nils-herrmann/clean_scirepeval huggingface dataset:
Monotonic Chunkwise Attention

Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction. To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed. We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time. When applied to online speech recognition, we obtain state-of-theart results and match the performance of a model using an offline soft attention mechanism. In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model.
Substrate-Integrated-Waveguide Circulators Suitable for Millimeter-Wave Integration

Over the years, many millimeter-wave circulator techniques have been presented, such as nonradiative dielectric and fin-line circulators. Although excellent results have been demonstrated in the literature, their proliferation in commercial devices has been hindered by complex assembly cost. This paper presents a study of substrate-integrated millimeter-wave degree-2 circulators. Although the substrate integrated-circuits technique may be applied to virtually any planar transmission medium, the one adopted in this paper is the substrate integrated waveguide (SIW). Two design configurations are possible: a planar one that is suitable for thin substrate materials and a turnstile one for thicker substrate materials. The turnstile circulator is ideal for systems where the conductor losses associated with the thin SIW cannot be tolerated. The design methodology adopted in this paper is to characterize the complex gyrator circuit as a preamble to design. This is done via a commercial finite-element package
The BigChaos Solution to the Netflix Prize 2008

The team “BellKor in BigChaos” is a combined team of team BellKor and BigChaos. The solution with a RMSE of 0.8616 is created by a linear blend of the results from both teams. In the following paper we describe the results of BigChaos. 1 Preface During the last 2 years of research we tried a variety of different collaborative filtering algorithms. In the following we describe all methods which turned out to be useful for the Netflix Prize competition. In sections 4 to 18 we describe all algorithms used to produce our predictions, listed in section 20. We minimized the RMSE of each individual predictor on the probe set. Whenever possible, we therefore take a simple automatic parameter tuner described in section 3 to tune the meta parameters. Details are reported in the section describing the individual algorithm, and for each predictor, the meta parameters used are reported in section 20. If an algorithm has been trained on the residuals of another, we note that explicitly; others are based on raw ratings. Exact details can again be found in the predictor listing in section 20. Here we note for every predictor, if it was based on the residuals of another algorithm. 2 Notation Throughout the document we keep the following notation. A user is denoted with u and item/movie with i. The rating given by user u to item i is denoted by rui. Predictions for the rating rui are denoted with r̂ui. N(i) stands for the set of users who voted the item i, and N(u) is the set of items voted by the user u. N(u, i) is the set of the K most similar items to item i which were rated by user u and is therefore a subset of N(u). The learning rate is called η and the regularization λ. Constants like α, β, γ, δ θ, θ, ... can have different meanings for every algorithm. 3 Parameter Tuner In several algorithms like in Section 5, there is a need for tuning parameters, in order to minimize the RMSE of a particular predictor. We use two algorithms to automatically tune them. The target is to adjust N parameters pi, to minimize the RMSE on the probe set with the error of a given set of parameters being denoted as E(p1, p2, ..., pN ). In general, finding good parameters is a mixture of good manual setting and some automatic fine tuning. Both methods described below, are sensitive to the initialization values of pi. They are not guaranteed to find a global optimum, and can easily get stuck in local optima. So starting with a random initialization of the parameters does not guarantee to get the best solution. To ensure that all our results are reproducible, we report all found parameter values for each individual predictor in section 20. Note that the reported values are these, which we found and used. For most algorithms there may be a much better parameterization.
Broadside-coupled multi-octave impedance transformer

This paper presents a novel structure for ultra broadband 4:1 broadside-coupled PCB impedance transformer. Analysis, simulations and measurements of the developed transformer are introduced and discussed. Three prototypes of the proposed structure are implemented at center frequencies 5.65 GHz, 4.35 GHz and 3.65 GHz, respectively with fractional bandwidth of greater than 180 %. The implemented transformers show an ultra broadband performance with a transmission loss less than 1 dB and return loss at least 10 dB across the desired bandwidth. During comparison, simulations and measurements are found very close to each other. To the author's best knowledge the achieved performance of the designed transformer is better than so far published state of the art results.
The Linux kernel as a case study in software evolution

We use 810 versions of the Linux kernel, released over a perio d of 14 years, to characterize the system’s evolution, using Lehman’s laws of software evolut i n as a basis. We investigate different possible interpretations of these laws, as reflected by diff erent metrics that can be used to quantify them. For example, system growth has traditionally been qua tified using lines of code or number of functions, but functional growth of an operating system l ike Linux can also be quantified using the number of system calls. In addition we use the availabili ty of the source code to track metrics, such as McCabe’s cyclomatic complexity, that have not been tr acked across so many versions previously. We find that the data supports several of Lehman’ s l ws, mainly those concerned with growth and with the stability of the process. We also make som e novel observations, e.g. that the average complexity of functions is decreasing with time, bu t this is mainly due to the addition of many small functions.
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models

This paper addresses the general problem of reinforcement learning (RL) in partially observable environments. In 2013, our large RL recurrent neural networks (RNNs) learned from scratch to drive simulated cars from high-dimensional video input. However, real brains are more powerful in many ways. In particular, they learn a predictive model of their initially unknown environment, and somehow use it for abstract (e.g., hierarchical) planning and reasoning. Guided by algorithmic information theory, we describe RNN-based AIs (RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending sequences of tasks, some of them provided by the user, others invented by the RNNAI itself in a curious, playful fashion, to improve its RNN-based world model. Unlike our previous model-building RNN-based RL machines dating back to 1990, the RNNAI learns to actively query its model for abstract reasoning and planning and decision making, essentially “learning to think.” The basic ideas of this report can be applied to many other cases where one RNN-like system exploits the algorithmic information content of another. They are taken from a grant proposal submitted in Fall 2014, and also explain concepts such as “mirror neurons.” Experimental results will be described in separate papers. 1 ar X iv :1 51 1. 09 24 9v 1 [ cs .A I] 3 0 N ov 2 01 5
Exploiting hierarchical domain structure to compute similarity

The notion of similarity between objects finds use in many contexts, for example, in search engines, collaborative filtering, and clustering. Objects being compared often are modeled as sets, with their similarity traditionally determined based on set intersection. Intersection-based measures do not accurately capture similarity in certain domains, such as when the data is sparse or when there are known relationships between items within sets. We propose new measures that exploit a hierarchical domain structure in order to produce more intuitive similarity scores. We extend our similarity measures to provide appropriate results in the presence of multisets (also handled unsatisfactorily by traditional measures), for example, to correctly compute the similarity between customers who buy several instances of the same product (say milk), or who buy several products in the same category (say dairy products). We also provide an experimental comparison of our measures against traditional similarity measures, and report on a user study that evaluated how well our measures match human intuition.
An Assessment of Name Matching Algorithms

In many computer applications involving the recording and processing of personal data there is a need to allow for variations in surname spelling, caused for example by transcription errors. A number of algorithms have been developed for name matching, i.e. which attempt to identify name spelling variations, one of the best known of which is the Soundex algorithm. This paper describes a comparative analysis of a number of these algorithms and, based on an analysis of their comparative strengths and weaknesses, proposes a new and improved name matching algorithm, which we call the Phonex algorithm. The analysis takes advantage of the recent creation of a large list of “equivalent surnames”, published in the book Family History Knowledge UK [Park1992]. This list is based on data supplied by some thousands of individual genealogists, and can be presumed to be representative of British surnames and their variations over the last two or three centuries. It thus made it possible to perform what we would argue were objective tests of name matching, the results of which provide a solid basis for the analysis that we have performed, and for our claims for the merits of the new algorithm, though these are unlikely to hold fully for surnames emanating largely from other countries.
Flexible End-to-End Dialogue System for Knowledge Grounded Conversation

In knowledge grounded conversation, domain knowledge plays an important role in a special domain such as Music. The response of knowledge grounded conversation might contain multiple answer entities or no entity at all. Although existing generative question answering (QA) systems can be applied to knowledge grounded conversation, they either have at most one entity in a response or cannot deal with out-ofvocabulary entities. We propose a fully data-driven generative dialogue system GenDS that is capable of generating responses based on input message and related knowledge base (KB). To generate arbitrary number of answer entities even when these entities never appear in the training set, we design a dynamic knowledge enquirer which selects different answer entities at different positions in a single response, according to different local context. It does not rely on the representations of entities, enabling our model deal with out-ofvocabulary entities. We collect a human-human conversation data (ConversMusic) with knowledge annotations. The proposed method is evaluated on CoversMusic and a public question answering dataset. Our proposed GenDS system outperforms baseline methods significantly in terms of the BLEU, entity accuracy, entity recall and human evaluation. Moreover,the experiments also demonstrate that GenDS works better even on small datasets.
Robust Physical-World Attacks on Deep Learning Visual Classification

Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. Given that that emerging physical systems are using DNNs in safety-critical situations, adversarial examples could mislead these systems and cause dangerous situations. Therefore, understanding adversarial examples in the physical world is an important step towards developing resilient learning algorithms. We propose a general attack algorithm, Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions. Using the real-world case of road sign classification, we show that adversarial examples generated using RP2 achieve high targeted misclassification rates against standard-architecture road sign classifiers in the physical world under various environmental conditions, including viewpoints. Due to the current lack of a standardized testing method, we propose a two-stage evaluation methodology for robust physical adversarial examples consisting of lab and field tests. Using this methodology, we evaluate the efficacy of physical adversarial manipulations on real objects. With a perturbation in the form of only black and white stickers, we attack a real stop sign, causing targeted misclassification in 100% of the images obtained in lab settings, and in 84.8% of the captured video frames obtained on a moving vehicle (field test) for the target classifier.
Association between Facebook Dependence and Poor Sleep Quality: A Study in a Sample of Undergraduate Students in Peru

Internet can accelerate information exchange. Social networks are the most accessed especially Facebook. This kind of networks might create dependency with several negative consequences in people's life. The aim of this study was to assess potential association between Facebook dependence and poor sleep quality.



A cross sectional study was performed enrolling undergraduate students of the Universidad Peruana de Ciencias Aplicadas, Lima, Peru. The Internet Addiction Questionnaire, adapted to the Facebook case, and the Pittsburgh Sleep Quality Index, were used. A global score of 6 or greater was defined as the cutoff to determine poor sleep quality. Generalized linear model were used to determine prevalence ratios (PR) and 95% confidence intervals (95%CI). A total of 418 students were analyzed; of them, 322 (77.0%) were women, with a mean age of 20.1 (SD: 2.5) years. Facebook dependence was found in 8.6% (95% CI: 5.9%-11.3%), whereas poor sleep quality was present in 55.0% (95% CI: 50.2%-59.8%). A significant association between Facebook dependence and poor sleep quality mainly explained by daytime dysfunction was found (PR = 1.31; IC95%: 1.04-1.67) after adjusting for age, sex and years in the faculty.



There is a relationship between Facebook dependence and poor quality of sleep. More than half of students reported poor sleep quality. Strategies to moderate the use of this social network and to improve sleep quality in this population are needed.
Factors hindering acceptance of using cloud services in university: A case study

The aim of this paper is to propose a novel evaluation framework to explore the “root causes” that hinder the acceptance of using internal cloud services in a university.  The proposed evaluation framework incorporates the duo-theme DEMATEL (decision making trial and evaluation laboratory) with TAM (technology acceptance model). The operational procedures were proposed and tested on a university during the post-implementation phase after introducing the internal cloud services.  According to the results, clear understanding and operational ease under the theme perceived ease of use (PEOU) are more imperative; whereas improved usefulness and productivity under the theme perceived usefulness (PU) are more urgent to foster the usage of internal clouds in the case university.  Based on the findings, some intervention activities were suggested to enhance the level of users’ acceptance of internal cloud solutions in the case university. However, the results should not be generalized to apply to other educational establishments.  To reduce the resistance from using internal clouds, some necessary intervention activities such as developing attractive training programs, creating interesting workshops, and rewriting user friendly manual or handbook are recommended.  The novel two-theme DEMATEL has greatly contributed to the conventional one-theme DEMATEL theory. The proposed two-theme DEMATEL procedures were the first attempt to evaluate the acceptance of using internal clouds in university. The results have provided manifest root-causes under two distinct themes, which help derive effectual intervention activities to foster the acceptance of usage of internal clouds in a university.
Privacy-awareness in Blockchain-based PKI

Conventional public key infrastructure (PKI) designs are not optimal and contain security flaws; there is much work underway in improving PKI. The properties given by the Bitcoin blockchain and its derivatives are a natural solution to some of the problems with PKI in particular, certificate transparency and elimination of single points of failure. Recently-proposed blockchain PKI designs are built as public ledgers linking identity with public key, giving no provision of privacy. We consider the suitability of a blockchain-based PKI for contexts in which PKI is required, but in which linking of identity with public key is undesirable; specifically, we show that blockchain can be used to construct a privacy-aware PKI while simultaneously eliminating some of the problems encountered in conventional PKI.
Imitating human playing styles in Super Mario Bros

We describe and compare several methods for generating game character controllers that mimic the playing style of a particular human player, or of a population of human players, across video game levels. Similarity in playing style is measured through an evaluation framework, that compares the play trace of one or several human players with the punctuated play trace of an AI player. The methods that are compared are either hand-coded, direct (based on supervised learning) or indirect (based on maximising a similarity measure). We find that a method based on neuroevolution performs best both in terms of the instrumental similarity measure and in phenomenological evaluation by human spectators. A version of the classic platform game “Super Mario Bros” is used as the testbed game in this study but the methods are applicable to other games that are based on character movement in space.
Cancellation of crosstalk-induced jitter

A novel jitter equalization circuit is presented that addresses crosstalk-induced jitter in high-speed serial links. A simple model of electromagnetic coupling demonstrates the generation of crosstalk-induced jitter. The analysis highlights unique aspects of crosstalk-induced jitter that differ from far-end crosstalk. The model is used to predict the crosstalk-induced jitter in 2-PAM and 4-PAM, which is compared to measurement. Furthermore, the model suggests an equalizer that compensates for the data-induced electromagnetic coupling between adjacent links and is suitable for pre- or post-emphasis schemes. The circuits are implemented using 130-nm MOSFETs and operate at 5-10 Gb/s. The results demonstrate reduced deterministic jitter and lower bit-error rate (BER). At 10 Gb/s, the crosstalk-induced jitter equalizer opens the eye at 10/sup -12/ BER from 17 to 45 ps and lowers the rms jitter from 8.7 to 6.3 ps.
A Simulation-Based Architecture for Smart Cyber-Physical Systems

In order to accurately predict future states of a smart cyber-physical system, which can change its behavior to a large degree in response to environmental influences, the existence of precise models of the system and its surroundings is demandable. In machine engineering, ultra-high fidelity simulations have been developed to better understand both constraints in system design and possible consequences of external influences during the system's operation. These digital twins enable further applications in software design for complex cyber-physical systems as online planning methods can utilize good simulations to continuously optimize the system behavior, yielding a software architecture framework based on the information flow between the cyber-physical system, its physical environment and the digital twin model.
XGBoost : Reliable Large-scale Tree Boosting System

Tree boosting is an important type of machine learning algorithms that is widely used in practice. In this paper, we describe XGBoost, a reliable, distributed machine learning system to scale up tree boosting algorithms. The system is optimized for fast parallel tree construction, and designed to be fault tolerant under the distributed setting. XGBoost can handle tens of millions of samples on a single node, and scales beyond billions of samples with distributed computing.
INTERNET OF THINGS : 1 A REVIEW OF APPLICATIONS & 2 TECHNOLOGIES

13 This paper presents the state-of-art of Internet of Things (IoT), an enabler of 14 new forms of communication between people and things, and between things. 15 The main strength of IoT concept is the high influence it has to everyday life 16 by creating new dimension to the world, resembling to what the Internet once 17 did. This paper describes the definitions of IoT, summarizes its main 18 enabling technologies. The content includes strengths and limitations of 19 applications based IoT in logistic, transportation, healthcare, and 20 environment and disaster. Finally, the open challenges of IoT are debated to 21 encourage more investigations into the domains. 22
Forest Species Recognition Using Deep Convolutional Neural Networks

Forest species recognition has been traditionally addressed as a texture classification problem, and explored using standard texture methods such as Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Gabor Filters. Deep learning techniques have been a recent focus of research for classification problems, with state-of-the art results for object recognition and other tasks, but are not yet widely used for texture problems. This paper investigates the usage of deep learning techniques, in particular Convolutional Neural Networks (CNN), for texture classification in two forest species datasets - one with macroscopic images and another with microscopic images. Given the higher resolution images of these problems, we present a method that is able to cope with the high-resolution texture images so as to achieve high accuracy and avoid the burden of training and defining an architecture with a large number of free parameters. On the first dataset, the proposed CNN-based method achieves 95.77% of accuracy, compared to state-of-the-art of 97.77%. On the dataset of microscopic images, it achieves 97.32%, beating the best published result of 93.2%.
BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram

The t-CWT, a novel method for feature extraction from biological signals, is introduced. It is based on the continuous wavelet transform (CWT) and Student's t-statistic. Applied to event-related brain potential (ERP) data in brain- computer interface (BCI) paradigms, the method provides fully automated detection and quantification of the ERP components that best discriminate between two samples of EEG signals and are, therefore, particularly suitable for classification of single-trial ERPs. A simple and fast CWT computation algorithm is proposed for the transformation of large data sets and single trials. The method was validated in the BCI Competition 2003 , where it was a winner (provided best classification) on two data sets acquired in two different BCI paradigms, P300 speller and slow cortical potential (SCP) self-regulation. These results are presented here.