nils-herrmann/clean_scirepeval huggingface dataset:
A self-tuning system based on application Profiling and Performance Analysis for optimizing Hadoop MapReduce cluster configuration

One of the most widely used frameworks for programming MapReduce-based applications is Apache Hadoop. Despite its popularity, however, application developers face numerous challenges in using the Hadoop framework, which stem from them having to effectively manage the resources of a MapReduce cluster, and configuring the framework in a way that will optimize the performance and reliability of MapReduce applications running on it. This paper addresses these problems by presenting the Profiling and Performance Analysis-based System (PPABS) framework, which automates the tuning of Hadoop configuration settings based on deduced application performance requirements. The PPABS framework comprises two distinct phases called the Analyzer, which trains PPABS to form a set of equivalence classes of MapReduce applications for which the most appropriate Hadoop config- uration parameters that maximally improve performance for that class are determined, and the Recognizer, which classifies an incoming unknown job to one of these equivalence classes so that its Hadoop configuration parameters can be self-tuned. The key research contributions in the Analyzer phase includes modifications to the well-known k - means + + clustering and Simulated Annealing algorithms, which were required to adapt them to the MapReduce paradigm. The key contributions in the Recognizer phase includes an approach to classify an unknown, incoming job to one of the equivalence classes and a control strategy to self-tune the Hadoop cluster configuration parameters for that job. Experimental results comparing the performance improvements for three different classes of applications running on Hadoop clusters deployed on Amazon EC2 show promising results.
Privacy awareness about information leakage: who knows what about me?

The task of protecting users' privacy is made more difficult by their attitudes towards information disclosure without full awareness and the economics of the tracking and advertising industry. Even after numerous press reports and widespread disclosure of leakages on the Web and on popular Online Social Networks, many users appear not be fully aware of the fact that their information may be collected, aggregated and linked with ambient information for a variety of purposes. Past attempts at alleviating this problem have addressed individual aspects of the user's data collection. In this paper we move towards a comprehensive and efficient client-side tool that maximizes users' awareness of the extent of their information leakage. We show that such a customizable tool can help users to make informed decisions on controlling their privacy footprint.
Cross-domain transfer for reinforcement learning

A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcement learning settings have shown considerable promise, but they typically transfer between pairs of very similar tasks. This work introduces Rule Transfer, a transfer algorithm that first learns rules to summarize a source task policy and then leverages those rules to learn faster in a target task. This paper demonstrates that Rule Transfer can effectively speed up learning in Keepaway, a benchmark RL problem in the robot soccer domain, based on experience from source tasks in the gridworld domain. We empirically show, through the use of three distinct transfer metrics, that Rule Transfer is effective across these domains.
A low power and high speed CMOS Voltage-Controlled Ring Oscillator

In this paper a new Voltage Controlled Ring Oscillator is presented. The proposed VCO uses partial positive feedback in its delay cell, allowing the circuit to operate with single two stages, achieving high speed with reduced power consumption. The new VCO has a wide range of operation frequencies (0.2 at 2.1 GHz), good linearity between the output frequency and the control voltage, phase noise of -90 dBc/Hz at 100 kHz offset and it only consumes 7.01 mW in its central frequency of 1.2 GHz using a 3.3 V power supply. The circuit was fabricated in a 0.35 /spl mu/m CMOS-AMS process and occupies an area of 67.5/spl times/77.5 /spl mu/m/sup 2/.
The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations

Swarm is a multi agent software platform for the simulation of complex adaptive systems In the Swarm system the basic unit of simulation is the swarm a collec tion of agents executing a schedule of actions Swarm supports hierarchical model ing approaches whereby agents can be composed of swarms of other agents in nested structures Swarm provides object oriented libraries of reusable components for build ing models and analyzing displaying and controlling experiments on those models Swarm is currently available as a beta version in full free source code form It requires the GNU C Compiler Unix and X Windows More information about Swarm can be obtained from our web pages http www santafe edu projects swarm Santa Fe Institute Hyde Park Rd Santa Fe NM nelson santafe edu Deere Company John Deere Road Moline IL roger ci deere com Santa Fe Institute Hyde Park Rd Santa Fe NM cgl santafe edu Santa Fe Institute Hyde Park Rd Santa Fe NM manor santafe edu Swarm has been developed at the Santa Fe Institute with the support of Grant No N from the O ce of Naval Research and Grant No N G from the Naval Research Laboratory both acting in cooperation with the Defense Advanced Research Projects Agency Swarm bene ted from earlier support from The Carol O Donnell Foundation Mr and Mrs Michael Grantham and the National Science Foundation SFI also gratefully acknowledges invaluable support from Deere Company for this
Sketching in circuits: designing and building electronics on paper

The field of new methods and techniques for building electronics is quickly growing - from research in new materials for circuit building, to modular toolkits, and more recently to untoolkits, which aim to incorporate more off-the-shelf parts. However, the standard mediums for circuit design and construction remain the breadboard, protoboard, and printed circuit board (PCB). As an alternative, we introduce a method in which circuits are hand-made on ordinary paper substrates, connected with conductive foil tape and off-the-shelf circuit components with the aim of supporting the durability, scalability, and accessibility needs of novice and expert circuit builders alike. We also used electrified notebooks to investigate how the circuit design and build process would be affected by the constraints and affordances of the bound book. Our ideas and techniques were evaluated through a series of workshops, through which we found our methods supported a wide variety of approaches and results - both technical and expressive - to electronics design and construction.
Forecasting Human Dynamics from Static Images

This paper presents the first study on forecasting human dynamics from static images. The problem is to input a single RGB image and generate a sequence of upcoming human body poses in 3D. To address the problem, we propose the 3D Pose Forecasting Network (3D-PFNet). Our 3D-PFNet integrates recent advances on single-image human pose estimation and sequence prediction, and converts the 2D predictions into 3D space. We train our 3D-PFNet using a three-step training strategy to leverage a diverse source of training data, including image and video based human pose datasets and 3D motion capture (MoCap) data. We demonstrate competitive performance of our 3D-PFNet on 2D pose forecasting and 3D structure recovery through quantitative and qualitative results.
StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning

Real-time strategy games have been an important field of game artificial intelligence in recent years. This paper presents a reinforcement learning and curriculum transfer learning method to control multiple units in StarCraft micromanagement. We define an efficient state representation, which breaks down the complexity caused by the large state space in the game environment. Then, a parameter sharing multi-agent gradient-descent Sarsa($\lambda$) algorithm is proposed to train the units. The learning policy is shared among our units to encourage cooperative behaviors. We use a neural network as a function approximator to estimate the action–value function, and propose a reward function to help units balance their move and attack. In addition, a transfer learning method is used to extend our model to more difficult scenarios, which accelerates the training process and improves the learning performance. In small-scale scenarios, our units successfully learn to combat and defeat the built-in AI with 100% win rates. In large-scale scenarios, the curriculum transfer learning method is used to progressively train a group of units, and it shows superior performance over some baseline methods in target scenarios. With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios.
SME e-readiness in Malaysia : Implications for Planning and Implementation

This study hoped to answer 2 main objectives. The first objective was to assess the level of e-readiness of SMEs in Northern Malaysia. The second objective was to investigate the factors contributing to the e-readiness of SMEs in Northern Malaysia. Questionnaires were distributed using a simple random sampling method to 300 SMEs in Penang, Kedah and Perlis. The findings of this study show that SMEs in Northern Malaysia are ready to go for e-business, e-commerce and Internet in general. The findings also showed that in general top management commitment and infrastructure and technology have significant impact on SMEs’ e-readiness. However, human capital, resistance to change, and information security do not have significant impact or contribution on e-readiness in SMEs.
XGBoost: A Scalable Tree Boosting System

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.
Designing and implementing future aerial communication networks

Providing "connectivity from the sky" is the new innovative trend in wireless communications. High and low altitude platforms, drones, aircrafts, and airships are being considered as candidates for deploying wireless communications complementing the terrestrial communication infrastructure. In this article we report the detailed account of the design and implementation challenges of an aerial network consisting of LTE-Advanced (LTE-A) base stations. In particular, we review achievements and innovations harnessed by an aerial network composed of Helikite platforms. Helikites can be raised in the sky to bring Internet access during special events and in the aftermath of an emergency. The trial phase of the system mounting LTE-A technology onboard Helikites to serve users on the ground yielded very encouraging results, and showed that such a system could offer a longer lasting solution, provided that inefficiency in powering the radio frequency equipment in the Helikite can be overcome.
Low-Cost Wideband Microstrip Antenna Array for 60-GHz Applications

A low-cost single-layer wideband microstrip antenna array is presented for 60-GHz band applications. Each microstrip radiation element is fed by a modified L-shaped probe to enhance the impedance bandwidth. The proposed microstrip antenna array is excited by a novel coplanar waveguide (CPW) feed network, which not only has a simple structure by abandoning the traditional air bridges (wire bonds) above the CPW T-junctions but also provides pairs of broadband differential outputs. Experimentally, two 4 × 4 antenna arrays with different polarizations were designed and fabricated on the double-sided single-layer printed circuit board. The linearly polarized array exhibits an impedance bandwidth ( SWR ≤ 2) of 25.5% and a gain of around 15.2 dBi. The circularly polarized array, employing the same CPW feed network to excite sequentially rotated circularly polarized elements, achieves an impedance bandwidth ( SWR ≤ 2) of 17.8%, a 3-dB axial ratio bandwidth of 15.6%, and a gain of around 14.5 dBi.
Featureless Visual Processing for SLAM in Changing Outdoor Environments

Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features.
Design for wearability

Digital Technology is constantly improving as information becomes wireless. These advances demand more wearable and mobile form factors for products that access information. A product that is wearable should have wearability. This paper explores the concept of dynamic wearability, through design research. Wearability is defined as the interaction between the human body and the wearable object. Dynamic wearability extends that definition to include the human body in motion. Our research has been to locate, understand, and define the spaces on the human body where solid and flexible forms can rest-without interfering with fluid human movement. The result is a set of design guidelines embodied in a set of wearable forms. These wearable forms describe the three dimensional spaces on the body best suited for comfortable and unobtrusive wearability by design.
Wideband Pattern-Reconfigurable Antenna Using Pair of Radial Radiators on Truncated Ground With Switchable Director and Reflector

A compact wideband pattern-reconfigurable antenna that is based on multiple radiators and switchable director/reflector is presented. The antenna consists of two quarter-wavelength radial radiators placed on two sides of a central feeder at the top layer. The bottom layer of the antenna includes a truncated quarter-wavelength ground plane and two quarter-wavelength parallel strips surrounding the radiators. Four p-i-n diodes, which are interlocked in pair, are used to control the radiation direction by selecting the radiator at the top layer and switching the strips at the bottom layer between being a director or reflector. Thus, the antenna achieves four-state pattern reconfigurability in the azimuthal plane. The antenna has a bandwidth of 1.88–2.64 GHz (34% fractional bandwidth) and is capable of steering the beam in the four directions 0°, 45°, 135°, and 180° in the azimuth plane. The antenna achieves a stable measured gain of more than 2.8 dBi across the band with a peak gain of 3.7 dBi. It has a low profile of 0.48λ<italic>e</italic> × 0.64λ<italic>e</italic> × 0.02λ<italic>e</italic> (λ<italic>e</italic> guided wavelength at 1.88 GHz) on a low-cost FR4 substrate. With the covered band, the antenna can be used in compact handheld and portable devices, indoor or short-range communications, WiMAX and WLAN.
The obstacle-restriction method for robot obstacle avoidance in difficult environments

This paper addresses the obstacle avoidance problem in difficult scenarios that usually are dense, complex and cluttered. The proposal is a method called the obstacle-restriction. At each iteration of the control cycle, this method addresses the obstacle avoidance in two steps. First there is procedure to compute instantaneous subgoals in the obstacle structure (obtained by the sensors). The second step associates a motion restriction to each obstacle, which are managed next to compute the most promising motion direction. The advantage of this technique is that it avoids common limitations of previous obstacle avoidance methods, improving their navigation performance in difficult scenarios. Furthermore, we obtain similar results to the recent methods that achieve navigation in troublesome scenarios. However, the new method improves their behavior in open spaces. The performance of this method is illustrated with experimental results obtained with a robotic wheelchair vehicle.
Probfuscation: An Obfuscation Approach Using Probabilistic Control Flows

Sensitive parts of a program, such as proprietary algorithms or licensing information, are often protected with the help of code obfuscation techniques. Many obfuscation schemes transform the control flow of the protected program. Typically, the control flow of obfuscated programs is deterministic, i. e., recorded execution traces do not differ for multiple executions using the same input values. An adversary can take advantage of this behavior and create multiple traces to perform analyses on the target program in order to deobfuscate it. In this paper, we introduce an obfuscation approach which yields probabilistic control flow within a given method. That is, for the same input values, multiple execution traces differ, whilst preserving semantics. This effectively renders analyses relying on multiple traces impractical. We have implemented a prototype and applied it to multiple different programs. Our experimental results show that our approach can be used effectively to ensure divergent traces for the same input values and it can significantly improve the resilience against dynamic analysis.
FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization

Probabilistic point-set registration methods have been gaining more attention for their robustness to noise, outliers and occlusions. However, these methods tend to be much slower than the popular iterative closest point (ICP) algorithms, which severely limits their usability. In this paper, we contribute a novel probabilistic registration method that achieves state-of-the-art robustness as well as substantially faster computational performance than modern ICP implementations. This is achieved using a rigorous yet computationally-efficient probabilistic formulation. Pointset registration is cast as a maximum likelihood estimation and solved using the EM algorithm. We show that with a simple augmentation, the E step can be formulated as a filtering problem, allowing us to leverage advances in efficient Gaussian filtering methods. We also propose a customized permutohedral filter  for improved efficiency while retaining sufficient accuracy for our task. Additionally, we present a simple and efficient twist parameterization that generalizes our method to the registration of articulated and deformable objects. For articulated objects, the complexity of our method is almost independent of the Degrees Of Freedom (DOFs), which makes it highly efficient even for high DOF systems. The results demonstrate the proposed method consistently outperforms many competitive baselines on a variety of registration tasks. The video demo and source code are available on our project page.
Design, modeling and control of a 5-DoF light-weight robot arm for aerial manipulation

The design, modeling and control of a 5 degrees-of-freedom light-weight robot manipulator is presented in this paper. The proposed robot arm, named Prisma Ultra-Lightweight 5 ARm (PUL5AR), is employed to execute manipulation tasks equipped on board of a vertical take-off and landing unmanned aerial vehicle. The arm is compact and light-weight. Its mechanics is designed such that it can fold on itself during landing manoeuvres. Moreover, the design is conceived to constrain the center of gravity of the arm as close as possible to vehicle base, thus reducing the total inertia and static unbalancing of the system. Experimental tests have been carried out in order to validate the dynamic model, the communication library, the developed electronics, and the control schemes implemented for the designed robot arm.
Camera/Laser/GPS Fusion Method for Vehicle Positioning Under Extended NIS-Based Sensor Validation

Vehicle localization and autonomous navigation consist of precisely positioning a vehicle on road by the use of different kinds of sensors. This paper presents a vehicle localization method by integrating a stereoscopic system, a laser range finder (LRF) and a global localization sensor GPS. For more accurate LRF-based vehicle motion estimation, an outlier-rejection invariant closest point method (ICP) is proposed to reduce the matching ambiguities of scan alignment. The fusion approach starts by a sensor selection step that is applied to validate the coherence of the observations from different sensors. Then the information provided by the validated sensors is fused with an unscented information filter. To demonstrate its performance, the proposed multisensor localization method is tested with real data and evaluated by RTK-GPS data as ground truth. The fusion approach also facilitates the incorporation of more sensors if needed.
A data modem for GSM Adaptive Multi Rate voice channel

This paper considers the problem of digital data transmission through the Global System for Mobile communications (GSM). A data modem is presented that utilizes codebooks of Speech-Like (SL) symbols to transmit data through the GSM Adaptive Multi Rate (AMR) voice codec. Using this codebook of finite alphabet, the continuous vocoder channel is modeled by a Discrete Memory less Channel (DMC). A heuristic optimization algorithm is proposed to select codebook symbols from a database of observed human speech such that the capacity of DMC is maximized. Simulation results show that the proposed data modem achieves higher data rates and lower symbol error rates compared to previously reported results while requiring lower computational complexity for codebook optimization.