nils-herrmann/clean_scirepeval huggingface dataset:
Learning Generic Sentence Representations Using Convolutional Neural Networks

We propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes. The model is learned by using a convolutional neural network as an encoder to map an input sentence into a continuous vector, and using a long short-term memory recurrent neural network as a decoder. Several tasks are considered, including sentence reconstruction and future sentence prediction. Further, a hierarchical encoderdecoder model is proposed to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods.
Efficient Processing of Deep Neural Networks: A Tutorial and Survey

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. This article aims to provide a comprehensive tutorial and survey about the recent advances toward the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic codesigns, being proposed in academia and industry. The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the tradeoffs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.
Advances in adsorption of surfactants and their mixtures at solid/solution interfaces.

Surfactants and their mixtures can drastically change the interfacial properties and hence are used in many industrial processes such as dispersion/flocculation, flotation, emulsification, corrosion inhibition, cosmetics, drug delivery, chemical mechanical polishing, enhanced oil recovery, and nanolithography. A review of studies on adsorption of single surfactant as well as mixtures of various types (anionic-cationic, anionic-nonionic, cationic-nonionic, cationic-zwitterionic and nonionic-nonionic) is presented here along with mechanisms involved. Results obtained using techniques such as zeta potential, flotation, AFM, specular neutron reflectivity, small angle neutron scattering, fluorescence, ESR, Raman spectroscopy, ellipsometry, HPLC and ATR-IR are reviewed along with those from traditional techniques to elucidate the mechanisms of adsorption and particularly to understand synergistic/antagonistic interactions at solution/liquid interfaces and nanostructures of surface aggregates. In addition, adsorption of several mixed surfactant systems is considered due to their industrial relevance. Finally an attempt is made to derive structure-property relationships to provide a solid foundation for the design and use of surfactant formulations for industrial applications.
Deformable ConvNets v2: More Deformable, Better Results

The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects. Through an examination of its adaptive behavior, we observe that while the spatial support for its neural features conforms more closely than regular ConvNets to object structure, this support may nevertheless extend well beyond the region of interest, causing features to be influenced by irrelevant image content. To address this problem, we present a reformulation of Deformable ConvNets that improves its ability to focus on pertinent image regions, through increased modeling power and stronger training. The modeling power is enhanced through a more comprehensive integration of deformable convolution within the network, and by introducing a modulation mechanism that expands the scope of deformation modeling. To effectively harness this enriched modeling capability, we guide network training via a proposed feature mimicking scheme that helps the network to learn features that reflect the object focus and classification power of RCNN features. With the proposed contributions, this new version of Deformable ConvNets yields significant performance gains over the original model and produces leading results on the COCO benchmark for object detection and instance segmentation.
Modeling Trust Relationships for Developing Trustworthy Information Systems

Developing a trustworthy information system is a challenging task. The overall trustworthiness of an information system depends on trust relationships that are generally assumed without adequate justification. However, lack of appropriate analysis of such relationships and of appropriate justification of relevant trust assumptions might lead to systems that fail to fully achieve their functionalities. Existing literature does not provide adequate guidelines for a systematic process or an appropriate modeling language to support such trust-focused analysis. This paper fills this gap by introducing a process that allows developers to capture possible trust relationships and to reason about them. The process is supported by a modeling language based on a set of concepts relating to trust and control and a CASE tool. An illustrative example from the UK health care domain is used to demonstrate the applicability and usefulness of the approach.
Multicasting over Emerging 5G Networks: Challenges and Perspectives

The number and variety of mobile multicast applications are growing at an unprecedented and unanticipated pace. Mobile network providers are in front of a dramatic increase in multicast traffic load, and this growth is forecasted to continue in fifth-generation (5G) networks. The major challenges come from the fact that multicast traffic not only targets groups of end-user devices; it also involves machine-type communications (MTC) for the Internet of Things (IoT). The increase in the MTC load, predicted for 5G, calls into question the effectiveness of the current multimedia broadcast multicast service (MBMS). The aim of this paper is to provide a survey of 5G challenges in the view of effective management of multicast applications, and to identify how to enhance the mobile network architecture to enable multicast applications in future 5G scenarios. By accounting for the presence of both human and machine-related traffic, strengths and weaknesses of the state-of-the-art achievements in multicasting are critically analyzed to provide guidelines for future research on 5G networks and more conscious design choices.
WebTables: exploring the power of tables on the web

The World-Wide Web consists of a huge number of unstructured documents, but it also contains structured data in the form of HTML tables. We extracted 14.1 billion HTML tables from Google’s general-purpose web crawl, and used statistical classification techniques to find the estimated 154M that contain high-quality relational data. Because each relational table has its own “schema” of labeled and typed columns, each such table can be considered a small structured database. The resulting corpus of databases is larger than any other corpus we are aware of, by at least five orders of magnitude. We describe the WebTables system to explore two fundamental questions about this collection of databases. First, what are effective techniques for searching for structured data at search-engine scales? Second, what additional power can be derived by analyzing such a huge corpus? First, we develop new techniques for keyword search over a corpus of tables, and show that they can achieve substantially higher relevance than solutions based on a traditional search engine. Second, we introduce a new object derived from the database corpus: the attribute correlation statistics database (AcsDB) that records corpus-wide statistics on cooccurrences of schema elements. In addition to improving search relevance, the AcsDB makes possible several novel applications: schema auto-complete, which helps a database designer to choose schema elements; attribute synonym finding, which automatically computes attribute synonym pairs for schema matching; and join-graph traversal, which allows a user to navigate between extracted schemas using automatically-generated join links. ∗Work done while all authors were at Google, Inc. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commer cial advantage, the VLDB copyright notice and the title of the publication an d its date appear, and notice is given that copying is by permission of the Very L arge Data Base Endowment. To copy otherwise, or to republish, to post o n servers or to redistribute to lists, requires a fee and/or special pe rmission from the publisher, ACM. VLDB ’08 Auckland, New Zealand Copyright 2008 VLDB Endowment, ACM 000-0-00000-000-0/00/ 00.
Video forgery detection using correlation of noise residue

We propose a new approach for locating forged regions in a video using correlation of noise residue. In our method, block-level correlation values of noise residual are extracted as a feature for classification. We model the distribution of correlation of temporal noise residue in a forged video as a Gaussian mixture model (GMM). We propose a two-step scheme to estimate the model parameters. Consequently, a Bayesian classifier is used to find the optimal threshold value based on the estimated parameters. Two video inpainting schemes are used to simulate two different types of forgery processes for performance evaluation. Simulation results show that our method achieves promising accuracy in video forgery detection.
Nitro: Network-Aware Virtual Machine Image Management in Geo-Distributed Clouds

Recently, most large cloud providers, like Amazon and Microsoft, replicate their Virtual Machine Images (VMIs) on multiple geographically distributed data centers to offer fast service provisioning. Provisioning a service may require to transfer a VMI over the wide-area network (WAN) and therefore is dictated by the distribution of VMIs and the network bandwidth in-between sites. Nevertheless, existing methods to facilitate VMI management (i.e., retrieving VMIs) overlook network heterogeneity in geo-distributed clouds. In this paper, we design, implement and evaluate Nitro, a novel VMI management system that helps to minimize the transfer time of VMIs over a heterogeneous WAN. To achieve this goal, Nitro incorporates two complementary features. First, it makes use of deduplication to reduce the amount of data which will be transferred due to the high similarities within an image and in-between images. Second, Nitro is equipped with a network-aware data transfer strategy to effectively exploit links with high bandwidth when acquiring data and thus expedites the provisioning time. Experimental results show that our network-aware data transfer strategy offers the optimal solution when acquiring VMIs while introducing minimal overhead. Moreover, Nitro outperforms state-of-the-art VMI storage systems (e.g., OpenStack Swift) by up to 77%.
Holistic 3D scene understanding from a single geo-tagged image

In this paper we are interested in exploiting geographic priors to help outdoor scene understanding. Towards this goal we propose a holistic approach that reasons jointly about 3D object detection, pose estimation, semantic segmentation as well as depth reconstruction from a single image. Our approach takes advantage of large-scale crowd-sourced maps to generate dense geographic, geometric and semantic priors by rendering the 3D world. We demonstrate the effectiveness of our holistic model on the challenging KITTI dataset [13], and show significant improvements over the baselines in all metrics and tasks.
Graph Databases: Neo4j Analysis

The volume of data is growing at an increasing rate. This growth is both in size and in connectivity, where connectivity refers to the increasing presence of relationships between data. Social networks such as Facebook and Twitter store and process petabytes of data each day. Graph databases have gained renewed interest in the last years, due to their applications in areas such as the Semantic Web and Social Network Analysis. Graph databases provide an effective and efficient solution to data storage and querying data in these scenarios, where data is rich in relationships. In this paper, it is analyzed the fundamental points of graph databases, showing their main characteristics and advantages. We study Neo4j, the top graph database software in the market and evaluate its performance using the Social Network Benchmark (SNB).
Spline Fusion: A continuous-time representation for visual-inertial fusion with application to rolling shutter cameras

This paper describes a general continuous-time framework for visual-inertial simultaneous localization and mapping and calibration. We show how to use a spline parameterization that closely matches the torque-minimal motion of the sensor. Compared to traditional discrete-time solutions, the continuous-time formulation is particularly useful for solving problems with high-frame rate sensors and multiple unsynchronized devices. We demonstrate the applicability of the method for multi-sensor visual-inertial SLAM and calibration by accurately establishing the relative pose and internal parameters of multiple unsynchronized devices. We also show the advantages of the approach through evaluation and uniform treatment of both global and rolling shutter cameras within visual and visual-inertial SLAM systems.
The Yale cTAKES extensions for document classification: architecture and application

Open-source clinical natural-language-processing (NLP) systems have lowered the barrier to the development of effective clinical document classification systems. Clinical natural-language-processing systems annotate the syntax and semantics of clinical text; however, feature extraction and representation for document classification pose technical challenges.



The authors developed extensions to the clinical Text Analysis and Knowledge Extraction System (cTAKES) that simplify feature extraction, experimentation with various feature representations, and the development of both rule and machine-learning based document classifiers. The authors describe and evaluate their system, the Yale cTAKES Extensions (YTEX), on the classification of radiology reports that contain findings suggestive of hepatic decompensation.



The F(1)-Score of the system for the retrieval of abdominal radiology reports was 96%, and was 79%, 91%, and 95% for the presence of liver masses, ascites, and varices, respectively. The authors released YTEX as open source, available at http://code.google.com/p/ytex.
Dynamic Coattention Networks For Question Answering

Several deep learning models have been proposed for question answering. However, due to their single-pass nature, they have no way to recover from local maxima corresponding to incorrect answers. To address this problem, we introduce the Dynamic Coattention Network (DCN) for question answering. The DCN first fuses co-dependent representations of the question and the document in order to focus on relevant parts of both. Then a dynamic pointing decoder iterates over potential answer spans. This iterative procedure enables the model to recover from initial local maxima corresponding to incorrect answers. On the Stanford question answering dataset, a single DCN model improves the previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains 80.4% F1.
PIXELDEFEND: LEVERAGING GENERATIVE MODELS

Adversarial perturbations of normal images are usually imperceptible to humans, but they can seriously confuse state-of-the-art machine learning models. What makes them so special in the eyes of image classifiers? In this paper, we show empirically that adversarial examples mainly lie in the low probability regions of the training distribution, regardless of attack types and targeted models. Using statistical hypothesis testing, we find that modern neural density models are surprisingly good at detecting imperceptible image perturbations. Based on this discovery, we devised PixelDefend, a new approach that purifies a maliciously perturbed image by moving it back towards the distribution seen in the training data. The purified image is then run through an unmodified classifier, making our method agnostic to both the classifier and the attacking method. As a result, PixelDefend can be used to protect already deployed models and be combined with other model-specific defenses. Experiments show that our method greatly improves resilience across a wide variety of state-of-the-art attacking methods, increasing accuracy on the strongest attack from 63% to 84% for Fashion MNIST and from 32% to 70% for CIFAR-10.
Double JPEG compression forensics based on a convolutional neural network

Double JPEG compression detection has received considerable attention in blind image forensics. However, only few techniques can provide automatic localization. To address this challenge, this paper proposes a double JPEG compression detection algorithm based on a convolutional neural network (CNN). The CNN is designed to classify histograms of discrete cosine transform (DCT) coefficients, which differ between single-compressed areas (tampered areas) and double-compressed areas (untampered areas). The localization result is obtained according to the classification results. Experimental results show that the proposed algorithm performs well in double JPEG compression detection and forgery localization, especially when the first compression quality factor is higher than the second.
STRATEGIC LEADERSHIP AND EXECUTIVE INNOVATION INFLUENCE : AN INTERNATIONAL MULTI-CLUSTER COMPARATIVE STUDY

This study investigates the relationship of strategic leadership behaviors with executive innovation influence and the moderating effects of top management team (TMT)’s tenure heterogeneity and social culture on that relationship. Using survey data from six countries comprising three social cultures, strategic leadership behaviors were found to have a strong positive relationship with executive influence on both product–market and administrative innovations. In addition, TMT tenure heterogeneity moderated the relationship of strategic leadership behaviors with executive innovation influence for both types of innovation, while social culture moderated that relationship only in the case of administrative innovation.
A subject identification method based on term frequency technique

The analyzing and extracting important information from a text document is crucial and has produced interest in the area of text mining and information retrieval. This process is used in order to notice particularly in the text. Furthermore, on view of the readers that people tend to read almost everything in text documents to find some specific information. However, reading a text document consumes time to complete and additional time to extract information. Thus, classifying text to a subject can guide a person to find relevant information. In this paper, a subject identification method which is based on term frequency to categorize groups of text into a particular subject is proposed. Since term frequency tends to ignore the semantics of a document, the term extraction algorithm is introduced for improving the result of the extracted relevant terms from the text. The evaluation of the extracted terms has shown that the proposed method is exceeded other extraction techniques.
Internet Use, Transparency, and Interactivity Effects on Trust in Government

This paper asks how internet use, citizen satisfaction with e-government and citizen trust in government are interrelated. Prior research has found that agencies stress information and service provision on the Web (oneway e-government strategy), but have generally ignore applications that would enhance citizen-government interaction (two-way e-government strategy). Based on a review of the literature, we develop hypotheses about how two facets of e-democracy – transparency and interactivity – may affect citizen trust in government. Using data obtained from the Council on Excellence in Government, we apply a two stage multiple equation model. Findings indicate that internet use is positively associated with transparency satisfaction but negatively associated with interactivity satisfaction, and that both interactivity and transparency are positively associated with citizen trust in government. We conclude that the one-way e-transparency strategy may be insufficient, and that in the future agencies should make and effort to enhance e-interactivity.
Exploring Deep Learning Models for Machine Comprehension on SQuAD

This paper explores the use of multiple models in performing question answering tasks on the Stanford Question Answering Database. We first implement and share results of a baseline model using bidirectional long short-term memory (BiLSTM) encoding of question and context followed a simple co-attention model . We then report on the use of match-LSTM and Pointer Net which showed marked improvements in question answering over the baseline model . Lastly, we extend the model by adding Dropout  and randomization strategies to account for unknown tokens. Final test score on Codalab under Username: yife, F1: 66.981, EM: 56.845.
High-Frequency-Link-Based Grid-Tied PV System With Small DC-Link Capacitor and Low-Frequency Ripple-Free Maximum Power Point Tracking

This paper proposes a grid-tied photovoltaic (PV) system consisting of modular current-fed dual-active-bridge (CF-DAB) dc-dc converter with cascaded multilevel inverter. The proposed converter allows a small dc-link capacitor in the three-phase wye-connected PV system; therefore, the system reliability can be improved by replacing electrolytic capacitors with film capacitors. The low-frequency ripple-free maximum power point tracking (MPPT) is also realized in the proposed converter. First of all, to minimize the influence resulting from reduced capacitance, a dc-link voltage synchronizing control is developed. Then, a detailed design of power mitigation control based on CF-DAB dynamic model is presented to prevent the large low-frequency voltage variation propagating from the dc-link to PV side. Finally, a novel variable step-size MPPT algorithm is proposed to ensure not only high MPPT efficiency, but also fast maximum power extraction under rapid irradiation change. A downscaled 5-kW PV converter module with a small dc-link capacitor was built in the laboratory with the proposed control and MPPT algorithm, and experimental results are given to validate the converter performance.