nils-herrmann/clean_scirepeval huggingface dataset:
Automated Planning and Acting

action into more concrete ones. Each dashed arrow maps a task into a plan of actions.  Automated Planning and Acting Malik Ghallab , Dana Nau , Paolo Traverso Excerpt More Information www.cambridge.org © in this web service Cambridge University Press 1.2 Conceptual View of an Actor 7 above it, and decides what activities need to be performed to carry out those tasks. Performing a task may involve reining it into lower-level steps, issuing subtasks to other components below it in the hierarchy, issuing commands to be executed by the platform, and reporting to the component that issued the task. In general, tasks in different parts of the hierarchymay involve concurrent use of different types of models and specialized reasoning functions. This example illustrates two important principles of deliberation: hierarchical organization and continual online processing. Hierarchically organized deliberation. Some of the actions the actor wishes to perform do not map directly into a command executable by its platform. An action may need further reinement and planning. This is done online and may require different representations, tools, and techniques from the ones that generated the task. A hierarchized deliberation process is not intended solely to reduce the search complexity of ofline plan synthesis. It is needed mainly to address the heterogeneous nature of the actions about which the actor is deliberating, and the corresponding heterogeneous representations and models that such deliberations require. Continual online deliberation.Only in exceptional circumstances will the actor do all of its deliberation ofline before executing any of its planned actions. Instead, the actor generally deliberates at runtime about how to carry out the tasks it is currently performing. The deliberation remains partial until the actor reaches its objective, including through lexible modiication of its plans and retrials. The actor’s predictive models are often limited. Its capability to acquire and maintain a broad knowledge about the current state of its environment is very restricted. The cost of minor mistakes and retrials are often lower than the cost of extensive modeling, information gathering, and thorough deliberation. Throughout the acting process, the actor reines and monitors its actions; reacts to events; and extends, updates, and repairs its plan on the basis of its perception focused on the relevant part of the environment. Different parts of the actor’s hierarchy often use different representations of the state of the actor and its environment. These representations may correspond to different amounts of detail in the description of the state and different mathematical constructs. In Figure 1.2, a graph of discrete locations may be used at the upper levels, while the lower levels may use vectors of continuous coniguration variables for the robot limbs. Finally, because complex deliberations can be compiled down by learning into lowlevel commands, the frontier between deliberation functions and the execution platform is not rigid; it evolves with the actor’s experience.
Pop-up assembly of 3 D structures actuated by heat shrinkable polymers

Folding 2D sheets into desired 3D structures is a promising fabrication technique that can find a wide range of applications. Compressive buckling provides an attractive strategy to actuate the folding and can be applied to a broad range of materials. Here a new and simple method is reported to achieve controlled compressive buckling, which is actuated by a heat shrinkable polymer sheet. The buckling deformation is localized at the pre-defined creases in the 2D sheet, resulting in sharp folding. Two approaches are developed to actuate the transformation, which follow similar geometric rules. In the first approach, the 2D precursor is pushed from outside, which leads to a 3D structure surrounded by the shrunk polymer sheet. Assembled 3D structures include prisms/pyramids with different base shapes, house roof, partial soccer ball, Miura-ori structure and insect wing. In the second approach, the 2D precursor is pulled from inside, which leads to a 3D structure enclosing the shrunk polymer sheet. Prisms/pyramids with different base shapes are assembled. The assembled structures are further tessellated to fabricate cellular structures that can be used as thermal insulator and crash energy absorber. They are also stacked vertically to fabricate complex multilayer structures.
TetriSched: global rescheduling with adaptive plan-ahead in dynamic heterogeneous clusters

TetriSched is a scheduler that works in tandem with a calendaring reservation system to continuously re-evaluate the immediate-term scheduling plan for all pending jobs (including those with reservations and best-effort jobs) on each scheduling cycle. TetriSched leverages information supplied by the reservation system about jobs' deadlines and estimated runtimes to plan ahead in deciding whether to wait for a busy preferred resource type (e.g., machine with a GPU) or fall back to less preferred placement options. Plan-ahead affords significant flexibility in handling mis-estimates in job runtimes specified at reservation time. Integrated with the main reservation system in Hadoop YARN, TetriSched is experimentally shown to achieve significantly higher SLO attainment and cluster utilization than the best-configured YARN reservation and CapacityScheduler stack deployed on a real 256 node cluster.
Using facebook after losing a job: differential benefits of strong and weak ties

Among those who have recently lost a job, social networks in general and online ones in particular may be useful to cope with stress and find new employment. This study focuses on the psychological and practical consequences of Facebook use following job loss. By pairing longitudinal surveys of Facebook users with logs of their online behavior, we examine how communication with different kinds of ties predicts improvements in stress, social support, bridging social capital, and whether they find new jobs. Losing a job is associated with increases in stress, while talking with strong ties is generally associated with improvements in stress and social support. Weak ties do not provide these benefits. Bridging social capital comes from both strong and weak ties. Surprisingly, individuals who have lost a job feel greater stress after talking with strong ties. Contrary to the "strength of weak ties" hypothesis, communication with strong ties is more predictive of finding employment within three months.
IrCube tracker: an optical 6-DOF tracker based on LED directivity

Six-degrees-of-freedom (6-DOF) trackers, which were mainly for professional computer applications, are now in demand by everyday consumer applications. With the requirements of consumer electronics in mind, we designed an optical 6-DOF tracker where a few photo-sensors can track the position and orientation of an LED cluster. The operating principle of the tracker is basically source localization by solving an inverse problem. We implemented a prototype system for a TV viewing environment, verified the feasibility of the operating principle, and evaluated the basic performance of the prototype system in terms of accuracy and speed. We also examined its application possibility to different environments, such as a tabletop computer, a tablet computer, and a mobile spatial interaction environment.
SocialTransfer: cross-domain transfer learning from social streams for media applications

The usage and applications of social media have become pervasive. This has enabled an innovative paradigm to solve multimedia problems (e.g., recommendation and popularity prediction), which are otherwise hard to address purely by traditional approaches. In this paper, we investigate how to build a mutual connection among the disparate social media on the Internet, using which cross-domain media recommendation can be realized. We accomplish this goal through SocialTransfer---a novel cross-domain real-time transfer learning framework. While existing transfer learning methods do not address how to utilize the real time social streams, our proposed SocialTransfer is able to effectively learn from social streams to help multimedia applications, assuming an intermediate topic space can be built across domains. It is characterized by two key components: 1) a topic space learned in real time from social streams via Online Streaming Latent Dirichlet Allocation (OSLDA), and 2) a real-time cross-domain graph spectra analysis based transfer learning method that seamlessly incorporates learned topic models from social streams into the transfer learning framework. We present as use cases of \emph{SocialTransfer} two video recommendation applications that otherwise can hardly be achieved by conventional media analysis techniques: 1) socialized query suggestion for video search, and 2) socialized video recommendation that features socially trending topical videos. We conduct experiments on a real-world large-scale dataset, including 10.2 million tweets and 5.7 million YouTube videos and show that \emph{SocialTransfer} outperforms traditional learners significantly, and plays a natural and interoperable connection across video and social domains, leading to a wide variety of cross-domain applications.
Printed Omni-Directional UWB Monopole Antenna With Very Compact Size

A compact rectangular monopole with an equal-width ground plane is presented. This novel structural configuration could significantly improve its radiation performance and decrease its size. The feeding structure composed of a trident-shaped strip and a tapered impedance transformer is presented and discussed. Its impedance bandwidth, defined by measured VSWR less than 2, is from 2.75 to 16.2 GHz with a ratio of about 5.9:1. Both numerical and experimental results show that the proposed monopole antenna has stably omni-directional H-plane radiation patterns with low cross-polarization level within its impedance bandwidth. This novel monopole antenna has ultrawide impedance bandwidth, very compact size (30 mm*8 mm), low fabrication lost, and omni-directional H-plane radiation patterns, which are suitable for various broadband applications.
Driver Gaze Tracking and Eyes Off the Road Detection System

Distracted driving is one of the main causes of vehicle collisions in the United States. Passively monitoring a driver's activities constitutes the basis of an automobile safety system that can potentially reduce the number of accidents by estimating the driver's focus of attention. This paper proposes an inexpensive vision-based system to accurately detect Eyes Off the Road (EOR). The system has three main components: 1) robust facial feature tracking; 2) head pose and gaze estimation; and 3) 3-D geometric reasoning to detect EOR. From the video stream of a camera installed on the steering wheel column, our system tracks facial features from the driver's face. Using the tracked landmarks and a 3-D face model, the system computes head pose and gaze direction. The head pose estimation algorithm is robust to nonrigid face deformations due to changes in expressions. Finally, using a 3-D geometric analysis, the system reliably detects EOR.
Digital Image Processing - An Algorithmic Introduction using Java

Read more and get great! That's what the book enPDFd digital image processing an algorithmic introduction using java will give for every reader to read this book. This is an on-line book provided in this website. Even this book becomes a choice of someone to read, many in the world also loves it so much. As what we talk, when you read more every page of this digital image processing an algorithmic introduction using java, what you will obtain is something great.
Intelligent agent-assisted adaptive order simulation system in the artificial stock market

Agent-based computational economics (ACE) has received increased attention and importance over recent years. Some researchers have attempted to develop an agent-based model of the stock market to investigate the behavior of investors and provide decision support for innovation of trading mechanisms. However, challenges remain regarding the design and implementation of such a model, due to the complexity of investors, financial information, policies, and so on. This paper will describe a novel architecture to model the stock market by utilizing stock agent, finance agent and investor agent. Each type of investor agent has a different investment strategy and learning method. A prototype system for supporting stock market simulation and evolution is also presented to demonstrate the practicality and feasibility of the proposed intelligent agent-based artificial stock market system architecture.
Imagine This! Scripts to Compositions to Videos

Imagining a scene described in natural language with realistic layout and appearance of entities is the ultimate test of spatial, visual, and semantic world knowledge. Towards this goal, we present the Composition, Retrieval and Fusion Network (Craft), a model capable of learning this knowledge from video-caption data and applying it while generating videos from novel captions. Craft explicitly predicts a temporal-layout of mentioned entities (characters and objects), retrieves spatio-temporal entity segments from a video database and fuses them to generate scene videos. Our contributions include sequential training of components of Craft while jointly modeling layout and appearances, and losses that encourage learning compositional representations for retrieval. We evaluate Craft on semantic fidelity to caption, composition consistency, and visual quality. Craft outperforms direct pixel generation approaches and generalizes well to unseen captions and to unseen video databases with no text annotations. We demonstrate Craft on Flintstones, a new richly annotated video-caption dataset with over 25000 videos. For a glimpse of videos generated by Craft, see https://youtu.be/688Vv86n0z8. Fred wearing a red hat is walking in the living room Retrieve Compose Retrieve Compose Retrieve Pebbles is sitting at a table in a room watching the television Retrieve Compose Retrieve Compose Compose Retrieve Retrieve Fuse
Robust, Deep and Inductive Anomaly Detection

PCA is a classical statistical technique whose simplicity and maturity has seen it find widespread use for anomaly detection. However, it is limited in this regard by being sensitive to gross perturbations of the input, and by seeking a linear subspace that captures normal behaviour. The first issue has been dealt with by robust PCA, a variant of PCA that explicitly allows for some data points to be arbitrarily corrupted; however, this does not resolve the second issue, and indeed introduces the new issue that one can no longer inductively find anomalies on a test set. This paper addresses both issues in a single model, the robust autoencoder. This method learns a nonlinear subspace that captures the majority of data points, while allowing for some data to have arbitrary corruption. The model is simple to train and leverages recent advances in the optimisation of deep neural networks. Experiments on a range of real-world datasets highlight the model’s effectiveness.
Inner engraving for the creation of a balanced LEGO sculpture

LEGO is a globally popular toy composed of colorful interlocking plastic bricks that can be assembled in many ways; however, this special feature makes designing a LEGO sculpture particularly challenging. Building a stable sculpture is not easy for a beginner; even an experienced user requires a good deal of time to build one. This paper provides a novel approach to creating a balanced LEGO sculpture for a 3D model in any pose, using centroid adjustment and inner engraving. First, the input 3D model is transformed into a voxel data structure. Next, the model’s centroid is adjusted to an appropriate position using inner engraving to ensure that the model stands stably. A model can stand stably without any struts when the center of mass is moved to the ideal position. Third, voxels are merged into layer-by-layer brick layout assembly instructions. Finally, users will be able to build a LEGO sculpture by following these instructions. The proposed method is demonstrated with a number of LEGO sculptures and the results of the physical experiments are presented.
Support vector machine parameter tuning using firefly algorithm

Performance of the support vector machine strongly depends on parameters settings. One of the most common algorithms for parameter tuning is grid search, combined with cross validation. This algorithm is often time consuming and inaccurate. In this paper we propose the use of stochastic metaheuristic algorithm, firefly algorithm, for effective support vector machine parameter tuning. The experimental results on 13 standard benchmark datasets show that our proposed method achieve better results compared to other state-of-the-art algorithms from literature.
Image Classification using Random Forests and Ferns

We explore the problem of classifying images by the object categories they contain in the case of a large number of object categories. To this end we combine three ingredients: (i) shape and appearance representations that support spatial pyramid matching over a region of interest. This generalizes the representation of Lazebnik et al., (2006) from an image to a region of interest (ROI), and from appearance (visual words) alone to appearance and local shape (edge distributions); (ii) automatic selection of the regions of interest in training. This provides a method of inhibiting background clutter and adding invariance to the object instance 's position; and (iii) the use of random forests (and random ferns) as a multi-way classifier. The advantage of such classifiers (over multi-way SVM for example) is the ease of training and testing. Results are reported for classification of the Caltech-101 and Caltech-256 data sets. We compare the performance of the random forest/ferns classifier with a benchmark multi-way SVM classifier. It is shown that selecting the ROI adds about 5% to the performance and, together with the other improvements, the result is about a 10% improvement over the state of the art for Caltech-256.
Lean/Agile Software Development Methodologies in Regulated Environments - State of the Art

Choosing the appropriate software development methodology is something which continues to occupy the minds of many IT professionals. The introduction of “Agile” development methodologies such as XP and SCRUM held the promise of improved software quality and reduced delivery times. Combined with a Lean philosophy, there would seem to be potential for much benefit. While evidence does exist to support many of the Lean/Agile claims, we look here at how such methodologies are being adopted in the rigorous environment of safety-critical embedded software development due to its high regulation. Drawing on the results of a systematic literature review we find that evidence is sparse for Lean/Agile adoption in these domains. However, where it has been trialled, “out-of-the-box” Agile practices do not seem to fully suit these environments but rather tailored Agile versions combined with more planbased practices seem to be making inroads.
Video Game Addiction Test: Validity and Psychometric Characteristics

The study explores the reliability, validity, and measurement invariance of the Video game Addiction Test (VAT). Game-addiction problems are often linked to Internet enabled online games; the VAT has the unique benefit that it is theoretically and empirically linked to Internet addiction. The study used data (n=2,894) from a large-sample paper-and-pencil questionnaire study, conducted in 2009 on secondary schools in Netherlands. Thus, the main source of data was a large sample of schoolchildren (aged 13-16 years). Measurements included the proposed VAT, the Compulsive Internet Use Scale, weekly hours spent on various game types, and several psychosocial variables. The VAT demonstrated excellent reliability, excellent construct validity, a one-factor model fit, and a high degree of measurement invariance across gender, ethnicity, and learning year, indicating that the scale outcomes can be compared across different subgroups with little bias. In summary, the VAT can be helpful in the further study of video game addiction, and it contributes to the debate on possible inclusion of behavioral addictions in the upcoming DSM-V.
60 GHz slot antenna array based on ridge gap waveguide technology enhanced with dielectric superstrate

A 2 × 2 planar slot antenna array is designed at 60 GHz. The slots are fed by a printed feeding network based on the ridge gap waveguide (RGW) technology. Such a feeding network requires the slots to be more than a full wavelength apart, which causes excitation of undesired grating lobes. In order to reduce the grating lobes level, a low-cost dielectric superstrate at a distance of half wavelength in the air above the slot antenna array is used. The presence of the superstrate acts as a planar lens that increases the slot array gain by 7 dB along the broadside direction as well as greatly reduces the grating lobes level. The single element and the array characteristics are provided.
Bow-tie rectenna arrays

This paper presents experimental results on several self-complementary bow-tie antenna arrays populated with rectifier diodes. Results with two different diodes are compared, and the number of diodes, antenna polarization, DC load, DC connection (parallel/series) and incident power density are varied. The transmitter is in the far field and the incident power density ranges from 10 to 125μW/cm2 at 2.3GHz. The main conclusion of this work is that it is possible to transmit useful power wirelessly, using low and safe power density levels (tens of μW/cm2), and receive it with a scalable device.
Low-loss 0.13-µm CMOS 50 – 70 GHz SPDT and SP4T switches

This paper presents 50–70 GHz single-pole double-throw (SPDT) and single-pole four-throw (SP4T) switches built using a low-cost 0.13-µm CMOS process. The switches are based on tuned λ/4 designs with output matching networks. High substrate resistance together with deep trenches and isolation moats are used for low insertion loss. The SPDT and SP4T switches result in a measured insertion loss of 2.0 and 2.3 dB at 60 GHz, with an isolation of ≫ 32 dB and ≫ 22 dB, respectively. The measured output port-to-port isolation is ≫ 27 dB for both designs. The P1dB is 13–14 dBm with a measured IIP3 of ≫ 23 dBm for both switches. Both designs have a return loss better than −10 dB at all ports from 50 to 70 GHz. The active chip area is 0.39×0.32 mm2 (SPDT) and 0.59×0.45 mm2 (SP4T). To our knowledge, this paper presents the lowest loss 60 GHz SPDT and SP4T switches and also the highest isolation SPDT switch in any CMOS technology to-date.
Scale space texture analysis for face anti-spoofing

Face spoofing detection (i.e. face anti-spoofing) is emerging as a new research area and has already attracted a good number of works during the past five years. This paper addresses for the first time the key problem of the variation in the input image quality and resolution in face anti-spoofing. In contrast to most existing works aiming at extracting multiscale descriptors from the original face images, we derive a new multiscale space to represent the face images before texture feature extraction. The new multiscale space representation is derived through multiscale filtering. Three multiscale filtering methods are considered including Gaussian scale space, Difference of Gaussian scale space and Multiscale Retinex. Extensive experiments on three challenging and publicly available face anti-spoofing databases demonstrate the effectiveness of our proposed multiscale space representation in improving the performance of face spoofing detection based on gray-scale and color texture descriptors.