Miracle-dz/UniFAR-FTUs huggingface dataset:
An Evaluation of VGG16 and YOLO v3 on Hand-drawn Images

This thesis evaluates the accuracy and performance of VGG16, a convolutional neural network (CNN), and YOLO v3, an object detector, on a dataset of 1000 hand-drawn images. Unlike with photographs, which possess high amounts of detail, sketches tend to lack much detail aside from the freehand lines that comprise them. This is further detailed in prior works about Sketch-based Image Retrieval (SBIR) [10], a classification task to map photographs to sketches; and SketchParse [10], a CNN that analyzes sketch features and assigns captions. In this paper, I show the differences in classification accuracy between VGG16 and YOLO v3. The former model, pretrained on ImageNet, showed a test accuracy as high as 79.6%. On the other hand, YOLO v3, pretrained on MS COCO, performed worse; it misclassified objects in the dog category and across all categories, made no detections on several images.
Comparison of Feature Selection Methods and Machine Learning Classifiers for Radiomics Analysis in Glioma Grading

Radiomics-based researches have shown predictive abilities with machine-learning approaches. However, it is still unknown whether different radiomics strategies affect the prediction performance. The aim of this study was to compare the prediction performance of frequently utilized radiomics feature selection and classification methods in glioma grading. Quantitative radiomics features were extracted from tumor regions in 210 Glioblastoma (GBM) and 75 low-grade glioma (LGG) MRI subjects. Then, the diagnostic performance of sixteen feature selection and fifteen classification methods were evaluated by using two different test modes: ten-fold cross-validation and percentage split. Balanced accuracy and area under the curve (AUC) of the receiver operating characteristic were used to evaluate prediction performance. In addition, the roles of the number of selected features, feature type, MRI modality, and tumor sub-region were compared to optimize the radiomics-based prediction. The results indicated that the combination of feature selection method L1-based linear support vector machine (L1-SVM) and classifier multi-layer perceptron (MLPC) achieved the best performance in the differentiation of GBM and LGG in both ten-fold cross validation (balanced accuracy:0.944, AUC:0.986) and percentage split (balanced accuracy:0.953, AUC:0.981). For radiomics feature extraction, the enhancing tumor region (ET) combined with necrotic and non-enhancing tumor (NCR/NET) regions in T1 post-contrast (T1-Gd) modality provided more considerable tumor-related phenotypes than other combinations of tumor region and MRI modality. Our comparative investigation indicated that both feature selection methods and machine learning classifiers affected the predictive performance in glioma grading. Also, the cross-combination strategy for comparison of radiomics feature selection and classification methods provided a way of searching optimal machine learning model for future radiomics-based prediction.
Digital sketch maps in teaching GIS for law enforcement students

Abstract. This paper presents a project-based learning method designed by teachers of GIS for students in public order. The aim of the method is to utilize GIS technologies for analysing digital sketch maps made after interviewing people by students. This method gives a hands-on experience of the students about the subjective feelings of the citizens of the examined settlements. For the projects, a paper-based survey and a web application in the form of digital sketch maps were used.The article shows the process of projects used in the studies of public order. The methods applied in the phase of planning, processing and visualising of the data are explained in the case of digital sketch maps and of paper-based surveys. Some general findings of the project are also introduced as well as the students’ opinion of the effectiveness of the projects.General findings show that the results of the students correlate with observations found in the literature. It is evident that more informatics the students acquire during their everyday routine, the more useful and beneficial they find the usage of Geographical Information Systems and the project of using digital sketch maps of fear of crime in different cities.
A new non-linear limb-darkening law for LTE stellar atmosphere models III - Sloan filters: Calculations for –5.0 ≤ log [M/H] ≤ +1, 2000 K ≤ T$\mathsf{_{eff}}$ ≤ 50 000 K at several surface gravities

Continuing our studies on stellar atmospheres (Claret [CITE], [CITE]), we present in this paper the limb-darkening coefficients for the Sloan photometric system. The calculations cover a wide range of values of log g , T eff , metallicities and microturbulent velocities. The atmosphere models used are ATLAS and PHOENIX. In addition to the traditional applications of the limb-darkening coefficients, the present ones will be useful since the Sloan filters are now being used in the interpretation of light curves of extrasolar transiting planets, for example. The present calculations may also be useful, as a complement, for the Kepler mission, dedicated to the search for terrestrial planets.
Prevalence and consequences of substance use among high school and college students in Ethiopia: A review of the literature

This paper is an overview of mind-altering substance use among high school and college students in Ethiopia in the past two decades. Alcohol, khat and cigarettes were commonly used by both high school and college students in urban as well as rural areas. While the use patterns of the substances were related to the gender, education/age and religion of the users, no clear-cut patterns were observed in relation to several other factors including geographic locations. Further, cannabis was used in selected high schools, and its abuse prevalence was greater in urban private schools, as it was for alcohol and tobacco use. Students who used khat, alcohol or cigarettes also displayed increased violent behaviours towards women and enhanced sexual activity, with increased risks for negative consequences. However, as a limited number of studies have been reported in the literature, this review provides only limited information on such substance use. Nonetheless, despite this limitation, the review can be a useful source of information for designing future research directions and for considering actions directed towards tackling this important problem. Keywords : alcohol, khat, tobacco, cannabis, students, Ethiopia
NotesKB: Web-Based Semantic Annotation Tool for Online Multimedia Learning Content

Due to the rapid growth of e-Learning contents, especially videos, published online, the usability of tools that help students keep track of such contents along with their thought traces is an issue. We addressed the problem by designing and building a web-based semantic annotation tool called NotesKB. Through technical and theoretical lenses, we aim to discover and improve upon the usability of such tools. The usability of NotesKB is evaluated and compared with two other predominant note-taking methods, namely Pen and Paper (PnP) and Microsoft OneNote. According to the SUS test scores, PnP is the most usable while NotesKB was the most preferred when students were interviewed. We have reaffirmed that cognitive load is key to design e-Learning systems and that note-taking tools with semantic capabilities need to help users learn how to use the greater power available.
Forward Guidance: Is it Useful Away from the Lower Bound?

During the recent economic crisis, when nominal interest rates were at their effective lower bounds, central banks used forward guidance announcements about future policy rates to conduct their monetary policy. Many policymakers believe that forward guidance will remain in use after the end of the crisis; however, there is uncertainty about its effectiveness. In this paper, we study the impact of forward guidance in a stylized new Keynesian economy away from the effective lower bound on nominal interest rates. Using closed-form solutions, we show that the impact of forward guidance on the economy depends critically on a specific monetary policy rule, ranging from non-existing to immediate and unrealistically large, the so-called forward guidance puzzle. We show that the size of the smallest root (or eigenvalue) captures model dynamics better than the underlying parameters. We argue that the puzzle occurs under very special empirically implausible and socially sub-optimal monetary policy rules, whereas empirically relevant Taylor rules lead to sensible implications.
Automated Testing Techniques for Event-Driven and Dynamically Typed Software Applications

Software testing is the process of executing a software application on a set of inputs, and determining if the application behaves as intended on these inputs. This thesis focuses on testing techniques for event-driven and dynamically typed applications. This is an important class of software, which includes mobile and web applications, that can be challenging to test thoroughly. When developers test their applications, they face several challenges. In particular, they need to: (i) generate a set of inputs to their application, along with oracles that specify intended behavior, (ii) react to test failures by repairing the application, and (iii) determine when the testing is adequate. The goal of this thesis is to design new techniques that can help developers in addressing these challenges. The thesis identifies opportunities for improving over state-of-the-art techniques, and proposes new techniques to address each of the challenges. We present a new methodology that extends the error detection capabilities of existing, manually written Android test suites. In the context of JavaScript web applications, we present practical race detectors for detecting AJAX and initialization races, and a technique that can prevent event race errors by restricting the nondeterminism. Finally, we present a notion of test completeness for dynamic languages, along with a hybrid static/dynamic analysis framework that approximates test completeness, and demonstrate the usefulness of test completeness facts. To demonstrate the usefulness of the proposed techniques, they have been implemented in publicly available prototypes, which have been evaluated on real-world software applications.
Utility-based Approach for Video Service Delivery Optimization

This work aims to introduce an Utility-based approach for Video Service Delivery Optimization (U-VSDO). Through this optimization, a global utility function is calculated based on different constraints. Those constraints are considered based on separate utility function for each actor in the video service delivery chain (Content Provider (CP), Operator (OP) and Client (CL)). However, each actor has a global score for his vision, the overall optimization aims to satisfy the three actors. Our proposed methodology for this optimization is validated through different types of evaluation. First, a simulation based utility function is done for obtaining the optimal values of our optimization problem. Then, a complete GUI (Graphical User Interface) interface is built based on the main parameters for each actor. Finally, a test-bed is conducted to differentiate between two types of flows using open source Software Defined Network (SDN) controller. This part considered the standard use case for ETSI (European Telecommunications Standards Interface) in CDN (Content Delivery Network) as a Service.
Speech Emotion Classification Using Attention-Based LSTM

Automatic speech emotion recognition has been a research hotspot in the field of human–computer interaction over the past decade. However, due to the lack of research on the inherent temporal relationship of the speech waveform, the current recognition accuracy needs improvement. To make full use of the difference of emotional saturation between time frames, a novel method is proposed for speech recognition using frame-level speech features combined with attention-based long short-term memory (LSTM) recurrent neural networks. Frame-level speech features were extracted from waveform to replace traditional statistical features, which could preserve the timing relations in the original speech through the sequence of frames. To distinguish emotional saturation in different frames, two improvement strategies are proposed for LSTM based on the attention mechanism: first, the algorithm reduces the computational complexity by modifying the forgetting gate of traditional LSTM without sacrificing performance and second, in the final output of the LSTM, an attention mechanism is applied to both the time and the feature dimension to obtain the information related to the task, rather than using the output from the last iteration of the traditional algorithm. Extensive experiments on the CASIA, eNTERFACE, and GEMEP emotion corpora demonstrate that the performance of the proposed approach is able to outperform the state-of-the-art algorithms reported to date.
Protocol to Avoid Multiple Black Hole Attacks in MANETs

—Mobile Ad Hoc Networks (MANETs) form a promising approach for applications that need fast installation with no infrastructure especially in disaster recovery and emergency operations. However, many challenges are facing MANETs including security, routing, transmission range and dynamically changing topology with high nodes mobility. Security is considered as the main obstacle for the widespread adoption of MANET applications. Black hole attack is a type of DoS attack that can disrupt the services of the network layer. It has the worst malicious impact on network performance as the number of malicious nodes increases. Several mechanisms and protocols have been proposed to detect and mitigate its effects using different strategies. However, many of these solutions impose more overhead and increase the average end-to-end delay. This paper proposes an enhanced and modified mechanism called "Enhanced RID-AODV", based on a preceding mechanism: RID-AODV. The proposed enhancement is based on creating dynamic blacklists for each node in the network. Each node, according to criteria depends on the number of mismatches of hash values of received packets as compared with some threshold values, can decide to add or remove other nodes to or from its blacklist. The threshold is a function of mobility ( variable threshold ) to cancel the effect of normal link failure. Enhanced RID-AODV was implemented in ns-2 simulator and compared with three previous solutions for mitigating multiple black hole attacks in terms of performance metrics. The results show an increase in throughput and packet delivery ratio and a decrease in end-to-end delay and overhead ratio.
The algebraic geometry of Harper operators

Following an approach developed by Gieseker, Knörrer and Trubowitz for discretized Schrödinger operators, we study the spectral theory of Harper operators in dimensions 2 and 1, as a discretized model of magnetic Laplacians, from the point of view of algebraic geometry. We describe the geometry of an associated family of Bloch varieties and compute their density of states. Finally, we also compute some spectral functions based on the density of states. We discuss the difference between the cases with rational or irrational parameters: for the two-dimensional Harper operator, the compactification of the Bloch variety is an ordinary variety in the rational case and an ind-pro-variety in the irrational case. This gives rise, at the algebro-geometric level of Bloch varieties, to a phenomenon similar to the Hofstadter butterfly in the spectral theory. In dimension 2, the density of states can be expressed in terms of period integrals over Fermi curves, where the resulting elliptic integrals are independent of the parameters. In dimension 1, for the almost Mathieu operator, with a similar argument, we find the usual dependence of the spectral density on the parameter, which gives rise to the well-known Hofstadter butterfly picture.
Bounding the distinguishing number of infinite graphs

A group of permutations G of a set V is k-distinguishable if there exists a partition of V into k parts such that only the identity permutation in G fixes setwise all of the cells of the partition. The least cardinal number k such that (G,V) is k-distinguishable is its distinguishing number. In particular, a graph X is k-distinguishable if its automorphism group Aut(X) has distinguishing number at most k in its action on the vertices of X. Various results in the literature demonstrate that when an infinite graph fails to have some property, then often some finite subgraph is similarly deficient. In this paper we show that whenever an infinite connected graph X is not k-distinguishable (for a given cardinal k), then it contains a ball B of finite radius whose distinguishing number is at least k. Moreover, this lower bound cannot be sharpened, since for any integer k greater than 3 there exists an infinite, locally finite, connected graph X that is not k-distinguishable but in which every ball of finite radius is k-distinguishable. In the second half of this paper we show that a large distinguishing number for an imprimitive graph X is traceable to a high distinguishing number either of a block of imprimitivity or of the induced action of Aut(X) on the corresponding system of imprimitivity. The distinguishing numbers of infinite primitive graphs have been examined in detail in a previous paper by the authors together with Tom W. Tucker.
Semantic Segmentation based Dense RGB-D SLAM in Dynamic Environments

Visual Simultaneous Location and Mapping (SLAM) based on RGB-D has developed as a fundamental capability for intelligent mobile robot. However, most of existing SLAM algorithms assume that the environment is static and not suitable for dynamic environments. This is because moving objects in dynamic environments can interfere with camera pose tracking, cause undesired objects to be integrated into the map. In this paper, we modify the existing framework for RGB-D SLAM in dynamic environments, which reduces the influence of moving objects and reconstructs the background. The method starts by semantic segmentation and motion points detection, then removing feature points on moving objects. Meanwhile, a clean and accurate semantic map is produced, which contains semantic information maintenance part. Quantitative experiments using TUM RGB-D dataset are conducted. The results show that the absolute trajectory accuracy and real-time performance in dynamic scenes can be improved.
INTEGRATED VEHICLE CHASSIS CONTROL BASED ON DIRECT YAW MOMENT AND ACTIVE FRONT STEERING

This paper presents an advanced integrated control system which combines two fuzzy logic controllers for the Direct Yaw Moment (DYC) and Active Front Steering (AFS) in order to improve vehicle handling and cornering stability. Based on a well-developed and validated fourteen degrees of freedom full vehicle model with non-linear tire characteristics, a reference bicycle car model is introduced to compare and therefore control both the yaw rate and side slip angle of the vehicle body. Three input variables are considered by the two controllers namely; the yaw rate, the sideslip angle, and the steering wheel angle. The control action of the direct yaw moment is carried out by generating a differential braking across the front wheels, while the control action of the active steering is carried out by modifying the steering wheel angle. The controller design is carried out by fuzzy logic which suits the non-linearity of the derived model. In addition, all necessary membership functions are given to define the linguistic variables of the input and output variables. Consequently, the correlation between input and output variables are defined by the rule base tables. The numerical modelling is carried out through the MATLAB / Simulink environment which suits the control and optimization process. Different simulation results are carried out by considering standard test maneuvers with different driving speeds such as J-turn, fishhook, and lane change. The simulation results are compered during four cases namely, the conventional system without control, the DYC controller only, the AFS controller only and the integrated DYC and AFS controllers. The results show a substantial improvement of the vehicle stability in terms of vehicle lateral acceleration, side slip angle, yaw rate and roll angle for the developed integrated DYC and AFS controllers compared to that of the individual controller or the conventional system without control.
Choice Architecture for Healthier Insurance Choices: Ordering and Partitioning Can Improve Decisions

Health insurance decisions are a challenge for many consumers and influence welfare, health outcomes, and longevity. Two choice architecture tools are examined that can improve these decisions: informed ordering of options (from best to worst) and choice set partitioning. It is hypothesized that these tools can improve choices by changing: (1) decision focus: the options in a set on which consumers focus their attention, and (2) decision strategy: how consumers integrate the different attributes that make up the options. The first experiment focuses on the mediating role of the hypothesized decision processes on consumer decision outcomes. The outcome results are validated further in a field study of over 40,000 consumers making actual health insurance choices and in two additional experiments. The results show that informed ordering and partitioning can reduce consumers’ mistakes by hundreds of dollars per year. They suggest that wise choice architecture interventions depend upon two factors: The quality of the user model possessed by the firm to predict consumers’ best choice and possible interactions among the ensemble of choice architecture tools.
Analysis and Evaluation of a Wired/Wireless Hybrid Architecture for Distributed Control Systems With Mobility Requirements

Wireless communications offer significant benefits over wired communications, which has increased their popularity in industrial applications. Nevertheless, the existing wireless standard technologies do not satisfy the requirements demanded by the most critical industrial applications and thus, wired communications cannot be directly replaced by wireless solutions. Moreover, the inclusion of movable nodes in the network brings new challenges, such as the handover mechanism. In this paper, a hybrid wired/wireless architecture designed for industrial control applications is proposed. To control the wired network, a time-sensitive network (TSN) is used and to control the wireless network a medium access control (MAC) protocol is designed. In order to communicate both networks, a bridge that acts as a deterministic access point (AP) with real-time features is also proposed. One of the fundamental parts of the proposed architecture is that it can be used in applications with mobility requirements. Hence, a soft-handover algorithm is designed which guarantees uninterrupted communication during its execution without the need for a second radio interface and with reduced growth in network overhead. The proposed architecture is evaluated in order to assess its performance. This paper extends our previous work, including both a theoretical analysis to determine the delay bounds of the proposed architecture and a comparison between the performances of the proposed handover algorithm with other algorithms proposed in the literature. The evaluation has been carried out through OMNeT++ simulations. The results demonstrate the superiority of the proposed handover algorithm compared with other state-of-the-art solutions.