nils-herrmann/clean_scirepeval huggingface dataset:
Ten key considerations for the successful implementation and adoption of large-scale health information technology.

The implementation of health information technology interventions is at the forefront of most policy agendas internationally. However, such undertakings are often far from straightforward as they require complex strategic planning accompanying the systemic organizational changes associated with such programs. Building on our experiences of designing and evaluating the implementation of large-scale health information technology interventions in the USA and the UK, we highlight key lessons learned in the hope of informing the on-going international efforts of policymakers, health directorates, healthcare management, and senior clinicians.
Dual-Band Transmitarrays With Dual-Linear Polarization at Ka-Band

Dual-band transmitarray antennas are demonstrated at Ka-band with the capability of forming independent linearly polarized beams with a given polarization in each frequency band, while sharing the same radiating aperture. The proposed three-layer unit-cell is based on identical narrow microstrip patches printed on both receiving and transmitting layers and connected by a metallized via hole. The metal layers are printed on two identical substrates bonded with a thin film, and the designed unit-cell exhibits a 180° phase resolution (i.e., 1-b phase quantization). The dual-band dual-polarized property of the transmitarray is achieved by interleaving unit-cells operating in the down-link and up-link frequency bands. Four different prototypes are characterized to demonstrate the relevance of the proposed concepts. A good agreement is obtained between the radiation patterns, gain curves, and cross-polarization levels measured and computed in both frequency bands and polarizations.
Interactive Protection System Simulation Using ATP MODELS and C++

This paper presents a new approach for interactive protection system simulation. In this approach, the power system network is modeled by the ATP program while the "compiled foreign model" mechanism of MODELS language is employed to model the digital protective relay in C++ language. This allows "object-oriented" relay modeling as well as building a "seamless" interface between the power system network model and the relay model. An example is used to demonstrate the interactive protection system simulation developed using the new approach
Efficient Symbolic Execution for Analysing Cryptographic Protocol Implementations

The analysis of code that uses cryptographic primitives is unfeasible with current state-of-the-art symbolic execution tools. We develop an extension that overcomes this limitation by treating certain concrete functions, like cryptographic primitives, as symbolic functions whose execution analysis is entirely avoided; their behaviour is in turn modelled formally via rewriting rules. Our code runs in a (simplified) LLVM virtual machine. We develop concrete and symbolic semantics for our LLVM, and we show our approach sound by proving operational correspondence between the two semantics. We present a prototype to illustrate our approach with several (sequential code) examples, and we discuss next milestones towards the symbolic analysis of fully concurrent cryptographic protocol implementations.
Artist-directed dynamics for 2D animation

Animation artists enjoy the benefits of simulation but do not want to be held back by its constraints. Artist-directed dynamics seeks to resolve this need with a unified method that combines simulation with classical keyframing techniques. The combination of these approaches improves upon both extremes: simulation becomes more customizable and keyframing becomes more automatic. Examining our system in the context of the twelve fundamental animation principles reveals that it stands out for its treatment of exaggeration and appeal. Our system accommodates abrupt jumps, large plastic deformations, and makes it easy to reuse carefully crafted animations.
Universal Language Model Fine-tuning for Text Classification

Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 1824% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100× more data. We opensource our pretrained models and code1.
Common-Duty-Ratio Control of Input-Parallel Output-Parallel (IPOP) Connected DC–DC Converter Modules With Automatic Sharing of Currents

Input-parallel output-parallel (IPOP) connected converter systems allow the use of low-power converter modules for high-power applications, with interleaving control scheme resulting in smaller filter size, better dynamic performance, and higher power density. In this paper, a new IPOP converter system is proposed, which consists of multiple dual-active half-bridge (DAHB) dc-dc converter modules. Moreover, by applying a common-duty-ratio control scheme, without a dedicated current-sharing controller, the automatic sharing of input currents or load currents is achieved in the IPOP converter even in the presence of substantial differences of 10% in various module parameters. The current-sharing performance of the proposed control method is analyzed using both a small-signal model and a steady-state dc model of the IPOP system. It is concluded that the equal sharing of currents among modules can be achieved by reducing the mismatches in various module parameters, which is achievable in practice. The current-sharing performance of the IPOP converter is also verified by Saber simulation and a 400-W experimental prototype consisting of two DAHB modules. The common-duty-ratio control method can be extended to any IPOP system that consists of three or more converter modules, including traditional dual-active bridge dc-dc converters, which have a characteristic of current source.
Classification and treatment of the anterior maxillary dentition affected by dental erosion: the ACE classification.

Erosive tooth wear is a serious problem with very costly consequences. Intercepting patients at the initial stages of the disease is critical to avoid significant irreversible damages to their dentition and to benefit from still favorable conditions when it comes to clinical performance of the restorative measures proposed. In this article, a new classification is proposed to quantify the severity of the dental destruction and to guide clinicians and patients in the therapeutic decision-making process. The classification is based on several parameters relevant for both the selection of treatment and the assessment of the prognosis, such as dentin exposure in the palatal tooth contact areas, alterations at the level of the incisal edges, and ultimately, loss of pulp vitality.
RMPflow: A Computational Graph for Automatic Motion Policy Generation

We develop a novel policy synthesis algorithm, RMPflow, based on geometrically consistent transformations of Riemannian Motion Policies (RMPs). RMPs are a class of reactive motion policies designed to parameterize non-Euclidean behaviors as dynamical systems in intrinsically nonlinear task spaces. Given a set of RMPs designed for individual tasks, RMPflow can consistently combine these local policies to generate an expressive global policy, while simultaneously exploiting sparse structure for computational efficiency. We study the geometric properties of RMPflow and provide sufficient conditions for stability. Finally, we experimentally demonstrate that accounting for the geometry of task policies can simplify classically difficult problems, such as planning through clutter on high-DOF manipulation systems.
Privacy-Preserving Multiple Linear Regression of Vertically Partitioned Real Medical Datasets

This paper studies the feasibility of privacy-preservingdata mining in epidemiological study. As for the data-miningalgorithm, we focus to a linear multiple regression thatcan be used to identify the most significant factorsamong many possible variables, such as the historyof many diseases. We try to identify the linear model to estimate a lengthof hospital stay from distributed dataset related tothe patient and the disease information. In this paper, we have done experiment usingthe real medical dataset related to stroke andattempt to apply multiple regression with sixpredictors of age, sex, the medical scales, e.g., Japan Coma Scale, and the modified Rankin Scale. Our contributions of this paper include(1) to propose a practical privacy-preserving protocols for linear multiple regressionwith vertically partitioned datasets, and(2) to show the feasibility of the proposed system usingthe real medical dataset distributed into two parties, the hospital who knows the technical details of diseasesduring the patients are in the hospital, and the local government who knows the residence even afterthe patients left hospital. (3) to show the accuracy and the performance of thePPDM system which allows us to estimate the expectedprocessing time with arbitrary number of predictors.
Neural systems responding to degrees of uncertainty in human decision-making.

Much is known about how people make decisions under varying levels of probability (risk). Less is known about the neural basis of decision-making when probabilities are uncertain because of missing information (ambiguity). In decision theory, ambiguity about probabilities should not affect choices. Using functional brain imaging, we show that the level of ambiguity in choices correlates positively with activation in the amygdala and orbitofrontal cortex, and negatively with a striatal system. Moreover, striatal activity correlates positively with expected reward. Neurological subjects with orbitofrontal lesions were insensitive to the level of ambiguity and risk in behavioral choices. These data suggest a general neural circuit responding to degrees of uncertainty, contrary to decision theory.
Interactive technical illustration

A rendering is an abstraction that favors, preserves, or even emphasizes some qualities while sacrificing, suppressing, or omitting other characteristics that are not the focus of attention. Most computer graphics rendering activities have been concerned with photorealism, i.e., trying to emulate an image that looks like a highquality photograph. This laudable goal is useful and appropriate in many applications, but not in technical illustration where elucidation of structure and technical information is the preeminent motivation. This calls for a different kind of abstraction in which technical communication is central, but art and appearance are still essential instruments toward this end. Work that has been done on computer generated technical illustrations has focused on static images, and has not included all of the techniques used to hand draw technical illustrations. A paradigm for the display of technical illustrations in a dynamic environment is presented. This display environment includes all of the benefits of computer generated technical illustrations, such as a clearer picture of shape, structure, and material composition than traditional computer graphics methods. It also includes the three-dimensional interactive strength of modem display systems. This is accomplished by using new algorithms for real time drawing of silhouette curves, algorithms which solve a number of the problems inherent in previous methods. We incorporate current non-photorealistic lighting methods, and augment them with new shadowing algorithms based on accepted techniques used by artists and studies carried out in human perception. This paper, all of the images, and a mpeg video clip are available at http://www.cs.utah.edu/~bgooch/ITI CR Categories: I.3.0 [Computer Graphics]: General; I.3.6 [Computer Graphics]: Methodology and Techniques.
Design-space exploration in model driven engineering : an initial pattern catalogue

A designer often has to evaluate alternative designs during the development of a system. A multitude of Design-Space Exploration (DSE) techniques exist in the literature. Integration of these techniques into the modelling paradigm is needed when a model-driven engineering approach is used for designing systems. To a greater or lesser extent, the integration of those different DSE techniques share characteristics with each other. Inspired by software design patterns, we introduce an initial pattern catalogue to categorise the embedding of different DSE techniques in an MDE context. We demonstrate their use by a literature survey and discuss the consequences of each pattern. Finally, we demonstrate the application of our initial pattern catalogue on two examples.
TED-LIUM: an Automatic Speech Recognition dedicated corpus

This paper presents the corpus developed by the LIUM for Automatic Speech Recognition (ASR), based on the TED Talks. This corpus was built during the IWSLT 2011 Evaluation Campaign, and is composed of 118 hours of speech with its accompanying automatically aligned transcripts. We describe the content of the corpus, how the data was collected and processed, how it will be publicly available and how we built an ASR system using this data leading to a WER score of 17.4%. The official results we obtained at the IWSLT 2011 evaluation campaign are also discussed.
Swarm Intelligence in Optimization

Optimization techniques inspired by swarm intelligence have become increasingly popular during the last decade. They are characterized by a decentralized way of working that mimics the behavior of swarms of social insects, flocks of birds, or schools of fish. The advantage of these approaches over traditional techniques is their robustness and flexibility. These properties make swarm intelligence a successful design paradigm for algorithms that deal with increasingly complex problems. In this chapter we focus on two of the most successful examples of optimization techniques inspired by swarm intelligence: ant colony optimization and particle swarm optimization. Ant colony optimization was introduced as a technique for combinatorial optimization in the early 1990s. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. In addition, particle swarm optimization was introduced for continuous optimization in the mid-1990s, inspired by bird flocking.
Accurate online power estimation and automatic battery behavior based power model generation for smartphones

This paper describes PowerBooter, an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation. PowerBooter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants, which each have different power consumption properties and therefore require different power models. PowerTutor is intended to ease the design and selection of power efficient software for embedded systems. Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.
An implicit segmentation-based method for recognition of handwritten strings of characters

This paper describes an implicit segmentation-based method for recognition of strings of characters (words or numerals). In a two-stage HMM-based method, an implicit segmentation is applied to segment either words or numeral strings, and in the verification stage, foreground and background features are combined to compensate the loss in terms of recognition rate when segmentation and recognition are performed in the same process. A rigorous experimental protocol shows the performance of the proposed method for isolated characters, numeral strings, and words.
A novel topology design approach using an integrated deep learning network architecture

Topology design optimization offers tremendous oppo rtunity in design and manufacturing freedoms by designing and producing a part from the ground-up without a meaningful initial design as required by conventional shape design opt imization approaches. Ideally, with adequate problem statements, to formulate and solve the topo logy design problem using a standard topology optimization process, such as SIMP (Simpli fied Isotropic Material with Penalization) is possible. In reality, an estimated over thousands o f design iterations is often required for just a few design variables, the conventional optimization approach is in general impractical or computationally unachievable for real world applica tions significantly diluting the development of the topology optimization technology. There is, therefore, a need for a different approach that will be able to optimize the initial design topolog y effectively and rapidly. Therefore, this work presents a new topology design procedure to generat e optimal structures using an integrated Generative Adversarial Networks (GANs) and convolut i nal neural network architecture. The discriminator in the GANs as well as the convolutio nal network are initially trained through the dataset of 3024 true optimized planner structure im ages generated from a conventional topology design approach (SIMP). The convolutional network m aps the optimized structure to the volume fraction, penalty and radius of the smoothening fil ter. Once the GAN is trained, the generator produced a large number of new unseen structures sa ti fying the design requirements. The corresponding input variables of these new structur es can be evaluated using the trained convolutional network. The structures generated by the GANs are also minutely post-processed to aberrations. Validation of the results is made b y generating the images with same conditions using existing topology optimization algorithms. T his paper presents a proof of concept which 1 Corresponding Author, email: shen.1@osu.edu
Evaluating Neural Machine Translation in English-Japanese Task

Weblio Inc. We evaluated Neural Machine Translation (NMT) models in English-Japanese translation task. Various network architectures with different recurrent units are tested. Additionally, we examine the effect of using pre-reordered data for the training. Our experiments show that even simple NMT models can produce better translations compared with all SMT baselines. For NMT models, recovering unknown words is another key to obtaining good translations. We describe a simple workaround to find missing translations with a back-off system. Surprisingly, performing prereordering on the training data hurts the model performance. We provide a qualitative analysis demonstrates a specific error pattern in NMT translations which omits partial information and thus fail to preserve the complete meaning. BLEU RIBES HUMAN
Web Engineering: A New Discipline for Development of Web-Based Systems

In most cases, development of Web-based systems has been ad hoc, lacking systematic approach and quality control and assurance procedures. Hence, there is now legitimate and growing concern about the manner in which Web-based systems are developed and their long-term quality and integrity. Web Engineering, an emerging new discipline, advocates a process and a systematic approach to development of high quality Web-based systems. It promotes the establishment and use of sound scientific, engineering and management principles, and disciplined and systematic approaches to development, deployment and maintenance of Web-based systems. This paper gives an introductory overview on Web Engineering. It presents the principles and roles of Web Engineering, assesses the similarities and differences between development of traditional software and Web-based systems, identifies key Web engineering activities and reviews some of the ongoing work in this area. It also highlights the prospects of Web engineering and the areas that need further study.
Parallel-Array Implementations of a Non-Restoring Square Root Algorithm

In this paper, we present a parallel-array implementation of a new non-restoring square root algorithm (PASQRT). The carry-save adder (CSA) is used in the parallel array. The PASQRT has several features unlike other implementations. First, it does not use redundant representation for square root result. Second, each iteration generates an exact resulting value. Next, it does not require any conversion on the inputs of the CSA. And last, a precise remainder can be obtained immediately. Furthermore, we present an improved version — a root-select parallel-array implementation (RS-PASQRT) for fast result value generation. The RS-PASQRT is capable of achieving up to about 150% speedup ratio over the PASQRT. The simplicity of the implementations indicates that the proposed approach is an alternative to consider when designing a fully pipelined square root unit.
Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices

To verify cluster separation in high-dimensional data, analysts often reduce the data with a dimension reduction (DR) technique, and then visualize it with 2D Scatterplots, interactive 3D Scatterplots, or Scatterplot Matrices (SPLOMs). With the goal of providing guidance between these visual encoding choices, we conducted an empirical data study in which two human coders manually inspected a broad set of 816 scatterplots derived from 75 datasets, 4 DR techniques, and the 3 previously mentioned scatterplot techniques. Each coder scored all color-coded classes in each scatterplot in terms of their separability from other classes. We analyze the resulting quantitative data with a heatmap approach, and qualitatively discuss interesting scatterplot examples. Our findings reveal that 2D scatterplots are often 'good enough', that is, neither SPLOM nor interactive 3D adds notably more cluster separability with the chosen DR technique. If 2D is not good enough, the most promising approach is to use an alternative DR technique in 2D. Beyond that, SPLOM occasionally adds additional value, and interactive 3D rarely helps but often hurts in terms of poorer class separation and usability. We summarize these results as a workflow model and implications for design. Our results offer guidance to analysts during the DR exploration process.
Language processing and learning models for community question answering in Arabic

In this paper we focus on the problem of question ranking in community question answering (cQA) forums in Arabic. We address the task with machine learning algorithms using advanced Arabic text representations. The latter are obtained by applying tree kernels to constituency parse trees combined with textual similarities, including word embeddings. Our two main contributions are: (i) an Arabic language processing pipeline based on UIMA —from segmentation to constituency parsing— built on top of Farasa, a state-of-the-art Arabic language processing toolkit; and (ii) the application of long short-term memory neural networks to identify the best text fragments in questions to be used in our tree-kernel-based ranker. Our thorough experimentation on a recently released cQA dataset shows that the Arabic linguistic processing provided by Farasa produces strong results and that neural networks combined with tree kernels further boost the performance in terms of both efficiency and accuracy. Our approach also enables an implicit comparison between different processing pipelines as our tests on Farasa and Stanford parsers demonstrate.