nils-herrmann/clean_scirepeval huggingface dataset:
Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System

For medical students, virtual patient dialogue systems can provide useful training opportunities without the cost of employing actors to portray standardized patients. This work utilizes wordand character-based convolutional neural networks (CNNs) for question identification in a virtual patient dialogue system, outperforming a strong wordand characterbased logistic regression baseline. While the CNNs perform well given sufficient training data, the best system performance is ultimately achieved by combining CNNs with a hand-crafted pattern matching system that is robust to label sparsity, providing a 10% boost in system accuracy and an error reduction of 47% as compared to the pattern-matching system alone.
A Survey on Cloud Storage

As technology in the cloud increases, there has been a lot of improvements in the maturity and firmness of cloud storage technologies. Many end-users and IT managers are getting very excited about the potential benefits of cloud storage, such as being able to store and retrieve data in the cloud and capitalizing on the promise of higher-performance, more scalable and cut-price storage. In this thesis, we present a typical Cloud Storage system architecture, a referral Cloud Storage model and Multi-Tenancy Cloud Storage model, value the past and the state-ofthe-art of Cloud Storage, and examine the Edge and problems that must be addressed to implement Cloud Storage. Use cases in diverse Cloud Storage offerings were also abridged. Cloud Storage, Cloud Computing, referral model, Multi-Tenancy, survey
Text categorization based on improved Rocchio algorithm

Text categorization is used to assign each text document to predefined categories. This paper presents a new text classification method for classifying Chinese text based on Rocchio algorithm. We firstly use the TFIDF to extract document vectors from the training documents which have been correctly categorized, and then use those document vectors to generate codebooks as classification models using the LBG and Rocchio algorithm. The codebook is then used to categorize the target documents using vector scores. We tested this method in the experiment and the result shows that this method can achieve better performance.
A meta-analysis of after-school programs that seek to promote personal and social skills in children and adolescents.

A meta-analysis of after-school programs that seek to enhance the personal and social skills of children and adolescents indicated that, compared to controls, participants demonstrated significant increases in their self-perceptions and bonding to school, positive social behaviors, school grades and levels of academic achievement, and significant reductions in problem behaviors. The presence of four recommended practices associated with previously effective skill training (SAFE: sequenced, active, focused, and explicit) moderated several program outcomes. One important implication of current findings is that ASPs should contain components to foster the personal and social skills of youth because youth can benefit in multiple ways if these components are offered. The second implication is that further research is warranted on identifying program characteristics that can help us understand why some programs are more successful than others.
Efficiency Analysis of Drive Train Topologies Applied to Electric/Hybrid Vehicles

One of the most important research topics in drive train topologies applied to electric/hybrid vehicles is the efficiency analysis of the power train components, including the global drive efficiency. In this paper, two basic traction electric drive systems of electric/hybrid vehicles are presented and evaluated, with a special focus on the efficiency analysis. The first topology comprises a traditional pulsewidth-modulation (PWM) battery-powered inverter, whereas in the second topology, the battery is connected to a bidirectional dc-dc converter, which supplies the inverter. Furthermore, a variable-voltage control technique applied to this second topology is presented, which allows for the improvement of the drive overall performance. Some simulation results are presented, considering both topologies and a permanent-magnet synchronous motor (PMSM). An even more detailed analysis is performed through the experimental validation. Particular attention is given to the evaluation of the main drive components efficiency, including the global drive efficiency, presented in the form of efficiency maps. Other parameters such as motor voltage distortion and power factor are also considered. In addition, the comparison of the two topologies takes into account the drive operation under the motoring and regenerative-braking modes.
Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagery

The quantity of small scale solar photovoltaic (PV) arrays in the United States has grown rapidly in recent years. As a result, there is substantial interest in high quality information about the quantity, power capacity, and energy generated by such arrays, including at a high spatial resolution (e.g., cities, counties, or other small regions). Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution. This work presents a computer algorithm that automatically detects PV panels using very high resolution color satellite imagery. The approach potentially offers a fast, scalable method for obtaining accurate information on PV array location and size, and at much higher spatial resolutions than are currently available. The method is validated using a very large (135 km) collection of publicly available (Bradbury et al., 2016) aerial imagery, with over 2700 human annotated PV array locations. The results demonstrate the algorithm is highly effective on a per-pixel basis. It is likewise effective at object-level PV array detection, but with significant potential for improvement in estimating the precise shape/size of the PV arrays. These results are the first of their kind for the detection of solar PV in aerial imagery, demonstrating the feasibility of the approach and establishing a baseline performance for future investigations.
Studies comparing Numerical Rating Scales, Verbal Rating Scales, and Visual Analogue Scales for assessment of pain intensity in adults: a systematic literature review.

The use of unidimensional pain scales such as the Numerical Rating Scale (NRS), Verbal Rating Scale (VRS), or Visual Analogue Scale (VAS) is recommended for assessment of pain intensity (PI). A literature review of studies specifically comparing the NRS, VRS, and/or VAS for unidimensional self-report of PI was performed as part of the work of the European Palliative Care Research Collaborative on pain assessment.



To investigate the use and performance of unidimensional pain scales, with specific emphasis on the NRSs.



A systematic search was performed, including citations through April 2010. All abstracts were evaluated by two persons according to specified criteria.



Fifty-four of 239 papers were included. Postoperative PI was most frequently studied; six studies were in cancer. Eight versions of the NRS (NRS-6 to NRS-101) were used in 37 studies; a total of 41 NRSs were tested. Twenty-four different descriptors (15 for the NRSs) were used to anchor the extremes. When compared with the VAS and VRS, NRSs had better compliance in 15 of 19 studies reporting this, and were the recommended tool in 11 studies on the basis of higher compliance rates, better responsiveness and ease of use, and good applicability relative to VAS/VRS. Twenty-nine studies gave no preference. Many studies showed wide distributions of NRS scores within each category of the VRSs. Overall, NRS and VAS scores corresponded, with a few exceptions of systematically higher VAS scores.



NRSs are applicable for unidimensional assessment of PI in most settings. Whether the variability in anchors and response options directly influences the numerical scores needs to be empirically tested. This will aid in the work toward a consensus-based, standardized measure.
Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio

Spectrum awareness is currently one of the most challenging problems in cognitive radio (CR) design. Detection and classification of very low SNR signals with relaxed information on the signal parameters being detected is critical for proper CR functionality as it enables the CR to react and adapt to the changes in its radio environment. In this work, the cycle frequency domain profile (CDP) is used for signal detection and preprocessing for signal classification. Signal features are extracted from CDP using a threshold-test method. For classification, a Hidden Markov Model (HMM) has been used to process extracted signal features due to its robust pattern-matching capability. We also investigate the effects of varied observation length on signal detection and classification. It is found that the CDP-based detector and the HMM-based classifier can detect and classify incoming signals at a range of low SNRs.
Opinion mining in legal blogs

We perform a survey into the scope and utility of opinion mining in legal Weblogs (a.k.a. blawgs). The number of 'blogs' in the legal domain is growing at a rapid pace and many potential applications for opinion detection and monitoring are arising as a result. We summarize current approaches to opinion mining before describing different categories of blawgs and their potential impact on the law and the legal profession. In addition to educating the community on recent developments in the legal blog space, we also conduct some introductory opinion mining trials. We first construct a Weblog test collection containing blog entries that discuss legal search tools. We subsequently examine the performance of a language modeling approach deployed for both subjectivity analysis (i.e., is the text subjective or objective?) and polarity analysis (i.e., is the text affirmative or negative towards its subject?). This work may thus help establish early baselines for these core opinion mining tasks.
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models

In the last few years, we have seen the transformative impact of deep learning in many applications, particularly in speech recognition and computer vision. Inspired by Google's Inception-ResNet deep convolutional neural network (CNN) for image classification, we have developed"Chemception", a deep CNN for the prediction of chemical properties, using just the images of 2D drawings of molecules. We develop Chemception without providing any additional explicit chemistry knowledge, such as basic concepts like periodicity, or advanced features like molecular descriptors and fingerprints. We then show how Chemception can serve as a general-purpose neural network architecture for predicting toxicity, activity, and solvation properties when trained on a modest database of 600 to 40,000 compounds. When compared to multi-layer perceptron (MLP) deep neural networks trained with ECFP fingerprints, Chemception slightly outperforms in activity and solvation prediction and slightly underperforms in toxicity prediction. Having matched the performance of expert-developed QSAR/QSPR deep learning models, our work demonstrates the plausibility of using deep neural networks to assist in computational chemistry research, where the feature engineering process is performed primarily by a deep learning algorithm.
Multitask Learning for Mental Health Conditions with Limited Social Media Data

Language contains information about the author’s demographic attributes as well as their mental state, and has been successfully leveraged in NLP to predict either one alone. However, demographic attributes and mental states also interact with each other, and we are the first to demonstrate how to use them jointly to improve the prediction of mental health conditions across the board. We model the different conditions as tasks in a multitask learning (MTL) framework, and establish for the first time the potential of deep learning in the prediction of mental health from online user-generated text. The framework we propose significantly improves over all baselines and single-task models for predicting mental health conditions, with particularly significant gains for conditions with limited data. In addition, our best MTL model can predict the presence of conditions (neuroatypicality) more generally, further reducing the error of the strong feed-forward baseline.
Multiband millimeter wave antenna array for 5G communication

This paper presents a simulated design of millimeter wave square patch antenna 1×6 array on silicon and Roger RO4003 substrate for prominent multiple bands i.e. 58GHz-60GHz, 65GHz-68GHz, 72GHz-77GHz. Designed antenna can serve 5G cellular network as well as advance device-to-device (D2D) network which is special feature of 5G communication system to reduce end-to-end latency and to implement Mission Critical Push-To-Talk Communication (MCPTT) and Vehicle-to-Anything (V2X) Communication. Designed antenna has peakgain of 9 dB and very high efficiency. Return loss for given bands at their resonant frequencies are as low as -35dB and total bandwidth of 9.57 GHz. Silicon is used under feeding network to enhance the bandwidth and reduce the size of feeding network and low dielectric material under patch to reduce dielectric loss thus maintaining the efficiency. Symmetrical parallel feeding network is used to enhance gain. Inset fed with quarter wave transformers are used for feeding and matching, along with maintaining the conformity. A novel design is used to kill the spurious radiation due to feed network, thus shaping the radiation pattern for cellular application. Overall size of antenna is 6.7mm×30mm×1.2mm compatible with miniaturized devices and is printable.
Congestion Attacks to Autonomous Cars Using Vehicular Botnets

The increasing popularity and acceptance of VANETs will make the deployment of autonomous vehicles easier and faster since the VANET will reduce dependence on expensive sensors. However, these benefits are counterbalanced by possible security attacks. We demonstrate a VANET-based botnet attack in an autonomous vehicle scenario that can cause serious congestion by targeting hot spot road segments. We show via simulation that the attack can increase the trip times of the cars in the targeted area by orders of magnitude. After 5 minutes, the targeted road becomes completely unusable. More importantly, the effect of such an attack is not confined to a specific hotspot; the congestion can spread to multiple roads and significantly affect the entire urban grid. We show that current countermeasures are not effective, and point to new possible defenses.
Revealing the Traces of Median Filtering Using High-Order Local Ternary Patterns

Recently, detecting the traces introduced by the content-preserving image manipulations has received a great deal of attention from forensic analyzers. It is well known that the median filter is a widely used nonlinear denoising operator. Therefore, the detection of median filtering is of important realistic significance in image forensics. In this letter, a novel local texture operator, named the second-order local ternary pattern (LTP), is proposed for median filtering detection. The proposed local texture operator encodes the local derivative direction variations by using a 3-valued coding function and is capable of effectively capturing the changes of local texture caused by median filtering. In addition, kernel principal component analysis (KPCA) is exploited to reduce the dimensionality of the proposed feature set, making the computational cost manageable. The experiment results have shown that the proposed scheme performs better than several state-of-the-art approaches investigated.
Generation of THz wave with orbital angular momentum by graphene patch reflectarray

A graphene patch reflectarray is proposed for the first time to generate THz wave with orbital angular momentum (OAM). It is shown that by properly assigning the graphene patches with specific reflection coefficients, an OAM-carrying beam can be generated. The graphene reflectarray, which consists of 8 regions and each region contains about 170 patches with uniform chemical potential and size, has successfully produced the OAM-carrying beam of either 1 or -1 mode.
Analysis and Design Techniques Applied to Hybrid Vehicle Drive Machines—Assessment of Alternative IPM and Induction Motor Topologies

In this paper, different analysis and design techniques are used to analyze the drive motor in the 2004 Prius hybrid vehicle and to examine alternative spoke-type magnet rotor (buried magnets with magnetization which is orthogonal to the radial direction) and induction motor arrangements. These machines are characterized by high transient torque requirement, compactness, and forced cooling. While rare-earth magnet machines are commonly used in these applications, there is an increasing interest in motors without magnets, hence the investigation of an induction motor. This paper illustrates that the machines operate under highly saturated conditions at high torque and that care should be taken when selecting the correct analysis technique. This is illustrated by divergent results when using I-Psi loops and dq techniques to calculate the torque.
Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression

The application of boosting technique to regression problems has received relatively little attention in contrast to research aimed at classification problems. This letter describes a new boosting algorithm, AdaBoost.RT, for regression problems. Its idea is in filtering out the examples with the relative estimation error that is higher than the preset threshold value, and then following the AdaBoost procedure. Thus, it requires selecting the suboptimal value of the error threshold to demarcate examples as poorly or well predicted. Some experimental results using the M5 model tree as a weak learning machine for several benchmark data sets are reported. The results are compared to other boosting methods, bagging, artificial neural networks, and a single M5 model tree. The preliminary empirical comparisons show higher performance of AdaBoost.RT for most of the considered data sets.
Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: a confirmatory factor analysis.

The present study was designed to assess selected psychometric properties of the Intrinsic Motivation Inventory (IMI) (Ryan, 1982), a multidimensional measure of subjects' experience with regard to experimental tasks. Subjects (N = 116) competed in a basketball free-throw shooting game, following which they completed the IMI. The LISREL VI computer program was employed to conduct a confirmatory factor analysis to assess the tenability of a five factor hierarchical model representing four first-order factors or dimensions and a second-order general factor representing intrinsic motivation. Indices of model acceptability tentatively suggest that the sport data adequately fit the hypothesized five factor hierarchical model. Alternative models were tested but did not result in significant improvements in the goodness-of-fit indices, suggesting the proposed model to be the most accurate of the models tested. Coefficient alphas for the four dimensions and the overall scale indicated adequate reliability. The results are discussed with regard to the importance of accurate assessment of psychological constructs and the use of linear structural equations in confirming the factor structures of measures.
The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News

A large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. Despite the existence of these systems, fake news is still wildly shared by online users. It indicates that these systems may not be fully utilized. After detecting fake news, what is the next step to stop people from sharing it? How can we improve the utilization of these fact-check systems? To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities. We found that the guardians usually took less than one day to reply to claims in online conversations and took another day to spread verified information to hundreds of millions of followers. Our proposed recommendation model outperformed four state-of-the-art models by 11%~33%. Our source code and dataset are available at http://web.cs.wpi.edu/~kmlee/data/gau.html.
A 10-bit 205-MS/s 1.0- $\hbox{mm}^{2}$ 90-nm CMOS Pipeline ADC for Flat Panel Display Applications

This paper describes a 10-bit 205-MS/s pipeline analog-to-digital converter (ADC) for flat panel display applications with the techniques to alleviate the design limitations in the deep-submicron CMOS process. The switched source follower combined with a resistor-switch ladder eliminates the sampling switches and achieves high linearity for a large single-ended input signal. Multistage amplifiers adopting the complementary common-source topology increase the output swing range with lower transconductance variation and reduce the power consumption. The supply voltage for the analog blocks is provided by the low drop-out regulator for a high power-supply rejection ratio (PSRR) under the noisy operation environment. The pipeline stages of the ADC are optimized in the aspect of power consumption through the iterated calculation of the sampling capacitance and transconductance. The ADC occupies an active area of 1.0 mm2 in a 90-nm CMOS process and achieves a 53-dB PSRR for a 100-MHz noise tone with the regulator and a 55.2-dB signal-to-noise-and-distortion ratio for a 30-MHz 1.0-VPP single-ended input at 205 MS/s. The ADC core dissipates 40 mW from a 1.0-V nonregulated supply voltage.
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure

We introduce a system that recognizes concurrent activities from real-world data captured by multiple sensors of different types. The recognition is achieved in two steps. First, we extract spatial and temporal features from the multimodal data. We feed each datatype into a convolutional neural network that extracts spatial features, followed by a long-short term memory network that extracts temporal information in the sensory data. The extracted features are then fused for decision making in the second step. Second, we achieve concurrent activity recognition with a single classifier that encodes a binary output vector in which elements indicate whether the corresponding activity types are currently in progress. We tested our system with three datasets from different domains recorded using different sensors and achieved performance comparable to existing systems designed specifically for those domains. Our system is the first to address the concurrent activity recognition with multisensory data using a single model, which is scalable, simple to train and easy to deploy.