nils-herrmann/clean_scirepeval huggingface dataset:
Consumer Learning and 3-D Ecommerce : The Effects of Sequential Exposure of a Virtual Experience Relative to Indirect and Direct Product Experience on Product Knowledge , Brand Attitude and Purchase Intention

The Internet has the ability to serve as a more powerful medium because consumers are able to interact with products in 3-D multimedia environments thus simulating a new form of experience – a virtual experience. However, very little research has explored the impact of this new type of experience especially in combination with indirect (advertising) and direct (product trial) experience. Therefore, in a laboratory experiment (n=166) this study empirically tested the sequential impact of consumer exposure to indirect, direct, and virtual experiences on brand attitude, product knowledge, and purchase intention when evaluating a digital video camcorder. The findings indicate that exposure to a virtual experience preceding both indirect and direct product experience is more effective at influencing brand attitudes. However, direct experience preceding exposure to a virtual experience was found to have the greatest impact on product knowledge. Consumers exposed to an indirect experience preceding both virtual and direct product experience indicated they were significantly less likely to purchase the test product. Overall, the results suggest that an interactive virtual product experience has the strongest impact on consumer behavior as a first order effect prior to exposure from advertising or product trial. Implications for business-to-consumer ecommerce and Internet marketing are discussed.
Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.
Deep Learning approach for sentiment analysis of short texts

Unstructured text data produced on the internet grows rapidly, and sentiment analysis for short texts becomes a challenge because of the limit of the contextual information they usually contain. Learning good vector representations for sentences is a challenging task and an ongoing research area. Moreover, learning long-term dependencies with gradient descent is difficult in neural network language model because of the vanishing gradients problem. Natural Language Processing (NLP) systems traditionally treat words as discrete atomic symbols; the model can leverage small amounts of information regarding the relationship between the individual symbols. In this paper, we propose ConvLstm, neural network architecture that employs Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) on top of pre-trained word vectors. In our experiments, ConvLstm exploit LSTM as a substitute of pooling layer in CNN to reduce the loss of detailed local information and capture long term dependencies in sequence of sentences. We validate the proposed model on two sentiment datasets IMDB, and Stanford Sentiment Treebank (SSTb). Empirical results show that ConvLstm achieved comparable performances with less parameters on sentiment analysis tasks.
Trending Cardiovascular Nutrition Controversies.

The potential cardiovascular benefits of several trending foods and dietary patterns are still incompletely understood, and nutritional science continues to evolve. However, in the meantime, a number of controversial dietary patterns, foods, and nutrients have received significant media exposure and are mired by hype. This review addresses some of the more popular foods and dietary patterns that are promoted for cardiovascular health to provide clinicians with accurate information for patient discussions in the clinical setting.
Through-skull fluorescence imaging of the brain in a new near-infrared window

To date, brain imaging has largely relied on X-ray computed tomography and magnetic resonance angiography with limited spatial resolution and long scanning times. Fluorescence-based brain imaging in the visible and traditional near-infrared regions (400-900 nm) is an alternative but currently requires craniotomy, cranial windows and skull thinning techniques, and the penetration depth is limited to 1-2 mm due to light scattering. Here, we report through-scalp and through-skull fluorescence imaging of mouse cerebral vasculature without craniotomy utilizing the intrinsic photoluminescence of single-walled carbon nanotubes in the 1.3-1.4 micrometre near-infrared window. Reduced photon scattering in this spectral region allows fluorescence imaging reaching a depth of >2 mm in mouse brain with sub-10 micrometre resolution. An imaging rate of ~5.3 frames/s allows for dynamic recording of blood perfusion in the cerebral vessels with sufficient temporal resolution, providing real-time assessment of blood flow anomaly in a mouse middle cerebral artery occlusion stroke model.
Joint Copying and Restricted Generation for Paraphrase

Many natural language generation tasks, such as abstractive summarization and text simplification, are paraphrase-orientated. In these tasks, copying and rewriting are two main writing modes. Most previous sequence-to-sequence (Seq2Seq) models use a single decoder and neglect this fact. In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder. The copying decoder finds the position to be copied based on a typical attention model. The generative decoder produces words limited in the source-specific vocabulary. To combine the two decoders and determine the final output, we develop a predictor to predict the mode of copying or rewriting. This predictor can be guided by the actual writing mode in the training data. We conduct extensive experiments on two different paraphrase datasets. The result shows that our model outperforms the stateof-the-art approaches in terms of both informativeness and language quality.
Greedy Fun tion Approximation : A Gradient Boosting

Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additive expansions based on any tting criterion. Speciic algorithms are presented for least{squares, least{absolute{deviation, and Huber{M loss functions for regression, and multi{class logistic likelihood for classiication. Special enhancements are derived for the particular case where the individual additive components are decision trees, and tools for interpreting such \TreeBoost" models are presented. Gradient boosting of decision trees produces competitive, highly robust, interpretable procedures for regression and classiication, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire 1996, and Friedman, Hastie, and Tibshirani 1998 are discussed. In the function estimation problem one has a system consisting of a random \output" or \re-sponse" variable y and a set of random \input" or \explanatory" variables x = fx 1 ; ; x n g. Given a \training" sample fy i ; x i g N 1 of known (y; x){values, the goal is to nd a function F (x) that maps x to y, such that over the joint distribution of all (y; x){values, the expected value of some speciied loss function (y; F(x)) is minimized F (x) = arg min F(x) Frequently employed loss functions (y; F) include squared{error (y ? F) 2 and absolute error jy ? Fj for y 2 R 1 (regression), and negative binomial log{likelihood, log(1 + e ?2yF), when y 2 f?1; 1g (classiication). A common procedure is to take F(x) to be a member of a parameterized class of functions
Comparison of Performance of Decision Tree Algorithms and Random Forest: An Application on OECD Countries Health Expenditures

Decision trees and Random Forest are most popular methods of machine learning techniques. C4.5 which is an extension version of ID.3 algorithm and CART are one of these most commonly use algorithms to generate decision trees. Random Forest which constructs a lot of number of trees is one of another useful technique for solving both classification and regression problems. This study compares classification performances of different decision trees (C4.5, CART) and Random Forest which was generated using 50 trees. Data came from OECD countries health expenditures for the year 2011. AUC and ROC curve graph was used for performance comparison. Experimental results show that Random Forest outperformed in classification accuracy [AUC=0.98] in comparison with CART (0.95) and C4.5 (0.90) respectively. Future studies more focus on performance comparisons of different machine learning techniques using several datasets and different hyperparameter optimization techniques.
American Economic Association Corruption and Development : A Review of Issues

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A large annotated corpus for learning natural language inference

Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources. To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning. At 570K pairs, it is two orders of magnitude larger than all other resources of its type. This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time.
A cross-language acoustic study of initial and final allophones of /l/

Formant frequency data for /l/ in 23 languages/dialects where the consonant may be typically clear or dark show that the two varieties of /l/ are set in contrast mostly in the context of /i/ but also next to /a/, and that a few languages/dialects may exhibit intermediate degrees of darkness in the consonant. F2 for /l/ is higher utterance initially than utterance finally, more so if the lateral is clear than if it is dark; moreover, the initial and final allophones may be characterized as intrinsic (in most languages/dialects) or extrinsic (in several English dialects, Czech and Dutch) depending on whether the position-dependent frequency difference in question is below or above 200/ 300 Hz. The paper also reports a larger degree of vowel coarticulation for clear /l/ than for dark /l/ and in initial than in final position. These results are interpreted in terms of the production mechanisms involved in the realization of the two /l/ varieties in the different positional and vowel context conditions subjected to investigation.
Automated Selection of Materialized Views and Indexes in SQL Databases

Automatically selecting an appropriate set of materialized views and indexes for SQL databases is a non-trivial task. A judicious choice must be cost-driven and influenced by the workload experienced by the system. Although there has been work in materialized view selection in the context of multidimensional (OLAP) databases, no past work has looked at the problem of building an industry-strength tool for automated selection of materialized views and indexes for SQL workloads. In this paper, we present an end-to-end solution to the problem of selecting materialized views and indexes. We describe results of extensive experimental evaluation that demonstrate the effectiveness of our techniques. Our solution is implemented as part of a tuning wizard that ships with Microsoft SQL Server 2000.
A multi-view context-aware approach to Android malware detection and malicious code localization

Many existing Machine Learning (ML) based Android malware detection approaches use a variety of features such as security-sensitive APIs, system calls, control-flow structures and information flows in conjunction with ML classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps’ behaviors with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterize several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevents them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localization. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. MKLDroid uses a graph kernel to capture structural and contextual information from apps’ dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid’s unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Malicious code localization caters several important applications such as supporting human analysts studying malware behaviors, engineering malware signatures, and other counter-measures. Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localization experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94% average recall. Our work opens up two new avenues in malware research: (i) enables the research community to elegantly look at Android malware behaviors in multiple perspectives simultaneously, and (ii) performing precise and scalable malicious code localization.
What's in a p-value in NLP?

In NLP, we need to document that our proposed methods perform significantly better with respect to standard metrics than previous approaches, typically by reporting p-values obtained by rankor randomization-based tests. We show that significance results following current research standards are unreliable and, in addition, very sensitive to sample size, covariates such as sentence length, as well as to the existence of multiple metrics. We estimate that under the assumption of perfect metrics and unbiased data, we need a significance cut-off at ⇠0.0025 to reduce the risk of false positive results to <5%. Since in practice we often have considerable selection bias and poor metrics, this, however, will not do alone.
The restless mind.

This article reviews the hypothesis that mind wandering can be integrated into executive models of attention. Evidence suggests that mind wandering shares many similarities with traditional notions of executive control. When mind wandering occurs, the executive components of attention appear to shift away from the primary task, leading to failures in task performance and superficial representations of the external environment. One challenge for incorporating mind wandering into standard executive models is that it often occurs in the absence of explicit intention--a hallmark of controlled processing. However, mind wandering, like other goal-related processes, can be engaged without explicit awareness; thus, mind wandering can be seen as a goal-driven process, albeit one that is not directed toward the primary task.
BAG OF REGION EMBEDDINGS VIA LOCAL CONTEXT UNITS FOR TEXT CLASSIFICATION

Contextual information and word orders are proved valuable for text classification task. To make use of local word order information, n-grams are commonly used features in several models, such as linear models. However, these models commonly suffer the data sparsity problem and are difficult to represent large size region. The discrete or distributed representations of n-grams can be regarded as region embeddings, which are representations of fixed size regions. In this paper, we propose two novel text classification models that learn task specific region embeddings without hand crafted features, hence the drawbacks of n-grams can be overcome. In our model, each word has two attributes, a commonly used word embedding, and an additional local context unit which is used to interact with the word’s local context. Both the units and word embeddings are used to generate representations of regions, and are learned as model parameters. Finally, bag of region embeddings of a document is fed to a linear classifier. Experimental results show that our proposed methods achieve state-of-the-art performance on several benchmark datasets. We provide visualizations and analysis illustrating that our proposed local context unit can capture the syntactic and semantic information.
Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks

A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser range finders, sonar sensors or artificial landmarks. In this paper, we describe a vision-based mobile robot localization and mapping algorithm, which uses scale-invariant image features as natural landmarks in unmodified environments. The invariance of these features to image translation, scaling and rotation makes them suitable landmarks for mobile robot localization and map building. With our Triclops stereo vision system, these landmarks are localized and robot ego-motion is estimated by least-squares minimization of the matched landmarks. Feature viewpoint variation and occlusion are taken into account by maintaining a view direction for each landmark. Experiments show that these visual landmarks are robustly matched, robot pose is estimated and a consistent three-dimensional map is built. As image features are not noise-free, we carry out error analysis for the landmark positions and the robot pose. We use Kalman filters to track these landmarks in a dynamic environment, resulting in a database map with landmark positional uncertainty. localization, mapping, visual landmarks, mobile robot
A Convolutional Fuzzy Neural Network for Image Classification

A model of Convolutional Fuzzy Neural Network for real world objects and scenes images classification is proposed. The Convolutional Fuzzy Neural Network consists of convolutional, pooling and fully-connected layers and a Fuzzy Self Organization Layer. The model combines the power of convolutional neural networks and fuzzy logic and is capable of handling uncertainty and impreciseness in the input pattern representation. The Training of The Convolutional Fuzzy Neural Network consists of three independent steps for three components of the net.
EXPLICATING DYNAMIC CAPABILITIES : THE NATURE AND MICROFOUNDATIONS OF ( SUSTAINABLE ) ENTERPRISE PERFORMANCE

This paper draws on the social and behavioral sciences in an endeavor to specify the nature and microfoundations of the capabilities necessary to sustain superior enterprise performance in an open economy with rapid innovation and globally dispersed sources of invention, innovation, and manufacturing capability. Dynamic capabilities enable business enterprises to create, deploy, and protect the intangible assets that support superior longrun business performance. The microfoundations of dynamic capabilities—the distinct skills, processes, procedures, organizational structures, decision rules, and disciplines—which undergird enterprise-level sensing, seizing, and reconfiguring capacities are difficult to develop and deploy. Enterprises with strong dynamic capabilities are intensely entrepreneurial. They not only adapt to business ecosystems, but also shape them through innovation and through collaboration with other enterprises, entities, and institutions. The framework advanced can help scholars understand the foundations of long-run enterprise success while helping managers delineate relevant strategic considerations and the priorities they must adopt to enhance enterprise performance and escape the zero profit tendency associated with operating in markets open to global competition.
A Review of Motion Planning Techniques for Automated Vehicles

Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.
Stating the Obvious: Extracting Visual Common Sense Knowledge

Obtaining common sense knowledge using current information extraction techniques is extremely challenging. In this work, we instead propose to derive simple common sense statements from fully annotated object detection corpora such as the Microsoft Common Objects in Context dataset. We show that many thousands of common sense facts can be extracted from such corpora at high quality. Furthermore, using WordNet and a novel submodular k-coverage formulation, we are able to generalize our initial set of common sense assertions to unseen objects and uncover over 400k potentially useful facts.