Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning with data augmentation

Convolutional neural networks (CNNs) learn better when more training data is presented to the network. Data augmentation techniques artificially increase the amount of image data that can be passed to a CNN. This allows the CNNs to extract class-defining features more accurately, resulting in better classification accuracies. In this thesis several data techniques are presented and their performances are compared. Two deep learning architectures are used, GoogLeNet and ResNet, and they are both trained on a plant image dataset ('Tropic10'). The data augmentation techniques used in this paper are rotation, flipping, shifting, cutout, and mix-up. Their performances are compared, as well as the performance of some combination of techniques. This results in 11 different augmentation methods. Both deep learning architectures are configured with either pre-trained weights, trained on the 'ImageNet' dataset, or with randomly initialized weights. Both configurations benefit from data augmentation techniques, in some experiments leading to a 3% increase of classification accuracy. Especially fipping or shifting the images, or combining them, resulted in the best performance.
A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation

ABSTRACT The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. In this work, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data suggest social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish.
A Space Mapping Based 0–1 Linear Model for Onboard Conflict Resolution of Heterogeneous Unmanned Aerial Vehicles

This paper addresses the onboard locally centralized conflict resolution problem of heterogeneous unmanned aerial vehicles (UAVs) with variable headings. First, the geometric properties of the conflict resolution problem are analyzed in a two-dimensional (2-D) space. By mapping the nonlinear safe separation constraint to the sine value space, a linear constraint for safe separation is established for pairwise conflicting UAVs. Then, a mixed integer linear programming (MILP) model is built to cope with the conflict resolution problem of heterogeneous UAVs, where the objective is to minimize the overall costs. A coordination coefficient $\lambda $ is defined to enhance the coordination performance between UAVs. The proposed MILP model is extended to the conflict resolution problem of UAVs in a 3-D space. By using the CPLEX solver, the MILP model can be efficiently solved. Numerical simulations are presented to demonstrate the effectiveness of the proposed approach. The simulation results show that the proposed approach can generate conflict-free solutions more efficiently when compared with the existing short-term coordination algorithm. The proposed approach is able to realize UAV coordination in highly congested air traffic and satisfies the onboard conflict resolution requirement.
Topology Aware Worm Simulation and Analysis

This paper provides a complete packet-level topology aware worm simulation model based on worm targets discovery, worm code transmission and worm activation, and designs a directed Small World topology generation algorithm. Then, through selective abstraction, this paper implements a complement packet-level topology aware worm simulation system. Finally, with simulation experiments, the paper analyzes the impacts of topology structure and worm activation on worm propagation. The results of simulation experiments show that this simulation system can provide great support for topology aware worm research.
The Human Facial Combined Expression Recognition System

This paper proposed based on wavelet transform and elastic graph match expression recognition program. It feature extraction with wavelet transform. First this program transform the image of the face with wavelet transform. In order to be on the base of a small loss of image information, achieve the purpose of the amount of compressed data. Then use the elastic graph matching for feature matching. Elastic graph matching algorithm have good performance. So it guarantee the performance of this expression recognition program. The experimental results show that this algorithm can identified the common basic expressions sample fast and more correct.
A queueing network approach for appointment scheduling of blood donors

This paper presents a MINLP model (Mixed integer non linear programming) to schedule appointments of plasma and platelets donors in a blood collection system. Apart from these scheduled donors, there are also some random walk-in whole blood donors. The goal is to determine the optimal appointment schedules of scheduled donors in order to minimize the total waiting time of all donors in a day. We approximate the waiting times of all patients arrived in an hourly period by the corresponding steady-state performances of the queueing network. The appointment scheduling is then modeled as a mixed integer non linear programming problem. The resulting appointment schedules are then compared with other schedules by simulation. Numerical experiments are then performed to assess the efficiency of the proposed appointment scheduling approach.
PROTECTIVE FACTORS OF ACADEMIC RESILIENCY

iii ACKNOWLEDGEMENTS . iv LIST OF TABLES . xiii LIST OF FIGURES . xiv CHAPTER ONE: INTRODUCTION Introductory Background Information . 1 Statement of the Problem . 2 Changing the Perspective . 6 Purpose Statement . 7 Research Framework . 8 Worldview . 9 Design and Method . 9 Theoretical Underpinning . 10 Socio-Ecological Model of Resilience . 10 Hierarchy of Needs . 12 Input-Environment-Outcome Model . 15 Research Questions . 17
Study on Algorithm of Digital Image Watermarking Based on DWT

In this article, watermarking algorithm is studied using digital watermarking technology based on the discrete wavelet (DWT) multi-resolution decomposition algorithm. Three-level wavelet decomposition is employed so that images are divided into three serial sub-graphs with high frequency band and one sub-graph with low frequency band. Since low-frequency information is more sensitive to human eyes than high-frequency information, watermarking is embedded into the high-frequency coefficients of the wavelet image. In our experiment, watermarking information is embedded into the original images by Matlab simulation. Besides, salt and pepper noise, Gaussian low-pass filter, amplifying 200% after narrowing 50% attack as well as JPEG coding are used to test the algorithm. Results show that our algorithm is robust and the visual effect is satisfactory.
Recurrent fuzzy systems

Besides their linguistic interface, we believe fuzzy controllers not only to be universal approximators but also more general and efficient than their similar neural counterparts: radial basis functions. Consequently like recurrent neural networks, this paper aims at extending the fuzzy controllers approximation capacity to dynamic processes of unknown order. We propose a new type of architecture called recurrent fuzzy system together with a learning algorithm for adapting the membership functions. <<ETX>>
An Interval Type-2 Fuzzy Neural Network for Cognitive Decisions

In an ambient assisted living environment, raw data can often be very noisy making is difficulty to interrupt by a decision and reasoning system. To help reduce the effects of noise, we propose a decision and reasoning system which combines an interval fuzzy system and a self-organising fuzzy neural network (SOFNN) is presented in this paper. The method exploits the use of a trained standard SOFNN structure from a fuzzy neural network to initialise the proposed approach. Simulation results show that the proposed structure is more suitable for uncertain situations demonstrating a high level of robustness.
Naive Bayes and similarity based methods for identifying computer users using keystroke patterns

In this dissertation, we present two methods for identifying computer users using keystroke patterns. In the first method "Competition between naive Bayes models for user identification," a naive Bayes model is created for each user. In the training phase of this method, the model of a user is trained using maximum likelihood estimation on the key press latency values extracted from the texts typed by the user. In the user identification phase of this method, for each user we determine the probabilistic likelihood that the typed text belongs to a user. Finally, the typed text is assigned to the user with the highest likelihood value. In the second method "Similarity based user identification," each user is represented by a distinct model. In the training phase of this method, the model parameters of a user are estimated using the extracted key press latency values from the texts typed by the user. In the user identification phase of this method, we assign a similarity score to each user given a typed text. The similarity score of a user is determined by finding the ratio between (1) the number of key press latency values extracted from the typed text similar to the estimated model parameters of the user and (2) the total number of key press latency values extracted from the typed text. Finally, the typed text is assigned to the user with the highest similarity score. We also present a novel application of distance based outlier detection method for discarding outliers in the extracted key press latency values from a users' typed text. Outliers are detected using the following three-step procedure: (1) for each extracted
Parametric Design and Fabrication Strategies by Practice

The rapid development of parametrical tools for architectural design has implicated a big challenge for contemporary architectural education. In the last years many universities have been introducing digital design and fabrication in there syllabus to provide and teach a broader understanding of parametrical design. In order to make virtual parametrical models also buildable for architectural usage a huge amount of knowledge and skills are needed, which induces a big task for teachers and a big challenge for students. Mathematics and geometry play again an important role in order to understand these new tools. This paper presents our teaching approach to the design process through parametrical modelling to realisation in scale 1:1 strongly based on geometry and mathematics. With the design project “Wooden sleeper”, which is made from solid wood, we will present our teaching line. The core of this is the geometrical understanding of the different relations between the involved objects and its translation into a geometrical and mathematical language in terms of computer algorithms, including the mathematical transformation of building materials behaviour.
A three-dimensional risk management model of port logistics for hazardous goods

ABSTRACT Ports are the distribution centers of hazardous goods in the global transportation system. Once hazardous goods accidents occur in ports, they may cause catastrophic losses to humans and the environment. This research investigates the port logistics risks of hazardous goods and provides an overview of port logistics regarding hazardous goods risks. With this investigation, we conducted a detailed analysis of the underlying causes of the port logistics accidents related to hazardous goods. To manage these risks, this article proposes a three-dimensional risk management model that includes human, governance and goods and provides a risk level identification framework for port hazardous goods logistics. Applying the three-dimensional risk management model, we use the Tianjin port explosion as a case study. Finally, we provide several potential preventative measures to prevent such accidents. The findings of this article contribute to the implementation and monitoring of port strategies that will allow for more sustainable and secure development.
Service-delivery success and failure under the sharing economy

Purpose Informed by the role and script theory, this paper aims to explore workforce and customer experiences related to service delivery under the sharing economy. The study is expedient as the sharing economy spreads its disruptive innovation across the hospitality industry. Design/methodology/approach This paper applies a qualitative approach, sourcing online data from virtual platforms. Findings The results indicate both positive and negative experiences for customers and the workforce, and the paper highlights the challenges of fragmented service delivery as a result of service delivery mediated through mobile applications. Practical implications Food delivery mediated through mobile applications is likely to expand as more households are lured by its convenience. This change has implications for both restaurants and the workforce. Originality/value Although a growing number of studies analyse the impact of the sharing economy on the hospitality industry, much of the focus has been on accommodation, and the impact and implications for the food-service segment has not been thoroughly investigated yet.
Analysis of Recreational Carrying Capacity of Urban Parks and Urban Forests in DKI Jakarta Province

Green Open Space (GOS) in the Capital Special Area (Daerah Khusus Ibu Kota/DKI) Jakarta, both in the form of urban park and urban forest has very important function for recreation or any other greening function. In term of recreation & tourism, one of the most important arrangement aspect in GOS management is the carrying capacity (CC), which is not only important to be acknowledged in order to enforce the aspects of sustainability, but also important to be managed well to reach customer satisfaction aspects. Therefore, by using purposive sampling methode there have been done a series of research about the carrying capacity of three GOS, namely urban park (Taman) Suropati (well established area), urban forest-park (Taman Hutan Kota/THK) Honda-Tebet (established area), and THK Penjaringan (less established area); specifically related to physical carrying capacity aspect and psychologycal carrying capacity aspect. Primary data retrieval is done by direct measuring of recreational space usage (for the physical CC/PCC) and by spreading the closed-ended questionnaires instrument (for the psychology CC/CFp) that data adopted the One Score One Indicator Criteria Scoring System. The result of study shows that at the three GOS’s the CFp take an important role to reduce the PCC significantly range from 78% up to 92% to become its real carrying capacity (RCC).
Teachers' Voice: Teacher Participation in Curriculum Development Process.

The purpose of this research was to understand the phenomenon of participation in the curriculum development process through the eyes of teachers. In this research, qualitative instrumental case study design was adopted. The participants of this research consisted of teachers (n = 27) working in five public high schools in the province of Niğde, Turkey. The data of the research were collected by using a semi-structured interview form. For the data analysis, content analysis was used to identify the concepts and relations regarding the collected data. In this research, thick descriptions, prolonged engagement, expert examination, and participant confirmation techniques were used to provide evidence for the trustworthiness of the findings. The research identified four sub-categories for each main category, including curriculum development at the central level and curriculum development at the local level.
SHORT CONTRARIAN INVESTMENT STRATEGY : PENGUJIAN WINNER-LOSER ANOMALY PADA SAHAM-SAHAM DI BURSA EFEK INDONESIA

This research is aimed to examine the winner-loser anomaly based on the overreaction hypothesis that can be decided whether the contrarian investment strategy in the short term can be applied in Indonesia Stock Exchange to obtain the return. The overreaction hypothesis is a manifestation of the inefficiency of the market (De Bondt and Thaler, 1985). Research on Short Contrarian Investment Strategy used a sample of 23 shares in the company's property and real estate sectors. The data used is stock price during the period of 2004-2008. Results obtained by using market-adjusted abnormal return indicates that the average six-month test period only loser in the stock portfolio return reversal happens. This research also examines the possibility of other factors that could explain the market overreaction return reversal, namely size and risk control. Results obtained using regression Zero Cost Investment Portfolio with the method of size and risk adjusted returns indicates that average during the six month test period showed that the loser stocks can not provide a return that is higher than the winner stocks. Thus the research indicates only loser anomaly, this is caused by an overreaction to the bad news. So it can be said that there is no potential for profit if the short-term contrarian strategy applied to the property and real estate sectors in BEI.
DO NEGATIVE OIL PRICE SHOCKS AFFECT THE INDUSTRIAL SECTOR STOCK PRICES MORE THAN POSITIVE SHOCKS? A BIVARIATE EGARCH ANALYSIS FOR TURKEY.

Petrol dunyada en cok takip edilen emtia olma ozelligine sahiptir. Gerek arz ve talebi gerekse bunun sonucu olarak fiyatlari bakimindan petrol yalnizca makroekonomik gostergeler uzerinde degil ayni zamanda finansal piyasalar uzerinde de etkin bir rol oynayan bir degiskendir. Bu calismada da petrol fiyatlarindan Turkiye’deki toplam ve alt sektorler bazinda Borsa Istanbul’da islem goren sanayi sektorune ait hisse senedi fiyatlarina dogru asimetrik oynaklik yayiliminin olup olmadigi incelenmistir. Bu baglamda, 03.08. 2009-30.07. 2016 tarihleri arasinda gunluk olarak derlenen ve getiri serisi haline donusturulen veri setine iki degiskenli VAR-EGARCH modeli uygulanmistir. Elde edilen sonuclara gore, petrolden madencilik sektoru haric tum sektorlere ve BIST 100 endeksine dogru asimetrik bir oynaklik yayilimi vardir. Buna gore petrol fiyatlarinda meydana gelen negatif oynakliklarin hisse senetleri uzerinde pozitif olanlara gore daha fazla etkisi oldugu soylenebilir.
DISEÑO SÍSMICO SIMPLIFICADO DE ESTRUCTURAS CON MUROS DE MAMPOSTERÍA AISLADAS SÍSMICAMENTE

Se presenta una propuesta de metodo simplificado para el analisis de estructuras con base en muros de carga aisladas sismicamente. La propuesta adapta el metodo simplificado de diseno de estructuras con base en muros de mamposteria de las normas de diseno sismico de Mexico y la propuesta para el diseno del sistema de aislamiento sismico del metodo estatico de los reglamentos de los Estados Unidos. Ademas de discutir los conceptos medulares del metodo, se ilustra su aplicacion con el diseno de una estructura tipo, y se demuestra su eficacia en llegar a disenos seguros mediante analisis dinamicos no lineales (paso a paso) de la estructura disenada ante la accion de una familia de registros de aceleracion asociados al espectro de diseno sismico. Se espera que este metodo simplificado de diseno ayude a promover el uso del aislamiento sismico en estructuras de baja altura con base en muros de mamposteria y, con ello, reducir su vulnerabilidad ante la accion de sismos intensos. Se presenta una propuesta de metodo simplificado para el analisis de estructuras con base en muros de carga aisladas sismicamente. La propuesta adapta el metodo simplificado de diseno de estructuras con base en muros de mamposteria de las normas de diseno sismico de Mexico y la propuesta para el diseno del sistema de aislamiento sismico del metodo estatico de los reglamentos de los Estados Unidos. Ademas de discutir los conceptos medulares del metodo, se ilustra su aplicacion con el diseno de una estructura tipo, y se demuestra su eficacia en llegar a disenos seguros mediante analisis dinamicos no lineales (paso a paso) de la estructura disenada ante la accion de una familia de registros de aceleracion asociados al espectro de diseno sismico. Se espera que este metodo simplificado de diseno ayude a promover el uso del aislamiento sismico en estructuras de baja altura con base en muros de mamposteria y, con ello, reducir su vulnerabilidad ante la accion de sismos intensos.
Time-ordered Online Training of Convolutional Neural Networks

Learned algorithms, especially deep neural networks, have proliferated in recent years, primarily due to their adaptability and performance. For applications where humans are required to monitor a datastream, e.g., a video feed from a UAS, such algorithms can greatly reduce the cognitive load required of human operators. But these algorithms are learned, not learning. After deployment to an end device such as a UAS, they are generally unchanged until the next software update. This makes them inflexible to changingrequirements and scenarios, a sharp contrast to adaptable human operators. Furthermore, retraining requires data collection and annotation—sometimes upwards of thousands of images—and must be done offline, not while a particular mission is unfolding. In this work, we tackle the problem of online, real-time training of a deep learning algorithm. We refer to this type of problem as time-ordered online training (ToOT), of which the UAS use case is an example. We begin with the observation that in human students, learning requires both study and curiosity. A goodlearner is not only good at extracting information from the data given to it, but also skilled at finding the right new information to learn from. They may have help from a mentor or teacher, but must use this assistance wisely and effectively. For a real-time learning algorithm onboard a UAS, this means being able to learn from an incoming video feed while minimizing the human annotations and time required to do so, and moving around the environment to augment its learning. We define a metric, incremental trainingbenefit (ITB) per annotation, that seeks to capture the value extracted for each annotation provided by the user.We present a combined system that addresses both study and curiosity when deployed onboard a small UAS. This system has two components: ClickBAIT and autonomous curiosity. ClickBAIT is a framework designed to enable real-time training of a learning algorithm from mouse clicks on a video feed. We apply this framework to classification as well as detection models, showing that it increases the ITB ofeach annotation by 3-7 times across both cases. Furthermore, we show that such techniques can be usedto enable learning from a separate low-power sensor network, without clicks at all. Autonomous curiosityis a system, trained through deep reinforcement learning, that accomplishes the task of both guiding the UAS’ motion and choosing when to ask for annotations from the human operator. Our trained approach is able to increase average ITB by 10-14 times versus a random search. In addition, we examine how autonomous curiosity agents can be trained to consider the time-cost as well as the annotation cost of itsactions, adapting its behavior to optimize for the characteristics of a particular UAS.