nils-herrmann/clean_scirepeval huggingface dataset:
Quantization-based hashing: a general framework for scalable image and video retrieval

Nowadays, due to the exponential growth of user generated images and videos, there is an increasing interest in learning-based hashing methods. In computer vision, the hash functions are learned in such a way that the hash codes can preserve essential properties of the original space (or label information). Then the Hamming distance of the hash codes can approximate the data similarity. On the other hand, vector quantization methods quantize the data into different clusters based on the criteria of minimal quantization error, and then perform the search using look-up tables. While hashing methods using Hamming distance can achieve faster search speed, their accuracy is often outperformed by quantization methods with the same code length, due to the low quantization error and more flexible distance lookups. To improve the effectiveness of the hashing methods, in this work, we propose Quantization-based Hashing (QBH), a general framework which incorporates the advantages of quantization error reduction methods into conventional property preserving hashing methods. The learned hash codes simultaneously preserve the properties in the original space and reduce the quantization error, and thus can achieve better performance. Furthermore, the hash functions and a quantizer can be jointly learned and iteratively updated in a unified framework, which can be readily used to generate hash codes or quantize new data points. Importantly, QBH is a generic framework that can be integrated to different property preserving hashing methods and quantization strategies, and we apply QBH to both unsupervised and supervised hashing models as showcases in this paper. Experimental results on three large-scale unlabeled datasets (i.e., SIFT1M, GIST1M, and SIFT1B), three labeled datastes (i.e., ESPGAME, IAPRTC and MIRFLICKR) and one video dataset (UQ VIDEO) demonstrate the superior performance of our QBH over existing unsupervised and supervised hashing methods. 1Jingkuan Song and Lianli Gao (lianli.gao@gmail.com, corresponding author) are with the Department of Computer Science, University of Electronic Science and Technology of China, China, 611731. Xiaofeng Zhu is with Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guangxi, China, 541004. Liu. Li is with University of East Anglia, UK. Nicu Sebe is with the Department of Information Engineering and Computer Science, University of Trento, Trento, Italy, 38100  2017
Executive control of visual attention in dual-task situations.

A theory of executive control is presented that proposes that executive processes control subordinate processes by manipulating their parameters, reconfiguring them to respond in accord with the current task set. It adopts C. Bundesen's (1990) theory of visual attention (TVA) and R. M. Nosofsky and T. J. Palmeri's (1997) exemplar-based random walk (EBRW) as the theory of subordinate processes. It assumes that a task set is a set of TVA and EBRW parameters sufficient to perform a task and that set switching involves changing those parameters. The theory solves 2 computational problems that emerge in dual-task situations: the binding problem and the serial order problem. It can perform dual tasks in series or in parallel but prefers the serial strategy because it is faster and it solves the binding problem naturally. The theory accounts for concurrence cost, set-switching cost, crosstalk between tasks, and the modulation of crosstalk by task set.
Development of Highly Secured Cloud Rendered Log Management System

A log is a collection of record of events that occurs within an organization containing systems and network. These logs are very important for any organization, because log file will able to record all user activities. As this log files plays vital role and also it contains sensitive information , it should be maintained highly secure. So, management and securely maintenance of log records are very tedious task. However, deploying such a system for high security and privacy of log records is an overhead for an organization and also it requires additional cost. Many techniques have been design so far for security of log records. The alternative solution is to maintaining log records over a cloud database. Log files over cloud environment leads to challenges about privacy, confidentiality and integrity of log files. In this paper, we propose highly secured cloud rendered log management and also use of some cryptographic algorithms for dealing the issues to access a cloud based data storage. To the best of knowledge, this is the strong work to provide a complete solution to the cloud based secure log management problem.
EMG analysis of the scapular muscles during a shoulder rehabilitation program.

The purpose of this study was to determine which exercises most effectively use the scapular muscles. Eight muscles in 9 healthy subjects were studied with indwelling electromyographic electrodes and cinematography while performing 16 exercises. The 8 muscles studied were the upper, middle, and lower trapezius; levator scapula; rhomboids; pectoralis minor; and the middle and lower serratus anterior. Each exercise was divided into arcs of motion and the electromyographic activity was quantified as a percentage of the maximal manual muscle test. The optimal exercises for each muscle were identified based on intensity (greater than 50% maximal manual muscle test) and duration (over at least 3 consecutive arcs of motion) of the muscle activity. Twelve of the exercises qualified as top exercises for all of the muscles. On further analysis, a group of 4 exercises was shown to make up the core of a scapular muscle strengthening program. Those 4 exercises include scaption (scapular plane elevation), rowing, push-up with a plus, and press-up.
Lesion-specific coronary artery calcium quantification better predicts cardiac events

CT-based coronary artery calcium (CAC) scanning has been introduced as a non-invasive, low-radiation imaging technique for the assessment of the overall coronary arterial atherosclerotic burden. A three dimensional CAC volume contains significant clinically relevant information, which is unused by conventional whole-heart CAC quantification methods. In this paper, we have developed a more detailed distance-weighted lesion-specific CAC quantification framework that predicts cardiac events better than the conventional whole-heart CAC measures. This framework consists of (1) a novel lesion-specific CAC quantification tool that measures each calcific lesion's attenuation, morphologic and geometric statistics; (2) a distance-weighted event risk model to estimate the risk probability caused by each lesion, and (3) a Naive Bayesian technique for risk integration. We have tested our lesion-specific event predictor on 30 CAC positive scans (10 with events and 20 without events), and compared it with conventional whole-heart CAC scores. Experiment results showed our novel approach significantly improves the prediction accuracy, including AUC of ROC analysis was improved from 66 ∼ 68% to 75%, and sensitivities was improved by 20 ∼ 30% at the cutpoints of 80% specificity.
Automatic categorization of figures in scientific documents

Figures are very important non-textual information contained in scientific documents. Current digital libraries do not provide users tools to retrieve documents based on the information available within the figures. We propose an architecture for retrieving documents by integrating figures and other information. The initial step in enabling integrated document search is to categorize figures into a set of pre-defined types. We propose several categories of figures based on their functionalities in scholarly articles. We have developed a machine-learning-based approach for automatic categorization of figures. Both global features, such as texture, and part features, such as lines, are utilized in the architecture for discriminating among figure categories. The proposed approach has been evaluated on a testbed document set collected from the CiteSeer scientific literature digital library. Experimental evaluation has demonstrated that our algorithms can produce acceptable results for realworld use. Our tools will be integrated into a scientific document digital library.
Connotation Frames: A Data-Driven Investigation

Through a particular choice of a predicate (e.g., “x violated y”), a writer can subtly connote a range of implied sentiments and presupposed facts about the entities x and y: (1) writer’s perspective: projecting x as an “antagonist” and y as a “victim”, (2) entities’ perspective: y probably dislikes x, (3) effect: something bad happened to y, (4) value: y is something valuable, and (5) mental state: y is distressed by the event. We introduce connotation frames as a representation formalism to organize these rich dimensions of connotation using typed relations. First, we investigate the feasibility of obtaining connotative labels through crowdsourcing experiments. We then present models for predicting the connotation frames of verb predicates based on their distributional word representations and the interplay between different types of connotative relations. Empirical results confirm that connotation frames can be induced from various data sources that reflect how people use language and give rise to the connotative meanings. We conclude with analytical results that show the potential use of connotation frames for analyzing subtle biases in online news media.
Effect of Gen2 protocol parameters on RFID tag performance

In this paper, we analyze the effect of Gen2 protocol parameters on RFID tag performance (tag sensitivity and backscatter efficiency). We describe our measurement methodology and perform characterization of several tags with different latest Gen2 ICs available on the market (Monza, UCODE, and Higgs families). To confirm our findings, we repeat measurements using conducted tag setup. We analyze data and draw conclusions on how the protocol parameters affect the tag performance in forward and reverse links.
A Hybrid Combining Hard and Soft Robots

This article describes a hybrid robotic system combining hard and soft subsystems. This hybrid comprises a wheeled robot (an iRobot Create; hard) and a four-legged quadruped (soft). It is capable (using a simple, wireless control system) of rapid locomotion over flat terrain (using the wheeled hard robot) and of gripping and retrieving an object (using the independent locomotive capabilities of the soft robot). The utility of this system is demonstrated by performing a mission requiring the capabilities of both components: retrieving an object (iPod Nano) from the center of a room. This class of robot—hybrids comprising hard and soft systems functioning synergistically—is capable of performing tasks that neither can do alone. In contrast to specialized hard robotic arms with grippers (capable of performing some of the functions we describe here), which are complex, relatively expensive, and require sophisticated controls, this hybrid system is easy to construct, simple to control, and low in cost. The soft robotic system in the hybrid is lightweight, disposable if contaminated or damaged, and capable of multiple functions.
A Review of Sleep EEG Patterns . Part I : A Compilation of Amended Rules for Their Visual Recognition according to Rechtschaffen and Kales

Task Force ‘Scoring of Polysomnographic Recordings’ of the German Sleep Society (DGSM): Andrea Rodenbeck, Ralf Binder, Peter Geisler, Heidi Danker-Hopfe, Reimer Lund, Friedhart Raschke, Hans-Günther Weeß, and Hartmut Schulz (Chairman) Department of Psychiatry, University of Göttingen, Göttingen, Germany Department of Psychiatry, Pfalzklinikum, Klingenmünster, Germany Department of Psychiatry, University of Regensburg, Regensburg, Germany Department of Psychiatry, Charité-CBF, Berlin, Germany Grüntenstr. 11, 80686 München (formerly Dept. of Pneumology, ASKLEPIOS Clinic Gauting, Germany) Institute of Rehabilitation Norderney, Norderney, Germany Department of Neurology, HELIOS-Klinikum Erfurt, and Free University of Berlin, Biopsychology, Germany
On the (In)Security of Mobile Two-Factor Authentication

Two-factor authentication (2FA) schemes aim at strengthening the security of login password-based authentication by deploying secondary authentication tokens. In this context, mobile 2FA schemes require no additional hardware (e.g., a smartcard) to store and handle the secondary authentication token, and hence are considered as a reasonable trade-off between security, usability and costs. They are widely used in online banking and increasingly deployed by Internet service providers. In this paper, we investigate 2FA implementations of several well-known Internet service providers such as Google, Dropbox, Twitter and Facebook. We identify various weaknesses that allow an attacker to easily bypass them, even when the secondary authentication token is not under attacker’s control. We then go a step further and present a more general attack against mobile 2FA schemes. Our attack relies on crossplatform infection that subverts control over both end points (PC and a mobile device) involved in the authentication protocol. We apply this attack in practice and successfully circumvent diverse schemes: SMS-based TAN solutions of four large banks, one instance of a visual TAN scheme, 2FA login verification systems of Google, Dropbox, Twitter and Facebook accounts, and the Google Authenticator app currently used by 32 third-party service providers. Finally, we cluster and analyze hundreds of real-world malicious Android apps that target mobile 2FA schemes and show that banking Trojans already deploy mobile counterparts that steal 2FA credentials like TANs.
DjiNN and Tonic: DNN as a service and its implications for future warehouse scale computers

As applications such as Apple Siri, Google Now, Microsoft Cortana, and Amazon Echo continue to gain traction, web-service companies are adopting large deep neural networks (DNN) for machine learning challenges such as image processing, speech recognition, natural language processing, among others. A number of open questions arise as to the design of a server platform specialized for DNN and how modern warehouse scale computers (WSCs) should be outfitted to provide DNN as a service for these applications.
 In this paper, we present DjiNN, an open infrastructure for DNN as a service in WSCs, and Tonic Suite, a suite of 7 end-to-end applications that span image, speech, and language processing. We use DjiNN to design a high throughput DNN system based on massive GPU server designs and provide insights as to the varying characteristics across applications. After studying the throughput, bandwidth, and power properties of DjiNN and Tonic Suite, we investigate several design points for future WSC architectures. We investigate the total cost of ownership implications of having a WSC with a disaggregated GPU pool versus a WSC composed of homogeneous integrated GPU servers. We improve DNN throughput by over 120x for all but one application (40x for Facial Recognition) on an NVIDIA K40 GPU. On a GPU server composed of 8 NVIDIA K40s, we achieve near-linear scaling (around 1000x throughput improvement) for 3 of the 7 applications. Through our analysis, we also find that GPU-enabled WSCs improve total cost of ownership over CPU-only designs by 4-20x, depending on the composition of the workload
OpinionFinder: A System for Subjectivity Analysis

Vancouver, October 2005.  A system for subjectivity analysis Theresa Wilson‡, Paul Hoffmann‡, Swapna Somasundaran†, Jason Kessler†, Janyce Wiebe†‡, Yejin Choi§, Claire Cardie§, Ellen Riloff∗, Siddharth Patwardhan∗ ‡Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260 †Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260 §Department of Computer Science, Cornell University, Ithaca, NY 14853 ∗School of Computing, University of Utah, Salt Lake City, UT 84112
Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach

In this paper, we introduce a synergistic approach to integrated lane and vehicle tracking for driver assistance. The approach presented in this paper results in a final system that improves on the performance of both lane tracking and vehicle tracking modules. Further, the presented approach introduces a novel approach to localizing and tracking other vehicles on the road with respect to lane position, which provides information on higher contextual relevance that neither the lane tracker nor vehicle tracker can provide by itself. Improvements in lane tracking and vehicle tracking have been extensively quantified. Integrated system performance has been validated on real-world highway data. Without specific hardware and software optimizations, the fully implemented system runs at near-real-time speeds of 11 frames per second.
Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object-Oriented Approach

The objective of the current study was to analyze the seasonal effect on differentiating tree species in an urban environment using multi-temporal hyperspectral data, Light Detection And Ranging (LiDAR) data, and a tree species database collected from the field. Two Airborne Imaging Spectrometer for Applications (AISA) hyperspectral images were collected, covering the Summer and Fall seasons. In order to make both datasets spatially and spectrally compatible, several preprocessing steps, including band reduction and a spatial degradation, were performed. An object-oriented classification was performed on both images using training data collected randomly from the tree species database. The seven dominant tree species (Gleditsia triacanthos, Acer saccharum, Tilia Americana, Quercus palustris, Pinus strobus and Picea glauca) were used in the classification. The results from this analysis did not show any major difference in overall accuracy between the two seasons. Overall accuracy was approximately 57% for the Summer dataset and 56% for the Fall dataset. However, the Fall dataset provided more consistent results for all tree species while the Summer dataset had a few higher individual class accuracies. Further, adding LiDAR into the classification improved the results by 19% for both fall and summer. This is mainly due to the removal of shadow effect and the addition of elevation data to separate low and high vegetation.
Data programming with DDLite: putting humans in a different part of the loop

Populating large-scale structured databases from unstructured sources is a critical and challenging task in data analytics. As automated feature engineering methods grow increasingly prevalent, constructing sufficiently large labeled training sets has become the primary hurdle in building machine learning information extraction systems. In light of this, we have taken a new approach called data programming [7]. Rather than hand-labeling data, in the data programming paradigm, users generate large amounts of noisy training labels by programmatically encoding domain heuristics as simple rules. Using this approach over more traditional distant supervision methods and fully supervised approaches using labeled data, we have been able to construct knowledge base systems more rapidly and with higher quality. Since the ability to quickly prototype, evaluate, and debug these rules is a key component of this paradigm, we introduce DDLite, an interactive development framework for data programming. This paper reports feedback collected from DDLite users across a diverse set of entity extraction tasks. We share observations from several DDLite hackathons in which 10 biomedical researchers prototyped information extraction pipelines for chemicals, diseases, and anatomical named entities. Initial results were promising, with the disease tagging team obtaining an F1 score within 10 points of the state-of-the-art in only a single day-long hackathon's work. Our key insights concern the challenges of writing diverse rule sets for generating labels, and exploring training data. These findings motivate several areas of active data programming research.
Monitoring of gait performance using dynamic time warping on IMU-sensor data

In this paper, a novel method for monitoring the changes in gait joint angle trajectories recorded using the low-cost and wearable Inertial Measurement Units (IMU) is presented. The introduced method is based on Dynamic Time Warping (DTW), an algorithm commonly used for evaluating the similarity of two time series which may vary in time and speed. DTW is employed as the measure of distance between two gait trajectories taken in different time instances, which could be used as an intuitive and effective measure for the evaluation of gait performances. The experimental results presented in the paper demonstrate that the proposed method is applicable for clinically relevant applications and is consequently adaptable to patients with diseases characterized with gait disorders and to different walking scenarios. The proposed method was firstly validated by applying the DTW-based measure on gait trajectories of five healthy subjects recorded while simulating different levels of walking disabilities. Then proposed measure was applied to estimate the distance between the “healthy” gait trajectories and gait trajectories of three patients with Parkinson's disease (PD) while performing single-task and dual-task overground walking. Also, the proposed measure was demonstrated as an effective measure for monitoring the changes in gait patterns of a PD patient before and after medication-based treatment. This result indicates potential use of proposed method for effective pharmacological management of PD.
TwitterMonitor: trend detection over the twitter stream

We present TwitterMonitor, a system that performs trend detection over the Twitter stream. The system identifies emerging topics (i.e. 'trends') on Twitter in real time and provides meaningful analytics that synthesize an accurate description of each topic. Users interact with the system by ordering the identified trends using different criteria and submitting their own description for each trend.
 We discuss the motivation for trend detection over social media streams and the challenges that lie therein. We then describe our approach to trend detection, as well as the architecture of TwitterMonitor. Finally, we lay out our demonstration scenario.
VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations

Broad-coverage repositories of semantic relations between verbs could benefit many NLP tasks. We present a semi-automatic method for extracting fine-grained semantic relations between verbs. We detect similarity, strength, antonymy, enablement, and temporal happens-before relations between pairs of strongly associated verbs using lexicosyntactic patterns over the Web. On a set of 29,165 strongly associated verb pairs, our extraction algorithm yielded 65.5% accuracy. Analysis of error types shows that on the relation strength we achieved 75% accuracy. We provide the resource, called VERBOCEAN, for download at http://semantics.isi.edu/ocean/.