Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrative, Segregative, and Degenerate Harmonics of the Structural Connectome

Unifying integration and segregation in the brain has been a fundamental puzzle in neuroscience ever since the conception of the “binding problem. ” Here, we introduce a novel framework that places integration and segregation within a continuum based on a fundamental property of the brain–its structural connectivity graph Laplacian harmonics and a new feature we term the gap-spectrum. This framework organizes harmonics into three regimes–integrative, segregative, and degenerate–that together account for various group-level properties. Integrative and segregative harmonics occupy the ends of the continuum, and they share properties such as reproducibility across individuals, stability to perturbation, and involve “bottom-up” sensory networks. Degenerate harmonics are in the middle of the continuum, and they are subject-specific, flexible, and involve “top-down” networks. The proposed framework accommodates inter-subject variation, sensitivity to changes, and structure-function coupling in ways that offer promising avenues for studying development, precision medicine, and treatment response in the brain.
Analysis and retrofitting of security properties for proprietary software systems

In this thesis, we present new methods to analyze and extend the security properties of software systems. We concentrate on proprietary software environments in which no source code of the relevant components is available. Analyzing and retrofitting security aspects of proprietary software is an especially intricate task and requires the development and use of customized techniques and tools. The contributions of the thesis are thus twofold. First, we develop tools and revise existing techniques to analyze proprietary systems. To that end, static reverse engineering is used to extract algorithms from proprietary software. Second, we also provide solutions to overcome security-related shortcomings in proprietary systems by extending them, i.e., we retrofit proprietary systems. Overall, we present an analysis and/or retrofitting of four different software systems in this thesis. At first, we describe the reverse engineering of the proprietary GMR-1 satellite phone communication encryption algorithm. We present a generic approach to identify and extract unknown cryptographic algorithms in mobile firmware images and provide an in-depth description of such an analysis using the example of one concrete GMR-1 firmware. In the end, we were able to successfully extract the unknown GMR-1 encryption algorithm which later proved to be vulnerable to cryptographic attacks. Another contribution is the development of new side channel timing attacks against kernelspace ASLR implementations. We therefore reverse engineered the proprietary ASLR implementation of Windows operating systems and present three different side channel attacks which allow a local attacker to reconstruct large parts of the privileged kernel space by abusing side channels in the processor’s memory management facilities. This allows us to effectively bypass kernelspace ASLR protections. We also present a mitigation solution with negligible overhead that renders the attack infeasible. The third topic of this thesis is the design and implementation of the dynamic runtime components of the control flow integrity (CFI) framework MoCFI. MoCFI protects binary iOS applications against attackers that exploit software vulnerabilities to execute arbitrary code. We therefore developed techniques to allow for protecting arbitrary binary program images on iOS. Our evaluation shows that MoCFI is capable of protecting various popular applications with reasonable overhead. Finally, we present a new approach to detect malicious command and control (C&C) bot connections. By enriching network-level information with host-level data, we create so-called behavior graphs that connect system activity with network packet data. This is achieved by monitoring the proprietary native API of Windows. Our evaluation shows that behavior graphs can be used to accurately tell apart C&C connections from legitimate benign traffic.
Uso de Pesquisa Bibliográfica em Informática na Educacão: um Mapeamento Sistemático

Uma Pesquisa Bibliografica (PB) e um procedimento que visa identificar pesquisas ja publicadas, o estado da arte, tendencias ou lacunas sobre um determinado tema com vistas na extensao do conhecimento cientifico. Existem, pelo menos, 4 metodos de PB: tradicional, bibliometrica, mapeamento e, revisao. Quando executadas de forma sistematica as PBs apresentam maior rigor cientifico, maior confiabilidade, maior imparcialidade e maior reproducibilidade, apesar de exigirem a adocao e registro de uma quantidade maior de passos durante a sua realizacao. Este artigo tem como objetivo identificar quantos e quais metodos de PB estao sendo utilizados na area de Informatica na Educacao no Brasil e avaliar como estes estao sendo conduzidos. Para tal, foi executado mapeamento sistematico das publicacoes da area no Brasil, nos ultimos 10 anos. Um total de 3149 artigos oriundos de 4 revistas e 4 eventos da area no pais foram avaliados resultando na identificacao de 17 trabalhos que eram mapeamento ou revisoes sistematicas, mesmo que isto nao fosse evidente pelos seus titulos. Os resultados indicam que o numero de artigos de PB vem aumentando como um todo, em especial as PB sistematicas mas, a PB tradicional ainda e a mais frequente. Foi evidenciada uma proliferacao de termos usados para identificar artigos de PB e que estes podem ser encontrados em varias sub-areas. Os dados tambem indicam que ha uma falta de uniformidade e rigor nos procedimentos e ate desacordo com a terminologia (alguns artigos se auto-denominaram erroneamente conforme os criterios desta pesquisa) a ponto de dificultar uma pesquisa terciaria como esta. Concluimos que a comunidade cientifica de Informatica na Educacao se beneficiaria de (a) um maior rigor na execucao de PBs e, (b) da concentracao dos artigos num unico portal com recursos de busca. Estas medidas certamente facilitaria o desenvolvimento da area como um todo no Brasil.
Efficient approaches to cleaning with mobile robots

In the future, service robots are expected to perform many useful tasks in domestic environments such as cleaning or tidying up. If they performed these tasks autonomously, users would profit from a large amount of saved time. Furthermore, elderly or handicapped people could live more independent of care personnel. Most currently available service robots address the task of floor cleaning and are often not very efficient. This thesis presents several innovative approaches, which make cleaning robots more efficient and extend their capabilities. For many tasks in domestic environments, such as cleaning the floor or wiping a table, service robots require access to a precise model of the environment. We therefore investigate an approach to object reconstruction and 3D mapping for RGB-D cameras, which results in an accurate, globally consistent reconstruction of the camera trajectory. Given this trajectory, our system allows for computing different 3D environment representations such as voxel maps or point clouds. For robot mapping, we integrate the odometry information as an additional motion estimator. In extensive experiments on benchmark datasets, we show that our approach is highly accurate in real-world scenarios. Using a 3D point cloud representation of an object as a prerequisite, we present techniques for cleaning 3D surfaces. Our algorithm for coverage path planning enables manipulation robots to perform tasks such as dusting. In addition, it considers cost functions in the joint space of the robot at the time of planning. As a result, a manipulation robot can cover complex 3D surfaces while minimizing the time or effort needed for the task. A cleaning robot that knows which parts of the surface are dirty can focus on them and thus finish faster. We therefore propose a vision-based approach that allows a manipulation robot to identify the dirty parts of a surface by observing the outcome of its actions. As a result, the robot becomes more efficient and can verify its cleaning success. We extend the idea of selectively cleaning the dirty parts to floor cleaning robots and present techniques for modeling and learning the distribution of dirt in the environment and its dynamics. This allows a robot to make predictions about the amount of dirt in the environment before it starts to clean. Furthermore, we propose a fully probabilistic model for jointly estimating the distribution of dirt as well as the pose of the robot during a cleaning run. This enables a vacuum cleaning robot to reduce the dirt in the environment below a user-defined threshold with high confidence. In addition, it minimizes the required cleaning time. The techniques presented in this thesis were implemented and evaluated in simulation as well as with real-world data obtained from mobile robots or benchmark datasets. We performed extensive experiments to support our claims. The implementation of our approaches on several robotic platforms demonstrate their applicability in practice.
Users & Machine Learning-based Curation Systems

Users are increasingly interacting with machine learning (ML)-based curation systems. YouTube and Facebook, two of the most visited websites worldwide, utilize such systems to curate content for billions of users. Contemporary challenges such as fake news, filter bubbles, and biased predictions make the understanding of ML-based curation systems an important and timely concern. Despite their political, social, and cultural importance, practitioners’ framing of machine learning and users’ understanding of ML-based curation systems have not been investigated systematically. This is problematic since machine learning as a novel programming paradigm in which a mapping between input and output is inferred from data poses a variety of open research questions regarding users’ understanding. The first part of this thesis provides the first in-depth investigation of ML-based curation systems as sociotechnical systems. The second part of the thesis contributes recommendations on how ML-based curation systems can and should be explained and audited. The first part analyses practitioners’ framing of ML by examining how the term machine learning, ML applications, and ML algorithms are framed in tutorials. The thesis also investigates the beliefs that users have about YouTube and introduces a user belief framework of ML-based curation systems. Furthermore, it demonstrates how limited users’ capabilities for providing input data for ML-based curation systems are. The second part evaluates different explanations of ML-based systems. This evaluation uncovered an explanatory gap between what is available to explain ML-based curation systems and what users need to understand such systems. Informed by this explanatory gap, the second part of this thesis demonstrates that audits of ML systems can be an important alternative to explanations. This demonstration of audits also uncovers a popularity bias enacted by YouTube’s ML-based curation system. Based on these findings, the thesis recommends performing audits to ensure that ML-based systems act in the public’s interest.
Unrestricted Bridging Resolution

Anaphora plays a major role in discourse comprehension and accounts for the coherence of a text. In contrast to identity anaphora which indicates that a noun phrase refers back to the same entity introduced by previous descriptions in the discourse, bridging anaphora or associative anaphora links anaphors and antecedents via lexico-semantic, frame or encyclopedic relations. In recent years, various computational approaches have been developed for bridging resolution. However, most of them only consider antecedent selection, assuming that bridging anaphora recognition has been performed. Moreover, they often focus on subproblems, e.g., only part-of bridging or definite noun phrase anaphora. This thesis addresses the problem of unrestricted bridging resolution, i.e., recognizing bridging anaphora and finding links to antecedents where bridging anaphors are not limited to definite noun phrases and semantic relations between anaphors and their antecedents are not restricted to meronymic relations. In this thesis, we solve the problem using a two-stage statistical model. Given all mentions in a document, the first stage predicts bridging anaphors by exploring a cascading collective classification model. We cast bridging anaphora recognition as a subtask of learning fine-grained information status (IS). Each mention in a text gets assigned one IS class, bridging being one possible class. The model combines the binary classifiers for minority categories and a collective classifier for all categories in a cascaded way. It addresses the multi-class imbalance problem (e.g., the wide variation of bridging anaphora and their relative rarity compared to many other IS classes) within a multi-class setting while still keeping the strength of the collective classifier by investigating relational autocorrelation among several IS classes. The second stage finds the antecedents for all predicted bridging anaphors at the same time by exploring a joint inference model. The approach models two mutually supportive tasks (i.e., bridging anaphora resolution and sibling anaphors clustering) jointly, on the basis of the observation that semantically/syntactically related anaphors are likely to be sibling anaphors, and hence share the same antecedent. Both components are based on rich linguistically-motivated features and discriminatively trained on a corpus
Humanoid robot navigation in complex indoor environments (Navigation für Humanoide Roboter in komplexen Innenräumen)

Humanoid service robots promise a high adaptability to environments designed for humans due to their human-like body layout. They are thus well-suited to assist humans in domestic environments for everyday tasks or providing elderly care, but also to replace human workers in hazardous environments. The human-like structure allows versatile manipulation with two arms as well as stepping over or onto obstacles with bipedal locomotion. Compared to wheeled platforms, however, the kinematic structure of humanoids requires active balancing while walking and allows only a limited payload, e.g., for additional sensors. Inaccurate motion execution can lead to foot slippage and thus to a poor estimation of ego-motion. Furthermore, the high number of degrees of freedom make planning and control a challenging problem for humanoid robots. Autonomous navigation is a core capability of any service robot that performs highlevel tasks involving mobility. Before a robot can fulfill a task, it needs to know where it is in the environment, what the environment looks like, and how it can successfully reach its goal. In this thesis, we present several novel contributions to the field of humanoid robotics with a focus on navigation in complex environments. We hereby cope with challenges in the areas of 3D environment representations, localization, perception, motion planning, and mobile manipulation. As a basis, we first introduce a memory-efficient 3D environment representation along with techniques for building three-dimensional maps. In this representation, our probabilistic localization approach estimates the 6D pose of the humanoid based on data of its noisy onboard sensors. We compare different range sensors as well as sensor and motion models. For the critical task of climbing stairs, we develop an improved particle filter that additionally integrates vision data for a highly accurate pose estimate. We also present methods to perceive staircases in 3D range data. For reaching a navigation goal with a humanoid robot, we introduce and compare different search-based footstep planning approaches with anytime capabilities. We then investigate planning for the whole body of the humanoid while considering constraints such as maintaining balance during a manipulation task. This enables the robot to pick up objects or open doors. Finally, we present an efficient approach for navigation in three-dimensional cluttered environments that is particularly suited for mobile manipulation. All techniques developed in this thesis were thoroughly evaluated both with real robots and in simulation. Our contributions generally advance the navigation capabilities of humanoid robots in complex indoor environments.
Shallow Parsing by Inferencing with Classi ers

We study the problem of identifying phrase structure. We formalize it as the problem of combining the outcomes of several diierent clas-siiers in a way that provides a coherent inference that satisses some constraints, and develop two general approaches for it. The rst is a Markovian approach that extends standard HMMs to allow the use of a rich observations structure and of general classiiers to model state-observation dependencies. The second is an extension of constraint satisfaction formalisms. We also develop eecient algorithms under both models and study them experimentally in the context of shallow parsing. 1
A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection /Author=Singh, Bharat; Marks, Tim K.; Jones, Michael J.; Tuzel, C. Oncel; Shao, Ming /CreationDate=June 26, 2016 /Subject=Artificial Intelligence, Computer Vision, Machine Learning

We present a multi-stream bi-directional recurrent neural network for fine-grained action detection. Recently, twostream convolutional neural networks (CNNs) trained on stacked optical flow and image frames have been successful for action recognition in videos. Our system uses a tracking algorithm to locate a bounding box around the person, which provides a frame of reference for appearance and motion and also suppresses background noise that is not within the bounding box. We train two additional streams on motion and appearance cropped to the tracked bounding box, along with full frame streams. Our motion streams use pixel trajectories of a frame as raw features, in which the displacement values corresponding to a moving scene point are at the same spatial position across several frames. To model long-term temporal dynamics within and between actions, the multi-stream CNN is followed by a bidirectional Long Short-Term Memory (LSTM) layer. We show that our bi-directional LSTM network utilizes about 8 seconds of the video sequence to predict an action label. We test on two action detection datasets: the MPII Cooking 2 Dataset, and a new Shopping Dataset that we introduce and make available to the community with this paper. The results demonstrate that our method significantly outperforms state-ofthe-art action detection methods on both datasets. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c © Mitsubishi Electric Research Laboratories, Inc., 2016 201 Broadway, Cambridge, Massachusetts 02139 A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection Bharat Singh U. of Maryland bharat@cs.umd. edu Tim K. Marks Michael Jones Oncel Tuzel Mitsubish Electric Research Labs (MERL) {tmarks, mjones, oncel}@merl.com Ming Shao Northeastern University mingshao@ccs.neu. edu
Characteristics and genesis of Rare Earth Element (REE) in western Indonesia

Rare Earth Element (REE) has unique properties that have been used in many hightech applications. The demand of REE increased recently in the world due to its special properties. Although REE concentration in the crust is higher than gold, economically viable deposits are still rare. Reduction of REE exports by China cause increased prices of REE. Due to this condition, exploration of potential REE mines emerged. Indonesia also participates in this phenomenon, and explore the possibility of REE mines in its area. This review will discuss the characteristics and genesis of REE and its occurrence in western Indonesia; focused in Sumatera, Tin Island, and Kalimantan. The review is done based on literature research from several resources about characteristics of rare earth element in general and in the given area. The research shows that the potential REE mines can be found in several different locations in Indonesia, such as Tin Island, Sumatera, and Kalimantan. Most of them are composed of monazite, zircon, and xenotime as rare earth minerals. Monazite iss known for its elevated number of radioactive elements, so study about radioactive content and more environment friendly ore processing becomes compulsory.
Curvit : An open-source Python package to generate light curves from UVIT 4 data 5

13 Abstract. Curvit is an open-source Python package that facilitates the creation of light curves from the data 14 collected by the Ultra-Violet Imaging Telescope (UVIT) onboard AstroSat, India’s first multi-wavelength 15 astronomical satellite. The input to Curvit is the calibrated events list generated by the UVIT-Payload 16 Operation Center (UVIT-POC) and made available to the principal investigators through the Indian Space 17 Science Data Center. The features of Curvit include (i) automatically detecting sources and generating light 18 curves for all the detected sources and (ii) custom generation of light curve for any particular source of 19 interest. We present here the capabilities of Curvit and demonstrate its usability on the UVIT observations of 20 the intermediate polar FO Aqr as an example. Curvit is publicly available on GitHub at https://github.com/ 21 prajwel/curvit. 22 23 Keyword. AstroSat—UVIT—variability.
Heuristic Variants for A* Search in 3D Flight Planning

A crucial component of a ﬂight plan to be submitted for approval to a control authority in the pre-ﬂight phase is the prescription of a sequence of airways and airway points in the sky that an aircraft has to follow to cover a given route. The generation of such a path in the 3D network that models the airways must respect a number of constraints. They generally state that if a set of points or airways is visited then another set of points or airways must be avoided or visited. Paths are then selected on the basis of cost considerations. The cost of traversing an airway depends, directly, on fuel consumption and on traversing time, and, indirectly, on weight and on weather conditions. Path ﬁnding algorithms based on A ∗ search are commonly used in automatic planning. However, the constraints and the dependency structure of the costs invalidate the classic domination criterion in these algorithms. A common approach to tackle the increased computational eﬀort is to decompose the problem heuristically into a sequence of horizontal and vertical route optimizations. Using techniques recently designed for the simpliﬁed 2D context, we address the 3D problem directly. We compare the direct approach with the decomposition approach. We enhance both approaches with ad hoc heuristics that exploit the expected appeal of routes to speed-up the solution process. We show that, on data resembling those arising in the context of European airspaces, the direct approach is computationally practical and leads to results of better quality than the decomposition approach.