nils-herrmann/clean_scirepeval huggingface dataset:
Neon: A (Big) (Fast) Single-Chip 3D Workstation Graphics Accelerator

High-performance 3D graphics accelerators traditionally require multiple chips on multiple boards. Specialized chips perform geometry transformations and lighting computations, rasterizing, pixel processing, and texture mapping. Multiple chip designs are often scalable: they can increase performance by using more chips. Scalability has obvious costs: a minimal configuration needs several chips, and some configurations must replicate texture maps. A less obvious cost is the almost irresistible temptation to replicate chips to increase performance, rather than to design individual chips for higher performance in the first place. In contrast, Neon is a single chip that performs like a multichip design. Neon accelerates OpenGL 3D rendering, as well as X11 and Windows/NT 2D rendering. Since our pin budget limited peak memory bandwidth, we designed Neon from the memory system upward in order to reduce bandwidth requirements. Neon has no special-purpose memories; its eight independent 32-bit memory controllers can access color buffers, Z depth buffers, stencil buffers, and texture data. To fit our gate budget, we shared logic among different operations with similar implementation requirements, and left floating point calculations to Digital's Alpha CPUs. Neon’s performance is between HP’s Visualize fx and fx, and is well above SGI’s MXE for most operations. Neon-based boards cost much less than these competitors, due to a small part count and use of commodity SDRAMs.
Stacked generalization

This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second space whose inputs are (for example) the guesses of the original generalizers when taught with part of the learning set and trying to guess the rest of it, and whose output is (for example) the correct guess. When used with multiple generalizers, stacked generalization can be seen as a more sophisticated version of cross-validation, exploiting a strategy more sophisticated than cross-vali-dation's crude winner-takes-all for combining the individual generalizers. When used with a single generalizer, stacked generalization is a scheme for estimating (and then correcting for) the error of a generalizer which has been trained on a particular learning set and then asked a particular question. After introducing stacked generalization and justifying its use, this paper presents two numerical experiments. The first demonstrates how stacked generalization improves upon a set of separate generalizers for the NETtalk task of translating text to phonemes. The second demonstrates how stacked generalization improves the performance of a single surface-fitter. With the other experimental evidence in the literature, the usual arguments supporting cross-validation, and the abstract justifications presented in this paper, the conclusion is that for almost any real-world generalization problem one should use some version of stacked generalization to minimize the generalization error rate. This paper ends by discussing some of the variations of stacked generalization, and how it touches on other fields like chaos theory.
Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings

Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a finegrained sentiment analysis. For example, master, seasoned and familiar point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise: having a good knowledge of. In this paper, we propose a semisupervised technique that uses sentiment bearing word embeddings to produce a continuous ranking among adjectives that share common semantics. Our system demonstrates a strong Spearman’s rank correlation of 0.83 with the gold standard ranking. We show that sentiment bearing word embeddings facilitate a more accurate intensity ranking system than other standard word embeddings (word2vec and GloVe). Word2vec is the state-of-the-art for intensity ordering task.
An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms

This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implementation of two ICA algorithms (FastICA and InfoMax) with the use of the Mathematica software.
Stress Detection Using Wearable Physiological and Sociometric Sensors

Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and  see text]-nearest neighbor. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real-time stress detection. Finally, we present an study of the most discriminative features for stress detection.
Aerodynamic analysis of variable cant angle winglets for improved aircraft performance

Aircraft performance is highly affected by induced drag caused by wingtip vortices. Winglets, referred to as vertical or angled extensions at aircraft wingtips, are used to minimise vortices formation to improve fuel efficiency. This paper describes a wind tunnel experiment and a Computational Fluid Dynamics (CFD) analysis, performed on a rectangular wing prototype (with and without winglet) of NACA 653218 aerofoil section. The objectives of the analysis were to compare the aerodynamic characteristics and to investigate the performance of winglet at cant angles 0°, 30°, 45° and 60° at various angles of attack (AOA). The experimental analysis was performed in a closed-loop wind tunnel at sea-level conditions and free-stream velocity of 35 m/s. The CFD simulations were performed at low subsonic flow speed in ANSYS CFX solver using Finite Volume Method. Spalart-Allmaras turbulence model and 3-dimensional unstructured tetrahedral mesh were used to compute the flow around the model. The aerodynamic characteristics of lift coefficient (CL), drag coefficient (CD) and lift-to-drag ratio (L/D) were compared and it was found that each winglet configuration at a particular AOA had different CL, CD and L/D values, indicating that fixed winglets do not provide optimum aircraft performance at different phases of flight.
A static hand gesture recognition algorithm using k-mean based radial basis function neural network

The accurate classification of static hand gestures is a vital role to develop a hand gesture recognition system which is used for human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC) application. A vision-based static hand gesture recognition algorithm consists of three stages: preprocessing, feature extraction and classification. The preprocessing stage involves following three sub-stages: segmentation which segments hand region from its background images using a histogram based thresholding algorithm and transforms into binary silhouette; rotation that rotates segmented gesture to make the algorithm, rotation invariant; filtering that effectively removes background noise and object noise from binary image by morphological filtering technique. To obtain a rotation invariant gesture image, a novel technique is proposed in this paper by coinciding the 1st principal component of the segmented hand gestures with vertical axes. A localized contour sequence (LCS) based feature is used here to classify the hand gestures. A k-mean based radial basis function neural network (RBFNN) is also proposed here for classification of hand gestures from LCS based feature set. The experiment is conducted on 500 train images and 500 test images of 25 class grayscale static hand gesture image dataset of Danish/international sign language hand alphabet. The proposed method performs with 99.6% classification accuracy which is better than earlier reported technique.
A memory insensitive technique for large model simplification

In this paper we propose three simple, but significant improvements to the OoCS (Out-of-Core Simplification) algorithm of Lindstrom [20] which increase the quality of approximations and extend the applicability of the algorithm to an even larger class of compute systems.The original OoCS algorithm has memory complexity that depends on the size of the output mesh, but no dependency on the size of the input mesh. That is, it can be used to simplify meshes of arbitrarily large size, but the complexity of the output mesh is limited by the amount of memory available. Our first contribution is a version of OoCS that removes the dependency of having enough memory to hold (even) the simplified mesh. With our new algorithm, the whole process is made essentially independent of the available memory on the host computer. Our new technique uses disk instead of main memory, but it is carefully designed to avoid costly random accesses.Our two other contributions improve the quality of the approximations generated by OoCS. We propose a scheme for preserving surface boundaries which does not use connectivity information, and a scheme for constraining the position of the "representative vertex" of a grid cell to an optimal position inside the cell.
Mining distance-based outliers in near linear time with randomization and a simple pruning rule

Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.
Three-axis NC milling simulation based on adaptive triangular mesh

0360-8352/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.cie.2010.06.016 * Corresponding author. . NC milling simulation has become an important step in computer aided manufacturing (CAM). To achieve real-time simulation, the total number of polygons has to be reduced, which results in poor image quality. This paper presents an adaptive triangular mesh algorithm to reduce the number of polygons while image quality remains high. Binary tree is used to represent the milling surface, and the optimization of the mesh is performed dynamically in the process of simulation. In this algorithm, the resolution of triangles is automatically updated according to local surface flatness, thus greatly reducing the number of triangles at planar regions. By doing this, real-time and high quality of visual presentation is insured and the translation, rotation and zooming operations are still applicable. When machining precision is evaluated, or overcut, undercut and interference are inspected, full resolution model stored in memory is automatically loaded to ensure the accuracy and correctness of these inspections. Finally, an example is presented to illustrate the validity of proposed algorithm.
Hashdoop: A MapReduce framework for network anomaly detection

Anomaly detection is essential for preventing network outages and maintaining the network resources available. However, to cope with the increasing growth of Internet traffic, network anomaly detectors are only exposed to sampled traffic, so harmful traffic may avoid detector examination. In this paper, we investigate the benefits of recent distributed computing approaches for real-time analysis of non-sampled Internet traffic. Focusing on the MapReduce model, our study uncovers a fundamental difficulty in order to detect network traffic anomalies by using Hadoop. Since MapReduce requires the dataset to be divided into small splits and anomaly detectors compute statistics from spatial and temporal traffic structures, special care should be taken when splitting traffic. We propose Hashdoop, a MapReduce framework that splits traffic with a hash function to preserve traffic structures and, hence, profits of distributed computing infrastructures to detect network anomalies. The benefits of Hashdoop are evaluated with two anomaly detectors and fifteen traces of Internet backbone traffic captured between 2001 and 2013. Using a 6-node cluster Hashdoop increased the throughput of the slowest detector with a speed-up of 15; thus, enabling real-time detection for the largest analyzed traces. Hashdoop also improved the overall detectors accuracy as splits emphasized anomalies by reducing the surrounding traffic.
Unicorn: A System for Searching the Social Graph

Unicorn is an online, in-memory social graph-aware indexing system designed to search trillions of edges between tens of billions of users and entities on thousands of commodity servers. Unicorn is based on standard concepts in information retrieval, but it includes features to promote results with good social proximity. It also supports queries that require multiple round-trips to leaves in order to retrieve objects that are more than one edge away from source nodes. Unicorn is designed to answer billions of queries per day at latencies in the hundreds of milliseconds, and it serves as an infrastructural building block for Facebook’s Graph Search product. In this paper, we describe the data model and query language supported by Unicorn. We also describe its evolution as it became the primary backend for Facebook’s search offerings.
Real Time Object Detection Based on FPGA with Big Data

Over recent years, various types of vision-based object detection models are proposed for avoiding traffic accidents or self-driving. However, in vehicle, the electronic control of driving part is achieved by SOC(System On Chip). Due to limitations on data capacity and compute power of SOC, the existing methods cannot be directly implemented to vehicles. In this paper, we present Simple-FBY(Simple-FPGA Based YOLO), a vehicle and pedestrian detection model based on FPGA which is a real time, high speed, low power computing platform. It can provide accuracy, speed and be capable of learning. To this end, we introduce a specifically designed training data set, which consists of pictures of other public self-driving data sets and a Beijing's streets data set collecting by ourselves. Compared with state-of-the-art model YOLO, our FPGA-based model can provide high performance per watt of power consumption. We also adjust the YOLO algorithm to make it more adaptable to SOC. In the experiment, we compare performance of Simple-FBY on GPU and Xilinx Zynq-Z1 FPGA Board. As a result, our model achieves better accuracy on vehicle and pedestrian detection and shows a good speed performance at FPGA platform.
Demand-Weighted Completeness Prediction for a Knowledge Base

In this paper we introduce the notion of Demand-Weighted Completeness, allowing estimation of the completeness of a knowledge base with respect to how it is used. Defining an entity by its classes, we employ usage data to predict the distribution over relations for that entity. For example, instances of person in a knowledge base may require a birth date, name and nationality to be considered complete. These predicted relation distributions enable detection of important gaps in the knowledge base, and define the required facts for unseen entities. Such characterisation of the knowledge base can also quantify how usage and completeness change over time. We demonstrate a method to measure DemandWeighted Completeness, and show that a simple neural network model performs well at this prediction task.
Real-Time Continuous Pose Recovery of Human Hands Using Convolutional Networks

We present a novel method for real-time continuous pose recovery of markerless complex articulable objects from a single depth image. Our method consists of the following stages: a randomized decision forest classifier for image segmentation, a robust method for labeled dataset generation, a convolutional network for dense feature extraction, and finally an inverse kinematics stage for stable real-time pose recovery. As one possible application of this pipeline, we show state-of-the-art results for real-time puppeteering of a skinned hand-model.
A Microstrip Ultra-Wideband Bandpass Filter With Cascaded Broadband Bandpass and Bandstop Filters

This paper develops a novel ultra-wideband bandpass filter by cascading a broadband bandpass filter with another broadband bandstop filter. Properly selected impedances of transmission lines achieve broadband bandpass and bandstop filters and make independent designs possible. Detailed design and synthesis procedures are provided; moreover, agreement between measured and theoretically predicted results demonstrates feasibility of the proposed filter. Due to its simple structure, the ultra-wideband bandpass filter newly introduced in this paper is suitable for integration in the single-chipped circuit or implementation on printed circuit boards.
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users

Personalized recommendations for new users, also known as the cold-start problem, can be formulated as a contextual bandit problem. Existing contextual bandit algorithms generally rely on features alone to capture user variability. Such methods are inefficient in learning new users’ interests. In this paper we propose Latent Contextual Bandits. We consider both the benefit of leveraging a set of learned latent user classes for new users, and how we can learn such latent classes from prior users. We show that our approach achieves a better regret bound than existing algorithms. We also demonstrate the benefit of our approach using a large real world dataset and a preliminary user study.
Learning Attractor Dynamics for Generative Memory

A central challenge faced by memory systems is the robust retrieval of a stored pattern in the presence of interference due to other stored patterns and noise. A theoretically well-founded solution to robust retrieval is given by attractor dynamics, which iteratively clean up patterns during recall. However, incorporating attractor dynamics into modern deep learning systems poses difficulties: attractor basins are characterised by vanishing gradients, which are known to make training neural networks difficult. In this work, we avoid the vanishing gradient problem by training a generative distributed memory without simulating the attractor dynamics. Based on the idea of memory writing as inference, as proposed in the Kanerva Machine, we show that a likelihood-based Lyapunov function emerges from maximising the variational lower-bound of a generative memory. Experiments shows it converges to correct patterns upon iterative retrieval and achieves competitive performance as both a memory model and a generative model.
Large-signal-modulation of high-efficiency light-emitting diodes for optical communication

The dynamic behavior of high-efficiency light-emitting diodes (LEDs) is investigated theoretically and experimentally. A detailed theoretical description of the switch-on and switch-off transients of LEDs is derived. In the limit of small-signal modulation, the well-established exponential behavior is obtained. However, in the case of high injection, which is easily reached for thin active layer LEDs, the small-signal time constant is found to be up to a factor of two faster than the radiative recombination lifetime. Using such quantum-well LEDs, we have demonstrated optical data transfer with wide open eye diagrams at bit rates up to 2 Gbit/s. In addition, we have combined the use of thin active layers with the concept of surface-textured thin-film LEDs, which allow a significant improvement in the light extraction efficiency. With LEDs operating at 0.5 Gbit/s and 1 Gbit/s, we have achieved external quantum efficiencies of 36% and 29%, respectively.
Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement

Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. However, this technique is not very well suited to be implemented in consumer electronics, such as television, because the method tends to introduce unnecessary visual deterioration such as the saturation effect. One of the solutions to overcome this weakness is by preserving the mean brightness of the input image inside the output image. This paper proposes a new method, known as brightness preserving dynamic histogram equalization (BPDHE), which is an extension to HE that can produce the output image with the mean intensity almost equal to the mean intensity of the input, thus fulfill the requirement of maintaining the mean brightness of the image. First, the method smoothes the input histogram with one dimensional Gaussian filter, and then partitions the smoothed histogram based on its local maximums. Next, each partition will be assigned to a new dynamic range. After that, the histogram equalization process is applied independently to these partitions, based on this new dynamic range. For sure, the changes in dynamic range, and also histogram equalization process will alter the mean brightness of the image. Therefore, the last step in this method is to normalize the output image to the input mean brightness. Our results from 80 test images shows that this method outperforms other present mean brightness preserving histogram equalization methods. In most cases, BPDHE successfully enhance the image without severe side effects, and at the same time, maintain the mean input brightness1.
On the development of high-throughput and area-efficient multi-mode cryptographic hash designs in FPGAs

In this paper, area-efficient and high-throughput multi-mode architectures for the SHA-1 and SHA-2 hash families are proposed and implemented in several FPGA technologies. Additionally a systematic flow for designing multi-mode architectures (implementing more than one function) of these families is introduced. Compared to the corresponding architectures that are produced by a commercial synthesis tool, the proposed ones are better in terms of both area (at least 40%) and throughput/area (from 32% up to 175%). Finally, the proposed architectures outperform similar existing ones in terms of throughput and throughput/area, from 4.2 up to 279.4 and from 1.2 up to 5.5 , respectively. & 2014 Elsevier B.V. All rights reserved.