Miracle-dz/UniFAR-FTUs huggingface dataset:
A Large-Scale Time-Domain Seismic Modeling and Inversion Workflow in Julia

We present our initial steps towards the development of a large-scale seismic modeling workflow in Julia that provides a framework for wave equation based inversion methods like full waveform inversion or least squares migration. Our framework is based on the Devito, a finite difference domain specific language compiler that generates highly optimized and parallel code. We develop a flexible workflow that is based on abstract matrixfree linear operators and enables developers to write code that closely resembles the underlying math, while at the same time leveraging highly optimized wave equation solvers, allowing us to solve large-scale three-dimensional inverse problems.
Self-organizing mobile medium ad hoc network in NS-2

MANETs (Mobile Ad Hoc Networks) are mobile networks of wireless mobile devices capable of communicating with one another without any reliance on a fixed infrastructure. A M2ANET (Mobile Medium Ad hoc Network) is a set of mobile nodes forming a Mobile Medium and functioning as relays for facilitating communication between the users of this Mobile Medium. Movement of the nodes affects the performance of a M2ANET. We propose a scheme for controlling the movement of mobile nodes in a M2ANET based on an attraction/repulsion paradigm. The new node movement has an advantage over a random movement in keeping the nodes in an unbounded region in a sufficient density to allow for an efficient transfer of data over the Mobile Medium. Simulation results show tripling of the delivery ratio in a self-organizing M2ANET compared to a mobile network with all nodes moving randomly, in one experimental scenario.
EMPLOYING THE CLASSROOM FLIP TO MOVE "LECTURE" OUT OF THE CLASSROOM

A common challenge that professors face is to balance the need to lecture on important content with the desire to increase student engagement through the use of more active learning methods. This paper describes the use of the “classroom flip” technique to develop and implement a five-week module in a 100-student, architectural engineering course. The instructors “flipped” traditional content delivery out of the classroom by posting video lectures, and brought project based learning into the classroom where they could provide face-to-face assistance to the class. This paper describes how the classroom flip was implemented, including technology-related course changes that facilitated the implementation. Assessment of the classroom flip consisted of student surveys, comparisons of exam and project grades to a prior semester, and instructor insights. Student perceptions generally supported the use of the method for the purposes of providing instructor interaction while performing project activities and included valuable feedback about how the classroom flip should be used in the course. The statistical comparison of grade data between this course offering and the previous offering by the same instructors showed no significant change for either exam or project outcomes. The instructors considered the classroom flip to be a success based upon the increased student engagement.
Pen-based Methods For Recognition and Animation of Handwritten Physics Solutions

There has been considerable interest in constructing pen-based intelligent tutoring systems due to the natural interaction metaphor and low cognitive load afforded by pen-based interaction. We believe that pen-based intelligent tutoring systems can be further enhanced by integrating animation techniques. In this work, we explore methods for recognizing and animating sketched physics diagrams. Our methodologies enable an Intelligent Tutoring System (ITS) to understand the scenario and requirements posed by a given problem statement and to couple this knowledge with a computational model of the student’s handwritten solution. These pieces of information are used to construct meaningful animations and feedback mechanisms that can highlight errors in student solutions. We have constructed a prototype ITS that can recognize mathematics and diagrams in a handwritten solution and infer implicit relationships among diagram elements, mathematics and annotations such as arrows and dotted lines. We use natural language processing to identify the domain of a given problem, and use this information to select one or more of four domain-specific physics simulators to animate the user’s sketched diagram. We enable students to use their answers to guide animation behavior and also describe a novel algorithm for checking recognized student solutions. We provide examples of scenarios that can be modeled using our prototype system and discuss the strengths and weaknesses of our current prototype.
Managing Multiple Fuzzy Information from Statistical Viewpoint and its Engineering Applications

In the area of fuzzy theory, one of the important themes is how to extend Dempster-Shafer (D-S) theory to include the process of fuzzy events. In this paper, instead of extending D-S rules, we propose a direction of arguing that elements in a fuzzy domain are all singletons based on the statistical viewpoint and are independent of each other. Therefore, the D-S combination rule can consequently be applied as regularly as in non-fuzzy cases. We first examine the characteristics of how membership functions are defined, which leads to the conclusion that every element in a fuzzy domain is independent, mutually exclusive of each other and consequently is a singleton. We then discuss the relationships between the grades of a membership function and its corresponding statistical probability, and between the probability of a fuzzy event and the probability of every element in its associated data domain. The equations are then derived to calculate the probability of every element in a fuzzy event data domain when both the probability of the event and its membership functions are known. An example is finally given to illustrate how our proposed approach is applied to engineering values.
Enhancing Robustness to Extrapolate Synergies Learned from Motion Capture

Reproducing the characteristics of human movements, is a crucial issue in studying motion. In the context of this work, an explicit model of synergies which can be parametrized is used for reproducing the main features of reaching motions. This paper evaluates the possibility to extrapolate learned parameters from a captured motion to new targets and shows how learning process is a key issue to ensure the robustness of parameters. another target, some parameters displayed poor capacity to extend features of the sample motion. In this article, diﬀerences between robust and non robust set of parameters are discussed. The learning process is enhanced to ensure robustness of parameters.
COMPARISON OF SINGLE-PORT AND MULTI-PORT NoCs WITH CONTEMPORARY BUSES ON FPGAs

Systems-on-chips brought several cores onto a single chip i n order to increase the utilization of the available on-chip resources. It helped to reduce the ever increasing designproductivity gap. But, as more and more cores are being put on to a single chip, the on-chip communication resource, which is usually a bus started gett in overburdened. With its inherent problems like scaling, inability to support paral lelism etc. the on-chip communication soon became a performance bottleneck. Networks-on-chip (NoC) has been proposed as a solution for o n-chip communication. It has been an area of intensive research for the past de cade since the inception of the idea. Field Programmable Gate Arrays (FPGAs) with their increasi ng number of transistors on a single chip are also beginning to support multiple c or s on a single FPGA chip. But again the widely supported mode of communication on a FPG A chip remains to be the traditional bus, which with its certain shortcomings ma y turn out to be a liability in the future. NoCs can be an attractive alternative for these b u s given there ability to support scalability. But with that arise some basic questions a s to how would these NoCs fair against the contemporary buses available on FPGAs as cores, in t rms of performance on FPGAs. What are the associated overheads? Thus we aim to compare the relative performance of contempor ary buses available as cores on FPGAs against indigenously built cores of NoCs. R outers being at the heart of NoCs, we first built a router keeping in mind the area limita tions of FPGAs. Also to make it comparable against the buses we try to decrease the latency of the router by incorporating some advanced features of the buses in them. W e also study the improvement in performance brought in by the use of Multi-Port routers in NoC. Once we had both the cores of the bus and the NoC we extract the timing information by simulations of the cores on FPGAs. Finally, for comparison of buses and NoCs we d ev lop a cycle accurate simulator using the timing values obtained by simulation of the cores. The cycle accurate simulator was developed in C++ for both the bus and the NoC to c ompare between the two on a set of benchmarks. The results show the better performance of NoCs compared to c ontemporary buses in most of the cases but with an area penalty which can be of som e concern on FPGAs. They also give valuable insights into the relative performa nce of NoCs with Single-Port and Multi-Port routers. To my parents who have been a constant source of motivation to me. & To my teachers who have imparted the necessary knowledge and skills to me.
Data Movement Challenges and Solutions with Software Defined Networking

With the recent rise in cloud computing, applications are routinely accessing and interacting with data on remote resources. Interaction with such remote resources for the operation of media-rich applications in mobile environments is also on the rise. As a result, the performance of the underlying network infrastructure can have a significant impact on the quality of service experienced by the user. Despite receiving significant attention from both academia and industry, computer networks still face a number of challenges. Users oftentimes report and complain about poor experiences with their devices and applications, which can oftentimes be attributed to network performance when downloading or uploading application data. This dissertation investigates problems that arise with data movement across computer networks and proposes novel solutions to address these issues through software defined networking (SDN). SDN is lauded to be the paradigm of choice for next generation networks. While academia explores use cases in various contexts, industry has focused on data center and wide area networks. There is a significant range of complex and application-specific network services that can potentially benefit from SDN, but introduction and adoption of such solutions remains slow in production networks. One impeding factor is the lack of a simple yet expressive enough framework applicable to all SDN services across production network domains. Without a uniform framework, SDN developers create disjoint solutions, resulting in untenable management and maintenance overhead. The SDNbased solutions developed in this dissertation make use of a common agent-based approach. The architecture facilitates application-oriented SDN design with an abstraction composed of software agents on top of the underlying network. There are three key components modern and future networks require to deliver exceptional data transfer performance to the end user: (1) user and application mobility, (2) high throughput data transfer, and (3) efficient and scalable content distribution. Meeting these key components will
Optimal Design of an Interleaved DC-DC Switch Mode Converter

Boost converters help in meeting high DC voltage demands of many applications utilising low voltage renewable energy sources. The two phase interleaved power factor corrector, controlled by the average current mode control method is investigated. As is known, interleaving operation provides several advantages over the single Boundary Conduction Mode (BCM) Power Factor Correction (PFC). Modern AC/DC switchmode converters incorporate at least two cascaded stages. The first stage is an AC/DC conversion stage, interfacing the utility grid voltage with a compatible DC voltage level. The second stage is a DC/DC converter module to condition the DC output of first stage such that the performance requirements of the loads are satisfied. PFC techniques are widely utilized in the interleaved dual BCM PFC controllers suggested here, operate two parallel-connected boost power trains with a phase difference of 180(exp 0). Interleaving extends the maximum practical power level of the control technique from about 300W to greater than 800W. Unlike the continuous conduction mode (CCM) technique often used at higher power levels, BCM offers inherent zero current switching of the boost diodes. This permits the use of less expensive diodes without sacrificing efficiency. Authors of this contribution report a simulation experiment of proposed topology of a two stage interleaved converter with reduced inductor volume. It has been shown that the efficiency, size, and cost can be minimized. Possible extension to multistage topology is indicated power electronics.
Predicting protein function via multi-label supervised topic model on gene ontology

ABSTRACT As the biological datasets accumulate rapidly, computational methods designed to automate protein function prediction are critically needed. The problem of protein function prediction can be considered as a multi-label classification problem resulting in protein functional annotations. Nevertheless, biologists prefer to discover the correlations between protein attributes and functions. We introduce a multi-label supervised topic model into protein function prediction and investigate the advantages of this approach. This topic model can not only work out the function probability distributions over protein instances effectively, but also directly provide the words probability distributions over functions. To the best of our knowledge, this is the first effort to apply a multi-label supervised topic model to the protein function prediction. In this paper, we model a protein as a document and a function label as a topic. First, a set of protein sequences is formalized into a bag of words. Then, we perform inference and estimate the model parameters to predict protein functions. Experimental results on yeast and human datasets demonstrate the effectiveness of this multi-label supervised topic model on protein function prediction. Meanwhile, the experiments also show that this multi-label supervised topic model delivers superior results over the compared algorithms. In summary, the method discussed in this paper provides a new efficient approach to protein function prediction and reveals more information about functions.
When the System Becomes Your Personal Docent: Curated Book Recommendations

Curation is the act of selecting, organizing, and presenting content most often guided by professional or expert knowledge. While many popular applications have attempted to emulate this process by turning users into curators, we put an accent on a recommendation system which can leverage multiple data sources to accomplish the curation task. We introduce QBook, a recommender that acts as a personal docent by identifying and suggesting books tailored to the various preferences of each individual user. The goal of the designed system is to address several limitations often associated with recommenders in order to provide diverse and personalized book recommendations that can foster trust, effectiveness of the system, and improve the decision making process. QBook considers multiple perspectives, from analyzing user reviews, user historical data, and items’ metadata, to considering experts’ reviews and constantly evolving users’ preferences, to enhance the recommendation process, as well as quality and usability of the suggestions. QBook pairs each generated suggestion with an explanation that (i) showcases why a particular book was recommended and (ii) helps users decide which items, among the ones recommended, will best suit their individual interests. Empirical studies conducted using the Amazon/LibraryThing benchmark corpus demonstrate the correctness of the proposed methodology and QBook’s ability to outperform baseline and state-of-the-art methodologies for book recommendations.
ESTIMATING DEMAND FOR MOBILE APPLICATIONS IN THE NEW MOBILE ECONOMY

A fundamental change brought forth by the advent of the mobile internet has been the widespread adoption of mobile phone based applications (apps). Mobile apps are now being used worldwide to perform a variety of tasks - access social networks, read ebooks, play games, listen to music, watch videos and so on. As consumers increasingly use mobile apps, it is important to understand the drivers of demand for these apps. We build a builds a structural model of user demand for mobile apps and jointly estimate it with supply-side equations. We estimate the change in consumer surplus from the usage of mobile apps. We use a panel dataset consisting of apps’ sales, prices, and characteristics data from the two leading app stores – Apple App Store and Google Play for 4 months in the South Korean market. Our results show that demand increases with the file size, app age (time since release), app description length, and number of screenshots. In terms of age-restriction, compared to “everyone” (or 4+) apps, “low maturity” (or 9+) and “medium maturity” (or 12+) apps lower demand. Compared to lifestyle apps, gaming apps have a positive impact on app demand while multimedia and education apps have a negative impact on demand. Notably, we find consumer preferences show strong correlation across apps from the same group (i.e., either within free apps or paid apps). We incorporate consumer heterogeneity in the model and find that older and male consumers tend to be less sensitive to the price of apps than younger and female consumers, respectively. In the supply-side we find app file size and app age are major cost component in app development. Also, our findings suggest there exist significant returns to scale in app development. Our counterfactual experiments show price discount strategy results in a greater increase of app demand in Apple App Store compared to Google Play and the effects of price discounts increase non-linearly. Our findings also indicate strong substitution effects between game apps and other apps such as utility and education apps. Using the estimated demand function, we find the top ranked apps in both app stores enhanced consumer surplus by approximately US $158 million over the 4 month period in South Korea
GravPad

GravPad is the next major step in the evolution of the EtherPad, a real-time collaborative editor that was first developed at AppJet, Inc. (http://appjet.com/) and recently open-sourced when AppJet was acquired by Google. EtherPad's developer community, headed up by Egil Möller and John McLear, has made a plugin engine and added standard wiki features to the editor (http://github.com/ether/pad). My GravPad demonstration will show a hacked EtherPad running in a "sidewiki" format -- the basis of a platform for live web annotation and content discovery.
An Introduction to PEBL: The Psychology Experiment Building Language

PEBL (Psychology Experiment Building Language) is a new programming language developed especially for building psychology experiments involving human participants interacting with a computer. PEBL source code and compiled binary executables are available via the internet at http://pebl.sf. net. Technical Details PEBL is an interpreter/compiler written in C++, and uses a parser designed with the flex and yacc compiler design tools (Levine et al., 1992). PEBL makes heavy use of data structures from the C++ Standard Template Library, and uses a countedreference pointer system to enable automatic memory management. It is designed with several principles in mind: (1) The source code for important scientific tools should be freely available and modifiable; PEBL is Free software, allowing experimenters complete control over the system. (2) PEBL is designed to be cross-platform, allowing a single program to run on whatever computer system the interpreter is compiled for, and to allow today's experiments to be used on tomorrow's computer systems. (3) PEBL is designed to be used by psychologists, who are rarely skilled programmers. Consequently, aspects of the language are designed to enable common experiment idioms to be used easily, while having a forgiving syntax that encourages the creation of maintainable and readable programs. PEBL is Free Software PEBL is designed with the hope to providing a lingua franca within which psychology experiments can be created and exchanged freely, to be used without the need to license or purchase other software. The source code for PEBL is licensed under the GNU General Public License (Stallman & Moglen, 1989), which allows others to use and modify the source code as long as any changes are made available to others. The license covers distribution of the executable binaries and source code of the PEBL system, and does not include usercreated experiments that can be run with PEBL. Psychologists who design experiments using PEBL are encouraged to redistribute their PEBL programs freely, to foster easier exchange of scientific information. PEBL is Cross-Platform. PEBL was designed to allow it to be easily ported to different computing platforms. Together with the fact that Current versions are written using the Simple DirectMedia Layer Libray (SDL; Lantiga, 2004) an open source cross-platform game library. The use of this library enables multiple platforms to be targeted easily, but PEBL Paper read at the 34th annual meeting of the Society for Computers in Psychology (SCiP), November 18, 2004, Minneapolis, MN.
Handling a Small Dataset Problem in Prediction Model by employ Artificial Data Generation Approach: A Review

The emerging era of big data for past few years has led to large and complex data which needed faster and better decision making. However, the small dataset problems still arise in a certain area which causes analysis and decision are hard to make. In order to build a prediction model, a large sample is required as a training sample of the model. Small dataset is insufficient to produce an accurate prediction model. This paper will review an artificial data generation approach as one of the solution to solve the small dataset problem.
A new fast method for 3D indexing and retrieval based on mapping techniques

In this paper we present a new fast method for 3D objects indexing and retrieval based on two-dimensional (2-D) views. The set of views are automatically generated around OX, OY and OZ by rotating each 3D object of database through 360° with taking views at pose intervals of 45°, given 24 images per object. The obtained views are afterward described by two descriptors: Angular Radial transform adopted by MPEG-7 and Fourier descriptor. The similarity of 3D objects is calculated as a linear combination of euclidian metrics. In order to lower the cost of search, we propose to apply a mapping from metric space to vector space of 3D descriptors by using a contractive function. We have implemented an application for indexing and retrieval of 3D objects by using Princeton 3D Shape Benchmark database. The performance of our retrieval system was measured in terms of recall and precision and Computation efficiency. The obtained results prove the effectiveness of proposed method and its superiority over automatic selection based on Curvature Scale Space.