nils-herrmann/clean_scirepeval huggingface dataset:
SQL server column store indexes

The SQL Server 11 release (code named "Denali") introduces a new data warehouse query acceleration feature based on a new index type called a column store index. The new index type combined with new query operators processing batches of rows greatly improves data warehouse query performance: in some cases by hundreds of times and routinely a tenfold speedup for a broad range of decision support queries. Column store indexes are fully integrated with the rest of the system, including query processing and optimization. This paper gives an overview of the design and implementation of column store indexes including enhancements to query processing and query optimization to take full advantage of the new indexes. The resulting performance improvements are illustrated by a number of example queries.
What Users Ask a Search Engine: Analyzing One Billion Russian Question Queries

We analyze the question queries submitted to a large commercial web search engine to get insights about what people ask, and to better tailor the search results to the users' needs. Based on a dataset of about one billion question queries submitted during the year 2012, we investigate askers' querying behavior with the support of automatic query categorization. While the importance of question queries is likely to increase, at present they only make up 3-4% of the total search traffic.
 Since questions are such a small part of the query stream, and are more likely to be unique than shorter queries, click-through information is typically rather sparse. Thus, query categorization methods based on the categories of clicked web documents do not work well for questions. As an alternative, we propose a robust question query classification method that uses the labeled questions from a large community question answering platform (CQA) as a training set. The resulting classifier is then transferred to the web search questions. Even though questions on CQA platforms tend to be different to web search questions, our categorization method proves competitive with strong baselines with respect to classification accuracy.
 To show the scalability of our proposed method we apply the classifiers to about one billion question queries and discuss the trade-offs between performance and accuracy that different classification models offer.
Modeling of Wide Bandgap Power Semiconductor Devices—Part I

Wide bandgap power devices have emerged as an often superior alternative power switch technology for many power electronic applications. These devices theoretically have excellent material properties enabling power device operation at higher switching frequencies and higher temperatures compared with conventional silicon devices. However, material defects can dominate device behavior, particularly over time, and this should be strongly considered when trying to model actual characteristics of currently available devices. Compact models of wide bandgap power devices are necessary to analyze and evaluate their impact on circuit and system performance. Available compact models, i.e., models compatible with circuit-level simulators, are reviewed. In particular, this paper presents a review of compact models for silicon carbide power diodes and MOSFETs.
On the Capacity Comparison Between MIMO-NOMA and MIMO-OMA

Non-orthogonal multiple access (NOMA) has been shown in the literature to have a better performance than OMA in terms of sum channel capacity; however, the capacity superiority of NOMA over OMA has been only proved for single antenna systems, and the proof for the capacity superiority of multiple-input multiple-output NOMA (MIMO-NOMA) over conventional MIMO-OMA has not been available yet. In this paper, we will provide our proof to demonstrate that the MIMO-NOMA is strictly better than MIMO-OMA in terms of sum channel capacity (except for the case where only one user is being communicated to), i.e., for any rate pair achieved by MIMO-OMA, there is a power split for which MIMO-NOMA can achieve rate pairs that are strictly larger. Based on this result, we prove that the MIMO-NOMA can also achieve a larger sum ergodic capacity than MIMO-OMA. Our analytical results are verified by simulations.
Phase-Error Measurement and Compensation in PLL Frequency Synthesizers for FMCW Sensors—I: Context and Application

The synthesis of linear frequency sweeps or chirps is required, among others, in frequency-modulated continuous-wave radar systems for object position estimation. Low phase and frequency errors in sweeps with high bandwidth are a prerequisite for good accuracy and resolution, but, in certain applications where high measurement rates are desired, the additional demand for short sweep cycles has to be met. Transient phenomena in dynamic synthesizers as well as nonlinear system behavior usually cause unknown phase errors in the system output. For the class of phase-locked-loop (PLL)-based frequency synthesizers, a novel output phase-measurement method and dedicated circuitry are proposed that allow significant reduction of phase errors by adaptive input predistortion. The measurement procedure is implemented within the PLL control circuitry and does not require external equipment. The application of this method to PLL system identification and linearization of extremely short frequency sweeps is shown
An Approximate Analysis of the LRU and FIFO Buffer Replacement Schemes

In this paper, we develop approximate analytical models for predicting the buffer hit probability under the Least Recently Used (LRU) and First In First Out (FIFO) buffer replacement policies under the independent reference model. In the case of the analysis of the LRU policy, the computational complexity for estimating the buffer hit probability is O(KB) where B is the size of the buffer and K denotes the number of items having distinct access probabilities. In the case of the FIFO policy, the solution algorithm is iterative and the computational complexity of each iteration is O(K). Results from these models are compared to exact results for models originally developed by King [KING71] for small values of the buffer size, B, and the total number of items sharing the buffer, D. Results are also compared with results from a simulation for large values of B and D. In most cases, the error is extremely small (less than 0.1%) for both LRU and FIFO, and a maximum error of 3% is observed for very small buffer size (less than 5) when the access probabilities are extremely skewed. To demonstrate the usefulness of the model, we consider two applications. In our first application, we compare the LRU and FIFO policies to an optimal static buffer allocation policy for a database consisting of two classes of data items. We observe that the performance of LRU is close to that of the optimal allocation. As the optimal allocation requires knowledge of the access probabilities, the LRU policy is preferred when this information is unavailable. We also observe that the LRU policy always performs better than the FIFO policy in our experiments. In our second application, we show that if multiple independent reference streams on mutually disjoint sets of data compete for the same buffer, it is better to partition the buffer using an optimal allocation policy than to share a common buffer.
Big Data Analytics for Earth Sciences: the EarthServer approach

Large-Scale Scientific Information Systems, Jacobs University, Bremen, Germany; Rasdaman GmbH, Bremen, Germany; CNR-IIA, National Research Council of Italy, Institute of Atmospheric Pollution Research, Florence, Italy; EOX IT Services GmbH, Vienna, Austria; Consorzio COMETA, Catania, Italy; Division of Catania, Italian National Institute for Nuclear Physics, 15 Catania, Italy; Department of Physics and Astronomy, University of Catania, Catania, Italy; MEEO S.r.l., Ferrara, Italy; Plymouth Marine Laboratory, Plymouth, UK; Fraunhofer IGD, Darmstadt, Germany; Athena Research and Innovation Center in Information Communication & Knowledge Technologies, Athens, Greece; British Geological Survey, Edinburgh, UK; Software Engineering Italia S.r.l., Catania, Italy; British Geological Survey, Keyworth, UK; NASA Ames 20 Research Center, Moffett Field, CA, USA
Visualizing LSTM decisions

Long Short-Term Memory (LSTM) recurrent neural networks are renowned for being uninterpretable "black boxes". In the medical domain where LSTMs have shown promise, this is specifically concerning because it is imperative to understand the decisions made by machine learning models in such acute situations. This study employs techniques used in the Convolutional Neural Network domain to elucidate the operations that LSTMs perform on time series. The visualization techniques include input saliency by means of occlusion and derivatives, class mode visualization, and temporal outputs. Moreover, we demonstrate that LSTMs appear to extract features similar to those extracted by wavelets. It was found that deriving the inputs for saliency is a poor approximation and occlusion is a better approach. Moreover, analyzing LSTMs on different sets of data provide novel interpretations.
Maturity and change in personality: developmental trends of temperament and character in adulthood.

We studied the developmental trends of temperament and character in a longitudinal population-based sample of Finnish men and women aged 20-45 years using the Temperament and Character Inventory model of personality. Personality was assessed in 1997, 2001, and 2007 (n = 2,104, 2,095, and 2,056, respectively). Mean-level changes demonstrated qualitatively distinct developmental patterns for character (self-directedness, cooperativeness, and self-transcendence) and temperament (novelty seeking, harm avoidance, reward dependence, and persistence). Character developed toward greater maturity, although self-transcendence decreased with age. However, self-transcendence was the strongest predictor of overall personality change. Cohort effects indicated lower level of self-transcendence and higher level of self-directedness and cooperativeness in younger birth cohorts. Regarding temperament, novelty seeking decreased and persistence increased slightly with age. Both high novelty seeking and high persistence predicted overall personality change. These findings suggest that temperament and character traits follow different kinds of developmental trajectories.
Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this
Teaching cryptography with open-source software

Cryptography has become an important topic in undergraduate curricula in mathematics and computer science, not just for its intrinsic interest---``about the most fun you can have with mathematics''\cite{ferg04, but for its current standing as the basis for almost all computer security. From wireless networking to secure email to password protection, cryptographic methods are used to secure information, to protect users, and to protect data.
 At Victoria University, cryptography has been taught as part of a mathematics and computer science degree for several years. The students all have had at least a year of tertiary mathematics, and some exposure to a computer algebra system (Maple). However, the cost of Maple, and the current licensing agreement, means that students are unable to experiment with the software away from the computer laboratories at the University. For this reason we have decided to investigate the use of open-source computer algebra systems Maxima and Axiom. Although not as full-featured and powerful as the commercial systems Maple and Mathematica, we show they are in fact admirably suited for a subject such as cryptography. In some ways Maxima and Axiom even surpass Maple and Mathematica. Student response to the introduction of these systems has been very positive.
Interpersonal Attraction in Exchange and Communal Relationships

Communal relationships, in which the giving of a benefit in response to a need for the benefit is appropriate, are distinguished from exchange relationships, in which the giving of a benefit in response to the receipt of a benefit is appropriate. Based on this distinction, it was hypothesized that the receipt of a benefit after the person has been benefited leads to greater attraction when an exchange relationship is preferred and decreases attraction when a communal relationship is desired. These hypotheses were supported in Experiment 1, which used male subjects. Experiment 2, which used a different manipulation of exchange versus communal relationships and female subjects, supported the hypotheses that (a) a request for a benefit after the person is aided by the other leads to greater attraction when an exchange relationship is expected and decreases attraction when a communal relationship is expected, and (b) a request for a benefit in the absence of prior aid from the other decreases attraction when an exchange relationship is expected.
Building Sentiment Lexicons for All Major Languages

Sentiment analysis in a multilingual world remains a challenging problem, because developing language-specific sentiment lexicons is an extremely resourceintensive process. Such lexicons remain a scarce resource for most languages. In this paper, we address this lexicon gap by building high-quality sentiment lexicons for 136 major languages. We integrate a variety of linguistic resources to produce an immense knowledge graph. By appropriately propagating from seed words, we construct sentiment lexicons for each component language of our graph. Our lexicons have a polarity agreement of 95.7% with published lexicons, while achieving an overall coverage of 45.2%. We demonstrate the performance of our lexicons in an extrinsic analysis of 2,000 distinct historical figures’ Wikipedia articles on 30 languages. Despite cultural difference and the intended neutrality of Wikipedia articles, our lexicons show an average sentiment correlation of 0.28 across all language pairs.
Enhancing well-being and alleviating depressive symptoms with positive psychology interventions: a practice-friendly meta-analysis.

Do positive psychology interventions-that is, treatment methods or intentional activities aimed at cultivating positive feelings, positive behaviors, or positive cognitions-enhance well-being and ameliorate depressive symptoms? A meta-analysis of 51 such interventions with 4,266 individuals was conducted to address this question and to provide practical guidance to clinicians. The results revealed that positive psychology interventions do indeed significantly enhance well-being (mean r=.29) and decrease depressive symptoms (mean r=.31). In addition, several factors were found to impact the effectiveness of positive psychology interventions, including the depression status, self-selection, and age of participants, as well as the format and duration of the interventions. Accordingly, clinicians should be encouraged to incorporate positive psychology techniques into their clinical work, particularly for treating clients who are depressed, relatively older, or highly motivated to improve. Our findings also suggest that clinicians would do well to deliver positive psychology interventions as individual (versus group) therapy and for relatively longer periods of time.
Low voltage low power analog circuit design OTA using signal attenuation technique in universal filter application

This paper presents a new configuration for a linear operational transconductance amplifier (OTA) using a signal attenuation technique. The OTA is designed to operate with a ±0.8V supply voltage and consumes 0.45mW power. All simulations are performed by ELDO model BSIM3v3 technology CMOS TSMC 0.18μm. The simulation results of this circuit showed a high DC gain of 73.6dB with a unity frequency of 50.19MHz and a total harmonic distortion of -60.81dB at 100 kHz for an input voltage of 1Vpp. Based on this circuit, a voltage mode universal filter has been implemented. The simulation results are in a good agreement with the theoretical calculations.
Dynamic Android Malware Classification Using Graph-Based Representations

Malware classification for the Android ecosystem can be performed using a range of techniques. One major technique that has been gaining ground recently is dynamic analysis based on system call invocations recorded during the executions of Android applications. Dynamic analysis has traditionally been based on converting system calls into flat feature vectors and feeding the vectors into machine learning algorithms for classification. In this paper, we implement three traditional feature-vector-based representations for Android system calls. For each feature vector representation, we also propose a novel graph-based representation. We then use graph kernels to compute pair-wise similarities and feed these similarity measures into a Support Vector Machine (SVM) for classification. To speed up the graph kernel computation, we compress the graphs using the Compressed Row Storage format, and then we apply OpenMP to parallelize the computation. Experiments show that the graph-based representations are able to improve the classification accuracy over the corresponding feature-vector-based representations from the same input. Finally we show that different representations can be combined together to further improve classification accuracy.
Clustering and Embedding Using Commute Times

This paper exploits the properties of the commute time between nodes of a graph for the purposes of clustering and embedding and explores its applications to image segmentation and multibody motion tracking. Our starting point is the lazy random walk on the graph, which is determined by the heat kernel of the graph and can be computed from the spectrum of the graph Laplacian. We characterize the random walk using the commute time (that is, the expected time taken for a random walk to travel between two nodes and return) and show how this quantity may be computed from the Laplacian spectrum using the discrete Green's function. Our motivation is that the commute time can be anticipated to be a more robust measure of the proximity of data than the raw proximity matrix. In this paper, we explore two applications of the commute time. The first is to develop a method for image segmentation using the eigenvector corresponding to the smallest eigenvalue of the commute time matrix. We show that our commute time segmentation method has the property of enhancing the intragroup coherence while weakening intergroup coherence and is superior to the normalized cut. The second application is to develop a robust multibody motion tracking method using an embedding based on the commute time. Our embedding procedure preserves commute time and is closely akin to kernel PCA, the Laplacian eigenmap, and the diffusion map. We illustrate the results on both synthetic image sequences and real-world video sequences and compare our results with several alternative methods.
Modeling Analysis and Parameters Calculation of Permanent Magnet Linear Synchronous Motor

Permanent magnet linear synchronous motor (PMLSM) has several advantages, such as simple structure, small volume, high force, and so on. It is a research focus and difficulties in the research field of motor. Because PMLSM is different from the traditional rotary motors in structure and running state, it is difficult for to build its precise mathematical model by analytical method. The asymmetry of the three-phase winding electromagnetic and electrical parameters of open PMLSM in a low-speed working zone is impossible to take into account. In this paper, the difference between the permanent magnet linear synchronous motor and traditional motors are analyzed. And then, a non-linear, asymmetric and variable parameter space state model is built, considering the structural and running characteristics of PMLSM. The proposed model needs all kinds of parameters of PMLSM, including selfinductance, mutual inductance, flux linkage, etc. So, a finite element calculation and actual measurement of the required parameters of a prototype PMLSM are done, respectively. The influence of air-gap on the inductance parameters was analyzed.
Development of a SiC JFET-Based Six-Pack Power Module for a Fully Integrated Inverter

In this paper, a fully integrated silicon carbide (SiC)-based six-pack power module is designed and developed. With 1200-V, 100-A module rating, each switching element is composed of four paralleled SiC junction gate field-effect transistors (JFETs) with two antiparallel SiC Schottky barrier diodes. The stability of the module assembly processes is confirmed with 1000 cycles of -40°C to +200°C thermal shock tests with 1.3°C/s temperature change. The static characteristics of the module are evaluated and the results show 55 mΩ on-state resistance of the phase leg at 200°C junction temperature. For switching performances, the experiments demonstrate that while utilizing a 650-V voltage and 60-A current, the module switching loss decreases as the junction temperature increases up to 150°C. The test setup over a large temperature range is also described. Meanwhile, the shoot-through influenced by the SiC JFET internal capacitance as well as package parasitic inductances are discussed. Additionally, a liquid cooled three-phase inverter with 22.9 cm × 22.4 cm × 7.1 cm volume and 3.53-kg weight, based on this power module, is designed and developed for electric vehicle and hybrid electric vehicle applications. A conversion efficiency of 98.5% is achieved at 10 kHz switching frequency at 5 kW output power. The inverter is evaluated with coolant temperature up to 95°C successfully.
Social stories: mechanisms of effectiveness in increasing game play skills in children diagnosed with autism spectrum disorder using a pretest posttest repeated measures randomized control group design.

An increasing body of literature has indicated that social stories are an effective way to teach individuals diagnosed with autism appropriate social behavior. This study compared two formats of a social story targeting the improvement of social skills during game play using a pretest posttest repeated measures randomized control group design. A total of 45 children diagnosed with Autism Spectrum Disorder (ASD) ages 7-14 were randomly assigned to standard, directive, or control story conditions. Results demonstrated that the standard and directive story formats were equally as effective in eliciting, generalizing and maintaining the targeted social skills in participants who had prior game play experience and Verbal Comprehension Index (VCI) scores from the WISC-IV intelligence test in the borderline range or above.