nils-herrmann/clean_scirepeval huggingface dataset:
A systematic assessment of rigor in information systems ranking-type Delphi studies

The ranking-type Delphi method is well suited as a means for consensus-building by using a series of questionnaires to collect data from a panel of geographically dispersed participants. This method allows a group of experts to systematically approach a particular task or problem. While information systems researchers have been using this method for almost three decades, no research to date has attempted to assess the extent to which Delphi studies have been rigorously conducted. Using the guidelines that have been prescribed by the leading Delphi methodologists, our descriptive review reveals many positive signs of rigor such as ensuring the anonymity of experts and providing clear and precise instructions to participants. Nevertheless, there are still several areas for improvement, such as reporting response and retention rates, instrument pretesting, and explicitly justifying modifications to the ranking-type Delphi
Localizing and segmenting text in images and videos

Many images—especially those used for page design on web pages—as well as videos contain visible text. If these text occurrences could be detected, segmented, and recognized automatically, they would be a valuable source of high-level semantics for indexing and retrieval. In this paper, we propose a novel method for localizing and segmenting text in complex images and videos. Text lines are identified by using a complex-valued multilayer feed-forward network trained to detect text at a fixed scale and position. The network’s output at all scales and positions is integrated into a single text-saliency map, serving as a starting point for candidate text lines. In the case of video, these candidate text lines are refined by exploiting the temporal redundancy of text in video. Localized text lines are then scaled to a fixed height of 100 pixels and segmented into a binary image with black characters on white background. For videos, temporal redundancy is exploited to improve segmentation performance. Input images and videos can be of any size due to a true multiresolution approach. Moreover, the system is not only able to locate and segment text occurrences into large binary images, but is also able to track each text line with sub-pixel accuracy over the entire occurrence in a video, so that one text bitmap is created for all instances of that text line. Therefore, our text segmentation results can also be used for object-based video encoding such as that enabled by MPEG-4.
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group

Recently introduced cost-effective depth sensors coupled with the real-time skeleton estimation algorithm of Shotton et al.  have generated a renewed interest in skeleton-based human action recognition. Most of the existing skeleton-based approaches use either the joint locations or the joint angles to represent a human skeleton. In this paper, we propose a new skeletal representation that explicitly models the 3D geometric relationships between various body parts using rotations and translations in 3D space. Since 3D rigid body motions are members of the special Euclidean group SE(3), the proposed skeletal representation lies in the Lie group SE(3)×.. .×SE(3), which is a curved manifold. Using the proposed representation, human actions can be modeled as curves in this Lie group. Since classification of curves in this Lie group is not an easy task, we map the action curves from the Lie group to its Lie algebra, which is a vector space. We then perform classification using a combination of dynamic time warping, Fourier temporal pyramid representation and linear SVM. Experimental results on three action datasets show that the proposed representation performs better than many existing skeletal representations. The proposed approach also outperforms various state-of-the-art skeleton-based human action recognition approaches.
Detecting and Extracting Events from Text Documents

Events of various kinds are mentioned and discussed in text documents, whether they are books, news articles, blogs or microblog feeds. The paper starts by giving an overview of how events are treated in linguistics and philosophy. We follow this discussion by surveying how events and associated information are handled in computationally. In particular, we look at how textual documents can be mined to extract events and ancillary information. These days, it is mostly through the application of various machine learning techniques. We also discuss applications of event detection and extraction systems, particularly in summarization, in the medical domain and in the context of Twitter posts. We end the paper with a discussion of challenges and future directions.
Lossless Medical Image

Image compression has become an important process in today‟s world of information exchange. Image compression helps in effective utilization of high speed network resources. Medical Image Compression is very important in the present world for efficient archiving and transmission of images. In this paper two different approaches for lossless image compression is proposed. One uses the combination of 2D-DWT & FELICS algorithm for lossy to lossless Image Compression and another uses combination of prediction algorithm and Integer wavelet Transform (IWT). To show the effectiveness of the methodology used, different image quality parameters are measured and shown the comparison of both the approaches. We observed the increased compression ratio and higher PSNR values.
Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium

Regularized inversion methods for image reconstruction are used widely due to their tractability and their ability to combine complex physical sensor models with useful regularity criteria. Such methods were used in the recently developed Plug-and-Play prior method, which provides a framework to use advanced denoising algorithms as regularizers in inversion. However, the need to formulate regularized inversion as the solution to an optimization problem severely limits both the expressiveness of possible regularity conditions and the variety of provably convergent Plug-and-Play denoising operators. In this paper, we introduce the concept of consensus equilibrium (CE), which generalizes regularized inversion to include a much wider variety of regularity operators without the need for an optimization formulation. Consensus equilibrium is based on the solution of a set of equilibrium equations that balance data fit and regularity. In this framework, the problem of MAP estimation in regularized inversion is replaced by the problem of solving these equilibrium equations, which can be approached in multiple ways, including as a fixed point problem that generalizes the ADMM approach used in the Plug-and-Play method. We present the Douglas-Rachford (DR) algorithm for computing the CE solution as a fixed point and prove the convergence of this algorithm under conditions that include denoising operators that do not arise from optimization problems and that may not be nonexpansive. We give several examples to illustrate the idea of consensus equilibrium and the convergence properties of the DR algorithm and demonstrate this method on a sparse interpolation problem using electron microscopy data.
Graph-based Coherence Modeling For Assessing Readability

Readability depends on many factors ranging from shallow features like word length to semantic ones like coherence. We introduce novel graph-based coherence features based on frequent subgraphs and compare their ability to assess the readability of Wall Street Journal articles. In contrast to Pitler and Nenkova (2008) some of our graph-based features are significantly correlated with human judgments. We outperform Pitler and Nenkova (2008) in the readability ranking task by more than 5% accuracy thus establishing a new state-of-the-art on this dataset.
Workshop Track -iclr 2016 Visualizing and Understanding Recurrent Networks

Recurrent Neural Networks (RNNs), and specifically a variant with Long ShortTerm Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data. However, while LSTMs provide exceptional results in practice, the source of their performance and their limitations remain rather poorly understood. Using character-level language models as an interpretable testbed, we aim to bridge this gap by providing an analysis of their representations, predictions and error types. In particular, our experiments reveal the existence of interpretable cells that keep track of long-range dependencies such as line lengths, quotes and brackets. Moreover, our comparative analysis with finite horizon n-gram models traces the source of the LSTM improvements to long-range structural dependencies. Finally, we provide analysis of the remaining errors and suggests areas for further study.
AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine

We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models. AliMe Chat uses an attentive Seq2Seq based rerank model to optimize the joint results. Extensive experiments show our engine outperforms both IR and generation based models. We launch AliMe Chat for a real-world industrial application and observe better results than another public chatbot.
Optimal Transport for Domain Adaptation

Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but described by another observation system. Among the many strategies proposed, finding domain-invariant representations has shown excellent properties, in particular since it allows to train a unique classifier effective in all domains. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labeled samples of the same class in the source domain to remain close during transport. This way, we exploit at the same time the labeled samples in the source and the distributions observed in both domains. Experiments on toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches. In addition, numerical experiments show that our approach leads to better performances on domain invariant deep learning features and can be easily adapted to the semi-supervised case where few labeled samples are available in the target domain.
Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures

Datasets: KBP 2015 for training and news articles in KBP 2016, 2017 for testing. Model BCUB CEAFE MUC BLANC AV G KBP 2016 Local Classifier 51.47 47.96 26.29 30.82 39.13 Basic ILP 51.44 47.77 26.65 30.95 39.19 +Discourse 51.67 49.1 34.08 34.08 42.23 Joint Learning 50.16 48.59 32.41 32.72 40.97 KBP 2017 Local Classifier 50.24 48.47 30.81 29.94 39.87 Basic ILP 50.4 48.49 31.33 30.58 40.2 +Discourse 50.35 48.61 37.24 31.94 42.04 Table 2: Results for event coreference resolution systems on the KBP 2016 and 2017 corpus. Joint Learning results correspond to the result files evaluated in Lu and Ng, 2017.
Parallel Algorithms for Real-time Motion Planning

For decades, humans have dreamed of making cars that could drive themselves, so that travel would be less taxing, and the roads safer for everyone. Toward this goal, we have made strides in motion planning algorithms for autonomous cars, using a powerful new computing tool, the parallel graphics processing unit (GPU). We propose a novel five-dimensional search space formulation that includes both spatial and temporal dimensions, and respects the kinematic and dynamic constraints on a typical automobile. With this formulation, the search space grows linearly with the length of the path, compared to the exponential growth of other methods. We also propose a parallel search algorithm, using the GPU to tackle the curse of dimensionality directly and increase the number of plans that can be evaluated by an order of magnitude compared to a CPU implementation. With this larger capacity, we can evaluate a dense sampling of plans combining lateral swerves and accelerations that represent a range of effective responses to more on-road driving scenarios than have previously been addressed in the literature. We contribute a cost function that evaluates many aspects of each candidate plan, ranking them all, and allowing the behavior of the vehicle to be fine-tuned by changing the ranking. We show that the cost function can be changed on-line by a behavioral planning layer to express preferred vehicle behavior without the brittleness induced by top-down planning architectures. Our method is particularly effective at generating robust merging behaviors, which have traditionally required a delicate and failure-prone coordination between multiple planning layers. Finally, we demonstrate our proposed planner in a variety of on-road driving scenarios in both simulation and on an autonomous SUV, and make a detailed comparison with prior work.
Sherlock Holmes – An Expert’s View of Expertise For over a century, numerous studies in psychology have aimed at understanding what differentiates experts from novices in a given domain. If the mental functioning of experts

In recent years, there has been an intense research effort to understand the cognitive processes and structures underlying expert behaviour. Work in different fields, including scientific domains, sports, games, and mnemonics, has shown that there are vast differences in perceptual abilities between experts and novices, and that these differences may underpin other cognitive differences in learning, memory, and problem solving. In this article, we evaluate the progress made in the last years through the eyes of an outstanding, albeit fictional, expert: Sherlock Holmes. We first use the Sherlock Holmes character to illustrate expert processes as described by current research and theories. In particular, the role of perception, as well as the nature and influence of expert knowledge, are all present in the description of Conan Doyle’s hero. In the second part of the article, we discuss a number of issues that current research on expertise has barely addressed. These gaps include, for example, several forms of reasoning, the influence of emotions on cognition, and the effect of age on experts’ knowledge and cognitive processes. Thus, although nearly 120 years old, Conan Doyle’s books show remarkable illustrations of expert behaviour, including the coverage of themes that have mostly been overlooked by current research.
Towards Sensor Database Systems

Sensor networks are being widely deployed for measurement, detection and surveillance applications. In these new applications, users issue long-running queries over a combination of stored data and sensor data. Most existing applications rely on a centralized system for collecting sensor data. These systems lack flexibility because data is extracted in a predefined way; also, they do not scale to a large number of devices because large volumes of raw data are transferred regardless of the queries that are submitted. In our new concept of sensor database system, queries dictate which data is extracted from the sensors. In this paper, we define a model for sensor databases. Stored data are represented as relations while sensor data are represented as time series. Each long-running query formulated over a sensor database defines a persistent view, which is maintained during a given time interval. We also describe the design and implementation of the COUGAR sensor database system.
A stabilizing model predictive controller for voltage regulation of a DC/DC converter

Using a nonlinear converter model, a numerical on-line optimization free model predictive control (MPC) is proposed for a converter to regulate the capacitor voltage. The MPC is given analytically by minimizing an energy function of the tracking error with its global exponential stability under input constraints. The outer-loop PI controller is adopted to eliminate an offset errors caused by a plant-model mismatch.
Software project management tools: a brief comparative view

The task of managing a software project can be an extremely complex one, drawing on many personal, team, and organizational resources. The quality of a software product is dependent on the process in which the project is completed. Time delays in software development project and low productivity tend to fall right to the bottom line. Recently, the evolution of project management tools for both software and non-software applications has been accelerating at a rapid pace, and the number of available products have grown significantly. There are many project management tools and software being developed everyday to help managers to automate the administration of individual projects or groups of projects during their life-cycle. This article presents a short comparison of popular software project management tools.
What is Intermodal Transportation ?

This Chapter focuses on Intermodal Freight Transportation, broadly defined as the transportation of a load from its origin to its destination by a sequence of at least two transportation modes, the transfer from one mode to the next being performed at an intermodal terminal. Container-based transportation services are an important part of intermodal transportation and the backbone of international trade. This importance is reflected in this Chapter. We also examine issues related other intermodal systems or components, such as postal services, intermodal rail transportation, and seaport container terminals. Compared to several other application areas, Operations Research models and methods for intermodal freight transportation is still a very young domain. The goal of the chapter is thus to be informative and provide a starting point for future research, by providing an overview of the evolution of the field and presenting methodological developments proposed to address a number of important operations and planning issues: system and service design, container fleet management, planning of container terminal operations, and national planning. We focus on models. Algorithmic developments are indicated but not examined in any depth.
Interleaved Structured Sparse Convolutional Neural Networks

In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels. In addition to structured sparse kernels, low-rank kernels and the product of low-rank kernels, the product of structured sparse kernels, which is a framework for interpreting the recently-developed interleaved group convolutions (IGC) and its variants (e.g., Xception), has been attracting increasing interests. Motivated by the observation that the convolutions contained in a group convolution in IGC can be further decomposed in the same manner, we present a modularized building block, IGC-V2: interleaved structured sparse convolutions. It generalizes interleaved group convolutions, which is composed of two structured sparse kernels, to the product of more structured sparse kernels, further eliminating the redundancy. We present the complementary condition and the balance condition to guide the design of structured sparse kernels, obtaining a balance among three aspects: model size, computation complexity and classification accuracy. Experimental results demonstrate the advantage on the balance among these three aspects compared to interleaved group convolutions and Xception, and competitive performance compared to other state-of-the-art architecture design methods.
RFID tags: Positioning principles and localization techniques

RFID is an automatic identification technology that enables tracking of people and objects. Both identity and location are generally key information for indoor services. An obvious and interesting method to obtain these two types of data is to localize RFID tags attached to devices or objects or carried by people. However, signals in indoor environments are generally harshly impaired and tags have very limited capabilities which pose many challenges for positioning them. In this work, we propose a classification and survey the current state-of-art of RFID localization by first presenting this technology and positioning principles. Then, we explain and classify RFID localization techniques. Finally, we discuss future trends in this domain.
Generative Adversarial Network in Medical Imaging: A Review

Generative adversarial networks have gained a lot of attention in general computer vision community due to their capability of data generation without explicitly modelling the probability density function and robustness to overfitting. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into the training and imposing higher order consistency that is proven to be useful in many cases, such as in domain adaptation, data augmentation, and image-to-image translation. These nice properties have attracted researcher in the medical imaging community and we have seen quick adoptions in many traditional tasks and some novel applications. This trend will continue to grow based on our observation therefore we conducted a review of the recent advances in medical imaging using the adversarial training scheme in the hope of benefiting researchers that are interested in this technique.
Data-flow Aware CNN Accelerator with Hybrid Wireless Interconnection

Deep convolution neural networks (CNNs) are computationally intensive machine learning algorithms with a large amount of data that impose various challenges for their hardware implementation. To meet the high computing demands of CNNs, many accelerator designs are proposed that revolve around achieving high parallelization, increasing on-chip data reuse and efficient memory hierarchy, etc. However, very few works have attempted to address the communication challenges in these massively parallel accelerators architectures, which is the most anticipated performance bottleneck. Traditional interconnections like bus, crossbar and even Network-on-Chip ($N$ o $C$) topologies like mesh fail to achieve the peak performance required by the large number of processing elements on accelerators. In this work, we address the communication bottlenecks of accelerators by extensively studying the application data-flow. We propose an efficient accelerator architecture that employs broadcast enabled low latency wireless links along with traditional wired links to efficiently support the data-flow of accelerators and achieve high communication performance. Evaluation of the proposed design shows that it achieves 28 % latency reduction, 19x bandwidth improvement and 35% network energy saving as compared to baseline wired networks.