nils-herrmann/clean_scirepeval huggingface dataset:
An SiC-Based Half-Bridge Module With an Improved Hybrid Packaging Method for High Power Density Applications

SiC devices have the potential to structure high power density converters; however, SiC devices have high di /dt during switching. Therefore, the parasitic inductances in the power loop and gating loop must be reduced to restrain the induced voltage. This paper proposes a SiC-based, half-bridge (HB) module with a hybrid packaging method. Such a module is based on printed circuit board (PCB) plus direct bonding copper (DBC) structure. The DBC provides part of power loops, and the SiC bare dies can be directly soldered on these loops; the PCB provides another part of the power loops, as well as the gating loops. Such a design allows the power loops on the PCB to be soldered with those on the DBC directly; in the meantime, the SiC bare dies soldered on the DBC can be connected to the PCB with bounding wires. Such a structure provides extra degrees of freedom for design so that the parasitic inductances can be minimized by optimizing the current communication loops, driver locations, and the gating connections, and the heat can also be dissipated through the direct cooling structure easily. A 1200 V/24 A SiC HB module is fabricated with the proposed method in the laboratory, and based on the fabricated module, a synchronous buck converter with power density of 379.3 W/in3 is also structured. The double pulse test shows that the fabricated module can be switched within 10 ns and that the drain-source voltage overshoot is only 2.5%. The highest efficiency of the converter based on the fabricated module is up to 99.46%.
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach

The goal of sentiment-to-sentiment “translation” is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To solve this problem, we propose a cycled reinforcement learning method that enables training on unpaired data by collaboration between a neutralization module and an emotionalization module. We evaluate our approach on two review datasets, Yelp and Amazon. Experimental results show that our approach significantly outperforms the state-of-the-art systems. Especially, the proposed method substantially improves the content preservation performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to 14.06 on the two datasets, respectively.
A metamaterial absorber for the terahertz regime: design, fabrication and characterization.

We present a metamaterial that acts as a strongly resonant absorber at terahertz frequencies. Our design consists of a bilayer unit cell which allows for maximization of the absorption through independent tuning of the electrical permittivity and magnetic permeability. An experimental absorptivity of 70% at 1.3 terahertz is demonstrated. We utilize only a single unit cell in the propagation direction, thus achieving an absorption coefficient alpha = 2000 cm(-1). These metamaterials are promising candidates as absorbing elements for thermally based THz imaging, due to their relatively low volume, low density, and narrow band response.
Control of microgrids: Aspects and prospects

A microgrid is a controllable component of the smart grid defined as a part of distribution network capable of supplying its own local load even in the case of disconnection from the upstream network. Microgrids incorporate large amount of renewable and non-renewable distributed generation (DG) that are connected to the system either directly or by power electronics (PE) interface. The diversity of technologies used in DGs and loads, high penetration of DGs, economic operation of DGs, dynamics of low-inertia conventional DGs and PE interfaced inertialess DGs and smart operation by means of enhanced communication infrastructure have raised challenges in widespread utilization of microgrids as basis of smart grids. Power quality, protection, economic and secure operation, active management, communication, dynamics and control of microgrids are among the most important issues under research both in academy and industry. Technical concerns over dynamics of microgrids especially in autonomous (island) mode necessitate revision of current paradigms in control of energy systems. This paper addresses current challenges towards controlling microgrids and surveys dynamic modeling, stability and control of microgrids. Future trends in realizing smart grids through aggregation of microgrids and research needs in this path are discussed at the end of this paper.
Emotion analysis of Twitter using opinion mining

With the rise in use of micro-blogging sites like Twitter, people are able to express and share their opinions with each other on a common platform. Currently all work in opinion mining research has quantified & assessed the expression of opinion as positive, negative or neutral values, we intend to categorize the opinion on the basis of five emotions, namely Happiness, Anger, Fear, Sadness & Disgust, which have been globally accepted & defined in human psychology. This paper presents a method to assess these identified types of emotions in a tweet using opinion mining. A two-step approach is proposed, where firstly, to identify the sentiment; we extract the opinion words (a combination of the adjectives along with the verbs and adverbs) in the tweets and subsequently use a novel algorithm to find the emotion values of opinion words. The initial results show that it is a motivating technique, which may find potential applications in business intelligence, government policy making, amongst others.
Compact Dual Band-Notched UWB MIMO Antenna With High Isolation

A compact dual band-notched ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna with high isolation is designed on a FR4 substrate (27 × 30 × 0.8 mm3). To improve the input impedance matching and increase the isolation for the frequencies ≥ 4.0 GHz, the two antenna elements with compact size of 5.5 × 11 mm2 are connected to the two protruded ground parts, respectively. A 1/3 λ rectangular metal strip producing a 1.0 λ loop path with the corresponding antenna element is used to obtain the notched frequency from 5.15 to 5.85 GHz. For the rejected band of 3.30-3.70 GHz, a 1/4 λ open slot is etched into the radiator. Moreover, the two protruded ground parts are connected by a compact metal strip to reduce the mutual coupling for the band of 3.0-4.0 GHz. The simulated and measured results show a bandwidth with |S11| ≤ -10 dB, |S21| ≤ -20 dB and frequency ranged from 3.0 to 11.0 GHz excluding the two rejected bands, is achieved, and all the measured and calculated results show the proposed UWB MIMO antenna is a good candidate for UWB MIMO systems.
Optimization based full body control for the atlas robot

One popular approach to controlling humanoid robots is through inverse kinematics (IK) with stiff joint position tracking. On the other hand, inverse dynamics (ID) based approaches have gained increasing acceptance by providing compliant motions and robustness to external perturbations. However, the performance of such methods is heavily dependent on high quality dynamic models, which are often very difficult to produce for a physical robot. IK approaches only require kinematic models, which are much easier to generate in practice. In this paper, we supplement our previous work with ID-based controllers by adding IK, which helps compensate for modeling errors. The proposed full body controller is applied to three tasks in the DARPA Robotics Challenge (DRC) Trials in Dec. 2013.
Unsupervised Neural Machine Translation

In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs. There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, but they still require a strong cross-lingual signal. In this work, we completely remove the need of parallel data and propose a novel method to train an NMT system in a completely unsupervised manner, relying on nothing but monolingual corpora. Our model builds upon the recent work on unsupervised embedding mappings, and consists of a slightly modified attentional encoder-decoder model that can be trained on monolingual corpora alone using a combination of denoising and backtranslation. Despite the simplicity of the approach, our system obtains 15.56 and 10.21 BLEU points in WMT 2014 French → English and German → English translation. The model can also profit from small parallel corpora, and attains 21.81 and 15.24 points when combined with 100,000 parallel sentences, respectively. Our implementation is released as an open source project1.
Achievement goals and intrinsic motivation: a meta-analytic review.

This article presents a meta-analysis of the experimental literature that has examined the effect of performance and mastery achievement goals on intrinsic motivation. Summary analyses provided support for the hypothesis that the pursuit of performance goals has an undermining effect on intrinsic motivation relative to the pursuit of mastery goals. Moderator analyses were conducted in an attempt to explain significant variation in the magnitude and direction of this effect across studies. Results indicated that the undermining effect of performance goals relative to mastery goals was contingent on whether participants received confirming or nonconfirming competence feedback, and on whether the experimental procedures induced a performance-approach or performance-avoidance orientation. These findings provide conceptual clarity to the literature on achievement goals and intrinsic motivation and suggest numerous avenues for subsequent empirical work.
Context-Aware Service Integration for Elderly Care in A Smart Environment

Advances in healthcare have led to longer life expectancy and an aging population. The cost of caring for the elderly is rising progressively and threatens the economic well-being of many nations around the world. Instead of professional nursing facilities, many elderly people prefer living independently in their own homes. To enable the aging to remain active, this research explores the roles of technology in improving their quality of life while reducing the cost of healthcare to the elderly population. In particular, we propose a multi-agent service framework, called Context-Aware Service Integration System (CASIS), to integrate applications and services. This paper demonstrates several context-aware service scenarios these have been developed on the proposed framework to demonstrate how context technologies and mobile web services can help enhance the quality of care for an elder’s daily
Commonsense Knowledge Aware Conversation Generation with Graph Attention

Commonsense knowledge is vital to many natural language processing tasks. In this paper, we present a novel open-domain conversation generation model to demonstrate how large-scale commonsense knowledge can facilitate language understanding and generation. Given a user post, the model retrieves relevant knowledge graphs from a knowledge base and then encodes the graphs with a static graph attention mechanism, which augments the semantic information of the post and thus supports better understanding of the post. Then, during word generation, the model attentively reads the retrieved knowledge graphs and the knowledge triples within each graph to facilitate better generation through a dynamic graph attention mechanism. This is the first attempt that uses large-scale commonsense knowledge in conversation generation. Furthermore, unlike existing models that use knowledge triples (entities) separately and independently, our model treats each knowledge graph as a whole, which encodes more structured, connected semantic information in the graphs. Experiments show that the proposed model can generate more appropriate and informative responses than stateof-the-art baselines.
Low-Rank Discriminant Embedding for Multiview Learning

This paper focuses on the specific problem of multiview learning where samples have the same feature set but different probability distributions, e.g., different viewpoints or different modalities. Since samples lying in different distributions cannot be compared directly, this paper aims to learn a latent subspace shared by multiple views assuming that the input views are generated from this latent subspace. Previous approaches usually learn the common subspace by either maximizing the empirical likelihood, or preserving the geometric structure. However, considering the complementarity between the two objectives, this paper proposes a novel approach, named low-rank discriminant embedding (LRDE), for multiview learning by taking full advantage of both sides. By further considering the duality between data points and features of multiview scene, i.e., data points can be grouped based on their distribution on features, while features can be grouped based on their distribution on the data points, LRDE not only deploys low-rank constraints on both sample level and feature level to dig out the shared factors across different views, but also preserves geometric information in both the ambient sample space and the embedding feature space by designing a novel graph structure under the framework of graph embedding. Finally, LRDE jointly optimizes low-rank representation and graph embedding in a unified framework. Comprehensive experiments in both multiview manner and pairwise manner demonstrate that LRDE performs much better than previous approaches proposed in recent literatures.
Ontology-Based Semantic Recommendation for Context-Aware E-Learning

Nowadays, e-learning systems are widely used for education and training in universities and companies because of their electronic course content access and virtual classroom participation. However, with the rapid increase of learning content on the Web, it will be time-consuming for learners to find contents they really want to and need to study. Aiming at enhancing the efficiency and effectiveness of learning, we propose an ontology-based approach for semantic content recommendation towards context-aware e-learning. The recommender takes knowledge about the learner (user context), knowledge about content, and knowledge about the domain being learned into consideration. Ontology is utilized to model and represent such kinds of knowledge. The recommendation consists of four steps: semantic relevance calculation, recommendation refining, learning path generation, and recommendation augmentation. As a result, a personalized, complete, and augmented learning program is suggested for the learner.
Chicken Farm Monitoring System

In this paper, the chicken farm monitoring system is proposed and developed based on wireless communication unit to transfer data by using the wireless module combined with the sensors that enable to detect temperature, humidity, light and water level values. This system is focused on the collecting, storing, and controlling the information of the chicken farm so that the high quality and quantity of the meal production can be produced. This system is developed to solve several problems in the chicken farm which are many human workers is needed to control the farm, high cost in maintenance, and inaccurate data collected at one point. The proposed methodology really helps in finishing this project within the period given. Based on the research that has been carried out, the system that can monitor and control environment condition (temperature, humidity, and light) has been developed by using the Arduino microcontroller. This system also is able to collect data and operate autonomously.
A Course in Probability Theory

In recent years books on probability theory have mushroomed. The two books under review have this in common : they are concerned with probability theory proper, rather than with its foundations (like the recent elegant but austere book by Neveu), and they give a balanced view of the theory rather than dwelling at length on some specialized topics. What is probability theory? Chung's spirited preface at first tells us what it is not: the "specious utterance" that "probability is just a chapter of measure theory" finds no more favor with him than the use of probability "as a front for certain types of analysis such as combinatorial, Fourier, functional and whatnot." Elsewhere he says about the same utterance that it is "not so much false as it is fatuous, as it would be to say that number theory is just a chapter of algebra." Thus over the years Professor Chung continues his disagreement with Professor Doob who in his Stochastic processes writes: "Probability is simply a branch of measure theory, with its own special emphasis and field of application." On the positive side Professor Chung remarks in the preface of his book: "The random walks in Chapter 8 illustrate well the way probability theory transforms other parts of mathematics. I t does so by introducing the trajectories of the process, thereby turning what was static into a dynamic structure." As for Dr. Breiman, without defining probability he tells us about its right and left hand. "On the right is the rigorous foundational work using the tools of measure theory. The left hand i thinks probabilistically/ reduces problems to gambling situations, coin-tossing, motions of a physical particle." Breiman acknowledges M. Loève as his teacher of the first aspect of probability (and D. Blackwell as the teacher of the second) and Professor Loève in his treatise on the subject (p. 170) defines a probabilistic property as one which can be described in terms of distribution functions. Professor Neveu (Preface, op. cit.) strongly disagrees: "Attempts have been made, it is true, to treat convergence problems of probability theory within the restricted framework of the study of distribution functions; but this 1 J. Neveu, Mathematical foundations of the calculus of probability, Holden-Day, San Francisco, Calif., 1965. 2 In the review of A. Rényi's textbook,  (1964), 225.
The Research and Application of College Student Attendance System based on RFID Technology

College student attendance management of class plays an important position in the work of management of college student, this can help to urge student to class on time, improve learning efficiency, increase learning grade, and thus entirely improve the education level of the school. Therefore, colleges need an information system platform of check attendance management of class strongly to enhance check attendance management of class using the information technology which gathers the basic information of student automatically. According to current reality and specific needs of check attendance and management system of college students and the exist device of the system. Combined with the study of college attendance system, this paper gave the node design of check attendance system of class which based on RFID on the basic of characteristics of embedded ARM and RFID technology.
The aligned rank transform for nonparametric factorial analyses using only anova procedures

Nonparametric data from multi-factor experiments arise often in human-computer interaction (HCI). Examples may include error counts, Likert responses, and preference tallies. But because multiple factors are involved, common nonparametric tests (e.g., Friedman) are inadequate, as they are unable to examine interaction effects. While some statistical techniques exist to handle such data, these techniques are not widely available and are complex. To address these concerns, we present the Aligned Rank Transform (ART) for nonparametric factorial data analysis in HCI. The ART relies on a preprocessing step that "aligns" data before applying averaged ranks, after which point common ANOVA procedures can be used, making the ART accessible to anyone familiar with the F-test. Unlike most articles on the ART, which only address two factors, we generalize the ART to N factors. We also provide ARTool and ARTweb, desktop and Web-based programs for aligning and ranking data. Our re-examination of some published HCI results exhibits advantages of the ART.
Age simulation for face recognition

In this paper, an automatic age simulation method used for robust face recognition is proposed. We first use a shape and texture vectors to represent a facial image by projecting it in the eigenspace of shape or texture. Then we use age function combined with aging way classification to estimate age. And we use estimated age, typical vector creating function and the feature vector of the original test image to generate the synthesized feature vectors at target age. At last we reconstruct the shape and texture in eigenspaces and combine them to synthesize facial image at target age. Experiments show that the proposed method can effectively "change" the age of face images, and help to get face recognition result robust to age variation
Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis

Statistical parametric speech synthesis (SPSS) using deep neural networks (DNNs) has shown its potential to produce naturally-sounding synthesized speech. However, there are limitations in the current implementation of DNN-based acoustic modeling for speech synthesis, such as the unimodal nature of its objective function and its lack of ability to predict variances. To address these limitations, this paper investigates the use of a mixture density output layer. It can estimate full probability density functions over real-valued output features conditioned on the corresponding input features. Experimental results in objective and subjective evaluations show that the use of the mixture density output layer improves the prediction accuracy of acoustic features and the naturalness of the synthesized speech.
Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.10.050 ⇑ Corresponding author.  fax E-mail addresses: xiejuany@snnu.edu.cn (J. Xie) (C. Wang). In this paper, we developed a diagnosis model based on support vector machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our proposed hybrid feature selection method, named improved F-score and Sequential Forward Search (IFSFS), combines the advantages of filter and wrapper methods to select the optimal feature subset from the original feature set. In our IFSFS, we improved the original F-score from measuring the discrimination of two sets of real numbers to measuring the discrimination between more than two sets of real numbers. The improved Fscore and Sequential Forward Search (SFS) are combined to find the optimal feature subset in the process of feature selection, where, the improved F-score is an evaluation criterion of filter method, and SFS is an evaluation system of wrapper method. The best parameters of kernel function of SVM are found out by grid search technique. Experiments have been conducted on different training-test partitions of the erythemato-squamous diseases dataset taken from UCI (University of California Irvine) machine learning database. Our experimental results show that the proposed SVM-based model with IFSFS achieves 98.61% classification accuracy and contains 21 features. With these results, we conclude our method is very promising compared to the previously reported results.