nils-herrmann/clean_scirepeval huggingface dataset:
Fast userspace packet processing

In recent years, we have witnessed the emergence of high speed packet I/O frameworks, bringing unprecedented network performance to userspace. Using the Click modular router, we first review and quantitatively compare several such packet I/O frameworks, showing their superiority to kernel-based forwarding. We then reconsider the issue of software packet processing, in the context of modern commodity hardware with hardware multi-queues, multi-core processors and non-uniform memory access. Through a combination of existing techniques and improvements of our own, we derive modern general principles for the design of software packet processors. Our implementation of a fast packet processor framework, integrating a faster Click with both Netmap and DPDK, exhibits up-to about 2.3x speed-up compared to other software implementations, when used as an IP router.
Robust sensor-based grasp primitive for a three-finger robot hand

This paper addresses the problem of robot grasping in conditions of uncertainty. We propose a grasp controller that deals robustly with this uncertainty using feedback from different contact-based sensors. This controller assumes a description of grasp consisting of a primitive that only determines the initial configuration of the hand and the control law to be used. We exhaustively validate the controller by carrying out a large number of tests with different degrees of inaccuracy in the pose of the target objects and by comparing it with results of a naive grasp controller.
An Expectation Propagation Perspective on Approximate Message Passing

An alternative derivation for the well-known approximate message passing (AMP) algorithm proposed by Donoho is presented in this letter. Compared with the original derivation, which exploits central limit theorem and Taylor expansion to simplify belief propagation (BP), our derivation resorts to expectation propagation (EP) and the neglect of high-order terms in large system limit. This alternative derivation leads to a different yet provably equivalent form of message passing, which explicitly establishes the intrinsic connection between AMP and EP, thereby offering some new insights in the understanding and improvement of AMP.
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights

We derive a second-order ordinary differential equation (ODE), which is the limit of Nesterov’s accelerated gradient method. This ODE exhibits approximate equivalence to Nesterov’s scheme and thus can serve as a tool for analysis. We show that the continuous time ODE allows for a better understanding of Nesterov’s scheme. As a byproduct, we obtain a family of schemes with similar convergence rates. The ODE interpretation also suggests restarting Nesterov’s scheme leading to an algorithm, which can be rigorously proven to converge at a linear rate whenever the objective is strongly convex.
A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.
A programmable parallel accelerator for learning and classification

For learning and classification workloads that operate on large amounts of unstructured data with stringent performance constraints, general purpose processor performance scales poorly with data size. In this paper, we present a programmable accelerator for this workload domain. To architect the accelerator, we profile five representative workloads, and find that their computationally intensive portions can be formulated as matrix or vector operations generating large amounts of intermediate data, which are then reduced by a secondary operation such as array ranking, finding max/min and aggregation. The proposed accelerator, called MAPLE, has hundreds of simple processing elements (PEs) laid out in a two-dimensional grid, with two key features. First, it uses in-memory processing where on-chip memory blocks perform the secondary reduction operations. By doing so, the intermediate data are dynamically processed and never stored or sent off-chip. Second, MAPLE uses banked off-chip memory, and organizes its PEs into independent groups each with its own off-chip memory bank. These two features together allow MAPLE to scale its performance with data size. This paper describes the MAPLE architecture, explores its design space with a simulator, and illustrates how to automatically map application kernels to the hardware. We also implement a 512-PE FPGA prototype of MAPLE and find that it is 1.5-10x faster than a 2.5 GHz quad-core Xeon processor despite running at a modest 125 MHz.
Simple Method for High-Performance Digit Recognition Based on Sparse Coding

In this brief paper, we propose a method of feature extraction for digit recognition that is inspired by vision research: a sparse-coding strategy and a local maximum operation. We show that our method, despite its simplicity, yields state-of-the-art classification results on a highly competitive digit-recognition benchmark. We first employ the unsupervised Sparsenet algorithm to learn a basis for representing patches of handwritten digit images. We then use this basis to extract local coefficients. In a second step, we apply a local maximum operation to implement local shift invariance. Finally, we train a support vector machine (SVM) on the resulting feature vectors and obtain state-of-the-art classification performance in the digit recognition task defined by the MNIST benchmark. We compare the different classification performances obtained with sparse coding, Gabor wavelets, and principal component analysis (PCA). We conclude that the learning of a sparse representation of local image patches combined with a local maximum operation for feature extraction can significantly improve recognition performance.
Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions

This issue of JAMIA focuses on natural language processing (NLP) techniques for clinical-text information extraction. Several articles are offshoots of the yearly ‘Informatics for Integrating Biology and the Bedside’ (i2b2) (http://www.i2b2.org) NLP shared-task challenge, introduced by Uzuner et al (see page 552) and cosponsored by the Veteran’s Administration for the last 2 years. This shared task follows long-running challenge evaluations in other fields, such as the Message Understanding Conference (MUC) for information extraction, TREC for text information retrieval, and CASP for protein structure prediction. Shared tasks in the clinical domain are recent and include annual i2b2 Challenges that began in 2006, a challenge for multi-label classification of radiology reports sponsored by Cincinnati Children’s Hospital in 2007, a 2011 Cincinnati Children’s Hospital challenge on suicide notes, and the 2011 TREC information retrieval shared task involving retrieval of clinical cases from narrative records. Although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications for clinical text has been slow and lags behind progress in the general NLP domain. There are several barriers to NLP development in the clinical domain, and shared tasks like the i2b2/VA Challenge address some of these barriers. Nevertheless, many barriers remain and unless the community takes a more active role in developing novel approaches for addressing the barriers, advancement and innovation will continue to be slow.
A Self-adaptation Method for Human Skin Segmentation based on Seed Growing

Human skin segmentation has several applications in image and video processing fields, whose main purpose is to distinguish image portions between skin and non-skin regions. Despite the large number of methods available in the literature, accurate skin segmentation is still a challenging task. Many methods rely on color information, which does not completely discriminate the image regions due to variations in lighting conditions and ambiguity between skin and background color. Therefore, there is still need to adapt the segmentation to particular conditions of the images. In contrast to the methods that rely on faces, hands or any other body content detector, we describe a self-contained method for adaptive skin segmentation that makes use of spatial analysis to produce regions from which the overall skin can be estimated. A comparison with state-of-the-art methods using a well known challenging data set shows that our method provides significant improvement on the skin segmentation.
Quality-biased ranking for queries with commercial intent

Modern search engines are good enough to answer popular commercial queries with mainly highly relevant documents. However, our experiments show that users behavior on such relevant commercial sites may differ from one to another web-site with the same relevance label. Thus search engines face the challenge of ranking results that are equally relevant from the perspective of the traditional relevance grading approach. To solve this problem we propose to consider additional facets of relevance, such as trustability, usability, design quality and the quality of service. In order to let a ranking algorithm take these facets in account, we proposed a number of features, capturing the quality of a web page along the proposed dimensions. We aggregated new facets into the single label, commercial relevance, that represents cumulative quality of the site. We extrapolated commercial relevance labels for the entire learning-to-rank dataset and used weighted sum of commercial and topical relevance instead of default relevance labels. For evaluating our method we created new DCG-like metrics and conducted off-line evaluation as well as on-line interleaving experiments demonstrating that a ranking algorithm taking the proposed facets of relevance into account is better aligned with user preferences.
A Visual Approach to Sketched Symbol Recognition

There is increasing interest in building systems that can automatically interpret hand-drawn sketches. However, many challenges remain in terms of recognition accuracy, robustness to different drawing styles, and ability to generalize across multiple domains. To address these challenges, we propose a new approach to sketched symbol recognition that focuses on the visual appearance of the symbols. This allows us to better handle the range of visual and stroke-level variations found in freehand drawings. We also present a new symbol classifier that is computationally efficient and invariant to rotation and local deformations. We show that our method exceeds state-of-the-art performance on all three domains we evaluated, including handwritten digits, PowerPoint shapes, and electrical circuit symbols.
Transformation of generalized chebyshev lowpass filter prototype to Suspended Stripline Structure highpass filter for wideband communication systems

This paper presents the transformation of generalized Chebyshev lowpass filter prototype to highpass filter using Suspended Stripline Structure (SSS) technology. The study involves circuit analysis to determine generalized Chebyshev responses with a transmission zero at finite frequency. The transformation of the highpass filter from the lowpass filter prototype provides a cutoff frequency of 3.1 GHz with a return loss better than -20 dB. The design is simulated on a Roger Duroid RO4350 with a dielectric constant, εr of 3.48 and a thickness of 0.168 mm. The simulation performance results show promising results that could be further examined during the experimental works. This class of generalized Chebyshev highpass filter with finite transmission zero would be useful in any RF/ microwave communication systems particularly in wideband applications where the reduction of overall physical volume and weight as well as cost very important, while maintaining its excellent performance.
Automotive big data: Applications, workloads and infrastructures

Data is increasingly affecting the automotive industry, from vehicle development, to manufacturing and service processes, to online services centered around the connected vehicle. Connected, mobile and Internet of Things devices and machines generate immense amounts of sensor data. The ability to process and analyze this data to extract insights and knowledge that enable intelligent services, new ways to understand business problems, improvements of processes and decisions, is a critical capability. Hadoop is a scalable platform for compute and storage and emerged as de-facto standard for Big Data processing at Internet companies and in the scientific community. However, there is a lack of understanding of how and for what use cases these new Hadoop capabilities can be efficiently used to augment automotive applications and systems. This paper surveys use cases and applications for deploying Hadoop in the automotive industry. Over the years a rich ecosystem emerged around Hadoop comprising tools for parallel, in-memory and stream processing (most notable MapReduce and Spark), SQL and NOSQL engines (Hive, HBase), and machine learning (Mahout, MLlib). It is critical to develop an understanding of automotive applications and their characteristics and requirements for data discovery, integration, exploration and analytics. We then map these requirements to a confined technical architecture consisting of core Hadoop services and libraries for data ingest, processing and analytics. The objective of this paper is to address questions, such as: What applications and datasets are suitable for Hadoop? How can a diverse set of frameworks and tools be managed on multi-tenant Hadoop cluster? How do these tools integrate with existing relational data management systems? How can enterprise security requirements be addressed? What are the performance characteristics of these tools for real-world automotive applications? To address the last question, we utilize a standard benchmark (TPCx-HS), and two application benchmarks (SQL and machine learning) that operate on a dataset of multiple Terabytes and billions of rows.
Data-driven 3D Voxel Patterns for object category recognition

Despite the great progress achieved in recognizing objects as 2D bounding boxes in images, it is still very challenging to detect occluded objects and estimate the 3D properties of multiple objects from a single image. In this paper, we propose a novel object representation, 3D Voxel Pattern (3DVP), that jointly encodes the key properties of objects including appearance, 3D shape, viewpoint, occlusion and truncation. We discover 3DVPs in a data-driven way, and train a bank of specialized detectors for a dictionary of 3DVPs. The 3DVP detectors are capable of detecting objects with specific visibility patterns and transferring the meta-data from the 3DVPs to the detected objects, such as 2D segmentation mask, 3D pose as well as occlusion or truncation boundaries. The transferred meta-data allows us to infer the occlusion relationship among objects, which in turn provides improved object recognition results. Experiments are conducted on the KITTI detection benchmark [17] and the outdoor-scene dataset [41]. We improve state-of-the-art results on car detection and pose estimation with notable margins (6% in difficult data of KITTI). We also verify the ability of our method in accurately segmenting objects from the background and localizing them in 3D.
Text mining for the biocuration workflow

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on 'Text Mining for the BioCuration Workflow' at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.
Combining Models and Exemplars for Face Recognition : An Illuminating Example

We propose a modeland exemplar-based approach for face recognition. This problem has been previously tackled using either models or exemplars, with limited success. Our idea uses models to synthesize many more exemplars, which are then used in the learning stage of a face recognition system. To demonstrate this, we develop a statistical shape-fromshading model to recover face shape from a single image, and to synthesize the same face under new illumination. We then use this to build a simple and fast classifier that was not possible before because of a lack of training data.
A Novel Implementation of Phase Control Technique for Speed Control of Induction Motor Using ARDUINO

Induction motors are the most widely used motors for appliances, industrial control and automation. However there arises a problem in voltage levels, which affects the speed of induction motor. As the voltage (V) is directly proportional to speed (N), we need to control the stator voltage which controls the speed proportionally. In this Paper a novel Open loop phase control method is developed by coding a program using ARDUINO software in which ARDUINO controller takes input from the user and generates firing pulses for the TRIAC which controls the speed of the Induction motor. The total process is executed with the help of an ARDUINO controller kit where ARDUINO and Tera-Term softwares are used for Micro Controller and for serial monitor. This results in variable speed control of Induction motor.
Design of an Ultra-Short Pulse Generator

An ultra-short pulse generator is presented, whose frequency, pulse width and amplitude are tunable. The design method is based on Marx circuit, which is made up of avalanche transistors mainly. The paper describes the avalanche transistor’s characteristic, the basic pulse circuit, the generator’s working principle and the selection of components in detail. Finally, the simulation and experimental results are shown and they are in accordance with each other perfectly. With the features such as simple structure, stable and reliable performance and low cost, the ultra-short pulse generator can be applied in ultra-wideband wireless communication system. Keyword-pulse generator; short pulse; avalanche transistor; ultrawideband
Sparse matrix solvers on the GPU: conjugate gradients and multigrid

Many computer graphics applications require high-intensity numerical simulation. We show that such computations can be performed efficiently on the GPU, which we regard as a full function streaming processor with high floating-point performance. We implemented two basic, broadly useful, computational kernels: a sparse matrix conjugate gradient solver and a regular-grid multigrid solver. Real time applications ranging from mesh smoothing and parameterization to fluid solvers and solid mechanics can greatly benefit from these, evidence our example applications of geometric flow and fluid simulation running on NVIDIA's GeForce FX.
Real-time tracking management system using GPS, GPRS and Google earth

Due to the high cost of fossil-based energy, several methods are proposed to reduce the usage of the energy in logistics and fleet management to be even more. GPS tracking system is a common approach to get vehicle location information in real-time for fleet planning. We proposed a GPS tracking system called Goo-Tracking that is composed of commodity hardware, open source software and an easy-to-manage user interface via a Web server with Google Map or via Google Earth software. The system includes a GPS/GPRS module to location acquisition and message transmission, MMC to temporary store location information, and an 8-bit AVR microcontroller. Our system prototype is shown and tested on a trip from Bangkok to Chonburi. It has shown great stability and also robust message transfer protocol that most of locations are accurately acquired and transmitted to the server in real-time.