nils-herrmann/clean_scirepeval huggingface dataset:
Design of Chebyshev-Type IIR Filters with Approximately Linear Phase Characteristics

あらまし 直線位相特性を有するディジタルフィルタは信号処理の多くの応用において必要である.本論文で は,通過域に近似的直線位相特性を有するチェビシェフ型 IIRフィルタの設計について述べる.まず,阻止域の 指定された周波数点に多重零点を配置することにより,フィルタの平たんな阻止域が容易に実現できることを示 す.次に,通過域に複素 Remez アルゴリズムを適用し,フィルタの設計問題を固有値問題として定式化する. よって,固有値問題を解くことにより,フィルタ係数が容易に求められる.更に,反復計算を行い,通過域にお ける誤差関数の等リプル特性を得る.最後に,提案したチェビシェフ型フィルタと遅延器を並列接続し,近似的 直線位相特性を有する逆チェビシェフ型 IIRフィルタも同時に得られることを示す. キーワード IIR ディジタルフィルタ,チェビシェフ型フィルタ,近似的直線位相特性,固有値問題,複素 Remezアルゴリズム
Shift-Reduce CCG Parsing using Neural Network Models

We present a neural network based shiftreduce CCG parser, the first neural-network based parser for CCG. We also study the impact of neural network based tagging models, and greedy versus beam-search parsing, by using a structured neural network model. Our greedy parser obtains a labeled F-score of 83.27%, the best reported result for greedy CCG parsing in the literature (an improvement of 2.5% over a perceptron based greedy parser) and is more than three times faster. With a beam, our structured neural network model gives a labeled F-score of 85.57% which is 0.6% better than the perceptron based counterpart.
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings

We study a symmetric collaborative dialogue setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.
PixelGAN Autoencoders

In this paper, we describe the “PixelGAN autoencoder”, a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code. We show that different priors result in different decompositions of information between the latent code and the autoregressive decoder. For example, by imposing a Gaussian distribution as the prior, we can achieve a global vs. local decomposition, or by imposing a categorical distribution as the prior, we can disentangle the style and content information of images in an unsupervised fashion. We further show how the PixelGAN autoencoder with a categorical prior can be directly used in semi-supervised settings and achieve competitive semi-supervised classification results on the MNIST, SVHN and NORB datasets.
Age, Gender, and Fine-Grained Ethnicity Prediction Using Convolutional Neural Networks for the East Asian Face Dataset

This paper explores the difficulty of performing automatic demographic prediction on the East Asian population. We introduce the Wild East Asian Face Dataset (WEAFD), a new and unique dataset, to the research community. This dataset consists primarily of labeled face images of individuals from East Asian countries, including Vietnam, Burma, Thailand, China, Korea, Japan, Indonesia, and Malaysia. East Asian Amazon Mechanical Turk annotators were used to label the age, gender and fine grain ethnicity attributes to reduce the impact of the “other-race effect” and improve quality of annotations. We focus on predicting age, gender and fine-grained ethnicity of an individual by providing baseline results using a convolutional neural network (CNN). Fine-grained ethnicity prediction refers to predicting refined categorization of the human population (Chinese, Japanese, Korean, etc.). Performance of two CNN architectures is presented, highlighting the difficulty of these tasks and showcasing potential design considerations that improve network optimization by promoting region based feature extraction.
Network Random KeysA Tree Representation Scheme for Genetic and Evolutionary Algorithms

When using genetic and evolutionary algorithms for network design, choosing a good representation scheme for the construction of the genotype is important for algorithm performance. One of the most common representation schemes for networks is the characteristic vector representation. However, with encoding trees, and using crossover and mutation, invalid individuals occur that are either under or overspecified. When constructing the offspring or repairing the invalid individuals that do not represent a tree, it is impossible to distinguish between the importance of the links that should be used. These problems can be overcome by transferring the concept of random keys from scheduling and ordering problems to the encoding of trees. This paper investigates the performance of a simple genetic algorithm (SGA) using network random keys (NetKeys) for the one-max tree and a real-world problem. The comparison between the network random keys and the characteristic vector encoding shows that despite the effects of stealth mutation, which favors the characteristic vector representation, selectorecombinative SGAs with NetKeys have some advantages for small and easy optimization problems. With more complex problems, SGAs with network random keys significantly outperform SGAs using characteristic vectors. This paper shows that random keys can be used for the encoding of trees, and that genetic algorithms using network random keys are able to solve complex tree problems much faster than when using the characteristic vector. Users should therefore be encouraged to use network random keys for the representation of trees.
Hyper-heuristics: a survey of the state of the art

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.  2013; doi:10.1057/jors.2013.71
Investigating the contribution of short wavelengths in the alerting effect of bright light

Short-wavelengths can have an acute impact on alertness, which is allegedly due to their action on intrinsically photosensitive retinal ganglion cells. Classical photoreceptors cannot, however, be excluded at this point in time as contributors to the alerting effect of light. The objective of this study was to compare the alerting effect at night of a white LED light source while wearing blue-blockers or not, in order to establish the contribution of short-wavelengths.



20 participants stayed awake under dim light (< 5 lx) from 23:00 h to 04:00 h on two consecutive nights. On the second night, participants were randomly assigned to one light condition for 30 min starting at 3:00 h. Group A (5M/5F) was exposed to 500 μW/cm(2) of unfiltered LED light, while group B (4M/6F) was required to wear blue-blocking glasses, while exposed to 1500 μW/cm(2) from the same light device in order to achieve 500 μW/cm(2) at eye level (as measured behind the glasses). Subjective alertness, energy, mood and anxiety were assessed for both nights at 23:30 h, 01:30 h and 03:30 h using a visual analog scale (VAS). Subjective sleepiness was assessed with the Stanford Sleepiness Scale (SSS). Subjects also performed the Conners' Continuous Performance Test II (CPT-II) in order to assess objective alertness. Mixed model analysis was used to compare VAS, SSS and CPT-II parameters.



No difference between group A and group B was observed for subjective alertness, energy, mood, anxiety and sleepiness, as well as CPT-II parameters. Subjective alertness (p < 0.001), energy (p < 0.001) and sleepiness (p < 0.05) were, however improved after light exposure on the second night independently of the light condition.



The current study shows that when sleepiness is high, the alerting effect of light can still be triggered at night in the absence of short-wavelengths with a 30 minute light pulse of 500 μW/cm(2). This suggests that the underlying mechanism by which a brief polychromatic light exposure improves alertness is not solely due to short-wavelengths through intrinsically photosensitive retinal ganglion cells.
Predicción del Fracaso Escolar Mediante Técnicas de Minería de Datos

This paper proposes to apply data mining techniques to predict school failure and drop out. We use real data on 670 middle-school students from Zacatecas, México and employ white-box classification methods such as induction rules and decision trees. Experiments attempt to improve their accuracy for predicting which students might fail or drop out by: firstly, using all the available attributes; next, selecting the best attributes; and finally, rebalancing data, and using cost sensitive classification. The outcomes have been compared and the best resulting models are shown.
Big-ETL : Extracting-Transforming-Loading Approach for Big Data

ETL process (Extracting-Transforming-Loading) is responsible for (E)xtracting data from heterogeneous sources, (T)ransforming and finally (L)oading them into a data warehouse (DW). Nowadays, Internet and Web 2.0 are generating data at an increasing rate, and therefore put the information systems (IS) face to the challenge of big data. Data integration systems and ETL, in particular, should be revisited and adapted and the well-known solution is based on the data distribution and the parallel/distributed processing. Among all the dimensions defining the complexity of the big data, we focus in this paper on its excessive "volume" in order to ensure good performance for ETL processes. In this context, we propose an original approach called Big-ETL (ETL Approach for Big Data) in which we define ETL functionalities that can be run easily on a cluster of computers with MapReduce (MR) paradigm. Big-ETL allows, thereby, parallelizing/distributing ETL at two levels: (i) the ETL process level (coarse granularity level), and (ii) the functionality level (fine level); this allows improving further the ETL performance.
Business process analysis in advertising: An extension to a methodology based on process mining projects

Nowadays organizations generate large amount of data. Only a few make a good use to optimize the performance of the business. Process mining appears as a branch of the data science that tries to understand the actual operational processes in the organizations through different algorithms, allowing the discovery of process models to give insight of the processes and understand how they can be improved. In this work different process mining techniques are applied to a company dedicated to the advertisement market, specifically the process of dealing with contract issues with customers. The Process Mining Project Methodology was followed to execute a case study. Additional to the basic methodology, elements from the others areas of studies were added to generate better results and have a better understanding of the problem. The case study includes three scenarios with three different hypotheses that were validated through our method.
Automatic grasp planning using shape primitives

Automatic grasp planning for robotic hands is a difficult problem because of the huge number of possible hand configurations. However, humans simplify the problem by choosing an appropriate prehensile posture appropriate for the object and task to be performed. By modeling an object as a set of shape primitives, such as spheres, cylinders, cones and boxes, we can use a set of rules to generate a set of grasp starting positions and pregrasp shapes that can then be tested on the object model. Each grasp is tested and evaluated within our grasping simulator “GraspIt!”, and the best grasps are presented to the user. The simulator can also plan grasps in a complex environment involving obstacles and the reachability constraints of a robot arm.
Robust speaker recognition based on DNN/i-vectors and speech separation

Recent research shows that the i-vector framework for speaker recognition can significantly benefit from phonetic information. A common approach is to use a deep neural network (DNN) trained for automatic speech recognition to generate a universal background model (UBM). Studies in this area have been done in relatively clean conditions. However, strong background noise is known to severely reduce speaker recognition performance. This study investigates a phonetically-aware i-vector system in noisy conditions. We propose a front-end to tackle the noise problem by performing speech separation and examine its performance for both verification and identification tasks. The proposed separation system trains a DNN to estimate the ideal ratio mask of the noisy speech. The separated speech is then used to extract enhanced features for the i-vector framework. We compare the proposed system against a multi-condition trained baseline and a traditional GMM-UBM i-vector system. Our proposed system provides an absolute average improvement of 8% in identification accuracy and 1.2% in equal error rate.
The E2E Dataset: New Challenges For End-to-End Generation

This paper describes the E2E data, a new dataset for training end-to-end, datadriven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances. We also establish a baseline on this dataset, which illustrates some of the difficulties associated with this data.
Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation

We propose a novel segmentation approach based on deep convolutional encoder networks and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that has both convolutional and deconvolutional layers, and combines feature extraction and segmentation prediction in a single model. The joint training of the feature extraction and prediction layers allows the model to automatically learn features that are optimized for accuracy for any given combination of image types. In contrast to existing automatic feature learning approaches, which are typically patch-based, our model learns features from entire images, which eliminates patch selection and redundant calculations at the overlap of neighboring patches and thereby speeds up the training. Our network also uses a novel objective function that works well for segmenting underrepresented classes, such as MS lesions. We have evaluated our method on the publicly available labeled cases from the MS lesion segmentation challenge 2008 data set, showing that our method performs comparably to the state-of-theart. In addition, we have evaluated our method on the images of 500 subjects from an MS clinical trial and varied the number of training samples from 5 to 250 to show that the segmentation performance can be greatly improved by having a representative data set.
Im2Calories: Towards an Automated Mobile Vision Food Diary

We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories. The simplest version assumes that the user is eating at a restaurant for which we know the menu. In this case, we can collect images offline to train a multi-label classifier. At run time, we apply the classifier (running on your phone) to predict which foods are present in your meal, and we lookup the corresponding nutritional facts. We apply this method to a new dataset of images from 23 different restaurants, using a CNN-based classifier, significantly outperforming previous work. The more challenging setting works outside of restaurants. In this case, we need to estimate the size of the foods, as well as their labels. This requires solving segmentation and depth / volume estimation from a single image. We present CNN-based approaches to these problems, with promising preliminary results.
The Evolutionary Origins of Hierarchy

Hierarchical organization-the recursive composition of sub-modules-is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force-the cost of connections-promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.
Perception of midline deviations in smile esthetics by
laypersons

To evaluate the esthetic perception of upper dental midline deviation by laypersons and if adjacent structures influence their judgment.



An album with 12 randomly distributed frontal view photographs of the smile of a woman with the midline digitally deviated was evaluated by 95 laypersons. The frontal view smiling photograph was modified to create from 1 mm to 5 mm deviations in the upper midline to the left side. The photographs were cropped in two different manners and divided into two groups of six photographs each: group LCN included the lips, chin, and two-thirds of the nose, and group L included the lips only. The laypersons performed the rate of each smile using a visual analog scale (VAS). Wilcoxon test, Student's t-test and Mann-Whitney test were applied, adopting a 5% level of significance.



Laypersons were able to perceive midline deviations starting at 1 mm. Statistically significant results (p< 0.05) were found for all multiple comparisons of the values in photographs of group LCN and for almost all comparisons in photographs of group L. Comparisons between the photographs of groups LCN and L showed statistically significant values (p< 0.05) when the deviation was 1 mm.



Laypersons were able to perceive the upper dental midline deviations of 1 mm, and above when the adjacent structures of the smiles were included. Deviations of 2 mm and above when the lips only were included. The visualization of structures adjacent to the smile demonstrated influence on the perception of midline deviation.
A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations

Evidence-based dietary information represented as unstructured text is a crucial information that needs to be accessed in order to help dietitians follow the new knowledge arrives daily with newly published scientific reports. Different named-entity recognition (NER) methods have been introduced previously to extract useful information from the biomedical literature. They are focused on, for example extracting gene mentions, proteins mentions, relationships between genes and proteins, chemical concepts and relationships between drugs and diseases. In this paper, we present a novel NER method, called drNER, for knowledge extraction of evidence-based dietary information. To the best of our knowledge this is the first attempt at extracting dietary concepts. DrNER is a rule-based NER that consists of two phases. The first one involves the detection and determination of the entities mention, and the second one involves the selection and extraction of the entities. We evaluate the method by using text corpora from heterogeneous sources, including text from several scientifically validated web sites and text from scientific publications. Evaluation of the method showed that drNER gives good results and can be used for knowledge extraction of evidence-based dietary recommendations.
Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster–Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the extent of its deviation from good pattern. Shafer’s theory is used to combine multiple such evidences and an initial belief is computed. The transaction is classified as normal, abnormal or suspicious depending on this initial belief. Once a transaction is found to be suspicious, belief is further strengthened or weakened according to its similarity with fraudulent or genuine transaction history using Bayesian learning. Extensive simulation with stochastic models shows that fusion of different evidences has a very high positive impact on the performance of a credit card fraud detection system as compared to other methods.