Search results for: semantic segmentation

Authors: Muazzam A. Khan

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In order to avoid dangerous environmental disasters, robots are being recognized as good entrants to step in as human rescuers. Robots has been gaining interest of many researchers in rescue matters especially which are furnished with advanced sensors. In distributed wireless robot system main objective for a rescue system is to track the location of the object continuously. This paper provides a novel idea to track and locate human in disaster area using stereo vision system and ZigBee technology. This system recursively predict and updates 3D coordinates in a robot coordinate camera system of a human which makes the system cost effective. This system is comprised of ZigBee network which has many advantages such as low power consumption, self-healing low data rates and low cost.

Keywords: stereo vision, segmentation, classification, human tracking, ZigBee module

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Authors: Rabi Mouhcine, Amrouch Mustapha, Mahani Zouhir, Mammass Driss

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In this paper, we present a system for offline recognition cursive Arabic handwritten text based on Hidden Markov Models (HMMs). The system is analytical without explicit segmentation used embedded training to perform and enhance the character models. Extraction features preceded by baseline estimation are statistical and geometric to integrate both the peculiarities of the text and the pixel distribution characteristics in the word image. These features are modelled using hidden Markov models and trained by embedded training. The experiments on images of the benchmark IFN/ENIT database show that the proposed system improves recognition.

Keywords: recognition, handwriting, Arabic text, HMMs, embedded training

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Authors: Yuliia Skrynnik

Abstract:

This research focuses on the interaction of verbal, non-verbal, and super-verbal communicative components used by the speaker changing social roles in the marketing discourse. The changing/performing of social roles is implemented through communicative strategies and tactics, the structural, semantic, and linguo-pragmatic means of which are characterized by specific features and differ for the performance of either a role of a supplier or a customer. Communication within the marketing discourse is characterized by symmetrical roles’ relation between communicative opponents. The strategy of a supplier’s social role realization and the strategy of a customer’s role realization influence the discursive personality's linguistic repertoire in the marketing discourse. This study takes into account that one person can be both a supplier and a customer under different circumstances, thus, exploring the one individual who can be both a supplier and a customer. Cooperative and non-cooperative tactics are the instruments for the implementation of these strategies. In the marketing discourse, verbal and non-verbal behaviour of the speaker performing a customer’s social role is highly informative for speakers who perform the role of a supplier. The research methods include discourse, context-situational, pragmalinguistic, pragmasemantic analyses, the method of non-verbal components analysis. The methodology of the study includes 5 steps: 1) defining the configurations of speakers’ social roles on the selected material; 2) establishing the type of the discourse (marketing discourse); 3) describing the specific features of a discursive personality as a subject of the communication in the process of social roles realization; 4) selecting the strategies and tactics which direct the interaction in different roles configurations; 5) characterizing the structural, semantic and pragmatic features of the strategies and tactics realization, including the analysis of interaction between verbal and non-verbal components of communication. In the marketing discourse, non-verbal behaviour is usually spontaneous but not purposeful. Thus, the adequate decoding of a partner’s non-verbal behavior provides more opportunities both for the supplier and the customer. Super-verbal characteristics in the marketing discourse are crucial in defining the opponent's social status and social role at the initial stage of interaction. The research provides the scenario of stereotypical situations of the play of a supplier and a customer. The performed analysis has perspectives for further research connected with the study of discursive variativity of speakers' verbal and non-verbal behaviour considering the intercultural factor influencing the process of performing the social roles in the marketing discourse; and the formation of the methods for the scenario construction of non-stereotypical situations of social roles realization/change in the marketing discourse.

Keywords: discursive personality, marketing discourse, non-verbal component of communication, social role, strategy, super-verbal component of communication, tactic, verbal component of communication

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Authors: Benjamin Kuester, Bjoern Eilert, Malte Stonis, Ludger Overmeyer

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Increasing quality requirements make reliable and effective quality management indispensable. This includes the complaint handling in which the 8D method is widely used. The 8D report as a written documentation of the 8D method is one of the key quality documents as it internally secures the quality standards and acts as a communication medium to the customer. In practice, however, the 8D report is mostly faulty and of poor quality. There is no quality control of 8D reports today. This paper describes the use of natural language processing for the automated evaluation of 8D reports. Based on semantic analysis and text-mining algorithms the presented system is able to uncover content and formal quality deficiencies and thus increases the quality of the complaint processing in the long term.

Keywords: 8D report, complaint management, evaluation system, text-mining

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Authors: Konstantinos Sofianos, Michael Stefanidakis

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Nowadays, advances in information and communication technologies offer a range of opportunities for new approaches, methods, and tools in the field of education and training. Teacher-centered learning has changed to student-centered learning. E-learning has now matured and enables the design and construction of intelligent learning systems. A smart learning system fully adapts to a student's needs and provides them with an education based on their preferences, learning styles, and learning backgrounds. It is a wise friend and available at any time, in any place, and with any digital device. In this paper, we propose an intelligent learning system, which includes an ontology with all elements of the learning process (learning objects, learning activities) and a massive open online course (MOOC) system. This intelligent learning system can be used in music education.

Keywords: intelligent learning systems, e-learning, music education, ontology, semantic web

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Authors: Ágoston Nagy

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This paper introduces a method of using unsupervised machine learning to visualize the feature space of a dataset in 2D, in order to find most characteristic segments in the set. After dimension reduction, users can select clusters by manual drawing. Selected clusters are recorded into a data model that is used for later predictions, based on realtime data. Predictions are made with supervised learning, using Gesture Recognition Toolkit. The paper introduces two example applications: a semantic audio organizer for analyzing incoming sounds, and a gesture database organizer where gestural data (recorded by a Leap motion) is visualized for further manipulation.

Keywords: gesture recognition, machine learning, real-time interaction, visualization

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Authors: Sujin Hwang, Hyerang Park, Hyunchul Kim

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The purpose of this study is to find the effectiveness of the admission simplification policy. The number of online news articles about ‘high school record’ was collected and semantically analyzed to identify and analyze the social awareness during 2014 to 2015. The main results of the study are as follows: First, there was a difference in expectations that the burden of the examinees would decrease as announced by KCUE. Thus, there was still a strain on the university entrance exam after the enforcement of the policy. Second, private tutoring is expanding in different forms, rather than reducing the policy. It is different from the prediction that examinees can prepare for university admissions without the private tutoring. Thus, the college admission rules currently enforced needs to be improved. The reasonable college admission system changes are discussed.

Keywords: education policy, private tutoring, shadow education, education admission policy

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Authors: Naime Boudemagh

Abstract:

In this work, we aim to improve the detection performances of radar systems. To this end, we propose and analyze a novel censoring technique of undesirable samples, of priori unknown positions, that may be present in the environment under investigation. Therefore, we consider heterogeneous backgrounds characterized by the presence of some irregularities such that clutter edge transitions and/or interfering targets. The proposed detector, termed automatic censoring constant false alarm (AC-CFAR), operates exclusively in a Gaussian background. It is built to allow the segmentation of the environment to regions and switch automatically to the appropriate detector; namely, the cell averaging CFAR (CA-CFAR), the censored mean level CFAR (CMLD-CFAR) or the order statistic CFAR (OS-CFAR). Monte Carlo simulations show that the AC-CFAR detector performs like the CA-CFAR in a homogeneous background. Moreover, the proposed processor exhibits considerable robustness in a heterogeneous background.

Keywords: CFAR, automatic censoring, heterogeneous environments, radar systems

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Authors: Azam Fazelpour, Saeed Reza Dehghani, Vlastimil Masek, Yuri S. Muzychka

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The study uses a novel image analysis based on thermal imaging to detect temperature contours created on salt ice surface during transient phenomena. Thermal cameras detect objects by using their emissivities and IR radiance. The ice surface temperature is not uniform during transient processes. The temperature starts to increase from the boundary of ice towards the center of that. Thermal cameras are able to report temperature changes on the ice surface at every individual moment. Various contours, which show different temperature areas, appear on the ice surface picture captured by a thermal camera. Identifying the exact boundary of these contours is valuable to facilitate ice surface temperature analysis. Image processing techniques are used to extract each contour area precisely. In this study, several pictures are recorded while the temperature is increasing throughout the ice surface. Some pictures are selected to be processed by a specific time interval. An image segmentation method is applied to images to determine the contour areas. Color thermal images are used to exploit the main information. Red, green and blue elements of color images are investigated to find the best contour boundaries. The algorithms of image enhancement and noise removal are applied to images to obtain a high contrast and clear image. A novel edge detection algorithm based on differences in the color of the pixels is established to determine contour boundaries. In this method, the edges of the contours are obtained according to properties of red, blue and green image elements. The color image elements are assessed considering their information. Useful elements proceed to process and useless elements are removed from the process to reduce the consuming time. Neighbor pixels with close intensities are assigned in one contour and differences in intensities determine boundaries. The results are then verified by conducting experimental tests. An experimental setup is performed using ice samples and a thermal camera. To observe the created ice contour by the thermal camera, the samples, which are initially at -20° C, are contacted with a warmer surface. Pictures are captured for 20 seconds. The method is applied to five images ,which are captured at the time intervals of 5 seconds. The study shows the green image element carries no useful information; therefore, the boundary detection method is applied on red and blue image elements. In this case study, the results indicate that proposed algorithm shows the boundaries more effective than other edges detection methods such as Sobel and Canny. Comparison between the contour detection in this method and temperature analysis, which states real boundaries, shows a good agreement. This color image edge detection method is applicable to other similar cases according to their image properties.

Keywords: color image processing, edge detection, ice contour boundary, salt ice, thermal image

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Authors: Tomáš Dyk, Stanislav Procházka, Martin Drahanský

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The article proposes a new algorithm for detecting damaged areas of the tank barrel based on the image of the inner surface of the tank barrel. Damage position is calculated using image processing techniques such as edge detection, discrete wavelet transformation and image segmentation for accurate contour detection. The algorithm can detect surface damage in smoothbore and even in rifled tank barrels. The algorithm also calculates the volume of the detected damage from the depth map generated, for example, from the distance measurement unit. The proposed method was tested on data obtained by a tank barrel scanning device, which generates both surface image data and depth map. The article also discusses tank barrel scanning devices and how damaged surface impacts material resistance.

Keywords: barrel, barrel diagnostic, image processing, surface damage detection, tank

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Authors: Peter Schnell, Phillip Haag

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In the course of the structural transformation of the construction industry, the integration of industrialization and digitization has led to the development of construction methods with an increased degree of prefabrication, such as system or modular construction. Compared to conventional construction, these innovative construction methods are characterized by modified structural and procedural properties and expand the range of construction services. Faced with the supply side, it is possible to identify construction-specific customer types with different characteristics and certain preferences as far as the choice of construction method is concerned. The basis for this finding was qualitative expert interviews. By evaluating the stereotypical customer needs, a corresponding segmentation of the demand side can be made along with the basic orientation and decision behavior. This demarcation supports the target- and needs-oriented customer approach and contributes to cooperative and successful project management.

Keywords: differentiation of customer types, modular construction methods, conventional construction methods, stereotypical customer types

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Authors: Mahmoud M. S. Albattah

Abstract:

Urbanization results in the expansion of administrative boundaries, mainly at the periphery, ultimately leading to changes in landcover. Agricultural land, naturally vegetated land, and other land types are converted into residential areas with a high density of constructs, such as transportation systems and housing. In urban regions of rapid growth and change, urban planners need regular information on up to date ground change. Amman (the capital of Jordan) is growing at unprecedented rates, creating extensive urban landscapes. Planners interact with these changes without having a global view of their impact. The use of aerial photographs and satellite images data combined with topographic information and field survey could provide effective information to develop urban change and growth inventory which could be explored towards producing a very important signature for the built-up area changes.

Keywords: highway design, satellite technologies, remote sensing, GIS, image segmentation, classification

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Authors: Surajo Ladan

Abstract:

English has been in existence in Nigeria since colonial period. The advent of English in Nigeria has caused a lot of linguistic changes in Nigerian languages especially among the educated elites and to some extent, even the ordinary people were not spared from this phenomenon. This scenario has generated a linguistic situation which culminated into the creation of Nigerian Pidgin that are conglomeration of English and other Nigerian languages. English has infiltrated the Nigerian languages to a point that a typical Nigerian can hardly talk without code-switching or using one English word or the other. The existence of English loanwords in Nigerian languages has taken another dimension in this scientific and technological age. Most of scientific and technological inventions are products of English language which are virtually adopted into the languages with phonological, morphological, and sometimes semantic variations. This paper is of the view that there should be a re-think and agitation from Nigerians to protect their languages from the linguistic genocide of English which are invariably facing extinction.

Keywords: linguistic change, loanword, phenomenon, pidgin

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Authors: Midhun Xavier

Abstract:

This paper introduces a method to verify multi-agent systems in industrial control systems using blockchain technology. The proposed solution enables to record and verify each process that occurs while generating a customized product using Ethereum-based smart contracts. Node-Red software agents are developed with the help of semantic web technologies, and these software agents interact with IEC 61499 function blocks to execute the processes. The agent associated with each mechatronic component and its controller can communicate with the blockchain to record various events that occur during each process, and the latter smart contract helps to verify these process orders of the customized product.

Keywords: blockchain, Ethereum, node-red, IEC 61499, multi-agent system, MQTT

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Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

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In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

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Authors: Shiyin He, Zheng Huang

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In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable.

Keywords: cell detection, cell recognition, deep learning, Mask-RCNN, ResNet

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Authors: Szymon Rutkowski, Tomasz Korbak

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We present a reinforcement-learning-based method of training neural machine translation models without parallel corpora. The standard encoder-decoder approach to machine translation suffers from two problems we aim to address. First, it needs parallel corpora, which are scarce, especially for low-resource languages. Second, it lacks psychological plausibility of learning procedure: learning a foreign language is about learning to communicate useful information, not merely learning to transduce from one language’s 'encoding' to another. We instead pose the problem of learning to translate as learning a policy in a communication game between two agents: the translator and the classifier. The classifier is trained beforehand on a natural language inference task (determining the entailment relation between a premise and a hypothesis) in the target language. The translator produces a sequence of actions that correspond to generating translations of both the hypothesis and premise, which are then passed to the classifier. The translator is rewarded for classifier’s performance on determining entailment between sentences translated by the translator to disciple’s native language. Translator’s performance thus reflects its ability to communicate useful information to the classifier. In effect, we train a machine translation model without the need for parallel corpora altogether. While similar reinforcement learning formulations for zero-shot translation were proposed before, there is a number of improvements we introduce. While prior research aimed at grounding the translation task in the physical world by evaluating agents on an image captioning task, we found that using a linguistic task is more sample-efficient. Natural language inference (also known as recognizing textual entailment) captures semantic properties of sentence pairs that are poorly correlated with semantic similarity, thus enforcing basic understanding of the role played by compositionality. It has been shown that models trained recognizing textual entailment produce high-quality general-purpose sentence embeddings transferrable to other tasks. We use stanford natural language inference (SNLI) dataset as well as its analogous datasets for French (XNLI) and Polish (CDSCorpus). Textual entailment corpora can be obtained relatively easily for any language, which makes our approach more extensible to low-resource languages than traditional approaches based on parallel corpora. We evaluated a number of reinforcement learning algorithms (including policy gradients and actor-critic) to solve the problem of translator’s policy optimization and found that our attempts yield some promising improvements over previous approaches to reinforcement-learning based zero-shot machine translation.

Keywords: agent-based language learning, low-resource translation, natural language inference, neural machine translation, reinforcement learning

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Document Analysis is an important research field that aims to gather the information by analyzing the data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enriching the lexicon with adapting their content based on semantic patterns extraction. The proposed approach has been presented, and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.

Keywords: document analysis, sentimental analysis, emotion detection, WEKA tool, NRC lexicon

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Authors: Rim Frikha, Nouha Bouayed Abdelmoula, Afifa Sellami, Salima Daoud, Tarek Rebai

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Klippel-Feil Syndrome (KFS) is characterized by congenital vertebral fusion of the cervical spine resulting from faulty segmentation along the embryo's developing axis. A wide spectrum of associated anomalies may be present. This heterogeneity has complicated elucidation of the genetic etiology and management of the syndrome. We report a case of an isolated Klippel-Feil Syndrome with C5-C6 fusion on the cervical spine. It‘s the rarest form of congenital fused cervical vertebrae which is predisposed to the risk of spinal cord injury and neurologic problems. The aim of this paper was to review clinical heterogeneity; radiographic abnormalities and genetic etiology in Klippel-Feil Syndrome. We insist in comprehensive evaluation and delineation of diagnostic and prognostic classes.

Keywords: Klippel–Feil anomaly, genetic, clinical heterogeneity, radiographic abnormalities

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

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The steepness of an audio signal which is produced by the musical instruments, specifically percussive instruments is the perception of how high tone or low tone which can be considered as a frequency closely related to the fundamental frequency. This paper presents a novel method for silence removal and segmentation of music signals produced by the percussive instruments and the performance of proposed method is studied with the help of MATLAB simulations. This method is based on two simple features, namely the signal energy and the spectral centroid. As long as the feature sequences are extracted, a simple thresholding criterion is applied in order to remove the silence areas in the sound signal. The simulations were carried on various instruments like drum, flute and guitar and results of the proposed method were analyzed.

Keywords: percussive instruments, spectral energy, spectral centroid, silence removal

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Authors: Abdullah Khuwaileh

Abstract:

While teaching and translating vocabulary is no longer a neglected area in ELT in general and in translation in particular, the psychology of its acquisition has been a neglected area. Our paper aims at exploring some of the learning and translating conditions under which vocabulary is acquired and translated properly. To achieve this objective, two teaching methods (experiments) were applied on 4 translators to measure their acquisition of a number of transparent vocabulary items. Some of these items were knowingly chosen from 'deceptively transparent words'. All the data, sample, etc., were taken from Jordan University of Science and Technology (JUST) and Yarmouk University, where the researcher is employed. The study showed that translators might translate transparent words inaccurately, particularly if these words are uncontextualised. It was also shown that the morphological structures of words may lead translators or even EFL learners to misinterpretations of meaning.

Keywords: english, transparent, word, processing, translation

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Authors: Marc Dietrich

Abstract:

Youth cultures like Punk as much as media relevant to the specific scenes associated with them offer ample opportunity for young people or juvenile adults to construct their personal identities. However, developmental psychology has largely neglected such identity construction processes during the last decades. Such was not always the case: Early developmental psychologists intensely studied youth cultures and their meaningful objects and media in the early 20th century but lost interest when cultural studies and the social sciences occupied the field after World War II. Our project Constructions of Juvenility and Generation(ality), funded by the German Federal Ministry for Education and Research, reintegrates the study of youth cultures and their meaningful objects and media in a developmental psychology perspective. We present an empirical study of the ways in which youth, juvenility, and generation (ality) are constructed and negotiated in underground media like punk fanzines (a portmanteau of fan and magazine), including both semantic and aesthetic aspects of these construction processes within punk culture. The fanzine sample was accessed by the theoretical sampling strategy typical for GTM studies. Acknowledging fanzines as artful self-produced media by scene members for scene members, we conceptualize them as authentic documents of scene norms and values. Drawing on an analysis of both text and (cover) images in Punk fanzines published in Germany (and within a sample dating from 1981 until 2015) using a novel Visual Grounded Theory approach, we found that: a) Juvenility is a highly contested concept in punk culture. Its semantic quality and valuation varies with the perspectives present within the culture (e.g. embryo punks versus older punks); b) Juvenility is constructed as having energy and being socio-critical that does not depend on biological age; c) Juvenility is regarded not an ideal per se in German Punk culture; Punk culture constructs old age in a largely positive way (e.g., as marker of being real and a historical innovator); d) Juvenility is constructed as a habit that should be kept for life as it is constantly adapted to individual biographical trajectories like specific job situations or having a family. Consequently, identity negotiation as documented in the zines attempts to balance subculturally driven perspectives on life and society with the pragmatic requirements of a bourgeois life. The proposed paper will present the main results of this large-scale study of punk fanzines and show how developmental psychology perspectives as represented in the novel methodology applied in it can advance the study of youth cultures.

Keywords: construction of juvenility, developmental psychology, visual GTM, youth culture, fanzines

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Authors: Nour Charara, Hussein Charara, Omar Abou Khaled, Hani Abdallah, Elena Mugellini

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In this paper, we present a human behavior modeling approach in videos scenes. This approach is used to model the normal behaviors in the conference halls. We exploited the Probabilistic Latent Semantic Analysis technique (PLSA), using the 'Bag-of-Terms' paradigm, as a tool for exploring video data to learn the model by grouping similar activities. Our term vocabulary consists of 3D spatio-temporal patch groups assigned by the direction of motion. Our video representation ensures the spatial information, the object trajectory, and the motion. The main importance of this approach is that it can be adapted to detect abnormal behaviors in order to ensure and enhance human security.

Keywords: activity modeling, clustering, PLSA, video representation

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Authors: Muhammad Zeeshan

Abstract:

This case study is about a 54 years man named Mr. U, referred to Capital Hospital, Islamabad, with the presenting complaints of Generalized Anxiety Disorder (GAD). Contrary to his complaints, the client reported psychological symptoms such as restlessness, low mood and fear of darkness and fear from closed places from the last 30 days. He also had a fear of death and his existence in the grave. His sleep was also disturbed due to excessive urination due to diabetes. He was also suffering from semantic symptoms such as headache, numbness of feet and pain in the chest and blockage of the nose. A complete history was taken and informal assessment (clinical interview and MSE) and formal testing (BAI) was applied that showed the clear diagnosis of Generalized Anxiety Disorder. CBT, relaxation techniques, prayer chart and behavioural techniques were applied for the treatment purposes.

Keywords: generalized anxiety disorder, presenting complaints, formal and informal assessment, diagnosis

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Authors: Robert Krause

Abstract:

Primary progressive aphasia (PPA) is a neurodegenerative disease similar to frontotemporal and semantic dementia, while having a different clinical image and anatomic pathology topography. Nonetheless, they are often included under an umbrella term: frontotemporal lobar degeneration (FTLD). In the study, examples of diagnosing PPA are presented through the multidisciplinary lens of specialists from different fields (neurologists, psychiatrists, clinical speech therapists, clinical neuropsychologists and others) using a variety of diagnostic tools such as MR, PET/CT, genetic screening and neuropsychological and logopedic methods. Thanks to that, specialists can get a better and clearer understanding of PPA diagnosis. The study summarizes the concrete procedures and results of different specialists while diagnosing PPA in a patient of younger middle age and illustrates the importance of multidisciplinary approach to differential diagnosis of PPA.

Keywords: primary progressive aphasia, etiology, diagnosis, younger middle age

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Authors: Koray U. Erbas

Abstract:

Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Shankaran Sitarama

Abstract:

New Product Development involves team members coming together and working in teams to come up with innovative solutions to problems, resulting in new products. Thus, a core attribute of a successful NPD team is their creativity and innovation. They need to be creative as a group, generating a breadth of ideas and innovative solutions that solve or address the problem they are targeting and meet the user’s needs. They also need to be very efficient in their teamwork as they work through the various stages of the development of these ideas, resulting in a POC (proof-of-concept) implementation or a prototype of the product. There are two distinctive traits that the teams need to have, one is ideational creativity, and the other is effective and efficient teamworking. There are multiple types of tensions that each of these traits cause in the teams, and these tensions reflect in the team dynamics. Ideational conflicts arising out of debates and deliberations increase the collective knowledge and affect the team creativity positively. However, the same trait of challenging each other’s viewpoints might lead the team members to be disruptive, resulting in interpersonal tensions, which in turn lead to less than efficient teamwork. Teams that foster and effectively manage these creative tensions are successful, and teams that are not able to manage these tensions show poor team performance. In this paper, it explore these tensions as they result in the team communication social network and propose a Creative Tension Balance index along the lines of Degree of Balance in social networks that has the potential to highlight the successful (and unsuccessful) NPD teams. Team communication reflects the team dynamics among team members and is the data set for analysis. The emails between the members of the NPD teams are processed through a semantic analysis algorithm (LSA) to analyze the content of communication and a semantic similarity analysis to arrive at a social network graph that depicts the communication amongst team members based on the content of communication. This social network is subjected to traditional social network analysis methods to arrive at some established metrics and structural balance analysis metrics. Traditional structural balance is extended to include team interaction pattern metrics to arrive at a creative tension balance metric that effectively captures the creative tensions and tension balance in teams. This CTB (Creative Tension Balance) metric truly captures the signatures of successful and unsuccessful (dissonant) NPD teams. The dataset for this research study includes 23 NPD teams spread out over multiple semesters and computes this CTB metric and uses it to identify the most successful and unsuccessful teams by classifying these teams into low, high and medium performing teams. The results are correlated to the team reflections (for team dynamics and interaction patterns), the team self-evaluation feedback surveys (for teamwork metrics) and team performance through a comprehensive team grade (for high and low performing team signatures).

Keywords: team dynamics, social network analysis, new product development teamwork, structural balance, NPD teams

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Authors: Ayşe Yurdakul, Eckehard Schnieder

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Nowadays, there are multilingual and multidisciplinary communication problems because of the increasing technical progress. Each technical field has its own specific terminology and in each particular language, there are differences in relation to definitions of terms. Besides, there could be several translations in the certain target language for one term of the source language. First of all, these problems of semantic relations between terms include the synonymy, antonymy, hypernymy/hyponymy, ambiguity, risk of confusion and translation problems. Therefore, the iglos terminology management system of the Institute for Traffic Safety and Automation Engineering of the Technische Universität Braunschweig has the goal to avoid these problems by a methodological standardisation of term definitions on the basis of the iglos sign model and iglos relation types. The focus of this paper should be on standardisation of navigation terminology as an example.

Keywords: iglos, localisation, methodological approaches, navigation, positioning, definition problems, terminology

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Authors: Yufen Qin

Abstract:

Text sentiment transfer is an important branch of text style transfer. The goal is to generate text with another sentiment attribute based on a text with a specific sentiment attribute while maintaining the content and semantic information unrelated to sentiment unchanged in the process. There are currently two main challenges in this field: no parallel corpus and text attribute entanglement. In response to the above problems, this paper proposed a novel solution: Mask-Prompt-Rerank. Use the method of masking the sentiment words and then using prompt regeneration to transfer the sentence sentiment. Experiments on two sentiment benchmark datasets and one formality transfer benchmark dataset show that this approach makes the performance of small pre-trained language models comparable to that of the most advanced large models, while consuming two orders of magnitude less computing and memory.

Keywords: language model, natural language processing, prompt, text sentiment transfer

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Authors: Rania A. Aboalela, Javed I. Khan

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This research presents a learning assessment theory of Cognitive Skill in Concept Space (CS2) to measure the assessed knowledge in terms of cognitive skill levels of the concepts. The cognitive skill levels refer to levels such as if a student has acquired the state at the level of understanding, or applying, or analyzing, etc. The theory is comprised of three constructions: Graph paradigm of a semantic/ ontological scheme, the concept states of the theory and the assessment analytics which is the process to estimate the sets of concept state at a certain skill level. Concept state means if a student has already learned, or is ready to learn, or is not ready to learn a certain skill level. The experiment is conducted to prove the validation of the theory CS2.

Keywords: cognitive skill levels, concept states, concept space, knowledge assessment theory

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