nils-herrmann/clean_scirepeval huggingface dataset:
C ] 2 5 N ov 2 01 5 How predictable is technological progress ? ∗

Recently it has become clear that many technologies follow a generalized version of Moore’s law, i.e. costs tend to drop exponentially, at different rates that depend on the technology. Here we formulate Moore’s law as a correlated geometric random walk with drift, and apply it to historical data on 53 technologies. We derive a closed form expression approximating the distribution of forecast errors as a function of time. Based on hind-casting experiments we show that this works well, making it possible to collapse the forecast errors for many different technologies at different time horizons onto the same universal distribution. This is valuable because it allows us to make forecasts for any given technology with a clear understanding of the quality of the forecasts. As a practical demonstration we make distributional forecasts at different time horizons for solar photovoltaic modules, and show how our method can be used to estimate the probability that a given technology will outperform another technology ∗Acknowledgements: We would like to acknowledge Diana Greenwald and Aimee Bailey for their help in gathering and selecting data, as well as Glen Otero for help acquiring data on genomics, Chris Goodall for bringing us up to date on developments in solar PV, and Christopher Llewellyn Smith, Jeff Alstott, Michael Totten, our INET colleagues and three referees for comments. This project was supported by the European Commission project FP7-ICT-2013-611272 (GROWTHCOM) and by the U.S. Dept. of Solar Energy Technologies Office under grant DE-EE0006133. Contacts: doyne.farmer@inet.ox.ac.uk; francois.lafond@inet.ox.ac.uk at a given point in the future.
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults

In the last decade, empirical studies on object-oriented design metrics have shown some of them to be useful for predicting the fault-proneness of classes in object-oriented software systems. This research did not, however, distinguish among faults according to the severity of impact. It would be valuable to know how object-oriented design metrics and class fault-proneness are related when fault severity is taken into account. In this paper, we use logistic regression and machine learning methods to empirically investigate the usefulness of object-oriented design metrics, specifically, a subset of the Chidamber and Kemerer suite, in predicting fault-proneness when taking fault severity into account. Our results, based on a public domain NASA data set, indicate that 1) most of these design metrics are statistically related to fault-proneness of classes across fault severity, and 2) the prediction capabilities of the investigated metrics greatly depend on the severity of faults. More specifically, these design metrics are able to predict low severity faults in fault-prone classes better than high severity faults in fault-prone classes
Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval

Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only time-consuming but also tend to achieve unsatisfactory performance due to the complexity of remote sensing images. In this paper, we investigate how to extract deep feature representations based on convolutional neural networks (CNNs) for high-resolution remote sensing image retrieval (HRRSIR). To this end, several effective schemes are proposed to generate powerful feature representations for HRRSIR. In the first scheme, a CNN pre-trained on a different problem is treated as a feature extractor since there are no sufficiently-sized remote sensing datasets to train a CNN from scratch. In the second scheme, we investigate learning features that are specific to our problem by first fine-tuning the pre-trained CNN on a remote sensing dataset and then proposing a novel CNN architecture based on convolutional layers and a three-layer perceptron. The novel CNN has fewer parameters than the pre-trained and fine-tuned CNNs and can learn low dimensional features from limited labelled images. The schemes are evaluated on several challenging, publicly available datasets. The results indicate that the proposed schemes, particularly the novel CNN, achieve state-of-the-art performance.
Social Media and the Decision to Participate in Political Protest : Observations From Tahrir Square

Based on a survey of participants in Egypt’s Tahrir Square protests, we demonstrate that social media in general, and Facebook in particular, provided new sources of information the regime could not easily control and were crucial in shaping how citizens made individual decisions about participating in protests, the logistics of protest, and the likelihood of success. We demonstrate that people learned about the protests primarily through interpersonal communication using Facebook, phone contact, or face-to-face conversation. Controlling for other factors, social media use greatly increased the odds that a respondent attended protests on the first day. Half of those surveyed produced and disseminated visuals from the demonstrations, mainly through Facebook.
Graph-based Analysis for E-Commerce Recommendation

Recommender systems automate the process of recommending products and services to customers based on various types of data including customer demographics, product features, and, most importantly, previous interactions between customers and products (e.g., purchasing, rating, and catalog browsing). Despite significant research progress and growing acceptance in real-world applications, two major challenges remain to be addressed to implement effective e-commerce recommendation applications. The first challenge is concerned with making recommendations based on sparse transaction data. The second challenge is the lack of a unified framework to integrate multiple types of input data and recommendation approaches. This dissertation investigates graph-based algorithms to address these two problems. The proposed approach is centered on consumer-product graphs that represent sales transactions as links connecting consumer and product nodes. In order to address the sparsity problem, I investigate the network spreading activation algorithms and a newly proposed link analysis algorithm motivated by ideas from Web graph analysis techniques. Experimental results with several e-commerce datasets indicated that both classes of algorithms outperform a wide range of existing collaborative filtering algorithms, especially under sparse data. Two graph-based models that enhance the simple consumerproduct graph were proposed to provide unified recommendation frameworks. The first model, a two-layer graph model, enhances the consumer-product graph by incorporating the consumer/product attribute information as consumer and product similarity links.
Optimization of photovoltaic farm under partial shading effects using artificial intelligent based matrix switch controller

Partial shading on photovoltaic array due to weather conditions reduces the energy generated and worsening the entire photovoltaic farm efficiency. The purpose of this research was to electric physically reconfigurate PV string through switch matrix by control of smart controller. It is great to find current and voltage at TCT point approaching work of range from MPPT, despite being awarded shading effect. The research method was reflected on the algorithms design. The aim of algorithms was to clustering in two steps. The first step was clustering one or more photovoltaic string based on parallelly current. Result of first clustering continued of second step clustering, clustering the first step result in series. The algorithm work smartly by current input from current sensors. Input processing from sensor by smart controller was signal control which for switch on/off the switches function in switch matrix circuit. Switch matrix output will be forwarded to MPPT circuit. This mechanism being novelty of this research. Based on the testing, it's mechanism able to protect the current at 18A level for all cases with each case voltage consecutively 3060V, 1360V, 2040V and 1020 V. By this mechanism, can be optimality power system increased up to 9.S % .similarly be affected or not partial shading effect.
2D electronics - opportunities and limitations

2D materials (2Ds), in particular the 2Ds beyond graphene, have attracted considerable attention in the transistor community. While rapid progress on 2D transistors has been achieved recently, the prospects of 2D electronics is still under debate. In the present paper we discuss our view of the potential of 2D transistors for digital CMOS and for flexible electronics. We show that 2D MOSFETs show promise for ultimately scaled CMOS due their excellent electrostatic integrity and their ability to suppress source-drain tunneling. Moreover, the 2Ds represent a promising option for radio frequency (RF) flexible electronics since they offer an attractive combination of mechanical (bendability, stretchability), and electrical (reasonable mobility) properties.
Music genre classification with word and document vectors

In these days, music genre classification (MGC) is a quite popular research field. The main goal of the MGC studies is automatically detecting music genre (eg., rap, rock). In literature, features are generally extracted from the music's melodic content or lyrics for this task. In this study, we have performed lyrics based MGC on a Turkish dataset. We have just used lyrics as feature source and considered the MGC as a classical text classification problem. However, we represented the features using word (word2vec) and document (doc2vec) vector methods which are quite popular recently. Also, we have compared these methods with traditional Bag of Words (BoW) feature model. In addition, we have investigated the impact of preprocessing steps and vector dimension on both word and document vectors. We have conducted experiments on Support Vector Machine algorithm. Our experimental results show that word vector can be employed for feature representation.
DEVELOPMENT OF SEISMIC VULNERABILITY ASSESSMENT METHODOLOGIES OVER THE PAST 30 YEARS

Models capable of estimating losses in future earthquakes are of fundamental importance for emergency planners and for the insurance and reinsurance industries. One of the main ingredients in a loss model is an accurate, transparent and conceptually sound algorithm to assess the seismic vulnerability of the building stock and indeed many tools and methodologies have been proposed over the past 30 years for this purpose. This paper takes a look at some of the most significant contributions in the field of vulnerability assessment and identifies the key advantages and disadvantages of these procedures in order to distinguish the main characteristics of an ideal methodology.
Geometry-Based Populated Chessboard Recognition

Chessboards are commonly used to calibrate cameras, and many robust methods have been developed to recognize the unoccupied boards. However, when the chessboard is populated with chess pieces, such as during an actual game, the problem of recognizing the board is much harder. Challenges include occlusion caused by the chess pieces, the presence of outlier lines and low viewing angles of the chessboard. In this paper, we present a novel approach to address the above challenges and recognize the chessboard. The Canny edge detector and Hough transform are used to capture all possible lines in the scene. The k-means clustering and a k-nearest-neighbors inspired algorithm are applied to cluster and reject the outlier lines based on their Euclidean distances to the nearest neighbors in a scaled Hough transform space. Finally, based on prior knowledge of the chessboard structure, a geometric constraint is used to find the correspondences between image lines and the lines on the chessboard through the homography transformation. The proposed algorithm works for a wide range of the operating angles and achieves high accuracy in experiments.
We Can "See" You via Wi-Fi - WiFi Action Recognition via Vision-based Methods

Recently, Wi-Fi has caught tremendous attention for its ubiquity. Motivated by Wi-Fi’s low cost and privacy preservation, researchers have been putting lots of investigation into its potential on action recognition and even person identification. In this paper, for bringing a new modality for multimedia community, we offer an comprehensive overview on these two topics in Wi-Fi. Also, through looking at these two topics from an unprecedented perspective, we could achieve generality instead of designing specific ad-hoc features for each scenario. Observing the great resemblance of Channel State Information (CSI, a finegrained information captured from the received Wi-Fi signal) to texture, we propose a brand-new framework based on computer vision methods. To minimize the effect of location dependency embedded in CSI, we propose a novel de-noising method based on Singular Value Decomposition (SVD) to eliminate the background energy and effectively extract the channel information of signals reflected by human bodies. From the experiments conducted, we demonstrate the feasibility and efficacy of the proposed methods. Also, we conclude the factors that would affect the performance and highlight a few potential issues that require further deliberations.
A broadband achromatic metalens in the visible.

Metalenses consist of an array of optical nanoantennas on a surface capable of manipulating the properties of an incoming light wavefront. Various flat optical components, such as polarizers, optical imaging encoders, tunable phase modulators and a retroreflector, have been demonstrated using a metalens design. An open issue, especially problematic for colour imaging and display applications, is the correction of chromatic aberration, an intrinsic effect originating from the specific resonance and limited working bandwidth of each nanoantenna. As a result, no metalens has demonstrated full-colour imaging in the visible wavelength. Here, we show a design and fabrication that consists of GaN-based integrated-resonant unit elements to achieve an achromatic metalens operating in the entire visible region in transmission mode. The focal length of our metalenses remains unchanged as the incident wavelength is varied from 400 to 660 nm, demonstrating complete elimination of chromatic aberration at about 49% bandwidth of the central working wavelength. The average efficiency of a metalens with a numerical aperture of 0.106 is about 40% over the whole visible spectrum. We also show some examples of full-colour imaging based on this design.
Automatic Cloud I/O Configurator for I/O Intensive Parallel Applications

As the cloud platform becomes a promising alternative to traditional HPC (high performance computing) centers or in-house clusters, the I/O bottleneck problem is highlighted in this new environment, typically with top-of-the-line compute instances but sub-par communication and I/O facilities. It has been observed that changing the cloud I/O system configurations, such as choices of file systems, number of I/O servers and their placement strategies, etc., will lead to a considerable variation in the performance and cost efficiency of I/O intensive parallel applications. However, storage system configuration is tedious and error-prone to do manually, even for expert users, leading to solutions that are grossly over-provisioned (low cost inefficiency), substantially under-performing (poor performance) or, in the worst case, both. This paper proposes ACIC, a system which automatically searches for optimized I/O system configurations from many candidates for each individual application running on a given cloud platform. ACIC takes advantage of machine learning models to perform performance/cost predictions. To tackle the high-dimensional parameter exploration space, we enable affordable, reusable, and incremental training on cloud platforms, guided by the Plackett and Burman Matrices for experiment design. Our evaluation results with four representative parallel applications indicate that ACIC consistently identifies optimal or near-optimal configurations among a large group of candidate settings. The top ACIC-recommended configuration is capable of improving the applications' performance by a factor of up to 10.5 (3.1 on average), and cost saving of up to 89 percent (51 percent on average), compared with a commonly used baseline I/O configuration. In addition, we carried out a small-scale user study for one of the test applications, which found that ACIC consistently beat the user and even the application's developer, often by a significant margin, in selecting optimized configurations.
Revisiting the commons: local lessons, global challenges.

In a seminal paper, Garrett Hardin argued in 1968 that users of a commons are caught in an inevitable process that leads to the destruction of the resources on which they depend. This article discusses new insights about such problems and the conditions most likely to favor sustainable uses of common-pool resources. Some of the most difficult challenges concern the management of large-scale resources that depend on international cooperation, such as fresh water in international basins or large marine ecosystems. Institutional diversity may be as important as biological diversity for our long-term survival.
Multilevel inverters: a survey of topologies, controls, and applications

Multilevel inverter technology has emerged recently as a very important alternative in the area of high-power medium-voltage energy control. This paper presents the most important topologies like diode-clamped inverter (neutral-point clamped), capacitor-clamped (flying capacitor), and cascaded multicell with separate dc sources. Emerging topologies like asymmetric hybrid cells and soft-switched multilevel inverters are also discussed. This paper also presents the most relevant control and modulation methods developed for this family of converters: multilevel sinusoidal pulsewidth modulation, multilevel selective harmonic elimination, and space-vector modulation. Special attention is dedicated to the latest and more relevant applications of these converters such as laminators, conveyor belts, and unified power-flow controllers. The need of an active front end at the input side for those inverters supplying regenerative loads is also discussed, and the circuit topology options are also presented. Finally, the peripherally developing areas such as high-voltage high-power devices and optical sensors and other opportunities for future development are addressed.
Block diagonalization for multi-user MIMO with other-cell interference

Block diagonalization is one approach for linear preceding in the multiple-input multiple-output broadcast channel that sends multiple interference free data streams to different users in the same cell. Unfortunately, block diagonalization neglects other-cell interference (OCI), which limits the performance of users at the edge of the cell. This paper presents an OCI-aware enhancement to block diagonalization that uses a whitening filter for interference suppression at the receiver and a novel precoder using the interference-plus-noise covariance matrix for each user at the transmitter. For complex Gaussian matrix channels, the asymptotic sum rate of the proposed system is analyzed under a large antenna assumption for isotropic inputs and compared to conventional block diagonalization. The capacity loss due to OCI is quantified in terms of results from single-user MIMO capacity. Several numerical examples compare achievable sum rates, the proposed asymptotic rates, and the capacity loss, in low and high interference regimes.
Video-based emotion recognition in the wild using deep transfer learning and score fusion

Multimodal recognition of affective states is a difficult problem, unless the recording conditions are carefully controlled. For recognition “in the wild”, large variances in face pose and illumination, cluttered backgrounds, occlusions, audio and video noise, as well as issues with subtle cues of expression are some of the issues to target. In this paper, we describe a multimodal approach for video-based emotion recognition in the wild. We propose using summarizing functionals of complementary visual descriptors for video modeling. These features include deep convolutional neural network (CNN) based features obtained via transfer learning, for which we illustrate the importance of flexible registration and fine-tuning. Our approach combines audio and visual features with least squares regression based classifiers and weighted score level fusion. We report state-of-the-art results on the EmotiW Challenge for “in the wild” facial expression recognition. Our approach scales to other problems, and ranked top in the ChaLearn-LAP First Impressions Challenge 2016 from video clips collected in the wild.
Customer Relationship Management for SME ’ s

Customer Relationship Management (CRM) is getting more and more a key strategy for companies big and small. Customer care strategy and CRM software go hand in hand. In particular SME’s need a CRM software that easily adapts to their customer care needs while still being low cost. In this paper I discuss the benefits of CRM for SME’s and their special requirements wrt. CRM software. Further, I introduce the IST-project CARUSO whose objective is to provide SME’s with a software framework to implement low cost, customized CRM applications. This paper presents the rational behind the CARUSO project, the architecture of the framework, and the software development process used to build the framework.
CAPACITOR-LESS LOW-DROPOUT VOLTAGE REGULATOR

A 1.2-V 40-mA capacitor-free CMOS low-dropout regulator (LDO) for system-on-chip applications to reduce board space and external pins is presented. By utilizing damping-factor control frequency compensation on the advanced LDO structure, the proposed LDO provides high stability, as well as fast line and load transient responses, even in capacitorfree operation. The proposed LDO has been implemented in a tsmc65nm CMOS technology, and the total error of the output voltage due to line and load variations is less. Moreover, the output voltage can recover with ≈2.3μs for full load current changes. The power-supply rejection ratio at 1 MHz is 26 dB.
Customer Lifetime Value ( CLV ) Measurement Based on RFM Model

Nowadays companies increasingly derive revenue from the creation and sustenance of long-term relationships with their customers. In such an environment, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company’s customers. A frequently-encountered difficulty for companies wishing to measure customer profitability is that management accounting and reporting systems have tended to reflect product profitability rather than customer profitability. But in spite of these difficulties, Companies looking for methods to know how calculate their customers's CLV. In this paper, we used K-Mean clustering approach to determine customers's CLV and segment them based on recency, frequency and monetary (RFM) measures. We also used Discriminant analysis to approve clustering results. Data required applying this method gathered from one branch of an Iranian private bank which is established newly. Finally, in terms of this segmentation, we proposed customer retention strategies for treating with the bank customers.