nils-herrmann/clean_scirepeval huggingface dataset:
Understanding large text corpora via sparse machine learning

Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational cost. The approach has been successfully used in many areas, such as signal and image processing. This paper posits that these methods can be extremely useful in the analysis of large collections of text documents, without requiring user expertise in machine learning. Our approach relies on three main ingredients: (a) multi-document text summarization and (b) comparative summarization of two corpora, both using sparse regression or classification; (c) sparse principal components and sparse graphical models for unsupervised analysis and visualization of large text corpora. We validate our methods using a corpus of Aviation Safety Reporting System (ASRS) reports and demonstrate that the methods can reveal causal and contributing factors in runway incursions. Furthermore, we show that the methods automatically discover four main tasks that pilots perform during flight, which can aid in further understanding the causal and contributing factors to runway incursions and other drivers for aviation safety incidents. We also provide a comparative study involving other commonly used datasets, and report on the competitiveness of sparse machine learning compared to state-of-the-art methods such as Latent Dirichlet Allocation (LDA). ∗EECS Dept., UC Berkeley, elghaoui@berkeley.edu. †EECS Dept., UC Berkeley, ptvu@berkeley.edu. ‡EECS Dept., UC Berkeley, guanchengli@berkeley.edu. §Ecole des Mines d’Alès, School of Production & Systems Engineering, viet-an.duong@mines-ales.org. ¶System-Wide Safety and Assurance Technologies Project, NASA, ashok.n.srivastava@nasa.gov. ‖System-Wide Safety and Assurance Technologies Project, NASA, kanishka.bhaduri-1@nasa.gov.
TrustLogin: Securing Password-Login on Commodity Operating Systems

With the increasing prevalence of Web 2.0 and cloud computing, password-based logins play an increasingly important role on user-end systems. We use passwords to authenticate ourselves to countless applications and services. However, login credentials can be easily stolen by attackers. In this paper, we present a framework, TrustLogin, to secure password-based logins on commodity operating systems. TrustLogin leverages System Management Mode to protect the login credentials from malware even when OS is compromised. TrustLogin does not modify any system software in either client or server and is transparent to users, applications, and servers. We conduct two study cases of the framework on legacy and secure applications, and the experimental results demonstrate that TrustLogin is able to protect login credentials from real-world keyloggers on Windows and Linux platforms. TrustLogin is robust against spoofing attacks. Moreover, the experimental results also show TrustLogin introduces a low overhead with the tested applications.
Compact Wideband SIW Horn Antenna Fed by Elevated-CPW Structure

This communication presents a compact substrate-integrated waveguide (SIW) H-plane horn antenna fed by a novel elevated coplanar waveguide (ECPW) structure. First, the wideband characteristic of the SIW horn antenna is achieved through loading a dielectric slab with gradually decreasing dielectric constants, which can be realized through simply perforating different air vias on the extended slab. Second, in order to sustain an efficient feeding for the relatively thick substrate (0.27λ<sub>0</sub>), an additional metal ground is inserted in the middle of the grounded coplanar waveguide (GCPW). Moreover, a triangular-shaped transition structure is placed at the end of the ECPW to smoothly transmit the energy from the thin ECPW to the thick SIW horn antenna. Finally, a prototype is fabricated to validate the proposed concept. Measured results indicate that the proposed horn antenna operates from 17.4 to 24 GHz. Stable radiation patterns can be observed in the whole operating band. The measured results show good accordance with the simulated ones. Above all, the proposed antenna occupies an area of 22 × 56.5 × 4 mm<sup>3</sup> (1.47λ<sub>0</sub> × 3.77λ<sub>0</sub> × 0.27λ<sub>0</sub>), which is much more compact than the previous rectangular waveguide-fed horn antenna (2.33λ<sub>0</sub> × 9.21λ<sub>0</sub> × 0.31λ<sub>0</sub>) (where λ0 is the wavelength at 20 GHz in the free space).
A Framework for Developing Serious Games to meet Learner Needs

This paper will communicate preliminary findings from applied research exploring how to ensure that serious games are cost effective and engaging components of future training solutions. The applied research is part of a multimillion pound program for the Department of Trade and Industry, and involves a partnership between UK industry and academia to determine how bespoke serious games should be used to best satisfy learning needs in a range of contexts. The main objective of this project is to produce a minimum of three serious games prototypes for clients from different sectors (e.g., military, medical and business) each prototype addressing a learning need or learning outcome that helps solve a priority business problem or fulfill a specific training need. This paper will describe a development process that aims to encompass learner specifics and targeted learning outcomes in order to ensure that the serious game is successful. A framework for describing game-based learning scenarios is introduced, and an approach to the analysis that effectively profiles the learner within the learner group with respect to game-based learning is outlined. The proposed solution also takes account of relevant findings from serious games research on particular learner groups that might support the selection and specification of a game. A case study on infection control will be used to show how this approach to the analysis is being applied for a healthcare issue.
SMiLer: A Semi-Lazy Time Series Prediction System for Sensors

It is useful to predict future values in time series data, for example when there are many sensors monitoring environments such as urban space. The Gaussian Process (GP) model is considered as a promising technique for this setting. However, the GP model requires too high a training cost to be tractable for large data. Though approximation methods have been proposed to improve GP's scalability, they usually can only capture global trends in the data and fail to preserve small-scale patterns, resulting in unsatisfactory performance.
 We propose a new method to apply the GP for sensor time series prediction. Instead of (eagerly) training GPs on entire datasets, we custom-build query-dependent GPs on small fractions of the data for each prediction request.
 Implementing this idea in practice at scale requires us to overcome two obstacles. On the one hand, a central challenge with such a semi-lazy learning model is the substantial model-building effort at kNN query time, which could lead to unacceptable latency. We propose a novel two-level inverted-like index to support kNN search using the DTW on the GPU, making such "just-in-time" query-dependent model construction feasible for real-time applications.
 On the other hand, several parameters should be tuned for each time series individually since different sensors have different data generating processes in diverse environments. Manually configuring the parameters is usually not feasible due to the large number of sensors. To address this, we devise an adaptive auto-tuning mechanism to automatically determine and dynamically adjust the parameters for each time series with little human assistance.
 Our method has the following strong points: (a) it can make prediction in real time without a training phase; (b) it can yield superior prediction accuracy; and (c) it can effectively estimate the analytical predictive uncertainty.
 To illustrate our points, we present SMiLer, a semi-lazy time series prediction system for sensors. Extensive experiments on real-world datasets demonstrate its effectiveness and efficiency. In particular, by devising a two-level inverted-like index on the GPU with an enhanced lower bound of the DTW, SMiLer accelerates the efficiency of kNN search by one order of magnitude over its baselines. The prediction accuracy of SMiLer is better than the state-of-the-art competitors (up to 10 competitors) with better estimation of predictive uncertainty.
A hybrid grouping genetic algorithm for bin packing

The grouping genetic algorithm (GGA) is a genetic algorithm heavily modified to suit the structure of grouping problems. Those are the problems where the aim is to find a good partition of a set or to group together the members of the set. The bin packing problem (BPP) is a well known NP-hard grouping problem: items of various sires have to be grouped inside bins of tixed capacity. On the other hand, the reduction method of Martello and Toth, based on their dominance criterion, constitutes one of the best OR techniques for optimization of the BPP to date. In this article, we first describe the GGA paradigm as compared to me classic Holland-style GA and the ordering GA. We then show how the bin packing GGA can be enhanced with a local optimization inspired by the dominance criterion. An extensive experimental comparison shows that the combination yields an algorithm superior to either of its components.  grouping, partitioning, bin packing, genetic algorithm, solution encoding, dominance, reduction
When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features

With the prevalence of e-commence websites and the ease of online shopping, consumers are embracing huge amounts of various options in products. Undeniably, shopping is one of the most essential activities in our society and studying consumer’s shopping behavior is important for the industry as well as sociology and psychology. Not surprisingly, one of the most popular e-commerce categories is clothing business. There arises the needs for analysis of popular and attractive clothing features which could further boost many emerging applications, such as clothing recommendation and advertising. In this work, we design a novel system that consists of three major components: 1) exploring and organizing a large-scale clothing dataset from a online shopping website, 2) pruning and extracting images of best-selling products in clothing item data and user transaction history, and 3) utilizing a machine learning based approach to discovering clothing attributes as the representative and discriminative characteristics of popular clothing style elements. Through the experiments over a large-scale online clothing dataset, we demonstrate the effectiveness of our proposed system, and obtain useful insights on clothing consumption trends and profitable clothing features.
Mining sequential patterns by pattern-growth: the PrefixSpan approach

Sequential pattern mining is an important data mining problem with broad applications. However, it is also a difficult problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Most of the previously developed sequential pattern mining methods, such as GSP, explore a candidate generation-and-test approach [R. Agrawal et al. (1994)] to reduce the number of candidates to be examined. However, this approach may not be efficient in mining large sequence databases having numerous patterns and/or long patterns. In this paper, we propose a projection-based, sequential pattern-growth approach for efficient mining of sequential patterns. In this approach, a sequence database is recursively projected into a set of smaller projected databases, and sequential patterns are grown in each projected database by exploring only locally frequent fragments. Based on an initial study of the pattern growth-based sequential pattern mining, FreeSpan [ (2000)], we propose a more efficient method, called PSP, which offers ordered growth and reduced projected databases. To further improve the performance, a pseudoprojection technique is developed in PrefixSpan. A comprehensive performance study shows that PrefixSpan, in most cases, outperforms the a priori-based algorithm GSP, FreeSpan, and SPADE [M. Zaki, (2001)] (a sequential pattern mining algorithm that adopts vertical data format), and PrefixSpan integrated with pseudoprojection is the fastest among all the tested algorithms. Furthermore, this mining methodology can be extended to mining sequential patterns with user-specified constraints. The high promise of the pattern-growth approach may lead to its further extension toward efficient mining of other kinds of frequent patterns, such as frequent substructures.
BalanceFlow: Controller load balancing for OpenFlow networks

In the discussion about Future Internet, Software-Defined Networking (SDN), enabled by OpenFlow, is currently seen as one of the most promising paradigm. While the availability and scalability concerns rises as a single controller could be alleviated by using replicate or distributed controllers, there lacks a flexible mechanism to allow controller load balancing. This paper proposes BalanceFlow, a controller load balancing architecture for OpenFlow networks. By utilizing CONTROLLER X action extension for OpenFlow switches and cross-controller communication, one of the controllers, called “super controller”, can flexibly tune the flow-requests handled by each controller, without introducing unacceptable propagation latencies. Experiments based on real topology show that BalanceFlow can adjust the load of each controller dynamically.
An Introduction To Compressive Sampling

Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.
Design of compact Ku band microstrip antenna for satellite communication

In the recent years antenna design appears as a mature field of research. It really is not the fact because as the technology grows with new ideas, fitting expectations in the antenna design are always coming up. A Ku-band patch antenna loaded with notches and slit has been designed and simulated using Ansoft HFSS 3D electromagnetic simulation tool. Multi-frequency band operation is obtained from the proposed microstrip antenna. The design was carried out using Glass PTFE as the substrate and copper as antenna material. The designed antennas resonate at 15GHz with return loss over 50dB & VSWR less than 1, on implementing different slots in the radiating patch multiple frequencies resonate at 12.2GHz & 15.00GHz (Return Loss -27.5, -37.73 respectively & VSWR 0.89, 0.24 respectively) and another resonate at 11.16 GHz, 15.64GHz & 17.73 GHz with return loss -18.99, -23.026, -18.156 dB respectively and VSWR 1.95, 1.22 & 2.1 respectively. All the above designed band are used in the satellite application for non-geostationary orbit (NGSO) and fixed-satellite services (FSS) providers to operate in various segments of the Ku-band.
E-voting in Estonia

Estonia has one of the most established e-voting systems in the world. Internet voting remote e-voting using the voter’s own equipment was piloted in 2005  (with the first real elections using e-voting being conducted the same year) and has been in use ever since. So far, the Estonian internet voting system has been used for the whole country in three sets of local elections, two European Parliament elections and three parliamentary elections . This chapter begins by exploring the voting system in Estonia; we consider the organisation of the electoral system in the three main kinds of election (municipal, parliamentary and European Parliament), the traditional ways of voting and the methods used to tally votes and elect candidates. Next we investigate the Estonian national ID card, an identity document that plays a key part in enabling internet voting to be possible in Estonia. After considering these pre-requisites, we describe the current internet voting system, including how it has evolved over time and the relatively new verification mechanisms that are available to voters. Next we discuss the assumptions and choices that have been made in the design of this system and the challenges and criticism that it has received. Finally, we conclude by discussing how the system has performed over the 10 years it has been in use, and the impact it appears to have had on voter turnout and satisfaction.
Computer-Assisted Text Analysis for Comparative Politics

Recent advances in research tools for the systematic analysis of textual data are enabling exciting new research throughout the social sciences. For comparative politics, scholars who are often interested in nonEnglish and possibly multilingual textual datasets, these advances may be difficult to access. This article discusses practical issues that arise in the processing, management, translation, and analysis of textual data with a particular focus on how procedures differ across languages. These procedures are combined in two applied examples of automated text analysis using the recently introduced Structural Topic Model. We also show how the model can be used to analyze data that have been translated into a single language via machine translation tools. All the methods we describe here are implemented in open-source software packages available from the authors.
Chaotic time series Part I : Estimation of some invariant properties in state space

Certain deterministic non-linear systems may show chaotic behaviour. Time series derived from such systems seem stochastic when analyzed with linear techniques. However, uncovering the deterministic structure is important because it allows constructing more realistic and better models and thus improved predictive capabilities. This paper provides a review of two main key features of chaotic systems, the dimensions of their strange attractors and the Lyapunov exponents. The emphasis is on state space reconstruction techniques that are used to estimate these properties, given scalar observations. Data generated from equations known to display chaotic behaviour are used for illustration. A compilation of applications to real data from widely di erent elds is given. If chaos is found to be present, one may proceed to build non-linear models, which is the topic of the second paper in this series.
When Firmware Modifications Attack: A Case Study of Embedded Exploitation

The ability to update firmware is a feature that is found in nearly all modern embedded systems. We demonstrate how this feature can be exploited to allow attackers to inject malicious firmware modifications into vulnerable embedded devices. We discuss techniques for exploiting such vulnerable functionality and the implementation of a proof of concept printer malware capable of network reconnaissance, data exfiltration and propagation to general purpose computers and other embedded device types. We present a case study of the HP-RFU (Remote Firmware Update) LaserJet printer firmware modification vulnerability, which allows arbitrary injection of malware into the printer’s firmware via standard printed documents. We show vulnerable population data gathered by continuously tracking all publicly accessible printers discovered through an exhaustive scan of IPv4 space. To show that firmware update signing is not the panacea of embedded defense, we present an analysis of known vulnerabilities found in third-party libraries in 373 LaserJet firmware images. Prior research has shown that the design flaws and vulnerabilities presented in this paper are found in other modern embedded systems. Thus, the exploitation techniques presented in this paper can be generalized to compromise other embedded systems. Embedded system exploitation; Firmware modification attack; Embedded system rootkit; HP-RFU vulnerability.
Dynamic metrics for java

In order to perform meaningful experiments in optimizing compilation and run-time system design, researchers usually rely on a suite of benchmark programs of interest to the optimization technique under consideration. Programs are described as numeric, memory-intensive, concurrent, or object-oriented, based on a qualitative appraisal, in some cases with little justification. We believe it is beneficial to quantify the behaviour of programs with a concise and precisely defined set of metrics, in order to make these intuitive notions of program behaviour more concrete and subject to experimental validation. We therefore define and measure a set of unambiguous, dynamic, robust and architecture-independent metrics that can be used to categorize programs according to their dynamic behaviour in five areas: size, data structure, memory use, concurrency, and polymorphism. A framework computing some of these metrics for Java programs is presented along with specific results demonstrating how to use metric data to understand a program's behaviour, and both guide and evaluate compiler optimizations.
Highly integrated RF frontend module for high SHF wide-band massive MIMO in 5G, and switching-mode amplifiers beyond 4G

In order to realize a massive MIMO concept, small size and low power consumption over the wide-band frequency range are challenges for RF frontends module. This paper describes a highly integrated RF frontend module for high SHF wide-band massive MIMO in 5G. The RF frontend module is designed with 0.15 µm GaAs process and assembled on 5 × 5 mm2 QFN package. By employing Doherty amplifier configuration using a parasitic output capacitance neutralization technique, it achieves low power consumption over wide frequency band. The integrated frontend architecture is an attractive solution for massive MIMO systems in 5G, and will contribute to 5G deployment. Additionally, the use of digital techniques is attractive future option beyond 4G (toward 5G), and amplifiers with them potentially lead to reduction of power consumption. Some prototyped switching-mode amplifiers are also presented.
Are we ready for autonomous driving? The KITTI vision benchmark suite

Today, visual recognition systems are still rarely employed in robotics applications. Perhaps one of the main reasons for this is the lack of demanding benchmarks that mimic such scenarios. In this paper, we take advantage of our autonomous driving platform to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection. Our recording platform is equipped with four high resolution video cameras, a Velodyne laser scanner and a state-of-the-art localization system. Our benchmarks comprise 389 stereo and optical flow image pairs, stereo visual odometry sequences of 39.2 km length, and more than 200k 3D object annotations captured in cluttered scenarios (up to 15 cars and 30 pedestrians are visible per image). Results from state-of-the-art algorithms reveal that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world. Our goal is to reduce this bias by providing challenging benchmarks with novel difficulties to the computer vision community. Our benchmarks are available online at: www.cvlibs.net/datasets/kitti.