Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive motion planning for humanoid robots

Motion planning for robots with many degrees of freedom (DoF) is a generally unsolved problem in the robotics context. In this work an approach for trajectory planning is presented, which takes account of the different kinematic parts of a humanoid robot. Since not all joints of the robot are important for different planning phases, the RRT-based planner is able to adapt the number of DoF on the fly to improve the performance and the quality of the results. The runtime of the approach is evaluated in comparison to a standard RRT planner. Futhermore several extensions to the algorithm are investigated.
An approach to modeling labor and machine down time in semiconductor fabrication

The authors present an approach to modeling labor resources, random equipment failures, and preventative maintenance in a wafer fabrication facility, and address their inherent complexities. This approach was implemented in a simulation model of an ASIC (application-specific integrated circuit) wafer fabrication facility, developed using the SIMAN simulation language. The important features of the model are its ability to accurately model labor separately from pure equipment processing time, and to model both scheduled and unscheduled maintenance activities. The labor model considers operator skills, man-machine ratios, and proximity of resources being monitored. This allows the simulation analyst to consider the effects of changes in production starts on labor requirements. The equipment downtime model accurately reflects the impact of random machine failures, when the data are available. This allows the analyst to evaluate the impact on cycle times and throughput of improving the uptime performance of a machine. <<ETX>>
An optimal scheduling problem in distribution networks considering V2G

This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.
Benchmarking the performance of ultrathin body InAs-on-insulator transistors as a function of body thickness

The effect of body thickness (5-13 nm) on the leakage currents of top-gated, InAs-on-insulator field-effect-transistors with a channel length of ∼200 nm is explored. From a combination of experiments and simulation, it is found that the OFF-state currents are primarily dominated by Shockley Read Hall recombination/generation and trap-assisted tunneling. The OFF currents are shown to decrease with thickness reduction, highlighting the importance of the ultrathin body device configuration. The devices exhibit promising performances, with a peak extrinsic and intrinsic transconductances of ∼1.7 and 2.3 mS/μm, respectively, at a low source/drain voltage of 0.5 V and a body thickness of ∼13 nm.
Inclusive quasielastic charged-current neutrino-nucleus reactions

The quasielastic (QE) contribution of the nuclear inclusive electron scattering model developed by Gil, Nieves, and Oset [Nucl. Phys. A627, 543 (1997); A627, 599 (1997)] is extended to the study of electroweak charged current (CC) induced nuclear reactions, at intermediate energies of interest for future neutrino oscillation experiments. The model accounts for, among other nuclear effects, long range nuclear [random phase approximation (RPA)] correlations, final state interaction (FSI), and Coulomb corrections. Predictions for the inclusive muon capture in $^{12}\mathrm{C}$ and the reaction $^{12}\mathrm{C}$ $({\ensuremath{\nu}}_{\ensuremath{\mu}},{\ensuremath{\mu}}^{\ensuremath{-}})X$ near threshold are also given. RPA correlations are shown to play a crucial role and their inclusion leads to one of the best existing simultaneous description of both processes, with accuracies of the order of 10\char21{}15 % for the muon capture rate and even better for the Liquid Scintillating Neutrino Detector (LSND) measurement.
On the implementation of AMBIT, a language for symbol manipulation

A brief description is given of the implementation technique for the replacement rule of the AMBIT programming language. The algorithm for the “AMBIT scan” and an example of its application are given. The algorithm is applicable to other members of the family of string transformation languages of which AMBIT is a member, and it provides a rationale for the design of the AMBIT language.
1.51-GHz nanocrystalline diamond micromechanical disk resonator with material-mismatched isolating support

The first CVD nanocrystalline diamond micromechanical disk resonator with material-mismatched stem has been demonstrated at a record frequency of 1.51 GHz with an impressive Q of 11,555, which is more than 7X higher than demonstrated in a previous 1.14-GHz polysilicon disk resonator, and which achieves a frequency-Q product of 1.74/spl times/10/sup 13/ that now exceeds the 1/spl times/10/sup 13/ of some of the best quartz crystals. In addition, a 1.27-GHz version with a Q in the excess of 12,000 exhibits a measured motional resistance of only 100 k/spl Omega/ with a dc-bias voltage of 20 V, which is more than 34X lower than measured on a pure polysilicon counterpart at 1.14 GHz. At 498 MHz, Q's up to 55,300 in vacuum and 35,550 in air have been demonstrated, both of which set frequency-Q product records at 2.75/spl times/10/sup 13/ (vacuum) and 1.77/spl times/10/sup 13/ (air).
How Novices Sketch and Prototype Hand-Fabricated Objects

We are interested in how to create digital tools to support informal sketching and prototyping of physical objects by novices. Achieving this goal first requires a deeper understanding of how non-professional designers generate, explore, and communicate design ideas with traditional tools, i.e., sketches on paper and hands-on prototyping materials. We describe a study framed around two all-day design charrettes where participants perform a complete design process: ideation sketching, concept development and presentation, fabrication planning documentation and collaborative fabrication of hand-crafted prototypes. This structure allows us to control key aspects of the design process while collecting rich data about creative tasks, including sketches on paper, physical models, and videos of collaboration discussions. Participants used a variety of drawing techniques to convey 3D concepts. They also extensively manipulated physical materials, such as paper, foam, and cardboard, both to support concept exploration and communication with design partners. Based on these observations, we propose design guidelines for CAD tools targeted at novice crafters.
Application of Microsoft Kinect Sensor for Tracking Construction Workers

The image processing based human recognition is yet a challenging task because of series of complications such as variations in pose, lighting conditions and complexity of background in the tracking environment. This study introduces a novel methodology to track construction workers using image processing techniques and depth information generated from the Microsoft Kinect sensor. Kinect is a new game controller technology introduced by Microsoft in November 2010. This automated real-time worker tracking system provides an opportunity to track the construction worker location and their movements in a specified indoor work area. The research study proposes a properly color coded “construction hardhat” as a key tracking object which can be used to differentiate site personnel (worker, supervisor, engineer, etc.). The proposed method detects construction workers in three major stages which includes human recognition, hardhat recognition and 3D localization. The human recognition is done by analysing human body parts. 3D positions of body joints are accurately predicted from a single depth image. The construction hardhat detection is based on characteristics of the hardhat such as unique shape and color. Template based template matching is used as the pattern recognition technique.
An Approach to Testing Black-Box Components Using Contract-Based Mutation

Component Based Software Development (CBSD) is gaining popularity in recent years. In this way of software development, software components, which are typically black-box components, are intensively reused to construct new systems. To ensure the quality of software systems composed of black-box components, a primary concern is how to ensure the quality of black-box components. Thus, adequate testing of those black-box components that will be reused is a necessary step in CBSD. However, due to the unavailability of the source code of black-box components, ensuring test adequacy becomes one of the hardest issues for testing black-box components. To tackle this problem, it is a natural idea to apply mutation testing, which is a fault-based testing method used for measuring test adequacy, for component contracts, whose aim is to improve the testability of the component. Though powerful, mutation testing is usually very computation-expensive, as many mutants need to be produced and executed in mutation testing. In this paper, we propose a contract-based mutation technique for testing black-box components. Based on the discordance between contracts and specification, our approach employs a set of high level contract mutation operators. The experimental results show that these operators can greatly reduce the number of mutants compared with traditional mutation operators. At the same time, the contract-based mutation using our contract mutation operators can provide almost the same ability as that of using traditional mutation operators. Therefore, our approach can produce effective test suites to reduce the cost of testing black-box components.
Towards Generalized Centrality Measures with Applications to Information Networks

The problem of identifying “valuable” or “central” nodes in a given network has long been recognized as important by researchers and practitioners alike. There exists an abundance of measures that associate each node with an individual centrality score; the higher the score of a node the more central its position in the network. Link-analysis algorithms and node-centrality measures try to capture this intuition [1, 5, 6, 8, 10]. While existing, commonly used centrality measures give intuition about the relative value of individual nodes, they are not useful in assessing the collective value of groups of nodes, which is not necessarily nor typically reflected by the sum of the values of the nodes in the group. For example, assume that the centrality of a group of nodes is defined as the total number of shortest paths passing through at least one node in the group. In this case, a set of nodes with high group centrality may not necessarily include nodes with high individual centrality scores. A relatively small number of recent studies considered such combinatorial notions of nodes’ importance in conjunction with specific problems, including advertisement strategy design [4], virus containment [7], and shortest-path distance approximation [9].
Data fusion for reducing power consumption in Arduino-Xbee wireless sensor network platform

Data fusion is one of the subsystems in data aggregation that aims to conserve power consumption by reducing the size of the transmitted data. The purpose of this paper is to present a simple data fusion mechanism called local data differentiation (LDD) for actual implementation in micro-controlled sensor nodes. LDD requires the end node to calculate the average value of an initially sampled data and afterwards, to only transmit the difference between the newly collected data with the average value. Usage of buffer in conjunction with LDD is also explored and empirical results are shown based on a hardware prototype to verify the benefit of the proposed method. The hardware for the test run is based on the Arduino platform and Xbee modules, as both are very popular in small-scale research and wireless automation solution.
2PCP: Two-phase CP decomposition for billion-scale dense tensors

Tensors are multi-dimensional arrays - consequently, tensor decomposition operations (CP and Tucker) are the bases for many high-dimensional data analysis tasks, from clustering, trend detection, anomaly detection, to correlation analysis in various application domains, including science and engineering1. One key problem with tensor decomposition is its computational complexity and space requirements. Especially, as the relevant data sets get denser, in-memory schemes for tensor decomposition become increasingly ineffective; therefore out-of-core (secondary-memory supported, potentially parallel) computing is necessitated. However, existing techniques do not consider the I/O and network data exchange costs that out-of-core execution of the tensor decomposition operation will incur. In this paper, we note that when this operation is implemented with the help of secondary-memory and/or multiple servers to tackle the memory limitations, we would need intelligent buffer-management and task-scheduling techniques which take into account the cost of bringing the relevant blocks into the buffer to minimize I/O in the system. In this paper, we introduce 2PCP, a two-phase, block-based CP decomposition system with intelligent buffer sensitive task scheduling and buffer management mechanisms. 2PCP aims to reduce I/O costs in the analysis of relatively dense tensors common in scientific and engineering applications. Experiment results compare with current state of art tensor decomposition algorithms and show that our algorithms can significantly reduce the amount of I/O and execution time while maintaining decomposition accuracy.
A Gentle Introduction to Predictive Filters

Predictive filters are essential tools in modern science. They perform state prediction and parameter estimation in fields such as robotics, computer vision, and computer graphics. Sometimes also called Bayesian filters, they apply the Bayesian rule of conditional probability to combine a predicted behavior with some corrupted indirect observation. When we study and solve a problem, we first need its proper mathematical formulation. Finding the essential parameters that best describe the system is hard; modeling their behaviors over time is even more challenging. Usually, we also have an inspection mechanism that provides us with indirect measurements, the observations, of the hidden underlying parameters. We also need to deal with the concept of uncertainty, and use random variables to represent both the state and the observations. Predictive filters are a family of estimation techniques. They combine the uncertain prediction from the system’s dynamics and the corrupted observation. There are many different predictive filters, each dealing with different types of mathematical representations for random variables and system dynamics. Here, the reader will find a dense introduction to predictive filters. After a general introduction, we discuss briefly discussion about mathematical modeling of systems: state representation, dynamics, and observation. Then, we expose some basic issues related to random variables and uncertainty modeling, and discuss four implementations of predictive filters, in order of complexity: the Kalman filter, the extended Kalman filter, the particle filter, and the unscented Kalman filter. Keywords : Predictive Filters, Density Estimators, Kalman Filter, Particle Filter, Unscented Kalman Filter.
Internet scrapbook: automating Web browsing tasks by demonstration

This paper describes a programming-by-demonstration system, called Internet Scrapbook, which allows users with little programming skill to automate repetitive browsing tasks. With the system, the user can create a personal page by clipping only the necessary portions from multiple Web pages. Once the personal page is created, the system updates it on behalf of the user by extracting the specified parts from the latest Web pages. The data extraction method in Scrapbook is based on the regularity in modifications of Web pages, i.e. that headings and positions of articles are rarely changed even though the articles themselves are modified. In the experiments to examine the accuracy of the data extraction algorithm, 96 percent of user-specified portions were correctly extracted.
Effect of Persuasion on Information Diffusion in Social Networks

One of the key factors guiding the act of communication between individuals in a social network is the desire to persuade or influence one another. In this paper, we study the interplay between a person writing (selecting) a message to send to another and the effect that the message has on its recipient. Using large-scale online user studies, we focus on a single effect (persuading or changing a recipient’s opinion about a topic) and its relationship to various measurable properties of the written message often associated with persuasive techniques, namely the degree of emotional and logical content. We find that the persuasive efficacy of these properties varies by domain of discussion and by individual susceptibility, and that senders appear to strategically select their persuasion techniques. Based on these results, we develop a structural model of information diffusion and show through simulations that the emergent larger-scale behaviors are consistent with current models of information cascades and, moreover, are able to model as yet unexplained empirically observed variance in the structural virality of cascades.
Fractals in art and nature: why do we like them?

Fractals have experienced considerable success in quantifying the visual complexity exhibited by many natural patterns, and continue to capture the imagination of scientists and artists alike. Fractal patterns have also been noted for their aesthetic appeal, a suggestion further reinforced by the discovery that the poured patterns of the American abstract painter Jackson Pollock are also fractal, together with the findings that many forms of art resemble natural scenes in showing scale-invariant, fractal-like properties. While some have suggested that fractal-like patterns are inherently pleasing because they resemble natural patterns and scenes, the relation between the visual characteristics of fractals and their aesthetic appeal remains unclear. Motivated by our previous findings that humans display a consistent preference for a certain range of fractal dimension across fractal images of various types we turn to scale-specific processing of visual information to understand this relationship. Whereas our previous preference studies focused on fractal images consisting of black shapes on white backgrounds, here we extend our investigations to include grayscale images in which the intensity variations exhibit scale invariance. This scale-invariance is generated using a 1/f frequency distribution and can be tuned by varying the slope of the rotationally averaged Fourier amplitude spectrum. Thresholding the intensity of these images generates black and white fractals with equivalent scaling properties to the original grayscale images, allowing a direct comparison of preferences for grayscale and black and white fractals. We found no significant differences in preferences between the two groups of fractals. For both set of images, the visual preference peaked for images with the amplitude spectrum slopes from 1.25 to 1.5, thus confirming and extending the previously observed relationship between fractal characteristics of images and visual preference.
Mitigating multi-threats optimally in proactive threat management

In turbulent economic times, the cost effectiveness of security measures is of the utmost importance when designing safeguards or countermeasures. This paper presents an optimal approach: MMT-O, Mitigating Multi-Threats Optimally, to meet the above challenges. The proposed approach is based on an optimum mitigation path set generation algorithm that provides optimal plans for threat/vulnerability management which can be adopted at the design level of the software life cycle. In MMT-O, a multi-threat attack graph is generated by combining all of the individual threats responsible for security compromise of the system. It identifies a unique set of attacks needing mitigation by removing redundant nodes, as an attack can be a part of multiple threats. The proposed algorithm, implemented in Java, provides the minimum mitigation paths required to be blocked to avert the threat. Countermeasures using a multi-agent system are inducted in these identified mitigation paths to avert the threat optimally. The proposed approach has been applied on different test cases and the results validate its economic justification over traditional security solutions as a part of proactive threat management.
Emotional space improves emotion recognition

A number of recent studies have focused on the conceptualized expression of emotions as a three-dimensional space. This paper proposes a new approach to emotion recognition, making use of two of the emotional dimensions and their relationship with different kinds of features. The main idea consists in associating prosodic features, derived from pitch, loudness, and duration, with the activation or arousal dimension, and quality features, i.e. phonation type, articulation manner, voice timbre, with the evaluation and pleasure dimension, in a way that different classification methods can be applied for each specific case. Most important results are achieved for the speaker-independent case and three classes, with a recognition rate close to 80%.
Cofeatures in international bond and equity markets

International bond and equity markets are investigated for evidence of first and second order common features, using the Engle-Kozicki cofeature methodology. Tests are conducted both across countries (Canada, France, Germany, Japan, the Netherlands, UK and US) within a particular asset class, and across asset classes (equities, 1 year bonds and 5 year bonds) within a particular country. Evidence of three types of common feature is sought: two first order features a common stochastic trend and common serial correlation; and one second order feature a common ARCH factor. Existence of either of the first order features implies a Granger causal flow between the variables and one of the most instructive parts of this analysis is plots of Granger causality F-statistics, calculated over a 2-year rolling window, which illustrate the changing pattern of Granger causal influence over time. In general, evidence of common features is stronger between countries than between assets. For example, there is some evidence of cointegration and strong evidence of common serial correlation between different countries in all three asset classes. There are also common second order features between countries: In particular, Dutch and German bond markets show evidence of a common ARCH factor (in both 1 year and 5 year zero coupon bonds) and the equity markets in Canada and the United States, the United Kingdom and the Netherlands also show some evidence of a common ARCH factor. Common features between assets within a particular country are harder to find. The most significant relationships are: equities and bonds are cointegrated in Japan; 1 and 5 year bonds have a common serial correlation feature in all countries (except perhaps the UK) and a very significant common ARCH factor is found in German 1 and 5 year bonds.