Search results for: true/false self
1065 Mosaic Augmentation: Insights and Limitations
Authors: Olivia A. Kjorlien, Maryam Asghari, Farshid Alizadeh-Shabdiz
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The goal of this paper is to investigate the impact of mosaic augmentation on the performance of object detection solutions. To carry out the study, YOLOv4 and YOLOv4-Tiny models have been selected, which are popular, advanced object detection models. These models are also representatives of two classes of complex and simple models. The study also has been carried out on two categories of objects, simple and complex. For this study, YOLOv4 and YOLOv4 Tiny are trained with and without mosaic augmentation for two sets of objects. While mosaic augmentation improves the performance of simple object detection, it deteriorates the performance of complex object detection, specifically having the largest negative impact on the false positive rate in a complex object detection case.Keywords: accuracy, false positives, mosaic augmentation, object detection, YOLOV4, YOLOV4-Tiny
Procedia PDF Downloads 1211064 An Orphan Software Engineering Course: Supportive Ways toward a True Software Engineer
Authors: Haya Sammana
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A well-defined curricula must be adopted to meet the increasing complexity and diversity in the software applications. In reality, some IT majors such as computer science and computer engineering receive the software engineering education in a single course which is considered as a big challenged for the instructors and universities. Also, it requires students to gain the most of practical experiences that simulate the real work in software companies. Furthermore, we have noticed that there is no consensus on how, when and what to teach in that introductory course to gain the practical experiences that are required by the software companies. Because all of software engineering disciplines will not fit in just one course, so the course needs reasonable choices in selecting its topics. This arises an important question which is an essential one to ask: Is this course has the ability to formulate a true software engineer that meets the needs of industry? This question arises a big challenge in selecting the appropriate topics. So answering this question is very important for the next undergraduate students. During teaching this course in the curricula, the feedbacks from an undergraduate students and the keynotes of the annual meeting for an advisory committee from industrial side provide a probable answer for the proposed question: it is impossible to build a true software engineer who possesses all the essential elements of software engineering education such teamwork, communications skills, project management skills and contemporary industrial practice from one course and it is impossible to have a one course covering all software engineering topics. Besides the used teaching approach, the author proposes an implemented three supportive ways aiming for mitigating the expected risks and increasing the opportunity to build a true software engineer.Keywords: software engineering course, software engineering education, software experience, supportive approach
Procedia PDF Downloads 3551063 Track Initiation Method Based on Multi-Algorithm Fusion Learning of 1DCNN And Bi-LSTM
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Aiming at the problem of high-density clutter and interference affecting radar detection target track initiation in ECM and complex radar mission, the traditional radar target track initiation method has been difficult to adapt. To this end, we propose a multi-algorithm fusion learning track initiation algorithm, which transforms the track initiation problem into a true-false track discrimination problem, and designs an algorithm based on 1DCNN(One-Dimensional CNN)combined with Bi-LSTM (Bi-Directional Long Short-Term Memory )for fusion classification. The experimental dataset consists of real trajectories obtained from a certain type of three-coordinate radar measurements, and the experiments are compared with traditional trajectory initiation methods such as rule-based method, logical-based method and Hough-transform-based method. The simulation results show that the overall performance of the multi-algorithm fusion learning track initiation algorithm is significantly better than that of the traditional method, and the real track initiation rate can be effectively improved under high clutter density with the average initiation time similar to the logical method.Keywords: track initiation, multi-algorithm fusion, 1DCNN, Bi-LSTM
Procedia PDF Downloads 711062 Formative Assessment in an Introductory Python Programming Course
Authors: María José Núñez-Ruiz, Luis Álvarez-González, Cristian Olivares-Rodriguez, Benjamin Lazo-Letelier
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This paper begins with some concept of formative assessment and the relationship with learning objective: contents objectives, processes objectives, and metacognitive objectives. Two methodologies are describes Evidence-Based teaching and Question Drive Instruction. To do formative assessments in larges classes a Classroom Response System (CRS) is needed. But most of CRS use only Multiple Choice Questions (MCQ), True/False question, or text entry; however, this is insufficient to formative assessment. To do that a new CRS, call FAMA was developed. FAMA support six types of questions: Choice, Order, Inline choice, Text entry, Associated, and Slider. An experiment participated in 149 students from four engineering careers. For results, Kendall's Range Correlation Analysis and descriptive analysis was done. In conclusion, there is a strong relation between contents question, process questions (ask in formative assessment without a score) and metacognitive questions, asked in summative assessment. As future work, the lecturer can do personalized teaching, because knows the behavior of all students in each formative assessmentKeywords: Python language, formative assessment, classroom response systems, evidence-Based teaching, question drive instruction
Procedia PDF Downloads 1241061 Intelligent Prediction of Breast Cancer Severity
Authors: Wahab Ali, Oyebade K. Oyedotun, Adnan Khashman
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Breast cancer remains a threat to the woman’s world in view of survival rates, it early diagnosis and mortality statistics. So far, research has shown that many survivors of breast cancer cases are in the ones with early diagnosis. Breast cancer is usually categorized into stages which indicates its severity and corresponding survival rates for patients. Investigations show that the farther into the stages before diagnosis the lesser the chance of survival; hence the early diagnosis of breast cancer becomes imperative, and consequently the application of novel technologies to achieving this. Over the year, mammograms have used in the diagnosis of breast cancer, but the inconclusive deductions made from such scans lead to either false negative cases where cancer patients may be left untreated or false positive where unnecessary biopsies are carried out. This paper presents the application of artificial neural networks in the prediction of severity of breast tumour (whether benign or malignant) using mammography reports and other factors that are related to breast cancer.Keywords: breast cancer, intelligent classification, neural networks, mammography
Procedia PDF Downloads 4821060 Use of Landsat OLI Images in the Mapping of Landslides: Case of the Taounate Province in Northern Morocco
Authors: S. Benchelha, H. Chennaoui, M. Hakdaoui, L. Baidder, H. Mansouri, H. Ejjaaouani, T. Benchelha
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Northern Morocco is characterized by relatively young mountains experiencing a very important dynamic compared to other areas of Morocco. The dynamics associated with the formation of the Rif chain (Alpine tectonics), is accompanied by instabilities essentially related to tectonic movements. The realization of important infrastructures (Roads, Highways,...) represents a triggering factor and favoring landslides. This paper is part of the establishment of landslides susceptibility map and concerns the mapping of unstable areas in the province of Taounate. The landslide was identified using the components of the false color (FCC) of images Landsat OLI: i) the first independent component (IC1), ii) The main component (PC), iii) Normalized difference index (NDI). This mapping for landslides class is validated by in-situ surveys.Keywords: landslides, False Color Composite (FCC), Independent Component Analysis (ICA), Principal Component Analysis (PCA), Normalized Difference Index (NDI), Normalized Difference Mid Red Index (NDMIDR)
Procedia PDF Downloads 2801059 Multiple Linear Regression for Rapid Estimation of Subsurface Resistivity from Apparent Resistivity Measurements
Authors: Sabiu Bala Muhammad, Rosli Saad
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Multiple linear regression (MLR) models for fast estimation of true subsurface resistivity from apparent resistivity field measurements are developed and assessed in this study. The parameters investigated were apparent resistivity (ρₐ), horizontal location (X) and depth (Z) of measurement as the independent variables; and true resistivity (ρₜ) as the dependent variable. To achieve linearity in both resistivity variables, datasets were first transformed into logarithmic domain following diagnostic checks of normality of the dependent variable and heteroscedasticity to ensure accurate models. Four MLR models were developed based on hierarchical combination of the independent variables. The generated MLR coefficients were applied to another data set to estimate ρₜ values for validation. Contours of the estimated ρₜ values were plotted and compared to the observed data plots at the colour scale and blanking for visual assessment. The accuracy of the models was assessed using coefficient of determination (R²), standard error (SE) and weighted mean absolute percentage error (wMAPE). It is concluded that the MLR models can estimate ρₜ for with high level of accuracy.Keywords: apparent resistivity, depth, horizontal location, multiple linear regression, true resistivity
Procedia PDF Downloads 2691058 Linear Frequency Modulation-Frequency Shift Keying Radar with Compressive Sensing
Authors: Ho Jeong Jin, Chang Won Seo, Choon Sik Cho, Bong Yong Choi, Kwang Kyun Na, Sang Rok Lee
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In this paper, a radar signal processing technique using the LFM-FSK (Linear Frequency Modulation-Frequency Shift Keying) is proposed for reducing the false alarm rate based on the compressive sensing. The LFM-FSK method combines FMCW (Frequency Modulation Continuous Wave) signal with FSK (Frequency Shift Keying). This shows an advantage which can suppress the ghost phenomenon without the complicated CFAR (Constant False Alarm Rate) algorithm. Moreover, the parametric sparse algorithm applying the compressive sensing that restores signals efficiently with respect to the incomplete data samples is also integrated, leading to reducing the burden of ADC in the receiver of radars. 24 GHz FMCW signal is applied and tested in the real environment with FSK modulated data for verifying the proposed algorithm along with the compressive sensing.Keywords: compressive sensing, LFM-FSK radar, radar signal processing, sparse algorithm
Procedia PDF Downloads 4681057 Comparison of the Distillation Curve Obtained Experimentally with the Curve Extrapolated by a Commercial Simulator
Authors: Lívia B. Meirelles, Erika C. A. N. Chrisman, Flávia B. de Andrade, Lilian C. M. de Oliveira
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True Boiling Point distillation (TBP) is one of the most common experimental techniques for the determination of petroleum properties. This curve provides information about the performance of petroleum in terms of its cuts. The experiment is performed in a few days. Techniques are used to determine the properties faster with a software that calculates the distillation curve when a little information about crude oil is known. In order to evaluate the accuracy of distillation curve prediction, eight points of the TBP curve and specific gravity curve (348 K and 523 K) were inserted into the HYSYS Oil Manager, and the extended curve was evaluated up to 748 K. The methods were able to predict the curve with the accuracy of 0.6%-9.2% error (Software X ASTM), 0.2%-5.1% error (Software X Spaltrohr).Keywords: distillation curve, petroleum distillation, simulation, true boiling point curve
Procedia PDF Downloads 4321056 Effect of Variety and Fibre Type on Functional and organoleptic Properties of Plantain Flour Intended for Food "Fufu"
Authors: C. C. Okafor
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The effect of different varieties of plantain (Horn, false horn and French) and fibre types (soy bean residue, cassava sievette and rice bran) on functional and organoleptic properties of plantain-based flour was assessed. Horn, false horn french were processed by washing, peeling with knife, slicing into 3mm thickness and steam blanched at 80℃ for 5minutes, oven dried at 65℃ for 48 hours and milled into flours with attrition mill, sieved with 60 mesh sieve, separately. Fibre sources were processed, milled and fractionated into 60, 40 & 20 mesh sizes. Both flours were blended as 80:20, 70:30 and 60:40. Results obtained indicated that water absorption capacity is highest (2.68) in French plantain variety irrespective of the fibre type used. And in all variety tested the swelling capacity is highest (2.93) when the plantain flour is blended with soy residue (SR) and lowest (1.25) when blended with rice brain (RB). The results show that there is significant variety and fibre type interaction effect at (P < : 0.05). Again the results showed that texture mold ability and overall acceptability were best (7.00) when soy residue was used where as addition of rice bran into plantain flour resulted in fufu with poor texture. This trend was observed in all the verities of plantain tested and in all of the particle size of flour. Using cassava serviette also yield fufu similar to that produced with soy residue in all the parameter tested (mold ability, texture and overall acceptability. Generally, plantain flours from french and false horn yielded better quality fufu in terms of texture mold ability, overall acceptability, irrespective of the fibre type used.Keywords: functional, organoleptic, particle size, sieve mesh, variety
Procedia PDF Downloads 4031055 ANA Negative but FANA Positive Patients with Clinical Symptoms of Rheumatic Disease: The Suggestion for Clinicians
Authors: Abdolreza Esmaeilzadeh, Mehri Mirzaei
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Objective: Rheumatic disease is a chronic disease that causes pain, stiffness, swelling and limited motion and function of many joints. RA is the most common form of autoimmune arthritis, affecting more than 1.3 million Americans. Of these, about 75% are women. Materials and Methods: This study was formed due to the misconception about ANA test, which is frequently performed with methods based upon solid phase as ELISA. This experiment was conducted on 430 patients, with clinical symptoms that are likely affected with rheumatic diseases, simultaneously by means of ANA and FANA. Results: 36 cases (8.37%) of patients, despite positive ANA, have demonstrated negative results via Indirect Immunofluorescence Assay (IIFA), (false positive). 116 cases (27%) have demonstrated negative ANA results, by means of the ELISA technique, although they had positive IIFA results. Conclusion: Other advantages of IIFA are antibody titration and specific pattern detection that have the capability of distinguishing positive dsDNA results. According to the restrictions and false negative cases, in patients, IIFA test is highly recommended for these disease's diagnosis.Keywords: autoimmune disease, IIFA, EIA, rheumatic disease
Procedia PDF Downloads 4931054 TRNG Based Key Generation for Certificateless Signcryption
Authors: S.Balaji, R.Sujatha, M. Ramakrishnan
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Signcryption is a cryptographic primitive that fulfills both the functions of digital signature and public key encryption simultaneously in low cost when compared with the traditional signature-then-encryption approach. In this paper, we propose a novel mouse movement based key generation technique to generate secret keys which is secure against the outer and insider attacks. Tag Key Encapsulation Mechanism (KEM) process is implemented using True Random Number Generator (TRNG) method. This TRNG based key is used for data encryption in the Data Encapsulation Mechanism (DEM). We compare the statistical reports of the proposed system with the previous methods which implements TKEM based on pseudo random number generatorKeywords: pseudo random umber generator, signcryption, true random number generator, node deployment
Procedia PDF Downloads 3371053 Intensive Care Unit Patient Self-Determination When Facing Cardiovascular Surgery for the First Time
Authors: Hsiao-Lin Fang
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The Patient Self-Determination Act is based on the belief that each life is unique. The act regards each patient as an autonomous entity and explicitly protects the patient’s rights to know and make decisions and choices while ensuring that the patient’s wish for a peaceful end is granted. Even when the patient is unconscious and unable to express himself/herself, the patient’s self-determination and its exercise are still protected under the law. The act also ensures that healthcare professionals (HCPs) have a specific set of rules to follow and complete legal protection when their patients are unable to express themselves clearly. This report is about a 55-year-old female patient who weighed 110 kg and was diagnosed with acute type A aortic dissection. The case was that the patient suddenly felt backache and nausea during sleep before daybreak and was therefore transferred to this hospital from the original one. After the doctor explained the patient’s conditions, it was concluded that surgery was necessary. However, the patient’s family was immediately against the surgery after having heard its possible complications. Nevertheless, the patient was still willing to receive the surgery. Being at odds with her family, the patient decided to sign the surgery agreement herself and agreed to receive the two surgical procedures: (1) ascending aorta replacement and (2) innominate artery debranching. After the surgery, the patient did not regain consciousness and therefore received computed tomography scanning of the brain, which revealed false lumen involving proximal left common carotid artery, left subclavian artery and innominate artery, and severe compression of the true lumen with total/subtotal occlusion in the left common carotid artery. On the following day, the doctor discussed two further surgical procedures: (1) endografting for descending aorta and (2) endografting for left common carotid artery and subclavian artery with the family. However, as the patient’s postoperative recovery of consciousness only reached the level of stupor and her family had no intention of subsequent healthcare for the patient, the family made the joint decision three days later to have the endotracheal tube removed from the patient and let her die a natural death. Suggestion: An advance directive (AD) can be created beforehand. Once the patient is in a special clinical state (e.g., terminal illness, permanent vegetative state, etc.), the AD can determine whether to sustain the patient’s life through ‘medical intervention’ or to respect the patient’s rights to choose a peaceful end and receive palliative care. Through the expression of self-determination, it is possible to respect the patient’s medical practice autonomy and protect the patient’s dignity and right to a peaceful end, thereby respecting and supporting the patient’s decision. This also allows the three sides: the patient, the family and the medical team to understand the patient’s true wish in the process of advance care planning (ACP) and thereby promote harmony in the HCP-patient relationship.Keywords: intensive care unit patient, cardiovascular surgery, self-determination, advance directive
Procedia PDF Downloads 1691052 Identifying the True Extend of Glioblastoma Based on Preoperative FLAIR Images
Authors: B. Shukir, L. Szivos, D. Kis, P. Barzo
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Glioblastoma is the most malignant brain tumor. In general, the survival rate varies between (14-18) months. Glioblastoma consists a solid and infiltrative part. The standard therapeutic management of glioblastoma is maximum safe resection followed by chemo-radiotherapy. It’s hypothesized that the pretumoral hyperintense region in fluid attenuated inversion recovery (FLAIR) images includes both vasogenic edema and infiltrated tumor cells. In our study, we aimed to define the sensitivity and specificity of hyperintense FLAIR images preoperatively to examine how well it can define the true extent of glioblastoma. (16) glioblastoma patients included in this study. Hyperintense FLAIR region were delineated preoperatively as tumor mask. The infiltrative part of glioblastoma considered the regions where the tumor recurred on the follow up MRI. The recurrence on the CE-T1 images was marked as the recurrence masks. According to (AAL3) and (JHU white matter labels) atlas, the brain divided into cortical and subcortical regions respectively. For calculating specificity and sensitivity, the FLAIR and the recurrence masks overlapped counting how many regions affected by both . The average sensitivity and specificity was 83% and 85% respectively. Individually, the sensitivity and specificity varied between (31-100)%, and (100-58)% respectively. These results suggest that despite FLAIR being as an effective radiologic imaging tool its prognostic value remains controversial and probabilistic tractography remain more reliable available method for identifying the true extent of glioblastoma.Keywords: brain tumors, glioblastoma, MRI, FLAIR
Procedia PDF Downloads 391051 The Effect of Visual Access to Greenspace and Urban Space on a False Memory Learning Task
Authors: Bryony Pound
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This study investigated how views of green or urban space affect learning performance. It provides evidence of the value of visual access to greenspace in work and learning environments, and builds on the extensive research into the cognitive and learning-related benefits of access to green and natural spaces, particularly in learning environments. It demonstrates that benefits of visual access to natural spaces whilst learning can produce statistically significant faster responses than those facing urban views after only 5 minutes. The primary hypothesis of this research was that a greenspace view would improve short-term learning. Participants were randomly assigned to either a view of parkland or of urban buildings from the same room. They completed a psychological test of two stages. The first stage consisted of a presentation of words from eight different categories (four manmade and four natural). Following this a 2.5 minute break was given; participants were not prompted to look out of the window, but all were observed doing so. The second stage of the test involved a word recognition/false memory test of three types. Type 1 was presented words from each category; Type 2 was non-presented words from those same categories; and Type 3 was non-presented words from different categories. Participants were asked to respond with whether they thought they had seen the words before or not. Accuracy of responses and reaction times were recorded. The key finding was that reaction times for Type 2 words (highest difficulty) were significantly different between urban and green view conditions. Those with an urban view had slower reaction times for these words, so a view of greenspace resulted in better information retrieval for word and false memory recognition. Importantly, this difference was found after only 5 minutes of exposure to either view, during winter, and with a sample size of only 26. Greenspace views improve performance in a learning task. This provides a case for better visual access to greenspace in work and learning environments.Keywords: benefits, greenspace, learning, restoration
Procedia PDF Downloads 1231050 Framework for Detecting External Plagiarism from Monolingual Documents: Use of Shallow NLP and N-Gram Frequency Comparison
Authors: Saugata Bose, Ritambhra Korpal
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The internet has increased the copy-paste scenarios amongst students as well as amongst researchers leading to different levels of plagiarized documents. For this reason, much of research is focused on for detecting plagiarism automatically. In this paper, an initiative is discussed where Natural Language Processing (NLP) techniques as well as supervised machine learning algorithms have been combined to detect plagiarized texts. Here, the major emphasis is on to construct a framework which detects external plagiarism from monolingual texts successfully. For successfully detecting the plagiarism, n-gram frequency comparison approach has been implemented to construct the model framework. The framework is based on 120 characteristics which have been extracted during pre-processing the documents using NLP approach. Afterwards, filter metrics has been applied to select most relevant characteristics and then supervised classification learning algorithm has been used to classify the documents in four levels of plagiarism. Confusion matrix was built to estimate the false positives and false negatives. Our plagiarism framework achieved a very high the accuracy score.Keywords: lexical matching, shallow NLP, supervised machine learning algorithm, word n-gram
Procedia PDF Downloads 3531049 Genetic Algorithm for In-Theatre Military Logistics Search-and-Delivery Path Planning
Authors: Jean Berger, Mohamed Barkaoui
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Discrete search path planning in time-constrained uncertain environment relying upon imperfect sensors is known to be hard, and current problem-solving techniques proposed so far to compute near real-time efficient path plans are mainly bounded to provide a few move solutions. A new information-theoretic –based open-loop decision model explicitly incorporating false alarm sensor readings, to solve a single agent military logistics search-and-delivery path planning problem with anticipated feedback is presented. The decision model consists in minimizing expected entropy considering anticipated possible observation outcomes over a given time horizon. The model captures uncertainty associated with observation events for all possible scenarios. Entropy represents a measure of uncertainty about the searched target location. Feedback information resulting from possible sensor observations outcomes along the projected path plan is exploited to update anticipated unit target occupancy beliefs. For the first time, a compact belief update formulation is generalized to explicitly include false positive observation events that may occur during plan execution. A novel genetic algorithm is then proposed to efficiently solve search path planning, providing near-optimal solutions for practical realistic problem instances. Given the run-time performance of the algorithm, natural extension to a closed-loop environment to progressively integrate real visit outcomes on a rolling time horizon can be easily envisioned. Computational results show the value of the approach in comparison to alternate heuristics.Keywords: search path planning, false alarm, search-and-delivery, entropy, genetic algorithm
Procedia PDF Downloads 3551048 Rank-Based Chain-Mode Ensemble for Binary Classification
Authors: Chongya Song, Kang Yen, Alexander Pons, Jin Liu
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In the field of machine learning, the ensemble has been employed as a common methodology to improve the performance upon multiple base classifiers. However, the true predictions are often canceled out by the false ones during consensus due to a phenomenon called “curse of correlation” which is represented as the strong interferences among the predictions produced by the base classifiers. In addition, the existing practices are still not able to effectively mitigate the problem of imbalanced classification. Based on the analysis on our experiment results, we conclude that the two problems are caused by some inherent deficiencies in the approach of consensus. Therefore, we create an enhanced ensemble algorithm which adopts a designed rank-based chain-mode consensus to overcome the two problems. In order to evaluate the proposed ensemble algorithm, we employ a well-known benchmark data set NSL-KDD (the improved version of dataset KDDCup99 produced by University of New Brunswick) to make comparisons between the proposed and 8 common ensemble algorithms. Particularly, each compared ensemble classifier uses the same 22 base classifiers, so that the differences in terms of the improvements toward the accuracy and reliability upon the base classifiers can be truly revealed. As a result, the proposed rank-based chain-mode consensus is proved to be a more effective ensemble solution than the traditional consensus approach, which outperforms the 8 ensemble algorithms by 20% on almost all compared metrices which include accuracy, precision, recall, F1-score and area under receiver operating characteristic curve.Keywords: consensus, curse of correlation, imbalance classification, rank-based chain-mode ensemble
Procedia PDF Downloads 1301047 Feature Based Unsupervised Intrusion Detection
Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein
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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka
Procedia PDF Downloads 2911046 Face Recognition Using Body-Worn Camera: Dataset and Baseline Algorithms
Authors: Ali Almadan, Anoop Krishnan, Ajita Rattani
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Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors.Keywords: face recognition, body-worn cameras, deep learning, person identification
Procedia PDF Downloads 1581045 Using Corpora in Semantic Studies of English Adjectives
Authors: Oxana Lukoshus
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The methods of corpus linguistics, a well-established field of research, are being increasingly applied in cognitive linguistics. Corpora data are especially useful for different quantitative studies of grammatical and other aspects of language. The main objective of this paper is to demonstrate how present-day corpora can be applied in semantic studies in general and in semantic studies of adjectives in particular. Polysemantic adjectives have been the subject of numerous studies. But most of them have been carried out on dictionaries. Undoubtedly, dictionaries are viewed as one of the basic data sources, but only at the initial steps of a research. The author usually starts with the analysis of the lexicographic data after which s/he comes up with a hypothesis. In the research conducted three polysemantic synonyms true, loyal, faithful have been analyzed in terms of differences and similarities in their semantic structure. A corpus-based approach in the study of the above-mentioned adjectives involves the following. After the analysis of the dictionary data there was the reference to the following corpora to study the distributional patterns of the words under study – the British National Corpus (BNC) and the Corpus of Contemporary American English (COCA). These corpora are continually updated and contain thousands of examples of the words under research which make them a useful and convenient data source. For the purpose of this study there were no special needs regarding genre, mode or time of the texts included in the corpora. Out of the range of possibilities offered by corpus-analysis software (e.g. word lists, statistics of word frequencies, etc.), the most useful tool for the semantic analysis was the extracting a list of co-occurrence for the given search words. Searching by lemmas, e.g. true, true to, and grouping the results by lemmas have proved to be the most efficient corpora feature for the adjectives under the study. Following the search process, the corpora provided a list of co-occurrences, which were then to be analyzed and classified. Not every co-occurrence was relevant for the analysis. For example, the phrases like An enormous sense of responsibility to protect the minds and hearts of the faithful from incursions by the state was perceived to be the basic duty of the church leaders or ‘True,’ said Phoebe, ‘but I'd probably get to be a Union Official immediately were left out as in the first example the faithful is a substantivized adjective and in the second example true is used alone with no other parts of speech. The subsequent analysis of the corpora data gave the grounds for the distribution groups of the adjectives under the study which were then investigated with the help of a semantic experiment. To sum it up, the corpora-based approach has proved to be a powerful, reliable and convenient tool to get the data for the further semantic study.Keywords: corpora, corpus-based approach, polysemantic adjectives, semantic studies
Procedia PDF Downloads 3081044 Adiabatic Flame Temperature: New Calculation Methode
Authors: Muthana Abdul Mjed Jamel Al-gburi
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The present paper introduces the methane-air flame and its main chemical reaction, the mass burning rate, the burning velocity, and the most important parameter, the adiabatic and its evaluation. Those major important flame parameters will be mathematically formulated and computerized using the MATLAB program. The present program established a new technique to decide the true adiabatic flame temperature. The new technique implements the trial and error procedure to obtained the calculated total internal energy of the product species then evaluate of the reactants ones, from both, we can draw two energy lines their intersection will decide the true required temperature. The obtained results show accurate evaluation for the atmospheric Stoichiometric (Φ=1.05) methane-air flame, and the value was 2136.36 K.Keywords: 1- methane-air flame, 2-, adiabatic flame temperature, 3-, reaction model, 4- matlab program, 5-, new technique
Procedia PDF Downloads 671043 Exploring Bidirectional Encoder Representations from the Transformers’ Capabilities to Detect English Preposition Errors
Authors: Dylan Elliott, Katya Pertsova
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Preposition errors are some of the most common errors created by L2 speakers. In addition, improving error correction and detection methods remains an open issue in the realm of Natural Language Processing (NLP). This research investigates whether the bidirectional encoder representations from the transformers model (BERT) have the potential to correct preposition errors accurately enough to be useful in error correction software. This research finds that BERT performs strongly when the scope of its error correction is limited to preposition choice. The researchers used an open-source BERT model and over three hundred thousand edited sentences from Wikipedia, tagged for part of speech, where only a preposition edit had occurred. To test BERT’s ability to detect errors, a technique known as multi-level masking was used to generate suggestions based on sentence context for every prepositional environment in the test data. These suggestions were compared with the original errors in the data and their known corrections to evaluate BERT’s performance. The suggestions were further analyzed to determine if BERT more often agreed with the judgements of the Wikipedia editors. Both the untrained and fined-tuned models were compared. Finetuning led to a greater rate of error-detection which significantly improved recall, but lowered precision due to an increase in false positives or falsely flagged errors. However, in most cases, these false positives were not errors in preposition usage but merely cases where more than one preposition was possible. Furthermore, when BERT correctly identified an error, the model largely agreed with the Wikipedia editors, suggesting that BERT’s ability to detect misused prepositions is better than previously believed. To evaluate to what extent BERT’s false positives were grammatical suggestions, we plan to do a further crowd-sourcing study to test the grammaticality of BERT’s suggested sentence corrections against native speakers’ judgments.Keywords: BERT, grammatical error correction, preposition error detection, prepositions
Procedia PDF Downloads 1371042 Pupil Size: A Measure of Identification Memory in Target Present Lineups
Authors: Camilla Elphick, Graham Hole, Samuel Hutton, Graham Pike
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Pupil size has been found to change irrespective of luminosity, suggesting that it can be used to make inferences about cognitive processes, such as cognitive load. To see whether identifying a target requires a different cognitive load to rejecting distractors, the effect of viewing a target (compared with viewing distractors) on pupil size was investigated using a sequential video lineup procedure with two lineup sessions. Forty one participants were chosen randomly via the university. Pupil sizes were recorded when viewing pre target distractors and post target distractors and compared to pupil size when viewing the target. Overall, pupil size was significantly larger when viewing the target compared with viewing distractors. In the first session, pupil size changes were significantly different between participants who identified the target (Hits) and those who did not. Specifically, the pupil size of Hits reduced significantly after viewing the target (by 26%), suggesting that cognitive load reduced following identification. The pupil sizes of Misses (who made no identification) and False Alarms (who misidentified a distractor) did not reduce, suggesting that the cognitive load remained high in participants who failed to make the correct identification. In the second session, pupil sizes were smaller overall, suggesting that cognitive load was smaller in this session, and there was no significant difference between Hits, Misses and False Alarms. Furthermore, while the frequency of Hits increased, so did False Alarms. These two findings suggest that the benefits of including a second session remain uncertain, as the second session neither provided greater accuracy nor a reliable way to measure it. It is concluded that pupil size is a measure of face recognition strength in the first session of a target present lineup procedure. However, it is still not known whether cognitive load is an adequate explanation for this, or whether cognitive engagement might describe the effect more appropriately. If cognitive load and cognitive engagement can be teased apart with further investigation, this would have positive implications for understanding eyewitness identification. Nevertheless, this research has the potential to provide a tool for improving the reliability of lineup procedures.Keywords: cognitive load, eyewitness identification, face recognition, pupillometry
Procedia PDF Downloads 4001041 Enhancing a Recidivism Prediction Tool with Machine Learning: Effectiveness and Algorithmic Fairness
Authors: Marzieh Karimihaghighi, Carlos Castillo
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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism
Procedia PDF Downloads 1431040 Epileptic Seizure Onset Detection via Energy and Neural Synchronization Decision Fusion
Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin
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This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour.Keywords: epilepsy, EEG, seizure onset, electroencephalography, neuron, detection
Procedia PDF Downloads 4711039 Particle Filter Implementation of a Non-Linear Dynamic Fall Model
Authors: T. Kobayashi, K. Shiba, T. Kaburagi, Y. Kurihara
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For the elderly living alone, falls can be a serious problem encountered in daily life. Some elderly people are unable to stand up without the assistance of a caregiver. They may become unconscious after a fall, which can lead to serious aftereffects such as hypothermia, dehydration, and sometimes even death. We treat the subject as an inverted pendulum and model its angle from the equilibrium position and its angular velocity. As the model is non-linear, we implement the filtering method with a particle filter which can estimate true states of the non-linear model. In order to evaluate the accuracy of the particle filter estimation results, we calculate the root mean square error (RMSE) between the estimated angle/angular velocity and the true values generated by the simulation. The experimental results give the highest accuracy RMSE of 0.0141 rad and 0.1311 rad/s for the angle and angular velocity, respectively.Keywords: fall, microwave Doppler sensor, non-linear dynamics model, particle filter
Procedia PDF Downloads 2061038 The Budget Impact of the DISCERN™ Diagnostic Test for Alzheimer’s Disease in the United States
Authors: Frederick Huie, Lauren Fusfeld, William Burchenal, Scott Howell, Alyssa McVey, Thomas F. Goss
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Alzheimer’s Disease (AD) is a degenerative brain disease characterized by memory loss and cognitive decline that presents a substantial economic burden for patients and health insurers in the US. This study evaluates the payer budget impact of the DISCERN™ test in the diagnosis and management of patients with symptoms of dementia evaluated for AD. DISCERN™ comprises three assays that assess critical factors related to AD that regulate memory, formation of synaptic connections among neurons, and levels of amyloid plaques and neurofibrillary tangles in the brain and can provide a quicker, more accurate diagnosis than tests in the current diagnostic pathway (CDP). An Excel-based model with a three-year horizon was developed to assess the budget impact of DISCERN™ compared with CDP in a Medicare Advantage plan with 1M beneficiaries. Model parameters were identified through a literature review and were verified through consultation with clinicians experienced in diagnosis and management of AD. The model assesses direct medical costs/savings for patients based on the following categories: •Diagnosis: costs of diagnosis using DISCERN™ and CDP. •False Negative (FN) diagnosis: incremental cost of care avoidable with a correct AD diagnosis and appropriately directed medication. •True Positive (TP) diagnosis: AD medication costs; cost from a later TP diagnosis with the CDP versus DISCERN™ in the year of diagnosis, and savings from the delay in AD progression due to appropriate AD medication in patients who are correctly diagnosed after a FN diagnosis.•False Positive (FP) diagnosis: cost of AD medication for patients who do not have AD. A one-way sensitivity analysis was conducted to assess the effect of varying key clinical and cost parameters ±10%. An additional scenario analysis was developed to evaluate the impact of individual inputs. In the base scenario, DISCERN™ is estimated to decrease costs by $4.75M over three years, equating to approximately $63.11 saved per test per year for a cohort followed over three years. While the diagnosis cost is higher with DISCERN™ than with CDP modalities, this cost is offset by the higher overall costs associated with CDP due to the longer time needed to receive a TP diagnosis and the larger number of patients who receive a FN diagnosis and progress more rapidly than if they had received appropriate AD medication. The sensitivity analysis shows that the three parameters with the greatest impact on savings are: reduced sensitivity of DISCERN™, improved sensitivity of the CDP, and a reduction in the percentage of disease progression that is avoided with appropriate AD medication. A scenario analysis in which DISCERN™ reduces the utilization for patients of computed tomography from 21% in the base case to 16%, magnetic resonance imaging from 37% to 27% and cerebrospinal fluid biomarker testing, positive emission tomography, electroencephalograms, and polysomnography testing from 4%, 5%, 10%, and 8%, respectively, in the base case to 0%, results in an overall three-year net savings of $14.5M. DISCERN™ improves the rate of accurate, definitive diagnosis of AD earlier in the disease and may generate savings for Medicare Advantage plans.Keywords: Alzheimer’s disease, budget, dementia, diagnosis.
Procedia PDF Downloads 1361037 The Importance of Right Speech in Buddhism and Its Relevance Today
Authors: Gautam Sharda
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The concept of right speech is the third stage of the noble eightfold path as prescribed by the Buddha and followed by millions of practicing Buddhists. The Buddha lays a lot of importance on the notion of right speech (Samma Vacca). In the Angutara Nikaya, the Buddha mentioned what constitutes right speech, which is basically four kinds of abstentions; namely abstaining from false speech, abstaining from slanderous speech, abstaining from harsh or hateful speech and abstaining from idle chatter. The Buddha gives reasons in support of his view as to why abstaining from these four kinds of speeches is favourable not only for maintaining the peace and equanimity within an individual but also within a society. It is a known fact that when we say something harsh or slanderous to others, it eventually affects our individual peace of mind too. We also know about the many examples of hate speeches which have led to senseless cases of violence and which are well documented within our country and the world. Also, indulging in false speech is not a healthy sign for individuals within a group as this kind of a social group which is based on falsities and lies cannot really survive for long and will eventually lead to chaos. Buddha also told us to refrain from idle chatter or gossip as generally we have seen that idle chatter or gossip does more harm than any good to the individual and the society. Hence, if most of us actually inculcate this third stage (namely, right speech) of the noble eightfold path of the Buddha in our daily life, it would be highly beneficial both for the individual and for the harmony of the society.Keywords: Buddhism, speech, individual, society
Procedia PDF Downloads 2571036 Organotin (IV) Based Complexes as Promiscuous Antibacterials: Synthesis in vitro, in Silico Pharmacokinetic, and Docking Studies
Authors: Wajid Rehman, Sirajul Haq, Bakhtiar Muhammad, Syed Fahad Hassan, Amin Badshah, Muhammad Waseem, Fazal Rahim, Obaid-Ur-Rahman Abid, Farzana Latif Ansari, Umer Rashid
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Five novel triorganotin (IV) compounds have been synthesized and characterized. The tin atom is penta-coordinated to assume trigonal-bipyramidal geometry. Using in silico derived parameters; the objective of our study is to design and synthesize promiscuous antibacterials potent enough to combat resistance. Among various synthesized organotin (IV) complexes, compound 5 was found as potent antibacterial agent against various bacterial strains. Further lead optimization of drug-like properties was evaluated through in silico predictions. Data mining and computational analysis were utilized to derive compound promiscuity phenomenon to avoid drug attrition rate in designing antibacterials. Xanthine oxidase and human glucose- 6-phosphatase were found as only true positive off-target hits by ChEMBL database and others utilizing similarity ensemble approach. Propensity towards a-3 receptor, human macrophage migration factor and thiazolidinedione were found as false positive off targets with E-value 1/4> 10^-4 for compound 1, 3, and 4. Further, displaying positive drug-drug interaction of compound 1 as uricosuric was validated by all databases and docked protein targets with sequence similarity and compositional matrix alignment via BLAST software. Promiscuity of the compound 5 was further confirmed by in silico binding to different antibacterial targets.Keywords: antibacterial activity, drug promiscuity, ADMET prediction, metallo-pharmaceutical, antimicrobial resistance
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