Search results for: sequential change detection

Authors: Issa Qabaja, Fadi Thabtah

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Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set.

Keywords: data mining, email classification, phishing, online security

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Authors: Malek Jamaliah, Robert Powell

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In spite of the influence of climate change on tourism destinations, particularly those rely heavily on natural resources, little attention paid to study the appropriate adaptation efforts to cope with, moderate and benefit from the impacts of climate change. The existing literature indicated that the research of climate change adaptation in the tourism and outdoor recreation field is at least 5-7 years behind other sectors such as water resources and agriculture. In Jordan, there are many observed changes in climate patterns such as higher temperatures, decreased precipitation and increased severity and frequency of drought. Dana Biosphere Reserve (DBR), the largest protected area and the major eco-tourism destination in Jordan, is facing climate change, which gradually degrading environment, shifting tourism seasons and changing livelihood and lifestyle of local communities. This study aims to assess climate change adaptation practices and policies used in DBR to cope with climate change related-risks. We conducted qualitative semi-structured interviews with key informants in DBR to assess climate change adaptation practices. Direct content analysis (or a priori content analysis) was used to determine the components and indicators of climate change adaptation. The results found that DBR has implemented a wide range of adaptation practices, including infrastructure development, diversification of tourism products, environmentally-friendly practices, visitor management, land use management, rainwater collection, environmental monitoring and research, environmental education and collaboration with stakeholders. These diverse practices implicitly and explicitly play an important role in coping with the social, economic and environmental impacts caused by climate change. Finally, this study demonstrated that climate change adaptation is closely related to sustainable management of eco-tourism.

Keywords: climate change adaptation, dana biosphere reserve, ecotourism, sustainable management

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Authors: Kashif Jabbar

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Milk adulteration is a global concern, and the current study was plan to synthesize Selenium nanoparticles by green method using plant extract of garlic, Allium Sativum, and to characterize Selenium nanoparticles through different analytical techniques and to apply Selenium nanoparticles as fast and easy technique for the detection of salicylic acid in milk. The highly selective, sensitive, and quick interference green synthesis-based sensing of possible milk adulterants i.e., salicylic acid, has been reported here. Salicylic acid interacts with nanoparticles through strong bonding interactions, hence resulting in an interruption within the formation of selenium nanoparticles which is confirmed by UV-VIS spectroscopy, scanning electron microscopy, and x-ray diffraction. This interaction in the synthesis of nanoparticles resulted in transmittance wavelength that decrease with the increasing amount of salicylic acid, showing strong binding of selenium nanoparticles with adulterant, thereby permitting in-situ fast detection of salicylic acid from milk having a limit of detection at 10-3 mol and linear coefficient correlation of 0.9907. Conclusively, it can be draw that colloidal selenium could be synthesize successfully by garlic extract in order to serve as a probe for fast and cheap testing of milk adulteration.

Keywords: adulteration, green synthesis, selenium nanoparticles, salicylic acid, aggregation

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Authors: Ansah Iris Baffour, Dong-Ho Kim, Sung-Gyu Park

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The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus and is gradually shifting to an endemic phase which implies the outbreak is far from over and will be difficult to eradicate. Global cooperation has led to unified precautions that aim to suppress epidemiological spread (e.g., through travel restrictions) and reach herd immunity (through vaccinations); however, the primary strategy to restrain the spread of the virus in mass populations relies on screening protocols that enable rapid on-site diagnosis of infections. Herein, we employed surface enhanced Raman spectroscopy (SERS) for the rapid detection of SARS-CoV-2 lysate on an Au-modified Au nanodimple(AuND)electrode. Through in situone-pot Au electrodeposition on the AuND electrode, Au-lysate nanocomposites were synthesized, generating3D internal hotspots for large SERS signal enhancements within 30 s of the deposition. The capture of lysate into newly generated plasmonic nanogaps within the nanocomposite structures enhanced metal-spike protein contact in 3D spaces and served as hotspots for sensitive detection. The limit of detection of SARS-CoV-2 lysate was 5 x 10-2 PFU/mL. Interestingly, ultrasensitive detection of the lysates of influenza A/H1N1 and respiratory syncytial virus (RSV) was possible, but the method showed ultimate selectivity for SARS-CoV-2 in lysate solution mixtures. We investigated the practical application of the approach for rapid on-site diagnosis by detecting SARS-CoV-2 lysate spiked in normal human saliva at ultralow concentrations. The results presented demonstrate the reliability and sensitivity of the assay for rapid diagnosis of COVID-19.

Keywords: label-free detection, nanocomposites, SARS-CoV-2, surface-enhanced raman spectroscopy

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Authors: Jorge Maestre Vidal, Ana Lucila Sandoval Orozco, Luis Javier García Villalba

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With the increase in popularity of mobile devices, new and varied forms of malware have emerged. Consequently, the organizations for cyberdefense have echoed the need to deploy more effective defensive schemes adapted to the challenges posed by these recent monitoring environments. In order to contribute to their development, this paper presents a malware detection strategy for mobile devices based on sequence alignment algorithms. Unlike the previous proposals, only the system calls performed during the startup of applications are studied. In this way, it is possible to efficiently study in depth, the sequences of system calls executed by the applications just downloaded from app stores, and initialize them in a secure and isolated environment. As demonstrated in the performed experimentation, most of the analyzed malicious activities were successfully identified in their boot processes.

Keywords: android, information security, intrusion detection systems, malware, mobile devices

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Authors: Nadia Theuma

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Climate change is a reality that has started to leave an impact on the physical environment as well as on the built environment, in particular built heritage. This paper explores the argument that climate change is also a trigger which can lead to identifying a number of creative solutions that can transform built heritage into sustainable buildings. Using the Maltese Islands, and in particular the city of Valletta which is also a World Heritage Site, this paper illustrates some of the innovative solutions that are being developed to make heritage buildings more sustainable and in doing so, mitigating the negative impacts of climate change. The paper looks in detail at the most notable initiatives being developed, their implementation and application, which at times is not easy considering the restrictions within protected built heritage areas and the positive impacts that they will have on visitor experience and overall sustainability of the Maltese tourism product. The paper will conclude by outlining how these solutions can be adapted to buildings with similar climatic conditions.

Keywords: built heritage, creative solutions, climate change, Maltese Islands

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

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

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

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Authors: Chiara Cesali

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The effects of climate change are increasingly evident: increases in temperature (i.e. global warming), greater frequency of extreme weather events, i.e. storms, floods, which often affect transport infrastructures. Large-scale climatological models with long-term horizons (up to 2100) show the possibility of significant increases in precipitation in the future, according to the greenhouse gas emissions scenarios from IPCC. Consequently, the insufficiency of existing hydraulic works (i.e. bridges, culverts, drainage systems) may be more frequent, or those currently being designed may become insufficient in the future. Thus, the hydraulic design methods of transport infrastructure must begin to take into account the influence of climate change. To this purpose, criteria for applying to the hydraulic design of a railway infrastructure some of the approaches currently available for determining design rainfall intensity and/or peak discharge flow on the basis of possible climate change scenarios are defined and proposed in the paper. Some application cases are also described.

Keywords: climate change, hydraulic design, precipitation, railway

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Authors: Aika Umemuro, Mitsuru Sato, Mizuki Narita, Saya Hori, Saya Sakurai, Tomomi Nakayama, Ayano Nakazawa, Toshihiro Ogura

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Mammography requires the detection of very small calcifications, and physicians search for microcalcifications by magnifying the images as they read them. The mouse is necessary to zoom in on the images, but this can be tiring and distracting when many images are read in a single day. Therefore, an image magnification system combining an eye-detector and a simple electroencephalograph (EEG) scanner was devised, and its operability was evaluated. Two experiments were conducted in this study: the measurement of eye-detection error using an eye-detector and the measurement of the time required for image magnification using a simple EEG scanner. Eye-detector validation showed that the mean distance of eye-detection error ranged from 0.64 cm to 2.17 cm, with an overall mean of 1.24 ± 0.81 cm for the observers. The results showed that the eye detection error was small enough for the magnified area of the mammographic image. The average time required for point magnification in the verification of the simple EEG scanner ranged from 5.85 to 16.73 seconds, and individual differences were observed. The reason for this may be that the size of the simple EEG scanner used was not adjustable, so it did not fit well for some subjects. The use of a simple EEG scanner with size adjustment would solve this problem. Therefore, the image magnification system using the eye-detector and the simple EEG scanner is useful.

Keywords: EEG scanner, eye-detector, mammography, observers

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Authors: Szu-Kai Hsu, Po-Ching Lin

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As more and more multi-core processors emerge, traditional sequential programming paradigm no longer suffice. Yet only few modern dynamic programming languages can leverage such advantage. Ruby, for example, despite its wide adoption, only includes threads as a simple parallel primitive. The global virtual machine lock of official Ruby runtime makes it impossible to exploit full parallelism. Though various alternative Ruby implementations do eliminate the global virtual machine lock, they only provide developers dated locking mechanism for data synchronization. However, traditional locking mechanism error-prone by nature. Software Transactional Memory is one of the promising alternatives among others. This paper introduces a new virtual machine: GobiesVM to provide a native software transactional memory based solution for dynamic programming languages to exploit parallelism. We also proposed a simplified variation of Transactional Locking II algorithm. The empirical results of our experiments show that support of STM at virtual machine level enables developers to write straightforward code without compromising parallelism or sacrificing thread safety. Existing source code only requires minimal or even none modi cation, which allows developers to easily switch their legacy codebase to a parallel environment. The performance evaluations of GobiesVM also indicate the difference between sequential and parallel execution is significant.

Keywords: global interpreter lock, ruby, software transactional memory, virtual machine

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

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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

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Authors: Muqaddas Butt, Allah Bakhsh Malik

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The changing educational environment, the diverse educational needs of students, the high expectations from the public, and the policy reports demand a strong change & innovation in the curriculum. Effective change and innovation could not be possible without the involvement of two major tenants i.e. teachers and educational managers. Thus, the locus of this research was to explore the secondary school principals and teachers priorities regarding change and innovation in curriculum. The main research objectives were included to explore the secondary school teachers’ and principals’ views about existing Humanities group curriculum; to identify their priorities regarding change and innovation in curriculum and to make a comparison between the priorities of both (the teachers & principals). A total sample of 150 Secondary school teachers and 24 principals from Federal Government Secondary Schools was drawn. The data was obtained through a five point Likert scale questionnaire. The findings indicated a huge difference between principals and teachers priorities. Related to prevailed curriculum, teachers showed more satisfactory views than principals. It was also found that the principals in comparison with teachers showed more inclination towards change and innovation and emphasized on an interdisciplinary, practical and ICT Integrated curriculum. Inclusion of local environmental issues; creativity based and practical activities based curriculum; and orientation to citizenship education into curriculum were some of the aspects highly prioritized by both teachers and principals.

Keywords: curriculum change, curriculum innovation, humanities curriculum, curriculum priorities

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Authors: Hamza Zidoum

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This paper investigates the impact of human genetic variation on the function of human proteins using machine-learning algorithms. Single-Nucleotide Polymorphism represents the most common form of human genome variation. We focus on the single amino-acid polymorphism located in the coding region as they can affect the protein function leading to pathologic phenotypic change. We use several supervised Machine Learning methods to identify structural properties correlated with increased risk of the missense mutation being damaging. SVM associated with Principal Component Analysis give the best performance.

Keywords: single-nucleotide polymorphism, machine learning, feature selection, SVM

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Authors: Mukesh Singh Boori, Vít Voženílek

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Olomouc is a unique and complex landmark with widespread forestation and land use. This research work was conducted to assess important and complex land use change trajectories in Olomouc region. Multi-temporal satellite data from 1991, 2001 and 2013 were used to extract land use/cover types by object oriented classification method. To achieve the objectives, three different aspects were used: (1) Calculate the quantity of each transition; (2) Allocate location based landscape pattern (3) Compare land use/cover evaluation procedure. Land cover change trajectories shows that 16.69% agriculture, 54.33% forest and 21.98% other areas (settlement, pasture and water-body) were stable in all three decade. Approximately 30% of the study area maintained as a same land cove type from 1991 to 2013. Here broad scale of political and socio-economic factors was also affect the rate and direction of landscape changes. Distance from the settlements was the most important predictor of land cover change trajectories. This showed that most of landscape trajectories were caused by socio-economic activities and mainly led to virtuous change on the ecological environment.

Keywords: remote sensing, land use/cover, change trajectories, image classification

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Authors: Nita Mitra, Pranab Chanda

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In this paper, the issue of attitude-behavior consistency has been addressed in the context of climate change. Scientists (about 98 percent) opine that human behavior has a significant role in climate change. Such climate changes are harmful for human life. Thus, it is natural to conclude that only change of human behavior can avoid harmful consequences. Government and Non-Government Organizations are taking steps to bring in the desired changes in behavior. However, it seems that although the efforts are achieving changes in the attitudes to some degree, those steps are failing to materialize the corresponding behavioral changes. This has been a great concern for environmentalists. Psychologists have noticed the problem as a particular case of the general psychological problem of making attitude and behavior consistent with each other. The present study is in continuation of a previous work of the same author based upon descriptive research on the status of attitude and behavior of the people of a foot-hill region of the Himalayas in India regarding climate change. The observations confirm the mismatch of attitude and behavior of the people of the region with respect to climate change. While doing so an attitude-behavior mismatch has been noticed with respect to the acceptance of psychological findings by the public. People have been found to be interested in Psychology as an important subject, but they are reluctant to take the observations of psychologists seriously. A comparative study in this regard has been made with similar studies done elsewhere. Finally, an attempt has been made to perceive observations in the framework of observational learning due to Bandura's and behavior change due to Lewin.

Keywords: acceptance of psychological variables, attitude-behavior consistency, behavior change, climate change, observational learning

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Authors: Usama Shahid

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Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model.

Keywords: off topic, text detection, eco state network, machine learning

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Authors: Paraskevi Liosatou

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Climate change is considered to be of global concern being amplified by the fact that by its nature, cannot be spatially limited. This fact makes necessary the intergovernmental decision-making procedures. In the intergovernmental level, the institutions such as the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change develop efforts, methods, and practices in order to plan and suggest climate mitigation and adaptation measures. These measures are based on specific scientific findings and methods making clear the strong connection between science and policy. In particular, these scientific recommendations offer a series of practices, methods, and choices mitigating the problem by aiming at the indirect mitigation of the causes and the factors amplifying climate change. Moreover, modern production and economic context do not take into consideration the social, political, environmental and spatial dimensions of the problem. This work studies the decision-making process working in international and European level. In this context, this work considers the policy tools that have been implemented by various intergovernmental organizations. The methodology followed is based mainly on the critical study of standards and process concerning the connections and cooperation between science and policy as well as considering the skeptic debates developed. The finding of this work focuses on the links between science and policy developed by the institutional and scientific mechanisms concerning climate change mitigation. It also analyses the dimensions and the factors of the science-policy framework; in this way, it points out the causes that maintain skepticism in current scientific circles.

Keywords: climate change, climate change mitigation, climate change skepticism, IPCC, skepticism

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Authors: Weishou Shen, Chunyan Yang, Zhongliang Li

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The crop climate productivity and the distribution of cropland reflect long-term adaption of agriculture to climate. In order to fully understand the impact of climate change on cropland ecosystem in Tibet, the spatiotemporal changes of crop climate productivity and cropland distribution were analyzed with the help of GIS and RS software. Results indicated that the climate change to the direction of wet and warm in Tibet in the recent 30 years, with a rate of 0.79℃/10 yr and 23.28 mm/10yr respectively. Correspondingly, the climate productivity increased gradually, with a rate of 346.3kg/(hm2•10a), of which, the fastest-growing rate of the crop climate productivity is in Southern Tibet Mountain- plain-valley. During the study period, the total cropland area increased from 32.54 million ha to 37.13 million ha, and cropland has expanded to higher altitude area and northward. Overall, increased cropland area and crop climate productivity due to climate change plays a positive role for agriculture in Tibet.

Keywords: climate change, productivity, cropland area, Tibet plateau

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Authors: T. A. Mona M. Salem, Ali F. Bakr

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Given the potential devastation associated with future climate change related disasters, it is vital to change the way we build and manage our cities, through new strategies to reconfigure them and their infrastructures in ways that help secure their reproduction. This leads to a kaleidoscopic view of the city that recognizes the interrelationships of energy, water, transportation, and solid waste. These interrelationships apply across sectors and with respect to the built form of the city. The paper aims at a long-term climate resilience of cities and their critical infrastructures, and sets out an argument for including an eco-infrastructure-based approach in strategies to address climate change. As these ecosystems have a critical role to play in building resilience and reducing vulnerabilities in cities, communities and economies at risk, the enhanced protection and management of ecosystems, biological resources and habitats can mitigate impacts and contribute to solutions as nations and cities strive to adapt to climate change.

Keywords: ecology, ecosystem, infrastructure, climate change, urban

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Authors: Zhang Xingnan, Huang Jingjia, Feng Yue, Burra Venkata Durga Kumar

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This paper proposes a comprehensive approach to mitigate ROP (Return-Oriented Programming) attacks by combining internal operating system protection mechanisms and hardware-assisted techniques. Through extensive literature review, we identify the effectiveness of ASLR (Address Space Layout Randomization) and LBR (Last Branch Record) in preventing ROP attacks. We present a process involving buffer overflow detection, hardware-assisted ROP attack detection, and the use of Turing detection technology to monitor control flow behavior. We envision a specialized tool that views and analyzes the last branch record, compares control flow with a baseline, and outputs differences in natural language. This tool offers a graphical interface, facilitating the prevention and detection of ROP attacks. The proposed approach and tool provide practical solutions for enhancing software security.

Keywords: operating system, ROP attacks, returning-oriented programming attacks, ASLR, LBR, CFI, DEP, code randomization, hardware-assisted CFI

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Authors: Martin Pumera

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Graphene materials are being widely used in electrochemistry due to their versatility and excellent properties as platforms for biosensing. Here we present current trends in the electrochemical biosensing of pesticides and other toxic compounds. We explore two fundamentally different designs, (i) using graphene and other 2-D nanomaterials as an electrochemical platform and (ii) using these nanomaterials in the laboratory on chip design, together with paramagnetic beads. More specifically: (i) We explore graphene as transducer platform with very good conductivity, large surface area, and fast heterogeneous electron transfer for the biosensing. We will present the comparison of these materials and of the immobilization techniques. (ii) We present use of the graphene in the laboratory on chip systems. Laboratory on the chip had a huge advantage due to small footprint, fast analysis times and sample handling. We will show the application of these systems for pesticide detection and detection of other toxic compounds.

Keywords: graphene, 2D nanomaterials, biosensing, chip design

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Authors: El Harraj Abdeslam, Raissouni Naoufal

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The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: video surveillance, background subtraction, contrast limited histogram equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes

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Authors: M. Adamczak-Retecka, O. Hołub-Śniadach

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Dangerous of climate change is now global problem and as such has a strategic priority also for the European Union. Europe and European citizens try to do their best to cut greenhouse gas emissions, moreover they substantially encourage other nations and regions to follow the same way. The European Commission and a number of Member States have developed adaptation strategies in order to help strengthen EU's resilience to the inevitable impacts of climate change. The EU has long been a driving force in international negotiations on climate change and was instrumental in the development of the UN Framework Convention on Climate Change. As the world's leading donor of development aid, the EU also provides substantial funding to help developing countries tackle climate change problem. Global warming influences human health, biodiversity, ecosystems but also many social and economic sectors. The aim of this paper is to focus on impact of claimant change on for food security. Food security challenges are directly related to globalization, climate change. It means that current and future food policy is exposed to all cross-cutting and that must be linked with environmental and climate targets, which supposed to be achieved. In the 7th EAP —The new general Union Environment Action Program to 2020, called “Living well, within the limits of our planet” EU has agreed to step up its efforts to protect natural capital, stimulate resource efficient, low carbon growth and innovation, and safeguard people’s health and wellbeing– while respecting the Earth’s natural limits.

Keywords: climate change, food security, sustainable food consumption, climate governance

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Authors: Esther Burkitt

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Background: Evidence is mounting that mixed emotions can be experienced simultaneously in different ways across the lifespan. Four types of patterns of simultaneously mixed emotions (sequential, prevalent, highly parallel, and inverse types) have been identified in middle childhood and adolescence. Moreover, the recognition of these experiences tends to develop firstly when children consider peers rather than the self. This evidence from children and adolescents is based on examining the presence of experiences specified in adulthood. The present study, therefore, applied an exhaustive coding scheme to investigate whether children experience types of previously unidentified simultaneous mixed emotional experiences. Methodology: One hundred and twenty children (60 girls) aged 7 years 1 month - 9 years 2 months (X=8 years 1 month; SD = 10 months) were recruited from mainstream schools across the UK. Two age groups were formed (youngest, n = 61, 7 years 1 month- 8 years 1 months: oldest, n = 59, 8 years 2 months – 9 years 2 months) and allocated to one of two conditions hearing vignettes describing happy and sad mixed emotion events in age and gender-matched protagonist or themselves. Results: Loglinear analyses identified new types of flexuous, vertical, and other experiences along with established sequential, prevalent, highly parallel, and inverse types of experience. Older children recognised more complex experiences other than the self-condition. Conclusion: Several additional types of simultaneously mixed emotions are recognised in middle childhood. The theoretical relevance of simultaneous mixed emotion processing in childhood is considered, and the potential utility of the findings in emotion assessments is discussed.

Keywords: emotion, childhood, self, other

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Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

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Authors: Li Hui, Riyadh Hindi

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Concrete structures frequently suffer from crack formation, a critical issue that can significantly reduce their lifespan by allowing damaging agents to enter. Traditional methods of crack detection depend on manual visual inspections, which heavily relies on the experience and expertise of inspectors using tools. In this study, a more efficient, computer vision-based approach is introduced by using the lightweight MobileNetV2 neural network. A dataset of 40,000 images was used to develop a specialized crack evaluation algorithm. The analysis indicates that MobileNetV2 matches the accuracy of traditional CNN methods but is more efficient due to its smaller size, making it well-suited for mobile device applications. The effectiveness and reliability of this new method were validated through experimental testing, highlighting its potential as an automated solution for crack detection in concrete structures.

Keywords: Concrete crack, computer vision, deep learning, MobileNetV2 neural network

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Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

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A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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Authors: Zheng Yuxun

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This review critically assesses the advancements and prospective developments in defect detection methodologies within the semiconductor industry, an essential domain that significantly affects the operational efficiency and reliability of electronic components. As semiconductor devices continue to decrease in size and increase in complexity, the precision and efficacy of defect detection strategies become increasingly critical. Tracing the evolution from traditional manual inspections to the adoption of advanced technologies employing automated vision systems, artificial intelligence (AI), and machine learning (ML), the paper highlights the significance of precise defect detection in semiconductor manufacturing by discussing various defect types, such as crystallographic errors, surface anomalies, and chemical impurities, which profoundly influence the functionality and durability of semiconductor devices, underscoring the necessity for their precise identification. The narrative transitions to the technological evolution in defect detection, depicting a shift from rudimentary methods like optical microscopy and basic electronic tests to more sophisticated techniques including electron microscopy, X-ray imaging, and infrared spectroscopy. The incorporation of AI and ML marks a pivotal advancement towards more adaptive, accurate, and expedited defect detection mechanisms. The paper addresses current challenges, particularly the constraints imposed by the diminutive scale of contemporary semiconductor devices, the elevated costs associated with advanced imaging technologies, and the demand for rapid processing that aligns with mass production standards. A critical gap is identified between the capabilities of existing technologies and the industry's requirements, especially concerning scalability and processing velocities. Future research directions are proposed to bridge these gaps, suggesting enhancements in the computational efficiency of AI algorithms, the development of novel materials to improve imaging contrast in defect detection, and the seamless integration of these systems into semiconductor production lines. By offering a synthesis of existing technologies and forecasting upcoming trends, this review aims to foster the dialogue and development of more effective defect detection methods, thereby facilitating the production of more dependable and robust semiconductor devices. This thorough analysis not only elucidates the current technological landscape but also paves the way for forthcoming innovations in semiconductor defect detection.

Keywords: semiconductor defect detection, artificial intelligence in semiconductor manufacturing, machine learning applications, technological evolution in defect analysis

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Authors: Nura Suleiman

Abstract:

The democratic dispensation in Nigeria witnessed allot of changes since its beginning up to the 2015 election. The issues of zoning formula, rigging, money politics, god fatherism, and political thuggery among the youths became the centre stage from 1999-2014. But 2015 came with new tune that brings about a little shift from the traditional politics mentioned above, the political socialisation and knowledge penetrated into the sense of electorate where people suddenly change and look for the better option. The paper will examine the democratic change in relation to the 2015 General election which brings General MohammaduBuhari on the mantle of leadership of Nigeria. Many reasons were attributed to the sudden change of government in Nigeria, but the major ones are lack of good governance, corruption, insecurity, political parties’ merger to formed APCand change in INEC leadership. Others are weakness of the leadership and undemocratic nature of PDP government at different level in the country. The glamor for change became necessary because People become more informs about the manifestation of good hope and better Nigeria from the major opposition party (APC). During 2015 election the electorate voted the incumbent government out and replaced it with their choice.

Keywords: democracy, election, insecurity, good governance

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Authors: Mehdi Moradi Sarmeidani, Peyman Keyhani, Hasan Momtaz

Abstract:

Chlamydophila psittaci is a avian pathogen that may cause respiratory disorders in humans. Conjunctival and cloacal swabs from 54 captive psittacine birds presented at veterinary clinics were collected to determine the prevalence of C. psittaci in domestic birds in Isfahan. Samples were collected during 2014 from a total of 10 different species of parrots, with African gray(33), Cockatiel lutino(3), Cockatiel gray(2), Cockatiel cinnamon(1), Pearl cockatiel(6), Timneh African grey(1), Ringneck parakeet(2), Melopsittacus undulatus(1), Alexander parakeet(2), Green Parakeet(3) being the most representative species sampled. C. psittaci was detected in 27 (50%) birds using molecular detection (PCR) method. The detection of this bacterium in captive psittacine birds shows that there is a potential risk for human whom has a direct contact and there is a possibility of infecting other birds.

Keywords: chlamydophila psittaci, psittacine birds, PCR, Isfahan

Procedia PDF Downloads 351