nils-herrmann/clean_scirepeval huggingface dataset:
TopicSketch: Real-Time Bursty Topic Detection from Twitter

Twitter has become one of the largest microblogging platforms for users around the world to share anything happening around them with friends and beyond. A bursty topic in Twitter is one that triggers a surge of relevant tweets within a short period of time, which often reflects important events of mass interest. How to leverage Twitter for early detection of bursty topics has therefore become an important research problem with immense practical value. Despite the wealth of research work on topic modelling and analysis in Twitter, it remains a challenge to detect bursty topics in real-time. As existing methods can hardly scale to handle the task with the tweet stream in real-time, we propose in this paper <inline-formula><tex-math notation="LaTeX">$\sf {TopicSketch}$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="xie-ieq1-2556661.gif"/></alternatives></inline-formula>, a sketch-based topic model together with a set of techniques to achieve real-time detection. We evaluate our solution on a tweet stream with over 30 million tweets. Our experiment results show both efficiency and effectiveness of our approach. Especially it is also demonstrated that <inline-formula><tex-math notation="LaTeX">$\sf {TopicSketch}$</tex-math> <alternatives><inline-graphic xlink:type="simple" xlink:href="xie-ieq2-2556661.gif"/></alternatives></inline-formula> on a single machine can potentially handle hundreds of millions tweets per day, which is on the same scale of the total number of daily tweets in Twitter, and present bursty events in finer-granularity.
Toward integrating feature selection algorithms for classification and clustering

This paper introduces concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks, reveals unattempted combinations, and provides guidelines in selecting feature selection algorithms. With the categorizing framework, we continue our efforts toward-building an integrated system for intelligent feature selection. A unifying platform is proposed as an intermediate step. An illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms. An added advantage of doing so is to help a user employ a suitable algorithm without knowing details of each algorithm. Some real-world applications are included to demonstrate the use of feature selection in data mining. We conclude this work by identifying trends and challenges of feature selection research and development.
A Log-Linear Model for Unsupervised Text Normalization

We present a unified unsupervised statistical model for text normalization. The relationship between standard and non-standard tokens is characterized by a log-linear model, permitting arbitrary features. The weights of these features are trained in a maximumlikelihood framework, employing a novel sequential Monte Carlo training algorithm to overcome the large label space, which would be impractical for traditional dynamic programming solutions. This model is implemented in a normalization system called UNLOL, which achieves the best known results on two normalization datasets, outperforming more complex systems. We use the output of UNLOL to automatically normalize a large corpus of social media text, revealing a set of coherent orthographic styles that underlie online language variation.
Local Deep-Feature Alignment for Unsupervised Dimension Reduction

This paper presents an unsupervised deep-learning framework named local deep-feature alignment (LDFA) for dimension reduction. We construct neighbourhood for each data sample and learn a local stacked contractive auto-encoder (SCAE) from the neighbourhood to extract the local deep features. Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features. Moreover, we derive an approach from LDFA to map explicitly a new data sample into the learned low-dimensional subspace. The advantage of the LDFA method is that it learns both local and global characteristics of the data sample set: the local SCAEs capture local characteristics contained in the data set, while the global alignment procedures encode the interdependencies between neighbourhoods into the final low-dimensional feature representations. Experimental results on data visualization, clustering, and classification show that the LDFA method is competitive with several well-known dimension reduction techniques, and exploiting locality in deep learning is a research topic worth further exploring.
SPH Fluids in Computer Graphics

Smoothed Particle Hydrodynamics (SPH) has been established as one of the major concepts for fluid animation in computer graphics. While SPH initially gained popularity for interactive free-surface scenarios, it has emerged to be a fully fledged technique for state-of-the-art fluid animation with versatile effects. Nowadays, complex scenes with millions of sampling points, oneand two-way coupled rigid and elastic solids, multiple phases and additional features such as foam or air bubbles can be computed at reasonable expense. This state-of-the-art report summarizes SPH research within the graphics community.
Pragmatic model of patient satisfaction in general practice: progress towards a theory.

A major problem in the measurement of patient satisfaction is the lack of an adequate theory to explain the meaning of satisfaction, and hence how it should be measured and how the findings are interpreted. Because of the lack of a fully developed theory, when developing patient satisfaction questionnaires for use in general practice, a simple model was used. This model was pragmatic in that it linked together empirical evidence about patient satisfaction without recourse to more general social or psychological theory of behaviour, other than to define satisfaction as an attitude. Several studies with the questionnaires confirm in general the components of the model. However, the importance of personal care had not been sufficiently emphasised, and therefore the model has been revised. It can now serve as a basis for future research into patient satisfaction, in particular as a stimulus for investigating the links between components of the model and underlying psychological or other behavioural theories.
A Cubic 3-Axis Magnetic Sensor Array for Wirelessly Tracking Magnet Position and Orientation

In medical diagnoses and treatments, e.g., endoscopy, dosage transition monitoring, it is often desirable to wirelessly track an object that moves through the human GI tract. In this paper, we propose a magnetic localization and orientation system for such applications. This system uses a small magnet enclosed in the object to serve as excitation source, so it does not require the connection wire and power supply for the excitation signal. When the magnet moves, it establishes a static magnetic field around, whose intensity is related to the magnet's position and orientation. With the magnetic sensors, the magnetic intensities in some predetermined spatial positions can be detected, and the magnet's position and orientation parameters can be computed based on an appropriate algorithm. Here, we propose a real-time tracking system developed by a cubic magnetic sensor array made of Honeywell 3-axis magnetic sensors, HMC1043. Using some efficient software modules and calibration methods, the system can achieve satisfactory tracking accuracy if the cubic sensor array has enough number of 3-axis magnetic sensors. The experimental results show that the average localization error is 1.8 mm.
Twitter Sentiment Analysis with Recursive Neural Networks

In this paper, we explore the application of Recursive Neural Networks on the sentiment analysis task with tweets. Tweets, being a form of communication that has been largely infused with symbols and short-hands, are especially challenging as a sentiment analysis task. In this project, we experiment with different genres of neural net and analyze how models suit the data set in which the nature of the data and model structures come to play. The neural net structures we experimented include one-hidden-layer Recursive Neural Net (RNN), two-hidden-layer RNN and Recursive Neural Tensor Net (RNTN). Different data filtering layers, such as ReLU, tanh, and drop-out also yields many insights while different combination of them might affect the performance in different ways.
An Integrated Three-Port Bidirectional DC–DC Converter for PV Application on a DC Distribution System

In this paper, an integrated three-port bidirectional dc-dc converter for a dc distribution system is presented. One port of the low-voltage side of the proposed converter is chosen as a current source port which fits for photovoltaic (PV) panels with wide voltage variation. In addition, the interleaved structure of the current source port can provide the desired small current ripple to benefit the PV panel to achieve the maximum power point tracking (MPPT). Another port of the low-voltage side is chosen as a voltage source port interfaced with battery that has small voltage variation; therefore, the PV panel and energy storage element can be integrated by using one converter topology. The voltage port on the high-voltage side will be connected to the dc distribution bus. A high-frequency transformer of the proposed converter not only provides galvanic isolation between energy sources and high voltage dc bus, but also helps to remove the leakage current resulted from PV panels. The MPPT and power flow regulations are realized by duty cycle control and phase-shift angle control, respectively. Different from the single-phase dual-half-bridge converter, the power flow between the low-voltage side and high-voltage side is only related to the phase-shift angle in a large operation area. The system operation modes under different conditions are analyzed and the zero-voltage switching can be guaranteed in the PV application even when the dc-link voltage varies. Finally, system simulation and experimental results on a 3-kW hardware prototype are presented to verify the proposed technology.
Image Attributes and Quality for Evaluation of Tone Mapping Operators

The problem of reproducing high dynamic range images on devices with restricted dynamic range has gained a lot of interest in the computer graphics community. There exist various approaches to this issue, which span several research areas including computer graphics, image processing, color science, physiology, neurology, psychology, et c. These approaches assume a thorough knowledge of both the objective and subjective attributes of an image. However, no comprehensive overview and analysis of such attributes has been published so far. In this paper, we present an overview of image quality attributes of different tone mapping methods. Furthermore, we propose a scheme of relationships between these attributes, leading to the definition of an overall image qua lity measure. We present results of subjective psychophysic al tests that we have performed to prove the proposed relationship scheme. We also present the evaluation of existing tone mapping methods with regard to these attributes. Our effort is not just useful to get into the tone mapping field or when implementing a tone mapping operator, but it also sets the stage for well-founded quality comparisons between tone mapping operators. By providing good definitions of the different attributes, user-driven or fully a utomatic comparisons are made possible at all.
Advanced Control Architectures for Intelligent Microgrids—Part II: Power Quality, Energy Storage, and AC/DC Microgrids

This paper summarizes the main problems and solutions of power quality in microgrids, distributed-energy-storage systems, and ac/dc hybrid microgrids. First, the power quality enhancement of grid-interactive microgrids is presented. Then, the cooperative control for enhance voltage harmonics and unbalances in microgrids is reviewed. Afterward, the use of static synchronous compensator (STATCOM) in grid-connected microgrids is introduced in order to improve voltage sags/swells and unbalances. Finally, the coordinated control of distributed storage systems and ac/dc hybrid microgrids is explained.
Enhancing mobile apps to use sensor hubs without programmer effort

Always-on continuous sensing apps drain the battery quickly because they prevent the main processor from sleeping. Instead, sensor hub hardware, available in many smartphones today, can run continuous sensing at lower power while keeping the main processor idle. However, developers have to divide functionality between the main processor and the sensor hub. We implement MobileHub, a system that automatically rewrites applications to leverage the sensor hub without additional programming effort. MobileHub uses a combination of dynamic taint tracking and machine learning to learn when it is safe to leverage the sensor hub without affecting application semantics. We implement MobileHub in Android and prototype a sensor hub on a 8-bit AVR micro-controller. We experiment with 20 applications from Google Play. Our evaluation shows that MobileHub significantly reduces power consumption for continuous sensing apps.
Smartphone security evaluation The malware attack case

The adoption of smartphones, devices transforming from simple communication devices to ‘smart’ and multipurpose devices, is constantly increasing. Amongst the main reasons are their small size, their enhanced functionality and their ability to host many useful and attractive applications. However, this vast use of mobile platforms makes them an attractive target for conducting privacy and security attacks. This scenario increases the risk introduced by these attacks for personal mobile devices, given that the use of smartphones as business tools may extend the perimeter of an organization's IT infrastructure. Furthermore, smartphone platforms provide application developers with rich capabilities, which can be used to compromise the security and privacy of the device holder and her environment (private and/or organizational). This paper examines the feasibility of malware development in smartphone platforms by average programmers that have access to the official tools and programming libraries provided by smartphone platforms. Towards this direction in this paper we initially propose specific evaluation criteria assessing the security level of the well-known smartphone platforms (i.e. Android, BlackBerry, Apple iOS, Symbian, Windows Mobile), in terms of the development of malware. In the sequel, we provide a comparative analysis, based on a proof of concept study, in which the implementation and distribution of a location tracking malware is attempted. Our study has proven that, under circumstances, all smartphone platforms could be used by average developers as privacy attack vectors, harvesting data from the device without the users knowledge and consent.
The Token Economy : A Recent Review and Evaluation

This article presents a recent and inclusive review of the use of token economies in various environments (schools, home, etc.). Digital and manual searches were carried using the following databases: Google Scholar, Psych Info (EBSCO), and The Web of Knowledge. The search terms included: token economy, token systems, token reinforcement, behavior modification, classroom management, operant conditioning, animal behavior, token literature reviews, and token economy concerns. The criteria for inclusion were studies that implemented token economies in settings where academics were assessed. Token economies have been extensively implemented and evaluated in the past. Few articles in the peerreviewed literature were found being published recently. While token economy reviews have occurred historically (Kazdin, 1972, 1977, 1982), there has been no recent overview of the research. During the previous several years, token economies in relation to certain disorders have been analyzed and reviewed; however, a recent review of token economies as a field of study has not been carried out. The purpose of this literature review was to produce a recent review and evaluation on the research of token economies across settings.
Performance Modeling of PBFT Consensus Process for Permissioned Blockchain Network (Hyperledger Fabric)

While Blockchain network brings tremendous benefits, there are concerns whether their performance would match up with the mainstream IT systems. This paper aims to investigate whether the consensus process using Practical Byzantine Fault Tolerance (PBFT) could be a performance bottleneck for networks with a large number of peers. We model the PBFT consensus process using Stochastic Reward Nets (SRN) to compute the mean time to complete consensus for networks up to 100 peers. We create a blockchain network using IBM Bluemix service, running a production-grade IoT application and use the data to parameterize and validate our models. We also conduct sensitivity analysis over a variety of system parameters and examine the performance of larger networks
A Framework for Efficient Storage Security in RDBMS

With the widespread use of e-business coupled with the public’s awareness of data privacy issues and recent database security related legislations, incorporating security features into modern database products has become an increasingly important topic. Several database vendors already offer integrated solutions that provide data privacy within existing products. However, treating security and privacy issues as an afterthought often results in inefficient implementations. Some notable RDBMS storage models (such as the N-ary Storage Model) suffer from this problem. In this work, we analyze issues in storage security and discuss a number of trade-offs between security and efficiency. We then propose a new secure storage model and a key management architecture which enable efficient cryptographic operations while maintaining a very high level of security. We also assess the performance of our proposed model by experimenting with a prototype implementation based on the well-known TPC-H data set.
Dropout Training as Adaptive Regularization

Dropout and other feature noising schemes control overfitting by artificially corrupting the training data. For generalized linear models, dropout performs a form of adaptive regularization. Using this viewpoint, we show that the dropout regularizer is first-order equivalent to an L2 regularizer applied after scaling the features by an estimate of the inverse diagonal Fisher information matrix. We also establish a connection to AdaGrad, an online learning algorithm, and find that a close relative of AdaGrad operates by repeatedly solving linear dropout-regularized problems. By casting dropout as regularization, we develop a natural semi-supervised algorithm that uses unlabeled data to create a better adaptive regularizer. We apply this idea to document classification tasks, and show that it consistently boosts the performance of dropout training, improving on state-of-the-art results on the IMDB reviews dataset.
An Approach to Identify Duplicated Web Pages

A relevant consequence of the unceasing expansion of the Web and e-commerce is the growth of the demand ofnew Web sites and Web applications. As a result, Web sites and applications are usually developed without a fomlalized process, but Web pages are directly coded in an incremental way, where ne}10' pages are obtained by duplicating existing ones. Duplicated Web pages, having the same structure and just differing for the data they include, can be considered as clones. The identification of clones may reduce the effort devoted to test, maintain and evolve Web sites and applications. Moreover, clone detection among different Web sites aims to detect cases ofpossible plagiarism. In this paper we propose an approach, based on similarity metrics, to detect duplicated pages in Web sites and applications, implemented with HTML language and ASP technology. The proposed approach has been assessed by analyzing several Web sites and Web applications. The obtained results are reported in the paper with respect to some case studies.
Reasoning about Quantities in Natural Language

Little work from the Natural Language Processing community has targeted the role of quantities in Natural Language Understanding. This paper takes some key steps towards facilitating reasoning about quantities expressed in natural language. We investigate two different tasks of numerical reasoning. First, we consider Quantity Entailment, a new task formulated to understand the role of quantities in general textual inference tasks. Second, we consider the problem of automatically understanding and solving elementary school math word problems. In order to address these quantitative reasoning problems we first develop a computational approach which we show to successfully recognize and normalize textual expressions of quantities. We then use these capabilities to further develop algorithms to assist reasoning in the context of the aforementioned tasks.
Personalized In-store E-Commerce with the PromoPad: an Augmented Reality Shopping Assistant

This paper presents an in-store e-commerce system that provides shopping assistance and personalized advertising through the use of a new concept in context aware computing, dynamic contextualization. This system, PromoPad, utilizes augmented reality technologies on a hand-held Tablet PC to provide for dynamic modification of the contextual settings of products on store shelves through the use of see-through vision with augmentations. This real-time modification of the perception of context, dynamic contextualization, moves beyond the traditional concept of contextaware computing into context modification. The technical requirements for realizing dynamic contextualization using augmented reality technologies are described in detail. The target design of the PromoPad is a consumer friendly shopping assistant that requires minimum user effort and is practical in a public environment such as a shopping mall or a grocery store.
PAC model-free reinforcement learning

For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm---Delayed Q-Learning. We prove it is PAC, achieving near optimal performance except for Õ(SA) timesteps using O(SA) space, improving on the Õ(S2 A) bounds of best previous algorithms. This result proves efficient reinforcement learning is possible without learning a model of the MDP from experience. Learning takes place from a single continuous thread of experience---no resets nor parallel sampling is used. Beyond its smaller storage and experience requirements, Delayed Q-learning's per-experience computation cost is much less than that of previous PAC algorithms.
Unifying Count-Based Exploration and Intrinsic Motivation

We consider an agent’s uncertainty about its environment and the problem of generalizing this uncertainty across states. Specifically, we focus on the problem of exploration in non-tabular reinforcement learning. Drawing inspiration from the intrinsic motivation literature, we use density models to measure uncertainty, and propose a novel algorithm for deriving a pseudo-count from an arbitrary density model. This technique enables us to generalize count-based exploration algorithms to the non-tabular case. We apply our ideas to Atari 2600 games, providing sensible pseudo-counts from raw pixels. We transform these pseudo-counts into exploration bonuses and obtain significantly improved exploration in a number of hard games, including the infamously difficult MONTEZUMA’S REVENGE.