nils-herrmann/clean_scirepeval huggingface dataset:
Combined Effects of Attention and Motivation on Visual Task Performance: Transient and Sustained Motivational Effects

We investigated how the brain integrates motivational and attentional signals by using a neuroimaging paradigm that provided separate estimates for transient cue- and target-related signals, in addition to sustained block-related responses. Participants performed a Posner-type task in which an endogenous cue predicted target location on 70% of trials, while motivation was manipulated by varying magnitude and valence of a cash incentive linked to task performance. Our findings revealed increased detection performance (d') as a function of incentive value. In parallel, brain signals revealed that increases in absolute incentive magnitude led to cue- and target-specific response modulations that were independent of sustained state effects across visual cortex, fronto-parietal regions, and subcortical regions. Interestingly, state-like effects of incentive were observed in several of these brain regions, too, suggesting that both transient and sustained fMRI signals may contribute to task performance. For both cue and block periods, the effects of administering incentives were correlated with individual trait measures of reward sensitivity. Taken together, our findings support the notion that motivation improves behavioral performance in a demanding attention task by enhancing evoked responses across a distributed set of anatomical sites, many of which have been previously implicated in attentional processing. However, the effect of motivation was not simply additive as the impact of absolute incentive was greater during invalid than valid trials in several brain regions, possibly because motivation had a larger effect on reorienting than orienting attentional mechanisms at these sites.
Location Assisted Coding (LAC): Embracing Interference in Free Space Optical Communications

As the number of wireless devices grows, the increasing demand for the shared radio frequency (RF) spectrum becomes a critical problem. Unlike wired communications in which, theoretically, more fibers can be used to accommodate the increasing bandwidth demand, wireless spectrum cannot be arbitrarily increased due to the fundamental limitations imposed by the physical laws. On the other hand, recent advances in free space optical (FSO) technologies promise a complementary approach to increase wireless capacity. However, high-speed FSO technologies are currently confined to short distance transmissions, resulting in limited mobility. In this paper, we briefly describe WiFO, a hybrid WiFi-FSO network for Gbps wireless local area network (WLAN) femtocells that can provide up to one Gbps per user while maintaining seamless mobility. While typical RF femtocells are non-overlapped to minimize inter-cell interference, there are advantages of using overlapped femtocells to increase mobility and throughput when the number of users is small. That said, the primary contribution of this paper will be a novel location assisted coding (LAC) technique used in the WiFO network that aims to increase throughput and reduce interference for multiple users in a dense array of femtocells. Both theoretical analysis and numerical experiments show orders of magnitude increase in throughput using LAC over basic codes.
A direct AC LED driver with high power factor without the use of passive components

In this paper, a novel driver for high brightness LED applications that can directly run from AC voltage is presented. Conventional LED drivers for such applications consist of switching-type converters which require passive components like high value inductors and electrolytic capacitors for their operation. The use of such components increases the size and cost of the LED system while decreasing the overall life time. The proposed circuit only needs active devices like MOSFETs and op-amps for power processing. The input current is selectively controlled to follow a sinusoidal waveform to achieve low harmonic distortion and high power factor. A simple start-up circuit is also designed for self-sufficient operation with a more efficient set-up under development. The efficiency of the proposed system is around 82% for a 1.5W converter with 9% total harmonic distortion. Such an implementation is also more suitable for IC design. Both simulation and experimental results have been presented to validate the operation of the proposed set-up.
Doc 2 Cube : Automated Document Allocation to Text Cube via Dimension-Aware Joint Embedding

Data cube is a cornerstone architecture in multidimensional analysis of structured datasets. It is highly desirable to conduct multidimensional analysis on text corpora with cube structures for various text-intensive applications in healthcare, business intelligence, and social media analysis. However, one bottleneck to constructing text cube is to automatically put millions of documents into the right cells in such a text cube so that quality multidimensional analysis can be conducted afterwards—it is too expensive to allocate documents manually or rely on massively labeled data. We propose Doc2Cube, a method that constructs a text cube from a given text corpus in an unsupervised way. Initially, only the label names (e.g., USA, China) of each dimension (e.g., location) are provided instead of any labeled data. Doc2Cube leverages label names as weak supervision signals and iteratively performs joint embedding of labels, terms, and documents to uncover their semantic similarities. To generate joint embeddings that are discriminative for cube construction, Doc2Cube learns dimension-tailored document representations by selectively focusing on terms that are highly label-indicative in each dimension. Furthermore, Doc2Cube alleviates label sparsity by propagating the information from label names to other terms and enriching the labeled term set. Our experiments on a real news corpus demonstrate that Doc2Cube outperforms existing methods significantly. Doc2Cube is a technology transferred to U.S. Army Research Lab and is a core component of the EventCube system that is being deployed for multidimensional news and social media data analysis.
Big Data Meet Green Challenges: Big Data Toward Green Applications

Big data are widely recognized as being one of the most powerful drivers to promote productivity, improve efficiency, and support innovation. It is highly expected to explore the power of big data and turn big data into big values. To answer the interesting question whether there are inherent correlations between the two tendencies of big data and green challenges, a recent study has investigated the issues on greening the whole life cycle of big data systems. This paper would like to discover the relations between the trend of big data era and that of the new generation green revolution through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions.
Category fluency test: normative data for English- and Spanish-speaking elderly.

Category fluency tasks are an important component of neuropsychological assessment, especially when evaluating for dementia syndromes. The growth in the number of Spanish-speaking elderly in the United States has increased the need for appropriate neuropsychological measures and normative data for this population. This study provides norms for English and Spanish speakers, over the age of 50, on 3 frequently used measures of category fluency: animals, vegetables, and fruits. In addition, it examines the impact of age, education, gender, language, and depressed mood on total fluency scores and on scores on each of these fluency measures. A sample of 702 cognitively intact elderly, 424 English speakers, and 278 Spanish speakers, participated in the study. Normative data are provided stratified by language, age, education, and gender. Results evidence that regardless of the primary language of the examinee, age, education, and gender are the strongest predictors of total category fluency scores, with gender being the best predictor of performance after adjusting for age and education. English and Spanish speakers obtained similar scores on animal and fruit fluency, but English speakers generated more vegetable exemplars than Spanish speakers. Results also indicate that different fluency measures are affected by various factors to different degrees.
Cancer statistics, 2014.

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the National Cancer Institute, the Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data were collected by the National Center for Health Statistics. A total of 1,665,540 new cancer cases and 585,720 cancer deaths are projected to occur in the United States in 2014. During the most recent 5 years for which there are data (2006-2010), delay-adjusted cancer incidence rates declined slightly in men (by 0.6% per year) and were stable in women, while cancer death rates decreased by 1.8% per year in men and by 1.4% per year in women. The combined cancer death rate (deaths per 100,000 population) has been continuously declining for 2 decades, from a peak of 215.1 in 1991 to 171.8 in 2010. This 20% decline translates to the avoidance of approximately 1,340,400 cancer deaths (952,700 among men and 387,700 among women) during this time period. The magnitude of the decline in cancer death rates from 1991 to 2010 varies substantially by age, race, and sex, ranging from no decline among white women aged 80 years and older to a 55% decline among black men aged 40 years to 49 years. Notably, black men experienced the largest drop within every 10-year age group. Further progress can be accelerated by applying existing cancer control knowledge across all segments of the population.
Modelling and compensation of nonlinear loudspeaker

The nonlinear response of the loudspeaker is a undesired phenomenon which produces audible distortion. The construction of the loudspeaker is a trade-off between various factors and therefore makes it difficult to build a linear loudspeaker. This work takes another approach to address this issue. By using digital signal processing, the terminal voltage can be pre-distorted to compensate for the nonlinear behavior and render the system input-output linear. In order to compute a controller which achieves this, a model of the moving coil loudspeaker was implemented. This model was made from the extended ThieleSmall model. The theory of exact input-output linearization was used to compute the control law from this model. Since the control law assumes full-state feedback, a state estimator is used. Two types of state estimators, a feed-forward based and a observer based, were computed and analyzed. The feed-forward one simply uses the terminal voltage to estimate the states of the loudspeaker and can therefore be implemented without sensors. The observer one uses the theory of the unscented Kalman filter to estimate the states using the terminal voltage and the current passing through the loudspeaker. The simulation results showed that the loudspeaker can be rendered fully linear assuming that the hardware does not limit the control signal and that the loudspeaker model is perfect. Various simulation were done to simulate hardware limitation and process noise. This resulted as expected, that the system was able to compensate for the nonlinear behavior but its performance was still affected by the limitation and the noise. The unscented Kalman filter proved to be more capable of estimating the states than the feed-forward estimator when affected by process noise.
A Local Detection Approach for Named Entity Recognition and Mention Detection

In this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing. Instead of treating NER as a sequence labeling problem, we propose a new local detection approach, which relies on the recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode each sentence fragment and its left/right contexts into a fixedsize representation. Subsequently, a simple feedforward neural network (FFNN) is learned to either reject or predict entity label for each individual text fragment. The proposed method has been evaluated in several popular NER and MD tasks, including CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016 Tri-lingual Entity Discovery and Linking (EDL) tasks. Our method has yielded pretty strong performance in all of these examined tasks. This local detection approach has shown many advantages over the traditional sequence labeling methods.
Evolution of wireless sensor networks towards the Internet of Things: A survey

Wireless Sensor Networks (WSNs) are playing more and more a key role in several application scenarios such as healthcare, agriculture, environment monitoring, and smart metering. Furthermore, WSNs are characterized by high heterogeneity because there are many different proprietary and non-proprietary solutions. This wide range of technologies has delayed new deployments and integration with existing sensor networks. The current trend, however, is to move away from proprietary and closed standards, to embrace IP-based sensor networks using the emerging standard 6LoWPAN/IPv6. This allows native connectivity between WSN and Internet, enabling smart objects to participate to the Internet of Things (IoT). Building an all-IP infrastructure from scratch, however, would be difficult because many different sensors and actuators technologies (both wired and wireless) have already been deployed over the years. After a review of the state of the art, this paper sketches a framework able to harmonize legacy and new installations, allowing migrating to an all-IP environment at a later stage. The Building Automation use case has been chosen to discuss potential benefits of the proposed framework.
Euclidean Distance Mapping

Based on a two-component descriptor, a distance label for each point, it is shown that Euclidean distance maps can be generated by effective sequential algorithms. The map indicates, for each pixel in the objects (or the background) of the originally binary picture, the shortest distance to the nearest pixel in the background (or the objects). A map with negligible errors can be produced in two picture scans which has to include forward and backward movement for each line. Thus, for expanding/shrinking purposes it may compete very successfully with iterative parallel propagation in the binary picture itself. It is shown that skeletons can be produced by simple procedures and since these are based on Euclidean distances it is assumed that they are superior to skeletons based on d4, ds, and even octagonal metrics.
Collaborative Recommendation via Adaptive Association Rule Mining

Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among users' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are ine cient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few rules; this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm speci cally designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be speci ed in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a speci c target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is signi cantly better than that of traditional correlationbased approaches.   Department of Computer Science, Wellesley College, 106 Central Street, Wellesley, MA 02481 USA, e-mail: salvarez@wellesley.edu
A fuzzy ontology for semantic modelling and recognition of human behaviour

We propose a fuzzy ontology for human activity representation, which allows us to model and reason about vague, incomplete, and uncertain knowledge. Some relevant subdomains found to be missing in previous proposed ontologies for this domain were modelled as well. The resulting fuzzy OWL 2 ontology is able to model uncertain knowledge and represent temporal relationships between activities using an underlying fuzzy state machine representation. We provide a proof of concept of the approach in work scenarios such as the office domain, and also make experiments to emphasize the benefits of our approach with respect to crisp ontologies. As a result, we demonstrate that the inclusion of fuzzy concepts and relations in the ontology provide benefits during the recognition process with respect to crisp
Socio-emotional development: from infancy to young adulthood.

, which started in 1985, are reported in two sections. The first section gives a summary of longitudinal data from infancy to middle childhood (age 9 years; n = 96) concerning predictions of social functioning aspects from the theoretical perspectives of temperament, attachment, and health psychology (social factors). The second section presents the first results emanating from a follow-up when participants were 21 years old (n = 85). The developmental roots of social anxiety symptoms were studied from the same perspectives as above, although with a special focus on the predictive power of the temperament trait of shyness/inhibition. Results for middle childhood outcomes showed that temperament characteristics were relevant for most outcomes, whereas the contribution of attachment was most convincingly shown in relation to social competence and personality. Social factors were found to have moderating functions, but direct effects were also shown, the most interesting perhaps being positive effects of non-parental day care. Results from the 21-year data confirmed the expected predictive relation from shyness/inhibition to symptoms of social anxiety and further showed this relation to be specific; the relation to symptoms of depression did not survive control for social anxiety, although the opposite was true. The broad analysis of predictor associations with social anxiety, showing the relevance of other temperament factors as well as interactive effects, again attested to the need for multi-faceted models to analyze developmental trajectories.
Distributed Clustering-Task Scheduling for Wireless Sensor Networks Using Dynamic Hyper Round Policy

Prolonging the network life cycle is an essential requirement for many types of Wireless Sensor Network (WSN) applications. Dynamic clustering of sensors into groups is a popular strategy to maximize the network lifetime and increase scalability. In this strategy, to achieve the sensor nodes’ load balancing, with the aim of prolonging lifetime, network operations are split into rounds, i.e., fixed time intervals. Clusters are configured for the current round and reconfigured for the next round so that the costly role of the cluster head is rotated among the network nodes, i.e., Round-Based Policy (RBP). This load balancing approach potentially extends the network lifetime. However, the imposed overhead, due to the clustering in every round, wastes network energy resources. This paper proposes a distributed energy-efficient scheme to cluster a WSN, i.e., Dynamic Hyper Round Policy (DHRP), which schedules clustering-task to extend the network lifetime and reduce energy consumption. Although DHRP is applicable to any data gathering protocols that value energy efficiency, a Simple Energy-efficient Data Collecting (SEDC) protocol is also presented to evaluate the usefulness of DHRP and calculate the end-to-end energy consumption. Experimental results demonstrate that SEDC with DHRP is more effective than two well-known clustering protocols, HEED and M-LEACH, for prolonging the network lifetime and achieving energy conservation.
Hesitant Fuzzy Sets: State of the Art and Future Directions

The necessity of dealing with uncertainty in real wold problems has been a long-term research challenge that has originated different methodologies and theories. Fuzzy sets along with their extensions such as, type-2 fuzzy sets, interval valued fuzzy sets, Atanassov’s intuitionistic fuzzy sets, etc., have provided a wide range of tools able to deal with uncertainty in different type of problems. Recently, a new extension of fuzzy sets so-called hesitant fuzzy sets has been introduced to deal with hesitant situations which were not well managed by the previous tools. Hesitant fuzzy sets have attracted very quickly the attention of many researchers that have proposed diverse extensions, several type of operators to compute with such type of information, and eventually some applications have been developed. Because of such a growth, this paper presents an overview on hesitant fuzzy sets with the aim of providing a clear perspective on the different concepts, tools and trends related to this extension of fuzzy sets.
Modeling and Vector Control of Planar Magnetic Levitator

We designed and implemented a magnetically levitated stage with large planar motion capability. This planar magnetic levitator employs four novel permanent-magnet linear motors. Each motor generates vertical force for suspension against gravity, as well as horizontal force for drive. These linear levitation motors can be used as building blocks in the general class of multi-degree-of-freedom motion stages. In this paper, we discuss electromechanical modeling and real-time vector control of such a permanent-magnet levitator. We describe the dynamics in a dq frame introduced to decouple the forces acting on the magnetically levitated moving part, namely, the platen. A transformation similar to the Blondel–Park transformation is derived for commutation of the stator phase currents. We provide test results on step responses of the magnetically levitated stage. It shows 5-nm rms positioning noise inx and y, which demonstrates the applicability of such stages in the next-generation photolithography in semiconductor manufacturing.
Multi-channel ECG classification using forests of randomized shapelet trees

Data series of multiple channels occur at high rates and in massive quantities in several application domains, such as healthcare. In this paper, we study the problem of multi-channel ECG classification. We map this problem to multivariate data series classification and propose five methods for solving it, using a split-and-combine approach. The proposed framework is evaluated using three base-classifiers on real-world data for detecting Myocardial Infarction. Extensive experiments are performed on real ECG data extracted from the Physiobank data repository. Our findings emphasize the importance of selecting an appropriate base-classifier for multivariate data series classification, while demonstrating the superiority of the Random Shapelet Forest (0.825 accuracy) against competitor methods (0.664 accuracy for 1-NN under cDTW).