Miracle-dz/UniFAR-FTUs huggingface dataset:
RADCOP: Manipulation and visualization of SAMMY covariances

Resonance parameter representations of nuclear data are used by processor codes to compute quantities needed for criticality safety and other nuclear applications. Usually these calculations also require the associated parameter covariance matrices (PCMs) that contain both the variances of resonance parameters and the correlations between parameters. The SAMMY code is widely used to fit neutron cross section data to obtain both resonance parameter representations and the corresponding PCMs, which are output to binary files. We have written a code, RADCOP, that facilitates the manipulation and visualization of information from SAMMY PCMs. RADCOP produces both one- and two-dimensional correlation plots that permit one to quickly trace the important off-diagonal correlations. ENDF File 2 and File 32 files for the Reich-Moore representation may be output. (authors)
Scientific discovery: compulalional explorations of the creative process

Scientific discovery is often regarded as romantic and creative -- and hence unanalyzable -- whereas the everyday process of verifying discoveries is sober and more suited to analysis. Yet this fascinating exploration of how scientific work proceeds argues that however sudden the moment of discovery may seem, the discovery process can be described and modeled. Using the methods and concepts of contemporary information-processing psychology (or cognitive science) the authors develop a series of artificial-intelligence programs that can simulate the human thought processes used to discover scientific laws. The programs -- BACON, DALTON, GLAUBER, and STAHL -- are all largely data-driven, that is, when presented with series of chemical or physical measurements they search for uniformities and linking elements, generating and checking hypotheses and creating new concepts as they go along. Scientific Discovery examines the nature of scientific research and reviews the arguments for and against a normative theory of discovery; describes the evolution of the BACON programs, which discover quantitative empirical laws and invent new concepts; presents programs that discover laws in qualitative and quantitative data; and ties the results together, suggesting how a combined and extended program might find research problems, invent new instruments, and invent appropriate problem representations. Numerous prominent historical examples of discoveries from physics and chemistry are used as tests for the programs and anchor the discussion concretely in the history of science.
Quantifying grasp quality using an inverse reinforcement learning algorithm

Quantifying Grasp Quality Using an Inverse Reinforcement Learning Algorithm Matthew William Horn, M.S.E. The University of Texas at Austin, 2017 Supervisors: Sheldon Landsberger Co-Supervisor: Mitch Pryor This thesis considers the problem of using a learning algorithm to recognize when a mechanical gripper and sensor combination has achieved a robust grasp. Robotic hands are continuously evolving with finer motor control and higher degrees of freedom which can complicate the ability of an operator to determine if a gripper has achieved a successful grasp. Robots working in hazardous environments especially need confirmation of a successful grasp as the cost of failure is often higher than in traditional factory environments. The object set found in a nuclear environment is the focus of this effort. Objects in this environment are typically expensive (or one-of-a-kind), rigid, radioactive (or toxic), dense, and susceptible to dents, scratches, and oxidation. To validate the robustness of a grasp option, an online inverse reinforcement learning approach is evaluated as a method to quantify grasp quality. This approach is
How to Publish Linked Data on the Web - Proposal for a Half-day Tutorial at ISWC2008

The Web is increasingly understood as a global information space consisting not just of linked documents, but also of Linked Data. The Linked Data principles provide a basis for realizing this Web of Data, or Semantic Web. Since early 2007 numerous data sets have been published on the Web according to these principles, in domains as broad as music, books, geographical information, films, people, events, reviews and photos. In combination these data sets consist of over 2 billion RDF triples, interlinked by more than 3 million triples that cross data sets. As this Web of Linked Data continues to grow, and an increasing number of applications are developed that exploit these data sets, there is a growing need for data publishers, researchers, developers and Web practitioners to understand Linked Data principles and practice. Run by some of the leading members of the Linked Data community, this tutorial will address those needs, and provide participants with a solid foundation from which to begin publishing Linked Data on the Web, as well as to implement applications that consume Linked Data from the Web.
Study of Image steganography using LSB, DFT and DWT

In this paper we have studied and implement the color image steganography using LSB, DFT and DWT. First we have studied the various literatures of LSB, DFT and DWT and then implemented one system in which we can do steganography using these techniques, after implementation we have evaluated the results using these techniques applying various image formats against different message size. For each of the cases we have calculated the MSE and PSNR and presented here in a tabulated form. Our main intention is to show how MSE and PSNR changes with respect to different image formats and different message sizes using these techniques. Besides the analysis of MSE and PSNR we have also analyzed the message insertion and message extraction time for all the cases.
A Review of Network Coding and Multimedia Traffic over Wireless Networks within 2013

Multimedia transmission over wireless networks has grown in recent years, so there is more attention from research community. Providing a high quality multimedia transmission over wireless is a challenge, as it is surrounded with strict timing constraint and high bandwidth demand. In addition, wireless communication is associated with challenges like limited bandwidth, interference and mobility, which make it more difficult and challenging. Network Coding is technique, which allows and encourages mixing of data at intermediate network nodes. A receiver sees these data packets and deduces from them the messages that were originally intended for the data sink. Many studies have been performed on video streaming over wireless networking. Most of these studies were intended to increase the performance and efficiency of wireless networks in order to satisfy the highly increased data traffic requirement. We noticed that during the past two years, many scholars tried to use network coding in order to provide a better environment for video traffic over wireless networks. So, this paper gives a literature review of the most related work with providing better performance of multimedia streaming over wireless networks. This paper is limited only for 2013’s researches.
Using Machine Learning to Detect Malicious URLs

on the opinion of the heads of departments, without taking into account their relationship. It is noted that due to the lack of automation and correctly conducted analysis, program projects are formed with errors and inaccuracies. To improve the situation, it is proposed to use an integrated information system based on data array analytics and the use of AI. The system consists of three subsystems: "LifeControl" for automatic analysis of the needs of the population, the subsystem of the balanced scorecard, which sets the task of determining key indicators that allow assessing the development of the municipality, and the subsystem for supporting the development and analysis of program projects.
Recent Survey on Medical Image Segmentation

This chapter presents a survey on the techniques of medical image segmentation. Image segmentation methods are given in three groups based on image features used by the method. The advantages and disadvantages of the existing methods are evaluated, and the motivations to develop new techniques with respect to the addressed problems are given. Digital images and digital videos are pictures and films, respectively, which have been converted into a computer-readable binary format consisting of logical zeros and ones. An image is a still picture that does not change in time, whereas a video evolves in time and generally contains moving and/or changing objects. An important feature of digital images is that they are multidimensional signals, i.e., they are functions of more than a single variable. In the classical study of the digital signal processing the signals are usually one-dimensional functions of time. Images however, are functions of two, and perhaps three space dimensions in case of colored images, whereas a digital video as a function includes a third (or fourth) time dimension as well. A consequence of this is that digital image processing, meaning that significant computational and storage resources are required.
Design of Robotic Human Assistance Systems Using a Mobile Manipulator

Robotic systems have been widely used in many areas to assist human beings. Mobile manipulators are among the most popular choices. This paper investigates human assistance systems using a mobile manipulator, for example, to guide the blind and to transport objects. Distinct from existing systems, an integrated dynamic model and controller of the mobile manipulator are designed. Singularity, manipulability and safety are all considered in the system design. Furthermore, two human assistance modes – Robot-Human mode and Teleoperator-Robot-Human mode – are designed and analysed. The Teleoperator-Robot-Human mode can integrate human intelligence into the assistance system to further enhance the system efficiency and safety. The experimental results implemented on a mobile manipulator demonstrated the effectiveness of the designed systems.
Fuzzy Clustering and Visualization of Information for Web Search Results

Searching for information on the web is a common task. Often information on the web is distributed, semi-structured, overlapping and heterogeneous. Organization and Visualization of this information is an active area of research where the goal is to help users locate required information in web pages efficiently.The most widely used data organization technique is clustering. This paper introduces a new clustering algorithm to organize web pages, and a visualization method which facilitates users to search information efficiently from the web. The algorithm presented is a hierarchical fuzzy clustering algorithm which uses domain knowledge to determine input parameters as opposed to other existing algorithms in the literature. The comparative results show that the algorithm performs as well as existing algorithms. Next, we present a methodology to visualize the clustered collection of documents and their contents such that users can visually explore data and extract information. A detailed example is presented to demonstrate various views to visualize clusters, documents and the keywords present in the web pages.
Designing network-on-chips for throughput accelerators

Physical limits of power usage for integrated circuits have steered the microprocessor industry towards parallel architectures in the past decade. Modern Graphics Processing Units (GPU) are a form of parallel processor that harness chip area more effectively compared to traditional single threaded architectures by favouring application throughput over latency. Modern GPUs can be used as throughput accelerators: accelerating massively parallel non-graphics applications. As the number of compute cores in throughput accelerators increases, so does the importance of efficient memory subsystem design. In this dissertation, we present system-level microarchitectural analysis and optimizations with an emphasis on the memory subsystem of throughput accelerators that employ Bulk-Synchronous-Parallel programming models such as CUDA and OpenCL. We model the whole throughput accelerator as a closed-loop system in order to capture the effects of complex interactions of microarchitectural components: we simulate components such as compute cores, on-chip network and memory controllers with cycle-level accuracy. For this purpose, the first version of GPGPU-Sim simulator that was capable of running unmodified applications by emulating NVIDIA’s virtual instruction set was developed. We use this simulator to model and analyze several applications and explore various microarchitectural tradeoffs for throughput accelerators to better suit these applications. Based on our observations, we identify the Network-on-Chip (NoC) component of memory subsystem as our main optimization target and set out to design throughput effective NoCs for future throughput accelerators. We provide a new framework for NoC researchers to ensure the optimizations are “throughput effective”,
Developing flexible-adaptive reasoning and computing: Pedro’s understanding of the task “Prawn skewers”

The project ‘Numerical thinking and flexible calculation: critical issues’ aims to study students’ conceptual knowledge associated with the understanding of the different levels of learning numbers and operations. We follow the idea proposed by several authors that flexibility refers to the ability to manipulate numbers as mathematical objects which can be decomposed and recomposed in multiple ways using different symbolisms for the same objet (Gravemeijer, 2004; Gray T). The project plan is based on a qualitative and interpretative methodology (Denzin & Lincoln, 2005) with a design research approach (Gravemeijer & Cobb, 2006). This article focus the preparation of a teaching experience centered in the flexible learning of multiplication. It describes the analysis of a clinical interview where Pedro (9 years) solves the task 'Prawn skewers'. It illustrates how we identify and describe Pedro’s conceptual knowledge associated with the different levels of understanding of numbers and multiplication/division and analyzes if and how this knowledge facilitates adaptive thinking and flexible calculation.
COMPETITION IN A SOCIAL STRUCTURE

Complex adaptive agents develop strategies in the presence of competition. In modern human societies, there is an inherent sense of locality when describing inter-agent dynamics because of its network structure. One then wonders whether the traditional advertising schemes that are globally publicized and target random individuals are as effective in attracting a larger portion of the population as those that take advantage of local neighborhoods, such as "word-of-mouth" marketing schemes. Here, we demonstrate using a differential equation model that schemes targeting local cliques within the network are more successful at gaining a larger share of the population than those that target users randomly at a global scale (e.g., television commercials, print ads, etc.). This suggests that success in the competition is dependent not only on the number of individuals in the population but also on how they are connected in the network. We further show that the model is general in nature by considering examples of competition dynamics, particularly those of business competition and language death.
A TrustEnclave-Based Architecture for Ensuring Run-Time Security in Embedded Terminals

The run-time security guarantee is a hotspot in current cyberspace security research, especially on embedded terminals, such as smart hardware as well as wearable and mobile devices. Typically, these devices use universal hardware and software to connect with public networks via the Internet, and are probably open to security threats from Trojan viruses and other malware. As a result, the security of sensitive personal data is threatened and economic interests in the industry are compromised. To address the run-time security problems efficiently, first, a TrustEnclave-based secure architecture is proposed, and the trusted execution environment is constructed by hardware isolation technology. Then the prototype system is implemented on real TrustZone-enabled hardware devices. Finally, both analytical and experimental evaluations are provided. The experimental results demonstrate the effectiveness and feasibility of the proposed security scheme.
Towards Formalizing Assumptions on Architectural Level: A Proof-of-Concept

While designing an architecture, architects often make assumptions about different factors like execution environment, structural properties of the artifacts, properties of input/output data etc. Implicit and invalid assumptions have been identified as a primary reason for architectural mismatches. Such mismatches cause nightmares to the people working at the system integration phase. Today’s complex systems operate in dynamic and rapidly changing environments. Implicit assumptions in the reusable components often make it challenging to adopt the components in a changed operational domain. If not documented, assumptions are often forgotten and it is both difficult and expensive to find them out from previously built software. This paper aims to formally capture assumptions at the architecture level so that they can be checked automatically throughout the system development. Automated checking of assumptions would facilitate a practicable assumption management system for complex and large-scale software system development with smooth integration and evolution.
A swingogram representation for tracking micro-rhythmic variation in jazz performances

Abstract A typical micro-rhythmic trait of jazz performances is their ‘swing feel. ’ According to several studies, uneven eighth notes contribute decisively to this perceived quality. In this paper we analyze the swing ratio (beat-upbeat ratio) implied by the drummer on the ride cymbal. Extending previous work, we propose a new method for semi-automatic swing ratio estimation based on pattern recognition in onset sequences. As a main contribution, we introduce a novel time-swing ratio representation called swingogram, which locally captures information related to the swing ratio over time. Based on this representation, we propose to track the most plausible trajectory of the swing ratio of the ride cymbal pattern over time via dynamic programming. We show how this kind of visualization leads to interesting insights into the peculiarities of jazz musicians improvising together.
Embedding and latent variable models using maximal correlation

Finding low dimensional latent variable models is a useful technique in inferring unobserved affinity between unobserved co-occurrences. We explore using maximal correlation and the alternating conditional expectation algorithm to construct embeddings one dimensional at a time to maximally preserve the linear correlation in the embedding space. Each dimension is enforced to be orthogonal to all other dimensions to not encode redundant information. Intuitively, we want to map objects that frequently co-occur to be close in the embedding space. However, often there are unobserved or undersampled pairs that skew the result. We derive simple regularization techniques to compensate for those outliers. Additionally, optimizing for the preservation of maximal correlations after processing lets us induce informative soft clustering and mixture models. Empirical results on natural language processing datasets show that our technique performs comparably to popular word embedding algorithms. Thesis Supervisor: Lizhong Zheng Title: Professor of Electrical Engineering and Computer Science
Toward empirical IP host traffic measurement in passive network measurement

• Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. • Authors control the copyright of their thesis. You will recognise the author's right to be identified as the author of this thesis, and due acknowledgement will be made to the author where appropriate. • You will obtain the author's permission before publishing any material from their thesis.
The Problems of Large-Scale Refactoring: Learning from Eclipse RCP

Investing in planning big refactoring changes prior to implementing them has been promoted as a positive practice that guarantees a high quality code change process. However, there is no empirical evidence of the potential risks that may degrade the quality of such a change process, even if changes are planned in advance. This paper identifies and categorizes potential risks based on a real-world case of large-scale refactoring: that which produced the Eclipse Rich Client Platform (RCP). This case is interesting because it is industrially relevant and three publicly available data sources exist for it. We analyzed these data sources in retrospect, and found that when expert engineers were mapping out changes, (1) they were uncertain about what the code does, (2) they were unclear about what it affects if the code is changed, and (3) they misunderstood each other when changes were described. If such lack of knowledge, and miscommunication among the expert engineers were not resolved via peer discussions prior to applying the changes, we anticipate that such changes would result in a later wrong decision or action. We also found that (4) many small changes are enacted in the code for which the sequencing matters and that were poorly-communicated while planning. Thus, these changes cannot be reviewed by other engineers until after they are fully implemented. We hypothesize that such lack of knowledge and miscommunication would adversely affect the quality of a largescale refactoring task, especially when the complexity of the task increases and the level of expertise decreases.
Deviating Angular Feature for Image Recognition System Using the Improved Neural Network Classifier.

The ability to reconize images makes it possible to abstractly conceptualize the world. Many in the field of machine learning have attem pted to invent an image recogni­ tion system with the recognition capabilities of a human. This dissertation presents a method of modifications to existent image recognition systems, which greatly im­ proves the efficiency of existing data imaging methods. This modification, the Deviating Angular Feature(DAF), has two obvious applica­ tions: 1) the recognition of handwritten numerals; and 2) the automatic identification of aircraft. Modifications of feature extraction and classification processes of current image recognition systems can leads to the systemic enhancement of data imaging. This research proposes a customized blend of image curvature extraction algorithms and the neural network classifiers trained by the Epoch Gradual Increase in Accuracy(EGIA) training algorithm. Using the DAF, the recognition of handwritten numerals and the automatic identification of aircraft have been improved. According to the preliminary results, the recognition system achieved an accuracy rate of 98.7% when applied to handwritten numeral recognition. When applied to automatic aircraft identification, the system achieved a 100% rate of recognition. The novel design of the prototype is quite flexible; thus, the system is easy to maintain, modify, and distribute. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter