nils-herrmann/clean_scirepeval huggingface dataset:
Creating Appropriate Trust in Automated Vehicle Systems: A Framework for HMI Design

While automated vehicle technology progresses, potentially leading to a safer and more efficient traffic environment, many challenges remain within the area of human factors, such as user trust for automated driving (AD) vehicle systems. The aim of this paper is to investigate how an appropriate level of user trust for AD vehicle systems can be created via human–machine interaction (HMI). A guiding framework for implementing trust-related factors into the HMI interface is presented. This trust-based framework incorporates usage phases, AD events, trust-affecting factors, and levels explaining each event from a trust perspective. Based on the research findings, the authors recommend that HMI designers and automated vehicle manufacturers take a more holistic perspective on trust rather than focusing on single, “isolated” events, for example understanding that trust formation is a dynamic process that starts long before a user's first contact with the system, and continues long thereafter. Furthermore, factors-affecting trust change, both during user interactions with the system and over time; thus, HMI concepts need to be able to adapt. Future work should be dedicated to understanding how trust-related factors interact, as well as validating and testing the trust-based framework.
Associating Internet Usage with Depressive Behavior Among College Students

Depression is a serious mental health problem affecting a significant segment of American society today, and in particular college students. In a survey by the U.S. Centers for Disease Control (CDC) in 2009, 26.1% of U.S. students nationwide reported feeling so sad or hopeless almost every day for 2 or more weeks in a row that they stopped doing some usual activities. Similar statistics are also reported in mental health studies by the American College Health Association, and by independent surveys. In this article, the author report their findings from a month-long experiment conducted at Missouri University of Science and Technology on studying depressive symptoms among college students who use the Internet. This research was carried out using real campus Internet data collected continuously, unobtrusively, and while preserving privacy.
ZaliQL: Causal Inference from Observational Data at Scale

Causal inference from observational data is a subject of active research and development in statistics and computer science. Many statistical software packages have been developed for this purpose. However, these toolkits do not scale to large datasets. We propose and demonstrate ZaliQL: a SQL-based framework for drawing causal inference from observational data. ZaliQL supports the state-of-the-art methods for causal inference and runs at scale within PostgreSQL database system. In addition, we built a visual interface to wrap around ZaliQL. In our demonstration, we will use this GUI to show a live investigation of the causal effect of different weather conditions on flight delays.
On the Unfairness of Blockchain

The success of Bitcoin largely relies on the perception of a fair underlying peer-to-peer protocol: blockchain. Fairness here essentially means that the reward (in bitcoins) given to any participant that helps maintain the consistency of the protocol by mining, is proportional to the computational power devoted by that participant to the mining task. Without such perception of fairness, honest miners might be disincentivized to maintain the protocol, leaving the space for dishonest miners to reach a majority and jeopardize the consistency of the entire system. We prove, in this paper, that blockchain is actually unfair, even in a distributed system of only two honest miners. In a realistic setting where message delivery is not instantaneous, the ratio between the (expected) number of blocks committed by two miners is at least exponential in the product of the message delay and the difference between the two miners’ hashrates. To obtain our result, we model the growth of blockchain, which may be of independent interest. We also apply our result to explain recent empirical observations and vulnerabilities.
The reliability of estimated confidence intervals for classification error rates when only a single sample is available

Error estimation accuracy is the salient issue regarding the validity of a classifier model. When samples are small, training-data-based error estimates tend to suffer from inaccuracy and quantification of error estimation accuracy is difficult. Numerous methods have been proposed for estimating confidence intervals for the true error based on the estimated error. This paper surveys proposed methods and quantifies their performance. Monte Carlo methods are used to obtain accurate estimates of the true confidence intervals and compare these to the intervals estimated from samples. We consider different error estimators and several proposed confidence-bound estimators. Both synthetic and real genomic data are employed. Our simulations show the majority of the confidence intervals methods have poor performance because of the difference of shape between true and estimated intervals. According to our results, the best estimation strategy is to use the 10-time 10-fold cross-validation with a confidence interval based on the standard deviation. & 2012 Elsevier Ltd. All rights reserved.
Learning the easy things first: Self-paced visual category discovery

Objects vary in their visual complexity, yet existing discovery methods perform “batch” clustering, paying equal attention to all instances simultaneously — regardless of the strength of their appearance or context cues. We propose a self-paced approach that instead focuses on the easiest instances first, and progressively expands its repertoire to include more complex objects. Easier regions are defined as those with both high likelihood of generic objectness and high familiarity of surrounding objects. At each cycle of the discovery process, we re-estimate the easiness of each subwindow in the pool of unlabeled images, and then retrieve a single prominent cluster from among the easiest instances. Critically, as the system gradually accumulates models, each new (more difficult) discovery benefits from the context provided by earlier discoveries. Our experiments demonstrate the clear advantages of self-paced discovery relative to conventional batch approaches, including both more accurate summarization as well as stronger predictive models for novel data.
Robust observer-based fault diagnosis for an unmanned aerial vehicle

In this paper, a new robust fault detection and isolation (FDI) methodology for an unmanned aerial vehicle (UAV) is proposed. The fault diagnosis scheme is constructed based on observer-based techniques according to fault models corresponding to each component (actuator, sensor, and structure). The proposed fault diagnosis method takes advantage of the structural perturbation of the UAV model due to the icing (the main structural fault in aircraft), sensor, and actuator faults to reduce the error of observers that are used in the FDI module in addition to distinguishing among faults in different components. Moreover, the accuracy of the FDI module is increased by considering the structural perturbation of the UAV linear model due to wind disturbances which is the major environmental disturbance affecting an aircraft. Our envisaged FDI strategy is capable of diagnosing recurrent faults through properly designed residuals with different responses to different types of faults. Simulation results are provided to illustrate and demonstrate the effectiveness of our proposed FDI approach due to faults in sensors, actuators, and structural components of unmanned aerial vehicles.
Sports Specialization, Part II

Many coaches, parents, and children believe that the best way to develop elite athletes is for them to participate in only 1 sport from an early age and to play it year-round. However, emerging evidence to the contrary indicates that efforts to specialize in 1 sport may reduce opportunities for all children to participate in a diverse year-round sports season and can lead to lost development of lifetime sports skills. Early sports specialization may also reduce motor skill development and ongoing participation in games and sports as a lifestyle choice. The purpose of this review is to employ the current literature to provide evidence-based alternative strategies that may help to optimize opportunities for all aspiring young athletes to maximize their health, fitness, and sports performance.



Nonsystematic review with critical appraisal of existing literature.



Clinical review.



Based on the current evidence, parents and educators should help provide opportunities for free unstructured play to improve motor skill development and youth should be encouraged to participate in a variety of sports during their growing years to influence the development of diverse motor skills. For those children who do choose to specialize in a single sport, periods of intense training and specialized sport activities should be closely monitored for indicators of burnout, overuse injury, or potential decrements in performance due to overtraining. Last, the evidence indicates that all youth should be involved in periodized strength and conditioning (eg, integrative neuromuscular training) to help them prepare for the demands of competitive sport participation, and youth who specialize in a single sport should plan periods of isolated and focused integrative neuromuscular training to enhance diverse motor skill development and reduce injury risk factors.



B.
Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure

The problem of nonnegative blind source separation (NBSS) is addressed in this paper, where both the sources and the mixing matrix are nonnegative. Because many real-world signals are sparse, we deal with NBSS by sparse component analysis. First, a determinant-based sparseness measure, named D-measure, is introduced to gauge the temporal and spatial sparseness of signals. Based on this measure, a new NBSS model is derived, and an iterative sparseness maximization (ISM) approach is proposed to solve this model. In the ISM approach, the NBSS problem can be cast into row-to-row optimizations with respect to the unmixing matrix, and then the quadratic programming (QP) technique is used to optimize each row. Furthermore, we analyze the source identifiability and the computational complexity of the proposed ISM-QP method. The new method requires relatively weak conditions on the sources and the mixing matrix, has high computational efficiency, and is easy to implement. Simulation results demonstrate the effectiveness of our method.
Low-complexity color demosaicing algorithm based on integrated gradients

bstract. Color demosaicing is critical for digital cameras, because t converts a Bayer sensor mosaic output to a full color image, which etermines the output image quality of the camera. In this work, an fficient decision-based demosaicing method is presented. This ethod exploits a new edge-sensing measure called integrated graient (IG) to effectively extract gradient information in both color ntensity and color difference domains simultaneously. This meaure is reliable and supports full resolution, which allows one to nterpolate the missing samples along an appropriate direction and ence directly improves the demosaicing performance. By sharing it n different demosaicing stages to guide the interpolation of various olor planes, it guarantees the consistency of the interpolation direcion in different color channels and saves the effort required to reeatedly extract gradient information from intermediate interpolation esults at different stages. An IG-based green plane enhancement is lso proposed to further improve the method’s efficiency. Simulation esults confirm that the proposed demosaicing method outperforms p-to-date demosaicing methods in terms of output quality at a comlexity of around 80 arithmetic operations per pixel.
Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification.

The purpose of this work was to modernize recommendations for evaluation, staging, and response assessment of patients with Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL). A workshop was held at the 11th International Conference on Malignant Lymphoma in Lugano, Switzerland, in June 2011, that included leading hematologists, oncologists, radiation oncologists, pathologists, radiologists, and nuclear medicine physicians, representing major international lymphoma clinical trials groups and cancer centers. Clinical and imaging subcommittees presented their conclusions at a subsequent workshop at the 12th International Conference on Malignant Lymphoma, leading to revised criteria for staging and of the International Working Group Guidelines of 2007 for response. As a result, fluorodeoxyglucose (FDG) positron emission tomography (PET)–computed tomography (CT) was formally incorporated into standard staging for FDG-avid lymphomas. A modification of the Ann Arbor descriptive terminology will be used for anatomic distribution of disease extent, but the suffixes A or B for symptoms will only be included for HL. A bone marrow biopsy is no longer indicated for the routine staging of HL and most diffuse large B-cell lymphomas. However, regardless of stage, general practice is to treat patients based on limited (stages I and II, nonbulky) or advanced (stage III or IV) disease, with stage II bulky disease considered as limited or advanced disease based on histology and a number of prognostic factors. PET-CT will be used to assess response in FDG-avid histologies using the 5-point scale. The product of the perpendicular diameters of a single node can be used to identify progressive disease. Routine surveillance scans are discouraged. These recommendations should improve evaluation of patients with lymphoma and enhance the ability to compare outcomes of clinical trials.
Implementation of FDK Reconstruction Algorithm in Cone-Beam CT Based on the 3D Shepp-Logan Model

Cone-beam CT system has become a hot issue in current CT technique. Compared with the traditional 2D CT, cone beam CT can greatly reduce the scanning time, improve the utilization ratio of X-ray, and enhance the spatial resolution. In the article, simulation data based on the 3D Shepp-Logan Model was obtained by tracing the X-ray and applying the radial attenuation theory. FDK (Feldkamp, Davis and Kress) reconstruction algorithm was then adopted to reconstruct the 3D Shepp-Logan Mode. The reconstruction results indicate that for the central image the spatial resolution can reach 8linepairs/mm. Reconstructed images truthfully reveal the archetype.
Outcomes of children bridged to heart transplantation with ventricular assist devices: a multi-institutional study.

Current ventricular assist devices (VADs) in the United States are designed primarily for adult use. Data on VADs as a bridge to transplantation in children are limited.



A multi-institutional, prospectively maintained database of outcomes in children after listing for heart transplantation (n=2375) was used to analyze outcomes of VAD patients (n=99, 4%) listed between January 1993 and December 2003. Median age at VAD implantation was 13.3 years (range, 2 days to 17.9 years); diagnoses were cardiomyopathy (78%) and congenital heart disease (22%). Mean duration of support was 57 days (range, 1 to 465 days). Seventy-three percent were supported with a long-term device, with 39% requiring biventricular support. Seventy-seven patients (77%) survived to transplantation, 5 patients were successfully weaned from support and recovered, and 17 patients (17%) died on support. In the recent era (2000 to 2003), successful bridge to transplantation with VAD was achieved in 86% of patients. Peak hazard for death while waiting was the first 2 weeks after VAD placement. Risk factors for death while awaiting a transplant included earlier era of implantation (P=0.05), female gender (P=0.02), and congenital disease diagnosis (P=0.05). There was no difference in 5-year survival after transplantation for patients on VAD at time of transplantation as compared with those not requiring VAD.



VAD support in children successfully bridged 77% of patients to transplantation, with posttransplantation outcomes comparable to those not requiring VAD. These encouraging results emphasize the need to further understand patient selection and to delineate the impact of VAD technology for children.
Benefits of Network Coding for Unicast Application in Disruption-Tolerant Networks

In this paper, we investigate the benefits of applying a form of network coding known as random linear coding (RLC) to unicast applications in disruption-tolerant networks (DTNs). Under RLC, nodes store and forward random linear combinations of packets as they encounter each other. For the case of a single group of packets originating from the same source and destined for the same destination, we prove a lower bound on the probability that the RLC scheme achieves the minimum time to deliver the group of packets. Although RLC significantly reduces group delivery delays, it fares worse in terms of average packet delivery delay and network transmissions. When replication control is employed, RLC schemes reduce group delivery delays without increasing the number of transmissions. In general, the benefits achieved by RLC are more significant under stringent resource (bandwidth and buffer) constraints, limited signaling, highly dynamic networks, and when applied to packets in the same flow. For more practical settings with multiple continuous flows in the network, we show the importance of deploying RLC schemes with a carefully tuned replication control in order to achieve reduction in average delay, which is observed to be as large as 20% when buffer space is constrained.
Corolla: GPU-Accelerated FPGA Routing Based on Subgraph Dynamic Expansion

FPGAs are increasingly popular as application-specific accelerators because they lead to a good balance between flexibility and energy efficiency, compared to CPUs and ASICs. However, the long routing time imposes a barrier on FPGA computing, which significantly hinders the design productivity. Existing attempts of parallelizing the FPGA routing either do not fully exploit the parallelism or suffer from an excessive quality loss. Massive parallelism using GPUs has the potential to solve this issue but faces non-trivial challenges.
 To cope with these challenges, this work presents Corolla, a GPU-accelerated FPGA routing method. Corolla enables applying the GPU-friendly shortest path algorithm in FPGA routing, leveraging the idea of problem size reduction by limiting the search in routing subgraphs. We maintain the convergence after problem size reduction using the dynamic expansion of the routing resource subgraphs. In addition, Corolla explores the fine-grained single-net parallelism and proposes a hybrid approach to combine the static and dynamic parallelism on GPU. To explore the coarse-grained multi-net parallelism, Corolla proposes an effective method to parallelize mutli-net routing while preserving the equivalent routing results as the original single-net routing. Experimental results show that Corolla achieves an average of 18.72x speedup on GPU with a tolerable loss in the routing quality and sustains a scalable speedup on large-scale routing graphs. To our knowledge, this is the first work to demonstrate the effectiveness of GPU-accelerated FPGA routing.
Informed blending of databases for emotional speech synthesis

The goal of this project was to build a unit selection voice that could portray emotions with varying intensities. A suitable definition of an emotion was developed along with a descriptive framework that supported the work carried out. A single speaker was recorded portraying happy and angry speaking styles. Additionally a neutral database was also recorded. A target cost function was implemented that chose units according to emotion mark-up in the database. The Dictionary of Affect supported the emotional target cost function by providing an emotion rating for words in the target utterance. If a word was particularly ’emotional’, units from that emotion were favoured. In addition intensity could be varied which resulted in a bias to select a greater number emotional units. A perceptual evaluation was carried out and subjects were able to recognise reliably emotions with varying amounts of emotional units present in the target utterance.
WiFi-aware as a connectivity solution for IoT pairing IoT with WiFi aware technology: Enabling new proximity based services

Internet of Things is referred to a combination of physical devices having sensors and connection capabilities enabling them to interact with each other (machine to machine) and can be controlled remotely via cloud engine. Success of an IoT device depends on the ability of systems and devices to securely sample, collect, and analyze data, and then transmit over link, protocol, or media selections based on stated requirements, all without human intervention. Among the requirements of the IoT, connectivity is paramount. It's hard to imagine that a single communication technology can address all the use cases possible in home, industry and smart cities. Along with the existing low power technologies like Zigbee, Bluetooth and 6LoWPAN, 802.11 WiFi standards are also making its way into the market with its own advantages in high range and better speed. Along with IEEE, WiFi Alliance has a new standard for the proximity applications. Neighbor Awareness Network (NAN) popularly known as WiFi Aware is that standard which enables low power discovery over WiFi and can light up many proximity based used cases. In this paper we discuss how NAN can influence the emerging IoT market as a connectivity solution for proximity assessment and contextual notifications with its benefits in some of the scenarios. When we consider WiFi the infrastructure already exists in terms of access points all around in public and smart phones or tablets come with WiFi as a default feature hence enabling NAN can be easy and if we can pair them with IoT, many innovative use cases can evolve.
ROTOR AIRLOADS PREDICTION USING LOOSE AERODYNAMIC/STRUCTURAL COUPLING

This work couples a computational fluid dynamics (C FD) code and rotorcraft computational structural dy namics (CSD) code to calculate helicopter rotor airloads a cross a range of flight conditions. An iterative lo ose (weak) coupling methodology is used to couple the CFD and CSD codes n a per revolution, periodic basis. The CFD uses a high fidelity, Navier-Stokes, overset grid methodology with first principles-based wake capturing. Modifications are made to the CFD code for the aeroelastic analysis. For a UH-60A Bla ckh wk helicopter, four challenging level flight co nditions are computed: 1) low speed ( μ = 0.15) with blade-vortex interaction, 2) high spe ed (μ = 0.37) with advancing blade negative lift, 3) high thrust with dynamic stall ( μ = 0.24), and 4) hover. Results are compared with U H-60A Airloads Program flight test data. Most importantly, for all cases t he loose coupling methodology is shown to be stable , convergent, and robust with full coupling of normal force, pitching moment, and chord force. In comparison with flight test data, normal force and pitching moment magnitudes are in good ag reement. For the high speed and dynamic stall cases a phase lag in comparison with the data is seen, nonetheless, the shapes of the curves are very good. Overall, the re sults are a noteworthy improvement over lifting line aerodynami cs used in rotorcraft comprehensive codes.