Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis Tools for Small and Big Data Problems

Analysis Tools for Small and Big Data Problems Juan Chen The dissertation focuses on two separate problems. Each is informed by real-world applications. The first problem involves the assessment of an ordinal measurement system in a manufacturing setting. A random-effects model is proposed that is applicable to this repeatability and reproducibility context, and a Bayesian framework is adopted to facilitate inference. This first problem is an example of an analysis tool to solve a small data problem. The second problem involves statistical machine learning applied to big data problems. As more and more data become available, a need increases to automate the ability to identify particularly relevant features in a prediction or forecasting context. This often involves expanding features using kernel functions to better facilitate predictive capabilities. Simultaneously, there are often manifolds embedded within big data structures that can be exploited to improve predictive performance on real data sets. Bringing together manifold learning with kernel methods provides a powerful and novel tool developed in this dissertation. This dissertation has the advantage of contributing to a more-classical problem in statistics involving ordinal data and to cutting edge machine learning techniques for the analysis of big data. It is our contention that statisticians need to understand both problem types. The novel tools developed here are demonstrated on practical applications with strong results.
Time and Power Allocation for Energy Efficiency Maximization in Wireless-Powered Full-Duplex Relay Systems

In this paper, we propose an optimal time and power allocation scheme in a wireless power supply full-duplex (FD) relay system, where we consider the number of relay antennas in the energy harvesting stage. At the same time, the energy efficiency optimization problem of the system is structured, where optimization issues related to time allocation factors and power allocation are established. For the FD dual-antenna and the FD single-antenna energy harvesting system, energy efficiency function is proven to be a concave function over the time-switch factor, and the optimal time-switching factor is theoretically obtained using the Lambert function. Then, according to the given value range of the optimal time switching factor, the optimal power distribution scheme is obtained by analyzing the derivative function of the system energy efficiency and using the properties of the Lambert function. The time-switching factor and transmission power are optimally selected at the wireless power supply FD relay. Results reveal that the performance of energy efficiency of the dual-antenna energy harvesting at the FD relay outperforms that of the single-antenna. Moreover, our results demonstrate that FD relay systems always substantially boost the energy efficiency compared with half-duplex (HD) relay systems.
Acquisition of Email Service Based Android Using NIST

Email is one of the results of the development of information and communication technology. Email is widely used to exchange information by sending and receiving data, such as document files, images, correspondence and others. With the development of technology and information causing crimes in communicating also growing, the perpetrators of cyber crime commonly referred to as cybercrime. Any crime committed by cybercrime will surely leave the evidence, in this study will make the acquisition of android-based email using the method of national institute of standards and technology (NIST). The results obtained IP address of the sender's email header as digital evidence.
Optimal Design and Parametric Assessment of Grid-Connected Solar Power Plants in Iran, a Review

Solar energy is used for domestic, industrial, and power plant consumption. From a nation-wide perspective, it has attracted increasing attention due to creating opportunities, reducing fossil fuels consumption, and also meeting the requirements for reduction of environmental pollutants. Given its geographical location which has endowed Iran with a desirable level of solar energy as a renewable source of energy, it is the first paper aimed to conduct a potentiometric study of constructing a 20 kW power plant in 31 capital cities in Iran, considering all the existing losses. PVsyst 6.7 and Meteonorm 7.1 software packages are used for analysis. Results showed that the required area for monocrystalline solar panels was less than that for polycrystalline ones and for polycrystalline less than thin-film panels. Furthermore, solar cells with higher manufacturing technology incurred lower costs, so that monocrystalline cells produced the cheapest solar-powered electricity, while the electricity generated by thin-film panels was the most expensive. In addition, it was found that ventilation had less impact on monocrystalline solar cells than polycrystalline, and less on polycrystalline panels than thin-film ones. The highest (39˚) and lowest (27˚) annual optimum tilt angle were related to Bojnoord and Bandar Abbas, respectively. Also, the results revealed that the highest (0.833) and lowest (0.771) annual performance ratios were obtained for Ardabil and Ahvaz, respectively. The highest (35276) and lowest (24031) amount of annual energy injected to the grid (kWh) were associated with Zahedan and Sari, respectively. Average annual energy injected to the grid (kWh) for the studied stations was 30942. For a more detailed evaluation of the effect that each type of losses had on the energy produced, annual loss diagrams, which are the most important outputs of PVsyst software, were evaluated for Zahedan and Sari stations.
Comparison of Phonemic and Graphemic Word to Sub-Word Unit Mappings for Lithuanian Phone-Level Speech Transcription

Conventional large vocabulary automatic speech recognition (ASR) systems require a mapping from words into sub-word units to generalize over the words that were absent in the training data and to enable the robust estimation of acoustic model parameters. This paper surveys the research done during the last 15 years on the topic of word to sub-word mappings for Lithuanian ASR systems. It also compares various phoneme and grapheme based mappings across a broad range of acoustic modelling techniques including monophone and triphone based Hidden Markov models (HMM), speaker adaptively trained HMMs, subspace gaussian mixture models (SGMM), feed-forward time delay neural network (TDNN), and state-of-the-art low frame rate bidirectional long short term memory (LFR BLSTM) recurrent deep neural network. Experimental comparisons are based on a 50-hour speech corpus. This paper shows that the best phone-based mapping significantly outperforms a grapheme-based mapping. It also shows that the lowest phone error rate of an ASR system is achieved by the phoneme-based lexicon that explicitly models syllable stress and represents diphthongs as single phonetic units.
Interbot Mobile Robot: Human-Robot Interaction Modules

Robotic technology has had many advances, and yet mobile robot are still not completely present in our daily environments, workplaces or homes. For these robots to make part of a human environment, a significant development of Human-Robot Interaction (HRI) technology is still required, so that robots know how to behave while interacting with humans towards being socially accepted. When applied to mobile robots, HRI can cover a large technological spectrum ranging from teleoperation interfaces and their limitations, human and robot roles in a HRI, and how the dialogue between both parties is conducted. The main goal of this Master’s dissertation is to develop and implement several functionalities for InterbotSocial Robot, a mobile robot being developed at the Institute for Systems and Robotics (ISR). These new functionalities allow Interbot to interact with humans and to perform given tasks. With that goal in mind, two new components were added: a remote control station and a tablet, acting both as human-robot interfaces. The architecture of the developed software is designed to be modular and versatile, in a way that it is easy to use and understand by future users and researchers. To develop the required software for the new components, the ROS (Robot Operating Systems) and Qt Creator frameworks were used as development, test and debugging platforms of the methods and algorithms integrating the final Interbot system. Different types of control and HRI roles were analyzed and evaluated for this platform. A series of tests and experiments was done to measure the performance of the new Interbot components, using different test scenarios and control types. The developed human-robot interfaces provided a reliable way for users to interact and directly control Interbot, to be escorted during a guided tour of ISR floor 0, or to order Interbot to perform tasks such as delivering messages or items between laboratories and rooms. The remote station was found to be capable of planning tasks sequentially, while displaying the robot information in a perceivable way. Finally, a latency analysis was performed by examining its effects on the system performance, and possible solutions for future improvements are presented.
Inflamed appendix detection from laparoscopic video footage using edge detection and morphological image processing

1 CHAPTER 01: INTRODUCTION 1.1 Motivations 2 1.2 Contribution Summary 2 1.3 Thesis Orientation 3 CHAPTER 02: BACKGROUND INFORMATION 2.1 Appendix 4 2.2 Alternative Techniques 2.2.1 Needlescopic laparoscopic appendectomy 7 2.2.2 Two port laparoscopic appendectomy 7 2.2.3 Appendectomy with a single, tensom biblical trocar 7 2.2.4 Single access laparoscopic appendectomy 7 2.3 Complications of laparoscopic appendectomy 2.3. 1 Bleeding 8 2.3. 2 Fecalith 8 2.4 Effects of Laparoscopic Appendectomy 2.4. 1 Laparoscopic appendectomy in women of childbearing age 8 2.4. 2 Laparoscopic appendectomy in pregnant women 8 2.4. 3 Laparoscopic appendectomy in children 8 2.4. 4 Laparoscopic appendectomy in obese persons 9 2.5 Edge detection 9
AGN types as different states of massive black holes

Abstract After discovering microquasars – active stellar systems with relativistic outflows (Mirabel et al. 1992) – many papers bringing the observational arguments on similar nature of accreting systems with different BH masses (from stellar to galactic nuclei) have appeared. It became clear that a level of activity in such systems could depend on few parameters: BH mass and angular momentum; accretion rate and magnetization parameter of accretion disk (AD); relative orientation of BH and AD angular momentum. The bolometric luminosity and characteristic variation time in such systems is proportional to BH masses, which span an interval in 5–8 decades.
Neural Voice Activity Detection and its practical use

The task of producing a Voice Activity Detector (VAD) that is robust in the presence of non-stationary background noise has been an active area of research for several decades. Historically, many of the proposed VAD models have been highly heuristic in nature. More recently, however, statistical models, including Deep Neural Networks (DNNs) have been explored. In this thesis, I explore the use of a lightweight, deep, recurrent neural architecture for VAD. I also explore a variant that is fully end-toend, learning features directly from raw waveform data. In obtaining data for these models, I introduce a data augmentation methodology that allows for the artificial generation of large amounts of noisy speech data from a clean speech source. I describe how these neural models, once trained, can be deployed in a live environment with a real-time audio stream. I find that while these models perform well in their closeddomain testing environment, the live deployment scenario presents challenges related to generalizability. Thesis Supervisor: James Glass Title: Senior Research Scientist Thesis Supervisor: Hao Tang Title: Postdoctoral Associate
COMPARISON OF SOFTWARE FOR AIRBORNE LASER SCANNING DATA PROCESSING IN SMART CITY APPLICATIONS

Abstract. Problems of processing of point clouds of airborne laser scanning using different software for Smart City projects are considered. Results of comparison of suitable software on the base of a test point cloud are presented. For comparison we had chosen a criterion for how results of point cloud processing can be used in the smart city application. The following software were chosen for comparison: Erdas IMAGINE, ENVI Lidar, TerraSolid (without Terraslave), GlobalMapper, Autodesk InfraWorks. Comparison have been conducted in qualitative and quantitative terms. The results presented allowed us to create recommendations on the usage of specific software for airborne laser scanning data processing for Smart City projects.
Which Variables Should I Log?

Developers usually depend on inserting logging statements into the source code to collect system runtime information. Such logged information is valuable for software maintenance. A logging statement usually prints one or more variables to record vital system status. However, due to the lack of rigorous logging guidance and the requirement of domain-specific knowledge, it is not easy for developers to make proper decisions about which variables to log. To address this need, in this work, we propose an approach to recommend logging variables for developers during development by learning from existing logging statements. Different from other prediction tasks in software engineering, this task has two challenges: 1) Dynamic labels – different logging statements have different sets of accessible variables, which means in this task, the set of possible labels of each sample is not the same. 2) Out-of-vocabulary words – identifiers’ names are not limited to natural language words and the test set usually contains a number of program tokens which are out of the vocabulary built from the training set and cannot be appropriately mapped to word embeddings. To deal with the first challenge, we convert this task into a representation learning problem instead of a multi-label classification problem. Given a code snippet which lacks a logging statement, our approach first leverages a neural network with an RNN (recurrent neural network) layer and a self-attention layer to learn the proper representation of each program token, and then predicts whether each token should be logged through a unified binary classifier based on the learned representation. To handle the second challenge, we propose a novel method to map program tokens into word embeddings by making use of the pre-trained word embeddings of natural language tokens. We evaluate our approach on 9 large and high-quality Java projects. Our evaluation results show that the average MAP of our approach is over 0.84, outperforming random guess and an information-retrieval-based method by large margins.
User Behavior to Identify Malicious Activities in Large-Scale Social Networks

: The enormous growth and volume of online social networks and their features, along with the vast number of socially connected users, it has become difficult to explain the true semantic value of published content for the detection of user behaviors. Without understanding the contextual background, it is impractical to differentiate among various groups in terms of their relevance and mutual relations, or to identify the most significant representatives from the community at large. In this paper, we propose an integrated social media content analysis platform that leverages three levels of features, i.e., user-generated content, social graph connections, and user profile activities, to analyze and detect anomalous behaviors that deviate significantly from the norm in large-scale social networks. Several types of analyses have been conducted for a better understanding of the different user behaviors in the detection of highly adaptive malicious users.
A combined RRT*-optimal control approach for kinodynamic motion planning for mobile robots

An important challenge in underground mining is that of motion planning for excavators and hauling trucks because of the tight spaces available to maneuver and fuel consumption. During the last decades several path planning strategies for mobile robots have been developed. However, most of the existing methods only find a path that satisfies certain optimality criteria, but do not consider the feasibility of the path accordingly with the robot’s motion dynamics. Thus in this work two motion planning strategies are proposed that combine the RRT* path planning algorithm with the piecewise solution of an optimal control problem over a finite number subintervals of a simpler polygonal trajectory derived from the path found by RRT*. The proposed approaches take into account the motion model of the robot, hence yield kinodynamically feasible trajectories. One strategy solves a local two-point boundary value optimal control problem at the corners of the polygonal path, while the second solves the two-point boundary value optimal control problem taking consecutive points of the polygonal trajectory. Geometrically, the first approach connects straight lines with curves at the turning points of the path, and the second approach yields smooth curves. In both cases the trajectories can be tracked by the motion controller with negligible errors compared to the trajectories that follow the RRT* path. However, an important difference between the proposed methods lies in the fact that the second one is able to yield trajectories that can be followed with an almost constant longitudinal velocity and gradual changes in heading turning rates. The proposed approaches were evaluated in simulations and tested in field experiments using a robotic excavator implemented with a Caterpillar CAT 262C2 compact loader. The results show that proposed approaches are computationally feasible on current standard computers and reduce the combined tracking error and controller effort criteria by 30−70 %, depending on the complexity of the path.
Remote Sensing Image Change Saliency Detection Technology

High resolution remote sensing image target change detection technology has been widely used, and the traditional remote sensing change detection technology can not meet the requirements of high precision change detection. This paper proposes that visual saliency technology can be used for remote sensing change detection to improve the accuracy and reliability of image target change detection. First, two remote sensing images of different time are obtained, and the visual interesting region of the images are obtained by visual saliency detection. Then the difference between the region of interest is calculated, and finally binaryzation is used to get the clear changing region image, which can effectively improve the detection accuracy.
E-StopMotion: reconstructing and enhancing 3D animation of stop motion characters by reverse engineering plasticine deformation

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Estonia ’ s Digital Transformation Mission Mystique and the Hiding Hand

In 2017, The New Yorker published a story titled ‘Estonia, the Digital Republic’ (Heller 2017). The article’s subtitle was ‘Its Government is Virtual, Borderless, Blockchained, and Secure. Has this Tiny Post-Soviet Nation Found the Way of the Future? ’ It summarizes the buzz around Estonia’s digital transformation: from the outside, at least, it is seen as a major success and has been lauded in mainstream media such as the New York Times, Financial Times, and Forbes (Scott 2014; Crouch 2015; Gaskell 2017). In these, Estonia’s success in digital government is frequently described in parallel with its thriving ICT industry, best exemplified by Skype which originated and is still primarily based in Estonia and which was subsequently acquired by Microsoft. The Estonian e-government infrastructure and its success rest on two main pillars, both introduced in 2001, that essentially create a digital state and digital citizens: the data infrastructure X-Road and a compulsory national digital ID (Kalvet and Aaviksoo 2008; Kalvet 2012; Margetts and Naumann 2016). X-Road is an interoperability platform for existing decentralized databases and a data exchange layer that can be used by public and private sector actors. It is independent of platforms and architectures and provides secure interoperability for data exchanges and identification of trusted actors in digital service delivery. The digital ID makes it possible for citizens to be identified digitally and to use digital signatures. Together, X-Road and the digital ID make it possible to digitally sign any contract, access essentially any public service such citizens’ digital medical history, order prescriptions, file taxes, vote, and so forth. More than 2,300 public and private services use X-Road, and the digital signature has been used almost
The cognitive efficacy of diagrammatic representations in logical reasoning(審査報告)

semantic information extractable from sentences and diagrams rather than concrete syntactic manipulations of diagrams. To test this possibility, settheoretical expressions corresponding to Venn diagrams are introduced, such as A∩B = ∅ for “All A are B” and A∩B 6= ∅ for “Some A are B”. It is assumed that these expressions could only provide some information on the meaning of categorical sentences, but could not block specific interpretational errors caused by word order: i.e., conversion errors and figural effects. This experiment is conducted to check whether the difference between the Linguistic and Venn groups was due to the Venn diagrams preventing interpretational errors in categorical sentences. The work of this appendix was obtained by analyzing the data originally collected in Sato, Mineshima, and Takemura (2010b).
Laser-based localization methods for mobile robots: a SLAM perspective

A robot navigation system is composed of two essential modules: localization and mapping. Localization consists on tracking the robot’s pose (position and orientation) in a known environment, while mapping is focused on building a map of the environment. Although there have been many techniques of Simultaneous Localization and Mapping (SLAM) developed over the last years, these modules can be analysed independently. This dissertation focuses on the localization module. One of the technologies that has been emerging over the last century is the use of Automated Guide Vehicles (AGVs) for industrial purposes (pick up and move materials around a warehouse). These vehicles are usually guided by wires in the floor, magnetic tapes or laser. Even though using wires or magnetic tapes may be more reliable, laser navigation does not require the application of any material on the environment which constitutes a more versatile solution. This dissertation focuses on developing a robot’s localization system using only laser data. This is accomplished using a scan matching algorithm (Iterative Closest Point) to match a map built from environment natural features (essentially corners) with a known map of the environment. An algorithm to extract corners is developed, which is later compared with already validated feature extraction methods available in OpenSLAM. Lastly, the developed algorithm is applied in the framework of the HectorSLAM method in order to give the localization technique more reliability and robustness.