nils-herrmann/clean_scirepeval huggingface dataset:
Computational design of linkage-based characters

We present a design system for linkage-based characters, combining form and function in an aesthetically-pleasing manner. Linkage-based character design exhibits a mix of discrete and continuous problems, making for a highly unintuitive design space that is difficult to navigate without assistance. Our system significantly simplifies this task by allowing users to interactively browse different topology options, thus guiding the discrete set of choices that need to be made. A subsequent continuous optimization step improves motion quality and, crucially, safeguards against singularities. We demonstrate the flexibility of our method on a diverse set of character designs, and then realize our designs by physically fabricating prototypes.
A Sensor Network Simulator for the Contiki OS

This report introduces a new sensor network simulator for the Contiki OS the COOJA Simulator. The Contiki OS is a portable operating system designed speci cally for resource limited devices such as sensor nodes. It is built around an eventdriven kernel but supports pre-emptive multithreading at a per-process basis. It also supports a full TCP/IP stack via uIP and the programming abstraction Protothreads. The main design goal of the COOJA Simulator is extendibility, for which Interfaces and Plugins are used. An Interface represents a sensor node property or device, such as a position, a button or a radio transmitter. A Plugin is used to interact with a simulation, for example to control the simulation speed or to watch all network tra c between the simulated nodes. Both new Plugins and Interfaces can easily be created and added to the simulation environment. A number of other parts of the simulator, for example a radio medium responsible for forwarding radio network data, can also be implemented and added to the simulator. And by supporting several di erent simulation environments at the same time in one simulation, di erent underlying hardware platforms can be simulated in heterogeneous networks. Java Native Interface is used to connect the new simulator with Contiki, allowing simulated applications to run in a real Contiki system. By using this approach, any simulated application can then be run on a real sensor node unaltered.
Condition Monitoring for Device Reliability in Power Electronic Converters: A Review

Condition monitoring (CM) has already been proven to be a cost effective means of enhancing reliability and improving customer service in power equipment, such as transformers and rotating electrical machinery. CM for power semiconductor devices in power electronic converters is at a more embryonic stage; however, as progress is made in understanding semiconductor device failure modes, appropriate sensor technologies, and signal processing techniques, this situation will rapidly improve. This technical review is carried out with the aim of describing the current state of the art in CM research for power electronics. Reliability models for power electronics, including dominant failure mechanisms of devices are described first. This is followed by a description of recently proposed CM techniques. The benefits and limitations of these techniques are then discussed. It is intended that this review will provide the basis for future developments in power electronics CM.
Imaging With Nature: Compressive Imaging Using a Multiply Scattering Medium

The recent theory of compressive sensing leverages upon the structure of signals to acquire them with much fewer measurements than was previously thought necessary, and certainly well below the traditional Nyquist-Shannon sampling rate. However, most implementations developed to take advantage of this framework revolve around controlling the measurements with carefully engineered material or acquisition sequences. Instead, we use the natural randomness of wave propagation through multiply scattering media as an optimal and instantaneous compressive imaging mechanism. Waves reflected from an object are detected after propagation through a well-characterized complex medium. Each local measurement thus contains global information about the object, yielding a purely analog compressive sensing method. We experimentally demonstrate the effectiveness of the proposed approach for optical imaging by using a 300-micrometer thick layer of white paint as the compressive imaging device. Scattering media are thus promising candidates for designing efficient and compact compressive imagers.
The Six Pillars for Building Big Data Analytics Ecosystems

With almost everything now online, organizations look at the Big Data collected to gain insights for improving their services. In the analytics process, derivation of such insights requires experimenting-with and integrating different analytics techniques, while handling the Big Data high arrival velocity and large volumes. Existing solutions cover bits-and-pieces of the analytics process, leaving it to organizations to assemble their own ecosystem or buy an off-the-shelf ecosystem that can have unnecessary components to them. We build on this point by dividing the Big Data Analytics problem into six main pillars. We characterize and show examples of solutions designed for each of these pillars. We then integrate these six pillars into a taxonomy to provide an overview of the possible state-of-the-art analytics ecosystems. In the process, we highlight a number of ecosystems to meet organizations different needs. Finally, we identify possible areas of research for building future Big Data Analytics Ecosystems.
Kronecker Graphs: An Approach to Modeling Networks

How can we generate realistic networks? In addition, how can we do so with a mathematically tractable model that allows for rigorous analysis of networ k properties? Real networks exhibit a long list of surprising properties: Heavy tails for the ina nd out-degree distribution; heavy tails for the eigenvalues and eigenvectors; small diameters; and de sification and shrinking diameters over time. The present network models and generators either fail to match several of the above properties, are complicated to analyze mathematically, or both. In this chapter we propose a generative model for networks that is both mathematically tracta ble and can generate networks that have all the above mentioned structural properties. Our main ide a here is to use a non-standard matrix operation, theKronecker product , to generate graphs that we refer to as “Kronecker graphs”. First, we show that Kronecker graphs naturally obey common n etwork properties; in fact, we rigorously prove that they do so. We also provide empirical evidence showing t hat Kronecker graphs can effectively model the structure of real networks . We then present K RONFIT, a fast and scalable algorithm for fitting the Kronecker grap h generation model to large real networks. A naive approach to fitt ing would take super-exponential time. In contrast, KRONFIT takeslinear time, by exploiting the structure of Kronecker matrix multiplication and by using statistical simulation techni ques. Experiments on large real and synthetic networks show that K RONFIT finds accurate parameters that indeed very well mimic the properties of target net works. Once fitted, the model parameters can be used to gain insights about the network structure, and the resulting synthetic graphs can be used for null-models, anonymization, extrapolations, and graph summarization.
Effective Clipart Image Vectorization through Direct Optimization of Bezigons

Bezigons, i.e., closed paths composed of Bézier curves, have been widely employed to describe shapes in image vectorization results. However, most existing vectorization techniques infer the bezigons by simply approximating an intermediate vector representation (such as polygons). Consequently, the resultant bezigons are sometimes imperfect due to accumulated errors, fitting ambiguities, and a lack of curve priors, especially for low-resolution images. In this paper, we describe a novel method for vectorizing clipart images. In contrast to previous methods, we directly optimize the bezigons rather than using other intermediate representations; therefore, the resultant bezigons are not only of higher fidelity compared with the original raster image but also more reasonable because they were traced by a proficient expert. To enable such optimization, we have overcome several challenges and have devised a differentiable data energy as well as several curve-based prior terms. To improve the efficiency of the optimization, we also take advantage of the local control property of bezigons and adopt an overlapped piecewise optimization strategy. The experimental results show that our method outperforms both the current state-of-the-art method and commonly used commercial software in terms of bezigon quality.
Automated Summarization Evaluation with Basic Elements

As part of evaluating a summary automatically, it is usual to determine how much of the contents of one or more human-produced ‘ideal’ summaries it contains. Past automated methods such as ROUGE compare using fixed word ngrams, which are not ideal for a variety of reasons. In this paper we describe a framework in which summary evaluation measures can be instantiated and compared, and we implement a specific evaluation method using very small units of content, called Basic Elements, that address some of the shortcomings of ngrams. This method is tested on DUC 2003, 2004, and 2005 systems and produces very good correlations with human judgments.
Blockchain Based Distributed Control System for Edge Computing

Edge computing proposes a novel model for providing computational resources close to end devices that are connected to the network. It has numerous applications in Internet of Things, as well as smart grids, healthcare, smart home, etc. This paper presents ongoing research regarding the use of blockchain technology as a platform hierarchical and distributed control systems based on IEC 61499 standard. Hyperledger Fabric was selected as the blockchain solution, where function blocks are to be implemented as smart contracts on a supervisor level. The integration with the edge nodes that perform on the executive level responsible for actual process control is based on a micro-services architecture where Docker containers implement function blocks, and Kubernetes platform is used for orchestrating the execution of containers across the edge resources.
Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition

Recently, the connectionist temporal classification (CTC) model coupled with recurrent (RNN) or convolutional neural networks (CNN), made it easier to train speech recognition systems in an end-to-end fashion. However in real-valued models, time frame components such as mel-filter-bank energies and the cepstral coefficients obtained from them, together with their first and second order derivatives, are processed as individual elements, while a natural alternative is to process such components as composed entities. We propose to group such elements in the form of quaternions and to process these quaternions using the established quaternion algebra. Quaternion numbers and quaternion neural networks have shown their efficiency to process multidimensional inputs as entities, to encode internal dependencies, and to solve many tasks with less learning parameters than real-valued models. This paper proposes to integrate multiple feature views in quaternion-valued convolutional neural network (QCNN), to be used for sequence-to-sequence mapping with the CTC model. Promising results are reported using simple QCNNs in phoneme recognition experiments with the TIMIT corpus. More precisely, QCNNs obtain a lower phoneme error rate (PER) with less learning parameters than a competing model based on real-valued CNNs.
Ethnicity estimation with facial images

We have advanced an effort to develop vision based human understanding technologies for realizing human-friendly machine interfaces. Visual information, such as gender, age ethnicity, and facial expression play an important role in face-to-face communication. This paper addresses a novel approach for ethnicity classification with facial images. In this approach, the Gabor wavelets transformation and retina sampling are combined to extract key facial features, and support vector machines that are used for ethnicity classification. Our system, based on this approach, has achieved approximately 94% for ethnicity estimation under various lighting conditions.
Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization

This work proposes a new algorithm for automated and simultaneous phenotyping of multiple co–occurring medical conditions, also referred as comorbidities, using clinical notes from the electronic health records (EHRs). A basic latent factor estimation technique of non-negative matrix factorization (NMF) is augmented with domain specific constraints to obtain sparse latent factors that are anchored to a fixed set of chronic conditions. The proposed anchoring mechanism ensures a one-to-one identifiable and interpretable mapping between the latent factors and the target comorbidities. Qualitative assessment of the empirical results by clinical experts suggests that the proposed model learns clinically interpretable phenotypes while being predictive of 30 day mortality. The proposed method can be readily adapted to any non-negative EHR data across various healthcare institutions.
The Weibull-G Family of Probability Distributions

The Weibull distribution is the most important distribution for problems in reliability. We study some mathematical properties of the new wider Weibull-G family of distributions. Some special models in the new family are discussed. The properties derived hold to any distribution in this family. We obtain general explicit expressions for the quantile function, ordinary and incomplete moments, generating function and order statistics. We discuss the estimation of the model parameters by maximum likelihood and illustrate the potentiality of the extended family with two applications to real data.
Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems

A long-term goal of machine learning is to build intelligent conversational agents. One recent popular approach is to train end-to-end models on a large amount of real dialog transcripts between humans (Sordoni et al., 201 5; Vinyals & Le, 2015; Shang et al., 2015). However, this approach leaves many ques tions unanswered as an understanding of the precise successes and shortcomings of each model is hard to assess. A contrasting recent proposal are the bAbI tasks ( We ton et al., 2015b) which are synthetic data that measure the ability of learnin g machines at various reasoning tasks over toy language. Unfortunately, those te sts are very small and hence may encourage methods that do not scale. In this work, w e propose a suite of new tasks of a much larger scale that attempt to bridge the g ap between the two regimes. Choosing the domain of movies, we provide tasks tha t test the ability of models to answer factual questions (utilizing OMDB), pro vide personalization (utilizing MovieLens), carry short conversations about th e wo, and finally to perform on natural dialogs from Reddit. We provide a dataset cov ering∼75k movie entities and with∼3.5M training examples. We present results of various model s on these tasks, and evaluate their performance.
Requirements for tools for ambiguity identification and measurement in natural language requirements specifications

This paper proposes a two-step approach to identifying ambiguities in natural language (NL) requirements specifications (RSs). In the first step, a tool would apply a set of ambiguity measures to a RS in order to identify potentially ambiguous sentences in the RS. In the second step, another tool would show what specifically is potentially ambiguous about each potentially ambiguous sentence. The final decision of ambiguity remains with the human users of the tools. The paper describes several requirements-identification experiments with several small NL RSs using four prototypes of the first tool based on linguistic instruments and resources of different complexity and a manual mock-up of the second tool.
Penile Implants among Prisoners—A Cause for Concern?

We report the prevalence of penile implants among prisoners and determine the independent predictors for having penile implants. Questions on penile implants were included in the Sexual Health and Attitudes of Australian Prisoners (SHAAP) survey following concerns raised by prison health staff that increasing numbers of prisoners reported having penile implants while in prison.



Computer-Assisted Telephone Interviewing (CATI) of a random sample of prisoners was carried out in 41 prisons in New South Wales and Queensland (Australia). Men were asked, "Have you ever inserted or implanted an object under the skin of your penis?" If they responded Yes: "Have you ever done so while you were in prison?" Univariate logistic regression and logistic regression were used to determine the factors associated with penile implants.



A total of 2,018 male prisoners were surveyed, aged between 18 and 65 years, and 118 (5.8%) reported that they had inserted or implanted an object under the skin of their penis. Of these men, 87 (73%) had this done while they were in prison. In the multivariate analysis, a younger age, birth in an Asian country, and prior incarceration were all significantly associated with penile implants (p<0.001). Men with penile implants were also more likely to report being paid for sex (p<0.001), to have had body piercings (p<0.001) or tattoos in prison (p<0.001), and to have taken non-prescription drugs while in prison (p<0.05).



Penile implants appear to be fairly common among prisoners and are associated with risky sexual and drug use practices. As most of these penile implants are inserted in prison, these men are at risk of blood borne viruses and wound infection. Harm reduction and infection control strategies need to be developed to address this potential risk.
Cell Association and Interference Coordination in Heterogeneous LTE-A Cellular Networks

Embedding pico/femto base-stations and relay nodes in a macro-cellular network is a promising method for achieving substantial gains in coverage and capacity compared to macro-only networks. These new types of base-stations can operate on the same wireless channel as the macro-cellular network, providing higher spatial reuse via cell splitting. However, these base-stations are deployed in an unplanned manner, can have very different transmit powers, and may not have traffic aggregation among many users. This could potentially result in much higher interference magnitude and variability. Hence, such deployments require the use of innovative cell association and inter-cell interference coordination techniques in order to realize the promised capacity and coverage gains. In this paper, we describe new paradigms for design and operation of such heterogeneous cellular networks. Specifically, we focus on cell splitting, range expansion, semi-static resource negotiation on third-party backhaul connections, and fast dynamic interference management for QoS via over-the-air signaling. Notably, our methodologies and algorithms are simple, lightweight, and incur extremely low overhead. Numerical studies show that they provide large gains over currently used methods for cellular networks.
Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting

Artificial neural network is a valuable tool for time series forecasting. In the case of performing multiperiodic forecasting with artificial neural networks, two methods, namely iterative and direct, can be used. In iterative method, first subsequent period information is predicted through past observations. Afterwards, the estimated value is used as an input; thereby the next period is predicted. The process is carried on until the end of the forecast horizon. In the direct forecast method, successive periods can be predicted all at once. Hence, this method is thought to yield better results as only observed data is utilized in order to predict future periods. In this study, forecasting was performed using direct and iterative methods, and results of the methods are compared using grey relational analysis to find the method which gives a better result.
A practical model for subsurface light transport

This paper introduces a simple model for subsurface light transport in translucent materials. The model enables efficient simulation of effects that BRDF models cannot capture, such as color bleeding within materials and diffusion of light across shadow boundaries. The technique is efficient even for anisotropic, highly scattering media that are expensive to simulate using existing methods. The model combines an exact solution for single scattering with a dipole point source diffusion approximation for multiple scattering. We also have designed a new, rapid image-based measurement technique for determining the optical properties of translucent materials. We validate the model by comparing predicted and measured values and show how the technique can be used to recover the optical properties of a variety of materials, including milk, marble, and skin. Finally, we describe sampling techniques that allow the model to be used within a conventional ray tracer.
Locality Sim: Cloud Simulator with Data Locality

Cloud Computing (CC) is a model for enabling on-demand access to a shared pool of configurable computing resources. Testing and evaluating the performance of the cloud environment for allocating, provisioning, scheduling, and data allocation policy have great attention to be achieved. Therefore, using cloud simulator would save time and money, and provide a flexible environment to evaluate new research work. Unfortunately, the current simulators (e.g., CloudSim, NetworkCloudSim, GreenCloud, etc..) deal with the data as for size only without any consideration about the data allocation policy and locality. On the other hand, the NetworkCloudSim simulator is considered one of the most common used simulators because it includes different modules which support needed functions to a simulated cloud environment, and it could be extended to include new extra modules. According to work in this paper, the NetworkCloudSim simulator has been extended and modified to support data locality. The modified simulator is called LocalitySim. The accuracy of the proposed LocalitySim simulator has been proved by building a mathematical model. Also, the proposed simulator has been used to test the performance of the three-tire data center as a case study with considering the data locality feature.
On the Origin of CRISPR-Cas Technology: From Prokaryotes to Mammals.

Clustered regularly-interspaced short palindromic repeat (CRISPR) sequences cooperate with CRISPR-associated (Cas) proteins to form the basis of CRISPR-Cas adaptive immune systems in prokaryotes. For more than 20 years, these systems were of interest only to specialists, mainly molecular microbiologists, who tried to understand the properties of this unique defense mechanism. In 2012, the potential of CRISPR-Cas systems was uncovered and these were presented as genome-editing tools with an outstanding capacity to trigger targeted genetic modifications that can be applied to virtually any organism. Shortly thereafter, in early 2013, these tools were shown to efficiently drive specific modification of mammalian genomes. This review attempts to summarize, in a comprehensive manner, the key events and milestones that brought CRISPR-Cas technology from prokaryotes to mammals.