nils-herrmann/clean_scirepeval huggingface dataset:
Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping

The conventional wisdom is that backprop nets with excess hi dden units generalize poorly. We show that nets with excess capacity ge neralize well when trained with backprop and early stopping. Experim nts suggest two reasons for this: 1) Overfitting can vary significant ly i different regions of the model. Excess capacity allows better fit to reg ions of high non-linearity, and backprop often avoids overfitting the re gions of low non-linearity. 2) Regardless of size, nets learn task subco mponents in similar sequence. Big nets pass through stages similar to th ose learned by smaller nets. Early stopping can stop training the large n et when it generalizes comparably to a smaller net. We also show that co njugate gradient can yield worse generalization because it overfits regions of low non-linearity when learning to fit regions of high non-linea rity.
Context-Based Speech Act Classification in Intelligent Tutoring Systems

In intelligent tutoring systems with natural language dialogue, speech act classification, the task of detecting learners’ intentions, informs the system’s response mechanism. In this paper, we propose supervised machine learning models for speech act classification in the context of an online collaborative learning game environment. We explore the role of context (i.e. speech acts of previous utterances) for speech act classification. We compare speech act classification models trained and tested with contextual and non-contextual features (contents of the current utterance). The accuracy of the proposed models is high. A surprising finding is the modest role of context in automatically predicting the speech acts.
Motion Blur Kernel Estimation via Deep Learning

The success of the state-of-the-art deblurring methods mainly depends on the restoration of sharp edges in a coarse-to-fine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images. Motivated by the success of the existing filtering-based deblurring methods, the proposed model consists of two stages: suppressing extraneous details and enhancing sharp edges. We show that the two-stage model simplifies the learning process and effectively restores sharp edges. Facilitated by the learned sharp edges, the proposed deblurring algorithm does not require any coarse-to-fine strategy or edge selection, thereby significantly simplifying kernel estimation and reducing computation load. Extensive experimental results on challenging blurry images demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of visual quality and run-time.
Solid state circuit breakers for DC micrgrids: Current status and future trends

Short circuit protection remains one of the major technical barriers in DC microgrids. This paper reviews state of the art of DC solid state circuit breakers (SSCBs). A new concept of a self-powered SSCB using normally-on wideband gap (WBG) semiconductor devices as the main static switch is described in this paper. The new SSCB detects short circuit faults by sensing its terminal voltage rise, and draws power from the fault condition itself to turn and hold off the static switch. The new two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or extra wiring. Challenges and future trends in protecting low voltage distribution microgrids against short circuit and other faults are discussed.
Artificial evil and the foundation of computer ethics

Moral reasoning traditionally distinguishes two types of evil:moral (ME) and natural (NE). The standard view is that ME is theproduct of human agency and so includes phenomena such as war,torture and psychological cruelty; that NE is the product ofnonhuman agency, and so includes natural disasters such asearthquakes, floods, disease and famine; and finally, that morecomplex cases are appropriately analysed as a combination of MEand NE. Recently, as a result of developments in autonomousagents in cyberspace, a new class of interesting and importantexamples of hybrid evil has come to light. In this paper, it iscalled artificial evil (AE) and a case is made for considering itto complement ME and NE to produce a more adequate taxonomy. Byisolating the features that have led to the appearance of AE,cyberspace is characterised as a self-contained environment thatforms the essential component in any foundation of the emergingfield of Computer Ethics (CE). It is argued that this goes someway towards providing a methodological explanation of whycyberspace is central to so many of CE's concerns; and it isshown how notions of good and evil can be formulated incyberspace. Of considerable interest is how the propensity for anagent's action to be morally good or evil can be determined evenin the absence of biologically sentient participants and thusallows artificial agents not only to perpetrate evil (and forthat matter good) but conversely to `receive' or `suffer from'it. The thesis defended is that the notion of entropy structure,which encapsulates human value judgement concerning cyberspace ina formal mathematical definition, is sufficient to achieve thispurpose and, moreover, that the concept of AE can be determinedformally, by mathematical methods. A consequence of this approachis that the debate on whether CE should be considered unique, andhence developed as a Macroethics, may be viewed, constructively,in an alternative manner. The case is made that whilst CE issuesare not uncontroversially unique, they are sufficiently novel torender inadequate the approach of standard Macroethics such asUtilitarianism and Deontologism and hence to prompt the searchfor a robust ethical theory that can deal with them successfully.The name Information Ethics (IE) is proposed for that theory. Itis argued that the uniqueness of IE is justified by its beingnon-biologically biased and patient-oriented: IE is anEnvironmental Macroethics based on the concept of data entityrather than life. It follows that the novelty of CE issues suchas AE can be appreciated properly because IE provides a newperspective (though not vice versa). In light of the discussionprovided in this paper, it is concluded that Computer Ethics isworthy of independent study because it requires its ownapplication-specific knowledge and is capable of supporting amethodological foundation, Information Ethics.
RAPter: rebuilding man-made scenes with regular arrangements of planes

With the proliferation of acquisition devices, gathering massive volumes of 3D data is now easy. Processing such large masses of pointclouds, however, remains a challenge. This is particularly a problem for raw scans with missing data, noise, and varying sampling density. In this work, we present a simple, scalable, yet powerful data reconstruction algorithm. We focus on reconstruction of man-made scenes as regular arrangements of planes (RAP), thereby selecting both local plane-based approximations along with their global inter-plane relations. We propose a novel selection formulation to directly balance between data fitting and the simplicity of the resulting arrangement of extracted planes. The main technical contribution is a formulation that allows less-dominant orientations to still retain their internal regularity, and not become overwhelmed and regularized by the dominant scene orientations. We evaluate our approach on a variety of complex 2D and 3D pointclouds, and demonstrate the advantages over existing alternative methods.
Recurrent neural network language modeling for code switching conversational speech

Code-switching is a very common phenomenon in multilingual communities. In this paper, we investigate language modeling for conversational Mandarin-English code-switching (CS) speech recognition. First, we investigate the prediction of code switches based on textual features with focus on Part-of-Speech (POS) tags and trigger words. Second, we propose a structure of recurrent neural networks to predict code-switches. We extend the networks by adding POS information to the input layer and by factorizing the output layer into languages. The resulting models are applied to our task of code-switching language modeling. The final performance shows 10.8% relative improvement in perplexity on the SEAME development set which transforms into a 2% relative improvement in terms of Mixed Error Rate and a relative improvement of 16.9% in perplexity on the evaluation set which leads to a 2.7% relative improvement of MER.
Energy-optimal trajectory planning for robot manipulators with holonomic constraints

In this paper, we study two different trajectory planning problems for robotmanipulators. In the first case, the end-effector of the robot is constrained to move along a prescribed path in the workspace, whereas in the second case, the trajectory of the end-effector has to be determined in the presence of obstacles. Constraints of this type are called holonomic constraints. Both problems have been solved as optimal control problems. Given the dynamicmodel of the robotmanipulator, the initial state of the system, some specifications about the final state and a set of holonomic constraints, one has to find the trajectory and the actuator torques that minimize the energy consumption during the motion. The presence of holonomic constraints makes the optimal control problem particularly difficult to solve. Our method involves a numerical resolution of a reformulation of the constrained optimal control problem into an unconstrained calculus of variations problem in which the state space constraints and the dynamic equations, also regarded as constraints, are treated by means of special derivative multipliers. We solve the resulting calculus of variations problem using a numerical approach based on the Euler–Lagrange necessary condition in the integral form in which time is discretized and admissible variations for each variable are approximated using a linear combination of piecewise continuous basis functions of time. The use of the Euler–Lagrange necessary condition in integral form avoids the need for numerical corner conditions and thenecessity of patching together solutions between corners. In thisway, a generalmethod for the solution of constrained optimal control problems is obtained inwhich holonomic constraints can be easily treated. Numerical results of the application of thismethod to trajectory planning of planar horizontal robot manipulators with two revolute joints are reported.
Multi-Task Feature Learning

We present a method for learning a low-dimensional representation which is shared across a set of multiple related tasks. The method builds upon the wellknown 1-norm regularization problem using a new regularizer which controls the number of learned features common for all the tasks. We show that this problem is equivalent to a convex optimization problem and develop an iterative algorithm for solving it. The algorithm has a simple interpretation: it alternately performs a supervised and an unsupervised step, where in the latter step we learn commonacross-tasks representations and in the former step we learn task-specific functions using these representations. We report experiments on a simulated and a real data set which demonstrate that the proposed method dramatically improves the performance relative to learning each task independently. Our algorithm can also be used, as a special case, to simply select – not learn – a few common features across the tasks.
Treatment of Fordyce spots with 5-aminolaevulinic acid-photodynamic therapy.

1 Lawrence CM,  Selective sweat gland removal with minimal skin excision in the treatment of axillary hyperhidrosis: a retrospective clinical and histological review of 15 patients.  2006; 155:115–18. 2 Bechara FG, Sand M, Sand D et al. Surgical treatment of axillary hyperhidrosis: a study comparing liposuction cannulas with a suction-curettage cannula.  Postoperative situation after axillary suction-curettage: an endoscopical view.
Overfitting and Neural Networks: Conjugate Gradient and Backpropagation

Methods for controlling the bias/variance tradeoff typica lly assume that overfitting or overtraining is a global phenomenon. For multi-layer perceptron (MLP) neural netwo rks, global parameters such as the training time (e.g. based on validation tests), network size, or the amount of we ight decay are commonly used to control the bias/variance tradeoff. However, the degree of overfitting can vary signifi cantly throughout the input space of the model. We show that overselection of the degrees of freedom for an MLP train ed with backpropagation can improve the approximation in regions of underfitting, while not significantly overfitti ng in other regions. This can be a significant advantage over other models. Furthermore, we show that “better” learning a lgorithms such as conjugate gradient can in fact lead to worse generalization, because they can be more prone to crea ting v rying degrees of overfitting in different regions of the input space. While experimental results cannot cover all practical situations, our results do help to explain common behavior that does not agree with theoretical expect ations. Our results suggest one important reason for the relative success of MLPs, bring into question common bel iefs about neural network training regarding training algorithms, overfitting, and optimal network size, suggest alternate guidelines for practical use (in terms of the trai ning algorithm and network size selection), and help to direct fu ture work (e.g. regarding the importance of the MLP/BP training bias, the possibility of worse performance for “be tter” training algorithms, local “smoothness” criteria, a nd further investigation of localized overfitting).
A Template for Scatter Search and Path Relinking

Scatter search and its generalized form called path relinking are evolutionary methods that have recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, these methods use strategies for combining solution vectors that have proved effective for scheduling, routing, financial product design, neural network training, optimizing simulation and a variety of other problem areas. These approaches can be implemented in multiple ways, and offer numerous alternatives for exploiting their basic ideas. We identify a template for scatter search and path relinking methods that provides a convenient and "user friendly" basis for their implementation. The overall design can be summarized by a small number of key steps, leading to versions of scatter search and path relinking that are fully specified upon providing a handful of subroutines. Illustrative forms of these subroutines are described that make it possible to create methods for a wide range of optimization problems. Highlights of these components include new diversification generators for zero-one and permutation problems (extended by a mapping-byobjective technique that handles additional classes of problems), together with processes to avoid generating or incorporating duplicate solutions at various stages (related to the avoidance of cycling in tabu search) and a new method for creating improved solutions.  UPDATED AND EXTENDED: February 1998 *** Previous version appeared in Lecture Notes in Computer Science, 1363, J.K. Hao, E. Lutton, E. Ronald, M. Schoenauer, D. Snyers (Eds.), 13-54, 1997. This research was supported in part by the Air Force Office of Scientific Research Grant #F49620-97-1-0271.
A new bio-inspired optimisation algorithm: Bird Swarm Algorithm

A new bio-inspired optimisation algorithm: Bird Swarm Algorithm Xian-Bing Meng, X.Z. Gao, Lihua Lu, Yu Liu & Hengzhen Zhang a College of Information Engineering, Shanghai Maritime University, Shanghai, P.R. China b Chengdu Green Energy and Green Manufacturing R&D Center, Chengdu, P.R. China c Department of Electrical Engineering and Automation, Aalto University School of Electrical Engineering, Aalto, Finland d School of Computer Science, Fudan University, Shanghai, P.R. China e College of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou, P.R. China Published online: 17 Jun 2015.
A 79-GHz Radar Sensor in LTCC Technology Using Grid Array Antennas

Concept, design, and measurement results of a frequency-modulated continuous-wave radar sensor in low-temperature co-fired ceramics (LTCC) technology is presented in this paper. The sensor operates in the frequency band between 77–81 GHz. As a key component of the system, wideband microstrip grid array antennas with a broadside beam are presented and discussed. The combination with a highly integrated feeding network and a four-channel transceiver chip based on SiGe technology results in a very compact LTCC RF frontend (23 mm <formula formulatype="inline"><tex Notation="TeX">$\times$</tex></formula> 23 mm). To verify the feasibility of the concept, first radar measurement results are presented.
Medical Image Denoising Using Convolutional Denoising Autoencoders

Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be combined to boost sample size for increased denoising performance. Simplest of networks can reconstruct images with corruption levels so high that noise and signal are not differentiable to human eye.
Optimization of sparse matrix-vector multiplication on emerging multicore platforms

We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from cell phones to supercomputers confronts parallelism of unprecedented scale. To fully unleash the potential of these systems, the HPC community must develop multicore specific optimization methodologies for important scientific computations. In this work, we examine sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs. Our experimental platform includes the homogeneous AMD dual-core and Intel quad-core designs, the heterogeneous STI Cell, as well as the first scientific study of the highly multithreaded Sun Niagara2. We present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations. Additionally, we present key insights into the architectural tradeoffs of leading multicore design strategies, in the context of demanding memory-bound numerical algorithms.
Automatic Set Expansion for List Question Answering

This paper explores the use of set expansion (SE) to improve question answering (QA) when the expected answer is a list of entities belonging to a certain class. Given a small set of seeds, SE algorithms mine textual resources to produce an extended list including additional members of the class represented by the seeds. We explore the hypothesis that a noise-resistant SE algorithm can be used to extend candidate answers produced by a QA system and generate a new list of answers that is better than the original list produced by the QA system. We further introduce a hybrid approach which combines the original answers from the QA system with the output from the SE algorithm. Experimental results for several state-of-the-art QA systems show that the hybrid system performs better than the QA systems alone when tested on list question data from past TREC evaluations.
Cybercafés and their potential as Community Development Tools in India

Using public Internet facilities in order to access information and communication technologies (ICT) is the main model of use after the more common models of home use (individual ownership) and access at work or at school/university. Especially in developing countries, public and shared facilities help to create desperately needed access and are a main strategy in several Internet access programs. In the context of public access, cybercafes play an important role as the most common Internet access model, especially in the urban areas of India. It is often argued that cybercafes could help bridge the digital divide, as they provide Internet access to people who cannot afford to have Internet connections at their homes or who need help in order to make use of ICT. The following article will take this assumption as a starting point and will present findings from empirical research on cybercafes in urban India. The research was conducted in order to explore the problems and potential of cybercafes as development tools for different urban communities. In order to examine these relationships, the reach of cybercafes, the users of cybercafes and the usage patterns have been examined. This study is part of a doctoral thesis and the following article presents some of the findings. The article has to be seen as a preliminary report on ongoing research, and it presents some of the data collected to date in order to help build understanding concerning this complex access model and its importance for urban India.
GaML - A Modeling Language for Gamification

Gamification has become a trend over the last years, especially in non-game environments such as business systems. With the aim to increase the users' engagement and motivation, existing or new information systems are enriched with game design elements. Before the technical implementation, the gamification concept is created. However, creation of such concepts is an informal and error-prone process, i.e., the definition and exchange of game mechanics is done in natural language or using spreadsheets. This becomes especially relevant, if the gamification concept is handed over to the implementation phase in which IT-experts have to manually translate informal to formal concepts without having gamification expertise. In this paper, we describe a novel, declarative, and formal domain-specific language to define gamification concepts. Besides that the language is designed to be readable and partially write able by gamification experts, the language is automatically compilable into gamification platforms without involving IT-experts.
From Natural Language Specifications to Program Input Parsers

We present a method for automatically generating input parsers from English specifications of input file formats. We use a Bayesian generative model to capture relevant natural language phenomena and translate the English specification into a specification tree, which is then translated into a C++ input parser. We model the problem as a joint dependency parsing and semantic role labeling task. Our method is based on two sources of information: (1) the correlation between the text and the specification tree and (2) noisy supervision as determined by the success of the generated C++ parser in reading input examples. Our results show that our approach achieves 80.0% F-Score accuracy compared to an F-Score of 66.7% produced by a state-of-the-art semantic parser on a dataset of input format specifications from the ACM International Collegiate Programming Contest (which were written in English for humans with no intention of providing support for automated processing).1