Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automatic Diagnosis Scheme for Gliomas Using Gray Level Co- occurrence Matrix, Histogram of Oriented Gradient,Discrete Wavelet Transformation, Intensity-based Features andHierarchical PSO-SVM

Since the brain is one of the most important organs of human, the malignant brain tumors are the most dangerous diseases. Among these tumors, gliomas are the most prevalent, and the early detection of gliomas is conducive to improving prognosis conditions and thus increasing the survival time of patients. However, as the brain is the most complicated organ, it is a challenging task to diagnosis gliomas at the early stage. Generally, the artificial diagnosis process is tedious and inefficient, which can lead to misdiagnosis. To improve the accuracy and efficiency of the gliomas diagnosis process, an automatic scheme for gliomas diagnosis based on hybrid features and hierarchical PSO-SVM is presented in this paper. Firstly, standard images are generated by image preprocessing techniques, including size normalization and background removal, contrast stretching, and histogram equalization is applied to enhance the low-contrast standard brain images afterward. Furthermore, gray level co-occurrence matrix (GLCM), a histogram of oriented gradient (HOG), intensity-based features, Laplace of Gaussian (LOG) features and wavelet energy features are extracted from the enhanced images, and the dimension of the feature vectors is reduced by principal component analysis (PCA). Kernel support vector machine (KSVM) combined with particle swarm optimization (PSO) is eventually applied to train 2 classifiers, classifier 1 is used to label the brain images with “normal” and “tumors” while classifier 2 is implemented to label the brain images with “gliomas” and “the others”. The experiment results indicate that the accuracy of the classifier 1 can reach 97.94% while the result of the classifier 2 is 96.44%, and the overall accuracy of the proposed scheme is 94.34%.
Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension

State-of-the-art machine reading comprehension models are capable of producing answers for factual questions about a given piece of text. However, some type of questions requires commonsense knowledge which cannot be inferred from the given text passage. Thus, external semantic information could enhance the performance of these models. This PhD research proposal provides a brief overview of some existing machine reading comprehension datasets and models and outlines possible ways of their improvement.
Hybrid Electric Vehicle Two-Step Fuel Efficiency Optimization With Decoupled Energy Management and Speed Control

Hybrid electric vehicles (HEVs) offer an effective solution for emissions reduction and fuel energy savings. The pursuit of further improvements in their energy efficiency has led to the two fundamental optimization challenges of vehicle speed and powertrain energy management (EM), which are inherently coupled. This paper examines the vehicle speed and powertrain EM co-optimization problem for fuel economy for a series HEV following a prescribed route with expected traveling time. In order to overcome the computational burden of a large scale optimal control problem, this work presents a novel two-step optimal control strategy that suitably separates the co-optimization problem on the basis of involving the characteristics of the HEV powertrain power split and losses in the speed optimization step without an explicit use of a powertrain model. A benchmark method that simultaneously solves the optimal driving speed and the energy source power split is introduced, which is used to show the solution quality of the proposed approach. It is illustrated that the proposed method yields a driving speed solution close to the benchmark method, and additionally it outperforms the benchmark fuel economy, with much higher computational efficiency. The simplicity and effectiveness of the proposed two-step approach make it a practical and implementable EM control strategy.
From classroom lessons to exploratory learning progressions: mathematics + computational thinking

ABSTRACT This paper presents findings from a two-year qualitative study examining integration of computer science (CS) and computational thinking (CT) into elementary mathematics instruction. Integrated units were developed by elementary teachers and CS/CT coaches with support from university faculty with expertise in CS/CT and elementary mathematics. CS/CT instruction primarily relied on the Scratch environment, although some lessons made use of Code.org materials. This research primarily relied on two theories of integration (i.e. Kiray, 2012. A new model for the integration of science and mathematics: The balance model. Energy Education Science and Technology Part B: Social and Educational Studies, 4(3), 1181–1196) that provided insight into the level of interconnection between the disciplines and the relative amount of instructional time spent within each discipline. Findings revealed that cross-grade CS/CT concepts included sequencing, looping, and conditional logic. Within each category: (a) concepts were taught with increasing complexity across the grades, (b) the mathematics was dominant and CS/CT was important but secondary, and (c) three types of lessons emerged: No integration, partial integration, and full integration. Lastly, lessons generally included a transition from less integrated to more integrated activities with an initial focus on discipline-specific conceptual understanding prior to integrated activities.
Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks

In this article, we propose a cooperative edge caching scheme, a new paradigm to jointly optimize the content placement and content delivery in the vehicular edge computing and networks, with the aid of the flexible trilateral cooperations among a macro-cell station, roadside units, and smart vehicles. We formulate the joint optimization problem as a double time-scale Markov decision process (DTS-MDP), based on the fact that the time-scale of content timeliness changes less frequently as compared to the vehicle mobility and network states during the content delivery process. At the beginning of the large time-scale, the content placement/updating decision can be obtained according to the content popularity, vehicle driving paths, and resource availability. On the small time-scale, the joint vehicle scheduling and bandwidth allocation scheme is designed to minimize the content access cost while satisfying the constraint on content delivery latency. To solve the long-term mixed integer linear programming (LT-MILP) problem, we propose a nature-inspired method based on the deep deterministic policy gradient (DDPG) framework to obtain a suboptimal solution with a low computation complexity. The simulation results demonstrate that the proposed cooperative caching system can reduce the system cost, as well as the content delivery latency, and improve content hit ratio, as compared to the noncooperative and random edge caching schemes.
Building segmentation of remote sensing images using deep neural networks and domain transform CRF

Automatic building segmentation from remote sensing images is critical in the remote sensing image semantic segmentation. The success of deep neural networks has led to advances in using fully convolutional neural networks (FCN) to extract buildings from the high-resolution image. However, the downsampling processing inevitably leads to loss of details of the segmentation results. To solve this problem, some methods try to refine the results of FCN by using probability graph models such as fully connected CRF (Conditional Random Fields). Nevertheless, many fully connected CRF based methods are too time-consuming and not suitable for building segmentation tasks in some situations. In this paper, we propose a novel time-efficient end-to-end CRF model with the domain transform algorithm called DT-CRF. In the proposed model, in order to accelerate the message passing in the mean-field approximate inference algorithm, we take the edge maps as the joint image for DT-CRF and use the domain transformation algorithm to calculate the pair-wise potential instead of the Gaussian kernel function. Meanwhile, we design a multi-task network which can generate masks and edges simultaneously, and the network can make the DT-CRF to easily optimize the segmentation results using model information. The evaluation of remote sensing image datasets verifies the time and space efficiency of the proposed DTCRF and demonstrates a distinct improvement.
An Unbiased Candidate Selection using Clustering and Analytic Hierarchy Process

Decision making in selecting the best one among different competing alternatives is a crucial task for Humans in many situations. This selection process becomes a complex decision making task when there are many multi criteria objects and each object seems to be equally preferable for selection. AHP is a mathematical model for giving ranking to the objects based on matrix algebra and pair wise comparisons between different numerical and categorical criteria. CAA analysis assigns a value to the student based on regularity, overall performance in academic and nonacademic activities. K means clustering divides the students into homogeneous groups. This paper proposes a combination of CAA analysis, k-means clustering and AHP to find the optimal and unbiased solution for the problem of selecting best student of the department.
Analysis of causes that led to baby Ron James Douglas'cardiopulmonary arrest, bleeding (intracranial, retinal, and pulmonary), and rib fracture

Ron James Douglas was 16 weeks old when he choked after being fed and suffered from cardiopulmonary arrest. The paramedics resuscitated him and took him to a hospital. He was pronounced dead at about 25 hours following admission. An autopsy was performed and the medical examiner (ME) found subdural, subarachnoid, and retinal bleeding and an acute rib fracture of the left anterolateral 6 th rib. The ME alleged that Ron’s death was caused by blunt trauma to the head and trunk. Ron’s father was accused of killing his son. My investigation reveals that Ron had a serum calcium level of 6.2 mg/dL and suffered from a severe hypocalcemia. Hypocalcaemia in children has caused seizures, respiratory disorders, laryngospasm, and/or severe cardiac disorders which resulted in sudden death. Hypocalcemia is the likely cause of Ron’s choking and cardiopulmonary arrest. Ron was born 11 weeks premature and developed hyperphosphatemia at 39 days following birth. The likely causes of Ron’s hypocalcemia are hypoparathyroidism and hyperphosphatemia. He also developed hemolytic anemia and his thymus was involuted. His hemoglobin level and hematocrit value were 6.2 g/dL and 19.4%, respectively. Ron’s intracranial bleeding developed following admission to the hospital and the likely causes of his bleeding are acute liver injury, treatment with epinephrine, and disseminated intravascular coagulation (DIC). In addition, severe anemia contributed to his retinal bleeding. Cardiopulmonary resuscitation is the likely cause of Ron’s rib fracture. The clinical and the medical studies described in this report do not support the ME’s allegation that Ron was killed by blunt trauma. © Copyright 2009, Medical Veritas International, Inc. All rights reserved.
Development of Discrete-Cockroach Algorithm (DCA) for Feature Selection Optimization

One of the recently proposed algorithms in the field of bio-inspired algorithm is the Hungry Roach Infestation Optimization (HRIO) algorithm. Haven has developed optimization algorithms HRIO that is inspired by recent discoveries in the social behaviour of cockroaches. Result showed that HRIO was effective at finding the global optima of a suite of test functions. However, there is no researcher who has observed HRIO for solving discrete problems. Therefore, we try to develop a discrete-cockroach algorithm (DCA) as the modification of HRIO for solving discrete optimization problem. We test the algorithm to solve bio-computation problem using single and multi-objectives optimization. The results showed DCA has better performance compared to the existed bio-inspired optimization algorithms such as genetic algorithms (GA) and discrete-particle swarm optimization (discrete-PSO).
Interpretability of deep learning models

In recent years we have seen growth on interest for Deep Learning (DL) algorithms on a variety of problems, due to their outstanding performance. This is more palpable on a multitude of fields, where self-learning algorithms are becoming indispensable tools to help professionals solve complex problems. However as these models are getting better, they also tend to be more complex and are sometimes referred to as "Black Boxes". The lack of explanations for the resulting predictions and the inability of humans to understand those decisions seems problematic. In this project, different methods to increase the interpretability of Deep Neural Networks (DNN) such as Convolutional Neural Network (CNN) are studied. Additionally, how these interpretability methods or techniques can be implemented, evaluated and applied to real-world problems, by creating a python ToolBox.
Advancing the Multi-Solver Paradigm for Overset CFD Toward Heterogeneous Architectures

Title of dissertation: ADVANCING THE MULTI-SOLVER PARADIGM FOR OVERSET CFD TOWARD HETEROGENEOUS ARCHITECTURES Dylan Jude Doctor of Philosophy, 2019 Dissertation directed by: Professor James Baeder Department of Aerospace Engineering A multi-solver, overset, computational fluid dynamics framework is developed for efficient, large-scale simulation of rotorcraft problems. Two primary features distinguish the developed framework from the current state of the art. First, the framework is designed for heterogeneous compute architectures, making use of both traditional codes run on the Central Processing Unit (CPU) as well as codes run on the Graphics Processing Unit (GPU). Second, a framework-level implementation of the Generalized Minimal Residual linear solver is used to consider all meshes from all solvers in a single linear system. The developed GPU flow solver and framework are validated against conventional implementations, achieving a 5.35× speedup for a single GPU compared to 24 CPU cores. Similarly, the overset linear solver is compared to traditional techniques, demonstrating the same convergence order can be achieved using as few as half the number of iterations. Applications of the developed methods are organized into two chapters. First, the heterogeneous, overset framework is applied to a notional helicopter configuration based on the ROBIN wind tunnel experiments. A tail rotor and hub are added to create a challenging case representative of a realistic, full-rotorcraft simulation. Interactional aerodynamics between the different components are reviewed in detail. The second application chapter focuses on performance of the overset linear solver for unsteady applications. The GPU solver is used along with an unstructured code to simulate laminar flow over a sphere as well as laminar coaxial rotors designed for a Mars helicopter. In all results, the overset linear solver out-performs the traditional, de-coupled approach. Conclusions drawn from both the full-rotorcraft and overset linear solver simulations can have a significant impact on improving modeling of complex rotorcraft aerodynamics. ADVANCING THE MULTI-SOLVER PARADIGM FOR OVERSET CFD TOWARD HETEROGENEOUS ARCHITECTURES
Hybrid DES-based Vehicular Network Simulator with Multichannel Operations

Vehicular Ad-hoc Network (VANET) is considered to be a viable technology for intervehicle communications for the purpose of improving road safety and efficiency. The Enhanced Distribution Channel Access (EDCA) mechanism and multichannel operations are introduced to ensure the Quality of Service (QoS). Therefore, it is necessary to create an accurate vehicular network simulator that guarantees the vehicular communications will work as described in the protocols. A comprehensive vehicular network simulator should consider the interaction between mobility models and network protocols. In this dissertation, a novel vehicular network simulation environment, VANET Toolbox, designed using discrete-event system (DES) is presented. The APP layer DES Module of the proposed simulator integrates vehicular mobility operations with message generation functions. The MAC layer DES module supports single channel and multichannel EDCA operations. The PHY layer DES module supports bit-level processing. Compared with packet-based simulator such as NS-3, the proposed PHY layer is more realistic and accurate. The EDCA scheme is evaluated and compared with the traditional Carrier-Sensing Multiple Access (CSMA) scheme, with the simulations proving that data with different priorities can coexist in the same channel. The multichannel operation for the EDCA scheme is also analyzed in this dissertation. The multichannel switching operation and coordination may cause packet dropping or increased latency to the communication. The simulations show that with heavy network traffic, multichannel communication performs better than single channel communication. From the perspective of safety-related messages, the multichannel operation is able to isolate the interference from the non-safety messages in order to achieve a better packet delivery rate and latency. On the other hand, the non-safety messages can achieve high throughput with reasonable latency from multichannel communication under heavy load traffic scenario.
Enhancing the performance of optical VLC system based on asymmetric symmetric subcarriers OFDM

Recently, visible light communications (VLCs) based on the orthogonal frequency‐division multiplexing (OFDM) technique play an important role in the field of wireless communications. Researches in that field found techniques to improve performance and reduce the system complexity. This paper proposes a system named asymmetric symmetric subcarriers enhanced optical (ASSEO)‐OFDM applied to VLC based on OFDM technique. The proposed system enhances the transmitted data rate by a factor of 1/8 compared with other existing systems. Keeping the same transmitted data rate as the comparable techniques, simulation results show an enhancement of the Eb/No for the proposed system by at least 0.65 dB than asymmetrically and symmetrically clipping optical (ASCO)‐OFDM, by at least 2 dB than asymmetrically clipped optical (ACO)‐OFDM and FLIP‐OFDM, and by at least 0.9 dB than DC‐biased optical (DCO)‐OFDM system at bit error rate (BER) of 10−4. An analytical description of the ASSEO proposed system is also introduced in this paper besides a comparative study with other existing systems.
Learning-Based Energy Management System for Scheduling of Appliances inside Smart Homes

: Improper designs of the demand response programs can lead to numerous problems such as customer dissatisfaction and lower participation in these programs. In this paper, a home energy management system is designed which schedules appliances of smart homes based on the user’s specific behavior to address these issues. Two types of demand response programs are proposed for each house which are shifting-based and learning-based programs for shiftable and heating, ventilation and cooling appliances, respectively. The current structure uses machine learning techniques to design the best demand response programs for heating, ventilation and cooling devices of each user based on his/her behavior and desired comfort level. Doing so, the home energy management system is able to achieve energy cost and consumption reduction without causing dissatisfaction and discomfort to the users. Results demonstrate that by using this structure, energy cost and consumption are reduced by 20.32% and 27%, respectively for a single house located in the Austin, Texas area, in one day. The proposed home energy management structure is tested on three additional houses to show the effectiveness of it. Moreover, comparisons with other methods are performed to clarify the benefits of this structure over other methods. The proposed structure is formulated as a mixed-integer linear model with its optimization performed in the General Algebraic Modeling System environment. CPLEX solver is used to solve the optimization problem.
Study on Fingermark and Palmmark Identification Technologies for its Implementation in the Schengen Information System

4 Acknowledgements . 5 Executive summary . 7 List of recommendations . 10 List of figures. 15 List of tables . 16 Introduction . 17 I. Policy, technical and legal context of CS-SIS . 18 II. Objectives of the study . 21 III. Technology: Readiness and Availability . 21 IV. Methodology followed . 22 Phase 1: Analysis of the state of the art in ABIS-Fingermark, Palmmark and Palmprint technology . 22 Phase 2: Consultation with national ABIS-Fingermark and Palmmark operators . 22 Phase 3: Consultation with eu-LISA. 25 Phase 4: Consultation with ABIS technology vendors . 25 Phase 5: Review by the external board . 26 V. Structure of the report . 26 VI. Audience of the report . 27 VII. A note on terminology . 27 1. Overview of ABIS-Fingermark technology . 31 1.1. Feature extraction and fingermark mark-up . 32 1.2. Deep learning in fingermark image processing . 33 1.2.1. Image enhancement . 33 1.2. 2. CNN based automated fingermark comparison algorithm . 34 1.2. 3. CNN based feature extraction . 34 1.3. ABIS-Fingermark Accuracy Evolution . 36 1.4. Evaluation of performance of fingerprint examiners . 38 1.5. Fingermark crowd-based learning . 39 1.6. Fingermark Datasets . 41 1.7. FBI – NGI fingermarks . 42 2. ABIS-Palmmarks, Partial Palmprints and Palmmarks Technology . 45 2.1. Comparing Palmprints . 46 2.1.1. Online palmprint comparison . 46
Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

: Many restaurant review analysis have been done, however only few analysis have been done for specific aspects of a restaurant. In this context this paper proposes aspect based restaurant analysis which includes Physical environment, Food quality, Service quality and Price fairness. The analysis steps include Aspect Term Extraction (ATE), Aspect Keyword Extraction (AKE), Aspect Categorization (AC) and Sentiment Analysis (SA). ATE employs the modification of Double Propagation method and several Topic Modelling methods, AKE utilizes Term Frequency-Inverse Cluster Frequency (TF-ICF), in AC we propose Hybrid ELMo-Wikipedia (HEW), and in SA we propose Hybrid Expanded Opinion Lexicon-SentiCircle (HEOLS). The results show that our modification of the methods used in ATE could increase the f1measure of the AC by average 2%, then the HEW that we proposed had better f1measure compared to other similar methods by average 6%. Other than that, our proposed HEOLS can expand and redetermine the Opinion Lexicon polarity and can increase f1measure of SA by 6%.