nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Room Segmentation From Unstructured 3-D Data of Indoor Environments

We present an automatic approach for the task of reconstructing a 2-D floor plan from unstructured point clouds of building interiors. Our approach emphasizes accurate and robust detection of building structural elements and, unlike previous approaches, does not require prior knowledge of scanning device poses. The reconstruction task is formulated as a multiclass labeling problem that we approach using energy minimization. We use intuitive priors to define the costs for the energy minimization problem and rely on accurate wall and opening detection algorithms to ensure robustness. We provide detailed experimental evaluation results, both qualitative and quantitative, against state-of-the-art methods and labeled ground-truth data.
The Fragility of Time: Time-Insensitivity and Valuation of the Near and Far Future

W propose that the temporal dimension is fragile in that choices are insufficiently sensitive to it, and second, such sensitivity as exists is exceptionally malleable, unlike other dimensions such as money, which are attended by default. To test this, we axiomatize a “constant-sensitivity” discount function, and in four studies, we show that the degree of time-sensitivity is inadequate relative to the compound discounting norm, and strongly susceptible to manipulation. Time-sensitivity is increased by a comparative within-subject presentation (Experiment 1), direct instruction (Experiment 3), and provision of a visual cue for time duration (Experiment 4); time-sensitivity is decreased using a time pressure manipulation (Experiment 2). In each study, the sensitivity manipulation has an opposite effect on near-future and far-future valuations: Increased sensitivity decreases discounting in the near future and increases discounting in the far future. In contrast, such sensitivity manipulations have little effect on the money dimension.
VERBAL IRONY USE IN FACE-TO-FACE AND COMPUTER-MEDIATED CONVERSATIONS

can be found at: Journal of Language and Social Psychology Additional services and information for http://jls.sagepub.com/cgi/alerts  Email Alerts: http://jls.sagepub.com/subscriptions  Subscriptions: http://www.sagepub.com/journalsReprints.nav Reprints: http://www.sagepub.com/journalsPermissions.nav Permissions: http://jls.sagepub.com/cgi/content/refs/23/4/447 SAGE Journals Online and HighWire Press platforms): (this article cites 16 articles hosted on the Citations
Extraction and Approximation of Numerical Attributes from the Web

We present a novel framework for automated extraction and approximation of numerical object attributes such as height and weight from the Web. Given an object-attribute pair, we discover and analyze attribute information for a set of comparable objects in order to infer the desired value. This allows us to approximate the desired numerical values even when no exact values can be found in the text. Our framework makes use of relation defining patterns and WordNet similarity information. First, we obtain from the Web and WordNet a list of terms similar to the given object. Then we retrieve attribute values for each term in this list, and information that allows us to compare different objects in the list and to infer the attribute value range. Finally, we combine the retrieved data for all terms from the list to select or approximate the requested value. We evaluate our method using automated question answering, WordNet enrichment, and comparison with answers given in Wikipedia and by leading search engines. In all of these, our framework provides a significant improvement.
An empirical comparison of four initialization methods for the K-Means algorithm

In this paper, we aim to compare empirically four initialization methods for the K-Means algorithm: random, Forgy, MacQueen and Kaufman. Although this algorithm is known for its robustness, it is widely reported in literature that its performance depends upon two key points: initial clustering and instance order. We conduct a series of experiments to draw up (in terms of mean, maximum, minimum and standard deviation) the probability distribution of the square-error values of the nal clusters returned by the K-Means algorithm independently on any initial clustering and on any instance order when each of the four initialization methods is used. The results of our experiments illustrate that the random and the Kauf-man initialization methods outperform the rest of the compared methods as they make the K-Means more eeective and more independent on initial clustering and on instance order. In addition, we compare the convergence speed of the K-Means algorithm when using each of the four initialization methods. Our results suggest that the Kaufman initialization method induces to the K-Means algorithm a more desirable behaviour with respect to the convergence speed than the random initial-ization method.
Identifying User Behavior by Analyzing Web Server Access Log File

Web usage mining is application of data mining techniques to discover usage patterns from web data, in order to better serve the needs of web based applications. The user access log files present very significant information about a web server. This paper is concerned with the in-depth analysis of Web Log Data of NASA website to find information about a web site, top errors, potential visitors of the site etc. which help system administrator and Web designer to improve their system by determining occurred systems errors, corrupted and broken links by using web using mining. The obtained results of the study will be used in the further development of the web site in order to increase its effectiveness.
EEG-Based Classification of Fast and Slow Hand Movements Using Wavelet-CSP Algorithm

A brain-computer interface (BCI) acquires brain signals, extracts informative features, and translates these features to commands to control an external device. This paper investigates the application of a noninvasive electroencephalography (EEG)based BCI to identify brain signal features in regard to actual hand movement speed. This provides a more refined control for a BCI system in terms of movement parameters. An experiment was performed to collect EEG data from subjects while they performed right-hand movement at two different speeds, namely fast and slow, in four different directions. The informative features from the data were obtained using the Wavelet-Common Spatial Pattern (W-CSP) algorithm that provided high-temporal-spatial-spectral resolution. The applicability of these features to classify the two speeds and to reconstruct the speed profile was studied. The results for classifying speed across seven subjects yielded a mean accuracy of 83.71% using a Fisher Linear Discriminant (FLD) classifier. The speed components were reconstructed using multiple linear regression and significant correlation of 0.52 (Pearson's linear correlation coefficient) was obtained between recorded and reconstructed velocities on an average. The spatial patterns of the W-CSP features obtained showed activations in parietal and motor areas of the brain. The results achieved promises to provide a more refined control in BCI by including control of movement speed.
Data cache prefetching using a global history buffer

Over the past couple of decades, trends in both microarchitecture and underlying semiconductor technology have significantly reduced microprocessor clock periods. These trends have significantly increased relative main-memory latencies as measured in processor clock cycles. To avoid large performance losses caused by long memory access delays, microprocessors rely heavily on a hierarchy of cache memories. But cache memories are not always effective, either because they are not large enough to hold a program's working set, or because memory access patterns don't exhibit behavior that matches a cache memory's demand-driven, line-structured organization. To partially overcome cache memories' limitations, we organize data cache prefetch information in a new way, a GHB (global history buffer) supports existing prefetch algorithms more effectively than conventional prefetch tables. It reduces stale table data, improving accuracy and reducing memory traffic. It contains a more complete picture of cache miss history and is smaller than conventional tables.
The Forest Convolutional Network: Compositional Distributional Semantics with a Neural Chart and without Binarization

According to the principle of compositionality, the meaning of a sentence is computed from the meaning of its parts and the way they are syntactically combined. In practice, however, the syntactic structure is computed by automatic parsers which are far-from-perfect and not tuned to the specifics of the task. Current recursive neural network (RNN) approaches for computing sentence meaning therefore run into a number of practical difficulties, including the need to carefully select a parser appropriate for the task, deciding how and to what extent syntactic context modifies the semantic composition function, as well as on how to transform parse trees to conform to the branching settings (typically, binary branching) of the RNN. This paper introduces a new model, the Forest Convolutional Network, that avoids all of these challenges, by taking a parse forest as input, rather than a single tree, and by allowing arbitrary branching factors. We report improvements over the state-of-the-art in sentiment analysis and question classification.
SPACLE: investigating learning across virtual and physical spaces using spatial replays

Classroom experiments that evaluate the effectiveness of educational technologies do not typically examine the effects of classroom contextual variables (e.g., out-of-software help-giving and external distractions). Yet these variables may influence students' instructional outcomes. In this paper, we introduce the Spatial Classroom Log Explorer (SPACLE): a prototype tool that facilitates the rapid discovery of relationships between within-software and out-of-software events. Unlike previous tools for retrospective analysis, SPACLE replays moment-by-moment analytics about student and teacher behaviors in their original spatial context. We present a data analysis workflow using SPACLE and demonstrate how this workflow can support causal discovery. We share the results of our initial replay analyses using SPACLE, which highlight the importance of considering spatial factors in the classroom when analyzing ITS log data. We also present the results of an investigation into the effects of student-teacher interactions on student learning in K-12 blended classrooms, using our workflow, which combines replay analysis with SPACLE and causal modeling. Our findings suggest that students' awareness of being monitored by their teachers may promote learning, and that "gaming the system" behaviors may extend outside of educational software use.
INTERNATIONAL INVESTMENT AND INTERNATIONAL TRADE IN THE PRODUCT CYCLE »

Anyone who has sought to understand the shifts in international trade and international investment over the past twenty years has chafed from time to time under an acute sense of the inadequacy of the available analytical tools. While the comparative cost concept and other basic concepts have rarely failed to provide some help, they have usually carried the analyst only a very little way toward adequate understanding. For the most part, it has been necessary to formulate new concepts in order to explore issues such as the strengths and limitations of import substitution in the development process, the implications of common market arrangements for trade and investment, the underlying reasons for the Leontief paradox, and other critical issues of the day. As theorists have groped for some more efiBcient tools, there has been a flowering in international trade and capital theory. But the very proliferation of theory has increased the urgency of the search for unifying concepts. It is doubtful that we shall flnd many propositions that can match the simplicity, power, and universality of application of the theory of comparative advantage and the international equilibrating mechanism; but unless the search for better tools goes on, the usefulness of economic theory for the solution of problems in international trade and capital movements will probably decline. The present paper deals with one promising line of generalization and synthesis which seems to me to have been somewhat neglected by the main stream of trade theory. It puts less emphasis upon comparative cost doctrine and more upon the timing of innovation, the effects of scale economies, and the roles of ignorance and uncertainty in influencing trade patterns. It is an approach
Learning image representations equivariant to ego-motion

Understanding how images of objects and scenes behave in response to specific ego-motions is a crucial aspect of proper visual development, yet existing visual learning methods are conspicuously disconnected from the physical source of their images. We propose to exploit proprioceptive motor signals to provide unsupervised regularization in convolutional neural networks to learn visual representations from egocentric video. Specifically, we enforce that our learned features exhibit equivariance i.e. they respond systematically to transformations associated with distinct ego-motions. With three datasets, we show that our unsupervised feature learning system significantly outperforms previous approaches on visual recognition and next-best-view prediction tasks. In the most challenging test, we show that features learned from video captured on an autonomous driving platform improve large-scale scene recognition in a disjoint domain.
Context sensitive stemming for web search

Traditionally, stemming has been applied to Information Retrieval tasks by transforming words in documents to the their root form before indexing, and applying a similar transformation to query terms. Although it increases recall, this naive strategy does not work well for Web Search since it lowers precision and requires a significant amount of additional computation.
 In this paper, we propose a context sensitive stemming method that addresses these two issues. Two unique properties make our approach feasible for Web Search. First, based on statistical language modeling, we perform context sensitive analysis on the query side. We accurately predict which of its morphological variants is useful to expand a query term with before submitting the query to the search engine. This dramatically reduces the number of bad expansions, which in turn reduces the cost of additional computation and improves the precision at the same time. Second, our approach performs a context sensitive document matching for those expanded variants. This conservative strategy serves as a safeguard against spurious stemming, and it turns out to be very important for improving precision. Using word pluralization handling as an example of our stemming approach, our experiments on a major Web search engine show that stemming only 29% of the query traffic, we can improve relevance as measured by average Discounted Cumulative Gain (DCG5) by 6.1% on these queriesand 1.8% over all query traffic.
Pixel-wise classification of weeds and crops in images by using a Fully Convolutional neural network

Effective weed control, using either mechanical or chemical means, relies on knowledge of the crop and weed plant occurrences in the field. This knowledge can be obtained automatically by analyzing images collected in the field. Many existing methods for plant detection in images make the assumption that plant foliage does not overlap. This assumption is often violated, reducing the performance of existing methods. This study overcomes this issue by training a convolutional neural network to create a pixel-wise classification of crops, weeds and soil in RGB images from fields, in order to know the exact position of the plants. This training is based on simulated top-down images of weeds and maize in fields. The results show an pixel accuracy over 94% and a 100% detection rate of both maize and weeds, when tested on real images, while a high intersection over union is kept. The system can handle 2.4 images per second for images with a resolution of 1MPix, when using an Nvidia Titan X GPU.
New monitoring approach for Neonatal Intensive Care Unit

The Neonatal Intensive Care Unit (NICU) represents a complex and multi-in/output context aimed at monitoring and controlling biological signals and parameters in premature newborns. This paper details some methodological and design options for developing technologies that allow end-user composition and control through new approaches that integrate wearable monitoring, pervasive and unobtrusive computing research that already are introducing new perspectives in a wide range of applications. These options enhance biosignals monitoring capabilities and provide consistent user experiences in environments where different devices, services and processes typically co-exist. In particular we describe the notion of assemblies of monitoring devices, interpreted as the combination of sensors, tools and services in a distributed monitoring environment where they interact. We report on the importance of flexibility and user control in the use of such technological assemblies in a NICU, describing a prototype and preliminary results of such monitoring system.
Crowdsourcing Language Generation Templates for Dialogue Systems

We explore the use of crowdsourcing to generate natural language in spoken dialogue systems. We introduce a methodology to elicit novel templates from the crowd based on a dialogue seed corpus, and investigate the effect that the amount of surrounding dialogue context has on the generation task. Evaluation is performed both with a crowd and with a system developer to assess the naturalness and suitability of the elicited phrases. Results indicate that the crowd is able to provide reasonable and diverse templates within this methodology. More work is necessary before elicited templates can be automatically plugged into the system.
Evaluating digital libraries

"So far, evaluation has not kept pace with efforts in digital libraries (or with digital libraries themselves), has not become part of their integral activity, and has not been even specified as to what it means, and how to do it." - [1]Conducting a comprehensive evaluation of a digital library requires a "triangulation" approach including multiple models, procedures, and tools. Carrying out valid evaluations of digital libraries in a timely and efficient manner is the focus of this tutorial. Why is evaluation of digital libraries so important? Each year sees the introduction of new digital libraries promoted as valuable resources for education and other needs. Yet systematic evaluation of the implementation and efficacy of these digital library systems is often lacking. This tutorial is specifically designed to establish evaluation as a key strategy throughout the design, development, and implementation of digital libraries. The tutorial focuses on a decision-oriented model for evaluating digital libraries using multiple methods such as: service evaluation, usability evaluation, information retrieval, biometrics evaluation, transaction log analysis survey methods, interviews and focus groups, observations, and experimental methods. Participants in this tutorial will learn how to implement models and procedures for evaluating digital libraries at all levels of education. The tutorial includes presentations with actual case studies that are focused on a variety of digital library evaluation strategies. Participants will also receive a copy of Evaluating Digital Libraries: A User-Friendly Guide.
Real-time smart traffic management system for smart cities by using Internet of Things and big data

Smart Traffic management system (STMS) is a one of the important feature for smart city. Currently traffic management and alert systems are not fulling needs of STMS. It is more expensive and highly configurable to provide better service for traffic management. This paper proposes a low cost Real-Time smart traffic Management System to provide better service by deploying traffic indicators to update the traffic details instantly. Low cost vehicle detecting sensors are embed in the middle of road for every 500 meters or 1000 meters. Internet of Things (IoT) are being used to acquire traffic data quickly and send it for processing. The Real time streaming data is sent for Big Data analytics. There are several analytical scriptures to analyze the traffic density and provide solution through predictive analytics. A mobile application is developed as user interface to explore the density of traffic at various places and provides an alternative way for managing the traffic.
Action Recognition with Dynamic Image Networks

We introduce the concept of dynamic image, a novel compact representation of videos useful for video analysis, particularly in combination with convolutional neural networks (CNNs). A dynamic image encodes temporal data such as RGB or optical flow videos by using the concept of ‘rank pooling’. The idea is to learn a ranking machine that captures the temporal evolution of the data and to use the parameters of the latter as a representation. We call the resulting representation dynamic image because it summarizes the video dynamics in addition to appearance. This powerful idea allows to convert any video to an image so that existing CNN models pre-trained with still images can be immediately extended to videos. We also present an efficient approximate rank pooling operator that runs two orders of magnitude faster than the standard ones with any loss in ranking performance and can be formulated as a CNN layer. To demonstrate the power of the representation, we introduce a novel  four stream CNN architecture which can learn from RGB and optical flow frames as well as from their dynamic image representations. We show that the proposed network achieves state-of-the-art performance, 95.5 and 72.5 percent accuracy, in the UCF101 and HMDB51, respectively.
An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL-KDD Data Set

Information security is extremely critical issues for every organization to protect information from unauthorized access. Intrusion detection system has one of the important roles to prevent data or information from malicious behaviours. Basically Intrusion detection system is a classifier that can classify the data as normal or attacks. In this research paper, we have proposed ANN-Bayesian Net-GR technique that means ensemble of Artificial Neural Network (ANN) and Bayesian Net with Gain Ratio (GR) feature selection technique. We have applied various individual classification techniques and its ensemble model on KDD99 and NSL-KDD data set to check the robustness of model. Due to irrelevant features in data set, also applied Gain Ratio feature selection technique on best model. Finally our proposed model produces highest accuracy compare to others.
Evolutionary architecture search for deep multitask networks

Multitask learning, i.e. learning several tasks at once with the same neural network, can improve performance in each of the tasks. Designing deep neural network architectures for multitask learning is a challenge: There are many ways to tie the tasks together, and the design choices matter. The size and complexity of this problem exceeds human design ability, making it a compelling domain for evolutionary optimization. Using the existing state of the art soft ordering architecture as the starting point, methods for evolving the modules of this architecture and for evolving the overall topology or routing between modules are evaluated in this paper. A synergetic approach of evolving custom routings with evolved, shared modules for each task is found to be very powerful, significantly improving the state of the art in the Omniglot multitask, multialphabet character recognition domain. This result demonstrates how evolution can be instrumental in advancing deep neural network and complex system design in general.