Search results for: facial recognition

Authors: Farzad Khajavi

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Recognition of the interaction between active pharmaceutical ingredients (API) and excipients is a pivotal factor in the development of all pharmaceutical dosage forms. By predicting the interaction between API and excipients, we will be able to prevent the advent of impurities or at least lessen their amount. In this study, we used principle component analysis (PCA) to predict the interaction between Gliclazide as a secondary amine with Lactose in pharmaceutical solid dosage forms. The infrared spectra of binary mixtures of Gliclazide with Lactose at different mole ratios were recorded, and the obtained matrix was analyzed with PCA. By plotting score columns of the analyzed matrix, the incompatibility between Gliclazide and Lactose was observed. This incompatibility was seen experimentally. We observed the appearance of the impurity originated from the Maillard reaction between Gliclazide and Lactose at the chromatogram of the manufactured tablets in room temperature and under accelerated stability conditions. This impurity increases at the stability months. By changing Lactose to Mannitol and using Calcium Dibasic Phosphate in the tablet formulation, the amount of the impurity decreased and was in the acceptance range defined by British pharmacopeia for Gliclazide Tablets. This method is a fast and simple way to predict the existence of incompatibility between excipients and active pharmaceutical ingredients.

Keywords: PCA, gliclazide, impurity, infrared spectroscopy, interaction

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Authors: A. M. Byrne, E. Bonfiglio, C. Rigby, N. Edelstyn

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Introduction: Neuromarketing employs numerous methodologies when investigating products and advertisement effectiveness. Electroencephalography (EEG), a non-invasive measure of electrical activity from the brain, is commonly used in neuromarketing. EEG data can be considered using time-frequency (TF) analysis, where changes in the frequency of brainwaves are calculated to infer participant’s mental states, or event-related potential (ERP) analysis, where changes in amplitude are observed in direct response to a stimulus. This presentation discusses the findings of a systematic review of EEG measures in neuromarketing. A systematic review summarises evidence on a research question, using explicit measures to identify, select, and critically appraise relevant research papers. Thissystematic review identifies which EEG measures are the most robust predictor of customer preference and purchase intention. Methods: Search terms identified174 papers that used EEG in combination with marketing-related stimuli. Publications were excluded if they were written in a language other than English or were not published as journal articles (e.g., book chapters). The review investigated which TF effect (e.g., theta-band power) and ERP component (e.g., N400) most consistently reflected preference and purchase intention. Machine-learning prediction was also investigated, along with the use of EEG combined with physiological measures such as eye-tracking. Results: Frontal alpha asymmetry was the most reliable TF signal, where an increase in activity over the left side of the frontal lobe indexed a positive response to marketing stimuli, while an increase in activity over the right side indexed a negative response. The late positive potential, a positive amplitude increase around 600 ms after stimulus presentation, was the most reliable ERP component, reflecting the conscious emotional evaluation of marketing stimuli. However, each measure showed mixed results when related to preference and purchase behaviour. Predictive accuracy was greatly improved through machine-learning algorithms such as deep neural networks, especially when combined with eye-tracking or facial expression analyses. Discussion: This systematic review provides a novel catalogue of the most effective use of each EEG measure commonly used in neuromarketing. Exciting findings to emerge are the identification of the frontal alpha asymmetry and late positive potential as markers of preferential responses to marketing stimuli. Predictive accuracy using machine-learning algorithms achieved predictive accuracies as high as 97%, and future research should therefore focus on machine-learning prediction when using EEG measures in neuromarketing.

Keywords: EEG, ERP, neuromarketing, machine-learning, systematic review, time-frequency

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Authors: Eric Agrie Ambele

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The norms and models for learning pronunciation in relation to the teaching and learning of English pronunciation are key issues nowadays in English Language Teaching in ESL contexts. This paper discusses these issues based on a study on the attitudes of some Francophone university students in Cameroon towards three English accents spoken in Cameroon: Cameroon Francophone English (CamFE), Cameroon English (CamE), and Hyperlectal Cameroon English (near standard British English). With the desire to know more about the treatment that these English accents receive among these students, an aspect that had hitherto received little attention in the literature, a language attitude questionnaire, and the matched-guise technique was used to investigate this phenomenon. Two methods of data analysis were employed: (1) the percentage count procedure, and (2) the semantic differential scale. The findings reveal that the participants’ attitudes towards the selected accents vary in degree. Though Hyperlectal CamE emerged first, CamE second and CamFE third, no accent, on average, received a negative evaluation. It can be deduced from this findings that, first, CamE is gaining more and more recognition and can stand as an autonomous accent; second, that the participants all rated Hyperlectal CamE higher than CamE implies that they would be less motivated in a context where CamE is the learning model. By implication, in the teaching of English pronunciation to francophone learners learning English in Cameroon, Hyperlectal Cameroon English should be the model.

Keywords: teaching pronunciation, English accents, Francophone learners, attitudes

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Authors: Yuying Liu

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Despite the increasing popularity of Chinese and the recognition of the growing commodifying ideology of Chinese language in many contexts (Liu and Gao, 2020; Guo, Shin and Shen 2020), the ideological orientations of the Chinese diaspora community towards the Chinese language remain under-researched. This research contributes seeks to bridge this gap by investigating the micro-level language ideologies embedded in the Chinese complementary schools in the Republic of Ireland. Informed by Ruíz’s (1984) metaphorical representations of language, 11 Chinese complementary schools’ websites were analysed as discursive texts that signal the language policy and ideology to prospective learners and parents were analysed. The results of the analysis suggest that a move from a portrayal of Chinese as linked to student heritage identity, to the commodification of linguistic and cultural diversity, is evident. It denotes the growing commodifying ideology among the Chinese complementary schools in the Republic of Ireland. The changing profile of the complementary school, from serving an ethnical community to teaching Chinese as a foreign language for the wider community, indicates the possibility of creating the a positive synergy between the Complementary school and the mainstream education. This study contributes to the wider discussions of language ideology and language planning, with regards to modern language learning and heritage language maintenance.

Keywords: the Chinese language;, Chinese as heritage language, Chinese as foreign language, Chinese community schools

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Authors: Omer Abdalla Ishag, Magdi Baker Amien

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Physicians used the stethoscope for listening patient heart sounds in order to make a diagnosis. However, the determination of heart conditions by acoustic stethoscope is a difficult task so it requires special training of medical staff. This study developed an accurate model for analyzing the phonocardiograph signal based on PC and DSP processor. The system has been realized into two phases; offline and real time phase. In offline phase, 30 cases of heart sounds files were collected from medical students and doctor's world website. For experimental phase (real time), an electronic stethoscope has been designed, implemented and recorded signals from 30 volunteers, 17 were normal cases and 13 were various pathologies cases, these acquired 30 signals were preprocessed using an adaptive filter to remove lung sounds. The background noise has been removed from both offline and real data, using wavelet transform, then graphical and statistics features vector elements were extracted, finally a look-up table was used for classification heart sounds cases. The obtained results of the implemented system showed accuracy of 90%, 80% and sensitivity of 87.5%, 82.4% for offline data, and real data respectively. The whole system has been designed on TMS320VC5509a DSP Platform.

Keywords: code composer studio, heart sounds, phonocardiograph, wavelet transform

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Authors: Luiz Daniel Garay Trindade, Antonio De Freitas Valle Neto, Fabio Paulo Basso, Elder De Macedo Rodrigues, Maicon Bernardino, Daniel Welfer, Daniel Muller

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Agriculture plays an important role in society since it is one of the main sources of food in the world. To help the production and yield of crops, precision agriculture makes use of technologies aiming at improving productivity and quality of agricultural commodities. One of the problems hampering quality of agricultural production is the disease affecting crops. Failure in detecting diseases in a short period of time can result in small or big damages to production, causing financial losses to farmers. In order to provide a map of the contributions destined to the early detection of plant diseases and a comparison of the accuracy of the selected studies, a systematic literature review of the literature was performed, showing techniques for digital image processing and neural networks. We found 35 interesting tool support alternatives to detect disease in 19 plants. Our comparison of these studies resulted in an overall average accuracy of 87.45%, with two studies very closer to obtain 100%.

Keywords: pattern recognition, image processing, deep learning, precision agriculture, smart farming, agricultural automation

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Authors: Diana López-Soto, Soumaya Yacout, Francisco Ángel-Bello

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Although inventories are considered as stocks of money sitting on shelve, they are needed in order to secure a constant and continuous production. Therefore, companies need to have control over the amount of inventory in order to find the balance between excessive and shortage of inventory. The classification of items according to certain criteria such as the price, the usage rate and the lead time before arrival allows any company to concentrate its investment in inventory according to certain ranking or priority of items. This makes the decision making process for inventory management easier and more justifiable. The purpose of this paper is to present a new approach for the classification of new items based on the already existing criteria. This approach is called the Logical Analysis of Data (LAD). It is used in this paper to assist the process of ABC items classification based on multiple criteria. LAD is a data mining technique based on Boolean theory that is used for pattern recognition. This technique has been tested in medicine, industry, credit risk analysis, and engineering with remarkable results. An application on ABC inventory classification is presented for the first time, and the results are compared with those obtained when using the well-known AHP technique and the ANN technique. The results show that LAD presented very good classification accuracy.

Keywords: ABC multi-criteria inventory classification, inventory management, multi-class LAD model, multi-criteria classification

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Authors: Ebenezer O. Olaniyi, Oyebade K. Oyedotun, Khashman Adnan

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In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry.

Keywords: banana, food processing, identification system, neural network

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Authors: Fatma El-Zahraa El-Taher, Ayman Taha, Jane Courtney, Susan Mckeever

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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated.

Keywords: satellite images, remote sensing images, data acquisition, autonomous vehicles

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Authors: Peter Adu

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Background: There is a paucity of mental health research in Ghana. Little is known about the beliefs and attitudes regarding specific mental disorders in Ghana. Method: A vignette study was conducted to examine the relationship between causal attributions, help-seeking, and stigma towards depression and schizophrenia using lay Ghanaians (N = 410). This adapted questionnaire presented two unlabelled vignettes about a hypothetical person with the above disorders for participants to provide their impressions. Next, participants answered questions on beliefs and attitudes regarding this person. Results: The results showed that causal beliefs about mental disorders were related to treatment options and stigma: spiritual causal attributions associated positively with spiritual help-seeking and perceived stigma for the mental disorders, whilst biological and psychosocial causal attribution of the mental disorders was positively related with professional help-seeking. Finally, contrary to previous literature, belonging to a particular religious group did not negatively associate with professional help-seeking for mental disorders. Conclusion: In conclusion, results suggest that Ghanaians may benefit from exposure to corrective information about depression and schizophrenia. Our findings have implications for mental health literacy and anti-stigma campaigns in Ghana and other developing countries in the region.

Keywords: stigma, mental health literacy, depression, schizophrenia, spirituality, religion

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Authors: Timothy J. Grebec

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This study investigated whether using a voice dictation software program (i.e., Google Voice Typing) has an impact on student writing quality. The research took place in a third-grade general education classroom in a suburban school setting. Because the study involved minors, all data was encrypted and deidentified before analysis. The students completed a series of writings prior to the beginning of the intervention to determine their thoughts and skill level with writing. During the intervention phase, the students were introduced to the voice dictation software, given an opportunity to practice using it, and then assigned writing prompts to be completed using the software. The prompts written by nineteen student participants and surveys of student opinions on writing established a baseline for the study. The data showed that using the dictation software resulted in a 34% increase in the response quality (compared to the Pennsylvania State Standardized Assessment [PSSA] writing guidelines). Of particular interest was the increase in students' proficiency in demonstrating mastery of the English language and conventions and elaborating on the content. Although this type of research is relatively no, it has the potential to reshape the strategies educators have at their disposal when instructing students on written language.

Keywords: educational technology, accommodations, students with disabilities, writing instruction, 21st century education

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Authors: Michal Wolk, Bat-Sheva Hadad, Orly Rubinsten

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Objective: The visuospatial perception system process that allows us to decompose and recompose small quantities into a whole is often called “groupitizing.” Previous studies have been found that adults use groupitizing processes in quantity estimation tasks and link this ability of subgroups recognition to arithmetic proficiency. This pilot study examined if adults with math difficulties benefit from visuospatial grouping cues when asked to estimate the quantity of a given set. It also compared the tipping point in which a significant improvement occurs in adults with typical development compared to adults with math difficulties. Method: In this pilot research, we recruited adults with low arithmetic abilities and matched controls. Participants were asked to estimate the quantity of a given set. Different grouping cues were displayed (space, color, or none) with different visual configurations (different quantities-different shapes, same quantities- different shapes, same quantities- same shapes). Results: Both groups showed significant performance improvement when grouping cues appeared. However, adults with low arithmetic abilities benefited from the grouping cues already in very small quantities as four. Conclusion: impaired perceptual groupitizing abilities may be a characteristic of low arithmetic abilities.

Keywords: groupitizing, math learning disability, quantity estimation, visual perception system

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Authors: Deniz Alca

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This research aims to study the themes of autonomy, democracy and the legitimacy of power under the headline of Arab Spring. After the first wave of Arab Spring, among the frequently mentioned ideals of self-recognition, awakening, democracy, autonomy, freedom etc. main concern of the border neighbors and the western governments was to see a “legitimate power.” Although the metaphor of spring was still pointing at emancipation, the principal focus was mostly not on the people but on the governments. So the question of what makes a government legitimate has come to the forefront. However, democracy and freedom, seems to be the main subject matters of the discussions, this rush about establishment of “legitimate governments” lead other countries, to indulge or worse endorse armed oppositionists. So essence of “power” changed from legitimate to rulership. It seems that the civil initiative or autonomy and clearly democracy are still far away from us. The need to a savior is overpowering. This cultural and traditional and almost hereditary miss orientation of the people, both the ones who are playing the role of god and the ones who believed the inevitable need to be freed by someone else, seems to be leading the Arabs to a new autocracy or worse. Middle East is waiting for the ex machina to operate. But what it gets is a spreading warfare. This darkness falling down on Middle East under the concept of spring may be explained by the confrontation of the concepts of emancipation and liberation. So the question is, if the era of emancipation really over or is there still a chance for autonomy and grassroots democracy operating as constituent power?

Keywords: autonomy, awakening, civil initiative, democracy, emancipation, legitimacy, liberation

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Authors: Sheila Santharamohana, Susan Bennett

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Historically, the attrition rates in secondary schools of Indigenous people or Orang Asli of Malaysia have been a cause for nationwide concern. Efforts to increase student engagement focusing on curriculum re-design and aid have not had the targeted impact. The scope of the research explored a change model incorporating project-based learning and wrap-around support through school-community partnerships to increase Orang Asli engagement, student outcomes and improve cultural connectedness. The evaluation methodology was mixed-method comprising a student questionnaire, interviews, and document analysis. Data and evidence were gathered from school staff, community, the Orang Asli governmental authority (JAKOA) and external agencies. Findings from the year-long research suggests shared values and goals in school-community partnerships foster responsive leadership and is key to safeguarding vulnerable Orang Asli, resulting in improved student outcomes. The research highlighted the barriers to the recognition and distinct needs and unique values of the Orang Asli that impact their educational equity and outcomes.

Keywords: Indigenous Education, Cultural Connectedness, School-Community Partnership, Student Outcomes

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Authors: M. Dabirinezhad, M. Bayat Pour, A. Dabirinejad

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In 2020, the technology was introduced to solve some of the deficiencies of direct digital radiology. SDDR is an invention that is capable of capturing dental images without human intervention, and it was invented by the authors of this paper. Adjusting the radiology wave dose is a part of the dentists, radiologists, and dental nurses’ tasks during the radiology photography process. In this paper, an improvement will be added to enable SDDR to set the suitable radiology wave dose according to the density and age of the patients automatically. The separate sensors will be included in the sensors’ package to use the ultrasonic wave to detect the density of the teeth and change the wave dose. It facilitates the process of dental photography in terms of time and enhances the accuracy of choosing the correct wave dose for each patient separately. Since the radiology waves are well known to trigger off other diseases such as cancer, choosing the most suitable wave dose can be helpful to decrease the side effect of that for human health. In other words, it decreases the exposure time for the patients. On the other hand, due to saving time, less energy will be consumed, and saving energy can be beneficial to decrease the environmental impact as well.

Keywords: dental direct digital imaging, environmental impacts, SDDR technology, wave dose

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Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan

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This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.

Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee

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Authors: M. Bodner, M. Scampicchio

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Fourier Transform Infrared (FT-IR) spectroscopy coupled with chemometrics was used to distinguish between butter samples and non-butter samples. Further, quantification of the content of margarine in adulterated butter samples was investigated. Fingerprinting region (1400-800 cm^–1) was used to develop unsupervised pattern recognition (Principal Component Analysis, PCA), supervised modeling (Soft Independent Modelling by Class Analogy, SIMCA), classification (Partial Least Squares Discriminant Analysis, PLS-DA) and regression (Partial Least Squares Regression, PLS-R) models. PCA of the fingerprinting region shows a clustering of the two sample types. All samples were classified in their rightful class by SIMCA approach; however, nine adulterated samples (between 1% and 30% w/w of margarine) were classified as belonging both at the butter class and at the non-butter one. In the two-class PLS-DA model’s (R² = 0.73, RMSEP, Root Mean Square Error of Prediction = 0.26% w/w) sensitivity was 71.4% and Positive Predictive Value (PPV) 100%. Its threshold was calculated at 7% w/w of margarine in adulterated butter samples. Finally, PLS-R model (R² = 0.84, RMSEP = 16.54%) was developed. PLS-DA was a suitable classification tool and PLS-R a proper quantification approach. Results demonstrate that FT-IR spectroscopy combined with PLS-R can be used as a rapid, simple and safe method to identify pure butter samples from adulterated ones and to determine the grade of adulteration of margarine in butter samples.

Keywords: adulterated butter, margarine, PCA, PLS-DA, PLS-R, SIMCA

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Authors: Olteanu Constanta, Olteanu Lucian

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Researchers note that algebraic reasoning and sense making is essential for building conceptual knowledge in school mathematics. Consequently, pre-service teachers’ own reasoning and sense making are useful in fostering and developing students’ algebraic reasoning and sense making. This article explores the forms of reasoning and sense making that pre-service mathematics teachers exhibit and use in the process of analysing problem-posing tasks with a focus on first-degree equations. Our research question concerns the characteristics of the problem-posing tasks used for reasoning and sense making of first-degree equations as well as the characteristics of pre-service teachers’ reasoning and sense making in problem-posing tasks. The analyses are grounded in a post-structuralist philosophical perspective and variation theory. Sixty-six pre-service primary teachers participated in the study. The results show that the characteristics of reasoning in problem-posing tasks and of pre-service teachers are selecting, exploring, reconfiguring, encoding, abstracting and connecting. The characteristics of sense making in problem-posing tasks and of pre-service teachers are recognition, relationships, profiling, comparing, laddering and verifying. Beside this, the connection between reasoning and sense making is rich in line of flight in problem-posing tasks, while the connection is rich in line of rupture for pre-service teachers.

Keywords: first-degree equations, problem posing, reasoning, rhizomatic assemblage, sense-making, variation theory

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Authors: Cheng Fang, Lingwei Quan, Cunyue Lu

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Human pose estimation and tracking are to accurately identify and locate the positions of human joints in the video. It is a computer vision task which is of great significance for human motion recognition, behavior understanding and scene analysis. There has been remarkable progress on human pose estimation in recent years. However, more researches are needed for human pose tracking especially for online tracking. In this paper, a framework, called PoseSRPN, is proposed for online single-person pose estimation and tracking. We use Siamese network attaching a pose estimation branch to incorporate Single-person Pose Tracking (SPT) and Visual Object Tracking (VOT) into one framework. The pose estimation branch has a simple network structure that replaces the complex upsampling and convolution network structure with deconvolution. By augmenting the loss of fully convolutional Siamese network with the pose estimation task, pose estimation and tracking can be trained in one stage. Once trained, PoseSRPN only relies on a single bounding box initialization and producing human joints location. The experimental results show that while maintaining the good accuracy of pose estimation on COCO and PoseTrack datasets, the proposed method achieves a speed of 59 frame/s, which is superior to other pose tracking frameworks.

Keywords: computer vision, pose estimation, pose tracking, Siamese network

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Authors: Menelaos N. Katsantonis, Athanasios Manikas, Alexandros Chatzis, Stavros Doropoulos, Anastasios Avramis, Ioannis Mavridis

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Museums enhance their potential by adopting new technologies and techniques to appeal to more visitors and engage them in creative and joyful activities. In this study, the Haptic Cycle is presented, a cycle of museum activities proposed for the development of museum learning approaches with optimized effectiveness and engagement. Haptic Cycle envisages the improvement of the museum’s services by offering a wide range of activities. Haptic Cycle activities make the museum’s exhibitions more approachable by bringing them closer to the visitors. Visitors can interact with the museum’s artifacts and explore them haptically and sonically. Haptic Cycle proposes constructivist learning activities in which visitors actively construct their knowledge by exploring the artifacts, experimenting with them and realizing their importance. Based on the Haptic Cycle, we developed the HapticSOUND system, an innovative virtual reality system that includes an advanced user interface that employs gesture-based technology. HapticSOUND’s interface utilizes the leap motion gesture recognition controller and a 3D-printed traditional Cretan lute, utilized by visitors to perform various activities such as exploring the lute and playing notes and songs.

Keywords: haptic cycle, HapticSOUND, museum learning, gesture-based, leap motion

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Authors: Xinyuan Chen, Mohd Nizam Husen, Zhongmei Zhou, Gongde Guo, Wei Gao

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Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention for its enormous value; however, existing models lack capabilities to capture both local interactions and global dependencies simultaneously with evolutionary dynamics, while the latest achievements in convolutions and Transformers haven't been employed in this area. What’s more, periodic patterns in TKGs haven’t been fully explored either. To this end, a multi-stage hybrid architecture with convolution-backed Transformers is introduced in TKGC tasks for the first time combining the Hawkes process to model evolving event sequences in a continuous-time domain. In addition, the seasonal-trend decomposition is adopted to identify periodic patterns. Experiments on six public datasets are conducted to verify model effectiveness against state-of-the-art (SOTA) methods. An extensive ablation study is carried out accordingly to evaluate architecture variants as well as the contributions of independent components in addition, paving the way for further potential exploitation. Besides complexity analysis, input sensitivity and safety challenges are also thoroughly discussed for comprehensiveness with novel methods.

Keywords: temporal knowledge graph completion, convolution, transformer, Hawkes process, periodicity

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Authors: Mete Ulaş Aksoy

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In contemporary politics, the state of emergency has become a permanent and salient feature of politics. This study aims to clarify the anthropological and ontological dimensions of the permanent state of emergency. It pays special attention to the structural relation between the exception and play. Focusing on the play in the context of emergency and exception enables the recognition of the difference and sometimes the discrepancy between the exception and emergency, which has passed into oblivion because of the frequency and normalization of emergency situations. This study coins the term “ludic exception” in order to highlight the difference between the exceptions in which exuberance and paroxysm rule over the socio-political life and the permanent emergency that protects the authority with a sort of extra-legality. The main thesis of the study is that the ludic elements such as risk, conspicuous consumption, sacrificial gestures, agonism, etc. circumscribe the exceptional moments temporarily, preventing them from being routine and normal. The study also emphasizes the decline of ludic elements in modernity as the main factor in the transformation of the exceptions into permanent emergency situations. In the introduction, the relationship between play and exception is taken into consideration. In the second part, the study elucidates the concept of ludic exceptions and dwells on the anthropological examples of the ludic exceptions. In the last part, the decline of ludic elements in modernity is addressed as the main factor for the permanent emergency.

Keywords: emergency, exception, ludic exception, play, sovereignty

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Authors: Eleni Peristeri, Georgia Fotiadou, Ianthi-Maria Tsimpli

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The local-global dimension has been studied extensively in healthy controls and preference for globally processed stimuli has been validated in both the visual and auditory modalities. Critically, the local-global dimension has an inherent interference resolution component, a type of cognitive control, and left-prefrontal-cortex-damaged (LPFC) individuals have exhibited inability to override habitual response behaviors in item recognition tasks that involve representational interference. Eight patients with damage in the left PFC (age range: 32;5 to 69;0. Mean age: 54;6 yrs) and twenty age- and education-matched language-unimpaired adults (mean age: 56;7yrs) have participated in the study. Distinct performance patterns were found between the language-unimpaired and the LPFC-damaged group which have mainly stemmed from the latter’s difficulty with inhibiting global stimuli in incongruent trials. Overall, the local-global attentional dimension affects LPFC-damaged individuals with non-fluent aphasia in non-language domains implicating distinct types of inhibitory processes depending on the level of processing.

Keywords: left lateral prefrontal cortex damage (LPFC), local-global non-language attention, representational interference, non-fluent aphasia

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Authors: Candy Chieh Lee

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Objective: The purpose of this study is to investigate the relationship between visual perception and social function in patients with schizophrenia. The specific aims are: 1) To explore performances in visual perception and social function in patients with schizophrenia 2) to examine the correlation between visual perceptual skills and social function in patients with schizophrenia The long-term goal is to be able to provide the most adequate intervention program for promoting patients’ visual perceptual skills and social function, as well as compensatory techniques. Background: Perceptual deficits in schizophrenia have been well documented in the visual system. Clinically, a considerable portion (up to 60%) of schizophrenia patients report distorted visual experiences such as visual perception of motion, color, size, and facial expression. Visual perception is required for the successful performance of most activities of daily living, such as dressing, making a cup of tea, driving a car and reading. On the other hand, patients with schizophrenia usually exhibit psychotic symptoms such as auditory hallucination and delusions which tend to alter their perception of reality and affect their quality of interpersonal relationship and limit their participation in various social situations. Social function plays an important role in the prognosis of patients with schizophrenia; lower social functioning skills can lead to poorer prognosis. Investigations on the relationship between social functioning and perceptual ability in patients with schizophrenia are relatively new but important as the results could provide information for effective intervention on visual perception and social functioning in patients with schizophrenia. Methods: We recruited 50 participants with schizophrenia in the mental health hospital (Taipei City Hospital, Songde branch, Taipei, Taiwan) acute ward. Participants who have signed consent forms, diagnosis of schizophrenia and having no organic vision deficits were included. Participants were administered the test of visual-perceptual skills (non-motor), third edition (TVPS-3) and the personal and social performance scale (PSP) for assessing visual perceptual skill and social function. The assessments will take about 70-90 minutes to complete. Data Analysis: The IBM SPSS 21.0 will be used to perform the statistical analysis. First, descriptive statistics will be performed to describe the characteristics and performance of the participants. Lastly, Pearson correlation will be computed to examine the correlation between PSP and TVPS-3 scores. Results: Significant differences were found between the means of participants’ TVPS-3 raw scores of each subtest with the age equivalent raw score provided by the TVPS-3 manual. Significant correlations were found between all 7 subtests of TVPS-3 and PSP total score. Conclusions: The results showed that patients with schizophrenia do exhibit visual perceptual deficits and is correlated social functions. Understanding these facts of patients with schizophrenia can assist health care professionals in designing and implementing adequate rehabilitative treatment according to patients’ needs.

Keywords: occupational therapy, social function, schizophrenia, visual perception

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Authors: Olivia Borgue, Massimo Panarotto, Ola Isaksson

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This article presents a neural networks-based strategy for reducing the need for support structures when designing for additive manufacturing (AM). Additive manufacturing is a relatively new and immature industrial technology, and the information to make confident decisions when designing for AM is limited. This lack of information impacts especially the early stages of engineering design, for instance, it is difficult to actively consider the support structures needed for manufacturing a part. This difficulty is related to the challenge of designing a product geometry accounting for customer requirements, manufacturing constraints and minimization of support structure. The approach presented in this article proposes an automatized geometry modification technique for reducing the use of the support structures while designing for AM. This strategy starts with a neural network-based strategy for shape recognition to achieve product classification, using an STL file of the product as input. Based on the classification, an automatic part geometry modification based on MATLAB© is implemented. At the end of the process, the strategy presents different geometry modification alternatives depending on the type of product to be designed. The geometry alternatives are then evaluated adopting a QFD-like decision support tool.

Keywords: additive manufacturing, engineering design, geometry modification optimization, neural networks

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Authors: Cosette Maiky

Abstract:

Background: In the context of the Syrian crisis, the past few years have witnessed an exponential growth in the number of refugee mental health studies, which have essentially focused either on the affected Syrian population and/or host communities. However, the Palestinian communities in the region did not receive sufficient that much of attention. Aim: The study aimed at identifying trends and patterns of mental health and and psychosocial conditions among Palestinian refugees in the context of the Syrian crisis, including the recognition of gaps in appropriate services. Methods: The research model comprised a systematic documentary review, a mapping of available contextual analyses, a quantitative survey, focus group discussions as well as key informant interviews (with relevant stakeholders and beneficiaries). Findings: Content analysis revealed multiple effects of transgenerational transmission of trauma among Palestinian refugees in the context of the Syrian crisis, which showed to be neither linear nor one-dimensional occurrence. In addition to highlights on exposure to traumatic events and psychological sequelae, the review outlines the most prevailing coping mechanisms and essential protective factors. Conclusion: Away from a trauma-centered or symptom-focused exercise, practitioners may take account of the present study to better focus research and intervention methodologies.

Keywords: Palestine refugees, Syria crisis, psychosocial, mental health

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Authors: Rana Zeina

Abstract:

Objective: The association between Sensory problems and cognitive abilities has been studied in individuals with Autism Spectrum Disorders (ASDs). In this study, we used a neuropsychological test to evaluate memory and attention in ASDs children with sensory problems compared to the ASDs children without sensory problems. Methods: Four visual memory tests of Cambridge Neuropsychological Test Automated Battery (CANTAB) including Big/Little Circle (BLC), Simple Reaction Time (SRT), Intra/Extra Dimensional Set Shift (IED), Spatial Recognition Memory (SRM), were administered to 14 ASDs children with sensory problems compared to 13 ASDs without sensory problems aged 3 to 12 with IQ of above 70. Results: ASDs Individuals with sensory problems performed worse than the ASDs group without sensory problems on comprehension, learning, reversal and simple reaction time tasks, and no significant difference between the two groups was recorded in terms of the visual memory and visual comprehension tasks. Conclusion: The findings of this study suggest that ASDs children with sensory problems are facing deficits in learning, comprehension, reversal, and speed of response to stimuli.

Keywords: visual memory, attention, autism spectrum disorders, CANTAB eclipse

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Authors: Pavel Adámek

Abstract:

This study examines perception of environmental approach in small and medium-sized enterprises (SMEs) – the process by which firms integrate environmental concern into business. Based on a review of the literature, the paper synthesizes focus on environmental issues with the reflection in a case study in the Czech Republic. Two themes of corporate environmentalism are discussed – corporate environmental orientation and corporate stances toward environmental concerns. It provides theoretical material on greening organizational culture that is helpful in understanding the response of contemporary business to environmental problems. We integrate theoretical predictions with empirical findings confronted with reality. Scales to measure these themes are tested in a survey of managers in 229 Czech firms. We used the process of in-depth questioning. The research question was derived and answered in the context of the corresponding literature and conducted research. A case study showed us that environmental approach is variety different (depending on the size of the firm) in SMEs sector. The results of the empirical mapping demonstrate Czech company’s approach to environment and define the problem areas and pinpoint the main limitation in the expansion of environmental aspects. We contribute to the debate for recognition of the particular role of environmental issues in business reality.

Keywords: corporate environmentalism, Czech Republic, empirical mapping, environmental performance

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Authors: Seyong Oh, Jin-Hong Park

Abstract:

Recently, neuromorphic computing based on brain-inspired artificial neural networks (ANNs) has attracted huge amount of research interests due to the technological abilities to facilitate massively parallel, low-energy consuming, and event-driven computing. In particular, research on artificial synapse that imitate biological synapses responsible for human information processing and memory is in the spotlight. Here, we demonstrate a photosynaptic device, wherein a synaptic weight is governed by a mixed spike consisting of voltage and light spikes. Compared to the device operated only by the voltage spike, ∆G in the proposed photosynaptic device significantly increased from -2.32nS to 5.95nS with no degradation of nonlinearity (NL) (potentiation/depression values were changed from 4.24/8 to 5/8). Furthermore, the Modified National Institute of Standards and Technology (MNIST) digit pattern recognition rates improved from 36% and 49% to 50% and 62% in ANNs consisting of the synaptic devices with 20 and 100 weight states, respectively. We expect that the photosynaptic device technology processed by optoelectronic spike will play an important role in implementing the neuromorphic computing systems in the future.

Keywords: optoelectronic synapse, IGZO (Indium-Gallium-Zinc Oxide) photosynaptic device, optoelectronic spiking process, neuromorphic computing

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