nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Action Repetition for Deep Reinforcement Learning

One of the long standing goals of Artificial Intelligence (AI) is to build cognitive agents which can perform complex tasks from raw sensory inputs without explicit supervision (Lake et al. 2016). Recent progress in combining Reinforcement Learning objective functions and Deep Learning architectures has achieved promising results for such tasks. An important aspect of such sequential decision making problems, which has largely been neglected, is for the agent to decide on the duration of time for which to commit to actions. Such action repetition is important for computational efficiency, which is necessary for the agent to respond in real-time to events (in applications such as self-driving cars). Action Repetition arises naturally in real life as well as simulated environments. The time scale of executing an action enables an agent (both humans and AI) to decide the granularity of control during task execution. Current state of the art Deep Reinforcement Learning models, whether they are off-policy (Mnih et al. 2015; Wang et al. 2015) or on-policy (Mnih et al. 2016), consist of a framework with a static action repetition paradigm, wherein the action decided by the agent is repeated for a fixed number of time steps regardless of the contextual state while executing the task. In this paper, we propose a new framework Dynamic Action Repetition which changes Action Repetition Rate (the time scale of repeating an action) from a hyper-parameter of an algorithm to a dynamically learnable quantity. At every decision-making step, our models allow the agent to commit to an action and the time scale of executing the action. We show empirically that such a dynamic time scale mechanism improves the performance on relatively harder games in the Atari 2600 domain, independent of the underlying Deep Reinforcement Learning algorithm used.
Vectorization in PyPy's Tracing Just-In-Time Compiler

PyPy is a widely known virtual machine for the Python programming language. PyPy itself is implemented in the statically typed subset of Python called RPython. RPython includes a tracing Just-In-Time (JIT) compiler and is capable of generating the compiler for a language from the specification of the interpreter for that language. In PyPy 4.0.0 we extended the tracing JIT compiler to support vectorization of loops and emit code for the SSE4 vector operations of the x86 instruction set. This article presents the details of the new vectorizer of PyPy. The vectorizer uses a loop unrolling approach to vectorization. It has been designed for efficient compilation as the compilation is done during the execution of the application. The scientific library NumPy introduced arrays which are homogeneous, primitive typed and contiguous in memory. These kind of arrays are used to avoid the problems with dynamic typing. Our contribution to PyPy's new vectorizer supports scalar and constant expansion, accumulator splitting for reductions, guard strengthening and array bounds check removal. The empirical evaluation shows that the vectorizer can gain speedups close to the theoretical optimum of the SSE4 instruction set.
Soft Switching Circuit for Interleaved Boost Converters

A zero-voltage switching-zero-current switching interleaved boost converter is proposed in this paper. An active circuit branch in parallel with the main switches is added and it is composed of an auxiliary switch and a snubber capacitor. By using this interleaved converter topology, zero current turn-on and zero voltage turn-off of the main switches can be achieved and the reverse-recovery loss of boost diode can be reduced. In addition, the auxiliary switches are zero-voltage transmission during the whole switching transition. A prototype of boost converter rated at 1.2kW has been built to confirm the effectiveness of the converter
Harvesting rib cartilage grafts for secondary rhinoplasty.

Reconstruction of the nasal osseocartilaginous framework is the foundation of successful secondary rhinoplasty.



Achieving this often requires large quantities of cartilage to correct both contour deformities and functional problems caused by previous procedures. Satisfactory and consistent long-term results rely on using grafts with low resorption rates and sufficient strength to offer adequate support. Auricular cartilage, irradiated cartilage, and alloplastic materials have all been used as implantable grafts with limited success.



In the senior author's experience (J.P.G.), rib cartilage has proven to be a reliable, abundant, and relatively accessible donor with which to facilitate successful secondary rhinoplasty surgery.



: The authors describe in detail the techniques that they have found to be integral in harvesting rib cartilage grafts for secondary rhinoplasty.
Taking Empowerment to the Next Level : A Multiple-Level Model of Empowerment , Performance , and Satisfaction

Most research to date has approached employee empowerment as an individual-level phenomenon. In this study we proposed a work-unit-level construct, empowerment climate, and tested a multiple-level model integrating macro and micro approaches to empowerment. Empowerment climate was shown to be empirically distinct from psychological empowerment and positively related to manager ratings of work-unit performance. A cross-level mediation analysis using hierarchical linear modeling showed that psychological empowerment mediated the relationships between empowerment climate and individual performance and job satisfaction.
Geometric continuous-curvature path planning for automatic parallel parking

This paper deals with path planning for car-like vehicle in parallel parking problems. Our path planning method uses simple geometry of the vehicle kinematic model. The presented strategy consists in two parts: create a simple geometric path for the parallel parking in one or more maneuvers, formed by circle arcs and then transform it to a continuous-curvature path with the use of clothoids. Accordingly, a car-like vehicle can follow such path without reorienting its front wheels at stop. The proposed planning method is independent of the initial position and of the orientation of the vehicle. Control inputs for steering angle and longitudinal velocity are generated so that the vehicle can park by following them.
3D spatial pyramid: descriptors generation from point clouds for indoor scene classification

Traditionally, the indoor scene classification problem has been approached from a 2D image recognition point of view. In most visual scene classification systems, a descriptor for the input image is generated to obtain a suitable representation that includes features related to color, shape or spatial information. Techniques based on the use of a spatial pyramid have proven to be adequate to perform this step. In the past years, on the other hand, 3D sensors have become widely available, which allows to include new information sources to the framework previously described. In this work we rely on RGB-D data to extend the spatial pyramid approach, aimed at building descriptors that can lead to a more robust representation against changing lighting conditions. The proposed descriptors are evaluated on the RobotVision@ImageCLEF-2013 benchmark dataset, remarkably outperforming state-of-the-art 3D local and global descriptors.
Conceptualizing Sources in Online News

This study attempts a new conceptualization of communication “sources” by proposing a typology of sources that would apply not only to traditional media but also to new online media. Ontological rationale for the distinctions in the typology is supplemented by psychological evidence via an experiment that investigated the effects of different types of source attributions upon receivers’ perception of online news content. Participants (N = 48) in a 4-condition, between-participants experiment read 6 identical news stories each through an online service. Participants were told that the stories were selected by 1 of 4 sources: news editors, the computer terminal on which they were accessing the stories, other audience members (or users) of the online news service, or (using a pseudo-selection task) the individual user (self). After reading each online news story, all participants filled out a paperand-pencil questionnaire indicating their perceptions of the story they had just read. In confirmation of the distinctions made in the typology, attribution of identical content to 4 different types of online sources was associated with significant variation in news story perception. Theoretical implications of the results as well as the typology are discussed.
Detecting Android Malware Based on Extreme Learning Machine

To relieve increasingly prominent security issues of Android applications, static malware-detection techniques have become essential, due to their rapid and convenient detection processes which do not require running the detected applications. Most of current commercial anti-malware tools utilize signatures of known malicious Android codes for static detection, but are unable to find out unknown, especially newly created, malware. Many existing malware-detection researches rely on traditional machine learning techniques to analyze some static features of Android applications such as permissions and API calls, but the detection approaches still have room for improvement with respect to simplicity, effectiveness or efficiency. To overcome the limitations of the above detection techniques, we propose a novel static approach to detect malicious Android applications by proposing a set of Android program features, consisting of sensitive permissions and sensitive API calls, and by utilizing Extreme Learning Machine. We implemented our approach with an automated testing tool called WaffleDetector. Controlled experiments have been conducted to compare our approach and the existing ones on detecting malicious Android applications, and the results show that our approach excels the existing ones with minimal human intervention, better detection effectiveness and less detection time.
Automated Irrigation System-IoT Based Approach

Agriculture is a major source of earning of Indians and agriculture has made a big impact on India's economy. The development of crops for a better yield and quality deliver is exceptionally required. So suitable conditions and suitable moisture in beds of crop can play a major role for production.. Mostly irrigation is done by tradition methods of stream flows from one end to other. Such supply may leave varied moisture levels in filed. The administration of the water system can be enhanced utilizing programmed watering framework This paper proposes a programmed water system with framework for the terrains which will reduce manual labour and optimizing water usage increasing productivity of crops. For formulating the setup, Arduino kit is used with moisture sensor with Wi-Fi module. Our experimental setup is connected with cloud framework and data is acquisition is done. Then data is analysed by cloud services and appropriate recommendations are given.
Survey on Bioinspired Adhesive Methods and Design and Implementation of A Multi-Mode Biomimetic Wall-Climbing Robot

Wall-climbing robot is an essential branch of robotics, and the adhesive method is the key technology. Nowadays, suction cups, magnets and grippers are three conventional modes for adhering. However, there are many limitations of the traditional methods, which make it difficult for the wall-climbing robots to adapt to most of the complicated environments. Bionics gives us inspiration to solve the limitation problems of conventional wall-climbing robots, and biomimetic wall-climbing robots have become a hot research area. Spiny feet and dry adhesives are two main bioinspired adhesive methods applied into wall-climbing robots, and relevant research status and development situation have been surveyed. In order to improve the adaption of biomimetic wall-climbing robots to both smooth and rough surfaces, we have developed a brand new multi-mode biomimetic wall-climbing robot employing four modes: spiny wheels, adhesive treads, spiny treads and a suction cup, which can switch different modes to conform to different terrains. The robot employs spiny wheels and spiny treads while meeting with rough surfaces and employs adhesive treads while encountering smooth surfaces. And a suction cup is applied all the time for assistive adhesive function. Design and prototype experiment about this robot have been represented.
Health Cloud: An Enabler for Healthcare Transformation

An unprecedented volume of data is being generated in healthcare and life sciences, ranging across medical records, claims, lab data, genomics data, medical images, emerging exogenous data, and knowledge. Much of this data is moving to the cloud. In this paper, we describe examples of how the data from systems of record, exogenous data sources and knowledge sources can be combined at cloud scale and speed to create industry-transforming insights to improve health outcomes. We then describe a cloud architecture and building blocks that enable these solutions, and the compliance aspects that are critical to healthcare solutions. Finally, we outline a realization of this architecture and outline further research topics in this domain.
Solid state circuit breaker protection devices for DC microgrid in review

The advent of alternating current (AC) is due to its ability to transmit power over long distances from transformers, does not mark the end of utilizing direct current (DC). The development of renewable energy sources is because of high usage of DC because the generated power is directed to the main grid for conversion into electric power and the source of energy cannot tolerate high voltage. Fault detection is a major problem that arises when an excess amount of current flows in the circuit leading to the necessity of having circuit breakers. Circuit breakers are made of light materials, which creates a problem for dealing with high voltage DC applications with the protection of circuit breakers have become popularized, and different papers have outlined various studies on the effectiveness of Solid State Circuit Breakers (SSCBs) protection for a direct current microgrid. This paper reviews the benefits and shortfalls of The Wide bandgap (WBG) SSCBs and its application with photovoltaic (PV) generators.
Broadband Class-E Power Amplifier for HF and VHF

The power amplifier described here uses a 50-V gemini MOSFET and operates in class E to produce up to 200 W from 1.8 to 128 MHz. The circuit incorporates a broadband input so that frequency changes require only retuning or switching of the output filter. The efficiency varies from 90 percent at 1.8 MHz to 70 percent at 128 MHz. The amplitude-modulation linearity is excellent, making it well suited for use in a Kahn-technique (EER) transmitter
Data Mining Classification Techniques for Human Talent Forecasting

In knowledge management process, data mining technique can be used to extract and discover the valuable and meaningful knowledge from a large amount of data. Nowadays, data mining has given a great deal of concern and attention in the information industry and in society as a whole. This technique is an approach that is currently receiving great attention in data analysis and it has been recognized as a newly emerging analysis tool (Osei-Bryson, 2010; Park, 2001; Sinha, 2008; Tso & Yau, 2007; Wan, 2009; Zanakis, 2005; Zhuang et al., 2009). Additionally, among the major tasks in data mining are classification and prediction; concept description; rule association; cluster analysis; outlier analysis; trend and evaluation analysis; statistical analysis and others. Classification and prediction tasks are among the popular tasks in data mining; and widely used in many areas especially for trend analysis and future planning. In fact, classification technique is supervised learning, which is the class level or prediction target is already known. As a result, the classification model which is represented through rules structures will be constructed in the classification process. In this case, the constructed model will be representing the precious knowledge and it can be used for future planning. There are many areas which adapted this approach to solve their problems such as in finance, medical, marketing, stock, telecommunication, manufacturing, health care, customer relationship and etc. However, the data mining application has not attracted much attention from people in Human Resource (HR) field (Chien & Chen, 2008; Ranjan, 2008). Besides that, in our previous study, most of the prediction applications are used to predict stock, demand, rate, risk, event and others; but there are quite limited studies on human prediction. In addition prediction applications are mainly developed in business and industrious fields; and quite restricted studies involved human talent in an organization (Jantan et al., 2009). HR data can provide a rich resource for knowledge discovery and for decision support system development. Recently, an organization has to struggle effectively in term of cost, quality, service or innovation. All these depend on having enough right people with the right skills, employed
Feature Extraction by Rotation-Invariant Matrix Representation for Object Detection in Aerial Image

This letter proposes a novel rotation-invariant feature for object detection in optical remote sensing images. Different from previous rotation-invariant features, the proposed rotation-invariant matrix (RIM) can incorporate partial angular spatial information in addition to radial spatial information. Moreover, it can be further calculated between different rings for a redundant representation of the spatial layout. Based on the RIM, we further propose an RIM_FV_RPP feature for object detection. For an image region, we first densely extract RIM features from overlapping blocks; then, these RIM features are encoded into Fisher vectors; finally, a pyramid pooling strategy that hierarchically accumulates Fisher vectors in ring subregions is used to encode richer spatial information while maintaining rotation invariance. Both of the RIM and RIM_FV_RPP are rotation invariant. Experiments on airplane and car detection in optical remote sensing images demonstrate the superiority of our feature to the state of the art.
60GHz pattern reconfigurable quasi-Yagi antenna — Proof through computational design

The paper demonstrates the possibility of designing pattern reconfigurable quasi-Yagi antenna. Adjusting the length of dipole arms and delay line, the E-plane radiation direction is steered from -20° to +20°, in 5° steps, without compromising the realized radiation gain or shifting the operating spectrum as high as 60 GHz band. In this work, nine individual Yagi antennas with different beam directions are designed. They all have bands centered at 60 GHz, with wide bandwidths covered from 55 GHz to 65 GHz.
Sign language recognition with recurrent neural network using human keypoint detection

We study the sign language recognition problem which is to translate the meaning of signs from visual input such as videos. It is well-known that many problems in the field of computer vision require a huge amount of dataset to train deep neural network models. We introduce the KETI sign language dataset which consists of 10,480 videos of high resolution and quality. Since different sign languages are used in different countries, the KETI sign language dataset can be the starting line for further research on the Korean sign language recognition.
 Using the sign language dataset, we develop a sign language recognition system by utilizing the human keypoints extracted from face, hand, and body parts. The extracted human keypoint vector is standardized by the mean and standard deviation of the keypoints and used as input to recurrent neural network (RNN). We show that our sign recognition system is robust even when the size of training data is not sufficient. Our system shows 89.5% classification accuracy for 100 sentences that can be used in emergency situations.
Fostering Effective Early Learning (FEEL) through a professional development programme for early childhood educators to improve professional practice and child outcomes in the year before formal schooling: study protocol for a cluster randomised controlled trial

A substantial research base documents the benefits of attendance at high-quality early childhood education and care (ECEC) for positive behavioural and learning outcomes. Research has also found that the quality of many young children's experiences and opportunities in ECEC depends on the skills, dispositions and understandings of the early childhood adult educators. Increasingly, research has shown that the quality of children's interactions with educators and their peers, more than any other programme feature, influence what children learn and how they feel about learning. Hence, we sought to investigate the extent to which evidence-based professional development (PD) - focussed on promoting sustained shared thinking through quality interactions - could improve the quality of ECEC and, as a consequence, child outcomes.



The Fostering Effective Early Learning (FEEL) study is a cluster randomised controlled trial for evaluating the benefits of a professional development (PD) programme for early childhood educators, compared with no extra PD. Ninety long-day care and preschool centres in New South Wales, Australia, will be selected to ensure representation across National Quality Standards (NQS) ratings, location, centre type and socioeconomic areas. Participating centres will be randomly allocated to one of two groups, stratified by centre type and NQS rating: (1) an intervention group (45 centres) receiving a PD intervention or (2) a control group (45 centres) that continues engaging in typical classroom practice. Randomisation to these groups will occur after the collection of baseline environmental quality ratings. Primary outcomes, at the child level, will be two measures of language development: verbal comprehension and expressive vocabulary. Secondary outcomes at the child level will be measures of early numeracy, social development and self-regulation. Secondary outcomes at the ECEC room level will be measures of environmental quality derived from full-day observations. In all cases, data collectors will be blinded to group allocation.



This is the first randomised controlled trial of a new approach to PD, which is focussed on activities previously found to be influential in children's early language, numeracy, social and self-regulatory development. Results should inform practitioners, policy-makers and families of the value of specific professional development for early childhood educators.


 This trial was retrospectively registered, given the first participant (centre) had been enrolled at the time of registration.
Implementation of a fuzzy logic speed controller for a permanent magnet dc motor using a low-cost Arduino platform

Implementation of a Fuzzy Logic speed controller for a Permanent Magnet DC (PMDC) motor using a low-cost Arduino interfaced with a standard xPC Target MATLAB environment is demonstrated. The Arduino solution is acceptable at kHz sampling frequencies. The study of speed control of PMDC motor based on Arduino and L298N H-Bridge using an artificial intelligence control method, such as Fuzzy PID with only four rules, has been validated by experiments on the physical system.