Search results for: probabilistic matching model for binary images

Authors: Ramayanam Suresh, A. Nagaraja Rao, B. Eswara Reddy

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Texture features of mammogram images are useful for finding masses or cancer cases in mammography, which have been used by radiologists. Textures are greatly succeeded for segmented images rather than normal images. It is necessary to perform segmentation for exclusive specification of cancer and non-cancer regions separately. Region of interest (ROI) is most commonly used technique for mammogram segmentation. Limitation of this method is that it is unable to explore segmentation for large collection of mammogram images. Therefore, this paper is proposed multi-ROI segmentation for addressing the above limitation. It supports greatly in finding the best texture features of mammogram images. Experimental study demonstrates the effectiveness of proposed work using benchmarked images.

Keywords: texture features, region of interest, multi-ROI segmentation, benchmarked images

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Authors: Hengki Eko Putra, Dennish Ari Putro, Tri Wahyu Hadi, Edi Riawan, Junnaedhi Dewa Gede, Aditia Rojali, Fariza Dian Prasetyo, Yudhistira Satya Pribadi, Dita Fatria Andarini, Mila Khaerunisa, Raditya Hanung Prakoswa

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Catastrophe risk management can only be done if we are able to calculate the exposed risks. Jakarta is an important city economically, socially, and politically and in the same time exposed to severe floods. On the other hand, flood risk calculation is still very limited in the area. This study has calculated the risk of flooding for Jakarta using 2-Dimensional Model ANUGA. 2-Dimensional model ANUGA and 1-Dimensional Model HEC-RAS are used to calculate the risk of flooding from 13 major rivers in Jakarta. ANUGA can simulate physical and dynamical processes between the streamflow against river geometry and land cover to produce a 1-meter resolution inundation map. The value of streamflow as an input for the model obtained from hydrological analysis on rainfall data using hydrologic model HEC-HMS. The probabilistic streamflow derived from probabilistic rainfall using statistical distribution Log-Pearson III, Normal and Gumbel, through compatibility test using Chi Square and Smirnov-Kolmogorov. Flood event on 2007 is used as a comparison to evaluate the accuracy of model output. Property damage estimations were calculated based on flood depth for 1, 5, 10, 25, 50, and 100 years return period against housing value data from the BPS-Statistics Indonesia, Centre for Research and Development of Housing and Settlements, Ministry of Public Work Indonesia. The vulnerability factor was derived from flood insurance claim. Jakarta's flood loss estimation for the return period of 1, 5, 10, 25, 50, and 100 years, respectively are Rp 1.30 t; Rp 16.18 t; Rp 16.85 t; Rp 21.21 t; Rp 24.32 t; and Rp 24.67 t of the total value of building Rp 434.43 t.

Keywords: 2D hydrodynamic model, ANUGA, flood, flood modeling

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Authors: Sittipong Jarernprasert, Enrique Bazan-Zurita, Paul C. Rizzo

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Currently, seismic probabilistic risk assessments (SPRA) for nuclear facilities use In-Structure Response Spectra (ISRS) in the calculation of fragilities for systems and components. ISRS are calculated via dynamic analyses of the host building subjected to two orthogonal components of horizontal ground motion. Each component is defined as the median motion in any horizontal direction. Structural engineers applied the components along selected X and Y Cartesian axes. The ISRS at different locations in the building are also calculated in the X and Y directions. The choice of the directions of X and Y are not specified by the ground motion model with respect to geographic coordinates, and are rather arbitrarily selected by the structural engineer. Normally, X and Y coincide with the “principal” axes of the building, in the understanding that this practice is generally conservative. For SPRA purposes, however, it is desirable to remove any conservatism in the estimates of median ISRS. This paper examines the effects of the direction of horizontal seismic motion on the ISRS on typical nuclear structure. We also evaluate the variability of ISRS calculated along different horizontal directions. Our results indicate that some central measures of the ISRS provide robust estimates that are practically independent of the selection of the directions of the horizontal Cartesian axes.

Keywords: seismic, directionality, in-structure response spectra, probabilistic risk assessment

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Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

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Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

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Authors: Mansir Abubakar, Hazlina Hamdan, Norwati Mustapha, Teh Noranis Mohd Aris

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In order to build a matching pattern for each class correspondences of ontology, it is required to specify a set of attribute correspondences across two corresponding classes by clustering. Clustering reduces the size of potential attribute correspondences considered in the matching activity, which will significantly reduce the computation workload; otherwise, all attributes of a class should be compared with all attributes of the corresponding class. Most existing ontology matching approaches lack scalable attributes discovery methods, such as cluster-based attribute searching. This problem makes ontology matching activity computationally expensive. It is therefore vital in ontology matching to design a scalable element or attribute correspondence discovery method that would reduce the size of potential elements correspondences during mapping thereby reduce the computational workload in a matching process as a whole. The objective of this work is 1) to design a clustering method for discovering similar attributes correspondences and relationships between ontologies, 2) to discover element correspondences by classifying elements of each class based on element’s value features using K-medoids clustering technique. Discovering attribute correspondence is highly required for comparing instances when matching two ontologies. During the matching process, any two instances across two different data sets should be compared to their attribute values, so that they can be regarded to be the same or not. Intuitively, any two instances that come from classes across which there is a class correspondence are likely to be identical to each other. Besides, any two instances that hold more similar attribute values are more likely to be matched than the ones with less similar attribute values. Most of the time, similar attribute values exist in the two instances across which there is an attribute correspondence. This work will present how to classify attributes of each class with K-medoids clustering, then, clustered groups to be mapped by their statistical value features. We will also show how to map attributes of a clustered group to attributes of the mapped clustered group, generating a set of potential attribute correspondences that would be applied to generate a matching pattern. The K-medoids clustering phase would largely reduce the number of attribute pairs that are not corresponding for comparing instances as only the coverage probability of attributes pairs that reaches 100% and attributes above the specified threshold can be considered as potential attributes for a matching. Using clustering will reduce the size of potential elements correspondences to be considered during mapping activity, which will in turn reduce the computational workload significantly. Otherwise, all element of the class in source ontology have to be compared with all elements of the corresponding classes in target ontology. K-medoids can ably cluster attributes of each class, so that a proportion of attribute pairs that are not corresponding would not be considered when constructing the matching pattern.

Keywords: attribute correspondence, clustering, computational workload, k-medoids clustering, ontology matching

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Authors: T. G. Asmitha Chandini

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Web-based image search re-ranking, as an successful method to get better the results. In a query keyword, the first stair is store the images is first retrieve based on the text-based information. The user to select a query keywordimage, by using this query keyword other images are re-ranked based on their visual properties with images.Now a day to day, people projected to match images in a semantic space which is used attributes or reference classes closely related to the basis of semantic image. though, understanding a worldwide visual semantic space to demonstrate highly different images from the web is difficult and inefficient. The re-ranking images, which automatically offline part learns dissimilar semantic spaces for different query keywords. The features of images are projected into their related semantic spaces to get particular images. At the online stage, images are re-ranked by compare their semantic signatures obtained the semantic précised by the query keyword image. The query-specific semantic signatures extensively improve both the proper and efficiency of image re-ranking.

Keywords: Query, keyword, image, re-ranking, semantic, signature

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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A simple adaptive voice activity detector (VAD) is implemented using Gabor and gammatone atomic decomposition of speech for high Gaussian noise environments. Matching pursuit is used for atomic decomposition, and is shown to achieve optimal speech detection capability at high data compression rates for low signal to noise ratios. The most active dictionary elements found by matching pursuit are used for the signal reconstruction so that the algorithm adapts to the individual speakers dominant time-frequency characteristics. Speech has a high peak to average ratio enabling matching pursuit greedy heuristic of highest inner products to isolate high energy speech components in high noise environments. Gabor and gammatone atoms are both investigated with identical logarithmically spaced center frequencies, and similar bandwidths. The algorithm performs equally well for both Gabor and gammatone atoms with no significant statistical differences. The algorithm achieves 70% accuracy at a 0 dB SNR, 90% accuracy at a 5 dB SNR and 98% accuracy at a 20dB SNR using 30dB SNR as a reference for voice activity.

Keywords: atomic decomposition, gabor, gammatone, matching pursuit, voice activity detection

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Authors: Ying Shen, Weihua Zhu

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Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.

Keywords: magnetic resonance image (MRI), c-means model, image segmentation, information entropy

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Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Broderick Crawford, Ricardo Soto, Natalia Berrios, Eduardo Olguin

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In this paper, we present a binary cat swarm optimization for solving the Set covering problem. The set covering problem is a well-known NP-hard problem with many practical applications, including those involving scheduling, production planning and location problems. Binary cat swarm optimization is a recent swarm metaheuristic technique based on the behavior of discrete cats. Domestic cats show the ability to hunt and are curious about moving objects. The cats have two modes of behavior: seeking mode and tracing mode. We illustrate this approach with 65 instances of the problem from the OR-Library. Moreover, we solve this problem with 40 new binarization techniques and we select the technical with the best results obtained. Finally, we make a comparison between results obtained in previous studies and the new binarization technique, that is, with roulette wheel as transfer function and V3 as discretization technique.

Keywords: binary cat swarm optimization, binarization methods, metaheuristic, set covering problem

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Authors: Mehrnoosh Omati, Mahmod Reza Sahebi

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The information on land use/land cover changing plays an essential role for environmental assessment, planning and management in regional development. Remotely sensed imagery is widely used for providing information in many change detection applications. Polarimetric Synthetic aperture radar (PolSAR) image, with the discrimination capability between different scattering mechanisms, is a powerful tool for environmental monitoring applications. This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection. In this method, first, two PolSAR images are segmented using integration of marker-controlled watershed algorithm and coupled Markov random field (MRF). Then, object-based classification is performed to determine changed/no changed image objects. Compared with pixel-based support vector machine (SVM) classifier, this novel segmentation algorithm significantly reduces the speckle effect in PolSAR images and improves the accuracy of binary classification in object-based level. The experimental results on Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric images show a 3% and 6% improvement in overall accuracy and kappa coefficient, respectively. Also, the proposed method can correctly distinguish homogeneous image parcels.

Keywords: coupled Markov random field (MRF), environment, object-based analysis, polarimetric SAR (PolSAR) images

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Authors: Simone B. K. Aires, Cinthia O. de A. Freitas, Luiz E. S. Oliveira

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This paper contributes to the CBIR systems applied to trademark retrieval. The proposed model includes aspects from visual perception of the shapes, by means of feature extractor associated to a non-symmetrical perceptual zoning mechanism based on the Principles of Gestalt. Thus, the feature set were performed using Scale Invariant Feature Transform (SIFT). We carried out experiments using four different zonings strategies (Z = 4, 5H, 5V, 7) for matching and retrieval tasks. Our proposal method achieved the normalized recall (Rn) equal to 0.84. Experiments show that the non-symmetrical zoning could be considered as a tool to build more reliable trademark retrieval systems.

Keywords: CBIR, Gestalt, matching, non-symmetrical zoning, SIFT

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Authors: Hye-Kyung Kang

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Social justice is a central principle of the social work profession and education. However, scholars have long questioned the profession’s commitment to putting social justice values into practice. Clinical social work has been particularly criticized for its lack of attention to social justice and for failing to address the concerns of the oppressed. One prominent criticism of clinical social work is that it often relies on individual intervention and fails to take on system-level changes or advocacy. This concern evokes the historical macro-micro tension of the social work profession where micro (e.g., mental health counseling) and macro (e.g., policy advocacy) practices are conceptualized as separate domains, creating a false binary for social workers. One contributor to this false binary seems to be that most clinical practice models do not prepare social work students and practitioners to make a clear link between clinical practice and social justice. This paper presents a model of clinical social work practice that clearly recognizes the essential and necessary connection between social justice, advocacy, and clinical practice throughout the clinical process: engagement, assessment, intervention, and evaluation. Contemporary relational theories, critical social work frameworks, and anti-oppressive practice approaches are integrated to build a clinical social work practice model that addresses the urgent need for mental health practice that not only helps and heals the person but also challenges societal oppressions and aims to change them. The application of the model is presented through case vignettes.

Keywords: social justice, clinical social work, clinical social work model, integrative model

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Authors: Fahad Alhussein, Huaping Liu

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This paper compares the multipath mitigation performance of code correlation reference waveform receivers with variable and fixed window width, for binary offset carrier and multiplexed binary offset carrier signals typically used in global navigation satellite systems. In the variable window width method, such width is iteratively reduced until the distortion on the discriminator with multipath is eliminated. This distortion is measured as the Euclidean distance between the actual discriminator (obtained with the incoming signal), and the local discriminator (generated with a local copy of the signal). The variable window width have shown better performance compared to the fixed window width. In particular, the former yields zero error for all delays for the BOC and MBOC signals considered, while the latter gives rather large nonzero errors for small delays in all cases. Due to its computational simplicity, the variable window width method is perfectly suitable for implementation in low-cost receivers.

Keywords: correlation reference waveform receivers, binary offset carrier, multiplexed binary offset carrier, global navigation satellite systems

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Authors: Kevin Henry

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Introduction: This research identifies the main struggles of non-binary people in an organizational setting using the ZMET – method of in-depth interviews. The research was done in Iceland, a country that is repeatedly listed in the top countries for gender equality and found three main categories of non-binary struggles in organizations. These categories can be used to improve organizational non-binary inclusion. Aim: The main questions this paper will answer are: Which unique obstacles are non-binary people facing in their daily organizational life? Which organizational and individual measures help with more inclusion of non-binary people? How can organizational gender equality measures be made more inclusive of non-binary issues? Background: Even though gender equality is a much-researched topic, the struggles of non-binary people are often overlooked in gender equality research. Additionally, non-binary and transgender people are frequently researched together, even though their struggles can be very different. Research focused on non-binary people is, in many cases, done on a more structural or organizational level with quantitative data such as salary or position within an organization. This research focuses on the individual and their struggles with qualitative data to derive measures for non-binary inclusion and equality. Method: An adapted approach of the ZMET-Method (Zaltman Metaphor Elicitation Technique) will be used, during which in-depth interviews are held with individuals, utilizing pictures as a metaphorical starting point to discuss their main thoughts and feelings on being non-binary in an organizational setting. Interviewees prepared five pictures, each representing one key thought or feeling about their organizational life. The interviewer then lets the interviewee describe each picture and asks probing questions to get a deeper understanding of each individual topic. This method helps with a mostly unbiased data collection process by only asking probing questions during the interview and not leading the interviewee in any certain direction. Results: This research has identified three main categories of struggles non-binary people are facing in an organizational setting: internal (personal) struggles, external struggles and structural struggles. Internal struggles refer to struggles that originate from the person themselves (e.g., struggles with their own identity). External struggles refer to struggles from the outside (e.g. harassment from coworkers, exclusion). Structural struggles refer to struggles that are built into the organizational policy or facilities (e.g. restrooms, gendered language). Conclusion: This study shows that there are many struggles for non-binary people in organizations and that even in countries that pride themselves on being progressive and having a high level of gender equality, there is still much to be done for non-binary inclusion. Implications for Organizations: Organizations that strive to improve the inclusion of all genders should pay attention to how their structures are built, how their training is conducted, and how their policies affect people of various genders. Simple changes like making restrooms gender-neutral and using neutral language in company communications are good examples of small structural steps for more inclusion.

Keywords: gender equality, non-binary, organizations, ZMET

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Authors: Hatthaphone Silimanotham, Michael Henry

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The effective maintenance of road bridge infrastructure is becoming a widely researched topic in the civil engineering field. Deterioration is one of the main issues in bridge performance, and it is necessary to understand how bridges deteriorate to optimally plan budget allocation for bridge maintenance. In Laos, many bridges are in a deteriorated state, which may affect the performance of the bridge. Due to bridge deterioration, the Ministry of Public Works and Transport is interested in the deterioration model to allocate the budget efficiently and support the bridge maintenance planning. A deterioration model can be used to predict the bridge condition in the future based on the observed behavior in the past. This paper analyzes the available inspection data of road bridges on the road classifications network to build deterioration prediction models for the main bridge type found at the provincial level (concrete slab, concrete girder, and steel truss) using probabilistic deterioration modeling by linear regression method. The analysis targets there has three bridge types in the 18 provinces of Laos and estimates the bridge deterioration rating for evaluating the bridge's remaining life. This research thus considers the relationship between the service period and the bridge condition to represent the probability of bridge condition in the future. The results of the study can be used for a variety of bridge management tasks, including maintenance planning, budgeting, and evaluating bridge assets.

Keywords: deterioration model, bridge condition, bridge management, probabilistic modeling

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Authors: Jianguo Chen, Fenggang Zang, Yu Yang, Liangang Zheng

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Reactor Pressure Vessel (RPV) is an important security barrier in nuclear power plant. Crack like defects may be produced on RPV during the whole operation lifetime due to the harsh operation condition and irradiation embrittlement. During the severe loss of coolant accident, thermal shock happened as the injection of emergency cooling water into RPV, which results in re-pressurization of the vessel and very high tension stress on the vessel wall, this event called Pressurized Thermal Shock (PTS). Crack on the vessel wall may propagate even penetrate the vessel, so the safety of the RPV would undergo great challenge. Many assumptions in structure integrity evaluation make the result of deterministic fracture mechanics very conservative, which affect the operation lifetime of the plant. Actually, many parameters in the evaluation process, such as fracture toughness and nil-ductility transition temperature, have statistical distribution characteristics. So it is necessary to assess the structural integrity of RPV subjected to PTS event by means of Probabilistic Fracture Mechanics (PFM). Structure integrity evaluation methods of RPV subjected to PTS event are summarized firstly, then evaluation method based on probabilistic fracture mechanics are presented by considering the probabilistic characteristics of material and structure parameters. A comprehensive analysis example is carried out at last. The results show that the probability of crack penetrates through wall increases gradually with the growth of fast neutron irradiation flux. The results give advice for reactor life extension.

Keywords: fracture toughness, integrity evaluation, pressurized thermal shock, probabilistic fracture mechanics, reactor pressure vessel

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Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

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The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

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Authors: Yindi Zhao, Yun Zhang, Silu Xia, Lixin Wu

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The contours and areas of water surfaces, especially lakes, often change due to natural disasters and construction activities. It is an effective way to extract and update water contours from satellite images using image processing algorithms. However, to produce optimal water surface contours that are close to true boundaries is still a challenging task. This paper compares the performances of three different level set models, including the Chan-Vese (CV) model, the signed pressure force (SPF) model, and the region-scalable fitting (RSF) energy model for extracting lake contours. After experiment testing, it is indicated that the RSF model, in which a region-scalable fitting (RSF) energy functional is defined and incorporated into a variational level set formulation, is superior to CV and SPF, and it can get desirable contour lines when there are “holes” in the regions of waters, such as the islands in the lake. Therefore, the RSF model is applied to extracting lake contours from Landsat satellite images. Four temporal Landsat satellite images of the years of 2000, 2005, 2010, and 2014 are used in our study. All of them were acquired in May, with the same path/row (121/036) covering Xuzhou City, Jiangsu Province, China. Firstly, the near infrared (NIR) band is selected for water extraction. Image registration is conducted on NIR bands of different temporal images for information update, and linear stretching is also done in order to distinguish water from other land cover types. Then for the first temporal image acquired in 2000, lake contours are extracted via the RSF model with initialization of user-defined rectangles. Afterwards, using the lake contours extracted the previous temporal image as the initialized values, lake contours are updated for the current temporal image by means of the RSF model. Meanwhile, the changed and unchanged lakes are also detected. The results show that great changes have taken place in two lakes, i.e. Dalong Lake and Panan Lake, and RSF can actually extract and effectively update lake contours using multi-temporal satellite image.

Keywords: level set model, multi-temporal image, lake contour extraction, contour update

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Authors: Ryan A. Black, Stacey A. McCaffrey

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Over the past few decades, great strides have been made towards improving the science in the measurement of psychological constructs. Item Response Theory (IRT) has been the foundation upon which statistical models have been derived to increase both precision and accuracy in psychological measurement. These models are now being used widely to develop and refine tests intended to measure an individual's level of academic achievement, aptitude, and intelligence. Recently, the field of clinical psychology has adopted IRT models to measure psychopathological phenomena such as depression, anxiety, and addiction. Because advances in IRT measurement models are being made so rapidly across various fields, it has become quite challenging for psychologists and other behavioral scientists to keep abreast of the most recent developments, much less learn how to employ and decide which models are the most appropriate to use in their line of work. In the same vein, IRT measurement models vary greatly in complexity in several interrelated ways including but not limited to the number of item-specific parameters estimated in a given model, the function which links the expected response and the predictor, response option formats, as well as dimensionality. As a result, inferior methods (a.k.a. Classical Test Theory methods) continue to be employed in efforts to measure psychological constructs, despite evidence showing that IRT methods yield more precise and accurate measurement. To increase the use of IRT methods, this study endeavors to provide a comprehensive overview of binary IRT models; that is, measurement models employed on test data consisting of binary response options (e.g., correct/incorrect, true/false, agree/disagree). Specifically, this study will cover the most basic binary IRT model, known as the 1-parameter logistic (1-PL) model dating back to over 50 years ago, up until the most recent complex, 4-parameter logistic (4-PL) model. Binary IRT models will be defined mathematically and the interpretation of each parameter will be provided. Next, all four binary IRT models will be employed on two sets of data: 1. Simulated data of N=500,000 subjects who responded to four dichotomous items and 2. A pilot analysis of real-world data collected from a sample of approximately 770 subjects who responded to four self-report dichotomous items pertaining to emotional consequences to alcohol use. Real-world data were based on responses collected on items administered to subjects as part of a scale-development study (NIDA Grant No. R44 DA023322). IRT analyses conducted on both the simulated data and analyses of real-world pilot will provide a clear demonstration of how to construct, evaluate, and compare binary IRT measurement models. All analyses will be performed using the new IRT procedure in SAS 9.4. SAS code to generate simulated data and analyses will be available upon request to allow for replication of results.

Keywords: instrument development, item response theory, latent trait theory, psychometrics

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Authors: Rajib Mia, Badam Singh Kushvah

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In this present paper, we use the photogravitational version of the restricted three body problem (RTBP) in binary systems. In the photogravitational RTBP, an infinitesimal particle (dust grain) is moving under the gravitational attraction and radiation pressure from the two bigger primaries. The third particle does not affect the motion of two bigger primaries. The zero-velocity curves, zero-velocity surfaces and their projections on the plane are studied. We have used existing analytical method to solve the equations of motion. We have obtained the Lagrangian points in some binary stellar systems. It is found that mass reduction factor affects the Lagrangian points. The linear stability of Lagrangian points is studied and found that these points are unstable. Moreover, trajectories of the infinitesimal particle at the triangular points are studied.

Keywords: binary systems, Lagrangian points, linear stability, photogravitational RTBP, trajectories

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Authors: Vatsal Patel, Niraj Shah

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This paper presents the results of a laboratory study on the properties of binary blended High Performance cementitious systems containing blends of ordinary Portland cement (OPC), Porcelain Powder or Marble Powder blend proportions of 100:00, 95:05, 90:10, 85:15, 80:20 for OPC: Porcelain Powder/Marble Powder. Studies on the Engineering Properties of the cementitious concrete, namely compressive strength, flexural strength, sorptivity, rapid chloride penetration test and accelerated corrosion test have been performed and those of OPC concrete. The results show that the inclusion of Porcelain powder or Marble Powder as binary blended cement alters to a great degree the properties of the binder as well as the resulting concrete. In addition, the results show that the Porcelain powder with 85:15 proportions and Marble powder with 90:10 proportions as binary systems to produce high-performance concrete could potentially be used in the concrete construction industry particular in lowering down the volume of OPC used and lowering emission of CO2 produces during manufacturing of cement.

Keywords: accelerated corrosion, binary blended cementitious system, rapid chloride penetration, sorptivity

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Authors: Ebrahim Ghanbari, Mohammad Reza Nematollahi

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Integrating deterministic and probabilistic safety assessment (IDPSA) is one of the most commonly used issues in the field of safety analysis of power plant accident. It has also been recognized today that the role of human error in creating these accidents is not less than systemic errors, so the human interference and system errors in fault and event sequences are necessary. The integration of these analytical topics will be reflected in the frequency of core damage and also the study of the use of water resources in an accident such as the loss of all electrical power of the plant. In this regard, the SBO accident was simulated for the pressurized water reactor in the deterministic analysis issue, and by analyzing the operator's behavior in controlling the accident, the results of the combination of deterministic and probabilistic assessment were identified. The results showed that the best performance of the plant operator would reduce the risk of an accident by 10%, as well as a decrease of 6.82 liters/second of the water sources of the plant.

Keywords: IDPSA, human error, SBO, risk

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Authors: Peter Shi

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Algorithmic trading is a rapidly expanding domain within quantitative finance, constituting a substantial portion of trading volumes in the US financial market. The demand for rigorous and robust mathematical theories underpinning these trading algorithms is ever-growing. In this study, the author establishes a new stock market model that integrates the Efficient Market Hypothesis and the statistical arbitrage. The model, for the first time, finds probabilistic relations between the rational price and the market price in terms of the conditional expectation. The theory consequently leads to a mathematical justification of the old market adage: buy-low and sell-high. The thresholds for “low” and “high” are precisely derived using a max-min operation on Bayes’s error. This explicit connection harmonizes the Efficient Market Hypothesis and Statistical Arbitrage, demonstrating their compatibility in explaining market dynamics. The amalgamation represents a pioneering contribution to quantitative finance. The study culminates in comprehensive numerical tests using historical market data, affirming that the “buy-low” and “sell-high” algorithm derived from this theory significantly outperforms the general market over the long term in four out of six distinct market environments.

Keywords: efficient market hypothesis, behavioral finance, Bayes' decision, algorithmic trading, risk control, stock market

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Authors: Elham jassemi Zargani, Mohammad almasi

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Densities and viscosities for binary mixtures of diethylamine + 2 Alkanol (2 propanol up to 2 pentanol) were measured over the entire composition range and temperature interval of 293.15 to 323.15 K. Excess molar volumes V_m^E and viscosity deviations Δη were calculated and correlated by the Redlich−Kister type function to derive the coefficients and estimate the standard error. For mixtures of diethylamine with used 2-alkanols, V_m^E and Δη are negative over the entire range of mole fraction. The observed variations of these parameters, with alkanols chain length and temperature, are discussed in terms of the inter-molecular interactions between the unlike molecules of the binary mixtures.

Keywords: densities, viscosities, diethylamine, 2-alkanol, Redlich-Kister

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Authors: Hyoseong Lee, Duk-jin Kim, Jaehong Oh, Jungil Shin

Abstract:

Tidal flat in Korean peninsula is one of the largest biodiversity tidal flats in the world. Therefore, digital elevation models (DEM) is continuously demanded to monitor of the tidal flat. In this study, DEM of tidal flat, according to different times, was produced by means of the Drone and commercial software in order to measure seawater change during high tide at water-channel in tidal flat. To correct the produced DEMs of the tidal flat where is inaccessible to collect control points, the DEM matching method was applied by using the reference DEM instead of the survey. After the ortho-image was made from the corrected DEM, the land cover classified image was produced. The changes of seawater amount according to the times were analyzed by using the classified images and DEMs. As a result, it was confirmed that the amount of water rapidly increased as the time passed during high tide.

Keywords: tidal flat, drone, DEM, seawater change

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Authors: Tun Myat Aung, Ni Ni Hla

Abstract:

This paper begins by describing basic properties of finite field and elliptic curve cryptography over prime field and binary field. Then we discuss the discrete logarithm problem for elliptic curves and its properties. We study the general common attacks on elliptic curve discrete logarithm problem such as the Baby Step, Giant Step method, Pollard’s rho method and Pohlig-Hellman method, and describe in detail experiments of these attacks over prime field and binary field. The paper finishes by describing expected running time of the attacks and suggesting strong elliptic curves that are not susceptible to these attacks.c

Keywords: discrete logarithm problem, general attacks, elliptic curve, prime field, binary field

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Authors: Muhammad Atif, Cang Yan

Abstract:

The enhancement of low-light images is a significant area of study aimed at enhancing the quality of captured images in challenging lighting environments. Recently, methods based on convolutional neural networks (CNN) have gained prominence as they offer state-of-the-art performance. However, many approaches based on CNN rely on increasing the size and complexity of the neural network. In this study, we propose an alternative method for improving low-light images using an autoencoder-based multiscale knowledge transfer model. Our method leverages the power of three autoencoders, where the encoders of the first two autoencoders are directly connected to the decoder of the third autoencoder. Additionally, the decoder of the first two autoencoders is connected to the encoder of the third autoencoder. This architecture enables effective knowledge transfer, allowing the third autoencoder to learn and benefit from the enhanced knowledge extracted by the first two autoencoders. We further integrate the proposed model into the PIX to PIX GAN framework. By integrating our proposed model as the generator in the GAN framework, we aim to produce enhanced images that not only exhibit improved visual quality but also possess a more authentic and realistic appearance. These experimental results, both qualitative and quantitative, show that our method is better than the state-of-the-art methodologies.

Keywords: low light image enhancement, deep learning, convolutional neural network, image processing

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Authors: Zviad Ghadua, Biswa Bhattacharya

Abstract:

The Flash Flood Guidance (FFG) provides the rainfall amount of a given duration necessary to cause flooding. The approach is based on the development of rainfall-runoff curves, which helps us to find out the rainfall amount that would cause flooding. An alternative approach, mostly experimented with Italian Alpine catchments, is based on determining threshold discharges from past events and on finding whether or not an oncoming flood has its magnitude more than some critical discharge thresholds found beforehand. Both approaches suffer from large uncertainties in forecasting flash floods as, due to the simplistic approach followed, the same rainfall amount may or may not cause flooding. This uncertainty leads to the question whether a probabilistic model is preferable over a deterministic one in forecasting flash floods. We propose the use of a Bayesian probabilistic approach in flash flood forecasting. A prior probability of flooding is derived based on historical data. Additional information, such as antecedent moisture condition (AMC) and rainfall amount over any rainfall thresholds are used in computing the likelihood of observing these conditions given a flash flood has occurred. Finally, the posterior probability of flooding is computed using the prior probability and the likelihood. The variation of the computed posterior probability with rainfall amount and AMC presents the suitability of the approach in decision making in an uncertain environment. The methodology has been applied to the Posina basin in Italy. From the promising results obtained, we can conclude that the Bayesian approach in flash flood forecasting provides more realistic forecasting over the FFG.

Keywords: flash flood, Bayesian, flash flood guidance, FFG, forecasting, Posina

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

Abstract:

This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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