nils-herrmann/clean_scirepeval huggingface dataset:
Software Product Line Engineering - Foundations, Principles, and Techniques

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Single-view modelling of free-form scenes

This paper presents a novel approach for reconstructing free-form, texture-mapped, 3D scene models from a single painting or photograph. Given a sparse set of user-specified constraints on the local shape of the scene, a smooth 3D surface that satisfies the constraints is generated. This problem is formulated as a constrained variational optimization problem. In contrast to previous work in single view reconstruction, our technique enables high quality reconstructions of free-form curved surfaces with arbitrary reflectance properties. A key feature of the approach is a novel hierarchical transformation technique for accelerating convergence on a non-uniform, piecewise continuous grid. The technique is interactive and updates the model in real time as constraints are added, allowing fast reconstruction of photorealistic scene models. The approach is shown to yield high quality results on a large variety of im-
Classifier Systems and Genetic Algorithms

Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm). They typically operate in environments that exhibit one or more of the following characteristics: (1) perpetually novel events accompanied by large amounts of noisy or irrelevant data; (2) continual, often real-time, requirements for action; (3) implicitly or inexactly defined goals; and (4) sparse payoff or reinforcement obtainable only through long action sequences. Classifier systems are designed to absorb new information continuously from such environments, devising sets of competing hypotheses (expressed as rules) without disturbing significantly capabilities already acquired. This paper reviews the definition, theory, and extant applications of classifier systems, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifier systems.
Research on the Forecast of Shared Bicycle Rental Demand Based on Spark Machine Learning Framework

In recent years, the shared bicycle project has developed rapidly. In use of shared bicycles, a great deal of user riding information is recorded. How to extract effective knowledge from these vast amounts of information, how to use this knowledge to improve the shared bicycle system, and how to improve the user experience, are problems to solve. Citi Bike is selected as the research target. Data on Citi Bike’s user historical behavior, weather information, and holiday information are collected from three different sources, and converted into appropriate formats for model training. Spark MLlib is used to construct three different predictive models, advantages and disadvantages of different forecasting models are compared. Some techniques are used to enhance the accuracy of random forests model. The experimental results show that the root mean square error RMSE of the final model is reduced from 305.458 to 243.346.
Quarter-Mode Substrate Integrated Waveguide and Its Application to Antennas Design

A quarter-mode substrate integrated waveguide (QMSIW) which is the quadrant sector of a square waveguide resonator is proposed and investigated in this paper. The QMSIW is realized by bisecting the half-mode substrate integrated waveguide (HMSIW) into two parts along the fictitious quasi-magnetic wall when it operates with TE101 and TE202 modes as the way bisecting the substrate integrated waveguide (SIW) to HMSIW. The QMSIW can almost preserve the field distribution of original SIW and leaky wave is achieved from the dielectric aperture of the QMSIW. When the feeding port is placed at one corner of the QMSIW, a linearly polarized radiation is obtained when the QMSIW resonates in TE101QM mode, and when the QMSIW resonates in TE202QM mode, a circularly polarized (CP) wave is achieved. An antenna is designed, fabricated, and measured based on the proposed QMSIW. The measurement results match with the simulation results very well.
Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Pattern Detection

Holistically detecting interstitial lung disease (ILD) patterns from CT images is challenging yet clinically important. Unfortunately, most existing solutions rely on manually provided regions of interest, limiting their clinical usefulness. In addition, no work has yet focused on predicting more than one ILD from the same CT slice, despite the frequency of such occurrences. To address these limitations, we propose two variations of multi-label deep convolutional neural networks (CNNs). The first uses a deep CNN to detect the presence of multiple ILDs using a regression-based loss function. Our second variant further improves performance, using spatially invariant Fisher Vector encoding of the CNN feature activations. We test our algorithms on a dataset of 533 patients using five-fold cross-validation, achieving high area-under-curve (AUC) scores of 0.982, 0.972, 0.893 and 0.993 for Ground Glass, Reticular, Honeycomb and Emphysema, respectively. As such, our work represents an important step forward in providing clinically effective ILD detection.
A Survey of Green Mobile Networks: Opportunities and Challenges

The explosive development of Information and Communication Technology (ICT) has significantly enlarged both the energy demands and the CO2 emissions, and consequently contributes to make the energy crisis and global warming problems worse. However, as the main force of the ICT field, the mobile networks, are currently focusing on the capacity, variety and stability of the communication services, without paying too much severe concerns on the energy X. Wang · M. Chen · T. T. Kwon (B) School of Computer Science and Engineering, Seoul National University, Seoul, South Korea e-mail: tkkwon@snu.ac.kr X. Wang e-mail: dobby@mmlab.snu.ac.kr M. Chen School of Computer Science and Technology, Huazhong University of Science and Technology, Wuan, China e-mail: minchen@ieee.org A. V. Vasilakos Department of Computer and Telecomm. Engineering, University of Western Macedonia, Macedonia, Greece e-mail: vasilako@ath.forthnet.gr Y. Liu TNLIST and School of Software, Tsinghua University, Tsinghua, China e-mail: yunhao@greenorbs.com Y. Liu Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong efficiency. The escalating energy costs and environmental concerns have already created an urgent need for more energy-efficient “green” wireless communications. In this paper, we survey and discuss various remarkable techniques toward green mobile networks, to mainly targeting mobile cellular networks. We also summarize the current research projects related to green mobile networks, along with the taxonomy of energy-efficiency metrics. We finally discuss and elaborate future research opportunities and design challenges for green mobile networks.
Logic design with unipolar memristors

Memristors are novel devices that could naturally be designed as memory elements. Recently, several methods of designing memristors for logic operations have been proposed. These methods, mostly, make use of bipolar memristors. In this paper, we propose a method for performing logic with unipolar memristors based on OR and NOT logic gates. An integration of the basic building blocks into more complicated logic functions is described and demonstrated. Our results indicate that any logic function could be performed using an external controller. Thus, adding the capability of performing logic computations to numerous types of unipolar memristive materials in addition to their memory capability.
The effective use of Benford’s Law to assist in detecting fraud in accounting data

Benford’s law has been promoted as providing the auditor with a tool that is simple and effective for the detection of fraud. The purpose of this paper is to assist auditors in the most effective use of digital analysis based on Benford’s law. The law is based on a peculiar observation that certain digits appear more frequently than others in data sets. For example, in certain data sets, it has been observed that more than 30% of numbers begin with the digit one. After discussing the background of the law and development of its use in auditing, we show where digital analysis based on Benford’s law can most effectively be used and where auditors should exercise caution. Specifically, we identify data sets which can be expected to follow Benford’s distribution, discuss the power of statistical tests, types of frauds that would be detected and not be detected by such analysis, the potential problems that arise when an account contains too few observations, as well as issues related to base rate of fraud. An actual example is provided demonstrating where Benford’s law proved successful in identifying fraud in a population of accounting data.
Evidence of Time-Dependent Vertical Breakdown in GaN-on-Si HEMTs

This paper demonstrates and investigates the time-dependent vertical breakdown of GaN-on-Si power transistors. The study is based on electrical characterization, dc stress tests and electroluminescence measurements. We demonstrate the following original results: 1) when submitted to two-terminal (drain-to-substrate) stress, the AlGaN/GaN transistors show a time-dependent degradation process, which leads to the catastrophic failure of the devices; 2) time-to-failure follows a Weibull distribution and is exponentially dependent on stress voltage; 3) the degradation mechanism is strongly field dependent and weakly thermally activated, with an activation energy of 0.25 eV; and 4) emission microscopy suggests that vertical current flows under the whole drain area, possibly through extended defects. The catastrophic failure occurs at random positions under the drain contact. The time-dependent failure is ascribed to a percolation process activated by the high-electric field that leads to the generation of localized shunt paths between drain and substrate.
Enterprise: breadth-first graph traversal on GPUs

The Breadth-First Search (BFS) algorithm serves as the foundation for many graph-processing applications and analytics workloads. While Graphics Processing Unit (GPU) offers massive parallelism, achieving high-performance BFS on GPUs entails efficient scheduling of a large number of GPU threads and effective utilization of GPU memory hierarchy. In this paper, we present Enterprise, a new GPU-based BFS system that combines three techniques to remove potential performance bottlenecks: (1) streamlined GPU threads scheduling through constructing a frontier queue without contention from concurrent threads, yet containing no duplicated frontiers and optimized for both top-down and bottom-up BFS. (2) GPU workload balancing that classifies the frontiers based on different out-degrees to utilize the full spectrum of GPU parallel granularity, which significantly increases thread-level parallelism; and (3) GPU based BFS direction optimization quantifies the effect of hub vertices on direction-switching and selectively caches a small set of critical hub vertices in the limited GPU shared memory to reduce expensive random data accesses. We have evaluated Enterprise on a large variety of graphs with different GPU devices. Enterprise achieves up to 76 billion traversed edges per second (TEPS) on a single NVIDIA Kepler K40, and up to 122 billion TEPS on two GPUs that ranks No. 45 in the Graph 500 on November 2014. Enterprise is also very energy-efficient as No. 1 in the GreenGraph 500 (small data category), delivering 446 million TEPS per watt.
Supervised Learning of Semantics-Preserving Hashing via Deep Neural Networks for Large-Scale Image Search

This paper presents a supervised deep hashing approach that constructs binary hash codes from labeled data for large-scale image search. We assume that semantic labels are governed by a set of latent attributes in which each attribute can be on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semanticspreserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network in which binary codes are learned by the optimization of an objective function defined over classification error and other desirable properties of hash codes. With this design, SSDH has a nice property that classification and retrieval are unified in a single learning model, and the learned binary codes not only preserve the semantic similarity between images but also are efficient for image search. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a pointwised manner and thus is naturally scalable to large-scale datasets. SSDH is simple and can be easily realized by a slight modification of an existing deep architecture for classification; yet it is effective and outperforms other unsupervised and supervised hashing approaches on several benchmarks and one large dataset comprising more than 1 million images.
A retrieval model for both recognition and recall.

ion of categorical information. Journal of Experimental Psychology: Learning, Memory, and Cognition. Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning.  207238. Mclcalfe, J., & Murdock, B. B., Jr. (1981). An encoding and retrieval model of single-trial free recall. Journal of Verbal Learning and Verbal Behavior, 20, 161-189. Murdock, B. B., Jr. (1962). The serial position effect in free recall. Journal oj Experimental Psychology, 64, 482488. Murdock, B. B., Jr. (1974).  Potomac, MD: Erlbaum. Murdock, B. B., Jr. (1982). A theory for the storage and retrieval of item and associative information.  Encoding, storage and retrieval of item information. In R. L. Solso (Ed.), Information processing and cognition: The Loyola symposium. Hillsdale,  Erlbaum. Murdock, B. B., Jr., & Dufty, P. O. (1972). Strength theory and recognition memory. Journal of Experimental Psychology, 94, 284-290. Muter, P. (in press). Recognition and recall of words with a single meaning. Journal of Experimental Psychology: Learning, Memory, and Cognition. Nairnc, J. S. (1983). Associative processing during rote rehearsal. Journal of Experimental Psychology: Learning, Memory, and Cognition, 9, 3-20. Neely, J. H., & Balota, D. A. (1981). Test expectancy and semantic organization effects in recall and recognition. Memory and Cognition, 9, 283-306. Neely, J. H., Schmidt, S. R., & Roediger, H. L. III. (1983). Inhibition from related primes in recognition memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 9, 196-211. Nelson, D. L., & Davis, M. J. (1972). Transfer and false recognitions based on phonetic identities of words. Journal of Experimental Psychology, 92, 347-353. Norman, D. A., & Wickelgren, W. A. (1969). Strength theory of decision rules and latency in short-term memory.  (1973). Organizational factors in high-speed scanning. Journal of Experimental Psychology, 101, 77-81. Pachella, R. G. (1974). An interpretation of reaction time in information processing research. In B. Kantowitz (Ed.), Human information processing: Tutorials in performance and cognition. Hillsdale,  Erlbaum. Pike, R. (1973). Response latency models for signal detection.  (1977). A multipleobservations model for response latency and the latencies of correct and incorrect responses in recognition memory. Memory and Cognition, 5, 580-589. Raaijmakers, J. G. W., & Shiflrin, R. M. (1980).  A theory of probabilistic search of associative memory. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 14).  Raaijmakers, J. G. W., & Shifrrin, R. M. (1981a). Order effects in recall. In A. Long & A. Baddeley (Eds.), Attention and performance(Vol. 9). Hillsdale,  Raaijmakers, J. G. W., & Shiffrin, R. M. (198Ib). Search of Associative Memory.
A machine learning approach for stock price prediction

Data mining and machine learning approaches can be incorporated into business intelligence (BI) systems to help users for decision support in many real-life applications. Here, in this paper, we propose a machine learning approach for BI applications. Specifically, we apply structural support vector machines (SSVMs) to perform classification on complex inputs such as the nodes of a graph structure. We connect collaborating companies in the information technology sector in a graph structure and use an SSVM to predict positive or negative movement in their stock prices. The complexity of the SSVM cutting plane optimization problem is determined by the complexity of the separation oracle. It is shown that (i) the separation oracle performs a task equivalent to maximum a posteriori (MAP) inference and (ii) a minimum graph cutting algorithm can solve this problem in the stock price case in polynomial time. Experimental results show the practicability of our proposed machine learning approach in predicting stock prices.
A Novel Transient Wrinkle Detection Algorithm and Its Application for Expression Synthesis

Because facial wrinkle is a representative feature of facial expression, automatic wrinkle detection has been an important and challenging topic for expression simulation, recognition, and animation. Recently, most works about wrinkle detection have focused on permanent wrinkles (e.g., age wrinkles), which are usually linear shapes, whereas the detection of transient wrinkles (e.g., expression wrinkles) has not been sufficiently studied because of their shape diversity and complexity. In this work, a novel algorithm for automatic detection of transient wrinkles with linear, fixed, and chaotic shapes is proposed, which largely consists of edge pair matching, active-appearance-model-based wrinkle structure location, and support-vector-machine-based wrinkle classification. The proposed wrinkle detector is applied for expression synthesis and an improved Poisson wrinkle mapping approach is proposed. Experimental results illustrate the competitiveness of the proposed wrinkle detector in detecting different transient wrinkles. Compared with state-of-the-art algorithms, the proposed approach yields complete and accurate wrinkle centers. The expression synthesized by the improved wrinkle mapping is also much more realistic.
Correlating intrusion events and building attack scenarios through attack graph distances

We map intrusion events to known exploits in the network attack graph, and correlate the events through the corresponding attack graph distances. From this, we construct attack scenarios, and provide scores for the degree of causal correlation between their constituent events, as well as an overall relevancy score for each scenario. While intrusion event correlation and attack scenario construction have been previously studied, this is the first treatment based on association with network attack graphs. We handle missed detections through the analysis of network vulnerability dependencies, unlike previous approaches that infer hypothetical attacks. In particular, we quantify lack of knowledge through attack graph distance. We show that low-pass signal filtering of event correlation sequences improves results in the face of erroneous detections. We also show how a correlation threshold can be applied for creating strongly correlated attack scenarios. Our model is highly efficient, with attack graphs and their exploit distances being computed offline. Online event processing requires only a database lookup and a small number of arithmetic operations, making the approach feasible for real-time applications.
Edge Detection with Embedded Confidence

ÐComputing the weighted average of the pixel values in a window is a basic module in many computer vision operators. The process is reformulated in a linear vector space and the role of the different subspaces is emphasized. Within this framework wellknown artifacts of the gradient-based edge detectors, such as large spurious responses can be explained quantitatively. It is also shown, that template matching with a template derived from the input data is meaningful since it provides an independent measure of confidence in the presence of the employed edge model. The widely used three-step edge detection procedure: gradient estimation, nonmaxima suppression, hysteresis thresholding; is generalized to include the information provided by the confidence measure. The additional amount of computation is minimal and experiments with several standard test images show the ability of the new procedure to detect weak edges. Index TermsÐEdge detection, performance assessment, gradient estimation, window operators.