nils-herrmann/clean_scirepeval huggingface dataset:
DIAC+: a Professional Diacritics Recovering System

In languages that use diacritical characters, if these special signs are stripped-off from a word, the resulted string of characters may not exist in the language, and therefore its normative form is, in general, easy to recover. However, this is not always the case, as presence or absence of a diacritical sign attached to a base letter of a word which exists in both variants, may change its grammatical properties or even the meaning, making the recovery of the missing diacritics a difficult task, not only for a program but sometimes even for a human reader. We describe and evaluate an accurate knowledge-based system for automatic recovering the missing diacritics in MSOffice documents written in Romanian. For the rare cases when the system is not able to reliably make a decision, it either provides the user a list of words with their recovery suggestions, or probabilistically choose one of the possible changes, but leaves a trace (a highlighted comment) on each word the modification of which was uncertain.
Intentional attunement: A neurophysiological perspective on social cognition and its disruption in autism

A direct form of experiential understanding of others, "intentional attunement", is achieved by modeling their behavior as intentional experiences on the basis of the activation of shared neural systems underpinning what the others do an feel and what we do and feel. This modeling mechanism is embodied simulation. In parallel with the detached sensory description of the observed social stimuli, internal representations of the body states associated with actions, emotions, and sensations are evoked in the observer, as if he/she would be doing a similar action or experiencing a similar emotion or sensation. Mirror neuron systems are likely the neural correlate of this mechanism. By means of a shared neural state realized in two different bodies, the "objectual other" becomes "another self". A defective intentional attunement caused by a lack of embodies simulation might cause some of the social impairments of autistic individuals.
A tactile distribution sensor which enables stable measurement under high and dynamic stretch

Recaently, we have been studying various tactile distribution sensors based on Electrical Impedance Tomography (EIT) which is a non-invasive technique to measure the resistance distribution of a conductive material only from a boundary, and needs no wiring inside the sensing area. In this paper, we present a newly developed conductive structure which is pressure sensitive but stretch insensitive and is based on the concept of contact resistance between (1)a network of stretchable wave-like conductive yarns with high resistance and (2)a conductive stretchable sheet with low resistance. Based on this newly developed structure, we have realized a novel tactile distribution sensor which enables stable measurement under dynamic and large stretch from various directions. Stable measurement of pressure distribution under dynamic and complex deformation cases such as pinching and pushing on a balloon surface are demonstrated. The sensor has been originally designed for implementation over interactive robots with soft and highly deformable bodies, but can also be used as novel user interface devices, or ordinary pressure distribution sensors. Some of the most remarkable specifications of the developed tactile sensor are high stretchability up to 140% and toughness under adverse load conditions. The sensor also has a realistic potential of becoming as thin and stretchable as stocking fabric. A goal of this research is to combine this thin sensor with stretch distribution sensors so that richer and more sophisticated tactile interactions can be realized.
Design and simulation of triple band GSM900/DCS1800/UMTS2100 MHz microstrip antenna for base station

In this paper, a simple dual-band dual-polarized aperture stacked patch antenna with double sided notch is presented. This antenna is suitable for operation in telecommunication base stations. One of the bands covers the GSM band, while the other covers both DCS and UMTS bands. The proposed antenna shows good port decoupling of less than −30dB for dual linear polarization over its operating bands. The antenna is based upon an aperture stacked patch layout and incorporates a simple dual-layered feeding technique to achieve dual polarization radiation. A peak antenna gain of about 11.8dBi and 7dBi has been obtained for lower & upper band respectively. The effort was directed towards the design of a single stand alone antenna; however, the performance would be better when it is incorporated as an array element.
A Survey of Algorithmic Shapes

In the context of computer-aided design, computer graphics and geometry processing, the idea of generative modeling is to allow the generation of highly complex objects based on a set of formal construction rules. Using these construction rules, a shape is described by a sequence of processing steps, rather than just by the result of all applied operations: shape design becomes rule design. Due to its very general nature, this approach can be applied to any domain and to any shape representation that provides a set of generating functions. The aim of this survey is to give an overview of the concepts and techniques of procedural and generative modeling, as well as their applications with a special focus on archeology and architecture.
Music and language side by side in the brain: a PET study of the generation of melodies and sentences.

Parallel generational tasks for music and language were compared using positron emission tomography. Amateur musicians vocally improvised melodic or linguistic phrases in response to unfamiliar, auditorily presented melodies or phrases. Core areas for generating melodic phrases appeared to be in left Brodmann area (BA) 45, right BA 44, bilateral temporal planum polare, lateral BA 6, and pre-SMA. Core areas for generating sentences seemed to be in bilateral posterior superior and middle temporal cortex (BA 22, 21), left BA 39, bilateral superior frontal (BA 8, 9), left inferior frontal (BA 44, 45), anterior cingulate, and pre-SMA. Direct comparisons of the two tasks revealed activations in nearly identical functional brain areas, including the primary motor cortex, supplementary motor area, Broca's area, anterior insula, primary and secondary auditory cortices, temporal pole, basal ganglia, ventral thalamus, and posterior cerebellum. Most of the differences between melodic and sentential generation were seen in lateralization tendencies, with the language task favouring the left hemisphere. However, many of the activations for each modality were bilateral, and so there was significant overlap. While clarification of this overlapping activity awaits higher-resolution measurements and interventional assessments, plausible accounts for it include component sharing, interleaved representations, and adaptive coding. With these and related findings, we outline a comparative model of shared, parallel, and distinctive features of the neural systems supporting music and language. The model assumes that music and language show parallel combinatoric generativity for complex sound structures (phonology) but distinctly different informational content (semantics).
Facial duplication: case, review, and embryogenesis.

The craniofacial anatomy of an infant with facial duplication is described. There were four eyes, two noses, two maxillae, and one mandible. Anterior to the single pituitary the brain was duplicated and there was bilateral arhinencephaly. Portions of the brain were extruded into a large frontal encephalocele. Cases of symmetrical facial duplication reported in the literature range from two complete faces on a single head (diprosopus) to simple nasal duplication. The variety of patterns of duplication suggests that the doubling of facial components arises in several different ways: Forking of the notochord, duplication of the prosencephalon, duplication of the olfactory placodes, and duplication of maxillary and/or mandibular growth centers around the margins of the stomatodeal plate. Among reported cases, the female:male ratio is 2:1.
StreetStyle: Exploring world-wide clothing styles from millions of photos

Each day billions of photographs are uploaded to photo-sharing services and social media platforms. These images are packed with information about how people live around the world. In this paper we exploit this rich trove of data to understand fashion and style trends worldwide. We present a framework for visual discovery at scale, analyzing clothing and fashion across millions of images of people around the world and spanning several years. We introduce a largescale dataset of photos of people annotated with clothing attributes, and use this dataset to train attribute classifiers via deep learning. We also present a method for discovering visually consistent style clusters that capture useful visual correlations in this massive dataset. Using these tools, we analyze millions of photos to derive visual insight, producing a first-of-its-kind analysis of global and per-city ∗e-mail:kmatzen@cs.cornell.edu †e-mail:kb@cs.cornell.edu ‡e-mail:snavely@cs.cornell.edu fashion choices and spatio-temporal trends.
A meta-analysis of the relationship between socioeconomic status and executive function performance among children.

The relationship between childhood socioeconomic status (SES) and executive function (EF) has recently attracted attention within psychology, following reports of substantial SES disparities in children's EF. Adding to the importance of this relationship, EF has been proposed as a mediator of socioeconomic disparities in lifelong achievement and health. However, evidence about the relationship between childhood SES and EF is mixed, and there has been no systematic attempt to evaluate this relationship across studies. This meta-analysis systematically reviewed the literature for studies in which samples of children varying in SES were evaluated on EF, including studies with and without primary hypotheses about SES. The analysis included 8760 children between the ages of 2 and 18 gathered from 25 independent samples. Analyses showed a small but statistically significant correlation between SES and EF across all studies (rrandom  = .16, 95% CI [.12, .21]) without correcting for attenuation owing to range restriction or measurement unreliability. Substantial heterogeneity was observed among studies, and a number of factors, including the amount of SES variability in the sample and the number of EF measures used, emerged as moderators. Using only the 15 studies with meaningful SES variability in the sample, the average correlation between SES and EF was small-to-medium in size (rrandom  = .22, 95% CI [.17, .27]). Using only the six studies with multiple measures of EF, the relationship was medium in size (rrandom  = .28, 95% CI [.18, .37]). In sum, this meta-analysis supports the presence of SES disparities in EF and suggests that they are between small and medium in size, depending on the methods used to measure them.
Motion Planning for Mobile Robots: A Method for the Selection of a Combination of Motion-Planning Algorithms

A motion planner for mobile robots is commonly built out of a number of algorithms that solve the two steps of motion planning: 1) representing the robot and its environment and 2) searching a path through the represented environment. However, the available literature on motion planning lacks a generic methodology to arrive at a combination of representations and search algorithm classes for a practical application. This article presents a method to select appropriate algorithm classes that solve both the steps of motion planning and to select a suitable approach to combine those algorithm classes. The method is verified by comparing its outcome with three different motion planners that have been successfully applied on robots in practice.
Designing of Robust Image Steganography Technique Based on LSB Insertion and Encryption

This paper discusses the design of a Robust image steganography technique based on LSB (Least Significant Bit) insertion and RSA encryption technique. Steganography is the term used to describe the hiding of data in images to avoid detection by attackers. Steganalysis is the method used by attackers to determine if images have hidden data and to recover that data. The application discussed in this paper ranks images in a users library based on their suitability as cover objects for some data. By matching data to an image, there is less chance of an attacker being able to use steganalysis to recover the data. Before hiding the data in an image the application first encrypts it. The steganography method proposed in this paper and illustrated by the application is superior to that used by current steganography tools.
Computer-aided generation of multiple-choice tests

The paper describes a novel automatic procedure for the generation of multiple-choice tests from electronic documents. In addition to employing various NLP techniques including term extraction and shallow parsing, the system makes use of language resources such as corpora and ontologies. The system operates in a fully automatic mode and also a semiautomatic environment where the user is offered the option to post-edit the generated test items. The results from the conducted evaluation suggest that the new procedure is very effective saving time and labour considerably and that the test items produced with the help of the program are not of inferior quality to those produced manually.
Biomedical Signal and Image Processing Spring 2008

In this chapter we will examine how we can generalize the idea of transforming a time series into an alternative representation, such as the Fourier (frequency) domain, to facilitate systematic methods of either removing (filtering) or adding (interpolating) data. In particular, we will examine the techniques of Principal Component Analysis (PCA) using Singular Value Decomposition (SVD), and Independent Component Analysis (ICA). Both of these techniques utilize a representation of the data in a statistical domain rather than a time or frequency domain. That is, the data are projected onto a new set of axes that fulfill some statistical criterion, which implies independence, rather than a set of axes that represent discrete frequencies such as with the Fourier transform, where the independence is assumed. Another important difference between these statistical techniques and Fourier-based techniques is that the Fourier components onto which a data segment is projected are fixed, whereas PCA-or ICA-based transformations depend on the structure of the data being analyzed. The axes onto which the data are projected are therefore discovered. If the structure of the data (or rather the statistics of the underlying sources) changes over time, then the axes onto which the data are projected will change too 1. Any projection onto another set of axes (or into another space) is essentially a method for separating the data out into separate components or sources which will hopefully allow us to see important structure more clearly in a particular projection. That is, the direction of projection increases the signal-to-noise ratio (SNR) for a particular signal source. For example, by calculating the power spectrum of a segment of data, we hope to see peaks at certain frequencies. The power (amplitude squared) along certain frequency vectors is therefore high, meaning we have a strong component in the signal at that frequency. By discarding the projections that correspond to the unwanted sources (such as the noise or artifact sources) and inverting the transformation, we effectively perform a filtering of the recorded observation. This is true for both ICA and PCA as well as Fourier-based techniques. However, one important difference between these techniques is that Fourier techniques assume that the projections onto each frequency component are independent of the other frequency components. In PCA and ICA we attempt to find a set of axes which are independent of one another in some sense. We assume there are a set of independent 1 …
A Scalable Hierarchical Clustering Algorithm Using Spark

Clustering is often an essential first step in data mining intended to reduce redundancy, or define data categories. Hierarchical clustering, a widely used clustering technique, can offer a richer representation by suggesting the potential group structures. However, parallelization of such an algorithm is challenging as it exhibits inherent data dependency during the hierarchical tree construction. In this paper, we design a parallel implementation of Single-linkage Hierarchical Clustering by formulating it as a Minimum Spanning Tree problem. We further show that Spark is a natural fit for the parallelization of single-linkage clustering algorithm due to its natural expression of iterative process. Our algorithm can be deployed easily in Amazon's cloud environment. And a thorough performance evaluation in Amazon's EC2 verifies that the scalability of our algorithm sustains when the datasets scale up.
Learning Conditioned Graph Structures for Interpretable Visual Question Answering

Visual Question answering is a challenging problem requiring a combination of concepts from Computer Vision and Natural Language Processing. Most existing approaches use a two streams strategy, computing image and question features that are consequently merged using a variety of techniques. Nonetheless, very few rely on higher level image representations, which can capture semantic and spatial relationships. In this paper, we propose a novel graph-based approach for Visual Question Answering. Our method combines a graph learner module, which learns a question specific graph representation of the input image, with the recent concept of graph convolutions, aiming to learn image representations that capture question specific interactions. We test our approach on the VQA v2 dataset using a simple baseline architecture enhanced by the proposed graph learner module. We obtain promising results with 66.18% accuracy and demonstrate the interpretability of the proposed method. Code can be found at github.com/aimbrain/vqa-project.
Switchable constraints for robust pose graph SLAM

Current SLAM back-ends are based on least squares optimization and thus are not robust against outliers like data association errors and false positive loop closure detections. Our paper presents and evaluates a robust back-end formulation for SLAM using switchable constraints. Instead of proposing yet another appearance-based data association technique, our system is able to recognize and reject outliers during the optimization. This is achieved by making the topology of the underlying factor graph representation subject to the optimization instead of keeping it fixed. The evaluation shows that the approach can deal with up to 1000 false positive loop closure constraints on various datasets. This largely increases the robustness of the overall SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers.
Factor-based Compositional Embedding Models

Information about language structure is critical in many NLP tasks, where substructures of a sentence and its annotations inform downstream NLP task. Yet word representations alone do not capture such structure. For example, in relation extraction the sentence may be annotated with partof-speech tags, a dependency parse, and named entities, with the goal of predicting a relation label for a pair of target entities. Semantic role labeling has a similar form. In tasks such as these, it is important to capture information about both individual words and their interactions. The annotations evidence these interactions, and we often define features over substructures of these annotated sentences (e.g. relative positions of words, words that appear on a dependency path, words with their entity types) to make successful predictions of the label (e.g. relation type).
A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems

This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.
Potential astronauts' Speech and Language disorders. Case study: Astronaut's interviews analysis before and after space mission

In the context of lengthy human spaceflight missions and in the need for non-invasive, non-contact and non-obtrusive solutions for astronauts' psychological problems, the study expressed herein presents a distinctive and interdisciplinary perspective through a case study which analyses variations of certain Speech and Language (SL) characteristics residing to rhythm and fluency, oral language and voice disorders on an astronaut's SL within two separate interviews corresponding to different moments in time as before- and after-long duration space mission. Additionally, this case study proposes an approach for detecting syllable repetitions and SL hesitations by making the differences between the number of syllables detected on one side through an existent syllable detection algorithm, and on the other side through a speech-to-text analysis which has the capability to eliminate the deficits of morphology and syntax.
Extracting Aspect-Evaluation and Aspect-Of Relations in Opinion Mining

The technology of opinion extraction allows users to retrieve and analyze people’s opinions scattered over Web documents. We define an opinion unit as a quadruple consisting of the opinion holder, the subject being evaluated, the part or the attribute in which the subject is evaluated, and the value of the evaluation that expresses a positive or negative assessment. We use this definition as the basis for our opinion extraction task. We focus on two important subtasks of opinion extraction: (a) extracting aspect-evaluation relations, and (b) extracting aspect-of relations, and we approach each task using methods which combine contextual and statistical clues. Our experiments on Japanese weblog posts show that the use of contextual clues improve the performance for both tasks.
The cost of a cloud: research problems in data center networks

The data centers used to create cloud services represent a significant investment in capital outlay and ongoing costs. Accordingly, we first examine the costs of cloud service data centers today. The cost breakdown reveals the importance of optimizing work completed per dollar invested. Unfortunately, the resources inside the data centers often operate at low utilization due to resource stranding and fragmentation. To attack this first problem, we propose (1) increasing network agility, and (2) providing appropriate incentives to shape resource consumption. Second, we note that cloud service providers are building out geo-distributed networks of data centers. Geo-diversity lowers latency to users and increases reliability in the presence of an outage taking out an entire site. However, without appropriate design and management, these geo-diverse data center networks can raise the cost of providing service. Moreover, leveraging geo-diversity requires services be designed to benefit from it. To attack this problem, we propose (1) joint optimization of network and data center resources, and (2) new systems and mechanisms for geo-distributing state.