nils-herrmann/clean_scirepeval huggingface dataset:
Leveraging Crowdsourcing: Activation-Supporting Components for IT-Based Ideas Competition

Ideas competitions appear to be a promising tool for crowdsourcing and open innovation processes, especially for business-to-business software companies. active participation of potential lead users is the key to success. Yet a look at existing ideas competitions in the software field leads to the conclusion that many information technology (It)–based ideas competitions fail to meet requirements upon which active participation is established. the paper describes how activation-enabling functionalities can be systematically designed and implemented in an It-based ideas competition for enterprise resource planning software. We proceeded to evaluate the outcomes of these design measures and found that participation can be supported using a two-step model. the components of the model support incentives and motives of users. Incentives and motives of the users then support the process of activation and consequently participation throughout the ideas competition. this contributes to the successful implementation and maintenance of the ideas competition, thereby providing support for the development of promising innovative ideas. the paper concludes with a discussion of further activation-supporting components yet to be implemented and points to rich possibilities for future research in these areas.
A Game-Theoretic Framework for Resilient and Distributed Generation Control of Renewable Energies in Microgrids

The integration of microgrids that depend on the renewable distributed energy resources with the current power systems is a critical issue in the smart grid. In this paper, we propose a non-cooperative game-theoretic framework to study the strategic behavior of distributed microgrids that generate renewable energies and characterize the power generation solutions by using the Nash equilibrium concept. Our framework not only incorporates economic factors but also takes into account the stability and efficiency of the microgrids, including the power flow constraints and voltage angle regulations. We develop two decentralized update schemes for microgrids and show their convergence to a unique Nash equilibrium. Also, we propose a novel fully distributed PMU-enabled algorithm which only needs the information of voltage angle at the bus. To show the resiliency of the distributed algorithm, we introduce two failure models of the smart grid. Case studies based on the IEEE 14-bus system are used to corroborate the effectiveness and resiliency of the proposed algorithms.
Unsupervised convolutional neural networks for motion estimation

Traditional methods for motion estimation estimate the motion field F between a pair of images as the one that minimizes a predesigned cost function. In this paper, we propose a direct method and train a Convolutional Neural Network (CNN) that when, at test time, is given a pair of images as input it produces a dense motion field F at its output layer. In the absence of large datasets with ground truth motion that would allow classical supervised training, we propose to train the network in an unsupervised manner. The proposed cost function that is optimized during training, is based on the classical optical flow constraint. The latter is differentiable with respect to the motion field and, therefore, allows backpropagation of the error to previous layers of the network. Our method is tested on both synthetic and real image sequences and performs similarly to the state-of-the-art methods.
Android security framework: extensible multi-layered access control on Android

We introduce the Android Security Framework (ASF), a generic, extensible security framework for Android that enables the development and integration of a wide spectrum of security models in form of code-based security modules. The design of ASF reflects lessons learned from the literature on established security frameworks (such as Linux Security Modules or the BSD MAC Framework) and intertwines them with the particular requirements and challenges from the design of Android's software stack. ASF provides a novel security API that supports authors of Android security extensions in developing their modules. This overcomes the current unsatisfactory situation to provide security solutions as separate patches to the Android software stack or to embed them into Android's mainline codebase. This system security extensibility is of particular benefit for enterprise or government solutions that require deployment of advanced security models, not supported by vanilla Android. We present a prototypical implementation of ASF and demonstrate its effectiveness and efficiency by modularizing different security models from related work, such as dynamic permissions, inlined reference monitoring, and type enforcement.
Compact Planar Monopole UWB Antenna with Quadruple Band-Notched Characteristics

A compact planar monopole UWB antenna with quadruple band-notched characteristics is analyzed and presented. By introducing a C-shaped slot, nested C-shaped slot in the radiating patch and U-shaped slot in the feed line, quadruple band-notched characteristics are achieved at frequencies of 2.5, 3.7, 5.8 and 8.2 GHz. The proposed antenna has been fabricated and tested. Measured impedance bandwidth of the antenna is 2.35–12 GHz, which covers Bluetooth and UWB band, for VSWR < 2 and also has four stop bands of 2.44–2.77, 3.42–3.97, 5.45–5.98 and 8–8.68GHz, for VSWR > 2, for rejecting 2.5/3.5GHz WiMAX, WLAN and ITU 8 GHz band signals, respectively. The average gain of this antenna is 4.30 dBi with a variation of ±1.8 dBi over the whole impedance bandwidth. Significant gain reduction over the rejected band is also observed. The antenna shows good omnidirectional radiation patterns in the passband with a compact size of 40 mm× 34mm.
Robust Unit Commitment With Wind Power and Pumped Storage Hydro

As renewable energy increasingly penetrates into power grid systems, new challenges arise for system operators to keep the systems reliable under uncertain circumstances, while ensuring high utilization of renewable energy. With the naturally intermittent renewable energy, such as wind energy, playing more important roles, system robustness becomes a must. In this paper, we propose a robust optimization approach to accommodate wind output uncertainty, with the objective of providing a robust unit commitment schedule for the thermal generators in the day-ahead market that minimizes the total cost under the worst wind power output scenario. Robust optimization models the randomness using an uncertainty set which includes the worst-case scenario, and protects this scenario under the minimal increment of costs. In our approach, the power system will be more reliable because the worst-case scenario has been considered. In addition, we introduce a variable to control the conservatism of our model, by which we can avoid over-protection. By considering pumped-storage units, the total cost is reduced significantly.
Accelerating t-SNE using tree-based algorithms

The paper investigates the acceleration of t-SNE—an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots—using two treebased algorithms. In particular, the paper develops variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in O(N logN). Our experiments show that the resulting algorithms substantially accelerate t-SNE, and that they make it possible to learn embeddings of data sets with millions of objects. Somewhat counterintuitively, the Barnes-Hut variant of t-SNE appears to outperform the dual-tree variant.
Planar Leaky-Wave Antenna With Flexible Control of the Complex Propagation Constant

This communication demonstrates for the first time the capability to independently control the real and imaginary parts of the complex propagation constant in planar, printed circuit board compatible leaky-wave antennas. The structure is based on a half-mode microstrip line which is loaded with an additional row of periodic metallic posts, resulting in a substrate integrated waveguide SIW with one of its lateral electric walls replaced by a partially reflective wall. The radiation mechanism is similar to the conventional microstrip leaky-wave antenna operating in its first higher-order mode, with the novelty that the leaky-mode leakage rate can be controlled by virtue of a sparse row of metallic vias. For this topology it is demonstrated that it is possible to independently control the antenna pointing angle and main lobe beamwidth while achieving high radiation efficiencies, thus providing low-cost, low-profile, simply fed, and easily integrable leaky-wave solutions for high-gain frequency beam-scanning applications. Several prototypes operating at 15 GHz have been designed, simulated, manufactured and tested, to show the operation principle and design flexibility of this one dimensional leaky-wave antenna.
A Quasi-Static Analysis of Open-Ended Coaxial Lines (Short Papers)

A quasi-static analysis of an open-ended coaxial line terminated by a semi-infinite medium on ground plane is presented in this paper. The analysis is based on a vtiriation formulation of the problem. A comparison of results obtained by this method with the experimental and the other theoretical approaches shows an excellent agreement. This analysis is expected to be helpful in the inverse problem of calculating the pertnittivity of materials in oico for a given iuput impedance of the coaxial line.
Saliency detection via Cellular Automata

In this paper, we introduce Cellular Automata-a dynamic evolution model to intuitively detect the salient object. First, we construct a background-based map using color and space contrast with the clustered boundary seeds. Then, a novel propagation mechanism dependent on Cellular Automata is proposed to exploit the intrinsic relevance of similar regions through interactions with neighbors. Impact factor matrix and coherence matrix are constructed to balance the influential power towards each cell's next state. The saliency values of all cells will be renovated simultaneously according to the proposed updating rule. It's surprising to find out that parallel evolution can improve all the existing methods to a similar level regardless of their original results. Finally, we present an integration algorithm in the Bayesian framework to take advantage of multiple saliency maps. Extensive experiments on six public datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods.
Automatic Rule Extraction from Long Short Term Memory Networks

Although deep learning models have proven effective at solving problems in natural language processing, the mechanism by which they come to their conclusions is often unclear. As a result, these models are generally treated as black boxes, yielding no insight of the underlying learned patterns. In this paper we consider Long Short Term Memory networks (LSTMs) and demonstrate a new approach for tracking the importance of a given input to the LSTM for a given output. By identifying consistently important patterns of words, we are able to distill state of the art LSTMs on sentiment analysis and question answering into a set of representative phrases. This representation is then quantitatively validated by using the extracted phrases to construct a simple, rule-based classifier which approximates the output of the LSTM.
Sudarshan Kriya Yogic breathing in the treatment of stress, anxiety, and depression. Part II--clinical applications and guidelines.

Yogic breathing is a unique method for balancing the autonomic nervous system and influencing psychologic and stress-related disorders. Part I of this series presented a neurophysiologic theory of the effects of Sudarshan Kriya Yoga (SKY). Part II will review clinical studies, our own clinical observations, and guidelines for the safe and effective use of yoga breath techniques in a wide range of clinical conditions. Although more clinical studies are needed to document the benefits of programs that combine pranayama (yogic breathing) asanas (yoga postures), and meditation, there is sufficient evidence to consider Sudarshan Kriya Yoga to be a beneficial, low-risk, low-cost adjunct to the treatment of stress, anxiety, post-traumatic stress disorder (PTSD), depression, stress-related medical illnesses, substance abuse, and rehabilitation of criminal offenders. SKY has been used as a public health intervention to alleviate PTSD in survivors of mass disasters. Yoga techniques enhance well-being, mood, attention, mental focus, and stress tolerance. Proper training by a skilled teacher and a 30-minute practice every day will maximize the benefits. Health care providers play a crucial role in encouraging patients to maintain their yoga practices.
Intelligent Load Shedding Need for a Fast and Optimal Solution

To ensure system stability and availability during disturbances, industrial facilities equipped with on-site generation, generally utilize some type of load shedding scheme. In recent years, conventional underfrequency and PLC-based load shedding schemes have been integrated with computerized power management systems to provide an “automated” load shedding system. However, these automated solutions lack system operating knowledge and are still best-guess methods which typically result in excessive or insufficient load shedding. An intelligent load shedding system can provide faster and optimal load relief by utilizing actual operating conditions and knowledge of past system disturbances. This paper presents the need for an intelligent, automated load shedding system. Simulation of case studies for two industrial electrical networks are performed to demonstrate the advantages of an intelligent load shedding system over conventional load shedding methods from the design and operation perspectives. Index Terms — Load Shedding (LS), Intelligent Load Shedding (ILS), Power System Transient Stability, Frequency Relay, Programmable Logic Controller (PLC), Power Management System
A Fully-Integrated 77-GHz FMCW Radar Transceiver in 65-nm CMOS Technology

A fully-integrated FMCW radar system for automotive applications operating at 77 GHz has been proposed. Utilizing a fractional- synthesizer as the FMCW generator, the transmitter linearly modulates the carrier frequency across a range of 700 MHz. The receiver together with an external baseband processor detects the distance and relative speed by conducting an FFT-based algorithm. Millimeter-wave PA and LNA are incorporated on chip, providing sufficient gain, bandwidth, and sensitivity. Fabricated in 65-nm CMOS technology, this prototype provides a maximum detectable distance of 106 meters for a mid-size car while consuming 243 mW from a 1.2-V supply.
Opponent Modeling in Poker

Poker is an interesting test-bed for artificial intelligence research. It is a game of imperfect knowledge, where multiple competing agents must deal with risk management, agent modeling, unreliable information and deception, much like decision-making applications in the real world. Agent modeling is one of the most difficult problems in decision-making applications and in poker it is essential to achieving high performance. This paper describes and evaluates Loki, a poker program capable of observing its opponents, constructing opponent models and dynamically adapting its play to best exploit patterns in the opponents’ play.
SPICE simulation of surface acoustic wave interdigital transducers

Surface Acoustic Wave (SAW) devices, are not normally amenable to simulation through circuit simulators. In this letter, an electrical macromodel of Mason's Equivalent Circuit for an interdigital transducer (IDT) is proposed which is compatible to a widely used general purpose circuit simulator SPICE endowed with the capability to handle negative capacitances and inductances. Illustrations have been given to demonstrate the simplicity of ascertaining the frequency and time domain characteristics of IDT and amenability to simulate the IDT along with other external circuit elements.<<ETX>>
Indirect Speech Acts

In this paper, we address several puzzles concerning speech acts,particularly indirect speech acts. We show how a formal semantictheory of discourse interpretation can be used to define speech actsand to avoid murky issues concerning the metaphysics of action. Weprovide a formally precise definition of indirect speech acts, includingthe subclass of so-called conventionalized indirect speech acts. Thisanalysis draws heavily on parallels between phenomena at the speechact level and the lexical level. First, we argue that, just as co-predicationshows that some words can behave linguistically as if they're `simultaneously'of incompatible semantic types, certain speech acts behave this way too.Secondly, as Horn and Bayer (1984) and others have suggested, both thelexicon and speech acts are subject to a principle of blocking or ``preemptionby synonymy'': Conventionalized indirect speech acts can block their`paraphrases' from being interpreted as indirect speech acts, even ifthis interpretation is calculable from Gricean-style principles. Weprovide a formal model of this blocking, and compare it withexisting accounts of lexical blocking.
Computational and Experimental Approaches to Visual Aesthetics

Aesthetics has been the subject of long-standing debates by philosophers and psychologists alike. In psychology, it is generally agreed that aesthetic experience results from an interaction between perception, cognition, and emotion. By experimental means, this triad has been studied in the field of experimental aesthetics, which aims to gain a better understanding of how aesthetic experience relates to fundamental principles of human visual perception and brain processes. Recently, researchers in computer vision have also gained interest in the topic, giving rise to the field of computational aesthetics. With computing hardware and methodology developing at a high pace, the modeling of perceptually relevant aspect of aesthetic stimuli has a huge potential. In this review, we present an overview of recent developments in computational aesthetics and how they relate to experimental studies. In the first part, we cover topics such as the prediction of ratings, style and artist identification as well as computational methods in art history, such as the detection of influences among artists or forgeries. We also describe currently used computational algorithms, such as classifiers and deep neural networks. In the second part, we summarize results from the field of experimental aesthetics and cover several isolated image properties that are believed to have a effect on the aesthetic appeal of visual stimuli. Their relation to each other and to findings from computational aesthetics are discussed. Moreover, we compare the strategies in the two fields of research and suggest that both fields would greatly profit from a joined research effort. We hope to encourage researchers from both disciplines to work more closely together in order to understand visual aesthetics from an integrated point of view.
Synthetic Text Generation for Sentiment Analysis

Natural language is a common type of input for data processing systems. Therefore, it is often required to have a large testing data set of this type. In this context, the task to automatically generate natural language texts, which maintain the properties of real texts is desirable. However, current synthetic data generators do not capture natural language text data sufficiently. In this paper, we present a preliminary study on different generative models for text generation, which maintain specific properties of natural language text, i.e., the sentiment of a review text. In a series of experiments using different data sets and sentiment analysis methods, we show that generative models can generate texts with a specific sentiment and that hidden Markov model based text generation achieves less accuracy than Markov chain based text generation, but can generate a higher number of distinct texts.
SAD : Social Anxiety and Depression Monitoring System for College Students

Mental health problems are highly prevalent and increasing in frequency and severity among the college student population. The upsurge in mobile and wearable wireless technologies capable of intense, longitudinal tracking of individuals, provide enormously valuable opportunities in mental health research to examine temporal patterns and dynamic interactions of key variables. In this paper, we present an integrative framework for social anxiety and depression (SAD) monitoring, two of the most common disorders in the college student population. We have developed a smartphone application and the supporting infrastructure to collect both passive sensor data and active event-driven data. This supports intense, longitudinal, dynamic tracking of anxious and depressed college students to evaluate how their emotions and social behaviors change in the college campus environment. The data will provide critical information about how student mental health problems are maintained and, ultimately, how student patterns on campus shift following treatment.
Influence of Hyperparameters on Random Forest Accuracy

In this paper we present our work on the Random Forest (RF) family of classification methods. Our goal is to go one step further in the understanding of RF mechanisms by studying the parametrization of the reference algorithm Forest-RI. In this algorithm, a randomization principle is used during the tree induction process, that randomly selects K features at each node, among which the best split is chosen. The strength of randomization in the tree induction is thus led by the hyperparameter K which plays an important role for building accurate RF classifiers. We have decided to focus our experimental study on this hyperparameter and on its influence on classification accuracy. For that purpose, we have evaluated the Forest-RI algorithm on several machine learning problems and with different settings of K in order to understand the way it acts on RF performance. We show that default values of K traditionally used in the literature are globally near-optimal, except for some cases for which they are all significatively sub-optimal. Thus additional experiments have been led on those datasets, that highlight the crucial role played by feature relevancy in finding the optimal setting of K.
A High speed, Low Voltage to High Voltage Level Shifter in Standard 1.2V 0.13μm CMOS

The design of a high speed, low voltage to high voltage level shifter in a digital 1.2 V, 0.13 mum CMOS technology is presented. The topology uses two differentially switched cascoded transistor ladders. The output signal has an offset of two times the nominal supply voltage of the used technology with respect to the input signal. Oxide stress and hot carrier degradation is minimized since all transistors of the level shifter operate within the voltage limits imposed by the design rules of a mainstream CMOS technology.