nils-herrmann/clean_scirepeval huggingface dataset:
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this dataset, we train a 34-layer convolutional neural network which maps a sequence of ECG samples to a sequence of rhythm classes. Committees of boardcertified cardiologists annotate a gold standard test set on which we compare the performance of our model to that of 6 other individual cardiologists. We exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value).
Artificial and Computational Intelligence in Games: AI-Driven Game Design (Dagstuhl Seminar 17471)

With the dramatic growth of the game industry over the past decade, its rapid inclusion in many sectors of today’s society, and the increased complexity of games, game development has reached a point where it is no longer humanly possible to use only manual techniques to create games. Large parts of games need to be designed, built, and tested automatically. In recent years, researchers have delved into artificial intelligence techniques to support, assist, and even drive game development. Such techniques include procedural content generation, automated narration, player modelling and adaptation, and automated game design. This research is still very young, but already the games industry is taking small steps to integrate some of these techniques in their approach to design. The goal of this seminar was to bring together researchers and industry representatives who work at the forefront of artificial intelligence (AI) and computational intelligence (CI) in games, to (1) explore and extend the possibilities of AI-driven game design, (2) to identify the most viable applications of AI-driven game design in the game industry, and (3) to investigate new approaches to AI-driven game design. To this end, the seminar included a wide range of researchers and developers, including specialists in AI/CI for abstract games, commercial video games, and serious games. Thus, it fostered a better understanding of and unified vision on AI-driven game design, using input from both scientists as well as AI specialists from industry. Seminar November 19–24, 2017 – http://www.dagstuhl.de/17471 1998
The measurement of happiness: development of the Memorial University of Newfoundland Scale of Happiness (MUNSH).

Items of the Affect Balance Scale, the Life Satisfaction Index-Z and the Philadelphia Geriatric Center Scale together with 22 new items were used in the construction of a happiness scale for the elderly. Items were initially administered to 301 subjects from urban, rural, and institutional settings and correlated with ratings of happiness. A new scale consisting of 24 items was cross-validated on an additional 297 subjects. Test-retest reliability scores were obtained on 56 subjects. Results indicated that the new scale was a better predictor of "avowed happiness" in both validation and cross-validation samples than the existing scales used for comparison. Moreover, the new scale's test-rated reliability was within an acceptable range for this type of scale.
Entropy-based Selection of Graph Cuboids

Emerging applications face the need to store and analyze interconnected data that are naturally depicted as graphs. Recent proposals take the idea of data cubes that have been successfully applied to multidimensional data and extend them to work for interconnected datasets. In our work we revisit the graph cube framework and propose novel mechanisms inspired from information theory in order to help the analyst quickly locate interesting relationships within the rich information contained in the graph cube. The proposed entropy-based filtering of data reveals irregularities and non-uniformity which are often what the decision maker is looking for. We experimentally validate our techniques and demonstrate that the proposed entropy-based filtering can help eliminate large portions of the respective graph cubes.
i, Poet: Automatic Poetry Composition through Recurrent Neural Networks with Iterative Polishing Schema

Part of the long lasting cultural heritage of humanity is the art of classical poems, which are created by fitting words into certain formats and representations. Automatic poetry composition by computers is considered as a challenging problem which requires high Artificial Intelligence assistance. This study attracts more and more attention in the research community. In this paper, we formulate the poetry composition task as a natural language generation problem using recurrent neural networks. Given user specified writing intents, the system generates a poem via sequential language modeling. Unlike the traditional one-pass generation for previous neural network models, poetry composition needs polishing to satisfy certain requirements. Hence, we propose a new generative model with a polishing schema, and output a refined poem composition. In this way, the poem is generated incrementally and iteratively by refining each line. We run experiments based on large datasets of 61,960 classic poems in Chinese. A comprehensive evaluation, using perplexity and BLEU measurements as well as human judgments, has demonstrated the effectiveness of our proposed approach.
Advanced Integration Techniques on Broadband Millimeter-Wave Beam Steering for 5G Wireless Networks and Beyond

Recently, the desired very high throughput of 5G wireless networks drives millimeter-wave (mm-wave) communication into practical applications. A phased array technique is required to increase the effective antenna aperture at mm-wave frequency. Integrated solutions of beamforming/beam steering are extremely attractive for practical implementations. After a discussion on the basic principles of radio beam steering, we review and explore the recent advanced integration techniques of silicon-based electronic integrated circuits (EICs), photonic integrated circuits (PICs), and antenna-on-chip (AoC). For EIC, the latest advanced designs of on-chip true time delay (TTD) are explored. Even with such advances, the fundamental loss of a silicon-based EIC still exists, which can be solved by advanced PIC solutions with ultra-broad bandwidth and low loss. Advanced PIC designs for mm-wave beam steering are then reviewed with emphasis on an optical TTD. Different from the mature silicon-based EIC, the photonic integration technology for PIC is still under development. In this paper, we review and explore the potential photonic integration platforms and discuss how a monolithic integration based on photonic membranes fits the photonic mm-wave beam steering application, especially for the ease of EIC and PIC integration on a single chip. To combine EIC, for its accurate and mature fabrication techniques, with PIC, for its ultra-broad bandwidth and low loss, a hierarchical mm-wave beam steering chip with large-array delays realized in PIC and sub-array delays realized in EIC can be a future-proof solution. Moreover, the antenna units can be further integrated on such a chip using AoC techniques. Among the mentioned techniques, the integration trends on device and system levels are discussed extensively.
Control of BLDC motor based on adaptive fuzzy logic PID controller

This paper presents an Adaptive fuzzy logic PID controller for speed control of Brushless Direct current Motor drives which is widely used in various industrial systems, such as servo motor drives, medical, automobile and aerospace industry. BLDC motors were electronically commutated motor offer many advantages over Brushed DC Motor which includes increased efficiency, longer life, low volume and high torque. This paper presents an overview of performance of fuzzy PID controller and Adaptive fuzzy PID controller using Simulink model. Tuning Parameters and computing using Normal PID controller is difficult and also it does not give satisfied control characteristics when compare to Adaptive Fuzzy PID controller. From the Simulation results we verify that Adaptive Fuzzy PID controller give better control performance when compared to fuzzy PID controller. The software Package SIMULINK was used in control and Modelling of BLDC Motor.
Personality traits and knowledge sharing in online communities

Adopting diffusion theory and the concept of social value orientation, the effects of personality traits on knowledge sharing in a virtual open content community are investigated. In addition to the main effects of personality, it was hypothesized that intrinsic motivations would moderate the effects on knowledge sharing. A sample of N = 256 active users of Wikipedia provided measures of personality, motivation, and knowledge sharing. Latent regression analyses support the notion that authorship of Wikipedia is associated with higher levels of trendsetting and a prosocial value orientation. Moreover, moderation analyses demonstrate that the effect of the latter is moderated by individual differences in motivations to write. Differences with regard to opinion leadership could not be confirmed.
Compact Low-Loss Tunable  $X$-Band Bandstop Filter With Miniature RF-MEMS Switches

This paper presents a compact low-loss tunable X-band bandstop filter implemented on a quartz substrate using both miniature RF microelectromechanical systems (RF-MEMS) capacitive switches and GaAs varactors. The two-pole filter is based on capacitively loaded folded-λ/2 resonators that are coupled to a microstrip line, and the filter analysis includes the effects of nonadjacent inter-resonator coupling. The RF-MEMS filter tunes from 11.34 to 8.92 GHz with a - 20-dB rejection bandwidth of 1.18%-3.51% and a filter quality factor of 60-135. The GaAs varactor loaded filter tunes from 9.56 to 8.66 GHz with a - 20-dB bandwidth of 1.65%-2% and a filter quality factor of 55-90. Nonlinear measurements at the filter null with Δf = 1 MHz show that the RF-MEMS loaded filter results in > 25-dBm higher third-order intermodulation intercept point and P-1 dB compared with the varactor loaded filter. Both filters show high rejection levels ( > 24 dB) and low passband insertion loss ( <; 0.8 dB) from dc to the first spurious response at 19.5 GHz. The filter topology can be extended to higher order designs with an even number of poles.
Representing shape with a spatial pyramid kernel

The objective of this paper is classifying images by the object categories they contain, for example motorbikes or dolphins. There are three areas of novelty. First, we introduce a descriptor that represents local image shape and its spatial layout, together with a spatial pyramid kernel. These are designed so that the shape correspondence between two images can be measured by the distance between their descriptors using the kernel. Second, we generalize the spatial pyramid kernel, and learn its level weighting parameters (on a validation set). This significantly improves classification performance. Third, we show that shape and appearance kernels may be combined (again by learning parameters on a validation set).
 Results are reported for classification on Caltech-101 and retrieval on the TRECVID 2006 data sets. For Caltech-101 it is shown that the class specific optimization that we introduce exceeds the state of the art performance by more than 10%.
Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization

Accurately estimating a robot’s pose relative to a global scene model and precisely tracking the pose in real-time is a fundamental problem for navigation and obstacle avoidance tasks. Due to the computational complexity of localization against a large map and the memory consumed by the model, state-ofthe-art approaches are either limited to small workspaces or rely on a server-side system to query the global model while tracking the pose locally. The latter approaches face the problem of smoothly integrating the server’s pose estimates into the trajectory computed locally to avoid temporal discontinuities. In this paper, we demonstrate that large-scale, real-time pose estimation and tracking can be performed on mobile platforms with limited resources without the use of an external server. This is achieved by employing map and descriptor compression schemes as well as efficient search algorithms from computer vision. We derive a formulation for integrating the global pose information into a local state estimator that produces much smoother trajectories than current approaches. Through detailed experiments, we evaluate each of our design choices individually and document its impact on the overall system performance, demonstrating that our approach outperforms state-of-the-art algorithms for localization at scale.
Regionally localized thinning of the cerebral cortex in schizophrenia

Schizophrenia is characterized by small reductions in cortical gray matter volume, particularly in the temporal and prefrontal cortices. The question of whether cortical thickness is reduced in schizophrenia has not been addressed using magnetic resonance imaging (MRI) techniques. Our objectives were to test the hypothesis that cortical thinning in patients with schizophrenia (relative to control subjects) is greater in temporal and prefrontal regions of interest (ROIs) than in control ROIs (superior parietal, calcarine, postcentral, central, and precentral cortices), and to obtain an unbiased estimate of the distribution of cortical thinning in patients (relative to controls) by constructing mean and statistical cortical thickness difference maps.



Participants included 33 right-handed outpatients receiving medication and meeting DSM-IV criteria for schizophrenia and 32 healthy volunteers, matched on age and parental socioeconomic status. After high-resolution MRI scans, models of the gray-white and pial surfaces were generated for each individual's cortex, and the distance between these 2 surfaces was used to compute cortical thickness. A surface-based averaging technique that aligned the main cortical folds across individuals allowed between-group comparisons of thickness within ROIs, and at multiple, uniformly sampled loci across the cortical ribbon.



Relative to controls, patients showed greater cortical thinning in temporal-prefrontal ROIs than in control ROIs, as revealed by a significant (P<.009) interaction between group and region type. Cortical thickness difference maps revealed significant (at P<.05, corrected) thinning within the orbitofrontal cortices bilaterally; the inferior frontal, inferior temporal, and occipitotemporal cortices on the left; and within the medial temporal and medial frontal cortices on the right. Superior parietal and primary somatosensory and motor cortices were relatively spared, even at subthreshold significance levels.



Patients with chronic schizophrenia showed widespread cortical thinning that particularly affected the prefrontal and temporal cortices. This thinning might reflect underlying neuropathological abnormalities in cortical structure.
Sensitivity Improved SPR Biosensor Based on the MoS2/Graphene–Aluminum Hybrid Structure

MoS<sub>2</sub>-graphene-based hybrid structures are biocompatible and useful in the field of biosensors. Herein, we propose a heterostructured MoS<sub>2</sub>/aluminum (Al) film/MoS<sub>2</sub>/graphene as a highly sensitive surface plasmon resonance (SPR) biosensor based on the Otto configuration. The sensitivity of the proposed biosensor is enhanced by using three methods. First, prisms of different refractive index have been discussed and it is found that sensitivity can be enhanced by using a low refractive index prism. Second, the influence of the thickness of the air layer on the sensitivity is analyzed and the optimal thickness of air is obtained. Finally, the sensitivity improvement and mechanism by using molybdenum disulfide (MoS<sub>2</sub>)–graphene hybrid structure is revealed. The maximum sensitivity ∼ 190.83°/RIU is obtained with six layers of MoS<sub>2</sub> coating on both surfaces of Al thin film.
Interval type-2 fuzzy logic systems: theory and design

In this paper, we present the theory and design of interval type-2 fuzzy logic systems (FLSs). We propose an efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs; one that is based on a general inference formula for them. We introduce the concept of upper and lower membership functions (MFs) and illustrate our efficient inference method for the case of Gaussian primary MFs. We also propose a method for designing an interval type-2 FLS in which we tune its parameters. Finally, we design type-2 FLSs to perform time-series forecasting when a nonstationary time-series is corrupted by additive noise where SNR is uncertain and demonstrate improved performance over type-1 FLSs.
Part-of-Speech Tagging for Code-mixed Indian Social Media Text at ICON 2015

This paper discusses the experiments carried out by us at Jadavpur University as part of the participation in ICON 2015 task: POS Tagging for Code-mixed Indian Social Media Text. The tool that we have developed for the task is based on Trigram Hidden Markov Model that utilizes information from dictionary as well as some other word level features to enhance the observation probabilities of the known tokens as well as unknown tokens. We submitted runs for Bengali-English, Hindi-English and Tamil-English Language pairs. Our system has been trained and tested on the datasets released for ICON 2015 shared task: POS Tagging For Code-mixed Indian Social Media Text. In constrained mode, our system obtains average overall accuracy (averaged over all three language pairs) of 75.60% which is very close to other participating two systems (76.79% for IIITH and 75.79% for AMRITA_CEN) ranked higher than our system. In unconstrained mode, our system obtains average overall accuracy of 70.65% which is also close to the system (72.85% for AMRITA_CEN) which obtains the highest average overall accuracy.
A Switchable 3-D-Coverage-Phased Array Antenna Package for 5G Mobile Terminals

This letter proposes a new design of millimeter-wave (mm-Wave) array antenna package with beam steering characteristic for the fifth-generation (5G) mobile applications. In order to achieve a broad three-dimensional scanning coverage of the space with high-gain beams, three identical subarrays of patch antennas have been compactly arranged along the edge region of the mobile phone printed circuit board (PCB) to form the antenna package. By switching the feeding to one of the subarrays, the desired direction of coverage can be achieved. The proposed design has >10-dB gain in the upper spherical space, good directivity, and efficiency, which is suitable for 5G mobile communications. In addition, the impact of the user's hand on the antenna performance has been investigated.
Security analysis of IoT protocols: A focus in CoAP

Internet of things (IoT) or Web of Things (WoT) is a wireless network between smart products or smart things connected to the internet. It is a new and fast developing market which not only connects objects and people but also billions of gadgets and smart devices. With the rapid growth of IoT, there is also a steady increase in security vulnerabilities of the linked objects. For example, a car manufacturer may want to link the systems within a car to smart home network networks to increase sales, but if all the various people involved do not embrace security the system will be exposed to security risks. As a result, there are several new published protocols of IoT, which focus on protecting critical data. However, these protocols face challenges and in this paper, numerous solutions are provided to overcome these problems. The widely used protocols such as, 802.15.4, 6LoWPAN, and RPL are the resenting of the IoT layers PHY/MAC, Adoption and Network. While CoAP (Constrained Application Protocol) is the application layer protocol designed as replication of the HTTP to serve the small devices coming under class 1 and 2. Many implementations of CoAP has been accomplished which indicates it's crucial amd upcoming role in the future of IoT applications. This research article explored the security of CoAP over DTLS incurring many issues and proposed solutions as well as open challenges for future research.
Psychological stress and the human immune system: a meta-analytic study of 30 years of inquiry.

The present report meta-analyzes more than 300 empirical articles describing a relationship between psychological stress and parameters of the immune system in human participants. Acute stressors (lasting minutes) were associated with potentially adaptive upregulation of some parameters of natural immunity and downregulation of some functions of specific immunity. Brief naturalistic stressors (such as exams) tended to suppress cellular immunity while preserving humoral immunity. Chronic stressors were associated with suppression of both cellular and humoral measures. Effects of event sequences varied according to the kind of event (trauma vs. loss). Subjective reports of stress generally did not associate with immune change. In some cases, physical vulnerability as a function of age or disease also increased vulnerability to immune change during stressors.
Artificial Intelligence Techniques for Rational Decision Making

Now, we come to offer you the right catalogues of book to open. artificial intelligence techniques for rational decision making is one of the literary work in this world in suitable to be reading material. That's not only this book gives reference, but also it will show you the amazing benefits of reading a book. Developing your countless minds is needed; moreover you are kind of people with great curiosity. So, the book is very appropriate for you.
Cloud Computing Features, Issues, and Challenges: A Big Picture

Since the phenomenon of cloud computing was proposed, there is an unceasing interest for research across the globe. Cloud computing has been seen as unitary of the technology that poses the next-generation computing revolution and rapidly becomes the hottest topic in the field of IT. This fast move towards Cloud computing has fuelled concerns on a fundamental point for the success of information systems, communication, virtualization, data availability and integrity, public auditing, scientific application, and information security. Therefore, cloud computing research has attracted tremendous interest in recent years. In this paper, we aim to precise the current open challenges and issues of Cloud computing. We have discussed the paper in three-fold: first we discuss the cloud computing architecture and the numerous services it offered. Secondly we highlight several security issues in cloud computing based on its service layer. Then we identify several open challenges from the Cloud computing adoption perspective and its future implications. Finally, we highlight the available platforms in the current era for cloud research and development.