Miracle-dz/UniFAR-FTUs huggingface dataset:
SRTM X-SAR motion compensation: concept and first assessment of the interferometric observation geometry

The space shuttle Endeavour that flew from 11 until 22 February carried the radar systems for the Shuttle Radar Topography Mission (SRTM). In the course of this project the first space born single pass interferometer has mapped the Earth's topography. Two different radar systems operated on board of the space shuttle: the C-band radar of the American NASA/JPL and the X-band radar of the German DLR and the Italian ASI. It is the objective of this mission to generate a digital elevation model (DEM) of the Earth with an accuracy that has never been reached before on this global scale. The inevitable technically caused oscillation of the secondary antenna turns this task into a scientific and technological challenge. The Synthetic Aperture Radar (SAR) observation is influenced by a resulting sensor motion in the line of sight. The consequence of this effect on the secondary SAR scene and subsequently on the DEM is described in this paper. To obtain the requested height accuracy in spite of the antenna oscillation, the principle of motion compensation is applied. The concept of this key algorithm in the X-SAR SRTM project is explained in detail. The essential parameters of the interferometric observation geometry were recorded by the Attitude and Orbit Determination Avionics (AODA) system of the JPL. These AODA data are employed to investigate the movement of the secondary antenna.
Mining Extremes through Fuzzy Clustering

Archetypes are extreme points that synthesize data representing "pure" individual types. Archetypes are assigned by the most discriminating features of data points, and are almost always useful in applications when one is interested in extremes and not on commonalities. Recent applications include talent analysis in sports and science, fraud detection, profiling of users and products in recommendation systems, climate extremes, as well as other machine learning applications. The furthest-sum Archetypal Analysis (FS-AA) (Mørup and Hansen, 2012) and the Fuzzy Clustering with Proportional Membership (FCPM) (Nascimento, 2005) propose distinct models to find clusters with extreme prototypes. Even though the FCPM model does not impose its prototypes to lie in the convex hull of data, it belongs to the framework of data recovery from clustering (Mirkin, 2005), a powerful property for unsupervised cluster analysis. The baseline version of FCPM, FCPM-0, provides central prototypes whereas its smooth version, FCPM-2 provides extreme prototypes as AA archetypes. The comparative study between FS-AA and FCPM algorithms conducted in this dissertation covers the following aspects. First, the analysis of FS-AA on data recovery from clustering using a collection of 100 data sets of diverse dimensionalities, generated with a proper data generator (FCPM-DG) as well as 14 real world data. Second, testing the robustness of the clustering algorithms in the presence of outliers, with the peculiar behaviour of FCPM-0 on removing the proper number of prototypes from data. Third, a collection of five popular fuzzy validation indices are explored on accessing the quality of clustering results. Forth, the algorithms undergo a study to evaluate how different initializations affect their convergence as well as the quality of the clustering partitions. The Iterative Anomalous Pattern (IAP) algorithm allows to improve the convergence of FCPM algorithm as well as to fine-tune the level of resolution to look at clustering results, which is an advantage from FS-AA. Proper visualization functionalities for FS-AA and FCPM support the easy interpretation of the clustering results.
Neural Machine Translation with Reordering Embeddings

The reordering model plays an important role in phrase-based statistical machine translation. However, there are few works that exploit the reordering information in neural machine translation. In this paper, we propose a reordering mechanism to learn the reordering embedding of a word based on its contextual information. These learned reordering embeddings are stacked together with self-attention networks to learn sentence representation for machine translation. The reordering mechanism can be easily integrated into both the encoder and the decoder in the Transformer translation system. Experimental results on WMT’14 English-to-German, NIST Chinese-to-English, and WAT Japanese-to-English translation tasks demonstrate that the proposed methods can significantly improve the performance of the Transformer.
Simultaneous Localization and Mapping in Dynamic Scenes

The task of dynamic scene understanding is an open problem in computer vision with numerous applications in fields such as autonomous driving. Many modern three-dimensional reconstruction pipelines do not account for the presence of dynamic objects in the environment, or simply treat all measurement associated with such entities as noise. Moreover, many reconstruction pipelines which rely on stereo input are unable to effectively deal with the noise typically associated with estimating depth from stereo. We present a dense mapping system based on stereo cameras which is capable of robust operation in complex urban scenes. In addition to building a static map of its environment, the system also detects, tracks, and reconstructs the vehicles encountered within, while remaining memoryefficient thanks to a simple yet effective map pruning technique. By leveraging instance-aware semantic segmentation, our system is capable of detecting and reconstructing not just actively moving objects, but also stationary objects with the potential of becoming dynamic, such as parked cars, which is highly desirable in many challenging higher-level tasks, such as urban path planning for autonomous vehicles. We use the well-established InfiniTAM framework for performing volumetric reconstructions, adapting it to allow effective outdoor operation, and improving its scalability by reducing its memory requirements through a map pruning technique based on voxel garbage collection. Performing volumetric fusion for static objects and the environment map is straightforward, and only requires knowledge of the camera’s egomotion, which is computed using visual odometry. Measurement registration for independently moving objects is achieved by tracking their 3D pose over time using sparse feature correspondences. We perform a rigorous evaluation of our system’s dense reconstruction capabilities, 3D pose tracking accuracy, run time, and memory footprint using sequences from the well-established KITTI odometry and tracking benchmarks. Our system is capable of near real-time operation at roughly 2.5Hz, with the primary bottleneck being the instance-aware semantic segmentation, which is a limitation we hope to address in future work.
Development of automatic control system of satellite signal parameters with TDMA technology

The control of the frequency spectrum state under operating conditions of the satellite networks that include plenty of satellite earth stations is one of the main functions of ground segment of space communication systems. In this case, the control system of the satellite network must measure the parameters of separate network segments or separate earth station if it is necessary. The necessity for selective measurements is relevant in the following cases: the power level, noise level or spurious radiation level of any earth station is significantly higher than the nominal one. The overview of some existing monitoring systems that makes automatic control of the frequency spectrum is presented. The analysis of the problem arisen during the functioning of satellite TDMA networks that involve plenty of earth stations is presented. Based on the analysis, it was concluded that the existing control systems of the satellite networks do not allow measuring high frequency parameters of separate earth stations under TDMA technology. The analysis of the problem of measuring the channels parameters of satellite network functioned by using the TDMA technology is presented. The differences between computations of channel power under “single channel per carrier” technology and the TDMA technology are presented. The authors proposed an automatic control system of high-frequency (HF) parameters of satellite signals, functioning by using the time-division multiple access technology. The block diagram of this system and description of its operation are presented. The conditions for implementing this automatic control system are defined. The time chart and its characteristics were calculated during measurements executing by this automatic control system. The method of match making between the received signals through the satellite segment and the identifiers of earth stations of the satellite network is presented.
A Unit - Test Case Prioritization Technique Based on Source Code Analysis

While performing the white box testing there may be large number of test cases executed by the developer to ensure the correct functionality of their code. This process involves a lot of efforts. To reduce these efforts if somehow a developer is able to get the prioritized order of the test cases which he/she is going to execute to ensure the correct functionality during the process of white box testing is a challenging task. Keeping this idea in mind a test case prioritization technique for unit testing is proposed in this research paper. The proposed technique is based on the analysis of the source code written by the developers. To show the effectiveness of the proposed approach it is compared with non prioritized and random approach. The APFD value obtained by proposed approach is more, showing the efficacy of the proposed approach.
The code of many colors: semi-automated reasoning about multi-thread policy for java

Concurrent programming has proven to be difficult. One cause of this difficulty is that the relevant thread usage policy seldom appears either in documents or code comments. A second difficulty is that thread usage policy—even when it is known—imposes widespread consequences on the code to be written. Finally, finding and removing concurrency faults in existing code is very hard. This thesis introduces thread coloring, a language of discourse useful for concise expression of and reasoning about intended thread usage policies in a wide variety of code. Thread coloring addresses a range of concurrency issues—assuring single-thread access, identifying possibly-shared data regions and localizing knowledge about roles for threads—that have not previously been addressed. Using this language, programmers can model design intent about relationships among the roles of threads with respect to segments of executable code and shared state. Programmers formally link the model with their code by expressing the model as annotations in that code. This thesis describes a prototype analysis tool, integrated into an IDE, and its use in case studies to demonstrate that thread coloring is a feasible and practicable approach to expressing and understanding thread usage policies, including complex ones. The tool analyzes the consistency of the expressed model and the as-written code, and notifies programmers of discrepancies between them. The case studies use published code to demonstrate that developers can express useful models, identify concurrency faults and assure policy compliance. The thesis includes a demonstration of scaling to a medium-sized program of 140KSLOC and a demonstration of the potential to scale to much larger programs. By limiting the problem scope to thread usage policy, the prototype implementation requires one hundred times fewer annotations than are needed for full functional correctness—6.3 annotations per KSLOC, potentially reducible in future by another order of magnitude. This thesis provides five primary contributions to software engineering. First, it provides a language that developers can use to express thread usage policies. Second, it provides a systematic way to improve code quality by assuring that as-written code complies with expressed thread usage policy. Third, it uses a new combination of preexisting techniques to reduce the effort required to express models to very low levels. Fourth, it demonstrates techniques that permit the analysis to operate on very large programs—millions of lines of code appear to be within reach. Finally, it demonstrates techniques that permit straightforward and reliable incremental recomputation of results after a program change.
Multiscale Cancer Modelling in Terms of Copyright

Multiscale cancer modelling is a complex process, which requires interdisciplinary effort. Simulation of tumor progression across multiple scales by computer models is a challenge for scientists, and determining applicable legal protection is a challenge for lawyers. Insofar as a computer model is defined as a computer program, software copyright comes into play. At this stage several questions arise: What elements in computer modelling are copyrightable? Is the modelling work protected? What about copyright in a hypermodel design? The intellectual effort and investment deployed into the modelling have been tested against the requirements of copyright. In fact, various elements in cancer modelling, which express original creative input, qualify as copyright works and are protected as such. Keywords-computer models; software copyright; copyright in compilations; non-literal expression; program expression.
Performing digital ways of knowing: epistemic walks with methods-as-prototypes

Based on our experience in the project ‘The Importance of Being Digital: Exploring Digital Academic Practices and Methods’, we narrate our different trajectories of engagement with digital methods and digital practices. Inspired by emerging scholarship that looks at prototypes as a cultural and epistemic form, we delve into an exploration of methods (both traditional and digital) as prototypes – open-ended, non-instrumental explorative devices – for our knowledge processes. By opening up the craft of our research we illustrate and discuss what ‘digital ways of knowing’ – ways of knowing inspired by digital practices – might look like, and which reconfigurations of knowledge practices and trajectories they could enable.
Soft Representation Learning for Sparse Transfer

Transfer learning is effective for improving the performance of tasks that are related, and Multi-task learning (MTL) and Cross-lingual learning (CLL) are important instances. This paper argues that hard-parameter sharing, of hard-coding layers shared across different tasks or languages, cannot generalize well, when sharing with a loosely related task. Such case, which we call sparse transfer, might actually hurt performance, a phenomenon known as negative transfer. Our contribution is using adversarial training across tasks, to “soft-code” shared and private spaces, to avoid the shared space gets too sparse. In CLL, our proposed architecture considers another challenge of dealing with low-quality input.
Objective video presentation QoE predictor for smart adaptive video streaming

How to deliver videos to consumers over the network for optimal quality-of-experience (QoE) has been the central goal of modern video delivery services. Surprisingly, regardless of the large volume of videos being delivered everyday through various systems attempting to improve visual QoE, the actual QoE of end consumers is not properly assessed, not to say using QoE as the key factor in making critical decisions at the video hosting, network and receiving sites. Real-world video streaming systems typically use bitrate as the main video presentation quality indicator, but using the same bitrate to encode different video content could result in drastically different visual QoE, which is further affected by the display device and viewing condition of each individual consumer who receives the video. To correct this, we have to put QoE back to the driver's seat and redesign the video delivery systems. To achieve this goal, a major challenge is to find an objective video presentation QoE predictor that is accurate, fast, easy-to-use, display device adaptive, and provides meaningful QoE predictions across resolution and content. We propose to use the newly developed SSIMplus index (https://ece.uwaterloo.ca/~z70wang/research/ssimplus/) for this role. We demonstrate that based on SSIMplus, one can develop a smart adaptive video streaming strategy that leads to much smoother visual QoE impossible to achieve using existing adaptive bitrate video streaming approaches. Furthermore, SSIMplus finds many more applications, in live and file-based quality monitoring, in benchmarking video encoders and transcoders, and in guiding network resource allocations.
Speculative Scheduling for Stochastic HPC Applications

New emerging fields are developing a growing number of large-scale applications with heterogeneous, dynamic and data-intensive requirements that put a high emphasis on productivity and thus are not tuned to run efficiently on today's high performance computing (HPC) systems. Some of these applications, such as neuroscience workloads and those that use adaptive numerical algorithms, develop modeling and simulation workflows with stochastic execution times and unpredictable resource requirements. When they are deployed on current HPC systems using existing resource management solutions, it can result in loss of efficiency for the users and decrease in effective system utilization for the platform providers. In this paper, we consider the current HPC scheduling model and describe the challenge it poses for stochastic applications due to the strict requirement in its job deployment policies. To address the challenge, we present speculative scheduling techniques that adapt the resource requirements of a stochastic application on-the-fly, based on its past execution behavior instead of relying on estimates given by the user. We focus on improving the overall system utilization and application response time without disrupting the current HPC scheduling model or the application development process. Our solution can operate alongside existing HPC batch schedulers without interfering with their usage modes. We show that speculative scheduling can improve the system utilization and average application response time by 25-30% compared to the classical HPC approach.
Understanding the Benefits of Agile Software Development in Regulated Environments

Agile software development has become increasingly popular in recent years. Applying agile methods, companies expect flexible planning, early deliv-ery of the software product, and a continuous improvement of the development process itself. However, in regulated environments the use of agile development is not yet common practice. In such environments, various regulatory require-ments apply which affect the software development process. This paper examines the use of agile software development in the regulated medical device industry and explores reasons for using agile methods although their use is limited. We interviewed agile software development teams in three different companies using semi-structured interviews. Using grounded theory methodology, we identify reasons why companies are using agile methods, even though problems and bar-riers exist. Our main achievement is the development of four categories, which describe the benefit of agile software development in regulated environments. These categories are master complexity, reduce effort, improve usability, and promote collaboration.
Efficient Reinforcement Learning using Relational Aggregation

Much research in Reinforcement Learning (RL) has focused on learning algorithms and generalization using simple representation languages for states and actions. Recently, there is much interest in various kinds of abstraction. Abstractions over time or primitive actions, e.g. in hierarchical RL, are useful ways to abstract over specific sub-actions or time. Currently there is also interest in using more powerful representation languages for abstraction in RL, in which subsets of first-order logic are used for representing sets of states and actions. For an overview of these methods, see (van Otterlo., 2003; van Otterlo, 2002).
MANAGERIAL PLANNING: HOW SCHEDULING INFLUENCES TV ADVERTISING EFFECTIVENESS

The purpose of this research is to analyse the scheduling factors and their influence on TV advertising effectiveness. We deployed a quantitative approach based on a complete data set of advertisements aired on Czech TV in 2016 and 2017 for constructing a regression model. In contrast to several previous studies, our findings indicate that most analysed variables have only a negligible influence (length of spot, length of break, number of spots, position in break, category, day of the week, week, year, and interaction of week and year) on TV advertising exposure, while only the type of programme, daypart and TV channel show a relevant impact on advertising exposure.
An Automatic Industrial Robot Cell Calibration Method

In robot cell, especially electronic assembly cell, usually a lot of parts are feed with a tray, the tray needs to be pushed in and pulled back frequently, or the tool, need to be changed over after wear(such as the electrode in TIG arc welding) or after exhausted (such as a glue gun). For these cases, the work object coordinate and tool coordinate often change slightly, so they need to be recalibrated. This paper describes an automatic calibration method for work object coordinate and tool coordinate aims at robot cell in electronic industry that needs frequently but slightly changing of trays and tools. Devices used in this method include a displacement sensor, a ball and three fixed intersected planes in robot cell. Three intersected planes on a cubic are used to setup a work object coordinate, to orthogonalize these three planes, this paper proposes a cyclic cross method. To calibrate tool coordinate, using the tip of displacement sensor to touch a fixed ball, Levenberg-Marquardt fitting algorithm is used to fit the ball center and tool offset. All the calibration algorithms are implemented with ABB RAPID language. Experiments are carried out on an ABB IRB140 robot and results show that this method has great stability and high accuracy. The work object calibration accuracy can achieve ±0.03 mm, which is close to the repeat accuracy of this type of robot. The tool coordinate calibration accuracy is about ±0.4 mm because it introduced rotation movement of robot, so the error depends on absolute accuracy of robot. The whole method can run automatically and finish in 15 minutes. It has been tested firstly in lab and then onsite. With this technology, a robot program made for one robot cell can be transferred to another "identical" robot cell.