone were used for control. Every fourth generation, survived mosquitoes were observed for functional mortality. Hind legs lost were significantly (P< 0.05) higher in treated than the controls up to generation 24, thereafter no significant lost. In contrary, no significant forelegs lost among exposed mosquitoes. Middle legs lost were also not significant in the exposed mosquitoes except first generation (F1). The field strain (Chennai) did not show any significant loss of legs (fore or mid or hind) compared to the control. The selection pressure on mosquito population influences strong natural selection to develop various adaptive mechanisms.

Keywords: Anopheles stephensi, deltamethrin, functional mortality, synthetic pyrethroids

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Authors: Jaechun No

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As the technology of NAND flash memory rapidly grows, SSD is becoming an excellent alternative for storage solutions, because of its high random I/O throughput and low power consumption. These SSD potentials have drawn great attention from IT enterprises that seek for better I/O performance. However, high SSD cost per capacity makes it less desirable to construct a large-scale storage subsystem solely composed of SSD devices. An alternative is to build a hybrid storage subsystem where both HDD and SSD devices are incorporated in an economic manner, while employing the strengths of both devices. This paper presents a hybrid file system, called hybridFS, that attempts to utilize the advantages of HDD and SSD devices, to provide a single, virtual address space by integrating both devices. HybridFS not only proposes an efficient implementation for the file management in the hybrid storage subsystem but also suggests an experimental framework for making use of the excellent features of existing file systems. Several performance evaluations were conducted to verify the effectiveness and suitability of hybridFS.

Keywords: hybrid file mapping, data layout, hybrid device integration, extent allocation

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Authors: Bjorn-Ole Meyer, Dominik Marti, Peter E. Andersen

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A multimodal imaging system, combining spectrally resolved multiphoton microscopy (MPM) and optical coherence microscopy (OCM) is demonstrated. MPM and OCM are commonly integrated into multimodal imaging platforms to combine functional and morphological information. The MPM signals, such as two-photon fluorescence emission (TPFE) and signals created by second harmonic generation (SHG) are biomarkers which exhibit information on functional biological features such as the ratio of pyridine nucleotide (NAD(P)H) and flavin adenine dinucleotide (FAD) in the classification of cancerous tissue. While the spectrally resolved imaging allows for the study of biomarkers, using a spectrometer as a detector limits the imaging speed of the system significantly. To overcome those limitations, an OCM setup was added to the system, which allows for fast acquisition of structural information. Thus, after rapid imaging of larger specimens, navigation within the sample is possible. Subsequently, distinct features can be selected for further investigation using MPM. Additionally, by probing a different contrast, complementary information is obtained, and different biomarkers can be investigated. OCM images of tissue and cell samples are obtained, and distinctive features are evaluated using MPM to illustrate the benefits of the system.

Keywords: optical coherence microscopy, multiphoton microscopy, multimodal imaging, two-photon fluorescence emission

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Dembele Dembele, Philippe

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Several Second Language Acquisition (SLA) studies have stressed the benefits of Content and Language Integrated Learning (CLIL) and shown how CLIL students outperformed their non-CLIL counterparts in many L2 skills. However, numerous experimental CLIL programs seem to have mainly targeted above-average and rather highly motivated language learners. The need to understand the impact of the student’s language proficiency on code-switching in CLIL instruction motivated this study. Therefore, determining the implications of the students’ low-language proficiency for CLIL methodology, as well as the frequency with which CLIL teachers use the main pedagogical functions of code-switching, seemed crucial for a Spanish CLIL instruction on a large scale. In the mixed-method approach adopted, ten face-to-face interviews were conducted in nine Valencian public secondary education schools, while over 30 CLIL teachers also contributed with their experience in two online survey questionnaires. The results showed the crucial role language proficiency plays in the Valencian CLIL/Plurilingual selection criteria. The presence of a substantial number of low-language proficient students in CLIL groups, which in turn implied important methodological consequences, was another finding of the study. Indeed, though the pedagogical use of L1 was confirmed as an extended practice among CLIL teachers, more than half of the participants perceived that code-switching impaired attaining their CLIL lesson objectives. Therein, the dissertation highlights the need for more extensive empirical research on how code-switching could prove beneficial in CLIL instruction involving low-language proficient students while maintaining the maximum possible exposure to the target language.

Keywords: CLIL methodology, low language proficiency, code switching, selection criteria, code-switching functions

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Authors: Maryam Heidari, James H. Jones Jr.

Abstract:

Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

Procedia PDF Downloads 118

Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

Abstract:

Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: moving object detection, histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine

Procedia PDF Downloads 598

Authors: Alan S. Hoback

Abstract:

Most people see human faces in car front and back ends because of the process of pareidolia. 96 people were surveyed to see how many of them saw a face in the vehicle styling. Participants were aged 18 to 72 years. 94% of the participants saw faces in the front-end design of production models. All participants that recognized faces indicated that most styles showed some degree of an angry expression. It was found that women were more likely to see faces in inanimate objects. However, with respect to whether women were more likely to perceive anger in the vehicle design, the results need further clarification. Survey responses were correlated to the design features of vehicles to determine what cues the respondents were likely looking at when responding. Whether the features looked anthropomorphic was key to anger perception. Features such as the headlights which could represent eyes and the air intake that could represent a mouth had high correlations to trends in scores. Results are compared among models, makers, by groupings of body styles classifications for the top 12 brands sold in the US, and by year for the top 20 models sold in the US in 2016. All of the top models sold increased in perception of an angry expression over the last 20 years or since the model was introduced, but the relative change varied by body style grouping.

Keywords: aggressive driving, face recognition, road rage, vehicle styling

Procedia PDF Downloads 145

Authors: Ammar Khader Almasri

Abstract:

The software processes play a vital role for delivering a high quality software system that meets the user’s needs. There are many software development models which are used by most system developers, which can be categorized into two categories (traditional and new methodologies). Mobile applications like other desktop applications need appropriate and well-working software development process. Nevertheless, mobile applications have different features which limit their performance and efficiency like application size, mobile hardware features. Moreover, this research aims to help developers in using a standardized model for developing mobile applications.

Keywords: software development process, agile methods , moblile application development, traditional methods

Procedia PDF Downloads 390

Authors: R. S. Remya, U. S. Sethulekshmi

Abstract:

Detecting the authenticity of a video is an important issue in digital forensics as Video is used as a silent evidence in court such as in child pornography, movie piracy cases, insurance claims, cases involving scientific fraud, traffic monitoring etc. The biggest threat to video data is the availability of modern open video editing tools which enable easy editing of videos without leaving any trace of tampering. In this paper, we propose an efficient passive method for inter-frame video tampering detection, its type and location by estimating the optical flow of wavelet features of adjacent frames and thresholding the variation in the estimated feature. The performance of the algorithm is compared with the z-score thresholding and achieved an efficiency above 95% on all the tested databases. The proposed method works well for videos with dynamic (forensics) as well as static (surveillance) background.

Keywords: discrete wavelet transform, optical flow, optical flow variation, video tampering

Procedia PDF Downloads 363