nils-herrmann/clean_scirepeval huggingface dataset:
Tagging The Web: Building A Robust Web Tagger with Neural Network

In this paper, we address the problem of web-domain POS tagging using a twophase approach. The first phase learns representations that capture regularities underlying web text. The representation is integrated as features into a neural network that serves as a scorer for an easy-first POS tagger. Parameters of the neural network are trained using guided learning in the second phase. Experiment on the SANCL 2012 shared task show that our approach achieves 93.15% average tagging accuracy, which is the best accuracy reported so far on this data set, higher than those given by ensembled syntactic parsers.
Combining Confocal Imaging and Descattering

In translucent objects, light paths are affected by multiple scattering, which is polluting any observation. Confocal imaging reduces the influence of such global illumination effects by carefully focusing illumination and viewing rays from a large aperture to a specific location within the object volume. The selected light paths still contain some global scattering contributions, though. Descattering based on high frequency illumination serves the same purpose. It removes the global component from observed light paths. We demonstrate that confocal imaging and descattering are orthogonal and propose a novel descattering protocol that analyzes the light transport in a neighborhood of light transport paths. In combination with confocal imaging, our descattering method achieves optical sectioning in translucent media with higher contrast and better resolution.
Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter

With the recent rise in popularity and scale of social media, a growing need exists for systems that can extract useful information from huge amounts of data. We address the issue of detecting influenza epidemics. First, the proposed system extracts influenza related tweets using Twitter API. Then, only tweets that mention actual influenza patients are extracted by the support vector machine (SVM) based classifier. The experiment results demonstrate the feasibility of the proposed approach (0.89 correlation to the gold standard). Especially at the outbreak and early spread (early epidemic stage), the proposed method shows high correlation (0.97 correlation), which outperforms the state-of-the-art methods. This paper describes that Twitter texts reflect the real world, and that NLP techniques can be applied to extract only tweets that contain useful information.
What Is Seen Is Who You Are: Are Cues in Selfie Pictures Related to Personality Characteristics?

Developments and innovation in the areas of mobile information technology, digital media and social networks foster new reflections on computer-mediated communication research, especially in the field of self-presentation. In this context, the selfie as a self-portrait photo is interesting, because as a meaningful gesture, it actively and directly relates the content of the photo to the author of the picture. From the perspective of the selfie as an image and the impression it forms, in the first part of the research we explored the distinctive characteristics of selfie pictures; moreover, from the perspective of the potential reflection of a selfie image on the personality of its author, in the second part we related the characteristics of selfie pictures to various personality constructs (e.g., Big Five personality traits narcissism and femininity-masculinity). Important aspects of selfies especially in relation to gender include the tilt of the head, the side of the face exhibited, mood and head position, later related also to the context of the selfie picture. We found no significant relations between selfie cues and personality constructs. The face-ism index was related to entitlement, and selfie availability to neuroticism.
The ABET “ Professional Skills ” – Can They Be Taught ? Can They Be Assessed ?

In developing its new engineering accreditation criteria, ABET reaffirmed a set of “hard” engineering skills while introducing a second, equally important, set of six “professional” skills. These latter skills include communication, teamwork, and understanding ethics and professionalism, which we label process skills, and engineering within a global and societal context, lifelong learning, and a knowledge of contemporary issues, which we designate as awareness skills. We review these skills with an emphasis on how they can be taught, or more correctly learned, citing a number of examples of successful and/or promising implementations. We then examine the difficult issue of assessing these skills. We are very positive about a number of creative ways that these skills are being learned, particularly at institutions that are turning to global and/or service learning in combination with engineering design projects to teach and reinforce outcome combinations. We are also encouraged by work directed at assessing these skills, but recognize that there is considerable research that remains to be done.
A 37.5 /spl mu/W Body Channel Communication Wake-Up Receiver With Injection-Locking Ring Oscillator for Wireless Body Area Network

An ultra-low power wake-up receiver for body channel communication (BCC) is implemented in 130 nm CMOS process. The proposed wake-up receiver uses the injection-locking ring oscillator (ILRO) to replace the RF amplifier with low power consumption. Through the ILRO, the frequency modulated input signal is amplified to the full swing rectangular signal directly demodulated by the following low power PLL-based FSK demodulator. In addition, the energy-efficient BCC link mitigates the sensitivity and selectivity requirements for the receiver, which significantly reduces the power consumption. Furthermore, the auto frequency calibrator (AFC) is adopted to compensate the free running frequency of the ring oscillator which is caused by temperature variation and leakage current. The AFC reuses the PLL-based demodulator to periodically set the free running frequency to the desired frequency without any area overhead. As a result, the proposed wake-up receiver achieves a sensitivity of -62.7 dBm at a data rate of 200 kbps while consuming only 37.5 μW from the 0.7 V supply.
Deep learning in agriculture: A survey

https://doi.org/10.1016/j.compag.2018.02.016 Received 14 July 2017; Received in revised form 7 February 2018;  February 2018 ⁎ Corresponding author. E-mail address: andreas.kamilaris@irta.cat (A. Kamilaris). Computers and Electronics in  (2018) 70–90 0168-1699/
Degree Project in Business Administration Title : Blockchain in audit trails – an investigation of how blockchain can help auditors to implement audit trails

Blockchain have attracted a lot of attention in the last few years. It has been described as a technology that will increase the effectiveness of monitoring and improve the auditability of transactions which would have great implications for accountants and auditors.  The purpose of this study is to investigate how blockchain can help auditors to implement audit trails. The aim is to increase awareness about what the blockchain technology is, investigate if blockchain can be used in audit trails and if it can contribute to more cost-effective, reliable and secure audit trail.  The study is an exploratory research to increase the knowledge and understanding of blockchain and audit trails. It has a qualitative approach where primary data is collected from a semi-structured interview with Deloitte.  The results indicates that there exists a gap in the literature of previously research on blockchain in relation to audit trails that needs further investigation. Furthermore, this study shows that blockchain is a technology with a lot of potential, but knowledge is still limited. This study concludes that there is too little research conducted to be able to provide any conclusive evidence. Due to findings and limitations of this research, suggestions for further research is provided. Explanation of key terms In order to facilitate the reading, a number of concepts related to the subject of the study will be presented below.  Used to record a business transaction in the accounting records of a business.  A paper or electronic trail that gives a step by step documented history of a transaction.  A distributed ledger which store digital information in a chain of blocks. Storage of this distributed data creates reliability and security of the information, allowing it to function without a central unit.  A type of computer network that is spread over different network, provides a single data communication network which can be managed jointly or separately by each network.  A computer connected to the blockchain network.  A function that converts input of letters and numbers into an encrypted output of a fixed length.  The process by which transactions are verified and added to the public ledger, known as the blockchain.  The ones validating new transactions and record them to the public ledger, the blockchain.  A digital or virtual currency that uses cryptography for security.  A digital currency in which encryption techniques are used to regulate the generation of units of currency and verify the transfer of funds, operating independently of a central bank. Bitcoin transactions are stored in the distributed network – the blockchain. Smart contracts: Self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of codes, the code and the agreements exist on the blockchain network. 1. TABLE OF CONTENTS
New Direct Torque Control Scheme for BLDC Motor Drives Suitable for EV Applications

This paper proposes a simple and effective scheme for Direct Torque Control (DTC) of Brushless DC Motor. Unlike the traditional DTC where both the torque and flux must to be employed in order to deliver the switching table for the inverter, the proposed DTC scheme utilizes only the torque information. By considering the particular operating principle of the motor, the instantaneous torque can be estimated only by using back EMF ratio in an unified differential equation. The simulation results have shown the good performance of the proposed scheme. It can be seen thus a very suitable technique for EV drives which need fast and precise torque response.
Learning preferences for manipulation tasks from online coactive feedback

We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than simple geometric constraints on trajectories; they are rather governed by the surrounding context of various objects and human interactions in the environment. We propose a coactive online learning framework for teaching preferences in contextually rich environments. The key novelty of our approach lies in the type of feedback expected from the user: the human user does not need to demonstrate optimal trajectories as training data, but merely needs to iteratively provide trajectories that slightly improve over the trajectory currently proposed by the system. We argue that this coactive preference feedback can be more easily elicited than demonstrations of optimal trajectories. Nevertheless, theoretical regret bounds of our algorithm match the asymptotic rates of optimal trajectory algorithms. We implement our algorithm on two high-degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings, household chores and grocery store checkout, and show that users are able to train the robot with just a few feedbacks (taking only a few minutes).
Automatic detection of acute myeloid leukemia from microscopic blood smear image

Cancer of blood-forming tissues is called Leukemia. This disease hinders the body's ability to fight infection. Leukemia can be categorized into many types. Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) are the two main types. The blood cells' growth and bone marrow are affected by AML. Collection of myeloid blasts in the bone marrow is one of the main characteristics of AML. In this research, a novel method is analyzed to detect the presence of AML. The paper proposes a technique that automatically detects and segments the nucleus from white blood cells (WBCs) in the microscopic blood smear images. Segmentation and clustering is done using a K-Means algorithm, while classification is done using Support Vector Machine (SVM) with feature reduction.
Lagged correlation-based deep learning for directional trend change prediction in financial time series

Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriously difficult to predict example of such systems. We approach predictions of directional trend changes via complex lagged correlations between them, excluding any information about the target series from the respective inputs to achieve predictions purely based on such correlations with other series. We propose the use of deep neural networks that employ step-wise linear regressions with exponential smoothing in the preparatory feature engineering for this task, with regression slopes as trend strength indicators for a given time interval. We apply this method to historical stock market data from 2011 to 2016 as a use case example of lagged correlations between large numbers of time series that are heavily influenced by externally arising new information as a random factor. The results demonstrate the viability of the proposed approach, with state-of-the-art accuracies and accounting for the statistical significance of the results for additional validation, as well as important implications for modern financial economics.
Proof of Concept for Robot-Aided Upper Limb Rehabilitation Using Disturbance Observers

This paper presents a wearable upper body exoskeleton system with a model-based compensation control framework to support robot-aided shoulder-elbow rehabilitation and power assistance tasks. To eliminate the need for EMG and force sensors, we exploit off-the-shelf compensation techniques developed for robot manipulators. Thus, target rehabilitation tasks are addressed by using only encoder readings. A proof-of-concept evaluation was conducted with live able-bodied participants. The patient-active rehabilitation task was realized via observer-based user torque estimation, in which resistive forces were adjusted using virtual impedance. In the patient-passive rehabilitation task, the proposed controller enabled precise joint tracking with a maximum positioning error of 0.25°. In the power assistance task, the users' muscular activities were reduced up to 85% while exercising with a 5 kg dumbbell. Therefore, the exoskeleton system was regarded as being useful for the target tasks, indicating that it has a potential to promote robot-aided therapy protocols.
Quizz: targeted crowdsourcing with a billion (potential) users

We describe Quizz, a gamified crowdsourcing system that simultaneously assesses the knowledge of users and acquires new knowledge from them. Quizz operates by asking users to complete short quizzes on specific topics; as a user answers the quiz questions, Quizz estimates the user's competence. To acquire new knowledge, Quizz also incorporates questions for which we do not have a known answer; the answers given by competent users provide useful signals for selecting the correct answers for these questions. Quizz actively tries to identify knowledgeable users on the Internet by running advertising campaigns, effectively leveraging the targeting capabilities of existing, publicly available, ad placement services. Quizz quantifies the contributions of the users using information theory and sends feedback to the advertisingsystem about each user. The feedback allows the ad targeting mechanism to further optimize ad placement.
 Our experiments, which involve over ten thousand users, confirm that we can crowdsource knowledge curation for niche and specialized topics, as the advertising network can automatically identify users with the desired expertise and interest in the given topic. We present controlled experiments that examine the effect of various incentive mechanisms, highlighting the need for having short-term rewards as goals, which incentivize the users to contribute. Finally, our cost-quality analysis indicates that the cost of our approach is below that of hiring workers through paid-crowdsourcing platforms, while offering the additional advantage of giving access to billions of potential users all over the planet, and being able to reach users with specialized expertise that is not typically available through existing labor marketplaces.
An anatomically-constrained local deformation model for monocular face capture

We present a new anatomically-constrained local face model and fitting approach for tracking 3D faces from 2D motion data in very high quality. In contrast to traditional global face models, often built from a large set of blendshapes, we propose a local deformation model composed of many small subspaces spatially distributed over the face. Our local model offers far more flexibility and expressiveness than global blendshape models, even with a much smaller model size. This flexibility would typically come at the cost of reduced robustness, in particular during the under-constrained task of monocular reconstruction. However, a key contribution of this work is that we consider the face anatomy and introduce subspace skin thickness constraints into our model, which constrain the face to only valid expressions and helps counteract depth ambiguities in monocular tracking. Given our new model, we present a novel fitting optimization that allows 3D facial performance reconstruction from a single view at extremely high quality, far beyond previous fitting approaches. Our model is flexible, and can be applied also when only sparse motion data is available, for example with marker-based motion capture or even face posing from artistic sketches. Furthermore, by incorporating anatomical constraints we can automatically estimate the rigid motion of the skull, obtaining a rigid stabilization of the performance for free. We demonstrate our model and single-view fitting method on a number of examples, including, for the first time, extreme local skin deformation caused by external forces such as wind, captured from a single high-speed camera.
IP covert timing channels: design and detection

A network covert channel is a mechanism that can be used to leak information across a network in violation of a security policy and in a manner that can be difficult to detect. In this paper, we describe our implementation of a covert network timing channel, discuss the subtle issues that arose in its design, and present performance data for the channel. We then use our implementation as the basis for our experiments in its detection. We show that the regularity of a timing channel can be used to differentiate it from other traffic and present two methods of doing so and measures of their efficiency. We also investigate mechanisms that attackers might use to disrupt the regularity of the timing channel, and demonstrate methods of detection that are effective against them.
Segmentation of malaria parasites in peripheral blood smear images

Detection of malaria parasites in stained blood smears is critical for treatment of the disease. Automation of this process will help in reducing the time taken for diagnosis and the chance for human errors. However, the variability and artifacts in microscope images of blood samples pose significant challenges for accurate detection. A scheme based on HSV color space that segments Red Blood Cells and parasites by detecting dominant hue range and by calculating optimal saturation thresholds is presented in this paper. Methods that are less computation-intensive than existing approaches are proposed to remove artifacts. The scheme is evaluated using images taken from Leishman-stained blood smears. Sensitivity and specificity of the scheme are found to be 83% and 98% respectively.
Violent flows: Real-time detection of violent crowd behavior

Although surveillance video cameras are now widely used, their effectiveness is questionable. Here, we focus on the challenging task of monitoring crowded events for outbreaks of violence. Such scenes require a human surveyor to monitor multiple video screens, presenting crowds of people in a constantly changing sea of activity, and to identify signs of breaking violence early enough to alert help. With this in mind, we propose the following contributions: (1) We describe a novel approach to real-time detection of breaking violence in crowded scenes. Our method considers statistics of how flow-vector magnitudes change over time. These statistics, collected for short frame sequences, are represented using the VIolent Flows (ViF) descriptor. ViF descriptors are then classified as either violent or non-violent using linear SVM. (2) We present a unique data set of real-world surveillance videos, along with standard benchmarks designed to test both violent/non-violent classification, as well as real-time detection accuracy. Finally, (3) we provide empirical tests, comparing our method to state-of-the-art techniques, and demonstrating its effectiveness.
LD-VOWL: Extracting and Visualizing Schema Information for Linked Data Endpoints

Users currently face the problem that schema information for Linked Data is often not available. If it is available, it tends to be incomplete or does not adequately represent the data. It can therefore be hard for users to get an impression of the data provided by some Linked Data source. In this paper, we introduce LD-VOWL, a web-based tool that extracts and visualizes schema information of Linked Data sources based on the VOWL notation. SPARQL queries are used to infer the schema information from the data of the source, which is then gradually added to an interactive VOWL graph visualization. We tested LD-VOWL on a number of Linked Data endpoints with promising results.
Experimental evaluation of the schunk 5-Finger gripping hand for grasping tasks

In order to perform useful tasks, a service robot needs to manipulate objects in its environment. In this paper, we propose a method for experimental evaluation of the suitability of a robotic hand for grasping tasks in service robotics. The method is applied to the Schunk 5-Finger Gripping Hand, which is a mechatronic gripper designed for service robots. During evaluation, it is shown, that it is able to grasp various common household objects and execute the grasps from the well known Cutkosky grasp taxonomy . The result is, that it is a suitable hand for service robot tasks.
Occlusion Boundary Detection via Deep Exploration of Context

Occlusion boundaries contain rich perceptual information about the underlying scene structure. They also provide important cues in many visual perception tasks such as scene understanding, object recognition, and segmentation. In this paper, we improve occlusion boundary detection via enhanced exploration of contextual information (e.g., local structural boundary patterns, observations from surrounding regions, and temporal context), and in doing so develop a novel approach based on convolutional neural networks (CNNs) and conditional random fields (CRFs). Experimental results demonstrate that our detector significantly outperforms the state-of-the-art (e.g., improving the F-measure from 0.62 to 0.71 on the commonly used CMU benchmark). Last but not least, we empirically assess the roles of several important components of the proposed detector, so as to validate the rationale behind this approach.