nils-herrmann/clean_scirepeval huggingface dataset:
Cloud-dew architecture

Users derive many benefits by storing personal data in cloud computing services; however the drawback of storing data in these services is that the user cannot access his/her own data when an internet connection is not available. To solve this problem in an efficient and elegant way, we propose the cloud-dew architecture. Cloud-dew architecture is an extension of the client-server architecture. In the extension, servers are further classified into cloud servers and dew servers. The dew servers are web servers that reside on user’s local computers and have a pluggable structure so that scripts and databases of websites can be installed easily. The cloud-dew architecture not only makes the personal data stored in the cloud continuously accessible by the user, but also enables a new application: web-surfing without an internet connection. An experimental system is presented to demonstrate the ideas of the cloud-dew architecture.
Mining the web to predict future events

We describe and evaluate methods for learning to forecast forthcoming events of interest from a corpus containing 22 years of news stories. We consider the examples of identifying significant increases in the likelihood of disease outbreaks, deaths, and riots in advance of the occurrence of these events in the world. We provide details of methods and studies, including the automated extraction and generalization of sequences of events from news corpora and multiple web resources. We evaluate the predictive power of the approach on real-world events withheld from the system.
Improving Multi-Step Prediction of Learned Time Series Models

Most typical statistical and machine learning approaches to time series modeling optimize a singlestep prediction error. In multiple-step simulation, the learned model is iteratively applied, feeding through the previous output as its new input. Any such predictor however, inevitably introduces errors, and these compounding errors change the input distribution for future prediction steps, breaking the train-test i.i.d assumption common in supervised learning. We present an approach that reuses training data to make a no-regret learner robust to errors made during multi-step prediction. Our insight is to formulate the problem as imitation learning; the training data serves as a “demonstrator” by providing corrections for the errors made during multi-step prediction. By this reduction of multistep time series prediction to imitation learning, we establish theoretically a strong performance guarantee on the relation between training error and the multi-step prediction error. We present experimental results of our method, DAD, and show significant improvement over the traditional approach in two notably different domains, dynamic system modeling and video texture prediction. Determining models for time series data is important in applications ranging from market prediction to the simulation of chemical processes and robotic systems. Many supervised learning approaches have been proposed for this task, such as neural networks (Narendra and Parthasarathy 1990), Expectation-Maximization (Ghahramani and Roweis 1999; Coates, Abbeel, and Ng 2008), Support Vector Regression (Müller, Smola, and Rätsch 1997), Gaussian process regression (Wang, Hertzmann, and Blei 2005; Ko et al. 2007), Nadaraya-Watson kernel regression (Basharat and Shah 2009), Gaussian mixture models (Khansari-Zadeh and Billard 2011), and Kernel PCA (Ralaivola and D’Alche-Buc 2004). Common to most of these methods is that the objective being optimized is the single-step prediction loss. However, this criterion does not guarantee accurate multiple-step simulation accuracy in which the output of a prediction step is used as input for the next inference. The prevalence of single-step modeling approaches is a result of the difficulty in directly optimizing the multipleCopyright  As an example, consider fitting a simple linear dynamical system model for the multi-step error over the time horizon T from an initial condition x0,
DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images

Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm was designed and trained to digitally stain (i.e. highlight) 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall dice coefficient (mean of all tissues) was 0.91 ± 0.05 when assessed against manual segmentations performed by an expert observer. We offer here a robust segmentation framework that could be extended for the automated parametric study of the ONH tissues.
Total glossectomy: reconstruction and rehabilitation.

Patients with carcinoma of the tongue including the base of the tongue who underwent total glossectomy in a period of just over ten years since January 1979 have been reviewed. Total glossectomy may be indicated as salvage surgery or as a primary procedure. The larynx may be preserved or may have to be sacrificed depending upon the site of the lesion. When the larynx is preserved the use of laryngeal suspension facilitates early rehabilitation and preserves the quality of life to a large extent. Cricopharyngeal myotomy seems unnecessary.
Mental fatigue impairs physical performance in humans.

Mental fatigue is a psychobiological state caused by prolonged periods of demanding cognitive activity. Although the impact of mental fatigue on cognitive and skilled performance is well known, its effect on physical performance has not been thoroughly investigated. In this randomized crossover study, 16 subjects cycled to exhaustion at 80% of their peak power output after 90 min of a demanding cognitive task (mental fatigue) or 90 min of watching emotionally neutral documentaries (control). After experimental treatment, a mood questionnaire revealed a state of mental fatigue (P = 0.005) that significantly reduced time to exhaustion (640 +/- 316 s) compared with the control condition (754 +/- 339 s) (P = 0.003). This negative effect was not mediated by cardiorespiratory and musculoenergetic factors as physiological responses to intense exercise remained largely unaffected. Self-reported success and intrinsic motivation related to the physical task were also unaffected by prior cognitive activity. However, mentally fatigued subjects rated perception of effort during exercise to be significantly higher compared with the control condition (P = 0.007). As ratings of perceived exertion increased similarly over time in both conditions (P < 0.001), mentally fatigued subjects reached their maximal level of perceived exertion and disengaged from the physical task earlier than in the control condition. In conclusion, our study provides experimental evidence that mental fatigue limits exercise tolerance in humans through higher perception of effort rather than cardiorespiratory and musculoenergetic mechanisms. Future research in this area should investigate the common neurocognitive resources shared by physical and mental activity.
Incremental Parsing Models for Dialog Task Structure

In this paper, we present an integrated model of the two central tasks of dialog management: interpreting user actions and generating system actions. We model the interpretation task as a classication problem and the generation task as a prediction problem. These two tasks are interleaved in an incremental parsing-based dialog model. We compare three alternative parsing methods for this dialog model using a corpus of human-human spoken dialog from a catalog ordering domain that has been annotated for dialog acts and task/subtask information. We contrast the amount of context provided by each method and its impact on performance.
Understanding Graph Computation Behavior to Enable Robust Benchmarking

Graph processing is important for a growing range of applications. Current performance studies of parallel graph computation employ a large variety of algorithms and graphs. To explore their robustness, we characterize behavior variation across algorithms and graph structures at different scales. Our results show that graph computation behaviors, with up to 1000-fold variation, form a very broad space. Any inefficient exploration of this space may lead to narrow understanding and ad-hoc studies. Hence, we consider constructing an ensemble of graph computations, or graph-algorithm pairs, to most effectively explore this graph computation behavior space. We study different ensembles of parallel graph computations, and define two metrics to quantify how efficiently and completely an ensemble explores the space. Our results show that: (1) experiments limited to a single algorithm or a single graph may unfairly characterize a graph-processing system, (2) benchmarks exploring both algorithm and graph diversity can significantly improve the quality (30% more complete and 200% more efficient), but must be carefully chosen, (3) some algorithms are more useful than others in benchmarking, and (4) we can reduce the complexity (number of algorithms, graphs, runtime) while conserving the benchmarking quality.
Usefulness of Sonazoid contrast-enhanced ultrasonography for hepatocellular carcinoma: comparison with pathological diagnosis and superparamagnetic iron oxide magnetic resonance images

We investigated the usefulness of Sonazoid contrast-enhanced ultrasonography (Sonazoid-CEUS) in the diagnosis of hepatocellular carcinoma (HCC). The examination was performed by comparing the images during the Kupffer phase of Sonazoid-CEUS with superparamagnetic iron oxide magnetic resonance (SPIO-MRI). The subjects were 48 HCC nodules which were histologically diagnosed (well-differentiated HCC, n = 13; moderately differentiated HCC, n = 30; poorly differentiated HCC, n = 5). We performed Sonazoid-CEUS and SPIO-MRI on all subjects. In the Kupffer phase of Sonazoid-CEUS, the differences in the contrast agent uptake between the tumorous and non-tumorous areas were quantified as the Kupffer phase ratio and compared. In the SPIO-MRI, it was quantified as the SPIO-intensity index. We then compared these results with the histological differentiation of HCCs. The Kupffer phase ratio decreased as the HCCs became less differentiated (P < 0.0001; Kruskal–Wallis test). The SPIO-intensity index also decreased as HCCs became less differentiated (P < 0.0001). A positive correlation was found between the Kupffer phase ratio and the SPIO-MRI index (r = 0.839). In the Kupffer phase of Sonazoid-CEUS, all of the moderately and poorly differentiated HCCs appeared hypoechoic and were detected as a perfusion defect, whereas the majority (9 of 13 cases, 69.2%) of the well-differentiated HCCs had an isoechoic pattern. The Kupffer phase images of Sonazoid-CEUS and SPIO-MRI matched perfectly (100%) in all of the moderately and poorly differentiated HCCs. Sonazoid-CEUS is useful for estimating histological grading of HCCs. It is a modality that could potentially replace SPIO-MRI.
Ergonomics problems due to the use and design of dissector and needle holder: a survey in minimally invasive surgery.

This study analyzes the problems and consequences associated with prolonged use of laparoscopic instruments (dissector and needle holder) and equipments.



A total of 390 questionnaires were sent to the laparoscopic surgeons of the Spanish Health System. Questions were structured on the basis of 4 categories: demographics, assessment of laparoscopic dissector, assessment of needle holder, and other informations.



A response rate of 30.26% was obtained. Among them, handle shape of laparoscopic instruments was identified as the main element that needed to be improved. Furthermore, the type of instrument, electrocautery pedals and height of the operating table were identified as major causes of forced positions during the use of both surgical instruments.



As far as we know, this is the largest Spanish survey conducted on this topic. From this survey, some ergonomic drawbacks have been identified in: (a) the instruments' design, (b) the operating tables, and (c) the posture of the surgeons.
Efficient Skin Temperature Sensor and Stable Gel-Less Sticky ECG Sensor for a Wearable Flexible Healthcare Patch.

Wearable, flexible healthcare devices, which can monitor health data to predict and diagnose disease in advance, benefit society. Toward this future, various flexible and stretchable sensors as well as other components are demonstrated by arranging materials, structures, and processes. Although there are many sensor demonstrations, the fundamental characteristics such as the dependence of a temperature sensor on film thickness and the impact of adhesive for an electrocardiogram (ECG) sensor are yet to be explored in detail. In this study, the effect of film thickness for skin temperature measurements, adhesive force, and reliability of gel-less ECG sensors as well as an integrated real-time demonstration is reported. Depending on the ambient conditions, film thickness strongly affects the precision of skin temperature measurements, resulting in a thin flexible film suitable for a temperature sensor in wearable device applications. Furthermore, by arranging the material composition, stable gel-less sticky ECG electrodes are realized. Finally, real-time simultaneous skin temperature and ECG signal recordings are demonstrated by attaching an optimized device onto a volunteer's chest.
Traceability Patterns : An Approach to Requirement-Component Traceability in Agile Software Development

Requirements traceability, regardless of the process used to produce a software system, is an important quality attribute for the resulting software product. However, existing approaches to achieving requirements traceability have been specifically designed for traditional software development with formal requirements processes. With the wide industry adoption of agile development methodologies, where requirements are captured and communicated through informal channels, the applicability of existing traceability approaches to agile software projects has been restricted. In this paper, we introduce traceability patterns as a solution to requirementcomponent tractability in agile software development. In our approach, we achieve traceability through source code, which is the main development artifact in agile methodologies.  Requirements Traceability, Traceability Pattern, Agile Software Development
New grouping and fitting methods for interactive overtraced sketches

This paper describes a new method for recognizing overtraced strokes to 2D geometric primitives, which are further interpreted as 2D line drawings. This method can support rapid grouping and fitting of overtraced polylines or conic curves based on the classified characteristics of each stroke during its preprocessing stage. The orientation and its endpoints of a classified stroke are used in the stroke grouping process. The grouped strokes are then fitted with 2D geometry. This method can deal with overtraced sketch strokes in both solid and dash linestyles, fit grouped polylines as a whole polyline and simply fit conic strokes without computing the direction of a stroke. It avoids losing joint information due to segmentation of a polyline into line-segments. The proposed method has been tested with our freehand sketch recognition system (FSR), which is robust and easier to use by removing some limitations embedded with most existing sketching systems which only accept non-overtraced stroke drawing. The test results showed that the proposed method can support freehand sketching based conceptual design with no limitations on drawing sequence, directions and overtraced cases while achieving a satisfactory interpretation rate.
CATalyst: Defeating last-level cache side channel attacks in cloud computing

Cache side channel attacks are serious threats to multi-tenant public cloud platforms. Past work showed how secret information in one virtual machine (VM) can be extracted by another co-resident VM using such attacks. Recent research demonstrated the feasibility of high-bandwidth, low-noise side channel attacks on the last-level cache (LLC), which is shared by all the cores in the processor package, enabling attacks even when VMs are scheduled on different cores. This paper shows how such LLC side channel attacks can be defeated using a performance optimization feature recently introduced in commodity processors. Since most cloud servers use Intel processors, we show how the Intel Cache Allocation Technology (CAT) can be used to provide a system-level protection mechanism to defend from side channel attacks on the shared LLC. CAT is a way-based hardware cache-partitioning mechanism for enforcing quality-of-service with respect to LLC occupancy. However, it cannot be directly used to defeat cache side channel attacks due to the very limited number of partitions it provides. We present CATalyst, a pseudo-locking mechanism which uses CAT to partition the LLC into a hybrid hardware-software managed cache. We implement a proof-of-concept system using Xen and Linux running on a server with Intel processors, and show that LLC side channel attacks can be defeated. Furthermore, CATalyst only causes very small performance overhead when used for security, and has negligible impact on legacy applications.
ICT in Small Firms: Factors Affecting the Adoption and Use of ICT in Southeast England SMEs

This paper explores patterns of adoption and use of information and communications technology (ICT) by small and medium sized enterprises (SMEs) in the southwest London and Thames Valley region of England. The paper presents preliminary results of a survey of around 400 SMEs drawn from four economically significant sectors in the region: food processing, transport and logistics, media and Internet services. The main objectives of the study were to explore ICT adoption and use patterns by SMEs, to identify factors enabling or inhibiting the successful adoption and use of ICT, and to explore the effectiveness of government policy mechanisms at national and regional levels. While our main result indicates a generally favourable attitude to ICT amongst the SMEs surveyed, it also suggests a failure to recognise ICT’s strategic potential. A surprising result was the overwhelming ignorance of regional, national and European Union wide policy initiatives to support SMEs. This strikes at the very heart of regional, national and European policy that have identified SMEs as requiring specific support mechanisms. Our findings from one of the UK’s most productive regions therefore have important implications for policy aimed at ICT adoption and use by SMEs.
Blockage Robust and Efficient Scheduling for Directional mmWave WPANs

With the increase in emerging bandwidth-intensive applications, millimeter-wave (mmWave) communications in 60-GHz band have become a hot topic. There are two unique features that sharply distinguish mmWave wireless personal area networks (WPANs) from other networks using lower carrier frequencies. First, mmWave links use high-gain directional antennas to overcome the high propagation loss. Second, mmWave links are easily blocked by the human body and furniture. In this paper, we develop a blockage robust and efficient directional medium access control (MAC) protocol (BRDMAC), which overcomes the blockage problem by relaying. Relay selection and spatial reuse are jointly optimized to improve network performance. In BRDMAC, relay selection and transmission scheduling algorithms are proposed to compute near-optimal relay selection to maximize spatial reuse and compute near-optimal schedules with respect to the total transmission time, respectively. Finally, extensive simulation results show that BRDMAC performs better in terms of delay and throughput, compared with other existing protocols while attaining a good fairness performance.
Learning image representations from the pixel level via hierarchical sparse coding

We present a method for learning image representations using a two-layer sparse coding scheme at the pixel level. The first layer encodes local patches of an image. After pooling within local regions, the first layer codes are then passed to the second layer, which jointly encodes signals from the region. Unlike traditional sparse coding methods that encode local patches independently, this approach accounts for high-order dependency among patterns in a local image neighborhood. We develop algorithms for data encoding and codebook learning, and show in experiments that the method leads to more invariant and discriminative image representations. The algorithm gives excellent results for hand-written digit recognition on MNIST and object recognition on the Caltech101 benchmark. This marks the first time that such accuracies have been achieved using automatically learned features from the pixel level, rather than using hand-designed descriptors.
Pet Therapy: Enhancing Patient Care Through Time with Animals.

A collection of Curbside Consultation published in AFP is available at http://www.aafp.org/afp/ curbside. Case Scenario A 16-year-old girl and her parents presented to my office for her wellness evaluation. The patient has generalized anxiety disorder with comorbid major depression, for which she has been prescribed a serotonergic antidepressant. She is a high school student, lives with her parents, and is currently preparing college applications. She has occasional headaches and disturbed sleep. She takes daily three-mile walks and plays on her school tennis team, both of which help relieve her anxiety symptoms. The therapist she sees once a week has suggested enrollment in a therapeutic foster dog walking program to help further relieve her anxiety symptoms. Would animal-assisted therapy be helpful as a part of anxiety and depression management in this patient? Would such treatment be a helpful approach for other teenaged patients in my practice with similar diagnoses?
A Dynamic Model for Traffic Flow Prediction Using Improved DRN

Real-time traffic flow prediction can not only provide travelers with reliable traffic information so that it can save people’s time, but also assist the traffic management agency to manage traffic system. It can greatly improve the efficiency of the transportation system. Traditional traffic flow prediction approaches usually need a large amount of data but still give poor performances. With the development of deep learning, researchers begin to pay attention to artificial neural networks (ANNs) such as RNN and LSTM. However, these ANNs are very time-consuming. In our research, we improve the Deep Residual Network and build a dynamic model which previous researchers hardly use. We firstly integrate the input and output of the i layer to the input of the i + 1 layer and prove that each layer will fit a simpler function so that the error rate will be much smaller. Then, we use the concept of online learning in our model to update pre-trained model during prediction. Our result shows that our model has higher accuracy than some state-of-the-art models. In addition, our dynamic model can perform better in practical applications.