nils-herrmann/clean_scirepeval huggingface dataset:
BIM: Enabling Sustainability and Asset Management through Knowledge Management

Building Information Modeling (BIM) is the use of virtual building information models to develop building design solutions and design documentation and to analyse construction processes. Recent advances in IT have enabled advanced knowledge management, which in turn facilitates sustainability and improves asset management in the civil construction industry. There are several important qualifiers and some disadvantages of the current suite of technologies. This paper outlines the benefits, enablers, and barriers associated with BIM and makes suggestions about how these issues may be addressed. The paper highlights the advantages of BIM, particularly the increased utility and speed, enhanced fault finding in all construction phases, and enhanced collaborations and visualisation of data. The paper additionally identifies a range of issues concerning the implementation of BIM as follows: IP, liability, risks, and contracts and the authenticity of users. Implementing BIM requires investment in new technology, skills training, and development of new ways of collaboration and Trade Practices concerns. However, when these challenges are overcome, BIM as a new information technology promises a new level of collaborative engineering knowledge management, designed to facilitate sustainability and asset management issues in design, construction, asset management practices, and eventually decommissioning for the civil engineering industry.
Rhodiola rosea in stress induced fatigue--a double blind cross-over study of a standardized extract SHR-5 with a repeated low-dose regimen on the mental performance of healthy physicians during night duty.

The aim of this study was to investigate the effect of repeated low-dose treatment with a standardized extract SHR/5 of rhizome Rhodiola rosea L, (RRE) on fatigue during night duty among a group of 56 young, healthy physicians. The effect was measured as total mental performance calculated as Fatigue Index. The tests chosen reflect an overall level of mental fatigue, involving complex perceptive and cognitive cerebral functions, such as associative thinking, short-term memory, calculation and ability of concentration, and speed of audio-visual perception. These parameters were tested before and after night duty during three periods of two weeks each: a) a test period of one RRE/placebo tablet daily, b) a washout period and c) a third period of one placebo/RRE tablet daily, in a double-blind cross-over trial. The perceptive and cognitive cerebral functions mentioned above were investigated using 5 different tests. A statistically significant improvement in these tests was observed in the treatment group (RRE) during the first two weeks period. No side-effects were reported for either treatment noted. These results suggest that RRE can reduce general fatigue under certain stressful conditions.
A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR

Permanent Scatterer SAR Interferometry (PSInSAR) aims to identify coherent radar targets exhibiting high phase stability over the entire observation time period. These targets often correspond to point-wise, man-made objects widely available over a city, but less present in non-urban areas. To overcome the limits of PSInSAR, analysis of interferometric data-stacks should aim at extracting geophysical parameters not only from point-wise deterministic objects (i.e., PS), but also from distributed scatterers (DS). Rather than developing hybrid processing chains where two or more algorithms are applied to the same data-stack, and results are then combined, in this paper we introduce a new approach, SqueeSAR, to jointly process PS and DS, taking into account their different statistical behavior. As it will be shown, PS and DS can be jointly processed without the need for significant changes to the traditional PSInSAR processing chain and without the need to unwrap hundreds of interferograms, provided that the coherence matrix associated with each DS is properly “squeezed” to provide a vector of optimum (wrapped) phase values. Results on real SAR data, acquired over an Alpine area, challenging for any InSAR analysis, confirm the effectiveness of this new approach.
A hybrid genetic approach for stereo matching

In this paper we present a genetic algorithm (GA)-based approach for the stereo matching problem. More precisely, the approach presented is a combination of a simple dynamic programming algorithm, commonly used for stereo matching, with a practical GA-based optimization scheme. The performance of our scheme was evaluated on standard test data of the Middlebury benchmark. Specifically, the number of incorrect disparities on these data decreases by approximately 20% in comparison to the original approach (without the use of a GA).
Point to Set Similarity Based Deep Feature Learning for Person Re-Identification

Person re-identification (Re-ID) remains a challenging problem due to significant appearance changes caused by variations in view angle, background clutter, illumination condition and mutual occlusion. To address these issues, conventional methods usually focus on proposing robust feature representation or learning metric transformation based on pairwise similarity, using Fisher-type criterion. The recent development in deep learning based approaches address the two processes in a joint fashion and have achieved promising progress. One of the key issues for deep learning based person Re-ID is the selection of proper similarity comparison criteria, and the performance of learned features using existing criterion based on pairwise similarity is still limited, because only P2P distances are mostly considered. In this paper, we present a novel person Re-ID method based on P2S similarity comparison. The P2S metric can jointly minimize the intra-class distance and maximize the inter-class distance, while back-propagating the gradient to optimize parameters of the deep model. By utilizing our proposed P2S metric, the learned deep model can effectively distinguish different persons by learning discriminative and stable feature representations. Comprehensive experimental evaluations on 3DPeS, CUHK01, PRID2011 and Market1501 datasets demonstrate the advantages of our method over the state-of-the-art approaches.
A multi-modal floor-space for experiencing material deformation underfoot in virtual reality

We present a floor-space design that provides the impression of walking on various terrains by rendering graphical, audio and haptic stimuli synchronously with low latency. Currently, interactive floors tend to focus on visual and auditory feedback but have neglected to explore the role of haptics. Our design recreates these three modalities in a direct manner where the sensors and reactive cues are located in the same area. The goal of this project is the creation of a realistic and dynamic area of ground that allows multiple, untethered users to engage in intuitive interaction via locomotion in a virtual or augmented reality environment.
Nonparametric variable importance using an augmented neural network with multi-task learning

In predictive modeling applications, it is often of interest to determine the relative contribution of subsets of features in explaining the variability of an outcome. It is useful to consider this variable importance as a function of the unknown, underlying data-generating mechanism rather than the specific predictive algorithm used to fit the data. In this paper, we connect these ideas in nonparametric variable importance to machine learning, and provide a method for efficient estimation of variable importance when building a predictive model using a neural network. We show how a single augmented neural network with multi-task learning simultaneously estimates the importance of many feature subsets, improving on previous procedures for estimating importance. We demonstrate on simulated data that our method is both accurate and computationally efficient, and apply our method to both a study of heart disease and for predicting mortality in ICU patients.
An Attention-Based Syntax-Tree and Tree-LSTM Model for Sentence Summarization

Generative Summarization is of great importance in understanding large-scale textual data. In this work, we propose an attention-based Tree-LSTM model for sentence summarization, which utilizes an attention-based syntactic structure as auxiliary information. Thereinto, block-alignment is used to align the input and output syntax blocks, while inter-alignment is used for alignment of words within that of block pairs. To some extent, block-alignment can prevent structural deviations on the long sentences and inter-alignment is capable of increasing the flexibility of the generation in the blocks. This model can be easily trained to end-to-end mode and deal with any length of the input sentences. Compared with several relatively strong baselines, our model has achieved the state-of-art on DUC-2004 shared task.
No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics

The No Free Lunch (NFL) theorems for search and optimization are reviewed and their implications for the design of metaheurist ics are discussed. The theorems state that any two search or optimization algorith ms are equivalent when their performance is averaged across all possible problems and even over subsets of problems fulfilling certain constraints. The NFL results show that if there is no assumption regarding the relation between visited and unse en arch points, efficient search and optimization is impossible. There is no wel l performing universal metaheuristic, but the heuristics must be tailored to the pr oblem class at hand using prior knowledge. In practice, it is not likely that the pr econditions of the NFL theorems are fulfilled for a problem class and thus differenc es between algorithms exist. Therefore, tailored algorithms can exploit structu re nderlying the optimization problem. Given full knowledge about the problem class, it i in theory possible to construct an optimal algorithm.
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network

Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context. In this paper, we investigate matching a response with its multi-turn context using dependency information based entirely on attention. Our solution is inspired by the recently proposed Transformer in machine translation (Vaswani et al., 2017) and we extend the attention mechanism in two ways. First, we construct representations of text segments at different granularities solely with stacked self-attention. Second, we try to extract the truly matched segment pairs with attention across the context and response. We jointly introduce those two kinds of attention in one uniform neural network. Experiments on two large-scale multi-turn response selection tasks show that our proposed model significantly outperforms the state-of-the-art models.
A 1.4–2.3-GHz Tunable Diplexer Based on Reconfigurable Matching Networks

An idea for implementing reconfigurable matching networks to realize a tunable diplexer is investigated and demonstrated. The reconfigurable matching networks ensure that the rejection band impedance of every filter is transformed to an open circuit over a wide range of frequencies, allowing two tunable filters to be connected together to form a tunable diplexer without affecting each other. The tunable diplexer is built using Schottky diodes and combline resonators on Duroid substrates, and can operate from 1.4 to 2.3 GHz with a closest frequency separation of 110 MHz. Measurements show virtually no difference in the frequency response between a standalone filter and a filter placed in the tunable diplexer. The work shows that a wideband tunable diplexer can therefore be realized using tunable bandpass filters and reconfigurable matching networks.
Multiple Physical Layer Pipes performance for DVB-T2

The DVB-T2 terrestrial television standard is becoming increasingly important, and have been extensively studied and developed to provide many types of services with higher spectral efficiency and better performance. The Physical Layer Pipes in DVB-S2 are logical channels carrying one or more services with modulation scheme and robustness. The main changes are found in the physical layer where DVB-T2 incorporates a new physical layer pipe (PLP). Each physical layer pipe contains an individual configuration of modulation, coding and interleaving. This new concept allows a transmission with multiple physical layer pipes where each service can be transmitted with different physical layer configuration. The Advanced Television Systems Committee (ATSC3.0) standard will add value to broadcasting services, allowing a extending reach by adding new business models, providing higher quality, improved accessibility, personalization and interactivity and more flexible and efficient use of the spectrum.
Consumer perceptions on web advertisements and motivation factors to purchase in the online shopping

The principal objective of this study was to investigate the mediating and moderating effects of product involvement and trust toward websites in relation to the effects of the attributes of web advertisements on customers’ purchase intentions. We collected data consisting of a total of 264 responses from individuals with previous experience with purchasing products from online shopping mall sites, and utilized the PLS (partial least squares) method to analyze the collected data. Our findings demonstrated that consumers with greater product involvement tended to shop in shopping malls, where they could obtain more information on products, and that consumers with low product involvement tended to be more willing to consider entertainment. Additionally, consumers with higher levels of trust toward websites tended to be influenced by perceived entertainment and thus stay longer at shopping sites; conversely, consumers with low levels of trust toward websites tended to be more willing to consider perceived informativeness as minimizing environmental uncertainty. On the basis of our results, online shopping malls must stress the intrinsic attributes of products to attract highly-involved consumers and must present a benevolent corporate image for consumers concerned with better service. Our results also demonstrated that, for less-involved consumers who wish to search for products to present, it may be appropriate to mix entertainment components with product information, and also to implement creative web interface designs for more effective browsing.
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data

Neural networks exhibit good generalization behavior in the over-parameterized regime, where the number of network parameters exceeds the number of observations. Nonetheless, current generalization bounds for neural networks fail to explain this phenomenon. In an attempt to bridge this gap, we study the problem of learning a two-layer over-parameterized neural network, when the data is generated by a linearly separable function. In the case where the network has Leaky ReLU activations, we provide both optimization and generalization guarantees for overparameterized networks. Specifically, we prove convergence rates of SGD to a global minimum and provide generalization guarantees for this global minimum that are independent of the network size. Therefore, our result clearly shows that the use of SGD for optimization both finds a global minimum, and avoids overfitting despite the high capacity of the model. This is the first theoretical demonstration that SGD can avoid overfitting, when learning over-specified neural network classifiers.
Seeding Deep Learning using Wireless Localization

Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario. We believe advances in wireless localization, working in unison with cameras, can produce automated annotation of targets on images and videos captured in the wild. Using pedestrian and vehicle detection as examples, we demonstrate the feasibility, benefits, and challenges of an automatic image annotation system. Our work calls for new technical development on passive localization, mobile data analytics, and error-resilient ML models, as well as design issues in user privacy policies.
Flow Theory and Student Engagement

Most teachers across America would agree that the student who is engaged in school is more likely to be academically successful and successful as an adult later in life. In fact, research regarding student engagement has shown that increases in student engagement are correlated to increases in positive student learning outcomes (Appelton, Christenson, Kim, & Reschly, 2006; Nystrand & Gamoran, 1989) such as higher GPA, more credits earned in high school, higher attendance rates, and higher classroom participation (Appelton et al., 2006). While the relationship between student engagement and positive learning outcomes is not new to educators, understanding how to keep students engaged in the classroom is still an on-going concern. Regardless of the debate, educators generally agree that they must create learning environments that are challenging for all students, and in which students want to learn and feel good about learning. Flow Theory, though largely undiscovered by educators, was developed by Csikszentmihalyi (1990) as a theoretical perspective of student learning that integrated cognition, motivation, and emotion. While not a theory of student engagement, application of flow theory in classrooms will help teachers create learning environments in which there is an increase in student engagement.
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning--pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.
Tracking "gross community happiness" from tweets

Policy makers are calling for new socio-economic measures that reflect subjective well-being, to complement traditional measures of material welfare as the Gross Domestic Product (GDP). Self-reporting has been found to be reasonably accurate in measuring one's well-being and conveniently tallies with sentiment expressed on social media (e.g., those satisfied with life use more positive than negative words in their Facebook status updates). Social media content can thus be used to track well-being of individuals. A question left unexplored is whether such content can be used to track well-being of entire physical communities as well. To this end, we consider Twitter users based in a variety of London census communities, and study the relationship between sentiment expressed in tweets and community socio-economic well-being. We find that the two are highly correlated: the higher the normalized sentiment score of a community's tweets, the higher the community's socio-economic well-being. This suggests that monitoring tweets is an effective way of tracking community well-being too.
A 1 . 5V , 10-bit , 14 . 3-MS / s CMOS Pipeline Analog-to-Digital Converter

A 1.5-V, 10-bit, 14.3-MS/s pipeline analog-to-digital converter was implemented in a 0.6m CMOS technology. Emphasis was placed on observing device reliability constraints at low voltage. MOS switches were implemented without lowthreshold devices by using a bootstrapping technique that does not subject the devices to large terminal voltages. The converter achieved a peak signal-to-noise-and-distortion ratio of 58.5 dB, maximum differential nonlinearity of 0.5 least significant bit (LSB), maximum integral nonlinearity of 0.7 LSB, and a power consumption of 36 mW.
From the ephemeral to the enduring: how approach-oriented mindsets lead to greater status.

We propose that the psychological states individuals bring into newly formed groups can produce meaningful differences in status attainment. Three experiments explored whether experimentally created approach-oriented mindsets affected status attainment in groups, both immediately and over time. We predicted that approach-oriented states would lead to greater status attainment by increasing proactive behavior. Furthermore, we hypothesized that these status gains would persist longitudinally, days after the original mindsets had dissipated, due to the self-reinforcing behavioral cycles the approach-oriented states initiated. In Experiment 1, individuals primed with a promotion focus achieved higher status in their newly formed groups, and this was mediated by proactive behavior as rated by themselves and their teammates. Experiment 2 was a longitudinal experiment and revealed that individuals primed with power achieved higher status, both immediately following the prime and when the groups were reassembled 2 days later to work on new tasks. These effects were mediated by independent coders' ratings of proactive behavior during the first few minutes of group interaction. Experiment 3 was another longitudinal experiment and revealed that priming happiness led to greater status as well as greater acquisition of material resources. Importantly, these immediate and longitudinal effects were independent of the effects of a number of stable dispositional traits. Our results establish that approach-oriented psychological states affect status attainment, over and above the more stable characteristics emphasized in prior research, and provide the most direct test yet of the self-reinforcing nature of status hierarchies. These findings depict a dynamic view of status organization in which the same group may organize itself differently depending on members' incoming psychological states.
Separable spectro-temporal Gabor filter bank features: Reducing the complexity of robust features for automatic speech recognition.

To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [ (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor filter bank. A feature set that is extracted with these separate spectral and temporal modulation filter banks was introduced, the separate Gabor filter bank (SGBFB) features, and evaluated on the CHiME (Computational Hearing in Multisource Environments) keywords-in-noise recognition task. From the perspective of robust ASR, the results showed that spectral and temporal processing can be performed independently and are not required to interact with each other. Using SGBFB features permitted the signal-to-noise ratio (SNR) to be lowered by 1.2 dB while still performing as well as the GBFB-based reference system, which corresponds to a relative improvement of the word error rate by 12.8%. Additionally, the real time factor of the spectro-temporal processing could be reduced by more than an order of magnitude. Compared to human listeners, the SNR needed to be 13 dB higher when using Mel-frequency cepstral coefficient features, 11 dB higher when using GBFB features, and 9 dB higher when using SGBFB features to achieve the same recognition performance.