nils-herrmann/clean_scirepeval huggingface dataset:
Random Forests for multiclass classification: Random MultiNomial Logit

Several supervised learning algorithms are suited to classify instances into a multiclass value space. MultiNomial Logit (MNL) is recognized as a robust classifier and is commonly applied within the CRM (Customer Relationship Management) domain. Unfortunately, to date, it is unable to handle huge feature spaces typical of CRM applications. Hence, the analyst is forced to immerse himself into feature selection. Surprisingly, in sharp contrast with binary logit, current software packages lack any feature selection algorithm for MultiNomial Logit. Conversely, Random Forests, another algorithm learning multi class problems, is ju st like MNL robust but unlike MNL it easily handles high-dimensional feature spaces. This paper investigates the potential of applying the Random Forests principles to the MNL framework. We propose the Random MultiNomial Logit (RMNL), i.e. a random forest of MNLs, and compare its predictive performance to that of a) MNL with expert feature selection, b) Random Forests of classification trees. We illustrate the Random MultiNomial Logit on a cross-sell CRM problem within the home -appliances industry. The results indicate a substantial increase in model accuracy of the RMNL model to that of the MNL model with expert feature selection. multiclass classifier design and evaluation, feature evaluation and selection, data mining methods and algorithms, customer relationship management (CRM)
Using Hard Macros to Reduce FPGA Compilation Time

The FPGA compilation process (synthesis, map, placement, routing) is a time-consuming process that limits designer productivity. Compilation time can be reduced by using pre-compiled circuit blocks (hard macros). Hard macros consist of previously synthesized, mapped, placed and routed circuitry that can be relatively placed with short tool runtimes and that make it possible to reuse previous computational effort. Two experiments were performed to demonstrate feasibility that hard macros can reduce compilation time. These experiments demonstrated that an augmented Xilinx flow designed specifically to support hard macros can reduce overall compilation time by 3x. Though the process of incorporating hard macros in designs is currently manual and error-prone, it can be automated to create compilation flows with much lower compilation time.
MayoNLP at SemEval-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic Model

Given two sentences, participating systems assign a semantic similarity score in the range of 0-5. We applied two different techniques for the task: one is based on lexical semantic net (corresponding to run 1) and the other is based on deep learning semantic model (corresponding to run 2). We also combined these two runs linearly (corresponding to run 3). Our results indicate that the two techniques perform comparably while the combination outperforms the individual ones on four out of five datasets, namely answeranswer, headlines, plagiarism, and questionquestion, and on the overall weighted mean of STS 2016 and 2015 datasets.
The pet connection: pets as a conduit for social capital?

There is growing interest across a range of disciplines in the relationship between pets and health, with a range of therapeutic, physiological, psychological and psychosocial benefits now documented. While much of the literature has focused on the individual benefits of pet ownership, this study considered the potential health benefits that might accrue to the broader community, as encapsulated in the construct of social capital. A random survey of 339 adult residents from Perth, Western Australia were selected from three suburbs and interviewed by telephone. Pet ownership was found to be positively associated with some forms of social contact and interaction, and with perceptions of neighbourhood friendliness. After adjustment for demographic variables, pet owners scored higher on social capital and civic engagement scales. The results suggest that pet ownership provides potential opportunities for interactions between neighbours and that further research in this area is warranted. Social capital is another potential mechanism by which pets exert an influence on human health.
Depth-Aware CNN for RGB-D Segmentation

Convolutional neural networks (CNN) are limited by the lack of capability to handle geometric information due to the fixed grid kernel structure. The availability of depth data enables progress in RGB-D semantic segmentation with CNNs. State-of-the-art methods either use depth as additional images or process spatial information in 3D volumes or point clouds. These methods suffer from high computation and memory cost. To address these issues, we present Depth-aware CNN by introducing two intuitive, flexible and effective operations: depth-aware convolution and depth-aware average pooling. By leveraging depth similarity between pixels in the process of information propagation, geometry is seamlessly incorporated into CNN. Without introducing any additional parameters, both operators can be easily integrated into existing CNNs. Extensive experiments and ablation studies on challenging RGB-D semantic segmentation benchmarks validate the effectiveness and flexibility of our approach.
Media Multitasking Behavior: Concurrent Television and Computer Usage

Changes in the media landscape have made simultaneous usage of the computer and television increasingly commonplace, but little research has explored how individuals navigate this media multitasking environment. Prior work suggests that self-insight may be limited in media consumption and multitasking environments, reinforcing a rising need for direct observational research. A laboratory experiment recorded both younger and older individuals as they used a computer and television concurrently, multitasking across television and Internet content. Results show that individuals are attending primarily to the computer during media multitasking. Although gazes last longer on the computer when compared to the television, the overall distribution of gazes is strongly skewed toward very short gazes only a few seconds in duration. People switched between media at an extreme rate, averaging more than 4 switches per min and 120 switches over the 27.5-minute study exposure. Participants had little insight into their switching activity and recalled their switching behavior at an average of only 12 percent of their actual switching rate revealed in the objective data. Younger individuals switched more often than older individuals, but other individual differences such as stated multitasking preference and polychronicity had little effect on switching patterns or gaze duration. This overall pattern of results highlights the importance of exploring new media environments, such as the current drive toward media multitasking, and reinforces that self-monitoring, post hoc surveying, and lay theory may offer only limited insight into how individuals interact with media.
MULTIPLE-INPUT DESCRIBING FUNCTIONS AND NONLINEAR SYSTEM DESIGN

ABRAMSON Information theory and coding BATTIN Astronautical guidance BLACHMAN Noise and its effect on communication BREMER Superconductive devices BROXMEYER Inertial navigation systems GELB AND VANDER VELDE Multiple-input describing functions and nonlinear system design GILL Introduction to the theory of finite-state machines HANCOCK AND WINTZ Signal detection theory HUELSMAN Circuits, matrices, and linear vector spaces KELSO Radio ray propagation in the ionosphere MERRIAM Optimization theory and the design of feedback control systems MUUM Biological control systems analysis NEWCOMB Linear multiport synthesis PAPOULIS The fourier integral and its applications R. N. BRACEWELL) STEINBERG AND LEQUEUX (TRANSLATOR Radio astronomy WEEKS Antenna engineering PREFACE The theory of automatic control has been advanced in important ways during recent years, particularly with respect to stability and optimal control. These are significant contributions which appeal to many workers, including the writers, because they answer important questions and are both theoretically elegant and practically useful. These theories do not, however, lay to rest all questions of importance to the control engineer. The designer of the attitude control system for a space vehicle booster which, for simplicity, utilizes a rate-switched engine gimbal drive, must know the characteristics of the limit cycle oscillation that the system will sustain and must have some idea of how the system will respond to attitude commands while continuing to limit-cycle. The designer of a chemical process control system must be able to predict the transient oscillations the process may experience during start-up due to the limited magnitudes of important variables in the system. The designer of a radar antenna pointing system with limited torque capability must be able to predict the rms pointing error due to random wind disturbances on the antenna, and must understand how these random disturbances will influence the behavior of the system in its response to command inputs. But more important than just being able to evaluate how a given system will behave in a postulated situation is the fact that these control engineers must design their systems to meet specifications on important characteristics. Thus a complicated exact analytical tool, if one existed, would be of less value to the designer than an approximate tool which is simple enough in application to give insight into the trends in system behavior as a function of system parameter values or possible compensations, hence providing the basis for system design. As an analytical tool to answer questions such as these in a way …
Sentiment Classification of Drug Reviews Using a Rule-Based Linguistic Approach

Clause-level sentiment classification algorithm is developed and applied to drug reviews on a discussion forum. The algorithm adopts a pure linguistic approach of computing the sentiment of a clause from the prior sentiment scores assigned to individual words, taking into consideration the grammatical dependency structure of the clause using the sentiment analysis rules. MetaMap, a medical resource tool, is used to identify various disease terms in the review documents to utilize domain knowledge for sentiment classification. Experiment results with 1,000 clauses show the effectiveness of the proposed approach, and it performed significantly better than baseline machine learning approaches. Various challenging issues were identified through error analysis, and we will continue improving our linguistic algorithm.
Factorizing Scene Albedo and Depth from a Single Foggy Image

Atmospheric conditions induced by suspended particles, such as fog and haze, severely degrade image quality. Restoring the true scene colors (clear day image) from a single image of a weather-degraded scene remains a challenging task due to the inherent ambiguity between scene albedo and depth. In this paper, we introduce a novel probabilistic method that fully leverages natural statistics of both the albedo and depth of the scene to resolve this ambiguity. Our key idea is to model the image with a factorial Markov random field in which the. scene albedo and depth are. two statistically independent latent layers. We. show that we may exploit natural image and depth statistics as priors on these hidden layers and factorize a single foggy image via a canonical Expectation Maximization algorithm with alternating minimization. Experimental results show that the proposed method achieves more accurate restoration compared to state-of-the-art methods that focus on only recovering scene albedo or depth individually.
OR FORUM - Little's Law as Viewed on Its 50th Anniversary

Fifty years ago, the author published a paper in Operations Research with the title, “A proof for the queuing formula: L = W ” [Little, J. D. C. 1961. A proof for the queuing formula: L = W . ]. Over the years, L = W has become widely known as “Little’s Law.” Basically, it is a theorem in queuing theory. It has become well known because of its theoretical and practical importance. We report key developments in both areas with the emphasis on practice. In the latter, we collect new material and search for insights on the use of Little’s Law within the fields of operations management and computer architecture.
Web table taxonomy and formalization

The Web is the largest repository of data available, with over 150 million high-quality tables. Several works have combined efforts to allow queries on these tables, but there are still challenges, like the various different types of structures found on the Web. In this paper, we propose a taxonomy for the tabular structures and formalize the ones used with relational data and show, through an experimental evaluation, that WTClassifier, our supervised framework, classifies Web tables with high accuracy. Additionally, we use WTClassifier to categorize more than 300 thousandWeb tables into our taxonomy and found that 82.25% are not formatted similarly to relational structure.
Several Approaches for Tweet Topic Classification in COSET - IberEval 2017

These working notes summarize the different approaches we have explored in order to classify a corpus of tweets related to the 2015 Spanish General Election (COSET 2017 task from IberEval 2017). Two approaches were tested during the COSET 2017 evaluations: Neural Networks with Sentence Embeddings (based on TensorFlow) and N-gram Language Models (based on SRILM). Our results with these approaches were modest: both ranked above the “Most frequent baseline”, but below the “Bag-of-words + SVM” baseline. A third approach was tried after the COSET 2017 evaluation phase was over: Advanced Linear Models (based on fastText). Results measured over the COSET 2017 Dev and Test show that this approach is well above the “TF-IDF+RF” baseline.
PHD filters of higher order in target number

The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In this paper we show that this is indeed possible. We derive a closed-form cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the entire probability distribution on target number.
Loop closure detection for visual SLAM systems using deep neural networks

The detection of loop closure is of essential importance in visual simultaneous localization and mapping systems. It can reduce the accumulating drift of localization algorithms if the loops are checked correctly. Traditional loop closure detection approaches take advantage of Bag-of-Words model, which clusters the feature descriptors as words and measures the similarity between the observations in the word space. However, the features are usually designed artificially and may not be suitable for data from new-coming sensors. In this paper a novel loop closure detection approach is proposed that learns features from raw data using deep neural networks instead of common visual features. We discuss the details of the method of training neural networks. Experiments on an open dataset are also demonstrated to evaluate the performance of the proposed method. It can be seen that the neural network is feasible to solve this problem.
Origin-Destination Flow Data Smoothing and Mapping

This paper presents a new approach to flow mapping that extracts inherent patterns from massive geographic mobility data and constructs effective visual representations of the data for the understanding of complex flow trends. This approach involves a new method for origin-destination flow density estimation and a new method for flow map generalization, which together can remove spurious data variance, normalize flows with control population, and detect high-level patterns that are not discernable with existing approaches. The approach achieves three main objectives in addressing the challenges for analyzing and mapping massive flow data. First, it removes the effect of size differences among spatial units via kernel-based density estimation, which produces a measurement of flow volume between each pair of origin and destination. Second, it extracts major flow patterns in massive flow data through a new flow sampling method, which filters out duplicate information in the smoothed flows. Third, it enables effective flow mapping and allows intuitive perception of flow patterns among origins and destinations without bundling or altering flow paths. The approach can work with both point-based flow data (such as taxi trips with GPS locations) and area-based flow data (such as county-to-county migration). Moreover, the approach can be used to detect and compare flow patterns at different scales or in relatively sparse flow datasets, such as migration for each age group. We evaluate and demonstrate the new approach with case studies of U.S. migration data and experiments with synthetic data.
Phase relationship between hippocampal place units and the EEG theta rhythm.

Many complex spike cells in the hippocampus of the freely moving rat have as their primary correlate the animal's location in an environment (place cells). In contrast, the hippocampal electroencephalograph theta pattern of rhythmical waves (7-12 Hz) is better correlated with a class of movements that change the rat's location in an environment. During movement through the place field, the complex spike cells often fire in a bursting pattern with an interburst frequency in the same range as the concurrent electroencephalograph theta. The present study examined the phase of the theta wave at which the place cells fired. It was found that firing consistently began at a particular phase as the rat entered the field but then shifted in a systematic way during traversal of the field, moving progressively forward on each theta cycle. This precession of the phase ranged from 100 degrees to 355 degrees in different cells. The effect appeared to be due to the fact that individual cells had a higher interburst rate than the theta frequency. The phase was highly correlated with spatial location and less well correlated with temporal aspects of behavior, such as the time after place field entry. These results have implications for several aspects of hippocampal function. First, by using the phase relationship as well as the firing rate, place cells can improve the accuracy of place coding. Second, the characteristics of the phase shift constrain the models that define the construction of place fields. Third, the results restrict the temporal and spatial circumstances under which synapses in the hippocampus could be modified.
Power struggles: Revisiting the RISC vs. CISC debate on contemporary ARM and x86 architectures

RISC vs. CISC wars raged in the 1980s when chip area and processor design complexity were the primary constraints and desktops and servers exclusively dominated the computing landscape. Today, energy and power are the primary design constraints and the computing landscape is significantly different: growth in tablets and smartphones running ARM (a RISC ISA) is surpassing that of desktops and laptops running x86 (a CISC ISA). Further, the traditionally low-power ARM ISA is entering the high-performance server market, while the traditionally high-performance x86 ISA is entering the mobile low-power device market. Thus, the question of whether ISA plays an intrinsic role in performance or energy efficiency is becoming important, and we seek to answer this question through a detailed measurement based study on real hardware running real applications. We analyze measurements on the ARM Cortex-A8 and Cortex-A9 and Intel Atom and Sandybridge i7 microprocessors over workloads spanning mobile, desktop, and server computing. Our methodical investigation demonstrates the role of ISA in modern microprocessors' performance and energy efficiency. We find that ARM and x86 processors are simply engineering design points optimized for different levels of performance, and there is nothing fundamentally more energy efficient in one ISA class or the other. The ISA being RISC or CISC seems irrelevant.
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors

We present a novel algorithm for detection of certain types of unusual events. The algorithm is based on multiple local monitors which collect low-level statistics. Each local monitor produces an alert if its current measurement is unusual and these alerts are integrated to a final decision regarding the existence of an unusual event. Our algorithm satisfies a set of requirements that are critical for successful deployment of any large-scale surveillance system. In particular, it requires a minimal setup (taking only a few minutes) and is fully automatic afterwards. Since it is not based on objects' tracks, it is robust and works well in crowded scenes where tracking-based algorithms are likely to fail. The algorithm is effective as soon as sufficient low-level observations representing the routine activity have been collected, which usually happens after a few minutes. Our algorithm runs in real-time. It was tested on a variety of real-life crowded scenes. A ground-truth was extracted for these scenes, with respect to which detection and false-alarm rates are reported.
Analyzing the Business Model Concept - A Comprehensive Classification of Literature

The business model concept is characterized by numerous fields of application which are promising in business practice. Consequently, research on business models has attracted increasing attention in the scientific world. However, for a successful utilization, the widely-criticized lack of theoretical consensus in this field of research has to be overcome. Thus, this paper conducted a comprehensive and up-to-date literature analysis examining 30 relevant literature sources focusing mainly on business model research. To achieve this, the analysis was based on a classification framework containing 17 evaluation criteria. Hereby, a systematic and objective penetration of the research area could be achieved. Moreover, existing research gaps as well as the most important fields to be addressed in future research could be revealed.
Semidirect composite onlay with cavity sealing: a review of clinical procedures.

The evolution in adhesive dentistry has broadened the indication of esthetic restorative procedures especially with the use of resin composite material. Depending on the clinical situation, some restorative techniques are best indicated. As an example, indirect adhesive restorations offer many advantages over direct techniques in extended cavities. In general, the indirect technique requires two appointments and a laboratory involvement, or it can be prepared chairside in a single visit either conventionally or by the use of computer-aided design/computer-aided manufacturing systems. In both cases, there will be an extra cost as well as the need of specific materials. This paper describes the clinical procedures for the chairside semidirect technique for composite onlay fabrication without the use of special equipments. The use of this technique combines the advantages of the direct and the indirect restoration.



The semidirect technique for composite onlays offers the advantages of an indirect restoration and low cost, and can be the ideal treatment option for extended cavities in case of financial limitations.