nils-herrmann/clean_scirepeval huggingface dataset:
Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak

Surveys are popular methods to measure public perceptions in emergencies but can be costly and time consuming. We suggest and evaluate a complementary "infoveillance" approach using Twitter during the 2009 H1N1 pandemic. Our study aimed to: 1) monitor the use of the terms "H1N1" versus "swine flu" over time; 2) conduct a content analysis of "tweets"; and 3) validate Twitter as a real-time content, sentiment, and public attention trend-tracking tool.



Between May 1 and December 31, 2009, we archived over 2 million Twitter posts containing keywords "swine flu," "swineflu," and/or "H1N1." using Infovigil, an infoveillance system. Tweets using "H1N1" increased from 8.8% to 40.5% (R(2) = .788; p<.001), indicating a gradual adoption of World Health Organization-recommended terminology. 5,395 tweets were randomly selected from 9 days, 4 weeks apart and coded using a tri-axial coding scheme. To track tweet content and to test the feasibility of automated coding, we created database queries for keywords and correlated these results with manual coding. Content analysis indicated resource-related posts were most commonly shared (52.6%). 4.5% of cases were identified as misinformation. News websites were the most popular sources (23.2%), while government and health agencies were linked only 1.5% of the time. 7/10 automated queries correlated with manual coding. Several Twitter activity peaks coincided with major news stories. Our results correlated well with H1N1 incidence data.



This study illustrates the potential of using social media to conduct "infodemiology" studies for public health. 2009 H1N1-related tweets were primarily used to disseminate information from credible sources, but were also a source of opinions and experiences. Tweets can be used for real-time content analysis and knowledge translation research, allowing health authorities to respond to public concerns.
Co-segmentation by Composition

Given a set of images which share an object from the same semantic category, we would like to co-segment the shared object. We define 'good' co-segments to be ones which can be easily composed (like a puzzle) from large pieces of other co-segments, yet are difficult to compose from remaining image parts. These pieces must not only match well but also be statistically significant (hard to compose at random). This gives rise to co-segmentation of objects in very challenging scenarios with large variations in appearance, shape and large amounts of clutter. We further show how multiple images can collaborate and "score" each others' co-segments to improve the overall fidelity and accuracy of the co-segmentation. Our co-segmentation can be applied both to large image collections, as well as to very few images (where there is too little data for unsupervised learning). At the extreme, it can be applied even to a single image, to extract its co-occurring objects. Our approach obtains state-of-the-art results on benchmark datasets. We further show very encouraging co-segmentation results on the challenging PASCAL-VOC dataset.
Multimodal Similarity-Preserving Hashing

We introduce an efficient computational framework for hashing data belonging to multiple modalities into a single representation space where they become mutually comparable. The proposed approach is based on a novel coupled siamese neural network architecture and allows unified treatment of intra- and inter-modality similarity learning. Unlike existing cross-modality similarity learning approaches, our hashing functions are not limited to binarized linear projections and can assume arbitrarily complex forms. We show experimentally that our method significantly outperforms state-of-the-art hashing approaches on multimedia retrieval tasks.
Bimanual compliant tactile exploration for grasping unknown objects

Humans have an incredible capacity to learn properties of objects by pure tactile exploration with their two hands. With robots moving into human-centred environment, tactile exploration becomes more and more important as vision may be occluded easily by obstacles or fail because of different illumination conditions. In this paper, we present our first results on bimanual compliant tactile exploration, with the goal to identify objects and grasp them. An exploration strategy is proposed to guide the motion of the two arms and fingers along the object. From this tactile exploration, a point cloud is obtained for each object. As the point cloud is intrinsically noisy and un-uniformly distributed, a filter based on Gaussian Processes is proposed to smooth the data. This data is used at runtime for object identification. Experiments on an iCub humanoid robot have been conducted to validate our approach.
Keyphrase Extraction in Scientific Publications

We present a keyphrase extraction algorithm for scientific p ublications. Different from previous work, we introduce features that capture the positions of phrases in document with respect to logical section s f und in scientific discourse. We also introduce features that capture salient morphological phenomena found in scientific keyphrases, such as whether a candida te keyphrase is an acronyms or uses specific terminologically productive suffi xes. We have implemented these features on top of a baseline feature set used by Kea . In our evaluation using a corpus of 120 scientific publications mul tiply annotated for keyphrases, our system significantly outperformed Kea at th e p < .05 level. As we know of no other existing multiply annotated keyphrase do cument collections, we have also made our evaluation corpus publicly avai lable. We hope that this contribution will spur future comparative research.
COUNTERFACTUAL DECOMPOSITION OF CHANGES IN WAGE DISTRIBUTIONS USING QUANTILE REGRESSION

We propose a method to decompose the changes in the wage distribution over a period of time in several factors contributing to those changes. The method is based on the estimation of marginal wage distributions consistent with a conditional distribution estimated by quantile regression as well as with any hypothesized distribution for the covariates. Comparing the marginal distributions implied by different distributions for the covariates, one is then able to perform counterfactual exercises. The proposed methodology enables the identification of the sources of the increased wage inequality observed in most countries. Specifically, it decomposes the changes in the wage distribution over a period of time into several factors contributing to those changes, namely by discriminating between changes in the characteristics of the working population and changes in the returns to these characteristics. We apply this methodology to Portuguese data for the period 1986–1995, and find that the observed increase in educational levels contributed decisively towards greater wage inequality.
Sensing techniques for mobile interaction

We describe sensing techniques motivated by unique aspects of human-computer interaction with handheld devices in mobile settings. Special features of mobile interaction include changing orientation and position, changing venues, the use of computing as auxiliary to ongoing, real-world activities like talking to a colleague, and the general intimacy of use for such devices. We introduce and integrate a set of sensors into a handheld device, and demonstrate several new functionalities engendered by the sensors, such as recording memos when the device is held like a cell phone, switching between portrait and landscape display modes by holding the device in the desired orientation, automatically powering up the device when the user picks it up the device to start using it, and scrolling the display using tilt. We present an informal experiment, initial usability testing results, and user reactions to these techniques.
Exploring neural transducers for end-to-end speech recognition

In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition. We show that, without any language model, Seq2Seq and RNN-Transducer models both outperform the best reported CTC models with a language model, on the popular Hub5'00 benchmark. On our internal diverse dataset, these trends continue — RNN-Transducer models rescored with a language model after beam search outperform our best CTC models. These results simplify the speech recognition pipeline so that decoding can now be expressed purely as neural network operations. We also study how the choice of encoder architecture affects the performance of the three models — when all encoder layers are forward only, and when encoders downsample the input representation aggressively.
Learning User Preferences across Multiple Aspects for Merchant Recommendation

With the pervasive use of mobile devices, Location Based Social Networks(LBSNs) have emerged in past years. These LBSNs, allowing their users to share personal experiences and opinions on visited merchants, have very rich and useful information which enables a new breed of location-based services, namely, Merchant Recommendation. Existing techniques for merchant recommendation simply treat each merchant as an item and apply conventional recommendation algorithms, e.g., Collaborative Filtering, to recommend merchants to a target user. However, they do not differentiate the user's real preferences on various aspects, and thus can only achieve limited success. In this paper, we aim to address this problem by utilizing and analyzing user reviews to discover user preferences in different aspects. Following the intuition that a user rating represents a personalized rational choice, we propose a novel utility-based approach by combining collaborative and individual views to estimate user preference (i.e., rating). An optimization algorithm based on a Gaussian model is developed to train our merchant recommendation approach. Lastly we evaluate the proposed approach in terms of effectiveness, efficiency and cold-start using two real-world datasets. The experimental results show that our approach outperforms the state-of-the-art methods. Meanwhile, a real mobile application is implemented to demonstrate the practicability of our method.
nvm malloc: Memory Allocation for NVRAM

Non-volatile main memory (NVRAM) has the potential to fundamentally change the persistency of software. Applications can make their state persistent by directly placing data structures on NVRAM instead of volatile DRAM. However, the persistent nature of NVRAM requires significant changes for memory allocators that are now faced with the additional tasks of data recovery and failure-atomicity. In this paper, we present nvm malloc, a general-purpose memory allocator concept for the NVRAM era as a basic building block for persistent applications. We introduce concepts for managing named allocations for simplified recovery and using volatile and non-volatile memory in combination to provide both high performance and failure-atomic allocations.
Data Mining Techniques for Optimization of Liver Disease Classification

Patients with liver disease continue to increase and the symptoms of the disease is difficult to detect. Therefore many people suffer from liver damage but they feel healthy, it causes many medical practitioners to often fail to detect the disease. Failure to detect can mislead to improper medication and medical treatment. Therefore accurate detection is necessary to help the medical practitioner to give proper medication and medical treatment. Some researchers have been using data mining techniques to classify liver disease. The problem is, it is not easy to have the same consensus for a better algorithm in classifying liver disease. This study aims to identify if the patients have the liver disease based on the 10 important attributes of liver disease using a Decision Tree, Naive Bayes, and NBTree algorithms. The result shows NBTree algorithm has the highest accuracy, however the Naive Bayes algorithm gives the fastest computation time. This study presents promising results in giving recommendation if the patients have the disease.
Visualizing Digital Forensic Datasets: A Proof of Concept.

Digital forensic visualization is an understudied area despite its potential to achieve significant improvements in the efficiency of an investigation, criminal or civil. In this study, a three-stage forensic data storage and visualization life cycle is presented. The first stage is the decoding of data, which involves preparing both structured and unstructured data for storage. In the storage stage, data are stored within our proposed database schema designed for ensuring data integrity and speed of storage and retrieval. The final stage is the visualization of stored data in a manner that facilitates user interaction. These functionalities are implemented in a proof of concept to demonstrate the utility of the proposed life cycle. The proof of concept demonstrates the utility of the proposed approach for the storage and visualization of digital forensic data.
Picture Changes During Blinks : Looking Without Seeing and Seeing Without Looking

Observers inspected normal, high quality colour displays of everyday visual scenes while their eye movements were recorded. A large display change occurred each time an eye blink occurred. Display changes could either involve “Central Interest” or “Marginal Interest” locations, as determined from descriptions obtained from independent judges in a prior pilot experiment. Visual salience, as determined by luminance, colour, and position of the Central and Marginal Interest changes were equalized. The results obtained were very similar to those obtained in prior experiments showing failure to detect changes occurring simultaneously with saccades, flicker, or “mudsplashes” in the visual scene: Many changes were very hard to detect, and Marginal Interest changes were harder to detect than Central Interest changes. Analysis of eye movements showed, as expected, that the probability of detecting a change depended on the eye’s distance from the change location. However a surprising finding was that both for Central and Marginal Interest changes, even when observers were directly fixating the change locations (within 1 degree), more than 40% of the time they still failed to see the changes. It seems that looking at something does not guarantee you “ see” it.
Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety

This article presents an overview of highway cooperative collision avoidance (CCA), which is an emerging vehicular safety application using the IEEE- and ASTM-adopted Dedicated Short Range Communication (DSRC) standard. Along with a description of the DSRC architecture, we introduce the concept of CCA and its implementation requirements in the context of a vehicle-to-vehicle wireless network, primarily at the Medium Access Control (MAC) and the routing layer. An overview is then provided to establish that the MAC and routing protocols from traditional Mobile Ad Hoc networks arc not directly applicable for CCA and similar safety-critical applications. Specific constraints and future research directions are then identified for packet routing protocols used to support such applications in the DSRC environment. In order to further explain the interactions between CCA and its underlying networking protocols, we present an example of the safety performance of CCA using simulated vehicle crash experiments. The results from these experiments arc also used to demonstrate the need for network data prioritization for safety-critical applications such as CCA. Finally, the performance sensitivity of CCA to unreliable wireless channels is discussed based on the experimental results.
Design and control of a bio-inspired soft wearable robotic device for ankle-foot rehabilitation.

We describe the design and control of a wearable robotic device powered by pneumatic artificial muscle actuators for use in ankle-foot rehabilitation. The design is inspired by the biological musculoskeletal system of the human foot and lower leg, mimicking the morphology and the functionality of the biological muscle-tendon-ligament structure. A key feature of the device is its soft structure that provides active assistance without restricting natural degrees of freedom at the ankle joint. Four pneumatic artificial muscles assist dorsiflexion and plantarflexion as well as inversion and eversion. The prototype is also equipped with various embedded sensors for gait pattern analysis. For the subject tested, the prototype is capable of generating an ankle range of motion of 27° (14° dorsiflexion and 13° plantarflexion). The controllability of the system is experimentally demonstrated using a linear time-invariant (LTI) controller. The controller is found using an identified LTI model of the system, resulting from the interaction of the soft orthotic device with a human leg, and model-based classical control design techniques. The suitability of the proposed control strategy is demonstrated with several angle-reference following experiments.
A Nested Two Stage Game-Based Optimization Framework in Mobile Cloud Computing System

The rapidly developing cloud computing and virtualization techniques provide mobile devices with battery energy saving opportunities by allowing them to offload computation and execute applications remotely. A mobile device should judiciously decide whether to offload computation and which portion of application should be offloaded to the cloud. In this paper, we consider a mobile cloud computing (MCC) interaction system consisting of multiple mobile devices and the cloud computing facilities. We provide a nested two stage game formulation for the MCC interaction system. In the first stage, each mobile device determines the portion of its service requests for remote processing in the cloud. In the second stage, the cloud computing facilities allocate a portion of its total resources for service request processing depending on the request arrival rate from all the mobile devices. The objective of each mobile device is to minimize its power consumption as well as the service request response time. The objective of the cloud computing controller is to maximize its own profit. Based on the backward induction principle, we derive the optimal or near-optimal strategy for all the mobile devices as well as the cloud computing controller in the nested two stage game using convex optimization technique. Experimental results demonstrate the effectiveness of the proposed nested two stage game-based optimization framework on the MCC interaction system. The mobile devices can achieve simultaneous reduction in average power consumption and average service request response time, by 21.8% and 31.9%, respectively, compared with baseline methods.
Latent Semantic Word Sense Induction and Disambiguation

In this paper, we present a unified model for the automatic induction of word senses from text, and the subsequent disambiguation of particular word instances using the automatically extracted sense inventory. The induction step and the disambiguation step are based on the same principle: words and contexts are mapped to a limited number of topical dimensions in a latent semantic word space. The intuition is that a particular sense is associated with a particular topic, so that different senses can be discriminated through their association with particular topical dimensions; in a similar vein, a particular instance of a word can be disambiguated by determining its most important topical dimensions. The model is evaluated on the SEMEVAL-2010 word sense induction and disambiguation task, on which it reaches stateof-the-art results.
A Comparison and Analysis of Some Ontology Visualization Tools

This paper presents an analysis and comparison of three existing ontology visualization tools with a new ontology visualization tool developed at the Energy Informatics Laboratory of University of Regina, Canada. The new tool is called Onto3DViz, which is designed as a knowledge engineering support tool for ontology visualization. It aims to address the deficiency that existing tools have in their lack of support for dynamic knowledge visualization. The Onto3DViz is a tool that supports a new approach of visualization in that it supports: (1) dynamic knowledge visualization; and (2) complex ontology visualization in 3-dimensional (3D) computer graphics. This paper also discusses the strengths and weaknesses of the four visualization tool when they are measured against a set of assessment criteria.
Determination of 3D human body postures from a single view

In this paper a method is proposed to recover and interpret the 3D body structures of a person from a single view, provided that (1) at least six feature points on the head and a set of body joints are available on the image plane, and (2) the geometry of head and lengths of body segments formed by joints are known. First of all, the feature points on the head in the head-centered coordinate system and their image projections are used to determine a transformation matrix. Then, the camera position and orientations are extracted from the matrix. Finally, the 3D coordinates of the head points expressed in the camera-centered coordinate system are obtained. Starting from the coordinates of the neck, which is a head feature point, the 3D coordinates of other joints one-by-one are determined under the assumption of the fixed lengths of the body segments. A binary interpretation tree is used to represent the 2”-’ possible body structures, if a human body has n joints. To determine the final feasible body structures, physical and motion constraints are used to prune the interpretation tree. Formulas and rules required for the tree pruning are formulated. Experiments are used to illustrate the pruning powers of these constraints. In the two cases of input data chosen, a unique or nearly unique solution of the body structure is obtained.
Optimal Data-Dependent Hashing for Approximate Near Neighbors

We show an optimal data-dependent hashing scheme for the approximate near neighbor problem. For an n-point dataset in a d-dimensional space our data structure achieves query time O(d ⋅ nρ+o(1)) and space O(n1+ρ+o(1) + d ⋅ n), where ρ=1/(2c2-1) for the Euclidean space and approximation c>1. For the Hamming space, we obtain an exponent of ρ=1/(2c-1). Our result completes the direction set forth in (Andoni, Indyk, Nguyen, Razenshteyn 2014) who gave a proof-of-concept that data-dependent hashing can outperform classic Locality Sensitive Hashing (LSH). In contrast to (Andoni, Indyk, Nguyen, Razenshteyn 2014), the new bound is not only optimal, but in fact improves over the best (optimal) LSH data structures (Indyk, Motwani 1998) (Andoni, Indyk 2006) for all approximation factors c>1.
 From the technical perspective, we proceed by decomposing an arbitrary dataset into several subsets that are, in a certain sense, pseudo-random.
A modular robotic system using magnetic force effectors

One of the primary impediments to building ensembles of modular robots is the complexity and number of mechanical mechanisms used to construct the individual modules. As part of the Claytronics project - which aims to build very large ensembles of modular robots - we investigate how to simplify each module by eliminating moving parts and reducing the number of mechanical mechanisms on each robot by using force-at-a-distance actuators. Additionally, we are also investigating the feasibility of using these unary actuators to improve docking performance, implement intermodule adhesion, power transfer, communication, and sensing. In this paper we describe our most recent results in the magnetic domain, including our first design sufficiently robust to operate reliably in groups greater than two modules. Our work should be seen as an extension of systems such as Fracta [9], and a contrasting line of inquiry to several other researchers' prior efforts that have used magnetic latching to attach modules to one another but relied upon a powered hinge  or telescoping mechanism [12] within each module to facilitate self-reconfiguration.