nils-herrmann/clean_scirepeval huggingface dataset:
Terrain Analysis in Real-Time Strategy Games: An Integrated Approach to Choke Point Detection and Region Decomposition

Autonomous agents in real-time strategy (RTS) games lack an integrated framework for reasoning about choke points and regions of open space in their environment. This paper presents an algorithm which partitions the environment into a set of polygonal regions and computes optimal choke points between adjacent regions. This representation can be used as a component for AI agents to reason about terrain, plan multiple routes of attack, and make other tactical decisions. The algorithm is tested on a set of popular maps commonly used in international Starcraft competitions and evaluated against answers made by human participants. The algorithm identified 97% of the choke points that the participants found and also identified a number of bottlenecks that human participants did not recognize as choke points.
Enterprise Database Applications and the Cloud: A Difficult Road Ahead

There is considerable interest in moving DBMS applications from inside enterprise data centers to the cloud, both to reduce cost and to increase flexibility and elasticity. Some of these applications are "green field" projects (i.e., new applications), others are existing legacy systems that must be migrated to the cloud. In another dimension, some are decision support applications while others are update-oriented. In this paper, we discuss the technical and political challenges that these various enterprise applications face when considering cloud deployment. In addition, a requirement for quality-of-service (QoS) guarantees will generate additional disruptive issues. In some circumstances, achieving good DBMS performance on current cloud architectures and future hardware technologies will be non-trivial. In summary, there is a difficult road ahead for enterprise database applications.
A novel sentiment analysis of social networks using supervised learning

Online microblog-based social networks have been used for expressing public opinions through short messages. Among popular microblogs, Twitter has attracted the attention of several researchers in areas like predicting the consumer brands, democratic electoral events, movie box office, popularity of celebrities, the stock market, etc. Sentiment analysis over a Twitter-based social network offers a fast and efficient way of monitoring the public sentiment. This paper studies the sentiment prediction task over Twitter using machine-learning techniques, with the consideration of Twitter-specific social network structure such as retweet. We also concentrate on finding both direct and extended terms related to the event and thereby understanding its effect. We employed supervised machine-learning techniques such as support vector machines (SVM), Naive Bayes, maximum entropy and artificial neural networks to classify the Twitter data using unigram, bigram and unigram + bigram (hybrid) feature extraction model for the case study of US Presidential Elections 2012 and Karnataka State Assembly Elections (India) 2013. Further, we combined the results of sentiment analysis with the influence factor generated from the retweet count to improve the prediction accuracy of the task. Experimental results demonstrate that SVM outperforms all other classifiers with maximum accuracy of 88 % in predicting the outcome of US Elections 2012, and 68 % for Indian State Assembly Elections 2013.
Robust Convolutional Neural Networks for Image Recognition

Recently image recognition becomes vital task using several methods. One of the most interesting used methods is using Convolutional Neural Network (CNN). It is widely used for this purpose. However, since there are some tasks that have small features that are considered an essential part of a task, then classification using CNN is not efficient because most of those features diminish before reaching the final stage of classification. In this work, analyzing and exploring essential parameters that can influence model performance. Furthermore different elegant prior contemporary models are recruited to introduce new leveraging model. Finally, a new CNN architecture is proposed which achieves state-of-the-art classification results on the different challenge benchmarks. The experimented are conducted on MNIST, CIFAR-10, and CIFAR-100 datasets. Experimental results showed that the results outperform and achieve superior results comparing to the most contemporary approaches. Convolutional Neural Network; Image recognition; Multiscale input images
Trainable Sentence Planning for Complex Information Presentations in Spoken Dialog Systems

A challenging problem for spoken dialog systems is the design of utterance generation modules that are fast, flexible and general, yet produce high quality output in particular domains. A promising approach is trainable generation, which uses general-purpose linguistic knowledge automatically adapted to the application domain. This paper presents a trainable sentence planner for the MATCH dialog system. We show that trainable sentence planning can produce output comparable to that of MATCH’s template-based generator even for quite complex information presentations.
Occupational accidents aboard merchant ships.

To investigate the frequency, circumstances, and causes of occupational accidents aboard merchant ships in international trade, and to identify risk factors for the occurrence of occupational accidents as well as dangerous working situations where possible preventive measures may be initiated.



The study is a historical follow up on occupational accidents among crew aboard Danish merchant ships in the period 1993-7. Data were extracted from the Danish Maritime Authority and insurance data. Exact data on time at risk were available.



A total of 1993 accidents were identified during a total of 31 140 years at sea. Among these, 209 accidents resulted in permanent disability of 5% or more, and 27 were fatal. The mean risk of having an occupational accident was 6.4/100 years at sea and the risk of an accident causing a permanent disability of 5% or more was 0.67/100 years aboard. Relative risks for notified accidents and accidents causing permanent disability of 5% or more were calculated in a multivariate analysis including ship type, occupation, age, time on board, change of ship since last employment period, and nationality. Foreigners had a considerably lower recorded rate of accidents than Danish citizens. Age was a major risk factor for accidents causing permanent disability. Change of ship and the first period aboard a particular ship were identified as risk factors. Walking from one place to another aboard the ship caused serious accidents. The most serious accidents happened on deck.



It was possible to clearly identify work situations and specific risk factors for accidents aboard merchant ships. Most accidents happened while performing daily routine duties. Preventive measures should focus on workplace instructions for all important functions aboard and also on the prevention of accidents caused by walking around aboard the ship.
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq

The mammalian cerebral cortex supports cognitive functions such as sensorimotor integration, memory, and social behaviors. Normal brain function relies on a diverse set of differentiated cell types, including neurons, glia, and vasculature. Here, we have used large-scale single-cell RNA sequencing (RNA-seq) to classify cells in the mouse somatosensory cortex and hippocampal CA1 region. We found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex. We identified numerous marker genes, which allowed alignment with known cell types, morphology, and location. We found a layer I interneuron expressing Pax6 and a distinct postmitotic oligodendrocyte subclass marked by Itpr2. Across the diversity of cortical cell types, transcription factors formed a complex, layered regulatory code, suggesting a mechanism for the maintenance of adult cell type identity.
Low-Profile Dual Circularly Polarized Antenna Array for Satellite Communications in the X Band

A planar antenna is introduced that works as a portable system for X-band satellite communications. This antenna is low-profile and modular with dimensions of 40 × 40 × 2.5 × cm. It is composed of a square array of 144 printed circuit elements that cover a wide bandwidth (14.7%) for transmission and reception along with dual and interchangeable circular polarization. A radiation efficiency above 50% is achieved by a low-loss stripline feeding network. This printed antenna has a 3 dB beamwidth of 5°, a maximum gain of 26 dBi and an axial ratio under 1.9 dB over the entire frequency band. The complete design of the antenna is shown, and the measurements are compared with simulations to reveal very good agreement.
PMSG fault diagnosis in marine application

This paper presents a fault diagnosis (FD) method for on line monitoring of stator winding partial inter-turn fault and severe faults, in permanent magnet synchronous generator (PMSG) used in shaft generator system in marine applications. The faulty machine is represented in state space model based on the dq-frame transformation. The Extended Kalman Filter (EKF) parameters estimation technique is used to estimate the value of stator winding short-circuit parameters of the machine. The proposed technique has been simulated for different fault scenarios using Matlab®/Simulink®. Simulation results show that the proposed technique is robust and effective. Moreover it can be applied at different operating conditions to prevent development of complete failure.
Supernumerary nostril with a contralateral naso-ocular cleft

We present the rare case of a child with a supernumerary nostril and a contralateral naso-ocular cleft. The child also had a suggestion of a microform cleft lip. There are only six or seven cases of supernumerary nostrils reported in the literature. This case is also unique in that the additional nostril and the naso-ocular cleft were on opposite sides. We describe a simple method of dealing with the problem and review the existing literature on the topic.
Machine learning: a review of classification and combining techniques

Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy—ensembles of classifiers.
Security and privacy grand challenges for the Internet of Things

The growth of the Internet of Things (IoT) is driven by market pressures, and while security is being considered, the relationship between the unintended consequences of billions of such devices connecting to the Internet cannot be described with existing mathematical methods. The possibilities for unintended surveillance through lifestyle analysis, unauthorized access to information, and new attack vectors will continue to increase by 2020, when up to 50 billion devices may be connected. This paper discusses various kinds of vulnerabilities that can be expected to arise, and presents a research agenda for mitigating the worst of the impacts. We hope to draw research attention to the potential dangers of IoT so that many of these problems can be avoided.
Traffic management: a holistic approach to memory placement on NUMA systems

NUMA systems are characterized by Non-Uniform Memory Access times, where accessing data in a remote node takes longer than a local access. NUMA hardware has been built since the late 80's, and the operating systems designed for it were optimized for access locality. They co-located memory pages with the threads that accessed them, so as to avoid the cost of remote accesses. Contrary to older systems, modern NUMA hardware has much smaller remote wire delays, and so remote access costs per se are not the main concern for performance, as we discovered in this work. Instead, congestion on memory controllers and interconnects, caused by memory traffic from data-intensive applications, hurts performance a lot more. Because of that, memory placement algorithms must be redesigned to target traffic congestion. This requires an arsenal of techniques that go beyond optimizing locality. In this paper we describe Carrefour, an algorithm that addresses this goal. We implemented Carrefour in Linux and obtained performance improvements of up to 3.6 relative to the default kernel, as well as significant improvements compared to NUMA-aware patchsets available for Linux. Carrefour never hurts performance by more than 4% when memory placement cannot be improved. We present the design of Carrefour, the challenges of implementing it on modern hardware, and draw insights about hardware support that would help optimize system software on future NUMA systems.
Automatic Keyword Extraction for Text Summarization in Multi-document e-Newspapers Articles

Summarization is the way towards lessening the content of a text file to make it brief that holds all the critical purposes in the content of original text file. In the process of extractive summarization, one extracts only those sentences which are the most relevant sentences in the text document and that conveys the moral of the content. The extractive summarization techniques usually revolve around the idea of discovering most relevant and frequent keywords and then extract the sentences based on those keywords. Manual extraction or explanation of relevant keywords are a dreary procedure overflowing with errors including loads of manual exertion and time. In this paper, we proposed a hybrid approach to extract keyword automatically for multi-document text summarization in enewspaper articles. The performance of the proposed approach is compared with three additional keyword extraction techniques namely, term frequency-inverse document frequency (TF-IDF), term frequency-adaptive inverse document frequency (TF-AIDF), and a number of false alarm (NFA) for automatic keyword extraction and summarization in e-newspapers articles for better analysis. Finally, we showed that our proposed techniques had been outperformed over other techniques for automatic keyword extraction and summarization.
Light propagation with phase discontinuities: generalized laws of reflection and refraction.

Conventional optical components rely on gradual phase shifts accumulated during light propagation to shape light beams. New degrees of freedom are attained by introducing abrupt phase changes over the scale of the wavelength. A two-dimensional array of optical resonators with spatially varying phase response and subwavelength separation can imprint such phase discontinuities on propagating light as it traverses the interface between two media. Anomalous reflection and refraction phenomena are observed in this regime in optically thin arrays of metallic antennas on silicon with a linear phase variation along the interface, which are in excellent agreement with generalized laws derived from Fermat's principle. Phase discontinuities provide great flexibility in the design of light beams, as illustrated by the generation of optical vortices through use of planar designer metallic interfaces.
Giving too much social support: social overload on social networking sites

20 July 2012 Revised: 18 February 2013 2nd   1 February 2014  As the number of messages and social relationships embedded in social networking sites (SNS) increases, the amount of social information demanding a reaction from individuals increases as well. We observe that, as a consequence, SNS users feel they are giving too much social support to other SNS users. Drawing on social support theory (SST), we call this negative association with SNS usage ‘social overload’ and develop a latent variable to measure it. We then identify the theoretical antecedents and consequences of social overload and evaluate the social overload model empirically using interviews with 12 and a survey of 571 Facebook users. The results show that extent of usage, number of friends, subjective social support norms, and type of relationship (online-only vs offline friends) are factors that directly contribute to social overload while age has only an indirect effect. The psychological and behavioral consequences of social overload include feelings of SNS exhaustion by users, low levels of user satisfaction, and a high intention to reduce or even stop using SNS. The resulting theoretical implications for SST and SNS acceptance research are discussed and practical implications for organizations, SNS providers, and SNS users are drawn.
Analysis of Human Electrocardiogram for Biometric Recognition

Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Existing solutions for biometric recognition from electrocardiogram (ECG) signals are based on temporal and amplitude distances between detected fiducial points. Such methods rely heavily on the accuracy of fiducial detection, which is still an open problem due to the difficulty in exact localization of wave boundaries. This paper presents a systematic analysis for human identification from ECG data. A fiducial-detection-based framework that incorporates analytic and appearance attributes is first introduced. The appearance-based approach needs detection of one fiducial point only. Further, to completely relax the detection of fiducial points, a new approach based on autocorrelation (AC) in conjunction with discrete cosine transform (DCT) is proposed. Experimentation demonstrates that the AC/DCT method produces comparable recognition accuracy with the fiducial-detection-based approach.
Foundations of RDF⋆ and SPARQL⋆ (An Alternative Approach to Statement-Level Metadata in RDF)

The standard approach to annotate statements in RDF with metadata has a number of shortcomings including data size blow-up and unnecessarily complicated queries. We propose an alternative approach that is based on nesting of RDF triples and of query patterns. The approach allows for a more compact representation of data and queries, and it is backwards compatible with the standard. In this paper we present the formal foundations of our proposal and of different approaches to implement it. More specifically, we formally capture the necessary extensions of the RDF data model and its query language SPARQL, and we define mappings based on which our extended notions can be converted back to ordinary RDF and SPARQL. Additionally, for such type of mappings we define two desirable properties, information preservation and query result equivalence, and we show that the introduced mappings possess these properties.
Contactless DC Connector Based on GaN LLC Converter for Next-Generation Data Centers

An inductively coupled contactless dc connector has been proposed for the next-generation 380-V dc distribution system in data centers. A LLC resonant dc-dc converter topology with gallium nitride (GaN) power transistors has been applied to realize the short-distance highly efficient contactless power transfer. A prototype of a 1.2-kW 384- to 192-V connector has been fabricated and the conversion efficiency of over 95% with the power density of 8.1 W/cm3 has been confirmed experimentally under 1000-kHz operation. The design consideration has been carried out and the potential to achieve 10.0 W/cm3 has been also shown taking the feature of the GaN power device and the characteristics of the magnetic core material for the transformer into account. The contactless dc connector integrates the functioning of an isolated dc-dc converter into a connector for space saving, and the dc current can be cut off without arc because of the inductive coupling. The proposed connector contributes to realizing a highly efficient, space saving, and reliable future 380-V dc distribution system.
Learning Low-Dimensional Representations of Medical Concepts

We show how to learn low-dimensional representations (embeddings) of a wide range of concepts in medicine, including diseases (e.g., ICD9 codes), medications, procedures, and laboratory tests. We expect that these embeddings will be useful across medical informatics for tasks such as cohort selection and patient summarization. These embeddings are learned using a technique called neural language modeling from the natural language processing community. However, rather than learning the embeddings solely from text, we show how to learn the embeddings from claims data, which is widely available both to providers and to payers. We also show that with a simple algorithmic adjustment, it is possible to learn medical concept embeddings in a privacy preserving manner from co-occurrence counts derived from clinical narratives. Finally, we establish a methodological framework, arising from standard medical ontologies such as UMLS, NDF-RT, and CCS, to further investigate the embeddings and precisely characterize their quantitative properties.
Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning

Named entity recognition, and other information extraction tasks, frequently use linguistic features such as part of speech tags or chunkings. For languages where word boundaries are not readily identified in text, word segmentation is a key first step to generating features for an NER system. While using word boundary tags as features are helpful, the signals that aid in identifying these boundaries may provide richer information for an NER system. New state-of-the-art word segmentation systems use neural models to learn representations for predicting word boundaries. We show that these same representations, jointly trained with an NER system, yield significant improvements in NER for Chinese social media. In our experiments, jointly training NER and word segmentation with an LSTM-CRF model yields nearly 5% absolute improvement over previously published results.
A Monocephalus Diprosopus Fetus: Antenatal Sonographic Findings -

Monocephalus Diprosopus is the rarest form of conjoined twins. The etiology of this anomaly is stil obscure. We herein report a monocephalus diprosopus case that was diagnosed in week 19 of pregnancy was presented due to its rarity and the significance of its prenatal diagnosis. Recommended a pregnancy termination since it does not have a definitive treatment today, prenatal diagnosis made for such fetuses at an early stage bears importance in terms of lowering the severity of psychological trauma.
Electromagnetic Vortex Imaging Using Uniform Concentric Circular Arrays

Electromagnetic vortex imaging based on orbital angular momentum (OAM) modulation has the ability of azimuthal resolution without relative motion, which needs various OAM modes. However, the main-lobe directions of different OAM beams differ from each other so that the echo energy is limited. In this letter, several concentric circular arrays are employed to adjust the directivity, each array generating a beam carrying special OAM mode. With this scheme, the target can be illuminated by main lobes of all OAM beams simultaneously. Our proposal can benefit the image reconstruction in noisy environment. Moreover, this scheme can successfully reduce the artifacts caused by high sidelobes at the cost of reduction in azimuthal resolution. Simulation results demonstrate the effectiveness of this scheme.