Miracle-dz/UniFAR-FTUs huggingface dataset:
Conquering knowledge from images: improving image mining with region-based analysis and associated information

CAZZOLATO, M. T. Conquering knowledge from images: improving image mining with region-based analysis and associated information. 2019. 138 p. Tese (Doutorado em Ciências – Ciências de Computação e Matemática Computacional) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2019. The popularization of social media, combined with the widespread use of smartphones and the use of advanced equipment in hospitals and medical centers has generated sequences of complex data, including images of high quality and in large quantity. Providing appropriate tools to extract meaningful knowledge from such data is a big challenge. While many potential techniques have been proposed to analyze images, most of the processing performed by image mining techniques consider the entire image. Thus, regions that are not of interest are considered in the analysis step, without proper distinction and consequently damaging most tasks. This doctorate research focused on the thesis that by taking advantage of small representations of images we can improve the overall results of different image analysis tasks. Then, we employed classification, clustering, and temporal data analysis algorithms, according to the application. We evaluate this thesis in three application scenarios. In the first scenario, we analyzed regions of images from emergencies, gathered from social media and which depict smoke regions. We were able to segment smoke regions and improve the classification of smoke images by up to 23%, compared to global approaches. In the second scenario, we worked with images from the medical context, containing Interstitial Lung Diseases (ILD). We classified the images considering the uncertainty of each lung region to contain different abnormalities, representing the obtained results with a heat map visualization. Our approach outperformed its competitors in the classification of lung regions by up to four of five classes of abnormalities. In the third scenario, we dealt with sequences of microscopic images depicting embryos being developed over time. Using regionbased information of images, we were able to track and predict cells over time and build their motion vector. Our approaches showed an improvement of up to 57% in quality, and a speed-up of the tracking pipeline by up to 81.9%. Therefore, this doctorate research contributed to the state-of-the-art by introducing methods of region-based image analysis for the three application scenarios mentioned above.
Entropy-based Iterative Learning Estimation for Stochastic Non-linear Systems and Its Application to Neural Membrane Potential Interaction

This paper investigates the unknown parameter estimation problem for a class of stochastic non-linear systems which are subjected to non-Gaussian noises. Motivated by the iterative learning identification, an iterative learning estimation algorithm has been presented with an entropy-based performance criterion. Since the data sets from each iteration are collected and stored in memory, the entropy of the estimation error can be approximated by kernel density estimation and the optimal vector-valued estimated parameter can be obtained using the gradient descent searching while the learning rate is able to be selected properly to guarantee the convergence of the presented algorithm. In order to verify the effectiveness of this algorithm, one solution to biological neural membrane potential interaction characterization problem is given via a numerical simulation using the presented algorithm.
A Near Field Communication (NFC) – Enabled System in Smart Posters for University Application

This project involves the exploration and use of Near Field Communication (NFC) in the Education field. The goal is to design a new application for the use of NFC in the pretext of a university environment. This has been done by exploring the current application of NFC in the Education sector and looking into new possibilities for this upcoming technology. Upon research, the author is interested in the process of producing a tag that will contain URLs of references for smart posters. Through showing the capabilities of NFC in the form of a new application, this research proves the possibility of the expansion and successful application of NFC in the near future for the Education sector.
Concept Pointer Network for Abstractive Summarization

A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distant-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model’s abstractive abilities also supports the quality of the summaries produced within this framework.
Mathematical modeling in object localization based on signal strength

Object localization is used on a daily basis in the form of GPS use. Examples of indoor applications of positioning systems can be found in the care of the elderly, indoor navigation apps for museum visitors. When modeling the distance between sender and receiver in localization problems in wireless communication, the dependency of the received signal strength can be expressed mathematically by an exponential function. Therefore the distance is commonly expressed in a lin-log scale as a function of the received signal strength. For engineering students it is more than interesting to see that the discipline of mathematics is at the service of a typical engineering field as telecommunication. Moreover this STEM topic is useful to make students reflect on the error of the estimated parameters when using the technique of linear regression to model the distance based on measured data obtained from a real-life test bed. The measured quantity is the received power expressed in dBm. Localization algorithms are close enough to the real world of students and interesting enough form mathematical point of view to use it in STEM classes.
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space

Quadratic regression involves modeling the response as a (generalized) linear function of not only the features $x^{j_1}$ but also of quadratic terms $x^{j_1}x^{j_2}$. The inclusion of such higher-order “interaction terms" in regression often provides an easy way to increase accuracy in already-high-dimensional problems. However, this explodes the problem dimension from linear $O(p)$ to quadratic $O(p^2)$, and it is common to look for sparse interactions (typically via heuristics). In this paper, we provide a new algorithm – Interaction Hard Thresholding (IntHT) which is the first one to provably accurately solve this problem in sub-quadratic time and space. It is a variant of Iterative Hard Thresholding; one that uses the special quadratic structure to devise a new way to (approx.) extract the top elements of a $p^2$ size gradient in sub-$p^2$ time and space. Our main result is to theoretically prove that, in spite of the many speedup-related approximations, IntHT linearly converges to a consistent estimate under standard high-dimensional sparse recovery assumptions. We also demonstrate its value via synthetic experiments. Moreover, we numerically show that IntHT can be extended to higher-order regression problems, and also theoretically analyze an SVRG variant of IntHT.
End-to-end Reinforcement Learning for Autonomous Longitudinal Control Using Advantage Actor Critic with Temporal Context

Reinforcement learning has been used widely for autonomous longitudinal control algorithms. However, many existing algorithms suffer from sample inefficiency in reinforcement learning as well as the jerky driving behaviour of the learned systems. In this paper, we propose a reinforcement learning algorithm and a training framework to address these two disadvantages of previous algorithms proposed in this field. The proposed system uses an Advantage Actor Critic (A2C) learning system with recurrent layers to introduce temporal context within the network. This allows the learned system to evaluate continuous control actions based on previous states and actions in addition to current states. Moreover, slow training of the algorithm caused by its sample inefficiency is addressed by utilising another neural network to approximate the vehicle dynamics. Using a neural network as a proxy for the simulator has significant benefit to training as it reduces the requirement for reinforcement learning to query the simulation (which is a major bottleneck) in learning and as both reinforcement learning network and proxy network can be deployed on the same GPU, learning speed is considerably improved. Simulation results from testing in IPG CarMaker show the effectiveness of our recurrent A2C algorithm, compared to an A2C without recurrent layers.
Specified-Time Vehicular Platoon Control With Flexible Safe Distance Constraint

The platoon control for vehicles can efficiently reduce traffic pressure. Current control schemes are able to achieve stable platoon control, but cannot guarantee collision avoidance, connectivity preservation and convergence time at the same time. In this paper, we propose a novel control scheme that can simultaneously guarantee the safe distance and communication connectivity, which is called flexible safe distance constraint, and also can guarantee the specified convergence time. A dynamic gain based distributed control law is first designed for the vehicle with single-integral model. Using the prescribed transient and steady-state performance control approach, the flexible safe distance constraint can be satisfied. Using the time transformation method and Lyapunov stability theory, the closed-loop systems under the proposed protocols can achieve stable platoon within the specified time. Compared with some existing results, the novelty of this work is that the proposed approaches allow a vehicle to converge with the platoon within any preset time without dependence on the initial conditions or system parameters. Another contribution of this work is to propose specified-time platoon control protocols for the vehicle with double-integral model under a flexible safe distance constraint. Furthermore, in order to get closer to the actual system, the controller is extended to the triple-integral system, and some special cases are also discussed. Finally, some simulations are presented to show the effectiveness of the proposed protocols.
The spatial mapping of concepts in English and Mandarin

ABSTRACT English speakers have been shown to map abstract concepts in space, which occurs on both the vertical and horizontal dimensions. For example, words such as God are associated with up and right spatial locations, and words such as Satan with down and left. If the tendency to map concepts in space is a universal property of human cognition, then it is likely that such mappings may be at least partly culturally-specific, since many concepts are themselves language-specific and therefore cultural conventions. Here we investigated whether Mandarin speakers report spatial mapping of concepts, and how these mappings compare with English speakers (i.e. are words with the same meaning associated with the same spatial locations). Across two studies, results showed that both native English and Mandarin speakers reported spatial mapping of concepts, and that the distribution of mappings was highly similar for the two groups. Theoretical implications are discussed.
Identification of Effective Strategies in Facilitating Virtual Mobility

Background: Virtual mobility, as one of the emerging phenomena in the internationalization of higher education, is a process that all activities within it are done electronically or digitally. It is used, among others, to facilitate physical activities, such as student exchanges, with the help of information and communication technologies. Objectives: The current study aimed at the identification of effective strategies in facilitating virtual mobility. Methods: In order to achieve the objectives of the study, a qualitative approach and a phenomenological research method were used. The statistical population included all the experts experienced in the area of internationalization from the top five comprehensive and public universities in Iran, including the University of Tehran, Shahid Beheshti University, Tarbiat Modares University, Mashhad Ferdowsi University, and Shiraz University. The participants included 27 experts with international experience purposefully selected from the five universities and their comments were collected using semi-structured interviews to achieve the theoretical saturation of data. The content analysis of the data was done using MAXQDA version 2018 software. Results: Seventeen minor components were identified as effective strategies in facilitating virtual mobility in higher education. The main effective strategies in facilitating virtual mobility were organized in four major themes, including promoting university websites, promoting social networks, improving virtual infrastructure, and strengthening virtual branding. Conclusions: The results of the study showed that among the components, promoting university websites and promoting social networks were the most important factors highlighted by the experts as the factors that could facilitate the process of virtual mobility as an area in which more must be invested.
A Convolutional Neural Network Model Implementation for Speech Recognition

Speech recognition is the capability of an appliance to analyze vocable and diction in a phonetic language and turn them into a machine comprehensible arrangement. It is an interdisciplinary subfield of linguistics, computer science and electrical engineering that establishes processes and techniques that understands and converts speech to text. This paper presents a convolutional neural network model for recognition of speech data.
Recognition of horizontal gaze motion based on electrooculography using tsugeno fuzzy logic

Electrooculography is a biosignal activity generated from the human’s eyes activity. In this research, the eyes movements (left and right) were recognized by processing the EOG signal. The eyes movements in the horizontal movement were identified from the direction and the magnitude of the gaze angle. The direction of the eyes movements was detected using the polarity of the signal whereas the magnitude of gaze angle was computed using Tsugeno fuzzy method with two approaches, namely area of signal and peak of the signal. The system was able to detect the direction of gaze motion with the accuracy of 100%. The accuracy of the EOG signal in calculating the magnitude of the gaze angle was 93.46% for the right and 89.88% for the left. The number was higher compared to the performance of the peak point with the accuracy of 88.06% for the right and 84.79% for the left. The result in this paper showed that the system was able to detect the direction of eyes movements very well. However, the calculation of the magnitude of eyes movements is still a challenge to be improved in the future.
Academic Network Representation Learning Based on Metapath Tree

Network representation learning aims to use low-dimensional dense vectors to represent nodes in the graph, which can reflect the graph structure and can be used in a variety of machine learning tasks. The academic network contains richer information, which most of the current methods are unable to capture. This paper proposes a method to get better vectors in the academic network. This method first uses the metapath tree to guide the random walk process, and adds a sampling process to preserve multiple metapath information. The vector representation of the nodes is obtained by training using the skip-gram model on the academic network. Experiment results show that the proposed model outperforms several traditional network representation learning models in multi-label classification and clustering tasks.
Monitoring pada Server STMIK Amik Riau dengan Menggunakan Suricata Melalui Notifikasi Bot Telegram

Tindakan kejahatan dunia maya (cybercrime)saat ini berkembang cepatsesuai dengan perkembangan teknologi informasi, beberapa model tindakan cybercrime didasari juga oleh perkembangan tersebut. Salah satu bentuk kejahatan cyber yang ditimbulkan seperti serangan pada sistem server pada instansi atau perusahaan yang dapat merusak kerahasiaan, keaslian dan ketersediaan informasi.Menjadi sebuah tuntutan bagi administrator jaringan untuk memantau kondisi jaringan yang dikelolanya secara realtime begitu juga pada STMIK Amik Riau, sebuah perguruan tinggi yang memiliki beberapa server yang dikelola untuk menunjang keperluan akademik.Oleh karena itu dibutuhkan sebuah metode keamanan jaringan yang dapat melakukan pendeteksian, monitoring, dan mengidentifikasi aktifitas pada suatu host atau network salah satunya menggunakan Suricata.Suricata adalah Intrusion Detection and Prevention System (IDPS) yang mampu mendeteksi suatu aktifitas jaringan dan mengidentifikasi ancaman berdasarkan rules yang ter-integrasi. Rules pada suricata berperan dalam mengidentifikasi seranganÂ yang terjadi pada sebuah host. Agar alert serangan pada jaringan dapat di monitoring secara realtime. diperlukan sebuah penghubung antara notifikasi suricata ke administrator jaringan, salah satunya memanfaatkan bot Telegram yang berperan secara otomatis mengirimkan notifikasi ketika terjadi serangan pada server, dengan adanya botTelegram tersebut diharapkan akan memudahkan administrator jaringan dalam memantau aktifitas serangan dari mana saja tanpa harus membuka log suricata, sehingga administrator jaringan dapat megambil tindakan pencegahan lebih dini.
Leveraging Dependency Forest for Neural Medical Relation Extraction

Medical relation extraction discovers relations between entity mentions in text, such as research articles. For this task, dependency syntax has been recognized as a crucial source of features. Yet in the medical domain, 1-best parse trees suffer from relatively low accuracies, diminishing their usefulness. We investigate a method to alleviate this problem by utilizing dependency forests. Forests contain more than one possible decisions and therefore have higher recall but more noise compared with 1-best outputs. A graph neural network is used to represent the forests, automatically distinguishing the useful syntactic information from parsing noise. Results on two benchmarks show that our method outperforms the standard tree-based methods, giving the state-of-the-art results in the literature.
A Comprehensive Realization of Robot Skin: Sensors, Sensing, Control, and Applications

This article presents a holistic approach to the engineering of an artificial robot skin for robots. An example of a multimodal skin cell is given, one that supports multiple human-like sensing modalities, and support for skin cell network is also provided; this is essential to form large-area skin patches in order to cover the surfaces of robots. The essential elements of efficiently handling a large amount of tactile data are explained. A general control framework, which supports robots commanded in position, velocity, and torque, is provided and validated. Several applications of this robot skin will be presented, demonstrating the effectiveness and efficiency of our artificial robot skin to support a wide number of robotic platforms as well as its ease of use across different domains.
Wind Reliability of Transmission Line Models using Kriging-Based Methods

Risk assessment of power transmission systems against strong winds requires models that can accurately represent the realistic performance of the physical infrastructure. Capturing material nonlinearity, p-delta effects in towers, buckling of lattice elements, joint slippage, and joint failure requires nonlinear models. For this purpose, this study investigates the reliability of transmission line systems by utilizing a nonlinear model of steel lattice towers, generated in OpenSEES platform. This model is capable of considering various geometric and material nonlinearities mentioned earlier. In order to efficiently estimate the probability of failure of transmission lines, the current study adopts an advanced reliability method through Error rate-based Adaptive Kriging (REAK) proposed by the authors. This method is capable of significantly reducing the number of simulations compared to conventional Monte Carlo methods such that reliability analysis can be done within a reasonable time. Results indicate that REAK efficiently estimates the reliability of transmission lines with a maximum of 150 Finite Element simulations for various wind intensities.
Controlling Text Complexity in Neural Machine Translation

This work introduces a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a high quality dataset of news articles available in English and Spanish, written for diverse grade levels and propose a method to align segments across comparable bilingual articles. The resulting dataset makes it possible to train multi-task sequence to sequence models that can translate and simplify text jointly. We show that these multi-task models outperform pipeline approaches that translate and simplify text independently.
Does CNN Explain Tuning Properties of Macaque Face-Processing System?

Recent computational studies have emphasized quantitative similarity between convolutional neural networks (CNNs) and the visual ventral stream up to the primate inferotemporal (IT) cortex. However, whether such similarity holds for the face-selective areas, a subsystem of IT, is not clear. To address this question, we extensively investigate CNNs in terms of known tuning properties of the face-processing network in macaque IT. Specifically, we first trained an AlexNet-type CNN model with natural face images. Then, we conducted simulation of four physiological experiments (Freiwald, Tsao, & Livingstone, 2009; Freiwald & Tsao, 2010; Ohayon, Freiwald, & Tsao, 2012; Chang & Tsao, 2017) to make a correspondence between the model layers and the macaque face patches. As a result, we found that higher model layers explained well properties of anterior patches, while no layer had properties close to middle patches; this observation was consistent across model variation. Our results indicate that, although the near-goal representation of face-classifying CNNs has some similarity with the primate face processing system, the intermediate computational process might be rather different, thus calling for a more comprehensive model for better understanding of this system.