nils-herrmann/clean_scirepeval huggingface dataset:
Condition monitoring of helicopter drive shafts using quadratic-nonlinearity metric based on cross-bispectrum

Based on cross-bispectrum, quadratic-nonlinearity coupling between two vibration signals is proposed and used to assess health conditions of rotating shafts in an AH-64D helicopter tail rotor drive train. Vibration data are gathered from two bearings supporting the shaft in an experimental helicopter drive train simulating different shaft conditions, namely, baseline, misalignment, imbalance, and combination of misalignment and imbalance. The proposed metric shows better capabilities in distinguishing different shaft settings than the conventional linear coupling based on cross-power spectrum.
Using Filter Banks in Convolutional Neural Networks for Texture Classification

Deep learning has established many new state of the art solutions in the last decade in areas such as object, scene and speech recognition. In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks. Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself. However, some of its features are very similar to texture analysis methods. CNN layers can be thought of as filter banks of complexity increasing with the depth. Filter banks are powerful tools to extract texture features and have been widely used in texture analysis. In this paper we develop a simple network architecture named Texture CNN (T-CNN) which explores this observation. It is built on the idea that the overall shape information extracted by the fully connected layers of a classic CNN is of minor importance in texture analysis. Therefore, we pool an energy measure from the last convolution layer which we connect to a fully connected layer. We show that our approach can improve the performance of a network while greatly reducing the memory usage and computation.
Towards Robust Deep Neural Networks

We examine the relationship between the energy landscape of neural networks and their robustness to adversarial attacks. Combining energy landscape techniques developed in computational chemistry with tools drawn from formal methods, we produce empirical evidence that networks corresponding to lower-lying minima in the landscape tend to be more robust. The robustness measure used is the inverse of the sensitivity measure, which we define as the volume of an over-approximation of the reachable set of network outputs under all additive l∞ bounded perturbations on the input data. We present a novel loss function which contains a weighted sensitivity component in addition to the traditional task-oriented and regularization terms. In our experiments on standard machine learning and computer vision datasets (e.g., Iris and MNIST), we show that the proposed loss function leads to networks which reliably optimize the robustness measure as well as other related metrics of adversarial robustness without significant degradation in the classification error.
Temporal Summaries of News Topics

We discuss technology to help a person monitor changes in news coverage over time. We define temporal summaries of news stories as extracting a single sentence from each event within a news topic, where the stories are presented one at a time and sentences from a story must be ranked before the next story can be considered. We explain a method for evaluation, and describe an evaluation corpus that we have built. We also propose several methods for constructing temporal summaries and evaluate their effectiveness in comparison to degenerate cases. We show that simple approaches are effective, but that the problem is far from solved.
Pilonidal sinus disease. The conservative approach.

To examine the role of conservative, nonexcisional methods in the treatment of pilonidal sinus disease.



Pilot nonrandomized cohort study and follow-up retrospective study.



In the pilot study, all patients treated for pilonidal sinus disease consecutively over 3 years at an Army community hospital, and in the follow-up study within a closed federal population, all patients admitted with a diagnosis of pilonidal sinus disease over 17 years to an Army medical center.



Conservative, nonexcisional therapy (meticulous hair control by natal cleft shaving, improved perineal hygiene, and limited lateral incision and drainage for abscess) with initial comparison to excisional procedures.



Occupied-bed days for conservative vs excisional therapy during a 3-year pilot study and the number of admissions and procedures performed for pilonidal sinus disease at an institution dedicated to conservative treatment alone.



Complete healing over 83 occupied-bed days was demonstrated in 101 consecutive cases managed during 1 year with the conservative method, whereas slower healing over 4760 occupied-bed days was observed in 229 patients undergoing 240 operative procedures during the preceding 2 years. With application of conservative treatment over 17 years, only 23 excisional operations were performed.



Conservative therapy effectively controls pilonidal sinus disease in the nonoperative outpatient setting while promoting near-normal work status and is preferred over excisional operations.
Topologies of single-phase inverters for small distributed power generators: an overview

This paper presents an overview of single-phase inverters developed for small distributed power generators. The functions of inverters in distributed power generation (DG) systems include dc-ac conversion, output power quality assurance, various protection mechanisms, and system controls. Unique requirements for small distributed power generation systems include low cost, high efficiency and tolerance for an extremely wide range of input voltage variations. These requirements have driven the inverter development toward simpler topologies and structures, lower component counts, and tighter modular design. Both single-stage and multiple-stage inverters have been developed for power conversion in DG systems. Single-stage inverters offer simple structure and low cost, but suffer from a limited range of input voltage variations and are often characterized by compromised system performance. On the other hand, multiple-stage inverters accept a wide range of input voltage variations, but suffer from high cost, complicated structure and low efficiency. Various circuit topologies are presented, compared, and evaluated against the requirements of power decoupling and dual-grounding, the capabilities for grid-connected or/and stand-alone operations, and specific DG applications in this paper, along with the identification of recent development trends of single-phase inverters for distributed power generators.
MI&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification

A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented. To solve the problem of word2vec training word vector slowly, a method of training word vector by integrating word2vec and Convolutional Neural Network (CNN) is proposed. This training method not only improves the training speed of word2vec, but also makes the word vector more effective for the target task. Furthermore, the word2vec adopts a full connection between the input layer and the projection layer of the Continuous Bag-of-Words (CBOW) for acquiring the semantic information of the original sentence.
Designing a robotic assistant for healthcare applications

The population of the world is ageing rapidly. By 2050, the population aged 85 and over will be three times more than it is now. This phenomenon has caused several issues in the current health service system, especially workforce shortages in the health sector and a lack of space in aged care facilities (ACFs). In the face of these issues, home-based and community-based healthcare services have been identified as necessary in many developed countries to promote ageing-inplace and independent living in order to: 1. Lower the demands on health services and hence improve the quality of the services delivered, and 2. Maintain the quality of life of the older population by enabling them to be close to their families. For the last decade, a rising interest in personal robots as part of the technical solution in decentralised health services has led to an extensive range of research and implementations of health service and personal assistant robots. This paper describes a new research project to develop an assistant robot capable of interacting with patients, taking vital signs measurements and recording the data in healthcare environments such as aged care facilities, hospitals or personal homes. Current progress includes a comprehensive literature survey on recent health service robots with a list of issues in the area and an initial human-robot interaction study. The robot is currently interfaced with a blood pressure monitor and has a 3D face which is capable of displaying a range of different emotions with lips synchronized to speech.
Data Offloading Techniques in Cellular Networks: A Survey

One of the most engaging challenges for mobile operators today is how to manage the exponential data traffic increase. Mobile data offloading stands out as a promising and low-cost solution to reduce the burden on the cellular network. To make this possible, we need a new hybrid network paradigm that leverages the existence of multiple alternative communication channels. This entails significant modifications in the way data are handled, affecting also the behavior of network protocols. In this paper, we present a comprehensive survey of data offloading techniques in cellular networks and extract the main requirements needed to integrate data offloading capabilities into today's mobile networks. We classify existing strategies into two main categories, according to their requirements in terms of content delivery guarantees: delayed and nondelayed offloading. We overview the technical aspects and discuss the state of the art in each category. Finally, we describe in detail the novel functionalities needed to implement mobile data offloading in the access network, as well as current and future research challenges in the field, with an eye toward the design of hybrid architectures.
Data Sets: Word Embeddings Learned from Tweets and General Data

A word embedding is a low-dimensional, dense and realvalued vector representation of a word. Word embeddings have been used in many NLP tasks. They are usually generated from a large text corpus. The embedding of a word captures both its syntactic and semantic aspects. Tweets are short, noisy and have unique lexical and semantic features that are different from other types of text. Therefore, it is necessary to have word embeddings learned specifically from tweets. In this paper, we present ten word embedding data sets. In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets with the general data. The general data consist of news articles, Wikipedia data and other web data. These ten embedding models were learned from about 400 million tweets and 7 billion words from the general text. In this paper, we also present two experiments demonstrating how to use the data sets in some NLP tasks, such as tweet sentiment analysis and tweet topic classification tasks.
All-Distances Sketches, Revisited: HIP Estimators for Massive Graphs Analysis

Graph datasets with billions of edges, such as social and Web graphs, are prevalent. To be feasible, computation on such large graphs should scale linearly with graph size. All-distances sketches (ADSs) are emerging as a powerful tool for scalable computation of some basic properties of individual nodes or the whole graph.
 ADSs were first proposed two decades ago (Cohen 1994) and more recent algorithms include ANF (Palmer, Gibbons, and Faloutsos 2002) and hyperANF (Boldi, Rosa, and Vigna 2011). A sketch of logarithmic size is computed for each node in the graph and the computation in total requires only a near linear number of edge relaxations. From the ADS of a node, we can estimate neighborhood cardinalities (the number of nodes within some query distance) and closeness centrality. More generally we can estimate the distance distribution, effective diameter, similarities, and other parameters of the full graph. We make several contributions which facilitate a more effective use of ADSs for scalable analysis of massive graphs.
 We provide, for the first time, a unified exposition of ADS algorithms and applications. We present the Historic Inverse Probability (HIP) estimators which are applied to the ADS of a node to estimate a large natural class of queries including neighborhood cardinalities and closeness centralities. We show that our HIP estimators have at most half the variance of previous neighborhood cardinality estimators and that this is essentially optimal. Moreover, HIP obtains a polynomial improvement over state of the art for more general domain queries and the estimators are simple, flexible, unbiased, and elegant.
 The ADS generalizes Min-Hash sketches, used for approximating cardinality (distinct count) on data streams. We obtain lower bounds on Min-Hash cardinality estimation using classic estimation theory. We illustrate the power of HIP, both in terms of ease of application and estimation quality, by comparing it to the HyperLogLog algorithm (Flajolet et al. 2007), demonstrating a significant improvement over this state-of-the-art practical algorithm.
 We also study the quality of ADS estimation of distance ranges, generalizing the near-linear time factor-2 approximation of the diameter.
Tackling the Poor Assumptions of Naive Bayes Text Classifiers

Naive Bayes is often used as a baseline in text classification because it is fast and easy to implement. Its severe assumptions make such efficiency possible but also adversely affect the quality of its results. In this paper we propose simple, heuristic solutions to some of the problems with Naive Bayes classifiers, addressing both systemic issues as well as problems that arise because text is not actually generated according to a multinomial model. We find that our simple corrections result in a fast algorithm that is competitive with stateof-the-art text classification algorithms such as the Support Vector Machine.
AIMQ: a methodology for information quality assessment

Information Quality (IQ) is critical in organizations. Yet, despite a decade of active research and practice, the field lacks comprehensive methodologies for its assessment and improvement. Here, we develop such a methodology, which we call AIMQ (AIM Quality) to form a basis for IQ assessment and benchmarking. The methodology is illustrated through its application to five major organizations. The methodology encompasses a model of Information Quality, a questionnaire to measure IQ, and analysis techniques for interpreting the IQ measures. We develop and validate the questionnaire and use it to collect data on the status of organizational IQ. These data are used to assess and benchmark IQ for four quadrants of the model. These analysis techniques are applied to analyze the gap between an organization and best practices. They are also applied to analyze gaps between IS professionals and information consumers. The results of the techniques are useful for determining the best area for IQ improvement activities.
Artificial error generation for translation-based grammatical error correction

Automated grammatical error correction for language learners has attracted a lot of attention in recent years, especially after a number of shared tasks that have encouraged research in the area. Treating the problem as a translation task from 'incorrect' into 'correct' English using statistical machine translation has emerged as a state-of-the-art approach but it requires vast amounts of corrected parallel data to produce useful results. Because manual annotation of incorrect text is laborious and expensive, we can generate artificial error-annotated data by injecting errors deliberately into correct text and thus produce larger amounts of parallel data with much less effort. In this work, we review previous work on artificial error generation and investigate new approaches using random and probabilistic methods for constrained and general error correction. Our methods use error statistics from a reference corpus of learner writing to generate errors in native text that look realistic and plausible in context. We investigate a number of aspects that can play a part in the error generation process, such as the origin of the native texts, the amount of context used to find suitable insertion points, the type of information encoded by the error patterns and the output error distribution. In addition, we explore the use of linguistic information for characterising errors and train systems using different combinations of real and artificial data. Results of our experiments show that the use of artificial errors can improve system performance when they are used in combination with real learner errors, in line with previous research. These improvements are observed for both constrained and general correction, for which probabilistic methods produce the best results. We also demonstrate that systems trained on a combination of real and artificial errors can beat other highly-engineered systems and be more robust, showing that performance can be improved by focusing on the data rather than tuning system parameters. Part of our work is also devoted to the proposal of the I-measure, a new evaluation scheme that scores corrections in terms of improvement on the original text and solves known issues with existing evaluation measures. To my family and L.A. In memory of Kika.
Fast Component-Based QR Code Detection in Arbitrarily Acquired Images

Quick Response (QR) codes are a type of 2D barcode that is becoming very popular, with several application possibilities. Since they can encode alphanumeric characters, a rich set of information can be made available through encoded URL addresses. In particular, QR codes could be used to aid visually impaired and blind people to access web based voice information systems and services, and autonomous robots to acquire context-relevant information. However, in order to be decoded, QR codes need to be properly framed, something that robots, visually impaired and blind people will not be able to do easily without guidance. Therefore, any application that aims assisting robots or visually impaired people must have the capability to detect QR codes and guide them to properly frame the code. A fast component-based two-stage approach for detecting QR codes in arbitrarily acquired images is proposed in this work. In the first stage, regular components present at three corners of the code are detected, and in the second stage geometrical restrictions among detected components are verified to confirm the presence of a code. Experimental results show a high detection rate, superior to 90 %, at a fast speed compatible with real-time applications.
A Review on Knowledge-based Expert System: Concept and Architecture

A Knowledge-based expert system use human knowledge to solve problems that normally would require human intelligence. Expert systems are designed to carry the intelligence and information found in the intellect of experts and provide this knowledge to other members of the organization for problem solving purposes. With the growing importance of human resource management and increasing size of the organizations, maintenance of employee related data and generating appropriate reports are the crucial aspects of any organization. Therefore more and more organizations are adopting computer based human resource management systems (HRMS). This paper explains the architecture, knowledge representation techniques and application areas of knowledge-based expert system in Human Resource.
Compact Nonlinear Maps and Circulant Extensions

Kernel approximation via nonlinear random feature maps is w idely used in speeding up kernel machines. There are two main challenges for the conventional kernel ap proximation methods. First, before performing kernel approximation, a good kernel has to be chosen. Pickin g a good kernel is a very challenging problem in itself. Second, high-dimensional maps are often required i n order to achieve good performance. This leads to high computational cost in both generating the nonlinear ma ps, nd in the subsequent learning and prediction process. In this work, we propose to optimize the nonlinear m ps directly with respect to the classification objective in a data-dependent fashion. The proposed approa ch achieves kernel approximation and kernel learning in a joint framework. This leads to much more compact maps wit hout hurting the performance. As a by-product, the same framework can also be used to achieve more compact ke rnel maps to approximate a known kernel. We also introduce Circulant Nonlinear Maps, which uses a circu lant-structured projection matrix to speed up the nonlinear maps for high-dimensional data.
Challenges in Shared-Environment Human-Robot Collaboration

We present and discuss four important yet underserved research questions critical to the future of sharedenvironment human-robot collaboration. We begin with a brief survey of research surrounding individual components required for a complete collaborative robot control system, discussing the current state of the art in Learning from Demonstration, active learning, adaptive planning systems, and intention recognition. We motivate the exploration of the presented research questions by relating them to existing work and representative use cases from the domains of construction and cooking.
tress and anxiety detection using facial cues from videos

This study develops a framework for the detection and analysis of stress/anxiety emotional states through video-recorded facial cues. A thorough experimental protocol was established to induce systematic variability in affective states (neutral, relaxed and stressed/anxious) through a variety of external and internal stressors. The analysis was focused mainly on non-voluntary and semi-voluntary facial cues in order to estimate the emotion representation more objectively. Features under investigation included eye-related events, mouth activity, head motion parameters and heart rate estimated through camerabased photoplethysmography. A feature selection procedure was employed to select the most robust features followed by classification schemes discriminating between stress/anxiety and neutral states nxiety tress link rate ead motion with reference to a relaxed state in each experimental phase. In addition, a ranking transformation was proposed utilizing self reports in order to investigate the correlation of facial parameters with a participant perceived amount of stress/anxiety. The results indicated that, specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety.
The igraph software package for complex network research

There is no other package around that satisfies all the following requirements: •Ability to handle large graphs efficiently •Embeddable into higher level environments (like R [6] or Python ) •Ability to be used for quick prototyping of new algorithms (impossible with “click & play” interfaces) •Platform-independent and open source igraph aims to satisfy all these requirements while possibly remaining easy to use in interactive mode as well.