nils-herrmann/clean_scirepeval huggingface dataset:
A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives

Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches.



The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases.



From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords "flu", "influenza", "gripe", and "high fever". The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis.



Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay's Center and the Atlantic Avenue Terminal.



While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter's strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable.
Large-Scale Named Entity Disambiguation Based on Wikipedia Data

This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disambiguation paradigm employed and the information extraction process from Wikipedia. Through a process of maximizing the agreement between the contextual information extracted from Wikipedia and the context of a document, as well as the agreement among the category tags associated with the candidate entities, the implemented system shows high disambiguation accuracy on both news stories and Wikipedia articles.
Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning

Collaborative technologies support group work in project-based environments. In this study, we enhance the technology acceptance model to explain the factors that influence the acceptance of Google Applications for collaborative learning. The enhanced model was empirically evaluated using survey data collected from 136 students enrolled in a full-time degree program that used Google Applications to support project work. According to the research results, determinants of the technology acceptance model are the major factors influencing the adoption of the technology. In addition, the subjective norm represented by peers is found to significantly moderate the relationship between attitude and intention toward the technology. However, our results do not show a significant effect of subjective norms represented by instructors and mass media on students’ intentions to use the technology. The ability to share information in the collaborative learning environment is found to influence intention and behavior toward the Google Applications platform.
Battery-free wireless identification and sensing

The Wireless Identification and Sensing Platform (WISP) project explores an approach to provide power for sensor networks, based on passive radio-frequency-identification technology. In traditional passive RFID systems, ambient high-power readers interrogate battery-free devices, called tags, that modulate the interrogating signal to communicate a unique identifier to the reader. The WISP project aims to augment RFID tags with sensors so that tags can also send sensed data to the readers. We call these augmented tags wisps. Basing wisps on RFID has some immediate advantages. RFID tags communicate to ambient readers over distances of up to eight meters. Solutions compatible with RFID standards might therefore find quicker acceptance and see faster improvement than other solutions.
Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing

Bayesian compressive sensing (CS) is considered for signals and images that are sparse in a wavelet basis. The statistical structure of the wavelet coefficients is exploited explicitly in the proposed model, and, therefore, this framework goes beyond simply assuming that the data are compressible in a wavelet basis. The structure exploited within the wavelet coefficients is consistent with that used in wavelet-based compression algorithms. A hierarchical Bayesian model is constituted, with efficient inference via Markov chain Monte Carlo (MCMC) sampling. The algorithm is fully developed and demonstrated using several natural images, with performance comparisons to many state-of-the-art compressive-sensing inversion algorithms.
Improved median filtering algorithm for the reduction of impulse noise in corrupted 2d greyscale images

supervision of the undersigned. All works consulted have been duly acknowledged in the references.(SUPERVISOR) DATE SIGNATURE iii CERTIFICATION I certify that this thesis has been assessed and all correction has been made in accordance with the comments made by the Examiners. iv DEDICATION This work is dedicated to my Late Parents Mr. and Mrs. John C. Weyori and my family for the diverse support they gave me throughout my postgraduate study.  Digital images are often corrupted by Impulse noise due to errors generated in noisy sensor, errors that occur in the process of converting signals from analog-to-digital and also errors that are generated in the communication channels. This error that occurs inevitably alters some of the pixels intensity while some of the pixels remain unchanged. In order to remove impulse noise and enhance the affected image quality, the median filter has been studied and a method based on an improved median filtering algorithm has been proposed. This method removes or effectively suppresses the impulse noise in the image whiles preserving the image edges information and enhancing the image quality. The proposed method is a spatial domain approach and uses the overlapping window to filter the signal based on the selection of an effective median per window. The approach chosen in this work is based on a functional level 2n +1 window that makes the selection of the normal median easier, since the number of elements in the window is odd. The median so chosen is confirmed as the effective median or, where the median is an impulse a more representative value is sought and used as the effective median. The improved median filtering algorithms uses the median switching technique to compute an effective median when the active median of the window is an impulse. The performance of the proposed effective median filter has been evaluated in MATLAB using a 3 × 3 fixed window for simulations on an image that has been subjected to various degrees of corruption with impulse noise. The results demonstrate the effectiveness of the proposed algorithm vis-à-vis the standard and adaptive median filtering algorithms, and others. The peak signal-to-noise ratios of the filtered image using the various filtering techniques are computed quantitatively, to show the effectiveness and efficiency of the method of this vi thesis. The peak signal-to-noise ratio has been used to compare the performance of the proposed median filtering algorithm with other digital …
Review on cyber-physical systems

Cyber-physical systems U+0028 CPS U+0029 are complex systems with organic integration and in-depth collaboration of computation, communications and control U+0028 3C U+0029 technology. Subject to the theory and technology of existing network systems and physical systems, the development of CPS is facing enormous challenges. This paper first introduces the concept and characteristics of CPS and analyzes the present situation of CPS researches. Then the development of CPS is discussed from perspectives of system model, information processing technology and software design. At last it analyzes the main obstacles and key researches in developing CPS.
Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network

Word pairs, which are one of the most easily accessible features between two text segments, have been proven to be very useful for detecting the discourse relations held between text segments. However, because of the data sparsity problem, the performance achieved by using word pair features is limited. In this paper, in order to overcome the data sparsity problem, we propose the use of word embeddings to replace the original words. Moreover, we adopt a gated relevance network to capture the semantic interaction between word pairs, and then aggregate those semantic interactions using a pooling layer to select the most informative interactions. Experimental results on Penn Discourse Tree Bank show that the proposed method without using manually designed features can achieve better performance on recognizing the discourse level relations in all of the relations.
Determined to Die! Ability to Act Following Multiple Self-inflicted Gunshot Wounds to the Head. The Cook County Office of Medical Examiner Experience (2005-2012) and Review of Literature.

Cases of multiple (considered 2+) self-inflicted gunshot wounds are a rarity and require careful examination of the scene of occurrence; thorough consideration of the decedent's psychiatric, medical, and social histories; and accurate postmortem documentation of the gunshot wounds. We present a series of four cases of multiple self-inflicted gunshot wounds to the head from the Cook County Medical Examiner's Office between 2005 and 2012 including the first case report of suicide involving eight gunshot wounds to the head. In addition, a review of the literature concerning multiple self-inflicted gunshot wounds to the head is performed. The majority of reported cases document two gunshot entrance wound defects. Temporal regions are the most common affected regions (especially the right and left temples). Determining the capability to act following a gunshot wound to the head is necessary in crime scene reconstruction and in differentiation between homicide and suicide.
Structured Feature Learning for Pose Estimation

In this paper, we propose a structured feature learning framework to reason the correlations among body joints at the feature level in human pose estimation. Different from existing approaches of modeling structures on score maps or predicted labels, feature maps preserve substantially richer descriptions of body joints. The relationships between feature maps of joints are captured with the introduced geometrical transform kernels, which can be easily implemented with a convolution layer. Features and their relationships are jointly learned in an end-to-end learning system. A bi-directional tree structured model is proposed, so that the feature channels at a body joint can well receive information from other joints. The proposed framework improves feature learning substantially. With very simple post processing, it reaches the best mean PCP on the LSP and FLIC datasets. Compared with the baseline of learning features at each joint separately with ConvNet, the mean PCP has been improved by 18% on FLIC. The code is released to the public.
Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention’s context and a proposed target entity. These convolutional networks operate at multiple granularities to exploit various kinds of topic information, and their rich parameterization gives them the capacity to learn which n-grams characterize different topics. We combine these networks with a sparse linear model to achieve state-of-the-art performance on multiple entity linking datasets, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).1
Adaptive Neuro-Fuzzy Inference System for Health Monitoring at Home

Healthcare is approaching a critical situation. The ageing of population is increasing the prevalence of chronic diseases. Cardiovascular and respiratory diseases not only kill hundreds of thousands of people each year around the globe but also cost billions of dollars. Patients have to make frequent visits to their doctor to get their vital signs measured. People in remote places are deprived of proper healthcare. Hence, there is a need to develop a system which will help in reducing the frequent visits to the clinic and also help in early diagnosis of dangerous diseases. A system must be targeted both for monitoring elderly and for monitoring rehabilitation after hospitalization period and at the same time economically efficient. This paper presents our initial attempts to develop such a system with the help of Adaptive Neuro-Fuzzy Inference System (ANFIS) by adaptive learning mechanism. The MATLAB simulation results indicate that the performance of the ANFIS approach is much important and at the same time easy to implement. The study results are based on the ranges of diagnostic health parameters and the corresponding opinion of the expert. The developed healthcare system can be useful for the elderly and terminally ill patients confined within their homes and at the same time helpful to the pregnant women for their regular checkups without personally visiting to the clinic.
Round-Robin Arbiter Design and Generation

In this paper, we introduce a Round-robin Arbiter Generator (RAG) tool. The RAG tool can generate a design for a Bus Arbiter (BA). The BA is able to handle the exact number of bus masters for both on-chip and off-chip buses. RAG can also generate a distributed and parallel hierarchical Switch Arbiter (SA). The first contribution of this paper is the automated generation of a round-robin token passing BA to reduce time spent on arbiter design. The generated arbiter is fair, fast, and has a low and predictable worst-case wait time. The second contribution of this paper is the design and integration of a distributed fast arbiter, e.g., for a terabit switch, based on 2x2 and 4x4 switch arbiters (SAs). Using a .25µ TSMC standard cell library from LEDA Systems [10, 14], we show the arbitration time of a 256x256 SA for a terabit switch and demonstrate that the SA generated by RAG meets the time constraint to achieve approximately six terabits of throughput in a typical network switch design. Furthermore, our generated SA performs better than the Ping-Pong Arbiter and Programmable Priority Encoder by a factor of 1.9X and 2.4X, respectively.
Object Detection in High-Resolution Remote Sensing Images Using Rotation Invariant Parts Based Model

In this letter, we propose a rotation invariant parts-based model to detect objects with complex shape in high-resolution remote sensing images. Specifically, the geospatial objects with complex shape are firstly divided into several main parts, and the structure information among parts is described and regulated in polar coordinates to achieve the rotation invariance on configuration. Meanwhile, the pose variance of each part relative to the object is also defined in our model. In encoding the features of the rotated parts and objects, a new rotation invariant feature is proposed by extending histogram oriented gradients. During the final detection step, a clustering method is introduced to locate the parts in objects, and that method can also be used to fuse the detection results. By this way, an efficient detection model is constructed and the experimental results demonstrate the robustness and precision of our proposed detection model.
A Nested-Graph Model for the Representation and Manipulation of Complex Objects

Three recent trends in database research are object-oriented and deductive databases and graph-based user interfaces. We draw these trends together in a data model we call the Hypernode Model. The single data structure of this model is the hypernode, a graph whose nodes can themselves be graphs. Hypernodes are typed, and types, too, are nested graphs. We give the theoretical foundations of hypernodes and types, and we show that type checking is tractable. We show also how conventional type-forming operators can be simulated by our graph types, including cyclic types. The Hypernode Model comes equipped with a rule-based query language called Hyperlog, which is complete with respect to computation and update. We define the operational semantics of Hyperlog and show that the evaluation can be performed efficiently. We discuss also the use of Hyperlog for supporting database browsing, an essential feature of Hypertext databases. We compare our work with other graph-based data models—unlike previous graph-based models, the Hypernode Model provides inherent support for data abstraction via its nesting of graphs. Finally, we briefly discuss the implementation of a DBMS based on the Hypernode Model.
Optimizing CNNs on Multicores for Scalability, Performance and Goodput

Convolutional Neural Networks (CNN) are a class of Ar- tificial Neural Networks (ANN) that are highly efficient at the pattern recognition tasks that underlie difficult AI prob- lems in a variety of domains, such as speech recognition, object recognition, and natural language processing. CNNs are, however, computationally intensive to train. This paper presents the first characterization of the per- formance optimization opportunities for training CNNs on CPUs. Our characterization includes insights based on the structure of the network itself (i.e., intrinsic arithmetic inten- sity of the convolution and its scalability under parallelism) as well as dynamic properties of its execution (i.e., sparsity of the computation).
 Given this characterization, we present an automatic framework called spg-CNN for optimizing CNN training on CPUs. It comprises of a computation scheduler for efficient parallel execution, and two code generators: one that opti- mizes for sparsity, and the other that optimizes for spatial reuse in convolutions.
 We evaluate spg-CNN using convolutions from a variety of real world benchmarks, and show that spg-CNN can train CNNs faster than state-of-the-art approaches by an order of magnitude.
COAP: A Software-Defined Approach for Home WLAN Management through an Open API

In recent years, there has been a rapid growth in the adoption and usage of WiFi enabled networked devices at homes such as laptops, handheld device and wireless entertainment devices. In dense wireless deployments at homes, such as apartment buildings, neighboring home WLANs share the same unlicensed spectrum by deploying consumer-grade access points in their individual homes. In such environments, WiFi networks can suffer from intermittent performance issues such as wireless packet losses, interference from WiFi and non-WiFi sources due to the increasing diversity of devices that share the spectrum. In this paper, we propose a vendor-neutral cloud-based centralized framework called COAP to configure, co-ordinate and manage individual home APs using an open API implemented over the OpenFlow SDN framework. This paper describes the framework and motivates the potential benefits of the framework in home WLANs.
Continuous audit. A new paradigm of audit?

Continuous auditing is considered by many authors as the new paradigm of the audit, hence the need to understand its basic concepts, the main guidelines proposed by international organizations grouping accountants and auditors and the need and importance of this technology represents for modern auditing in an organizational environment dominated by information and communications technologies. This is one of the objectives of this document provides a basis for understanding the positions of various authors on the consideration of continuous auditing as a new paradigm audit.
Interactive Perception: Leveraging Action in Perception and Perception in Action

Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.
Realistic eavesdropping attacks on computer displays with low-cost and mobile receiver system

It is known that the computer display images can be reconstructed from display's radio frequency emanations. This is a big information leakage threat for information security. Recently published eavesdropping systems are very expensive and not portable. Furthermore all the published eavesdropping attacks are demonstrated for distance up to 10 meters away from display. In this work, a low-cost and all-in-one mobile receiver is combined to capture emanations from long distance approximately 50 meters away from display and to reconstruct the display images. In our eavesdropping scenario, receiver system is in a building and the target display is in another building. These experiments show that 26 points and bigger fonts can be read easily from the reconstructed images which are captured from the target display approximately 50 meters away. With this new system, we can also capture emanations from 3 meters away of the target display in an office environment for comparison. In this latter experiment 9 points and bigger fonts can be read easily.