nils-herrmann/clean_scirepeval huggingface dataset:
Aspect-based opinion mining from product reviews

"What other people think" has always been an important piece of information for most of us during the decision-making process. Today people tend to make their opinions available to other people via the Internet. As a result, the Web has become an excellent source of consumer opinions. There are now numerous Web resources containing such opinions, e.g., product reviews forums, discussion groups, and blogs. But, it is really difficult for a customer to read all of the reviews and make an informed decision on whether to purchase the product. It is also difficult for the manufacturer of the product to keep track and manage customer opinions. Also, focusing on just user ratings (stars) is not a sufficient source of information for a user or the manufacturer to make decisions. Therefore, mining online reviews (opinion mining) has emerged as an interesting new research direction. Extracting aspects and the corresponding ratings is an important challenge in opinion mining. An aspect is an attribute or component of a product, e.g. 'zoom' for a digital camera. A rating is an intended interpretation of the user satisfaction in terms of numerical values. Reviewers usually express the rating of an aspect by a set of sentiments, e.g. 'great zoom'. In this tutorial we cover opinion mining in online product reviews with the focus on aspect-based opinion mining. This problem is a key task in the area of opinion mining and has attracted a lot of researchers in the information retrieval community recently. Several opinion related information retrieval tasks can benefit from the results of aspect-based opinion mining and therefore it is considered as a fundamental problem. This tutorial covers not only general opinion mining and retrieval tasks, but also state-of-the-art methods, challenges, applications, and also future research directions of aspect-based opinion mining.
Active Suspension Control With Direct-Drive Tubular Linear Brushless Permanent-Magnet Motor

Recently, active suspension is gaining popularity in commercial automobiles. To develop the control methodologies for active suspension control, a quarter-car test bed was built employing a direct-drive tubular linear brushless permanent-magnet motor (LBPMM) as a force-generating component. Two accelerometers and a linear variable differential transformer (LVDT) are used in this quarter-car test bed. Three pulse-width-modulation (PWM) amplifiers supply the currents in three phases. Simulated road disturbance is generated by a rotating cam. Modified lead-lag control, linear-quadratic (LQ) servo control with a Kalman filter, fuzzy control methodologies were implemented for active-suspension control. In the case of fuzzy control, an asymmetric membership function was introduced to eliminate the DC offset in sensor data and to reduce the discrepancy in the models. This controller could attenuate road disturbance by up to 77% in the sprung mass velocity and 69% in acceleration. The velocity and the acceleration data of the sprung mass are presented to compare the controllers' performance in the ride comfort of a vehicle. Both simulation and experimental results are presented to demonstrate the effectiveness of these control methodologies.
Deep Linear Discriminant Analysis

We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns linearly separable latent representations in an end-to-end fashion. Classic LDA extracts features which preserve class separability and is used for dimensionality reduction for many classification problems. The central idea of this paper is to put LDA on top of a deep neural network. This can be seen as a non-linear extension of classic LDA. Instead of maximizing the likelihood of target labels for individual samples, we propose an objective function that pushes the network to produce feature distributions which: (a) have low variance within the same class and (b) high variance between different classes. Our objective is derived from the general LDA eigenvalue problem and still allows to train with stochastic gradient descent and back-propagation. For evaluation we test our approach on three different benchmark datasets (MNIST, CIFAR-10 and STL-10). DeepLDA produces competitive results on MNIST and CIFAR-10 and outperforms a network trained with categorical cross entropy (same architecture) on a supervised setting of STL-10.
Linux kernel projects for an undergraduate operating systems course

In this paper, we present a series of programming projects based on the Linux kernel for students in a senior-level undergraduate operating systems course. The projects we describe cover several key operating systems concepts, including process scheduling, I/O scheduling, memory management, and device drivers. In addition, we assess these projects along several dimensions, from their difficulty to their capacity to help students understand operating systems concepts, based on six terms (three years) of detailed student exit surveys along with observations and anecdotal evidence. Through this assessment, we conclude that our Linux-based projects are an effective means by which to teach operating systems concepts and, additionally, that students' response to these projects is overwhelmingly positive.
Incremental Learning with Support Vector Machines

Support Vector Machines (SVMs) have become a popular tool for learning with large amounts of high dimensional data. However, it may sometimes be preferable to learn incrementally from previous SVM results, as computing a SVM is very costly in terms of time and memory consumption or because the SVM may be used in an online learning setting. In this paper an approach for incremental learning with Support Vector Machines is presented, that improves existing approaches. Empirical evidence is given to prove that this approach can effectively deal with changes in the target concept that are results of the incremental learning setting.
Spatial Resolution Enhancement of Low-Resolution Image Sequences A Comprehensive Review with Directions for Future Research

Recent years have seen growing interest in the problem of sup er-resolution restoration of video sequences. Whereas in the traditional single image re storation problem only a single input image is available for processing, the task of reconst ructing super-resolution images from multiple undersampled and degraded images can take adv antage of the additional spatiotemporal data available in the image sequence. In particula r, camera and scene motion lead to frames in the source video sequence containing similar, b ut not identical information. The additional information available in these frames make poss ible reconstruction of visually superior frames at higher resolution than that of the original d ta. In this paper we review the current state of the art and identify promising directions f or future research. The authors are with the Laboratory for Image and Signal Analysis (LIS A), University of Notre Dame, Notre Dame, IN 46556.
Improving antibiotic prescribing for children in the resource-poor setting

Antibiotics are a critically important part of paediatric medical care in low- and middle-income countries (LMICs), where infectious diseases are the leading cause of child mortality. The World Health Organization estimates that >50% of all medicines are prescribed, dispensed or sold inappropriately and that half of all patients do not take their medicines correctly. Given the rising prevalence of antimicrobial resistance globally, inappropriate antibiotic use is of international concern, and countries struggle to implement basic policies promoting rational antibiotic use. Many barriers to rational paediatric prescribing in LMICs persist. The World Health Organization initiatives, such as 'Make medicines child size', the Model List of Essential Medicines for Children and the Model Formulary for Children, have been significant steps forward. Continued strategies to improve access to appropriate drugs and formulations, in conjunction with improved evidence-based clinical guidelines and dosing recommendations, are essential to the success of such initiatives on both a national and an international level. This paper provides an overview of these issues and considers future developments that may improve LMIC antibiotic prescribing.
Cloud computing service models: A comparative study

Cloud computing still suffer of many security issues that required the researchers to focus on it to make the users fully trust on it. In this paper we explain the security issues which attached to each service models Software as a service (SaaS), Platform as a service (PaaS) and Infrastructure as a service (IaaS). Furthermore, a comparative study has been presented for the three service models to evaluate their performance with rang of specific factors such as characteristics, Typical level of control granted to cloud consumer, provider and consumer activities.
A high-throughput HEVC deblocking filter VLSI architecture for 8k×4k application

As the next generation of video coding standard, High Efficiency Video Coding (HEVC) aims to reduce 50% bit rates in comparison with previous video coding standards. In order to increase the Deblocking Filter (DBF) throughput, we propose a memory of ping-pong and interlacing VLSI architecture to prevent DBF from unnecessarily waiting for pixels in both vertical and horizontal, which only takes 435 cycles at worst to process a LCU of 64×64 pixels size. Based on the memory organization, a four stage pipeline with a PreFilter was proposed to eliminate the data dependence in the filter processing and makes it working on 318M possible. As a result, our design can support 8k×4k@90fps real-time applications with SMIC 0.13um technology at the cost of 62.9k gates.
A Parallel Algorithm Development Model for the GPU Architecture

Parallel computing has been in use for decades, and throughout many researchers have sought to define a model for algorithm design for such a platform. Valiant developed a model for parallel computing, which was later extended to later include multi-core processors, but it still may not be best suited for the unique GPU architecture. With the current advances in high performance computing, it is easy to see the role that GPUs can play, and even easier to see the need for a model for GPU algorithm development. Here we propose a parallel GPU model which offers both a general design and a fine-grained approach, intended to accommodate nearly any GPU architecture. We show how our model can result in significant increases in performance when algorithms are designed based on its principles.
SwingPong: analysis and suggestion based on motion data from mobile sensors for table tennis strokes using decision tree

Table tennis is a sport that everyone can play, regardless of age or gender. It becomes a popular sport since it does not require much space and its equipment is affordable. For beginners, they may not have a chance to get some helpful advice to learn how to control a table tennis ball. Thus, we decided to analyze user's motion data and build a model to develop an application called SwingPong detecting basic table-tennis strokes in order to provide helpful suggestion to players.
 SwingPong is an android application on a smartphone analyzing user's forehand and backhand strokes and providing suggestion back to the user in order to improve his or her swing. There are two main modules in the application:
 Module #1: storing angular acceleration and angular velocity of strokes. When a table tennis ball is hit on the racket, the angular acceleration and angular velocity of user's swing are collected using accelerometer and gyroscope sensors in smartphones. We collected the training data set from table tennis national players, Thai youth national team table tennis athletes and coaches. J48 Decision Tree was used in order to build a model of hitting classification.
 Module #2: providing suggestions. We applied the model from Module#1 to evaluate user's basic strokes and provide alert sound when a ball is hit. After a user hits the ball in a given time, the application analyzes the user's strokes and shows suggestions.
 The experimental results show that SwingPong is able to recognize strokes whether the ball is good (or in), out, or hits the net. The accuracy of classification of forehand strokes is 77.21% and the accuracy of classification of backhand strokes is 69.63%. In addition, SwingPong can provide helpful suggestions so that users can use the feedbacks to improve their swings.
Garment-integrated wetness sensing for leak detection

Detecting fluid emissions (e.g. urination or leaks) that extend beyond containment systems (e.g. diapers or adult pads) is a cause of concern for users and developers of wearable fluid containment products. Immediate, automated detection would allow users to address the situation quickly, preventing medical conditions such as adverse skin effects and avoiding embarrassment. For product development, fluid emission detection systems would enable more accurate and efficient lab and field evaluation of absorbent products. This paper describes the development of a textile-based fluid-detection sensing method that uses a multi-layer "keypad matrix" sensing paradigm using stitched conductive threads. Bench characterization tests determined the effects of sensor spacing, spacer fabric property, and contact pressures on wetness detection for a 5mL minimum benchmark fluid volume. The sensing method and bench-determined requirements were then applied in a close-fitting torso garment for babies that fastens at the crotch (onesie) that is able to detect diaper leakage events. Mannequin testing of the resulting garment confirmed the ability of using wetness sensing timing to infer location of induced 5 mL leaks.
Superquadric Segmentation in Range Images via Fusion of Region and Boundary Information

The high potential of superquadrics as modeling elements for image segmentation tasks has been pointed out for years in the computer vision community. In this work, we employ superquadrics as modeling elements for multiple object segmentation in range images. Segmentation is executed in two stages: First, a hypothesis about the values of the segmentation parameters is generated. Second, the hypothesis is refined locally. In both stages, object boundary and region information are considered. Boundary information is derived via model-based edge detection in the input range image. Hypothesis generation uses boundary information to isolate image regions that can be accurately described by superquadrics. Within hypothesis refinement, a game-theoretic framework is used to fuse the two information sources by associating an objective function to each information source. Iterative optimization of the two objective functions in succession, outputs a precise description of all image objects. We demonstrate experimentally that this approach substantially improves the most established method in superquadric segmentation in terms of accuracy and computational efficiency. We demonstrate the applicability of our segmentation framework in real-world applications by constructing a novel robotic system for automatic unloading of jumbled box-like objects from platforms.
A Novel Approach for Event Detection by Mining Spatio-temporal Information on Microblogs

Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular microblogging services such as Twitter did provide information of top trending topics for selection, it is still unable to fully support users pickup all of the real-time event topics with a comprehensive spatio-temporal viewpoint to satisfy their information needs. This paper aims to enhance the understanding on how social networks can be used as a reliable source of spatio-temporal information, by analyzing the temporal and spatial dynamics of Twitter activity. In this work, we developed several algorithms for mining microblogging text stream to obtain real-time and geospatial event information. The goal of our approach is to effectively detecting and grouping emerging topics by making use of real-time messages and geolocation data provided by social network services.
The wisdom hierarchy: representations of the DIKW hierarchy

This paper revisits the data–information–knowledge–wisdom (DIKW) hierarchy by examining the articulation of the hierarchy in a number of widely read textbooks, and analysing their statements about the nature of data, information, knowledge, and wisdom. The hierarchy referred to variously as the ‘Knowledge Hierarchy’, the ‘Information Hierarchy’ and the ‘Knowledge Pyramid’ is one of the fundamental, widely recognized and ‘taken-for-granted’ models in the information and knowledge literatures. It is often quoted, or used implicitly, in definitions of data, information and knowledge in the information management, information systems and knowledge management literatures, but there has been limited direct discussion of the hierarchy. After revisiting Ackoff’s original articulation of the hierarchy, definitions of data, information, knowledge and wisdom as articulated in recent textbooks in information systems and knowledge management are reviewed and assessed, in pursuit of a consensus on definitions and transformation processes. This process brings to the surface the extent of agreement and dissent in relation to these definitions, and provides a basis for a discussion as to whether these articulations present an adequate distinction between data, information, and knowledge. Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge, but there is less consensus in the description of the processes that transform elements lower in the hierarchy into those above them, leading to a lack of definitional clarity. In addition, there is limited reference to wisdom in these texts.
The Post Adoption Switching Of Social Network Service: A Human Migratory Model

Becoming members of social networking websites is now one of the most popular on-line activities. Although the number of users has been growing exponentially in social network sites (SNS), some SNSs are facing a financial crisis and might be shut down in the near future. Therefore, understand users’ incentives to switch to another SNS has great influence on operators’ business performance. The study extended “Push-Pull” migratory theory to explain the switching behaviors of users in SNS. Structural equation modeling (SEM) was applied to analyze data collected from 618 users by questionnaire survey. The result shows that pull effects have the greatest influence on users’ switching behaviors, followed by push effects. As a result, the study suggests that operators should satisfy users’ pursuit of freshness, and carefully examine whether their services could address users’ needs of socializing and entertainment.
Applications of thermal infrared imaging for research in aeroecology.

The night sky remains a largely unexplored frontier for biologists studying the behavior and physiology of free-ranging, nocturnal organisms. Conventional imaging tools and techniques such as night-vision scopes, infrared-reflectance cameras, flash cameras, and radar provide insufficient detail for the scale and resolution demanded by field researchers. A new tool is needed that is capable of imaging noninvasively in the dark at high-temporal and spatial resolution. Thermal infrared imaging represents the most promising such technology that is poised to revolutionize our ability to observe and document the behavior of free-ranging organisms in the dark. Herein we present several examples from our research on free-ranging bats that highlight the power and potential of thermal infrared imaging for the study of animal behavior, energetics and censusing of large colonies, among others. Using never-before-seen video footage and data, we have begun to answer questions that have puzzled biologists for decades, as well as to generate new hypotheses and insight. As we begin to appreciate the functional significance of the aerosphere as a dynamic environment that affects organisms at different spatial and temporal scales, thermal infrared imaging can be at the forefront of the effort to explore this next frontier.
Computer-Automated Malaria Diagnosis and Quantitation Using Convolutional Neural Networks

The optical microscope remains a widely-used tool for diagnosis and quantitation of malaria. An automated system that can match the performance of well-trained technicians is motivated by a shortage of trained microscopists. We have developed a computer vision system that leverages deep learning to identify malaria parasites in micrographs of standard, field-prepared thick blood films. The prototype application diagnoses P. falciparum with sufficient accuracy to achieve competency level 1 in the World Health Organization external competency assessment, and quantitates with sufficient accuracy for use in drug resistance studies. A suite of new computer vision techniques-global white balance, adaptive nonlinear grayscale, and a novel augmentation scheme-underpin the system's state-of-the-art performance. We outline a rich, global training set; describe the algorithm in detail; argue for patient-level performance metrics for the evaluation of automated diagnosis methods; and provide results for P. falciparum.
Taste receptors in the gastrointestinal tract. V. Acid sensing in the gastrointestinal tract.

Luminal acidity is a physiological challenge in the foregut, and acidosis can occur throughout the gastrointestinal tract as a result of inflammation or ischemia. These conditions are surveyed by an elaborate network of acid-governed mechanisms to maintain homeostasis. Deviations from physiological values of extracellular pH are monitored by multiple acid sensors expressed by epithelial cells and sensory neurons. Acid-sensing ion channels are activated by moderate acidification, whereas transient receptor potential ion channels of the vanilloid subtype are gated by severe acidosis. Some ionotropic purinoceptor ion channels and two-pore domain background K(+) channels are also sensitive to alterations of extracellular pH.
A Framework for Decision-Theoretic Planning I: Combining the Situation Calculus, Conditional Plans, Probability and Utility

This paper shows how we can combine logical representations of actions and decision theory in such a manner that seems natural for both. In particular we assume an axiomatization of the domain in terms of situation calculus, using what is essentially Reiter’s solution to the frame problem, in terms of the completion of the axioms defining the state change. Uncertainty is handled in terms of the independent choice logic, which allows for independent choices and a logic program that gives the consequences of the choices. As part of the consequences are a specification of the utility of (final) states. The robot adopts robot plans, similar to the GOLOG programming language. Within this logic, we can define the expected utility of a conditional plan, based on the axiomatization of the actions, the uncertainty and the utility. The ‘planning’ problem is to find the plan with the highest expected utility. This is related to recent structured representations for POMDPs; here we use stochastic situation calculus rules to specify the state transition function and the reward/value function. Finally we show that with stochastic frame axioms, actions representations in probabilistic STRIPS are exponentially larger than using the representation proposed here.