nils-herrmann/clean_scirepeval huggingface dataset:
Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning

One of the open problems in neural network research is how to automatically determine network architectures for given applications. In this brief, we propose a simple and efficient approach to automatically determine the number of hidden nodes in generalized single-hidden-layer feedforward networks (SLFNs) which need not be neural alike. This approach referred to as error minimized extreme learning machine (EM-ELM) can add random hidden nodes to SLFNs one by one or group by group (with varying group size). During the growth of the networks, the output weights are updated incrementally. The convergence of this approach is proved in this brief as well. Simulation results demonstrate and verify that our new approach is much faster than other sequential/incremental/growing algorithms with good generalization performance.
Voice conversion using deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks

This paper investigates the use of Deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks (DBLSTM-RNNs) for voice conversion. Temporal correlations across speech frames are not directly modeled in frame-based methods using conventional Deep Neural Networks (DNNs), which results in a limited quality of the converted speech. To improve the naturalness and continuity of the speech output in voice conversion, we propose a sequence-based conversion method using DBLSTM-RNNs to model not only the frame-wised relationship between the source and the target voice, but also the long-range context-dependencies in the acoustic trajectory. Experiments show that DBLSTM-RNNs outperform DNNs where Mean Opinion Scores are 3.2 and 2.3 respectively. Also, DBLSTM-RNNs without dynamic features have better performance than DNNs with dynamic features.
Flexible frequency selective metamaterials for microwave applications

Metamaterials have attracted more and more research attentions recently. Metamaterials for electromagnetic applications consist of sub-wavelength structures designed to exhibit particular responses to an incident EM (electromagnetic) wave. Traditional EM (electromagnetic) metamaterial is constructed from thick and rigid structures, with the form-factor suitable for applications only in higher frequencies (above GHz) in microwave band. In this paper, we developed a thin and flexible metamaterial structure with small-scale unit cell that gives EM metamaterials far greater flexibility in numerous applications. By incorporating ferrite materials, the thickness and size of the unit cell of metamaterials have been effectively scaled down. The design, mechanism and development of flexible ferrite loaded metamaterials for microwave applications is described, with simulation as well as measurements. Experiments show that the ferrite film with permeability of 10 could reduce the resonant frequency. The thickness of the final metamaterials is only 0.3mm. This type of ferrite loaded metamaterials offers opportunities for various sub-GHz microwave applications, such as cloaks, absorbers, and frequency selective surfaces.
A performance evaluation of local descriptors

In this paper, we compare the performance of descriptors computed for local interest regions, as, for example, extracted by the Harris-Affine detector [Mikolajczyk, K and Schmid, C, 2004]. Many different descriptors have been proposed in the literature. It is unclear which descriptors are more appropriate and how their performance depends on the interest region detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector. Our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations. We compare shape context [Belongie, S, et al., April 2002], steerable filters [Freeman, W and Adelson, E, Setp. 1991], PCA-SIFT [Ke, Y and Sukthankar, R, 2004], differential invariants [Koenderink, J and van Doorn, A, 1987], spin images [Lazebnik, S, et al., 2003], SIFT [Lowe, D. G., 1999], complex filters [Schaffalitzky, F and Zisserman, A, 2002], moment invariants [Van Gool, L, et al., 1996], and cross-correlation for different types of interest regions. We also propose an extension of the SIFT descriptor and show that it outperforms the original method. Furthermore, we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best. Moments and steerable filters show the best performance among the low dimensional descriptors.
Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion

In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems. Disciplines Computer Sciences Comments Vanderwende, L., Suzuki, H., Brockett, C., & Nenkova, A., Beyond SumBasic: Task-Focused Summarization with Sentence Simplification and Lexical Expansion, Information Processing and Management, Special Issue on Summarization Volume 43, Issue 6, 2007, doi: 10.1016/j.ipm.2007.01.023 This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/736
Distilling free-form natural laws from experimental data.

For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. We propose a principle for the identification of nontriviality. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the "alphabet" used to describe those systems.
FreeSentry: protecting against use-after-free vulnerabilities due to dangling pointers

Use-after-free vulnerabilities have become an important class of security problems due to the existence of mitigations that protect against other types of vulnerabilities. The effects of their exploitation can be just as devastating as exploiting a buffer overflow, potentially resulting in full code execution within the vulnerable program. Few protections exist against these types of vulnerabilities and they are particularly hard to discover through manual code inspection. In this paper we present FreeSentry: a mitigation that protects against use-after-free vulnerabilities by inserting dynamic runtime checks that invalidate pointers when the associated memory is released. If such an invalidated pointer is accessed, the program will subsequently crash, preventing an attacker from exploiting the vulnerability. When checking dynamically allocated memory, our approach has a moderate performance overhead on the SPEC CPU benchmarks: running with a geometric mean performance impact of around 25%. It has no overhead when deployed on widely used server side daemons such as OpenSSH or the Apache HTTP daemon. FreeSentry also discovered a previously unknown use-after-free vulnerability in one of the programs in SPEC CPU2000 benchmarks: perlbmk. This vulnerability seems to have been missed by other mitigations.
A modified recursive x-y cut algorithm for solving block ordering problems

To achieve the best results from an OCR system, the pre-processing steps must be performed with a high degree of accuracy and reliability. There are two critically important steps in the OCR pre-processing phase. First, blocks must be extracted from each page of the scanned document. Secondly, all blocks resulting from the first step must be arranged in the correct order. One of the most notable techniques for block ordering in the second step is the recursive x-y cut (RXYC) algorithm. This technique works accurately only when applied to documents with a simple page layout but it causes incorrect block ordering when applied to documents with complex page layouts. This paper proposes a modified recursive x-y cut algorithm for solving block ordering problems for documents with complex page layouts. This proposed algorithm can solve problems such as (1) the overlapping block problem; (2) the blocks overlay problem, and (3) the L-Shaped block problem.
Player modeling using self-organization in Tomb Raider: Underworld

We present a study focused on constructing models of players for the major commercial title Tomb Raider: Underworld (TRU). Emergent self-organizing maps are trained on high-level playing behavior data obtained from 1365 players that completed the TRU game. The unsupervised learning approach utilized reveals four types of players which are analyzed within the context of the game. The proposed approach automates, in part, the traditional user and play testing procedures followed in the game industry since it can inform game developers, in detail, if the players play the game as intended by the game design. Subsequently, player models can assist the tailoring of game mechanics in real-time for the needs of the player type identified.
To Combat Multi-Class Imbalanced Problems by Means of Over-Sampling Techniques

Class imbalance problem is quite pervasive in our nowadays human practice. This problem basically refers to the skewness in the data underlying distribution which, in turn, imposes many difficulties on typical machine learning algorithms. To deal with the emerging issues arising from multi-class skewed distributions, existing efforts are mainly divided into two categories: model-oriented solutions and data-oriented techniques. Focusing on the latter, this paper presents a new over-sampling technique which is inspired by Mahalanobis distance. The presented over-sampling technique, called MDO (Mahalanobis Distance-based Over-sampling technique), generates synthetic samples which have the same Mahalanobis distance from the considered class mean as other minority class examples. By preserving the covariance structure of the minority class instances and intelligently generating synthetic samples along the probability contours, new minority class instances are modelled better for learning algorithms. Moreover, MDO can reduce the risk of overlapping between different class regions which are considered as a serious challenge in multi-class problems. Our theoretical analyses and empirical observations across wide spectrum multi-class imbalanced benchmarks indicate that MDO is the method of choice by offering statistical superior MAUC and precision compared to the popular over-sampling techniques.
Predicting Salient Updates for Disaster Summarization

During crises such as natural disasters or other human tragedies, information needs of both civilians and responders often require urgent, specialized treatment. Monitoring and summarizing a text stream during such an event remains a difficult problem. We present a system for update summarization which predicts the salience of sentences with respect to an event and then uses these predictions to directly bias a clustering algorithm for sentence selection, increasing the quality of the updates. We use novel, disaster-specific features for salience prediction, including geo-locations and language models representing the language of disaster. Our evaluation on a standard set of retrospective events using ROUGE shows that salience prediction provides a significant improvement over other approaches.
Design procedure of class-E amplifier for off-nominal operation at 50% duty ratio

Design equations for satisfying the off-nominal operating condition [i.e., only the zero-voltage switching (ZVS) condition] of the Class-E amplifier with a linear shunt capacitance at a duty ratio D=0.5 are derived. A new parameter s (V/s), called the slope of switch voltage when the switch turns on is introduced to obtain an image of the distance from the nominal conditions. By examining off-nominal Class-E operation degree of the design freedom of the Class-E amplifier increases by one. In addition various amplifier parameters such as operating frequency, output power, and load resistance range can be set as design specifications. For example, the peak switch voltage and switch current can be taken into account in the design procedure. Examples of a design procedure of the Class-E amplifier for off-nominal operation are given. The theoretical results were verified with PSpice simulation and experiments.
Exploiting Mobility for Energy Efficient Data Collection in Wireless Sensor Networks

There are two principal problems in collecting data in large and sparse ad-hoc sensor networks: 1) high energy expenditure in multihop routing between widely separated nodes; and 2) routing hotspots near the destination of the data that shortens the effective lifetime of the network. In this paper, we present and analyze an alternative architecture which addresses above problems. Our approach exploits mobile nodes present in the sensor field as forwarding agents. As a mobile node moves in close proximity to sensors, data is transferred to the mobile node for later depositing at the destination of the data. Transmitting data over these much shorter distances leads to substantial power savings at sensors. This is a natural scenario for a wide variety of monitoring and dissemination applications that span large geographic areas. We investigate the advantages and disadvantages of our approach and present an analytical model to understand the key performance metrics such as data transfer, latency to the destination, and power. Parameters for our model include: sensor buffer size, data generation rate, radio characteristics, and mobility patterns of mobile nodes. The modeling results provide insights and guidelines for the deployment of such systems. Through simulation we verify that our approach can provide substantial savings in energy as compared to traditional ad-hoc network approach.
Advanced High-Voltage Aqueous Lithium-Ion Battery Enabled by "Water-in-Bisalt" Electrolyte.

A new super-concentrated aqueous electrolyte is proposed by introducing a second lithium salt. The resultant ultra-high concentration of 28 m led to more effective formation of a protective interphase on the anode along with further suppression of water activities at both anode and cathode surfaces. The improved electrochemical stability allows the use of TiO2 as the anode material, and a 2.5 V aqueous Li-ion cell based on LiMn2 O4 and carbon-coated TiO2 delivered the unprecedented energy density of 100 Wh kg(-1) for rechargeable aqueous Li-ion cells, along with excellent cycling stability and high coulombic efficiency. It has been demonstrated that the introduction of a second salts into the "water-in-salt" electrolyte further pushed the energy densities of aqueous Li-ion cells closer to those of the state-of-the-art Li-ion batteries.
Russian Stress Prediction using Maximum Entropy Ranking

We explore a model of stress prediction in Russian using a combination of local contextual features and linguisticallymotivated features associated with the word’s stem and suffix. We frame this as a ranking problem, where the objective is to rank the pronunciation with the correct stress above those with incorrect stress. We train our models using a simple Maximum Entropy ranking framework allowing for efficient prediction. An empirical evaluation shows that a model combining the local contextual features and the linguistically-motivated non-local features performs best in identifying both primary and secondary stress.
Data X-Ray: A Diagnostic Tool for Data Errors

A lot of systems and applications are data-driven, and the correctness of their operation relies heavily on the correctness of their data. While existing data cleaning techniques can be quite effective at purging datasets of errors, they disregard the fact that a lot of errors are systematic, inherent to the process that produces the data, and thus will keep occurring unless the problem is corrected at its source. In contrast to traditional data cleaning, in this paper we focus on data diagnosis: explaining where and how the errors happen in a data generative process.
 We develop a large-scale diagnostic framework called DATA X-RAY. Our contributions are three-fold. First, we transform the diagnosis problem to the problem of finding common properties among erroneous elements, with minimal domain-specific assumptions. Second, we use Bayesian analysis to derive a cost model that implements three intuitive principles of good diagnoses. Third, we design an efficient, highly-parallelizable algorithm for performing data diagnosis on large-scale data. We evaluate our cost model and algorithm using both real-world and synthetic data, and show that our diagnostic framework produces better diagnoses and is orders of magnitude more efficient than existing techniques.
Robust and Rapid Generation of Animated Faces from Video Images: A Model-Based Modeling Approach

We have developed an easy-to-use and cost-effective system to construct textured 3D animated face models from videos with minimal user interaction. This is a particularly challenging task for faces due to a lack of prominent textures. We develop a robust system by following a model-based approach: we make full use of generic knowledge of faces in head motion determination, head tracking, model fitting, and multiple-view bundle adjustment. Our system first takes, with an ordinary video camera, images of a face of a person sitting in front of the camera turning their head from one side to the other. After five manual clicks on two images to indicate the position of the eye corners, nose tip and mouth corners, the system automatically generates a realistic looking 3D human head model that can be animated immediately (different poses, facial expressions and talking). A user, with a PC and a video camera, can use our system to generate his/her face model in a few minutes. The face model can then be imported in his/her favorite game, and the user sees themselves and their friends take part in the game they are playing. We have demonstrated the system on a laptop computer live at many events, and constructed face models for hundreds of people. It works robustly under various environment settings.
Sparse Subspace Clustering via Group Sparse Coding

We propose in this paper a novel sparse subspace clustering method that regularizes sparse subspace representation by exploiting the structural sharing between tasks and data points via group sparse coding. We derive simple, provably convergent, and computationally efficient algorithms for solving the proposed group formulations. We demonstrate the advantage of the framework on three challenging benchmark datasets ranging from medical record data to image and text clustering and show that they consistently outperforms rival methods.
Scalable browsing for large collections: a case study

Phrase browsing techniques use phrases extracted automatically from a large information collection as a basis for browsing and accessing it. This paper describes a case study that uses an automatically constructed phrase hierarchy to facilitate browsing of an ordinary large Web site. Phrases are extracted from the full text using a novel combination of rudimentary syntactic processing and sequential grammar induction techniques. The interface is simple, robust and easy to use.
To convey a feeling for the quality of the phrases that are generated automatically, a thesaurus used by the organization responsible for the Web site is studied and its degree of overlap with the phrases in the hierarchy is analyzed. Our ultimate goal is to amalgamate hierarchical phrase browsing and hierarchical thesaurus browsing: the latter provides an authoritative domain vocabulary and the former augments coverage in areas the thesaurus does not reach.
Introduction - Platforms and Infrastructures in the Digital Age

In the last few years, leading-edge research from information systems, strategic management, and economics have separately informed our understanding of platforms and infrastructures in the digital age. Our motivation for undertaking this special issue rests in the conviction that it is significant to discuss platforms and infrastructures concomitantly, while enabling knowledge from diverse disciplines to cross-pollinate to address critical, pressing policy challenges and inform strategic thinking across both social and business spheres. In this editorial, we review key insights from the literature on digital infrastructures and platforms, present emerging research themes, highlight the contributions developed from each of the six articles in this special issue, and conclude with suggestions for further research.
Uniting online social networks with places and things

As the Web of Things (WoT) broadens real world interaction via the internet, there is an increasing need for a user centric model for managing and interacting with real world objects. We believe that online social networks can provide that capability and can enhance existing and future WoT platforms leading to a Social WoT. As both social overlays and user interface containers, online social networks (OSNs) will play a significant role in the evolution of the web of things. As user interface containers and social overlays, they can be used by end users and applications as an on-line entry point for interacting with things, both receiving updates from sensors and controlling things. Conversely, access to user identity and profile information, content and social graphs can be useful in physical social settings like cafés. In this paper we describe some of the key features of social networks used by existing social WoT systems. We follow this with a discussion of open research questions related to integration of OSNs and how OSNs may evolve to be more suitable for integration with places and things. Several ongoing projects in our lab leverage OSNs to connect places and things to online communities.
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics

We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.