nils-herrmann/clean_scirepeval huggingface dataset:
Autonomous person following for telepresence robots

We present a method for a mobile robot to follow a person autonomously where there is an interaction between the robot and human during following. The planner takes into account the predicted trajectory of the human and searches future trajectories of the robot for the path with the highest utility. Contrary to traditional motion planning, instead of determining goal points close to the person, we introduce a task dependent goal function which provides a map of desirable areas for the robot to be at, with respect to the person. The planning framework is flexible and allows encoding of different social situations with the help of the goal function. We implemented our approach on a telepresence robot and conducted a controlled user study to evaluate the experiences of the users on the remote end of the telepresence robot. The user study compares manual teleoperation to our autonomous method for following a person while having a conversation. By designing a behavior specific to a flat screen telepresence robot, we show that the person following behavior is perceived as safe and socially acceptable by remote users. All 10 participants preferred our autonomous following method over manual teleoperation.
Autonomous hovering of a fixed-wing micro air vehicle

Recently, there is a need to acquire intelligence in hostile or dangerous environments such as caves, forests, or urban areas. Rather than risking human life, backpackable, bird-sized aircraft, equipped with a wireless camera, can be rapidly deployed to gather reconnaissance in such environments. However, they first must be designed to fly in tight, cluttered terrain. This paper discusses an additional flight modality for a fixed-wing aircraft, enabling it to supplement existing endurance superiority with hovering capabilities. An inertial measurement sensor and an onboard processing and control unit, used to achieve autonomous hovering, are also described. This is, to the best of our knowledge, the first documented success of hovering a fixed-wing micro air vehicle autonomously
Bioinformatics Curriculum Guidelines: Toward a Definition of Core Competencies

1 School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio, United States of America, 2 Bioinformatics and Research Computing, Whitehead Institute, Cambridge, Massachusetts, United States of America, 3 Department of Biological Sciences and School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, 4 European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom, 5 School of Informatics and Computing, Indiana University, Bloomington, Indiana, United States of America, 6 School of Computer Science and Engineering, The University of New South Wales, Sydney, New South Wales, Australia, 7 The Genome Analysis Centre, Norwich Research Park, Norwich, United Kingdom
A support vector machine classifier with automatic confidence and its application to gender classification

In this paper, we propose a support vector machine with automatic confidence (SVMAC) for pattern classification. The main contributions of this work to learning machines are twofold. One is that we develop an algorithm for calculating the label confidence value of each training sample. Thus, the label confidence values of all of the training samples can be considered in training support vector machines. The other one is that we propose a method for incorporating the label confidence value of each training sample into learning and derive the corresponding quadratic programming problems. To demonstrate the e ff ctiveness of the proposed SVMACs, a series of experiments are performed on three benchmarking pattern classification problems and a challenging gender classification problem. Experimental results show that the generalization performance of our SVMACs is superior to that of traditional SVMs.
Chapter IV Automatic Syllabus Classification Using Support Vector Machines

Syllabi are important educational resources. Gathering syllabi that are freely available and creating useful services on top of the collection presents great value for the educational community. However, searching for a syllabus on the Web using a generic search engine is an error-prone process and often yields too many irrelevant links. In this chapter, we describe our empirical study on automatic syllabus classification using support vector machines (SVM) to filter noise out from search results. We describe various steps in the classification process from training data preparation, feature selection, and classifier building using SVMs. Empirical results are provided and discussed. We hope our reported work will also benefit people who are interested in building other genre-specific repositories.
Diagnostic gateway based on DoIP for automotive systems

The Ethernet-based diagnosis protocol is widely used in automotive systems. Diagnostic gateways have been developed for seamlessly interconnect with Ethernet based diagnostic protocols and traditional in-vehicle network protocols, such as local interconnect network (LIN), controller area network (CAN), FlexRay. A diagnostic gateway is connected to the Internet and manages the interconnection between in-vehicle networks and the Internet. Therefore, a diagnostic gateway is the most important component for interconnecting a consumer electronics device to the automotive system. In this paper, we implement a diagnostic gateway for the automotive system, and propose various applications of a diagnostic gateway to consumer electronics devices.
Better managed than memorized? Studying the Impact of Managers on Password Strength and Reuse

Despite their well-known security problems, passwords are still the incumbent authentication method for virtually all online services. To remedy the situation, users are very often referred to password managers as a solution to the password reuse and weakness problems. However, to date the actual impact of password managers on password strength and reuse has not been studied systematically. We provide the first large-scale study of the password managers’ influence on users’ real-life passwords. By combining qualitative data on users’ password creation and management strategies, collected from 476 participants of an online survey, with quantitative data (incl. password metrics and entry methods) collected in situ with a browser plugin from 170 users, we were able to gain a more complete picture of the factors that influence our participants’ password strength and reuse. Our approach allows us to quantify for the first time that password managers indeed influence the password security, however, whether this influence is beneficial or aggravating existing problems depends on the users’ strategies and how well the manager supports the users’ password management right from the time of password creation. Given our results, we think research should further investigate how managers can better support users’ password strategies in order to improve password security as well as stop aggravating the existing problems.
A Solution to the Next Best View Problem for Automated Surface Acquisition

ÐA solution to the anext best viewo (NBV) problem for automated surface acquisition is presented. The NBV problem is to determine which areas of a scanner's viewing volume need to be scanned to sample all of the visible surfaces of an a priori unknown object and where to position/control the scanner to sample them. It is argued that solutions to the NBV problem are constrained by the other steps in a surface acquisition system and by the range scanner's particular sampling physics. A method for determining the unscanned areas of the viewing volume is presented. In addition, a novel representation, positional space (PS), is presented which facilitates a solution to the NBV problem by representing what must be and what can be scanned in a single data structure. The number of costly computations needed to determine if an area of the viewing volume would be occluded from some scanning position is decoupled from the number of positions considered for the NBV, thus reducing the computational cost of choosing one. An automated surface acquisition systems designed to scan all visible surfaces of an a priori unknown object is demonstrated on real
Convolutional Neural Network based Malignancy Detection of Pulmonary Nodule on Computer Tomography

Without performing biopsy that could lead physical damages to nerves and vessels, Computerized Tomography (CT) is widely used to diagnose the lung cancer due to the high sensitivity of pulmonary nodule detection. However, distinguishing pulmonary nodule inbetween malignant and benign is still not an easy task. As the CT scans are mostly in relatively low resolution, it is not easy for radiologists to read the details of the scan image. In the past few years, the continuing rapid growth of CT scan analysis system has generated a pressing need for advanced computational tools to extract useful features to assist the radiologist in reading progress. Computer-aided detection (CAD) systems have been developed to reduce observational oversights by identifying the suspicious features that a radiologist looks for during case review. Most previous CAD systems rely on low-level non-texture imaging features such as intensity, shape, size or volume of the pulmonary nodules. However, the pulmonary nodules have a wide variety in shapes and sizes, and also the high visual similarities between benign and malignant patterns, so relying on non-texture imaging features is difficult for diagnosis of the nodule types. To overcome the problem of non-texture imaging features, more recent CAD systems adopted the supervised or unsupervised learning scheme to translate the content of the nodules into discriminative features. Such features enable high-level imaging features highly correlated with shape and texture. Convolutional neural networks (ConvNets), supervised methods related to deep learning, have been improved rapidly in recent years. Due to their great success in computer vision tasks, they are also expected to be helpful in medical imaging. In this thesis, a CAD based on a deep convolutional neural network (ConvNet) is designed and evaluated for malignant pulmonary nodules on computerized tomography. The proposed ConvNet, which is the core component of the proposed CAD system, is trained on the LUNGx challenge database to classify benign and malignant pulmonary nodules on CT. The architecture of the proposed ConvNet consists of 3 convolutional layers with maximum pooling operations and rectified linear units (ReLU) activations, followed by 2 denser layers with full-connectivities, and the architecture is carefully tailored for pulmonary nodule classification by considering the
Multilingual Neural Network Acoustic Modelling for ASR of Under-Resourced English-isiZulu Code-Switched Speech

Although isiZulu speakers code-switch with English as a matter of course, extremely little appropriate data is available for acoustic modelling. Recently, a small five-language corpus of code-switched South African soap opera speech was compiled. We used this corpus to evaluate the application of multilingual neural network acoustic modelling to English-isiZulu code-switched speech recognition. Our aim was to determine whether English-isiZulu speech recognition accuracy can be improved by incorporating three other language pairs in the corpus: English-isiXhosa, English-Setswana and English-Sesotho. Since isiXhosa, like isiZulu, belongs to the Nguni language family, while Setswana and Sesotho belong to the more distant Sotho family, we could also investigate the merits of additional data from within and across language groups. Our experiments using both fully connected DNN and TDNN-LSTM architectures show that English-isiZulu speech recognition accuracy as well as language identification after code-switching is improved more by the incorporation of English-isiXhosa data than by the incorporation of the other language pairs. However additional data from the more distant language group remained beneficial, and the best overall performance was always achieved with a multilingual neural network trained on all four language pairs.
The Application of Non-parametric Method and Time Series Analysis to Predict the Machine Repair Times

This study focuses on the utilization of non-parametric to assess the distribution of repair times of a machine part as well as the prediction of the future values. There are two folds of objectives, namely, the distribution assessment of the repair times. The diagnostic graph, i.e., histogram and normal probability plot, as well as a non-parametric test, Kolmogorov-Smirnov (KS) method, is utilized to assess the distribution of data. According to the KS test, it can be used effectively to test the distribution of the repair times of a machine which are selected as a case study. Another objective is the prediction of the future repair time required to fix a designated part. The time series analysis based on autoregressive integrated moving average (ARIMA) model is deployed in order to forecast the repair times. It turned out that one of the simplest models of ARIMA, ARIMA (0, 1, 0) or random walk, is the most appropriate model for the prediction and this indicates that the pattern of repair times is non-stationary.
Gaussian process factorization machines for context-aware recommendations

Context-aware recommendation (CAR) can lead to significant improvements in the relevance of the recommended items by modeling the nuanced ways in which context influences preferences. The dominant approach in context-aware recommendation has been the multidimensional latent factors approach in which users, items, and context variables are represented as latent features in low-dimensional space. An interaction between a user, item, and a context variable is typically modeled as some linear combination of their latent features. However, given the many possible types of interactions between user, items and contextual variables, it may seem unrealistic to restrict the interactions among them to linearity.
 To address this limitation, we develop a novel and powerful non-linear probabilistic algorithm for context-aware recommendation using Gaussian processes. The method which we call Gaussian Process Factorization Machines (GPFM) is applicable to both the explicit feedback setting (e.g. numerical ratings as in the Netflix dataset) and the implicit feedback setting (i.e. purchases, clicks). We derive stochastic gradient descent optimization to allow scalability of the model. We test GPFM on five different benchmark contextual datasets. Experimental results demonstrate that GPFM outperforms state-of-the-art context-aware recommendation methods.
Spreadsheet Property Detection With Rule-assisted Active Learning

Spreadsheets are a critical and widely-used data management tool. Converting spreadsheet data into relational tables would bring benefits to a number of fields, including public policy, public health, and economics. Research to date has focused on designing domain-specific languages to describe transformation processes or automatically converting a specific type of spreadsheets. To handle a larger variety of spreadsheets, we have to identify various spreadsheet properties, which correspond to a series of transformation programs that contribute towards a general framework that converts spreadsheets to relational tables.
 In this paper, we focus on the problem of spreadsheet property detection. We propose a hybrid approach of building a variety of spreadsheet property detectors to reduce the amount of required human labeling effort. Our approach integrates an active learning framework with crude, easy-to-write, user-provided rules to save human labeling effort by generating additional high-quality labeled data especially in the initial training stage. Using a bagging-like technique, Our approach can also tolerate lower-quality user-provided rules. Our experiments show that when compared to a standard active learning approach, we reduced the training data needed to reach the performance plateau by 34-44% when a human provides relatively high-quality rules, and by a comparable amount with low-quality rules. A study on a large-scale web-crawled spreadsheet dataset demonstrates that it is crucial to detect a variety of spreadsheet properties in order to transform a large portion of the spreadsheets into a relational form.
Vision: towards real time epidemic vigilance through online social networks: introducing SNEFT -- social network enabled flu trends

Our vision is to achieve faster and near real time detection and prediction of the emergence and spread of an influenza epidemic, through sophisticated data collection and analysis of Online Social Networks (OSNs) such as Facebook, MySpace, and Twitter. In particular, we present the design of a system called SNEFT (Social Network Enabled Flu Trends), which will be developed in a 12-month SBIR (Small Business Innovation Research) project funded by the National Institutes of Health (NIH). We describe the innovative technologies that will be developed in this project for collecting and aggregating OSN data, extracting information from it, and integrating it with mathematical models of influenza. One of the monitoring tools used by the Centers for Disease Control and Prevention (CDC) is reports of Influenza-Like Illness (ILI) cases; these reports are authoritative but typically have a delay of one to two weeks due to the largely manual process. We describe the SNEFT prototype in the context of predicting ILI cases well in advance of the CDC reports. We observe that OSN data is individually noisy but collectively revealing, and speculate on other applications that can potentially be enabled by OSN data collection and analysis.
Abdominal Pain, Dysentery and Bilious V omiting: An Atypical Presentation of Henoch Schonlein Purpura: A Case Study

Adolescent male resident of Hazara, Pakistan presented with abdominal pain, bilious vomiting, dysentery and knee pain of 10 days duration in July of 2014 to the emergency department of the Aga Khan University Hospital. Knee pain later migrated to ankle and was associated with redness and oedema. Abdominal x-ray revealed thickened bowel wall that was suggestive of enteritis. He was being treated with IV antibiotics (ceftriaxone and metronidazole), which did not help in resolving his symptoms. Overnight he started developing palpable rashes on his feet, back and abdomen, which raised the suspicion of Henoch-Schonlein purpura. There were no signs to suggest any respiratory and urinary systems involvement. He was given intravenous prednisolone after which his rashes dramatically disappeared and his ankle pain and other symptoms resolved. On follow up his lab investigations were within normal range with complete disappearance of rash and resolution of his abdominal pain, dysentery and bilious vomiting. Rarely dysentery is accompanied with palpable rashes and should raise suspicion of Henoch-Schonlein Purpura. Knowledge and understanding of this rare disease can help early diagnosis and initiate prompt treatment.
Automatic assessment of macular edema from color retinal images

Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vision loss. In this paper, a two-stage methodology for the detection and classification of DME severity from color fundus images is proposed. DME detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Disease severity is assessed using a rotational asymmetry metric by examining the symmetry of macular region. The performance of the proposed methodology and features are evaluated against several publicly available datasets. The detection performance has a sensitivity of 100% with specificity between 74% and 90%. Cases needing immediate referral are detected with a sensitivity of 100% and specificity of 97%. The severity classification accuracy is 81% for the moderate case and 100% for severe cases. These results establish the effectiveness of the proposed solution.
Coordination in Software Development

Since its inception, the software industry has been in crisis. As Blazer noted 20 years ago, “[Software] is unreliable, delivered late, unresponsive to change, inefficient, and expensive … and has been for the past 20 years” . In a survey of software contractors and government contract officers, over half of the respondents believed that calendar overruns, cost overruns, code that required in-house modifications before being usable, and code that was difficult to modify were common problems in the software projects they supervised [22]. Even today, problems with software systems are common and highly-publicized occurrences.
Algorithms for RealTime Object Detection in Images

The field of Computer Vision (CV) is still in its infancy. It has many real-world applications, and many breakthroughs are yet to be made. Most of the companies in existence today that have products based on CV can be divided into three main categories: auto manufacturing, computer circuit manufacturing, and face recognition. There are other smaller categories of this field that are beginning to be developed in industry such as pharmaceutical manufacturing applications and traffic control. Auto manufacturing employs CV through the use of robots that put the cars together. Computer circuit manufacturers use CV to visually check circuits in a production line against a working template of that circuit. CV is used as quality control in this case. The third most common application of CV is in face recognition. This field has become popular in the last few years with the advent of more sophisticated and accurate methods of facial recognition. Applications of this technology are used in security situations like checking for hooligans at sporting events and identifying known thieves and cheats in casinos. There is also the related field of biometrics where retinal scans, fingerprint analysis, and other identification methods are conducted using CV methods. Traffic control is also of interest because CV software systems can be applied to already existing hardware in this field. By traffic control, we mean the regulation or overview of motor traffic by means of the already existing and functioning array of police monitoring equipment. Cameras are already present at busy intersections, highways, and other junctions for the purposes of regulating traffic, spotting problems, and enforcing laws such as running red lights. CV could be used to make all of these tasks automatic.