Search results for: trained graphic designers

Authors: John Kosmeh

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Introduction – For years, senior health care and skilled nursing facilities have been plagued with the dilemma of not having the necessary tools and equipment to adequately care for senior residents in their communities. This has led to high transport rates to emergency departments and high 30-day readmission rates, costing billions of unnecessary dollars each year, as well as quality assurance issues. Our Senior care telemedicine program is designed to solve this issue. Methods – We conducted a 1-year pilot program using our technology coupled with our 24/7 telemedicine program with skilled nursing facilities in different parts of the United States. We then compared transports rates and 30-day readmission rates to previous years before the use of our program, as well as transport rates of other communities of similar size not using our program. This data was able to give us a clear and concise look at the success rate of reducing unnecessary transport and readmissions as well as cost savings. Results – A 94% reduction nationally of unnecessary out-of-facility transports, and to date, complete elimination of 30-day readmissions. Our virtual platform allowed us to instruct facility staff on the utilization of our tools and system as well as deliver treatment by our ER-trained providers. Delay waiting for PCP callbacks was eliminated. We were able to obtain lung, heart, and abdominal ultrasound imaging, 12 lead EKG, blood labs, auscultate lung and heart sounds, and collect other diagnostic tests at the bedside within minutes, providing immediate care and allowing us to treat residents within the SNF. Are virtual capabilities allowed for loved ones, family members, and others who had medical power of attorney to virtually connect with us at the time of visit, to speak directly with the medical provider, providing increased confidence in the decision to treat the resident in-house. The decline in transports and readmissions will greatly reduce governmental cost burdens, as well as fines imposed on SNF for high 30-day readmissions, reduce the cost of Medicare A readmissions, and significantly impact the number of patients visiting overcrowded ERs. Discussion – By utilizing our program, SNF can effectively reduce the number of unnecessary transports of residents, as well as create significant savings from loss of day rates, transportation costs, and high CMS fines. The cost saving is in the thousands monthly, but more importantly, these facilities can create a higher quality of life and medical care for residents by providing definitive care instantly with ER-trained personnel.

Keywords: senior care, long term care, telemedicine, technology, senior care communities

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Authors: Lin Cheng, Zijiang Yang

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Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Keywords: program synthesis, flow chart, specification, graph recognition, CNN

Procedia PDF Downloads 115

Authors: Jiawei Dai, Xinrong Li, Yulong Sun, Yunxiao Hao

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Recently, participatory approaches have become popular in a variety of fields, including social work, community, and population health, as important research tools for researchers to understand and immerse communities and conceptualize social phenomena. The participatory visual research methods promote the diversification and depth of the exploration process and communication forms to support the feasibility and practicality of the scheme, which helps to further inspire designers and avoid blind spots caused by the solidification of single thinking. This paper focuses on how to develop visual toolkits for participatory methods to assist and shape crowd participation and trigger idea generation in community issues. This project helps to verify the value of participatory visual tools in shaping participation and arousing expression, which provides support for gaining community diversity insights and community problem-solving. In addition, a visual toolbox was developed based on an actual case in a community for field testing, and further discussion was carried out after the data results were analyzed.

Keywords: participatory design, community service, visual toolbox, visual metaphor

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Authors: Wanessa G. Oliveira, Fernando C. Capovilla

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Capovilla and Raphael’s Libras Dictionary documents a corpus of 4,200 Brazilian Sign Language (Libras) signs. Duduchi and Capovilla’s software SignTracking permits users to retrieve signs even when ignoring the gloss corresponding to it and to discover the meaning of all 4,200 signs sign simply by clicking on graphic menus of the sign characteristics (phonemes). Duduchi and Capovilla have discovered that the ease with which any given sign can be retrieved is an inverse function of the average popularity of its component phonemes. Thus, signs composed of rare (distinct) phonemes are easier to retrieve than are those composed of common phonemes. SignTracking offers a means of computing the average popularity of the phonemes that make up each one of 4,200 signs. It provides a precise measure of the degree of ease with which signs can be retrieved, and sign meanings can be discovered. Duduchi and Capovilla’s logarithmic model proved valid: The degree with which any given sign can be retrieved is an inverse function of the arithmetic mean of the logarithm of the popularity of each component phoneme. Capovilla, Raphael and Mauricio’s New Libras Dictionary documents a corpus of 10,400 Libras signs. The present analysis revealed Libras DNA structure by mapping the incidence of 501 sign phonemes resulting from the layered distribution of five parameters: 163 handshape phonemes (CherEmes-ManusIculi); 34 finger shape phonemes (DactilEmes-DigitumIculi); 55 hand placement phonemes (ArtrotoToposEmes-ArticulatiLocusIculi); 173 movement dimension phonemes (CinesEmes-MotusIculi) pertaining to direction, frequency, and type; and 76 Facial Expression phonemes (MascarEmes-PersonalIculi).

Keywords: Brazilian sign language, lexical retrieval, libras sign, sign phonology

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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Authors: Félix David Suárez Bonilla

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A sentiment analysis service to detect polarity (positive, neural, and negative), based on transfer learning, was built using a Spanish version of BERT and applied to tweets written in Spanish. The dataset that was used consisted of 11975 reviews, which were extracted from Google Play using the google-play-scrapper package. The BETO trained model used: the AdamW optimizer, a batch size of 16, a learning rate of 2x10⁻⁵ and 10 epochs. The system was tested using tweets of three presidential pre-candidates from Costa Rica. The system was finally validated using human labeled examples, achieving an accuracy of 83.3%.

Keywords: NLP, transfer learning, BERT, sentiment analysis, social media, opinion mining

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Authors: Felix Brito

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With the growth of online learning, several higher education institutions have attempted to incorporate technology in their curriculum. Successful technology implementation projects really on technology infrastructure and on the acceptance of education professionals towards innovation. This research study is aimed at illustrating the relevance of the human component in technology implementation projects in higher education by describing the Learning Management System implementation project executed by instructional designers working for a higher education institution in the southeast region of the United States. An analysis of the Transformative Leadership Theory, the Technology Acceptance Model, and the Diffusion of Innovation Process provide the support for a solid understanding of this issue and address recommendations for future technology implementation projects in higher education institutions.

Keywords: diffusion of innovation process, instructional design, leadership, learning management systems, online learning, technology acceptance model, transformative leadership theory

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Authors: Punnoose A. K.

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This paper presents an approach for identifying noisy speech recording using a multi-layer perception (MLP) trained to predict phonemes from acoustic features. Characteristics of the MLP posteriors are explored for clean speech and noisy speech at the frame level. Appropriate density functions are used to fit the softmax probability of the clean and noisy speech. A function that takes into account the ratio of the softmax probability density of noisy speech to clean speech is formulated. These phoneme independent scoring is weighted using a phoneme-specific weightage to make the scoring more robust. Simple thresholding is used to identify the noisy speech recording from the clean speech recordings. The approach is benchmarked on standard databases, with a focus on precision.

Keywords: noisy speech identification, speech pre-processing, noise robustness, feature engineering

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Mohamed Khallaf, Hossein M Rizeei

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Simulating wind pattern behavior over proposed urban features is critical in the early stage of the conceptual design of both architectural and urban disciplines. However, it is typically not possible for designers to explore the impact of wind flow profiles across new urban developments due to a lack of real data and inaccurate estimation of building parameters. Modeling the details of existing and proposed urban features and testing them against wind flows is the missing part of the conceptual design puzzle where architectural and urban discipline can focus. This research aims to develop a spatial decision-support design method utilizing LiDAR, GIS, and performance-based wind simulation technology to mitigate wind-related hazards on a design by simulating alternative design scenarios at the pedestrian level prior to its implementation in Sydney, Australia. The result of the experiment demonstrates the capability of the proposed framework to improve pedestrian comfort in relation to wind profile.

Keywords: spatial decision-support design, performance-based wind simulation, LiDAR, GIS

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Authors: Jingwen Shan

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In order to explore the key factors affecting the robot program learning intention of school students, this study takes the technology acceptance model as the theoretical basis and invites 167 students from Jiading District of Shanghai as the research subjects. In the robot course, the model of school students on their learning behavior is constructed. By verifying the causal path relationship between variables, it is concluded that teachers can enhance students’ perceptual usefulness to robotics courses by enhancing subjective norms, entertainment perception, and reducing technical anxiety, such as focusing on the gradual progress of programming and analyzing learner characteristics. Students can improve perceived ease of use by enhancing self-efficacy. At the same time, robot hardware designers can optimize in terms of entertainment and interactivity, which will directly or indirectly increase the learning intention of the robot course. By changing these factors, the learning behavior of primary and secondary school students can be more sustainable.

Keywords: TAM, learning behavior intentions, robot courses, primary and secondary school students

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Authors: Naomi Grigoryan, Alexandros Loutsioli Daskalakis, Anna Elisse Uy, Yihe Huang, Aude Laurent (Webanck)

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In response to the building and construction sectors accounting for a third of all energy demand and emissions, the European Union has placed new laws and regulations in the construction sector that emphasize material circularity, energy efficiency, biodiversity, and social impact. Existing design tools assess sustainability in early-stage design for products or buildings; however, there is no standardized methodology for measuring the circularity performance of building components. Existing assessment methods for building components focus primarily on carbon footprint but lack the comprehensive analysis required to design for circularity. The research conducted in this paper covers the parameters needed to assess sustainability in the design process of architectural products such as doors, windows, and facades. It maps a framework for a tool that assists designers with real-time sustainability metrics. Considering the life cycle of building components such as façades, windows, and doors involves the life cycle stages applied to product design and many of the methods used in the life cycle analysis of buildings. The current industry standards of sustainability assessment for metal building components follow cradle-to-grave life cycle assessment (LCA), track Global Warming Potential (GWP), and document the parameters used for an Environmental Product Declaration (EPD). Developed by the Ellen Macarthur Foundation, the Material Circularity Indicator (MCI) is a methodology utilizing the data from LCA and EPDs to rate circularity, with a "value between 0 and 1 where higher values indicate a higher circularity+". Expanding on the MCI with additional indicators such as the Water Circularity Index (WCI), the Energy Circularity Index (ECI), the Social Circularity Index (SCI), Life Cycle Economic Value (EV), and calculating biodiversity risk and uncertainty, the assessment methodology of an architectural product's impact can be targeted more specifically based on product requirements, performance, and lifespan. Broadening the scope of LCA calculation for products to incorporate aspects of building design allows product designers to account for the disassembly of architectural components. For example, the Material Circularity Indicator for architectural products such as windows and facades is typically low due to the impact of glass, as 70% of glass ends up in landfills due to damage in the disassembly process. The low MCI can be combatted by expanding beyond cradle-to-grave assessment and focusing the design process on disassembly, recycling, and repurposing with the help of real-time assessment tools. Design for Disassembly and Urban Mining has been integrated within the construction field on small scales as project-based exercises, not addressing the entire supply chain of architectural products. By adopting more comprehensive sustainability metrics and incorporating uncertainty calculations, the sustainability assessment of building components can be more accurately assessed with decarbonization and disassembly in mind, addressing the large-scale commercial markets within construction, some of the most significant contributors to climate change.

Keywords: architectural products, early-stage design, life cycle assessment, material circularity indicator

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Ahmed Emad Ahmed

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This paper aims to address the concept of human resources information systems (HRIS) and how to link it to new technologies such as artificial intelligence (AI) to be implemented in two human resources processes. A literature view has been collected to cover the main points related to HRIS, AI, and BC. A study case has been presented by generating a random HRIS to apply some AI operations to it. Then, an algorithm was applied to the database to complete some human resources processes, including training and performance appraisal, using a pre-trained AI model. After that, outputs and results have been presented and discussed briefly. Finally, a conclusion has been introduced to show the ability of new technologies such as AI and ML to be applied to the human resources management processes.

Keywords: human resources new technologies, HR artificial intelligence, HRIS AI models, construction AI HRIS

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Authors: Olivia Rybak-Karkosz

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This paper aims to present the results of scientific research on methods of forging art prints and their elements, such as signature or provenance and forensic science methods that might be used to verify their authenticity. In the last decades, the art market has observed significant interest in purchasing prints. They are considered an economical alternative to paintings and a considerable investment. However, the authenticity of an art print is difficult to establish as similar visual effects might be achieved with drawings or xerox. The latter is easy to make using a home printer. They are then offered on flea markets or internet auctions as genuine prints. This probable ease of forgery and, at the same time, the difficulty of distinguishing art print techniques were the main reasons why this research was undertaken. A lack of scientific methods dedicated to disclosing a forgery encouraged the author to verify the possibility of using forensic science's methods known and used in other fields of expertise. This research methodology consisted of completing representative forgery samples collected in selected museums based in Poland and a few in Germany and Austria. That allowed the author to present a typology of methods used to forge art prints. Given that one of the most famous graphic design examples is bills and securities, it seems only appropriate to propose in print verification the usage of methods of detecting counterfeit currency. These methods contain an examination of ink, paper, and watermarks. On prints, additionally, signatures and imprints of stamps, etc., are forged as well. So the examination should be completed with handwriting examination and forensic sphragistics. The paper contains a stipulation to conduct a complex analysis of authenticity with the participation of an art restorer, art historian, and forensic expert as head of this team.

Keywords: art forgery, examination of an artwork, handwriting analysis, prints

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Authors: Ollie Hall, Salma ElSayed

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Affective adaptation is a novel way for game designers to add an extra layer of engagement to their productions. When player’s emotions factor in game design, endless possibilities for creative gameplay emerge. Whilst gaining popularity, existing affective game research mostly runs controlled experiments carried in restrictive settings and relies on one or more specialist devices for measuring a player’s emotional state. These conditions, albeit effective, are not necessarily realistic. Moreover, the simplified narrative and intrusive wearables may not be suitable for the average player. This exploratory study investigates delivering an immersive affective experience in the wild with minimal requirements in an attempt for the average developer to reach the average player. A puzzle game is created with a rich narrative and creative mechanics. It employs both explicit and implicit adaptation and only requires a web camera. Participants played the game on their own machines in various settings. Whilst it was rated feasible, very engaging, and enjoyable, it remains questionable whether a fully immersive experience was delivered due to the limited sample size.

Keywords: affective games, dynamic adaptation, emotion recognition, game design

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Authors: Kadidiatou Toure

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Mali has started experimentation of teaching the national languages at school through the convergent pedagogy in 1987. Then, it is in 1994 that it will become widespread with eleven of the thirteen former national languages used at primary school. The aim was to improve the Malian educational system because the use of French as the only medium of instruction was considered a contributing factor to the significant number of student dropouts and the high rate of repetition. The Convergent pedagogy highlights the knowledge acquired by children at home, their vision of the world and especially the knowledge they have of their mother tongue. That pedagogy requires the use of a specific medium only during classroom practices and teachers have been trained in this sense. The specific medium depends on the learning content, which sometimes is French, other times, it is the national language. Research has shown that bilingual learners do not only use the required medium in their learning activities, but they code switch. It is part of their learning processes. Currently, many scholars agree on the importance of CS in bilingual classes, and teachers have been told about the necessity of integrating it into their classroom practices. One of the challenges of the Malian bilingual education curriculum is the question of ‘effective languages management’. Theoretically, depending on the classrooms, an average have been established for each of the involved language. Following that, teachers make use of CS differently, sometimes, it favors the learners, other times, it contributes to the development of some linguistic weaknesses. The present research tries to fill that gap through a tentative model of didactization of CS, which simply means the practical management of the languages involved in the bilingual classrooms. It is to know how to use CS for effective learning. Moreover, the didactization of CS tends to sensitize the teachers about the functional role of CS so that they may overcome their own weaknesses. The overall goal of this research is to make code switching a real tool for bilingual education. The specific objectives are: to identify the types of CS used during classroom activities to present the functional role of CS for the teachers as well as the pupils. to develop a tentative model of code-switching, which will help the teachers in transitional classes of bilingual schools to recognize the appropriate moment for making use of code switching in their classrooms. The methodology adopted is a qualitative one. The study is based on recorded videos of teachers of 3rd year of primary school during their classroom activities and interviews with the teachers in order to confirm the functional role of CS in bilingual classes. The theoretical framework adopted is the typology of CS proposed by Poplack (1980) to identify the types of CS used. The study reveals that teachers need to be trained on the types of CS and the different functions they assume and on the consequences of inappropriate use of language alternation.

Keywords: bilingual curriculum, code switching, didactization, national languages

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Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

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Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

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Authors: Sarah Abdourraziq, Rachid El Bachtiri

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The solar power source for pumping water is one of the most promising areas in photovoltaic applications. The implementation of these systems allows to protect the environment and reduce the CO₂ gas emission compared to systems trained by diesel generators. This paper presents a comparative study between the photovoltaic pumping system driven by DC motor, and AC motor to define the optimum design of this application. The studied system consists of PV array, DC-DC Boost Converter, inverter, motor-pump set and storage tank. The comparison was carried out to define the characteristics and the performance of each system. Each subsystem is modeled in order to simulate the whole system in MATLAB/ Simulink. The results show the efficiency of the proposed technique.

Keywords: photovoltaic water pumping system, DC motor-pump, AC motor-pump, DC-DC boost converter

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Authors: Mark Ashton Newman, Richard Blagrove, Jonathan Folland

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The force-velocity profile of an individual has been shown to influence success in ballistic movements, independent of the individuals' maximal power output; therefore, effective and accurate evaluation of an individual’s F-V characteristics and not solely maximal power output is important. The relatively narrow range of loads typically utilised during force-velocity profiling protocols due to the difficulty in obtaining force data at high velocities may bring into question the accuracy of the F-V slope along with predictions pertaining to the maximum force that the system can produce at a velocity of null (F₀) and the theoretical maximum velocity against no load (V₀). As such, the reliability of the slope of the force-velocity profile, as well as V₀, has been shown to be relatively poor in comparison to F₀ and maximal power, and it has been recommended to assess velocity at loads closer to both F₀ and V₀. The aim of the present study was to assess the relative and absolute reliability of an instrumented novel leg press machine which enables the assessment of force and velocity data at loads equivalent to ≤ 10% of one repetition maximum (1RM) through to 1RM during a ballistic leg press movement. The reliability of maximal and mean force, velocity, and power, as well as the respective force-velocity and power-velocity relationships and the linearity of the force-velocity relationship, were evaluated. Sixteen male strength-trained individuals (23.6 ± 4.1 years; 177.1 ± 7.0 cm; 80.0 ± 10.8 kg) attended four sessions; during the initial visit, participants were familiarised with the leg press, modified to include a mounted force plate (Type SP3949, Force Logic, Berkshire, UK) and a Micro-Epsilon WDS-2500-P96 linear positional transducer (LPT) (Micro-Epsilon, Merseyside, UK). Peak isometric force (IsoMax) and a dynamic 1RM, both from a starting position of 81% leg length, were recorded for the dominant leg. Visits two to four saw the participants carry out the leg press movement at loads equivalent to ≤ 10%, 30%, 50%, 70%, and 90% 1RM. IsoMax was recorded during each testing visit prior to dynamic F-V profiling repetitions. The novel leg press machine used in the present study appears to be a reliable tool for measuring F and V-related variables across a range of loads, including velocities closer to V₀ when compared to some of the findings within the published literature. Both linear and polynomial models demonstrated good to excellent levels of reliability for SFV and F₀ respectively, with reliability for V₀ being good using a linear model but poor using a 2nd order polynomial model. As such, a polynomial regression model may be most appropriate when using a similar unilateral leg press setup to predict maximal force production capabilities due to only a 5% difference between F₀ and obtained IsoMax values with a linear model being best suited to predict V₀.

Keywords: force-velocity, leg-press, power-velocity, profiling, reliability

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Authors: Sai On Cheung, Qiuwen Ma, Fong Chung Lee

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Due to increased project complexity and technological advancement in the last decade, the designers and contractors are expected to put more efforts to achieve project goals. To render extra efforts from the agents, incentivization has become one of the primary strategies for the client. Despite increased academia interest in the design of incentive strategies, there is still a need for discussion about the underlying motivations and favourable conditions to make incentives effective. Therefore, this study focuses on the effects of motivations and favourable conditions for the use of incentives in public projects. Questionnaire survey is used as the data collection tool. The questionnaire survey was piloted through interviews with professionals from Hong Kong public sector. A total of 100 responses were collected for this survey. Accountability and organizational effectiveness were found to be the prime objectives of incentives installed by public clients. Furthermore, a list of favourable conditions for incentivization and its consequent effects on cost, schedule, risk and public opinions were identified. To conclude, this study analyses the means and ends of the use of incentives in public projects in Hong Kong.

Keywords: incentives, public accountability, project effectiveness, public opinions

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Yanhua Ma, Lu Zhai, Xinchen Wang, Hongyu Liang

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In this paper, by understanding the development status of the human-machine interface in today's automobile cab, a subjective and objective evaluation system for evaluating the optimization of human-machine interface matching in automobile cab was established. The man-machine interface of the car cab was divided into a software interface and a hard interface. Objective evaluation method of software human factor analysis is used to evaluate the hard interface matching; The analytic hierarchy process is used to establish the evaluation index system for the software interface matching optimization, and the multi-level fuzzy comprehensive evaluation method is used to evaluate hard interface machine. This article takes Dongfeng Sokon (DFSK) C37 model automobile as an example. The evaluation method given in the paper is used to carry out relevant analysis and evaluation, and corresponding optimization suggestions are given, which have certain reference value for designers.

Keywords: analytic hierarchy process, fuzzy comprehension evaluation method, human-machine interface, matching optimization, software human factor analysis

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Authors: Galina Mihaleva, C. Koh

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Fashion, like many other design fields, undergoes numerous evolutions throughout the ages. This paper aims to recognize and evaluate the significance of advance technology in fashion design and examine how it changes the role of modern fashion designers by modifying the creation process. It also touches on how modern culture is involved in such developments and how it affects fashion design in terms of conceptualizing and fabrication. The methodology used is through surveying the various examples of technological applications to fashion design and drawing parallels between what was achievable then and what is achievable now. By comparing case studies, existing fashion design examples and crafting method experimentations; we then spot patterns in which to predict the direction of future developments in the field. A breakdown on the elements of technology in fashion design helps us understand the driving force behind such a trend. The results from explorations in the paper have shown that there is an observed pattern of a distinct increase in interest and progress in the field of fashion technology, which leads to the birth of hybrid crafting methods. In conclusion, it is shown that as fashion technology continues to evolve, their role in clothing crafting becomes more prominent and grows far beyond the humble sewing machine.

Keywords: fashion design, functional aesthetics, smart textiles, 3D printing

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Authors: Hussam Elias, Ninovic Perez, Holger Hirsch

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The completion of the EMC regulations worldwide is growing steadily as the usage of electronics in our daily lives is increasing more than ever. In this paper, we introduce a novel method to perform the final phase of Electromagnetic compatibility (EMC) measurement and to reduce the required test time according to the norm EN 55032 by using a developed tool and the conventional neural network(CNN). The neural network was trained using real EMC measurements, which were performed in the Semi Anechoic Chamber (SAC) by CETECOM GmbH in Essen, Germany. To implement our proposed method, we wrote software to perform the radiated electromagnetic interference (EMI) measurements and use the CNN to predict and determine the position of the turntable that meets the maximum radiation value.

Keywords: conventional neural network, electromagnetic compatibility measurement, mean absolute error, position error

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Authors: Jakub Berčík, Anna Mravcová, Jana Gálová, Katarína Neomániová

Abstract:

In an era when everything is increasingly digital, consumers are always looking for new options in solutions to their everyday needs. In this context, mobile apps are developing at an exponential pace. One of the fastest growing segments of mobile technologies is, obviously, e-commerce. It can be predicted that mobile commerce will record nearly three times the global growth of e-commerce across all platforms, which indicates its importance in the given segment. The current coronavirus pandemic is also changing many of the existing paradigms both socially, economically, and technologically, which has a major impact on changing consumer behaviour and the emphasis on simplification and clarity of mobile solutions. This is the area that user experience (UX) and user interface (UI) designers deal with. Their task is to design a sufficiently attractive and interesting solution that will be available on all mobile devices and at the same time will be easy enough for the customer/visitor to get to the destination or to get the necessary information in a few clicks. The basis for changes in UX design can now be obtained not only through online analytical tools but also through neuromarketing, especially in the case of mobile devices. The paper highlights new possibilities for testing UX design applications on mobile devices using a special platform that combines a stationary eye camera (eye tracking) and facial analysis (facial coding).

Keywords: emotions, mobile design, user experience, visual attention

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Authors: Mariem Kchaou, Wiem Khlif, Faiez Gargouri

Abstract:

Modeling business processes is an essential task when evaluating, improving, or documenting existing business processes. To be efficient in such tasks, a business process model (BPM) must have high structural quality and high performance. Evidently, evaluating the performance of a business process model is a necessary step to reduce time, cost, while assessing the structural quality aims to improve the understandability and the modifiability of the BPMN model. To achieve these objectives, a set of structural and performance measures have been proposed. Since the diversity of measures, we propose a framework that integrates both structural and performance aspects for classifying them. Our measure classification is based on business process model perspectives (e.g., informational, functional, organizational, behavioral, and temporal), and the elements (activity, event, actor, etc.) involved in computing the measures. Then, we implement this framework in a tool assisting the structural quality and the performance of a business process. The tool helps the designers to select an appropriate subset of measures associated with the corresponding perspective and to calculate and interpret their values in order to improve the structural quality and the performance of the model.

Keywords: performance, structural quality, perspectives, tool, classification framework, measures

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Authors: Szymon Rutkowski, Tomasz Korbak

Abstract:

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: Tsega Asresa, Getahun Tigistu, Melaku Bayih

Abstract:

Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Wei Zhang

Abstract:

With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator.

Keywords: deep learning, field programmable gate array, FPGA, hardware accelerator, convolutional neural networks, CNN

Procedia PDF Downloads 119