nils-herrmann/clean_scirepeval huggingface dataset:
Learning Perceptual Aspects of Diagnosis in Medicine via Eye Movement Modeling Examples on Patient Video Cases

Complex tasks with a visually rich component, like diagnosing seizures based on patient video cases, not only require the acquisition of conceptual but also of perceptual skills. Medical education has found that besides biomedical knowledge (knowledge of scientific facts) clinical knowledge (actual experience with patients) is crucial. One important aspect of clinical knowledge that medical education has hardly focused on, yet, are perceptual skills, like visually searching, detecting, and interpreting relevant features. Research on instructional design has shown that in a visually rich, but simple classification task perceptual skills could be conveyed by means of showing the eye movements of a didactically behaving expert. The current study applied this method to medical education in a complex task. This was done by example video cases, which were verbally explained by an expert. In addition the experimental groups saw a display of the expert’s eye movements recorded, while he performed the task. Results show that blurring non-attended areas of the expert enhances diagnostic performance of epileptic seizures by medical students in contrast to displaying attended areas as a circle and to a control group without attention guidance. These findings show that attention guidance fosters learning of perceptual aspects of clinical knowledge, if implemented in a spotlight manner.
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter

Twitter - a microblogging service that enables users to post messages ("tweets") of up to 140 characters - supports a variety of communicative practices; participants use Twitter to converse with individuals, groups, and the public at large, so when conversations emerge, they are often experienced by broader audiences than just the interlocutors. This paper examines the practice of retweeting as a way by which participants can be "in a conversation." While retweeting has become a convention inside Twitter, participants retweet using different styles and for diverse reasons. We highlight how authorship, attribution, and communicative fidelity are negotiated in diverse ways. Using a series of case studies and empirical data, this paper maps out retweeting as a conversational practice.
What You Need to Know About SDN Flow Tables

SDN deployments rely on switches that come from various vendors and differ in terms of performance and available features. Understanding these differences and performance characteristics is essential for ensuring successful deployments. In this paper we measure, report, and explain the performance characteristics of flow table updates in three hardware OpenFlow switches. Our results can help controller developers to make their programs efficient. Further, we also highlight differences between the OpenFlow specification and its implementations, that if ignored, pose a serious threat to network security and correctness.
Improved Crowbar Control Strategy of DFIG Based Wind Turbines for Grid Fault Ride-Through

Nowadays, the most widely used variable speed machine for wind turbine above 1MW is the doubly fed induction generator (DFIG). As the wind power penetration continues to increase, wind turbines are required to provide Low Voltage Ride-Through (LVRT) capability. Crowbars are commonly used to protect the power converters during voltage dips. Its main drawback is that the DFIG absorbs reactive power from the grid during grid faults. This paper proposes an improved control strategy for the crowbar protection to reduce its operation time. And a simple demagnetization method is adopted to decrease the oscillations of the transient current. Moreover, reactive power can be provided to assist the recovery of the grid voltage. Simulation results show the effectiveness of the proposed control schemes.
Techniques to Reduce IEEE 802 . 11 b MAC Layer Handover Time

In this paper we analyze link-layer handover times in wireless local area networks based on the IEEE 802.11b MAC protocol. Our measurements indicate that detection and search phases are the main contributors to the handover time. We show that detection time can be reduced by reacting quickly to packet losses and by using shorter beacon intervals. We also show that search time can be reduced by using active scanning. In this case, we calculate values for the two timers that control the duration of active scanning in order to reduce search time. Several simulations illustrate the achieved reduction in handover time.
Fast Visual Tracking via Dense Spatio-temporal Context Learning

In this paper, we present a simple yet fast and robust algorithm which exploits the dense spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its locally dense contexts in a Bayesian framework, which models the statistical correlation between the simple low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is then posed by computing a confidence map which takes into account the prior information of the target location and thereby alleviates target location ambiguity effectively. We further propose a novel explicit scale adaptation scheme, which is able to deal with target scale variations efficiently and effectively. The Fast Fourier Transform (FFT) is adopted for fast learning and detection in this work, which only needs 4 FFT operations. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.
The Interplay of Semantics and Morphology in Word Embeddings

We explore the ability of word embeddings to capture both semantic and morphological similarity, as affected by the different types of linguistic properties (surface form, lemma, morphological tag) used to compose the representation of each word. We train several models, where each uses a different subset of these properties to compose its representations. By evaluating the models on semantic and morphological measures, we reveal some useful insights on the relationship between semantics and morphology.
Parallax and Companion: Set-based Browsing for the Data Web

The arrival of the Data Web will bring an abundance of explicit semantics either complementary to or embedded within traditional web content. This body of semantics both demands and enables new interaction techniques to be introduced into the web experience. In this position paper, we proposes that the current web browsing paradigm of “one web page at a time” needs to be updated because the typical unit of web information to interact with will no longer be a whole and single web page but can be smaller and numerous bits of data. We introduce the set-based browsing paradigm that lets the user traverse graph-based data that will be found on the Data Web in an efficient manner, moving from a set of things to a related set of things rather than from one single thing to one single other thing. We demonstrate this paradigm as a standalone application on a web-like database and as a browser extension on existing web pages. categories and subject Descriptors H5.2 [Information Interfaces and Presentation]: User Interfaces – Graphical user interfaces (GUI). H5.4 [Information Interfaces and Presentation]: Hypertext/Hypermedia – User issues.
Fingerprint image enhancement method using directional median filter

The performance of any fingerprint recognizer highly depends on the fingerprint image quality. Different types of noises in the fingerprint images pose greater difficulty for recognizers. Most Automatic Fingerprint Identification Systems (AFIS) use some form of image enhancement. Although several methods have been described in the literature, there is still scope for improvement. In particular, effective methodology of cleaning the valleys between the ridge contours are lacking. We observe that noisy valley pixels and the pixels in the interrupted ridge flow gap are “impulse noises”. Therefore, this paper describes a new approach to fingerprint image enhancement, which is based on integration of Anisotropic Filter and directional median filter(DMF). Gaussian-distributed noises are reduced effectively by Anisotropic Filter, “impulse noises” are reduced efficiently by DMF. Usually, traditional median filter is the most effective method to remove pepper-and-salt noise and other small artifacts, the proposed DMF can not only finish its original tasks, it can also join broken fingerprint ridges, fill out the holes of fingerprint images, smooth irregular ridges as well as remove some annoying small artifacts between ridges. The enhancement algorithm has been implemented and tested on fingerprint images from FVC2002. Images of varying quality have been used to evaluate the performance of our approach. We have compared our method with other methods described in the literature in terms of matched minutiae, missed minutiae, spurious minutiae, and flipped minutiae(between end points and bifurcation points). Experimental results show our method to be superior to those described in the literature.
The impact of social media on children, adolescents, and families.

Using social media Web sites is among the most common activity of today's children and adolescents. Any Web site that allows social interaction is considered a social media site, including social networking sites such as Facebook, MySpace, and Twitter; gaming sites and virtual worlds such as Club Penguin, Second Life, and the Sims; video sites such as YouTube; and blogs. Such sites offer today's youth a portal for entertainment and communication and have grown exponentially in recent years. For this reason, it is important that parents become aware of the nature of social media sites, given that not all of them are healthy environments for children and adolescents. Pediatricians are in a unique position to help families understand these sites and to encourage healthy use and urge parents to monitor for potential problems with cyberbullying, "Facebook depression," sexting, and exposure to inappropriate content.
Improved Ring Oscillator PUF: An FPGA-friendly Secure Primitive

In this paper, we analyze ring oscillator (RO) based physical unclonable function (PUF) on FPGAs. We show that the systematic process variation adversely affects the ability of the RO-PUF to generate unique chip-signatures, and propose a compensation method to mitigate it. Moreover, a configurable ring oscillator (CRO) technique is proposed to reduce noise in PUF responses. Our compensation method could improve the uniqueness of the PUF by an amount as high as 18%. The CRO technique could produce nearly 100% error-free PUF outputs over varying environmental conditions without post-processing while consuming minimum area.
The WU-Minn Human Connectome Project: An overview

The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.
OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World

While deep reinforcement learning techniques have recently produced considerable achievements on many decision-making problems, their use in robotics has largely been limited to simulated worlds or restricted motions, since unconstrained trial-and-error interactions in the real world can have undesirable consequences for the robot or its environment. To overcome such limitations, we propose a novel reinforcement learning architecture, OptLayer, that takes as inputs possibly unsafe actions predicted by a neural network and outputs the closest actions that satisfy chosen constraints. While learning control policies often requires carefully crafted rewards and penalties while exploring the range of possible actions, OptLayer ensures that only safe actions are actually executed and unsafe predictions are penalized during training. We demonstrate the effectiveness of our approach on robot reaching tasks, both simulated and in the real world.
Cyber-physical system product line engineering: comprehensive domain analysis and experience report

Cyber-Physical Systems (CPSs) are the future generation of highly connected embedded systems having applications in diverse domains including Oil and Gas. Employing Product Line Engineering (PLE) is believed to bring potential benefits with respect to reduced cost, higher productivity, higher quality, and faster time-to-market. However, relatively few industrial field studies are reported regarding the application of PLE to develop large-scale systems, and more specifically CPSs. In this paper, we report about our experiences and insights gained from investigating the application of model-based PLE at a large international organization developing subsea production systems (typical CPSs) to manage the exploitation of oil and gas production fields. We report in this paper 1) how two systematic domain analyses (on requirements engineering and product configuration/derivation) were conducted to elicit CPS PLE requirements and challenges, 2) key results of the domain analysis (commonly observed in other domains), and 3) our initial experience of developing and applying two Model Based System Engineering (MBSE) PLE solution to address some of the requirements and challenges elicited during the domain analyses.
Eye-Model-Based Gaze Estimation by RGB-D Camera

This paper proposes a method of eye-model-based gaze estimation by RGB-D camera, Kinect sensor. Different from other methods, our method sets up a model to calibrate the eyeball center by gazing at a target in 3D space, not predefined. And then by detecting the pupil center, we can estimate the gaze direction. To achieve this algorithm, we first build a head model relying on Kinect sensor, then obtaining the 3D information of pupil center. As we need to know the eyeball center position in head model, we do a calibration by designing a target to gaze. Because the ray from eyeball center to target and the ray from eyeball center to pupil center should meet a relationship, we can have an equation to solve the real eyeball center position. After calibration, we can have a gaze estimation automatically at any time. Our method allows free head motion and it only needs a simple device, finally it also can run automatically in real-time. Experiments show that our method performs well and still has a room for improvement.
The design of the force.com multitenant internet application development platform

Force.com is the preeminent on-demand application development platform in use today, supporting some 55,000+ organizations. Individual enterprises and commercial software-as-a-service (SaaS) vendors trust the platform to deliver robust, reliable, Internet-scale applications. To meet the extreme demands of its large user population, Force.com's foundation is a metadatadriven software architecture that enables multitenant applications.
 The focus of this paper is multitenancy, a fundamental design approach that can dramatically improve SaaS application management. This paper defines multitenancy, explains its benefits, and demonstrates why metadata-driven architectures are the premier choice for implementing multitenancy.
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network

Automatic diagnosing lung cancer from computed tomography scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary malignancy. Currently, there are many studies about the first step, but few about the second step. Since the existence of nodule does not definitely indicate cancer, and the morphology of nodule has a complicated relationship with cancer, the diagnosis of lung cancer demands careful investigations on every suspicious nodule and integration of information of all nodules. We propose a 3-D deep neural network to solve this problem. The model consists of two modules. The first one is a 3-D region proposal network for nodule detection, which outputs all suspicious nodules for a subject. The second one selects the top five nodules based on the detection confidence, evaluates their cancer probabilities, and combines them with a leaky noisy-or gate to obtain the probability of lung cancer for the subject. The two modules share the same backbone network, a modified U-net. The overfitting caused by the shortage of the training data is alleviated by training the two modules alternately. The proposed model won the first place in the Data Science Bowl 2017 competition.
Andro-AutoPsy: Anti-malware system based on similarity matching of malware and malware creator-centric information

Mobile security threats have recently emerged because of the fast growth in mobile technologies and the essential role that mobile devices play in our daily lives. For that, and to particularly address threats associated with malware, various techniques are developed in the literature, including ones that utilize static, dynamic, on-device, off-device, and hybrid approaches for identifying, classifying, and defend against mobile threats. Those techniques fail at times, and succeed at other times, while creating a trade-off of performance and operation. In this paper, we contribute to the mobile security defense posture by introducing Andro-AutoPsy, an anti-malware system based on similarity matching of malware-centric and malware creator-centric information. Using Andro-AutoPsy, we detect and classify malware samples into similar subgroups by exploiting the profiles extracted from integrated footprints, which are implicitly equivalent to distinct characteristics. The experimental results demonstrate that Andro-AutoPsy is scalable, performs precisely in detecting and classifying malware with low false positives and false negatives, and is capable of identifying zero-day mobile malware.
Safety Benefits of Forward Collision Warning, Brake Assist, and Autonomous Braking Systems in Rear-End Collisions

This paper examines the potential effectiveness of the following three precollision system (PCS) algorithms: 1) forward collision warning only; 2) forward collision warning and precrash brake assist; and 3) forward collision warning, precrash brake assist, and autonomous precrash brake. Real-world rear-end crashes were extracted from a nationally representative sample of collisions in the United States. A sample of 1396 collisions, corresponding to 1.1 million crashes, were computationally simulated as if they occurred, with the driver operating a precollision-system-equipped vehicle. A probability-based framework was developed to account for the variable driver reaction to the warning system. As more components were added to the algorithms, greater benefits were realized. The results indicate that the exemplar PCS investigated in this paper could reduce the severity (i.e., ΔV) of the collision between 14% and 34%. The number of moderately to fatally injured drivers who wore their seat belts could have been reduced by 29% to 50%. These collision-mitigating algorithms could have prevented 3.2% to 7.7% of rear-end collisions. This paper shows the dramatic reductions in serious and fatal injuries that a PCS, which is one of the first intelligent vehicle technologies to be deployed in production cars, can bring to highway safety when available throughout the fleet. This paper also presents the framework of an innovative safety benefits methodology that, when adapted to other emerging active safety technologies, can be employed to estimate potential reductions in the frequency and severity of highway crashes.
Joint 2D-3D temporally consistent semantic segmentation of street scenes

In this paper we propose a novel Conditional Random Field (CRF) formulation for the semantic scene labeling problem which is able to enforce temporal consistency between consecutive video frames and take advantage of the 3D scene geometry to improve segmentation quality. The main contribution of this work lies in the novel use of a 3D scene reconstruction as a means to temporally couple the individual image segmentations, allowing information flow from 3D geometry to the 2D image space. As our results show, the proposed framework outperforms state-of-the-art methods and opens a new perspective towards a tighter interplay of 2D and 3D information in the scene understanding problem.
Classification of Three Types of Walking Activities Regarding Stairs Using Plantar Pressure Sensors

In this letter, we propose a novel method for classifying ambulatory activities using eight plantar pressure sensors within smart shoes. Using these sensors, pressure data of participants can be collected regarding level walking, stair descent, and stair ascent. Analyzing patterns of the ambulatory activities, we present new features with which to describe the ambulatory activities. After selecting critical features, a multi-class support vector machine algorithm is applied to classify these activities. Applying the proposed method to the experimental database, we obtain recognition rates up to 95.2% after six steps.
The challenge for multichannel services: Cross-channel free-riding behavior

In multichannel environments, consumers can move easily among different channels. They engage in cross-channel free-riding when they use one retailer’s channel to obtain information or evaluate products and then switch to another retailer’s channel to complete the purchase. Cross-channel free-riding erodes profits and is one of the most important issues that firms face in the multichannel era. The current study focuses on the most popular type of cross-channel free-riding: searching for product information in an online store and then purchasing in another brick-and-mortar store. It explores antecedents that may contribute to consumer switching behaviors through a questionnaire focused on cross-channel free-riding behavior. The empirical results reveal that when consumers perceive more multichannel self-efficacy, they engage in more cross-channel free-riding behavior. Perceived service quality of competitors’ offline store and the reduced risk in the brick-and-mortar channel influence the attractiveness of this behavior and increase cross-channel free-riding intentions. By increasing within-firm lock-in levels, firms can reduce consumers’ cross-channel free-riding intentions.
Toward clean and crackless transfer of graphene.

We present the results of a thorough study of wet chemical methods for transferring chemical vapor deposition grown graphene from the metal growth substrate to a device-compatible substrate. On the basis of these results, we have developed a "modified RCA clean" transfer method that has much better control of both contamination and crack formation and does not degrade the quality of the transferred graphene. Using this transfer method, high device yields, up to 97%, with a narrow device performance metrics distribution were achieved. This demonstration addresses an important step toward large-scale graphene-based electronic device applications.