Search results for: sign processing

Authors: Joaquin Navarro Perales, Ana Lidia Franzoni Velázquez, Francisco Cervantes Pérez

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The Mexican educational system faces diverse challenges related with the quality and coverage of education. The development of Intelligent Tutoring Systems (ITS) may help to solve some of them by helping teachers to customize their classes according to the performance of the students in online courses. In this work, we propose the adaptation of a functional ITS based on Bloom’s taxonomy called Sistema de Apoyo Generalizado para la Enseñanza Individualizada (SAGE), to measure student’s metacognition and their emotional response based on Marzano’s taxonomy. The students and the system will share the control over the advance in the course, so they can improve their metacognitive skills. The system will not allow students to get access to subjects not mastered yet. The interaction between the system and the student will be implemented through Natural Language Processing techniques, thus avoiding the use of sensors to evaluate student’s response. The teacher will evaluate student’s knowledge utilization, which is equivalent to the last cognitive level in Marzano’s taxonomy.

Keywords: intelligent tutoring systems, student modelling, metacognition, affective computing, natural language processing

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Authors: Ezekiel Ejiofor Nnadi

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Nigeria construction industry is known for its active construction activities. This has earmarked the industry to be the key to economic growth of the nation. It has largest employer of labour and gives sustenance to other industries like manufacturing industry. While this is a sign of growth and prosperity; the waste generated by the industry has always been a problem and a serious concern. It results in wastage of economic gain and has resultant health effect on the populace apart from injury being sustained on sites. This work provides a platform to learn more about construction waste, its management strategy and how to reduce waste production in Nigeria construction industry. Construction sites, waste management authority and public health institutions in Lagos as the centre of most construction activities in Nigeria were selected, and a set of questionnaire was administered to using the systematic sampling technique. Descriptive statistics and relative importance index (RII) technique were employed for the analysis of the data gathered. The findings of the analysis show that excessive wastes reduce contractors’ profit margin and also that some construction wastes contain hazardous and toxic elements such as lead, asbestos or radioactive materials which required proper handling and effective disposal. The conclusion was drawn that the check on waste on construction sites starts with the designers to the contractors who execute on site.

Keywords: construction cost, construction industry, economic growth, materials wastes

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Authors: Rihab Hamdi, Amel Hadri Hamida, Ouafae Bennis, Fatima Babaa, Sakina Zerouali

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This paper features a sliding mode controller (SMC) for closed-loop voltage control of DC-DC three-cells buck converter connected in parallel, operating in continuous conduction mode (CCM), based on pulse-width modulation (PWM). To maintain the switching frequency, the approach is to incorporate a pulse-width modulation that utilizes an equivalent control, inferred by applying the SM control method, to produce a control sign to be contrasted and the fixed-frequency within the modulator. Detailed stability and transient performance analysis have been conducted using Lyapunov stability criteria to restrict the switching frequency variation facing wide variations in output load, input changes, and set-point changes. The results obtained confirm the effectiveness of the proposed control scheme in achieving an enhanced output transient performance while faithfully realizing its control objective in the event of abrupt and uncertain parameter variations. Simulations studies in MATLAB/Simulink environment are performed to confirm the idea.

Keywords: DC-DC converter, pulse width modulation, power electronics, sliding mode control

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Authors: Jamie A. Prowse Turner, Jamie I. D. Campbell, Valerie A. Thompson

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The objective of the current research was to use a dual-process theory framework to explain these age-related differences in reasoning. Seventy-two older (M = 80.0 years) and 72 younger (M = 24.6 years) adults were given a variety of reasoning tests (i.e., a syllogistic task, base rate task, the Cognitive Reflection Test, and a perspective manipulation), as well as independent tests of capacity (working memory, processing speed, and inhibition), thinking styles, and metacognitive ability, to account for these age-related differences. It was revealed that age-related differences were limited to problems that required Type 2 processing and were related to differences in cognitive capacity, individual difference factors, and strategy choice. Furthermore, older adults’ performance can be improved by reasoning from another’s’ perspective and cannot, at this time, be explained by metacognitive differences between young and older adults. All of these findings fit well within a dual-process theory of reasoning, which provides an integrative framework accounting for previous findings and the findings presented in the current manuscript.

Keywords: aging, dual-process theory, performance, reasoning ability

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Authors: Shafiq Alam, Yen Ning Lee

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Conventional hydrometallurgical processing of metals involves the use of large quantities of toxic chemicals. Realizing a need to develop sustainable technologies, extensive research studies are being carried out to recover and recycle base, precious and rare earth metals from their pregnant leach solutions (PLS) using green chemicals/biomaterials prepared from biomass wastes derived from agriculture, marine and forest resources. Our innovative research showed that bio-adsorbents prepared from such biomass wastes can effectively adsorb precious metals, especially gold after conversion of their functional groups in a very simple process. The highly effective ‘Adsorption-coupled-Reduction’ phenomenon witnessed appears promising for the potential use of this gold biosorption process in the mining industry. Proper management and effective use of biomass wastes as value added green chemicals will not only reduce the volume of wastes being generated every day in our society, but will also have a high-end value to the mining and mineral processing industries as those biomaterials would be cheap, but very selective for gold recovery/recycling from low grade ore, leach residue or e-wastes.

Keywords: biosorption, hydrometallurgy, gold, adsorption, reduction, biomass, sustainability

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Authors: Anam Naveed, Asma Andleeb, Mehreen Sirshar

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In the article, Image processing techniques have been applied on cardiac images for enhancing the image quality. Two types of methodologies considers for survey, invasive techniques and non-invasive techniques. Different image processes for improvement of cardiac image quality and reduce the amount of radiation exposure for invasive techniques are explored. Different image processing algorithms for enhancing the noninvasive cardiac image qualities are described. Beside these two methodologies, third methodology has applied on live streaming of heart rate on ECG window for extracting necessary information, removing noise and enhancing quality. Sensitivity analyses have been carried out to investigate the impacts of cardiac images for diagnosis of cardiac arteries disease and how the enhancement on images will help the cardiologist to diagnoses disease. The paper evaluates strengths and weaknesses of different techniques applied for improved the image quality and draw a conclusion. Some specific limitations must be considered for whole survey, like the patient heart beat must be 70-75 beats/minute while doing the angiography, similarly patient weight and exposure radiation amount has some limitation.

Keywords: cardiac images, CT angiography, critical analysis, exposure radiation, invasive techniques, invasive techniques, non-invasive techniques

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Authors: Bowen Ding, Yihao Kuang

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In recent years, with the development of large-scale pre-trained lan-guage models, building vector representations of text through deep neural network models has become a standard practice for natural language processing tasks. From the performance on downstream tasks, we can know that the text representation constructed by these models contains linguistic information, but its encoding mode and extent are unclear. In this work, a structural probe is proposed to detect whether the vector representation produced by a deep neural network is embedded with a syntax tree. The probe is trained with the deep metric learning method, so that the distance between word vectors in the metric space it defines encodes the distance of words on the syntax tree, and the norm of word vectors encodes the depth of words on the syntax tree. The experiment results on ELMo and BERT show that the syntax tree is encoded in their parameters and the word representations they produce.

Keywords: deep metric learning, syntax tree probing, natural language processing, word representations

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Authors: Kenneth Christie

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Syria and Iraq are Arabian nightmares at the local, the regional and global levels in terms of human security and the protection of the vulnerable. Wracked by civil war, ethnic and political violence in the last 5 years in the case of Syria and 13 years in the case of Iraq, the body count now is staggering; the humanitarian crisis continues and there appears no end to this. A crisis that has claimed the lives of 200,000 people so far in Syria, sparked a humanitarian catastrophe fuelled violent Islamic extremism and exposed serious splits in the international community who appear to have no consensus. The international community’s failure to act is simply another sign of the desperate situation which has developed over conflicts that appears unsolvable in the immediate future and may be intractable in the long range. Three things are really at stake I’m going to argue in these continuing crises and how it will affect the human security dimensions of the conflict. Firstly, the protection of vulnerable individuals and civilians in the war, 2ndly, the dire consequences for regional instability as a result and thirdly the risks for minority and ethnic identities who are caught up in this, within and across these volatile borders. This paper will examine these elements and the consequences of the conflict in terms of human security, migration and development.

Keywords: human security, migration, Syria and Iraq, conflict and development

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Authors: M. Owlia, M. H. Azarsa, M. Khabbazan, A. Mirbagheri

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Proprioceptive neuromuscular facilitation exercises are a series of stretching techniques that are commonly used in rehabilitation and exercise therapy. Assessment of these exercises for true maneuvering requires extensive experience in this field and could not be down with patients themselves. In this paper, we developed software that uses Microsoft Kinect sensor, a spherical color marker, and real-time image processing methods to evaluate patient’s performance in generating true patterns of movements. The software also provides the patient with a visual feedback by showing his/her avatar in a Virtual Reality environment along with the correct path of moving hand, wrist and marker. Primary results during PNF exercise therapy of a patient in a room environment shows the ability of the system to identify any deviation of maneuvering path and direction of the hand from the one that has been performed by an expert physician.

Keywords: image processing, Microsoft Kinect, proprioceptive neuromuscular facilitation, upper extremities assessment, virtual reality

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Authors: Payel Datta, Abhishek Das, Abhishek Roychoudhury, Dhiman Chattopadhyay, Tanushyam Chattopadhyay

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Machine learning (ML) and deep learning (DL) is most predominantly used in image/video processing, natural language processing (NLP), audio and speech recognition but not that much used in system performance evaluation. In this paper, authors are going to describe the architecture of an abstraction layer constructed using ML/DL to detect the system failure. This proposed system is used to detect the system failure by evaluating the performance metrics of an IoT service deployment under constrained infrastructure environment. This system has been tested on the manually annotated data set containing different metrics of the system, like number of threads, throughput, average response time, CPU usage, memory usage, network input/output captured in different hardware environments like edge (atom based gateway) and cloud (AWS EC2). The main challenge of developing such system is that the accuracy of classification should be 100% as the error in the system has an impact on the degradation of the service performance and thus consequently affect the reliability and high availability which is mandatory for an IoT system. Proposed ML/DL classifiers work with 100% accuracy for the data set of nearly 4,000 samples captured within the organization.

Keywords: machine learning, system performance, performance metrics, IoT, edge

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Authors: Jorge Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder, Philipp Schuetz

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Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species.

Keywords: ring recognition, edge detection, X-ray computed tomography, dendrochronology

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Authors: Mengmeng Du, Noboru Noguchi, Hiroshi Okamoto, Noriko Kobayashi

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This paper introduced a topographic mapping system with time-saving and simplicity advantages based on integration of Light Detection and Ranging (LiDAR) data and Post Processing Kinematic Global Positioning System (PPK GPS) data. This topographic mapping system used a low-altitude Unmanned Aerial Vehicle (UAV) as a platform to conduct land survey in a low-cost, efficient, and totally autonomous manner. An experiment in a small-scale sugarcane farmland was conducted in Queensland, Australia. Subsequently, we synchronized LiDAR distance measurements that were corrected by using attitude information from gyroscope with PPK GPS coordinates for generation of precision topographic maps, which could be further utilized for such applications like precise land leveling and drainage management. The results indicated that LiDAR distance measurements and PPK GPS altitude reached good accuracy of less than 0.015 m.

Keywords: land survey, light detection and ranging, post processing kinematic global positioning system, precision agriculture, topographic map, unmanned aerial vehicle

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Authors: Janusz Bobulski, Mariusz Kubanek

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The main goal of the research is to develop a multimedia container structure containing three types of images: RGB, lidar and infrared, properly calibrated to each other. An additional goal is to develop program libraries for creating and saving this type of file and for restoring it. It will also be necessary to develop a method of data synchronization from lidar and RGB cameras as well as infrared. This type of file could be used in autonomous vehicles, which would certainly facilitate data processing by the intelligent autonomous vehicle management system. Autonomous cars are increasingly breaking into our consciousness. No one seems to have any doubts that self-driving cars are the future of motoring. Manufacturers promise that moving the first of them to showrooms is the prospect of the next few years. Many experts believe that creating a network of communicating autonomous cars will be able to completely eliminate accidents. However, to make this possible, it is necessary to develop effective methods of detection of objects around the moving vehicle. In bad weather conditions, this task is difficult on the basis of the RGB(red, green, blue) image. Therefore, in such situations, you should be supported by information from other sources, such as lidar or infrared cameras. The problem is the different data formats that individual types of devices return. In addition to these differences, there is a problem with the synchronization of these data and the formatting of this data. The goal of the project is to develop a file structure that could be containing a different type of data. This type of file is calling a multimedia container. A multimedia container is a container that contains many data streams, which allows you to store complete multimedia material in one file. Among the data streams located in such a container should be indicated streams of images, films, sounds, subtitles, as well as additional information, i.e., metadata. This type of file could be used in autonomous vehicles, which would certainly facilitate data processing by the intelligent autonomous vehicle management system. As shown by preliminary studies, the use of combining RGB and InfraRed images with Lidar data allows for easier data analysis. Thanks to this application, it will be possible to display the distance to the object in a color photo. Such information can be very useful for drivers and for systems in autonomous cars.

Keywords: an autonomous car, image processing, lidar, obstacle detection

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Authors: Aishatu Ibrahim Birma, Aminu Tukur, Abdulkarim Abbass Gora

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Machine translation plays a vital role in the Field of Natural Language Processing (NLP), breaking down language barriers and enabling communication across diverse communities. In the context of Hausa, a widely spoken language in West Africa, mainly in Nigeria, effective translation systems are essential for enabling seamless communication and promoting cultural exchange. However, due to the unique linguistic characteristics of Hausa, accurate translation remains a challenging task. The research proposes an approach to improving the machine learning translation of Hausa by integrating Named Entity Recognition (NER) techniques. Named entities, such as person names, locations, organizations, and dates, are critical components of a language's structure and meaning. Incorporating NER into the translation process can enhance the quality and accuracy of translations by preserving the integrity of named entities and also maintaining consistency in translating entities (e.g., proper names), and addressing the cultural references specific to Hausa. The NER will be incorporated into Neural Machine Translation (NMT) for the Hausa to English Translation.

Keywords: machine translation, natural language processing (NLP), named entity recognition (NER), neural machine translation (NMT)

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Authors: Jameela Ali Alkrimi, Abdul Rahim Ahmad, Azizah Suliman, Loay E. George

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Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. The lack of RBCs is a condition characterized by lower than normal hemoglobin level; this condition is referred to as 'anemia'. In this study, a software was developed to isolate RBCs by using a machine learning approach to classify anemic RBCs in microscopic images. Several features of RBCs were extracted using image processing algorithms, including principal component analysis (PCA). With the proposed method, RBCs were isolated in 34 second from an image containing 18 to 27 cells. We also proposed that PCA could be performed to increase the speed and efficiency of classification. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network ANN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained for a short time period with more efficient when PCA was used.

Keywords: red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC

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Authors: Hubert Pun

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When a manufacturer outsources the production of a product, it distributes its intellectual property (IP) into a supply chain that it may not be able to fully control. An IP agreement between a manufacturer and its suppliers is a popular solution to address the challenge of supplier-copycatting. The goal of this paper is to examine the impact of copycatting, from both the supplier and third-party firms, and the effectiveness of an IP agreement. Specifically, we use a game-theoretic approach to examine a system where a manufacturer outsources to a supplier. The supplier and a third-party firm decide whether or not to enter the market with copycat products while the manufacturer selects the level of marketing investment. The manufacturer can reduce the threat of supplier-copycatting by signing an IP agreement. We find that the manufacturer can be worse off from signing an IP agreement with its supplier, even if the IP agreement is costless and perfectly enforceable. We show that a manufacturer can deter copycat products through vertical integration and IP agreements and we outline the instances where each method is preferred. Furthermore, we find that the manufacturer may choose not to invest in quality improvements as a copycat deterrence strategy. We show that the supplier can benefit from the manufacturer’s decision to sign an IP agreement and that the supplier and the consumers can benefit from government regulations against copycat products. Our paper demonstrates the strengths and limitations of various copycat deterrence strategies when a supplier and third-party may produce copycat products.

Keywords: coopetitive supply chain, copycat, government regulation, intellectual property

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Authors: Gautam Sharda

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The concept of right speech is the third stage of the noble eightfold path as prescribed by the Buddha and followed by millions of practicing Buddhists. The Buddha lays a lot of importance on the notion of right speech (Samma Vacca). In the Angutara Nikaya, the Buddha mentioned what constitutes right speech, which is basically four kinds of abstentions; namely abstaining from false speech, abstaining from slanderous speech, abstaining from harsh or hateful speech and abstaining from idle chatter. The Buddha gives reasons in support of his view as to why abstaining from these four kinds of speeches is favourable not only for maintaining the peace and equanimity within an individual but also within a society. It is a known fact that when we say something harsh or slanderous to others, it eventually affects our individual peace of mind too. We also know about the many examples of hate speeches which have led to senseless cases of violence and which are well documented within our country and the world. Also, indulging in false speech is not a healthy sign for individuals within a group as this kind of a social group which is based on falsities and lies cannot really survive for long and will eventually lead to chaos. Buddha also told us to refrain from idle chatter or gossip as generally we have seen that idle chatter or gossip does more harm than any good to the individual and the society. Hence, if most of us actually inculcate this third stage (namely, right speech) of the noble eightfold path of the Buddha in our daily life, it would be highly beneficial both for the individual and for the harmony of the society.

Keywords: Buddhism, speech, individual, society

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Authors: Ebasa Girma, Nathnael Minyelshowa, Lebsework Negash

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The use of unmanned aerial vehicles (UAVs) in precision agriculture has seen a huge increase recently. As such, systems that aim to apply various algorithms on the field need a structured framework of abstractions. This paper defines the various tasks of the UAVs in precision agriculture and models them into an architectural framework. The presented architecture is built on the context that there will be minimal physical intervention to do the tasks defined with multiple coordinated and cooperative UAVs. Various tasks such as image processing, path planning, communication, data acquisition, and field mapping are employed in the architecture to provide an efficient system. Besides, different limitation for applying Multi-UAVs in precision agriculture has been considered in designing the architecture. The architecture provides an autonomous end-to-end solution, starting from mission planning, data acquisition, and image processing framework that is highly efficient and can enable farmers to comprehensively deploy UAVs onto their lands. Simulation and field tests show that the architecture offers a number of advantages that include fault-tolerance, robustness, developer, and user-friendliness.

Keywords: deep learning, multi-UAVs, precision agriculture, UAVs architecture

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Authors: Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal

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Diseases are fairly common in fruits and vegetables because of the changing climatic and environmental circumstances. Crop diseases, which are frequently difficult to control, interfere with the growth and output of the crops. Accurate disease detection and timely disease control measures are required to guarantee high production standards and good quality. In India, apples are a common crop that may be afflicted by a variety of diseases on the fruit, stem, and leaves. It is fungi, bacteria, and viruses that trigger the early symptoms of leaf diseases. In order to assist farmers and take the appropriate action, it is important to develop an automated system that can be used to detect the type of illnesses. Machine learning-based image processing can be used to: this research suggested a system that can automatically identify diseases in apple fruit and apple plants. Hence, this research utilizes the hybrid SVM-RBN model. As a consequence, the model may produce results that are more effective in terms of accuracy, precision, recall, and F1 Score, with respective values of 96%, 99%, 94%, and 93%.

Keywords: fruit plant disease, crop disease, machine learning, image processing, SVM-RBN

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Authors: Emad Alenany, M. Adel El-Baz

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In this paper, the flow of different classes of patients into a hospital is modelled and analyzed by using the queueing network analyzer (QNA) algorithm and discrete event simulation. Input data for QNA are the rate and variability parameters of the arrival and service times in addition to the number of servers in each facility. Patient flows mostly match real flow for a hospital in Egypt. Based on the analysis of the waiting times, two approaches are suggested for improving performance: Separating patients into service groups, and adopting different service policies for sequencing patients through hospital units. The separation of a specific group of patients, with higher performance target, to be served separately from the rest of patients requiring lower performance target, requires the same capacity while improves performance for the selected group of patients with higher target. Besides, it is shown that adopting the shortest processing time and shortest remaining processing time service policies among other tested policies would results in, respectively, 11.47% and 13.75% reduction in average waiting time relative to first come first served policy.

Keywords: queueing network, discrete-event simulation, health applications, SPT

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Authors: Qian Zaijian

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E-government is one of the hot issues in the current academic research of public policy and management. As the organic integration of information and communication technology (ICT) and public administration, e-government is one of the most important areas in contemporary information society. Policy information system is a basic subsystem of public policy system, its operation affects the overall effect of the policy process or even exerts a direct impact on the operation of a public policy and its success or failure. The basic principle of its operation is information collection, processing, analysis and release for a specific purpose. The function of E-government for public policy information system lies in the promotion of public access to the policy information resources, information transmission through e-participation, e-consultation in the process of policy analysis and processing of information and electronic services in policy information stored, to promote the optimization of policy information systems. However, due to many factors, the function of e-government to promote policy information system optimization has its practical limits. In the building of E-government in our country, we should take such path as adhering to the principle of freedom of information, eliminating the information divide (gap), expanding e-consultation, breaking down information silos and other major path, so as to promote the optimization of public policy information systems.

Keywords: China, e-consultation, e-democracy, e-government, e-participation, ICTs, public policy information systems

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Authors: Robert O'Mochain

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Over seven years have passed since the #MeToo movement first became prominent in Japan. Opinions vary on the degree of success of the movement along with its offshoot campaigns. This chapter suggests that a range of historical factors help to account for the relative lack of success of #MeToo in Japan, but that the gains achieved should also be highlighted. Our interviews with a range of activists provide insights into the nature of #MeToo in Japan and how it should be contextualized in the contemporary landscape of feminist movements here. In spite of the gains that have been made, it remains clear that levels of abuse and of non-reporting remain worryingly high and calls for well-resourced campaigns for ‘Awareness, Prevention, and Response’ are as timely as ever. Surprisingly, a case of sexual abuse involving boys and young men who were the victims of the sexual predator and entertainment mogul, Johnny Kitagawa, may be the clearest sign of change in Japanese society regarding victims of sexual abuse. In the post-#MeToo era, it was possible for these victims to come forward and attain some measure of justice. It was also possible for members of the public to strongly condemn the failures of the mass media journalists who ignored the evidence of sexual abuse for many years. Nevertheless, the case itself does not constitute reliable evidence that conditions have changed significantly for all victims of abuse or that media outlets will live up to their promises about reporting abuse.

Keywords: #MeToo, significance, Japan, prevention, response

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Authors: Toshitaka Higashino, Naoki Wakamiya

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Human beings perform a task by perceiving information from outside, recognizing them, and responding them. There have been various attempts to analyze and understand internal processes behind the reaction to a given stimulus by conducting psychological experiments and analysis from multiple perspectives. Among these, we focused on Model Human Processor (MHP). However, it was built based on psychological experiments and thus the relation with brain activity was unclear so far. To verify the validity of the MHP and propose our model from a viewpoint of neuroscience, EEG (Electroencephalography) measurements are performed during experiments in this study. More specifically, first, experiments were conducted where Latin alphabet characters were used as visual stimuli. In addition to response time, ERPs (event-related potentials) such as N100 and P300 were measured by using EEG. By comparing cycle time predicted by the MHP and latency of ERPs, it was found that N100, related to perception of stimuli, appeared at the end of the perceptual processor. Furthermore, by conducting an additional experiment, it was revealed that P300, related to decision making, appeared during the response decision process, not at the end. Second, by experiments using Japanese Hiragana characters, i.e. Japan's own phonetic symbols, those findings were confirmed. Finally, Japanese Kanji characters were used as more complicated visual stimuli. A Kanji character usually has several readings and several meanings. Despite the difference, a reading-related task and a meaning-related task exhibited similar results, meaning that they involved similar information processing processes of the brain. Based on those results, our model was proposed which reflects response time and ERP latency. It consists of three processors: the perception processor from an input of a stimulus to appearance of N100, the cognitive processor from N100 to P300, and the decision-action processor from P300 to response. Using our model, an application system which reflects brain activity can be established.

Keywords: brain activity, EEG, information processing model, model human processor

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Authors: Kayla Murray, Andrew Green, Gopi Paliyath, Keith Warriner

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Introduction: It is now acknowledged that control of human pathogens associated with fresh produce requires an integrated approach of several interventions as opposed to relying on post-harvest washes to remove field acquired contamination. To this end, current research is directed towards identifying such interventions that can be applied at different points in leafy green processing. Purpose: In the following the efficacy of different gas phase treatments to decontaminate whole lettuce heads during pre-processing storage were evaluated. Methods: Whole Cos lettuce heads were spot inoculated with L. monocytogenes, E. coli O157:H7 or Salmonella spp. The inoculated lettuce heads were then placed in a treatment chamber and exposed to ozone, chlorine dioxide or hydroxyl radicals at different time periods under a range of relative humidity. Survivors of the treatments were enumerated along with sensory analysis performed on the treated lettuce. Results: Ozone gas reduced L. monocytogenes by 2-log10 after ten-minutes of exposure with Salmonella and E. coli O157:H7 being decreased by 0.66 and 0.56-log cfu respectively. Chlorine dioxide gas treatment reduced L. monocytogenes and Salmonella on lettuce heads by 4 log cfu but only supported a 0.8 log cfu reduction in E. coli O157:H7 numbers. In comparison, hydroxyl radicals supported a 2.9 – 4.8 log cfu reduction of model human pathogens inoculated onto lettuce heads but required extended exposure times and relative humidity < 0.8. Significance: From the gas phase sanitizers tested, chlorine dioxide and hydroxyl radicals are the most effective. The latter process holds most promise based on the ease of delivery, worker safety and preservation of lettuce sensory characteristics. Although expose times for hydroxyl radicles was relatively long (24h) this should not be considered a limitation given the intervention is applied in store rooms or in transport containers during transit.

Keywords: gas phase sanitizers, iceberg lettuce heads, leafy green processing

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Authors: Trevor Kroeger, Hayden Williams, Edward Holzinger, David Coleman, Brian Haberman

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The network connectivity of medical devices is increasing at a rapid rate. Many medical devices, such as vital sign monitors, share information via wireless or wired connections. However, these connectivity options suffer from a variety of well-known limitations. Wireless connectivity, especially in the unlicensed radio frequency bands, can be disrupted. Such disruption could be due to benign reasons, such as a crowded spectrum, or to malicious intent. While wired connections are less susceptible to interference, they inhibit the mobility of the medical devices, which could be critical in a variety of scenarios. This work explores the application of Light Fidelity (Li-Fi) communication to enhance the security, performance, and mobility of medical devices in connected healthcare scenarios. A simple bridge for connected devices serves as an avenue to connect traditional medical devices to the Li-Fi network. This bridge was utilized to conduct bandwidth tests on a small Li-Fi network installed into a Mock-ICU setting with a backend enterprise network similar to that of a hospital. Mobile and stationary tests were conducted to replicate various different situations that might occur within a hospital setting. Results show that in room Li-Fi connectivity provides reasonable bandwidth and latency within a hospital like setting.

Keywords: hospital, light fidelity, Li-Fi, medical devices, security

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Authors: Rubén Valcarce-Álvarez, Francisco Jáñez-Martino, Rocío Alaiz-Rodríguez

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Cybersecurity challenges, including scams, drug sales, the distribution of child sexual abuse material, fake news, and hate speech on both the surface and deep web, have significantly increased over the past decade. Users who post such content often employ strategies to evade detection by automated filters. Among these tactics, text obfuscation plays an essential role in deceiving detection systems. This approach involves modifying words to make them more difficult for automated systems to interpret while remaining sufficiently readable for human users. In this work, we aim at spotting obfuscated words and the employed techniques, such as leetspeak, word inversion, punctuation changes, and mixed techniques. We benchmark Named Entity Recognition (NER) using models from the BERT family as well as two large language models (LLMs), Llama and Mistral, on XX_NER_WordCamouflage dataset. Our experiments evaluate these models by comparing their precision, recall, F1 scores, and accuracy, both overall and for each individual class.

Keywords: natural language processing (NLP), text obfuscation, named entity recognition (NER), deep learning

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Authors: Jacek Turski

Abstract:

The visual space geometries are constructed in the Riemannian geometry framework from simulated iso-disparity conics in the horizontalvisual plane of the binocular system with the asymmetric eyes (AEs). For the eyes fixating at the abathic distance, which depends on the AE’s parameters, the iso-disparity conics are frontal straight lines in physical space. For allother fixations, the iso-disparity conics consist of families of the ellipses or hyperbolas depending on both the AE’s parameters and the bifoveal fixation. However, the iso-disparity conic’s arcs are perceived in the gaze direction asthe frontal lines and are referred to as visual geodesics. Thus, geometriesof physical and visual spaces are different. A simple postulate that combines simulated iso-disparity conics with basic anatomy od the human visual system gives the relative depth for the fixation at the abathic distance that establishes the Riemann matric tensor. The resulting geodesics are incomplete in the gaze direction and, therefore, give thefinite distances to the horizon that depend on the AE’s parameters. Moreover, the curvature vanishes in this eyes posture such that visual space is flat. For all other fixations, only the sign of the curvature canbe inferred from the global behavior of the simulated iso-disparity conics: the curvature is positive for the elliptic iso-disparity curves and negative for the hyperbolic iso-disparity curves.

Keywords: asymmetric eye model, iso-disparity conics, metric tensor, geodesics, curvature

Procedia PDF Downloads 145

Authors: Maryam Heidari, James H. Jones Jr.

Abstract:

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

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

Procedia PDF Downloads 116

Authors: Comfort Mokgothu

Abstract:

This study investigated the relation between processing information and fitness level of active (fit) and sedentary (unfit) children drawn from rural and urban areas in Botswana. It was hypothesized that fit children would display faster simple reaction time (SRT), choice reaction times (CRT) and movement times (SMT). 60, third grade children (7.0 – 9.0 years) were initially selected and based upon fitness testing, 45 participated in the study (15 each of fit urban, unfit urban, fit rural). All children completed anthropometric measures, skinfold testing and submaximal cycle ergometer testing. The cognitive testing included SRT, CRT, SMT and Choice Movement Time (CMT) and memory sequence length. Results indicated that the rural fit group exhibited faster SMT than the urban fit and unfit groups. For CRT, both fit groups were faster than the unfit group. Collectively, the study shows that the relationship that exists between physical fitness and cognitive function amongst the elderly can tentatively be extended to the pediatric population. Physical fitness could be a factor in the speed at which we process information, including decision making, even in children.

Keywords: decision making, fitness, information processing, reaction time, cognition movement time

Procedia PDF Downloads 146

Authors: Hao Mi, Ming Yang, Tian-yue Yang

Abstract:

Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

Procedia PDF Downloads 225