nils-herrmann/clean_scirepeval huggingface dataset:
Explore/Exploit Schemes for Web Content Optimization

We propose novel multi-armed bandit (explore/exploit) schemes to maximize total clicks on a content module published regularly on Yahoo! Intuitively, one can ``explore'' each candidate item by displaying it to a small fraction of user visits to estimate the item's click-through rate (CTR), and then ``exploit'' high CTR items in order to maximize clicks. While bandit methods that seek to find the optimal trade-off between explore and exploit have been studied for decades, existing solutions are not satisfactory for web content publishing applications where dynamic set of items with short lifetimes, delayed feedback and non-stationary reward (CTR) distributions are typical. In this paper, we develop a Bayesian solution and extend several existing schemes to our setting. Through extensive evaluation with nine bandit schemes, we show that our Bayesian solution is uniformly better in several scenarios. We also study the empirical characteristics of our schemes and provide useful insights on the strengths and weaknesses of each. Finally, we validate our results with a ``side-by-side'' comparison of schemes through live experiments conducted on a random sample of real user visits to Yahoo!
Rotor surface ferrite magnet synchronous machine for generator use in a hybrid application — Electro-magnetic and thermal analysis

To reach a particular tangential stress in a PMSM the magnetic loading and the electric loading should have corresponding values. This means that if the magnetic loading is restricted by the characteristics of cheap and relatively weak ferrite permanent magnets, the electric loading should be increased to keep the tangential stress at desirable value. However, the latter is also limited because of the demagnetization risk of the permanent magnets by a high armature reaction. Therefore, surface ferrite PMSMs should have a low tangential stress in order to avoid the demagnetization risk, which consequently leads to a low torque density. This is one of the main drawbacks of ferrite magnets used in electric motor drives. This paper describes some possibilities for improving the torque density with surface ferrite PMSMs, describes the restrictions which one can meet while designing this type of the electric machines and observe the influence of temperature variation in the magnets on the electric machine performance. An example is done with the analytical and the FEM analyses of a 50 kW, 3000 rpm, permanent magnet generator for a series hybrid electric vehicle application.
What is Deep Learning ? Multi-layer representation methods to learn features from data

Deep learning is a popular technique in modern online and offline services. Deep neural network based learning systems have made groundbreaking progress in model size, training and inference speed, and expressive power in recent years, but to tailor the model to specific problems and exploit data and problem structures is still an ongoing research topic. We look into two types of deep ‘‘multi-’’ objective learning problems: multi-view learning, referring to learning from data represented by multiple distinct feature sets, and multi-label learning, referring to learning from data instances belonging to multiple class labels that are not mutually exclusive. Research endeavors of both problems attempt to base on existing successful deep architectures and make changes of layers, regularization terms or even build hybrid systems to meet the problem constraints. In this report we first explain the original artificial neural network (ANN) with the backpropagation learning algorithm, and also its deep variants, e.g. deep belief network (DBN), convolutional neural network (CNN) and recurrent neural network (RNN). Next we present a survey of some multi-view and multi-label learning frameworks based on deep neural networks. At last we introduce some applications of deep multi-view and multi-label learning, including e-commerce item categorization, deep semantic hashing, dense image captioning, and our preliminary work on x-ray scattering image classification.
Nucleic Acids Data Sequencing using Higher Order Logic-A Suggestion of Basic Computational Framework Towards Bio-Sensors and Gene-Chips Design, Implementation and Verification *

In this current research paper we highlight the usage of Higher Order Logic (HOL) to compute the sequences of Nucleic acids, DNA and RNA and illustrate the usefulness of higher order logic in computing the sequences for bio-sensor, probes, tags and gene-chip applications. What ever the application in question,sequences always form the heart and soul of bio-information processing in such demanding and promising tools. Higher order Logic provides a comfortable way of solving or computing the sequencing problems and related issues in the advancement of bio-informatics,hence we have focused in this interdisciplinary paper on HOL and its applications, in the design of such bio1 Jiangnan University, China and Ben-Gurion University, Israel, e-mail:tejdnk@gmail.com 2 Ben-Gurion University, Israel,  August 11, 2012.  September 14, 2012  2012 66 Nucleic Acids Data Sequencing using Higher Order Logic ... devices. Further more we are of the opinion that HOL is fast becoming the defacto verification and computational software tool, in verifying the electronic hardware in demanding applications, the same methodology could be extended to bio-inspired systems, bio-systems, theoretical biology etc. to explore the untouched relevant areas of computational design paradigms. This is one of the pioneering research efforts in this highly promising domain, to adopt and adapt HOL as the design tool to model and develop sequencing, in Bio-Computing or Bio-Sensing platforms.
Classifying Options for Deep Reinforcement Learning

In this paper we combine one method for hierarchical reinforcement learning—the options framework—with deep Q-networks (DQNs) through the use of different “option heads” on the policy network, and a supervisory network for choosing between the different options. We utilise our setup to investigate the effects of architectural constraints in subtasks with positive and negative transfer, across a range of network capacities. We empirically show that our augmented DQN has lower sample complexity when simultaneously learning subtasks with negative transfer, without degrading performance when learning subtasks with positive transfer.
S-PTAM: Stereo Parallel Tracking and Mapping

This paper describes a real-time feature-based stereo SLAM system that is robust and accurate in a wide variety of conditions –indoors, outdoors, with dynamic objects, changing light conditions, fast robot motions and large-scale loops. Our system follows a parallel-tracking-and-mapping strategy: a tracking thread estimates the camera pose at frame rate; and a mapping thread updates a keyframe-based map at a lower frequency. The stereo constraints of our system allow a robust initialization –avoiding the well-known bootstrapping problem in monocular systems– and the recovery of the real scale. Both aspects are essential for its practical use in real robotic systems that interact with the physical world. In this paper we provide the implementation details, an exhaustive evaluation of the system in public datasets and a comparison of most state-of-the-art feature detectors and descriptors on the presented system. For the benefit of the community, its code for ROS (Robot Operating System) has been released.
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning

Autoencoders are popular among neural-network-based matrix completion models due to their ability to retrieve potential latent factors from the partially observed matrices. Nevertheless, when training data is scarce their performance is significantly degraded due to overfitting. In this paper, we mitigate overfitting with a data-dependent regularization technique that relies on the principles of multi-task learning. Specifically, we propose an autoencoder-based matrix completion model that performs prediction of the unknown matrix values as a main task, and manifold learning as an auxiliary task. The latter acts as an inductive bias, leading to solutions that generalize better. The proposed model outperforms the existing autoencoder-based models designed for matrix completion, achieving high reconstruction accuracy in well-known datasets.
On security and privacy issues of fog computing supported Internet of Things environment

Recently, the concept of Internet of Things (IoT) is attracting much attention due to the huge potential. IoT uses the Internet as a key infrastructure to interconnect numerous geographically diversified IoT nodes which usually have scare resources, and therefore cloud is used as a key back-end supporting infrastructure. In the literature, the collection of the IoT nodes and the cloud is collectively called as an IoT cloud. Unfortunately, the IoT cloud suffers from various drawbacks such as huge network latency as the volume of data which is being processed within the system increases. To alleviate this issue, the concept of fog computing is introduced, in which foglike intermediate computing buffers are located between the IoT nodes and the cloud infrastructure to locally process a significant amount of regional data. Compared to the original IoT cloud, the communication latency as well as the overhead at the backend cloud infrastructure could be significantly reduced in the fog computing supported IoT cloud, which we will refer as IoT fog. Consequently, several valuable services, which were difficult to be delivered by the traditional IoT cloud, can be effectively offered by the IoT fog. In this paper, however, we argue that the adoption of IoT fog introduces several unique security threats. We first discuss the concept of the IoT fog as well as the existing security measures, which might be useful to secure IoT fog. Then, we explore potential threats to IoT fog.
Choice decision of e-learning system: Implications from construal level theory

This study investigates user acceptance of a new e-learning system when users can choose between the old and the new systems. Drawing upon construal level theory and technology acceptance model, this study proposes that users’ construal level of an e-learning system interacts with their perceptions of the system (i.e., PEoU and PU) and affects their adoption intention. Data collected from 131 participants in a laboratory experiment show that a higher construal level strengthened the effect of PEoU but mitigated the effect of PU on participants’ attitude toward using the system, thus affecting adoption intention. Theoretical contributions and implications are discussed.  Corresponding author at: University of Science and Technology of China, School of Management, 96 Jinzhai Road, Hefei, Anhui, China. Tel. E-mail addresses: candyho@hkbu.edu.hk (Candy K.Y. Ho), wke@clarkson.edu (W. Ke), liuhf@ustc.edu.cn (H. Liu).
One switching cycle current control strategy for triple active bridge phase-shifted DC-DC converter

The paper presents two types of one cycle current control method for Triple Active Bridge(TAB) phase-shifted DC-DC converter integrating Renewable Energy Source(RES), Energy Storage System(ESS) and a output dc bus. The main objective of the current control methods is to control the transformer current in each cycle so that dc transients are eliminated during phase angle change from one cycle to the next cycle. In the proposed current control methods, the transformer currents are sampled within a switching cycle and the phase shift angles for the next switching cycle are generated based on sampled current values and current references. The discussed one cycle control methods also provide an inherent power decoupling feature for the three port phase shifted triple active bridge converter. Two different methods, (a) sampling and updating twice in a switching cycle and (b) sampling and updating once in a switching cycle, are explained in this paper. The current control methods are experimentally verified using digital implementation technique on a laboratory made hardware prototype.
A survey of hierarchical classification across different application domains

In this survey we discuss the task of hierarchical classification. The literature about this field is scattered across very different application domains and for that reason research in one domain is often done unaware of methods developed in other domains. We define what is the task of hierarchical classification and discuss why some related tasks should not be considered hierarchical classification. We also present a new perspective about some existing hierarchical classification approaches, and based on that perspective we propose a new unifying framework to classify the existing approaches. We also present a review of empirical comparisons of the existing methods reported in the literature as well as a conceptual comparison of those methods at a high level of abstraction, discussing their advantages and disadvantages.
Research on Architecting Microservices: Trends, Focus, and Potential for Industrial Adoption

Microservices are a new trend rising fast from the enterprise world. Even though the design principles around microservices have been identified, it is difficult to have a clear view of existing research solutions for architecting microservices. In this paper we apply the systematic mapping study methodology to identify, classify, and evaluate the current state of the art on architecting microservices from the following three perspectives: publication trends, focus of research, and potential for industrial adoption. More specifically, we systematically define a classification framework for categorizing the research on architecting microservices and we rigorously apply it to the 71 selected studies. We synthesize the obtained data and produce a clear overview of the state of the art. This gives a solid basis to plan for future research and applications of architecting microservices.
Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends

Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) are the two most common types of acoustic models used in statistical parametric approaches for generating low-level speech waveforms from high-level symbolic inputs via intermediate acoustic feature sequences. However, these models have their limitations in representing complex, nonlinear relationships between the speech generation inputs and the acoustic features. Inspired by the intrinsically hierarchical process of human speech production and by the successful application of deep neural networks (DNNs) to automatic speech recognition (ASR), deep learning techniques have also been applied successfully to speech generation, as reported in recent literature. This article systematically reviews these emerging speech generation approaches, with the dual goal of helping readers gain a better understanding of the existing techniques as well as stimulating new work in the burgeoning area of deep learning for parametric speech generation.
Performance analysis and receiver architectures of DCF77 radio-controlled clocks

DCF77 is a longwave radio transmitter located in Germany. Atomic clocks generate a 77.5-kHz carrier which is amplitudeand phase-modulated to broadcast the official time. The signal is used by industrial and consumer radio-controlled clocks. DCF77 faces competition from the Global Positioning System (GPS) which provides higher accuracy time. Still, DCF77 and other longwave time services worldwide remain popular because they allow indoor reception at lower cost, lower power, and sufficient accuracy. Indoor longwave reception is challenged by signal attenuation and electromagnetic interference from an increasing number of devices, particularly switched-mode power supplies. This paper introduces new receiver architectures and compares them with existing detectors and time decoders. Simulations and analytical calculations characterize the performance in terms of bit error rate and decoding probability, depending on input noise and narrowband interference. The most promising detector with maximum-likelihood time decoder displays the time in less than 60 s after powerup and at a noise level of Eb/N0 = 2.7 dB, an improvement of 20 dB over previous receivers. A field-programmable gate array-based demonstration receiver built for the purposes of this paper confirms the capabilities of these new algorithms. The findings of this paper enable future high-performance DCF77 receivers and further study of indoor longwave reception.
Per-Pixel Feedback for improving Semantic Segmentation

Semantic segmentation is the task of assigning a label to each pixel in the image.In recent years, deep convolutional neural networks have been driving advances in multiple tasks related to cognition. Although, DCNNs have resulted in unprecedented visual recognition performances, they offer little transparency. To understand how DCNN based models work at the task of semantic segmentation, we try to analyze the DCNN models in semantic segmentation. We try to find the importance of global image information for labeling pixels. Based on the experiments on discriminative regions, and modeling of fixations, we propose a set of new training loss functions for fine-tuning DCNN based models. The proposed training regime has shown improvement in performance of DeepLab Large FOV(VGG-16) Segmentation model for PASCAL VOC 2012 dataset. However, further test remains to conclusively evaluate the benefits due to the proposed loss functions across models, and data-sets.
Leadership styles in nursing.

Nurses are often asked to think about leadership, particularly in times of rapid change in healthcare, and where questions have been raised about whether leaders and managers have adequate insight into the requirements of care. This article discusses several leadership styles relevant to contemporary healthcare and nursing practice. Nurses who are aware of leadership styles may find this knowledge useful in maintaining a cohesive working environment. Leadership knowledge and skills can be improved through training, where, rather than having to undertake formal leadership roles without adequate preparation, nurses are able to learn, nurture, model and develop effective leadership behaviours, ultimately improving nursing staff retention and enhancing the delivery of safe and effective care.
Learning 3D Keypoint Descriptors for Non-rigid Shape Matching

In this paper, we present a novel deep learning framework that derives discriminative local descriptors for 3D surface shapes. In contrast to previous convolutional neural networks (CNNs) that rely on rendering multi-view images or extracting intrinsic shape properties, we parameterize the multi-scale localized neighborhoods of a keypoint into regular 2D grids, which are termed as ‘geometry images’. The benefits of such geometry images include retaining sufficient geometric information, as well as allowing the usage of standard CNNs. Specifically, we leverage a triplet network to perform deep metric learning, which takes a set of triplets as input, and a newly designed triplet loss function is minimized to distinguish between similar and dissimilar pairs of keypoints. At the testing stage, given a geometry image of a point of interest, our network outputs a discriminative local descriptor for it. Experimental results for non-rigid shape matching on several benchmarks demonstrate the superior performance of our learned descriptors over traditional descriptors and the state-of-the-art learning-based alternatives.
A Meta-Analysis of Neuropsychological Outcome After Mild Traumatic Brain Injury : Re-analyses and Reconsiderations of Binder et al . ( ) , Frencham et al . ( ) , and Pertab et al . ( )

A Meta-Analysis of Neuropsychological Outcome After Mild Traumatic Brain Injury: Re-analyses and Reconsiderations of Binder et al. (), Frencham et al. (), and Pertab et al. () Martin L. Rohling a , Laurence M. Binder b , George J. Demakis c , Glenn J. Larrabee d , Danielle M. Ploetz a & Jennifer Langhinrichsen-Rohling a a University of South Alabama, Mobile, AL, USA b Oregon Health Sciences University, Portland, OR, USA c University of North Carolina, Charlotte, NC, USA d Independent Practice, Sarasota, FL, USA
InstaNAS: Instance-aware Neural Architecture Search

Neural Architecture Search (NAS) aims at finding one “single” architecture that achieves the best accuracy for a given task such as image recognition. In this paper, we study the instance-level variation, and demonstrate that instance-awareness is an important yet currently missing component of NAS. Based on this observation, we propose InstaNAS for searching toward instance-level architectures; the controller is trained to search and form a “distribution of architectures” instead of a single final architecture. Then during the inference phase, the controller selects an architecture from the distribution, tailored for each unseen image to achieve both high accuracy and short latency. The experimental results show that InstaNAS reduces the inference latency without compromising classification accuracy. On average, InstaNAS achieves 48.9% latency reduction on CIFAR-10 and 40.2% latency reduction on CIFAR-100 with respect to MobileNetV2 architecture.
WiC: 10, 000 Example Pairs for Evaluating Context-Sensitive Representations

By design, word embeddings are unable to model the dynamic nature of words’ semantics, i.e., the property of words to correspond to potentially different meanings depending on the context in which they appear. To address this limitation, dozens of specialized word embedding techniques have been proposed. However, despite the popularity of research on this topic, very few evaluation benchmarks exist that specifically focus on the dynamic semantics of words. In this paper we show that existing models have surpassed the performance ceiling for the standard de facto dataset, i.e., the Stanford Contextual Word Similarity. To address the lack of a suitable benchmark, we put forward a large-scale Word in Context dataset, called WiC, based on annotations curated by experts, for generic evaluation of contextsensitive word embeddings. WiC is released in https://pilehvar.github.io/wic/.