nils-herrmann/clean_scirepeval huggingface dataset:
Tensor2Tensor for Neural Machine Translation

Tensor2Tensor is a library for deep learning models that is very well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model. 1 Neural Machine Translation  Machine translation using deep neural networks achieved great success with sequence-tosequence models Sutskever et al. (2014); Bahdanau et al. (2014); Cho et al. (2014) that used recurrent neural networks (RNNs) with LSTM cells Hochreiter and Schmidhuber (1997). The basic sequence-to-sequence architecture is composed of an RNN encoder which reads the source sentence one token at a time and transforms it into a fixed-sized state vector. This is followed by an RNN decoder, which generates the target sentence, one token at a time, from the state vector. While a pure sequence-to-sequence recurrent neural network can already obtain good translation results Sutskever et al. (2014); Cho et al. (2014), it suffers from the fact that the whole input sentence needs to be encoded into a single fixed-size vector. This clearly manifests itself in the degradation of translation quality on longer sentences and was partially overcome in Bahdanau et al. (2014) by using a neural model of attention. Convolutional architectures have been used to obtain good results in word-level neural machine translation starting from Kalchbrenner and Blunsom (2013) and later in Meng et al. (2015). These early models used a standard RNN on top of the convolution to generate the output, which creates a bottleneck and hurts performance. Fully convolutional neural machine translation without this bottleneck was first achieved in Kaiser and Bengio (2016) and Kalchbrenner et al. (2016). The model in Kaiser and Bengio (2016) (Extended Neural GPU) used a recurrent stack of gated convolutional layers, while the model in Kalchbrenner et al. (2016) (ByteNet) did away with recursion and used left-padded convolutions in the decoder. This idea, introduced in WaveNet van den Oord et al. (2016), significantly improves efficiency of the model. The same technique was improved in a number of neural translation models recently, including Gehring et al. (2017) and Kaiser et al. (2017).
Deep Reinforcement Learning for Swarm Systems

Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making. However, concatenation scales poorly to swarm systems with a large number of homogeneous agents as it does not exploit the fundamental properties inherent to these systems: (i) the agents in the swarm are interchangeable and (ii) the exact number of agents in the swarm is irrelevant. Therefore, we propose a new state representation for deep multi-agent RL based on mean embeddings of distributions. We treat the agents as samples of a distribution and use the empirical mean embedding as input for a decentralized policy. We define different feature spaces of the mean embedding using histograms, radial basis functions and a neural network learned end–to–end. We evaluate the representation on two well known problems from the swarm literature (rendezvous and pursuit evasion), in a globally and locally observable setup. For the local setup we furthermore introduce simple communication protocols. Of all approaches, the mean embedding representation using neural network features enables the richest information exchange between neighboring agents facilitating the development of more complex collective strategies.
Examining The Adoption Of Human Resource Information System In The Context Of Saudi Arabia

The aim of this paper is to examine the factors influencing the adoption of the human resource information system (HRIS) in the Saudi context. A few researches have looked at the adoption of HRIS, but none of these studies have examined the adoption of such a system from the users’ perspective in general and Saudi Arabia (SA) in particular. This study has developed a model that integrates service quality with perceived usefulness and perceived ease of use to investigate the factors affecting the users’ adoption of HRIS. The model was empirically tested using the structure equation modelling by employing data collected from a survey of HRIS users. The findings and discussion presented in this study will help the Saudi organisations to understand the current status of their HRIS to improve it and benefit from it. This study provides guidelines for its implementation for theory and practices, limitations and future directions.
A survey of RDF data management systems

RDF is increasingly being used to encode data for the semantic web and data exchange. There have been a large number of works that address RDF data management following different approaches. In this paper we provide an overview of these works. This review considers centralized solutions (what are referred to as warehousing approaches), distributed solutions, and the techniques that have been developed for querying linked data. In each category, further classifications are provided that would assist readers in understanding the identifying characteristics of different approaches.
Learning Bayesian Belief Network Classifiers: Algorithms and System

This paper investigates the methods for learning Bayesian belief network (BN) based predictive models for classification. Our primary interests are in the unrestricted Bayesian network and Bayesian multi-net based classifiers. We present our algorithms for learning these classifiers and also the methods for fighting the overfitting problem. A natural method for feature subset selection is also studied. Using a set of standard classification problems, we empirically evaluate the performance of various BN based classifiers. The results show that the proposed BN and Bayes multi-net classifiers are competitive with (or superior to) the best known classifiers, based on both BN and other formalisms; and that the computational time for learning and using these classifiers is relatively small. We also briefly introduce our BN classifier learning system – BN PowerPredictor. We argue that BN based classifiers deserve more attention in the data mining community.
Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola

The least-squares "tting minimizes the squares sum of error-of-"t in prede"ned measures. By the geometric "tting, the error distances are de"ned with the orthogonal, or shortest, distances from the given points to the geometric feature to be "tted. For the geometric "tting of circle/sphere/ellipse/hyperbola/parabola, simple and robust nonparametric algorithms are proposed. These are based on the coordinate description of the corresponding point on the geometric feature for the given point, where the connecting line of the two points is the shortest path from the given point to the geometric feature to be "tted. 2001 Pattern Recognition Society.
A Survey on Different File System Approach

This paper, provide survey of the proposed namespace management schemes for file system. Namespace management can be used to reduce exhaustive search over all directories. Namespace using semantic correlation can also increase search ability. File system namespace as an information organizing infrastructure is a help to improve system’s quality of service such as performance, scalability, and ease of use. This paper discusses the improvement to be made for future proposed namespace schemes. This paper provides reader with the basis for research in namespace schemes for file system.
Super-resolution through neighbor embedding

In this paper, we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its high-resolution counterpart using a set of training examples. While this formulation resembles other learning-based methods for super-resolution, our method has been inspired by recent manifold teaming methods, particularly locally linear embedding (LLE). Specifically, small image patches in the lowand high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is very flexible and gives good empirical results.
A Cautionary Note Regarding Evaluation of AES Candidates on Smart-Cards

NIST has considered the performance of AES candidates on smart-cards as an important selection criterion and many submitters have highlighted the compactness and e ciency of their submission on low end smart cards. However, in light of recently discovered power based attacks, we strongly argue that evaluating smart-card suitability of AES candidates requires a very cautious approach. We demonstrate that straightforward implementations of AES candidates on smart cards, are highly vulnerable to power analysis and readily leak away all secret keys. To illustrate our point, we describe a power based attack on the Two sh Reference 6805 code which we implemented on a ST16 smart card. The attack required power samples from only 100 independent block encryptions to fully recover the 128-bit secret key. We also describe how all other AES candidates are susceptible to similar attacks. We review the basis of power attacks and suggest countermeasures for a secure implementation. Unfortunately, it appears that these software countermeasures result in signi cant memory and e ciency overhead and therefore the most e cient smart card implementation cannot serve as a guide in evaluating AES candidates.
Fuzzy Logic-Based Natural Language Processing and Its Application to Speech Recognition

In this paper we describe a fuzzy logic-based language processing method, which is applied to speech recognition. Our purpose is to create a system that can learn from a linguistic corpus the fuzzy semantic relations between the concepts represented by words and use such relations to process the word sequences generated by speech recognition systems. In particular, the system will be able to predict the words failed to be recognized by a speech recognition system. This will help to increase the accuracy of a speech recognition system. This will also serve as the first stage of deep semantic processing of speech recognition results by providing “semantic relatedness” between the recognized words. We report the fuzzy inference rule learning system, which we have developed and also report the experimental results based on the system.  Fuzzy logic, Natural Language Analysis, Speech Recognition, Corpus Linguistics.
Ear Biometrics Based on Geometrical Feature Extraction

Biometrics identification methods proved to be very efficient, more natural and easy for users than traditional methods of human identification. In fact, only biometrics methods truly identify humans, not keys and cards they posses or passwords they should remember. The future of biometrics will surely lead to systems based on image analysis as the data acquisition is very simple and requires only cameras, scanners or sensors. More importantly such methods could be passive, which means that the user does not have to take active part in the whole process or, in fact, would not even know that the process of identification takes place. There are many possible data sources for human identification systems, but the physiological biometrics seem to have many advantages over methods based on human behaviour. The most interesting human anatomical parts for such passive, physiological biometrics systems based on images acquired from cameras are face and ear. Both of those methods contain large volume of unique features that allow to distinctively identify many users and will be surely implemented into efficient biometrics systems for many applications. The article introduces to ear biometrics and presents its advantages over face biometrics in passive human identification systems. Then the geometrical method of feature extraction from human ear images in order to perform human identification is presented.
The Influence of Culture on Memory

The study of cognition across cultures offers a useful approach to both identifying bottlenecks in information processing and suggesting culturespecific strategies to alleviate these limitations. The recent emphasis on applying cognitive neuroscience methods to the study of culture further aids in specifying which processes differ cross-culturally. By localizing cultural differences to distinct neural regions, the comparison of cultural groups helps to identify candidate information processing mechanisms that can be made more efficient with augmented cognition and highlights the unique solutions that will be required for different groups of information processors.
Supplementary Material : Video Summarization with Long Short-term Memory

– Section 1: converting between different formats of ground-truth annotations (Section 3.1 in the main text) – Section 2: details of the datasets (Section 4.1 in the main text) – Section 3: details of our LSTM-based models, including the learning objective for dppLSTM and the generating process of shot-based summaries for both vsLSTM and dppLSTM (Section 3.4 and 3.5 in the main text) – Section 4: comparing different network structures for dppLSTM (Section 3.4 in the main text) – Section 5: Other implementation details – Section 6: Additional discussions on video summarization
On the Effectiveness of Minimal Context Selection for Robust Question Answering

Machine learning models for question-answering (QA), where given a question 1 and a passage, the learner must select some span in the passage as an answer, are 2 known to be brittle. By inserting a single nuisance sentence into the passage, an 3 adversary can fool the model into selecting the wrong span. A promising new 4 approach for QA decomposes the task into two stages: (i) select relevant sentences 5 from the passage; and (ii) select a span among those sentences. Intuitively, if 6 the sentence selector excludes the offending sentence, then the downstream span 7 selector will be robust. While recent work has hinted at the potential robustness 8 of two-stage QA, these methods have never, to our knowledge, been explicitly 9 combined with adversarial training. This paper offers a thorough empirical in10 vestigation of adversarial robustness, demonstrating that although the two-stage 11 approach lags behind single-stage span selection, adversarial training improves its 12 performance significantly, leading to an improvement of over 22 points in F1 score 13 over the adversarially-trained single-stage model.
Multiclass Posterior Probability Support Vector Machines

Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density estimator to calculate the posterior probabilities and is a binary classifier. We propose a neighbor-based density estimator and also extend the model to the multiclass case. Our bias-variance analysis shows that the decrease in error by PPSVM is due to a decrease in bias. On 20 benchmark data sets, we observe that PPSVM obtains accuracy results that are higher or comparable to those of canonical SVM using significantly fewer support vectors.
Semantic-Based Location Recommendation With Multimodal Venue Semantics

In recent years, we have witnessed a flourishing of location -based social networks. A well-formed representation of location knowledge is desired to cater to the need of location sensing, browsing, navigation and querying. In this paper, we aim to study the semantics of point-of-interest (POI) by exploiting the abundant heterogeneous user generated content (UGC) from different social networks. Our idea is to explore the text descriptions, photos, user check-in patterns, and venue context for location semantic similarity measurement. We argue that the venue semantics play an important role in user check-in behavior. Based on this argument, a unified POI recommendation algorithm is proposed by incorporating venue semantics as a regularizer. In addition to deriving user preference based on user-venue check-in information, we place special emphasis on location semantic similarity. Finally, we conduct a comprehensive performance evaluation of location semantic similarity and location recommendation over a real world dataset collected from Foursquare and Instagram. Experimental results show that the UGC information can well characterize the venue semantics, which help to improve the recommendation performance.
The smear layer in endodontics - a review.

Root canal instrumentation produces a layer of organic and inorganic material called the smear layer that may also contain bacteria and their by-products. It can prevent the penetration of intracanal medicaments into dentinal tubules and influence the adaptation of filling materials to canal walls. This article provides an overview of the smear layer, focusing on its relevance to endodontics. The PubMed database was used initially; the reference list for smear layer featured 1277 articles, and for both smear layer dentine and smear layer root canal revealed 1455 publications. Smear layer endodontics disclosed 408 papers. A forward search was undertaken on selected articles and using some author names. Potentially relevant material was also sought in contemporary endodontic texts, whilst older books revealed historic information and primary research not found electronically, such that this paper does not represent a 'classical' review. Data obtained suggests that smear layer removal should enhance canal disinfection. Current methods of smear removal include chemical, ultrasonic and laser techniques - none of which are totally effective throughout the length of all canals or are universally accepted. If smear is to be removed, the method of choice seems to be the alternate use of ethylenediaminetetraacetic acid and sodium hypochlorite solutions. Conflict remains regarding the removal of the smear layer before filling root canals, with investigations required to determine the role of the smear layer in the outcomes of root canal treatment.
Vision-Based State Estimation and Trajectory Control Towards High-Speed Flight with a Quadrotor

This paper addresses the development of a lightweight autonomous quadrotor that uses cameras and an inexpensive IMU as its only sensors and onboard processors for estimation and control. We describe a fully-functional, integrated system with a focus on robust visual-inertial state estimation, and demonstrate the quadrotor’s ability to autonomously travel at speeds up to 4 m/s and roll and pitch angles exceeding 20◦. The performance of the proposed system is demonstrated via challenging experiments in three dimensional indoor environments.
Management Dimension for Assessing Organizations' Readiness toward Business Intelligence Systems

The improvements and capabilities of Business Intelligence (BI) systems are making them highly required by organizations in many of the fields. Day by day, the demand is increasing on these systems. Figures show that billions of US dollars are spent annually on BI systems. However, half of these BI projects are ending with unrealized benefit, according to some studies. As a participation to reduce this failure rate, the authors have developed a conceptual model, based on the existing studies in the field, which may help organizations to assess their readiness toward BI systems. The conceptual model is tested empirically on the Context of Malaysian Organizations(CMOs) as they are emerging widely to BI systems and they lack for this kind of studies to be done them. The conceptual model is consisting of seven dimensions. Depending on the results of the conducted study, this paper will disclose the results of management dimension, which will help to know the degree of its importance for organizations' to be ready for such systems. A brief introduction on BI systems and the challenges of their implementation will be presented. In Addition, this paper will go through some important studies have been done in the field. The details of the study will be provided as well.
Using association rules to guide a search for best fitting transfer models of student learning

We say a transfer model is a mapping between the questions in an intelligent tutoring system and the knowledge components (i.e., skills, strategies, declarative knowledge, etc) needed to answer a question correctly.  showed how you could take advantage of 1) the Power Law Of Learning, 2) an existing transfer model, and 3) set of tutorial log files, to learn a function (using logistic regression) that will predict when a student will get a question correct. In the main conference proceeding [CHK2004] give an example of using this technique for transfer model selection. Koedinger and Junker [KJ99] also conceptualized a search space where each state is a new transfer model. The operators in this search space split, add or merge knowledge components based upon factors that are tagged to questions. Koedinger and Junker called this method learning factors analysis emphasizing that this method can be used to study learning. Unfortunately, the search space is huge and searching for good fitting transfer models is exponential. The main goal of this paper is show a technique that will make searching for transfer models more efficient. Our procedure implements a search method using association rules as a means of guiding the search. The association rules are mined from a dataset derived from student-tutor interaction logs. The association rules found in the mining process determine what operations to perform on the current transfer model. We report on the speed up achieved. Being able to find good transfer models quicker will help intelligent tutor system builders as well as cognitive science researchers better assess what makes certain problems hard and other problems easy for students.
Operations research/management contributions to emergency department patient flow optimization: Review and research prospects

Operations research/management contributions to emergency department patient flow optimization: Review and research prospects Soroush Saghafian, Garrett Austin & Stephen J. Traub a Industrial Engineering, School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA b Department of Emergency Medicine, Mayo Clinic, Phoenix, AZ, 85054, USA Published online: 09 Jun 2015.
Blind detection for MIMO systems with low-resolution ADCs using supervised learning

This paper considers a multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we propose a novel detection framework that performs data symbol detection without explicitly knowing channel state information at a receiver. The underlying idea of the proposed framework is to exploit supervised learning. Specifically, during channel training, the proposed approach sends a sequence of data symbols as pilots so that the receiver learns a nonlinear function that is determined by both a channel matrix and a quantization function of the ADCs. During data transmission, the receiver uses the learned nonlinear function to detect which data symbols were transmitted. In this context, we propose two blind detection methods to determine the nonlinear function from the training-data set. We also provide an analytical expression for the symbol-vector-error probability of the MIMO systems with one-bit ADCs when employing the proposed framework. Simulations demonstrate the performance improvement of the proposed framework compared to existing detection techniques.