nils-herrmann/clean_scirepeval huggingface dataset:
Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement

Community question answering services (CQAS) (e.g., Yahoo! Answers) provides a platform where people post questions and answer questions posed by others. Previous works analyzed the answer quality (AQ) based on answer-related features, but neglect the question-related features on AQ. Previous work analyzed how asker- and question-related features affect the question quality (QQ) regarding the amount of attention from users, the number of answers and the question solving latency, but neglect the correlation between QQ and AQ (measured by the rating of the best answer), which is critical to quality of service (QoS). We handle this problem from two aspects. First, we additionally use QQ in measuring AQ, and analyze the correlation between a comprehensive list of features (including answer-related features) and QQ. Second, we propose the first method that estimates the probability for a given question to obtain high AQ. Our analysis on the Yahoo! Answers trace confirmed that the list of our identified features exert influence on AQ, which determines QQ. For the correlation analysis, the previous classification algorithms cannot consider the mutual interactions between multiple ($>$ 2) classes of features. We then propose a novel Coupled Semi-Supervised Mutual Reinforcement-based Label Propagation (CSMRLP) algorithm for this purpose. Our extensive experiments show that CSMRLP outperforms the Mutual Reinforcement-based Label Propagation (MRLP) and five other traditional classification algorithms in the accuracy of AQ classification, and the effectiveness of our proposed method in AQ prediction. Finally, we provide suggestions on how to create a question that will receive high AQ, which can be exploited to improve the QoS of CQAS.
Positive academic emotions moderate the relationship between self-regulation and academic achievement.

Research has shown how academic emotions are related to achievement and to cognitive/motivational variables that promote achievement. Mediated models have been proposed to account for the relationships among academic emotions, cognitive/motivational variables, and achievement, and research has supported such mediated models, particularly with negative emotions.



The study tested the hypotheses: (1) self-regulation and the positive academic emotions of enjoyment and pride are positive predictors of achievement; and (2) enjoyment and pride both moderate the relationship between self-regulation and achievement.


SAMPLE
Participants were 1,345 students enrolled in various trigonometry classes in one university.



Participants answered the Academic Emotions Questionnaire-Math (Pekrun, Goetz, & Frenzel, 2005) and a self-regulation scale (Pintrich, Smith, Garcia, & McKeachie, 1991) halfway through their trigonometry class. The students' final grades in the course were regressed to self-regulation, positive emotions, and the interaction terms to test the moderation effects.



Enjoyment and pride were both positive predictors of grades; more importantly, both moderated the relationship between self-regulation and grades. For students who report higher levels of both positive emotions, self-regulation was positively associated with grades. However, for those who report lower levels of pride, self-regulation was not related to grades; and, for those who reported lower levels of enjoyment, self-regulation was negatively related to grades. The results are discussed in terms of how positive emotions indicate positive appraisals of task/outcome value, and thus enhance the positive links between cognitive/motivational variables and learning.
Design and Analysis of 42-V Coreless Axial-Flux Permanent-Magnet Generators for Automotive Applications

This paper presents the design and analysis of a novel structure of an axial-flux permanent-magnet machine with iron less stator to generate sinusoidal electromotive force with very low harmonics. The structure is of a special design that is suitable for flux weakening in variable speeds. Flux weakening is done by a mechanical actuator to change the position of windings relative to each other. To confirm the machine performance and the design, analytical and three-dimensional finite-element numerical methods are implemented. The simulations results of the proposed generator coincide very well with the experimental results on a prototype 42-V system realized in the laboratory.
Why Deep Learning Works: A Manifold Disentanglement Perspective

Deep hierarchical representations of the data have been found out to provide better informative features for several machine learning applications. In addition, multilayer neural networks surprisingly tend to achieve better performance when they are subject to an unsupervised pretraining. The booming of deep learning motivates researchers to identify the factors that contribute to its success. One possible reason identified is the flattening of manifold-shaped data in higher layers of neural networks. However, it is not clear how to measure the flattening of such manifold-shaped data and what amount of flattening a deep neural network can achieve. For the first time, this paper provides quantitative evidence to validate the flattening hypothesis. To achieve this, we propose a few quantities for measuring manifold entanglement under certain assumptions and conduct experiments with both synthetic and real-world data. Our experimental results validate the proposition and lead to new insights on deep learning.
Forecasting Foreign Exchange Rates Using Recurrent Neural Networks

This article proposes the use of recurrent neural networks in order to forecast foreign exchange rates. Artificial neural networks have proven to be efficient and profitable in forecasting financial time series. In particular, recurrent networks, in which activity patterns pass through the network more than once before they generate an output pattern, can learn extremely complex temporal sequences. Three recurrent architectures are compared in terms of prediction accuracy of futures forecast for Deutsche mark currency. A trading strategy is then devised and optimized. The profitability of the trading strategy, taking into account transaction costs, is shown for the different architectures. The methods described here, which have obtained promising results in real-time trading, are applicable to other markets.
Comparative study on power conversion methods for wireless battery charging platform

In this paper, four different power conversion methods (voltage control, duty-cycle control, frequency control and phase-shift control) are compared for wireless power transfer applications by considering the energy transfer efficiency, electromagnetic interference, stability, and implementation complexity. The phase-shift control is found to be the optimal scheme with good efficiency and low electromagnetic interference. Its constant frequency feature is also within the framework of the new international wireless charging standard called ‘Qi’. A high system efficiency of 72% for 5W wireless charging applications has been achieved practically.
An AOI algorithm for PCB based on feature extraction

With the development of the micro-electronic industry, electronical components assembled on the printed circuit board (PCB) become more and more microsize and its mounted density is increasing. It is out of date to depend on manual inspection to assure the joints quality. Instead, automated optical inspection (AOI) for solder joints based on the machine vision has become more and more important. In this paper, based on the image acquired from a 3-CCD color camera and a 3-color hemispherical LED arrays light resource (red, green, and blue), the place, shape, and logical features of solder joints of chip components are extracted. The place features are related to solder joint place. As for shape features, we divide solder joint into several regions and extract its shape features by its color, occupancy ratio of area, center of gravity and continuous pixels. The logical features come from their close relationships of different regions of shape features, color distributing and place features. On the basis of the features, an AOI algorithm is developed. The defects of lacking solder, surplus solder, no solder, pseudo joints, wrong component, damaged component, component absent, shift, tomb stone, wrong polarity, etc. can be identified properly by using the proposed algorithm. Finally, some experiment results are presented to show the validity of the algorithm.
Metabolic systems modeling for cell factories improvement.

Techniques for modeling microbial bioproduction systems have evolved over many decades. Here, we survey recent literature and focus on modeling approaches for improving bioproduction. These techniques from systems biology are based on different methodologies, starting from stoichiometry only to various stoichiometry with kinetics approaches that address different issues in metabolic systems. Techniques to overcome unknown kinetic parameters using random sampling have emerged to address meaningful questions. Among those questions, pathway robustness seems to be an important issue for metabolic engineering. We also discuss the increasing significance of databases in biology and their potential impact for biotechnology.
Image reconstruction with locally adaptive sparsity and nonlocal robust regularization

Sparse representation based modeling has been successfully used in many image-related inverse problems such as deblurring, super-resolution and compressive sensing. The heart of sparse representations lies on how to find a space (spanned by a dictionary of atoms) where the local image patch exhibits high sparsity and how to determine the image local sparsity. To identify the locally varying sparsity, it is necessary to locally adapt the dictionary learning process and the sparsity-regularization parameters. However, spatial adaptation alone runs into the risk of over-fitting the data because variation and invariance are two sides of the same coin. In this work, we propose two sets of complementary ideas for regularizing image reconstruction process: (1) the sparsity regularization parameters are locally estimated for each coefficient and updated along with adaptive learning of PCA-based dictionaries; (2) a nonlocal self-similarity constraint is introduced into the overall cost functional to improve the robustness of the model. An efficient alternative minimization algorithm is present to solve the proposed objective function and then an effective image reconstruction algorithm is presented. The experimental results on image deblurring, super-resolution and compressive sensing demonstrate that the proposed image reconstruct method outperforms many existing image reconstruction methods in both PSNR and visual quality assessment. & 2012 Elsevier B.V. All rights reserved.
Classification of EEG with Recurrent Neural Networks

3-D perception is a task that is growing in popularity in television and entertainment. Algorithms and innovations that mimic 3-D perception are of great importance to those in this industry, and as such they need a metric for how well a particular innovation is working. Electroencephalogram (EEG) recordings are an accurate and objective method of evaluating brain activity, and so the primary task is to use EEG recordings score different methods of mimicking 3-D perception. As a first step in doing this we must find the best features and methods to classify EEG recorded when participants are viewing regular 2D stimuli, and actual 3D stimuli. Hence, in this paper, we explore methods to address the following goal:
W-Band CMOS on-chip energy harvester and rectenna

This paper presents the first fully on-chip integrated energy harvester and rectenna at the W-Band in 65nm CMOS technology. The designs are based on a 1-stage Dickson voltage multiplier. The rectenna consists of an on-chip integrated dipole antenna with a reflector underneath the substrate to enhance the directivity and realized gain. The energy harvester and rectenna achieve a power conversion efficiency of 10% and 2% respectively at 94GHz. The stand-alone harvester occupies only 0.0945mm2 including pads, while the fully integrated rectenna occupies a minimal chip area of 0.48mm2.
3DMatch: Learning the Matching of Local 3D Geometry in Range Scans

Establishing correspondences between 3D geometries is essential to a large variety of graphics and vision applications, including 3D reconstruction, localization, and shape matching. Despite significant progress, geometric matching on real-world 3D data is still a challenging task due to the noisy, low-resolution, and incomplete nature of scanning data. These difficulties limit the performance of current state-of-art methods which are typically based on histograms over geometric properties. In this paper, we introduce 3DMatch1, a data-driven local feature learner that jointly learns a geometric feature representation and an associated metric function from a large collection of realworld scanning data. We represent 3D geometry using accumulated distance fields around key-point locations. This representation is suited to handle noisy and partial scanning data, and concurrently supports deep learning with convolutional neural networks directly in 3D. To train the networks, we propose a way to automatically generate correspondence labels for deep learning by leveraging existing RGB-D reconstruction algorithms. In our results, we demonstrate that we are able to outperform state-of-theart approaches by a significant margin. In addition, we show the robustness of our descriptor in a purely geometric sparse bundle adjustment pipeline for 3D reconstruction. 1All of our code, datasets, and trained models are publicly available: http://3dmatch.cs.princeton.edu/
Women “ Take Care , ” Men “ Take Charge ” : Managers ’ Stereotypic Perceptions of Women and Men Leaders

This study explored possible underpinnings of findings from earlier research in which women’s overall leadership competence was less favorably evaluated than men’s. The authors examined perceptions held by senior managers, 34% of whom were CEOs, of women’s and men’s effectiveness at 10 key leadership behaviors. Respondents generally perceived that women were more effective than men at caretaking leader behaviors; and that men were more effective than women at actionoriented, “take-charge” leader behaviors. Notably, male respondents perceived that the behavior at which men leaders most outperformed women was problemsolving. The authors propose that this perception could potentially undermine the influence of women leaders.
Congruent evidence from α-tubulin and β-tubulin gene phylogenies for a zygomycete origin of microsporidia

The origin of microsporidia and the evolutionary relationships among the major lineages of fungi have been examined by molecular phylogeny using a-tubulin and b-tubulin. Chytrids, basidiomycetes, ascomycetes, and microsporidia were all recovered with high support, and the zygomycetes were consistently paraphyletic. The microsporidia were found to branch within zygomycetes, and showed relationships with members of the Entomophthorales and Zoopagales. This provides support for the microsporidia having evolved from within the fungi, however, the tubulin genes are difficult to interpret unambiguously since fungal and microsporidian tubulins are very divergent. Rapid evolutionary rates a characteristic of practically all microsporidian genes studied, so determining their evolutionary history will never be completely free of such difficulties. While the tubulin phylogenies do not provide a decisive conclusion, they do further narrow the probable origin of microsporidia to a zygomycete-like ancestor.
Inter-satellite links for cubesats

Realizing inter-satellite links is a must for ensuring the success of cubesat swarm missions. Nevertheless, it has hardly been considered until now. The communication systems for cubesats have to deal with a few peculiar demands regarding consumed power, geometry and throughput. Depending on the type of application, required data rates can go up to tens of megabits per second, while power consumption and physical size are limited by the platform. The proposed communication scheme will combine power-efficient modulation and channel coding with multiple access and spread spectrum techniques, enabling the deployment of multiple satellites. Apart from this, the antenna system has to be designed such that links can be established and maintained independent of the satellites' orientation. An electrically steerable radiation pattern is achieved by placing antennas on each face of the cube. Conformal beamforming provides the system with 5 dBi gain for any desired direction of transmission, eliminating the need for attitude control. Furthermore, using planar antennas reduces the complexity of the mechanical part as they require no deployment.
Interaction techniques using head gaze for virtual reality

Virtual Reality (VR) has finally caught the attention of major technology companies, giving it a significant boost towards becoming market-ready. VR has the capacity to create profoundly engaging experiences by synthesizing highly detailed sensory input in real time, making games the most obvious applications of VR. While the promises of VR may be exciting due to the problems it can solve and new experiences it can offer, there are still numerous issues to be addressed in order to achieve maximal immersion in VR. In this paper, we present the issues encountered during open testing of our applications on commercially available VR goggles and hand input device. The inability to effectively use the hands and feet to control and navigate the environment degrades the level of immersion of VR. With users preferring reliability over intuitiveness, we explore the use of gaze as a substitute input device. Results of experiments conducted on numerous volunteers showed significant improvement in terms of user control.
Decision Networks for Security Risk Assessment of Critical Infrastructures

We exploit Decision Networks (DN) for the analysis of attack/defense scenarios in critical infrastructures. DN extend Bayesian Networks (BN) with decision and value nodes. DN inherit from BN the possibility to naturally address uncertainty at every level, making possible the modeling of situations that are not limited to Boolean combinations of events. By means of decision nodes, DN can include the interaction level of attacks and countermeasures. Inference algorithms can be directly exploited for implementing a probabilistic analysis of both the risk and the importance of the attacks. Thanks to value nodes, a sound decision theoretic analysis has the goal of selecting the optimal set of countermeasures to activate.
Evolutionary Generative Adversarial Networks

Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from training problems such as instability and mode collapse. In this paper, we propose a novel GAN framework called evolutionary generative adversarial networks (E-GAN) for stable GAN training and improved generative performance. Unlike existing GANs, which employ a pre-defined adversarial objective function alternately training a generator and a discriminator, we utilize different adversarial training objectives as mutation operations and evolve a population of generators to adapt to the environment (i.e., the discriminator). We also utilize an evaluation mechanism to measure the quality and diversity of generated samples, such that only well-performing generator(s) are preserved and used for further training. In this way, E-GAN overcomes the limitations of an individual adversarial training objective and always preserves the best offspring, contributing to progress in and the success of GANs. Experiments on several datasets demonstrate that E-GAN achieves convincing generative performance and reduces the training problems inherent in existing GANs.
A hybrid CAD-based construction site layout planning system using genetic algorithms

The efficient layout planning of a construction site is a fundamental task to any project undertaking. In an attempt to enhance the general practice of layout planning of construction sites, the paper introduces a novel approach for producing the sought layouts. This approach integrates the highly sophisticated graphical capabilities of computer-aided design (CAD) platforms with the robust search and optimization capabilities of genetic algorithms (GAs). In this context, GAs are utilized from within the CAD environment to optimize the location of temporary facilities on site. The functional interaction between GAs and CAD and the details of the GA-based layout optimization procedure are presented. A fully automated computer system is further developed to demonstrate the practicality of the chosen approach. In order to evaluate the system’s performance, a local construction project with a 24,000m site is used. The automated system produced highly satisfactory results and showed notable flexibility through its CAD-based input/output media. D 2003 Elsevier B.V. All rights reserved.
A wideband substrate integrated waveguide slotted array antenna with multimode and multidirectional characteristics

This paper presents the design of a substrate integrated waveguide slotted array antenna (SIW-SAA) with wideband, multimode and multidirectional characteristics. The slotted array consists of three transverse slots in the top wall of SIW which are inclined at 45° with respect to a single longitudinal slot. The number of slots and the position of the slotted array determine the impedance bandwidth. It is found from the simulated and measured results that the impedance bandwidth of the antenna is 92% (10 GHz–27 GHz) for S11 ≤ −10 dB. Multiple modes get excited in the SIW-SAA resulting in multidirectional characteristics and an equivalent circuit model is used to explain the physics of this phenomenon. In the X band, the lowest mode (TE10) of SIW exists and the combination of slotted array and SIW is responsible for unidirectional radiation. Bidirectional and omnidirectional patterns are observed in the elevation plane for Ku and K bands, respectively due to higher order modes that are excited in the bottom wall of SIW. With these multidirectional characteristics, the proposed antenna is an attractive choice in airborne control systems, long distance communications and broadcasting.
A Two-Handed Interface for Object Manipulation in Virtual Environments

A two-handed direct manipulation VE (virtual environment) interface has been developed as an intuitive manipulation metaphor for graphical objects. A new input device called ChordGloves introduces a simple technique for rapid and repeatable gesture recognition; the Chordgloves emulate a pair of 3-D mice and a keyboard. A drafting table is isomorphically mapped into the VE and provides hand support for 2-D interface techniques, as well as a reference frame for calibrating the mapping between real and virtual worlds. A cursor gravity function is used to grab vertices, edges, or faces and establish precisely aligned differential constraints between objects called anchors. The capability of subjects to translate, rotate, scale, align, and glue objects is tested with a puzzle building task. An approximation of the puzzle task is done in Adobe Illustrator to provide a performance reference. Results and informal user observations as well as topics for future work are presented.