nils-herrmann/clean_scirepeval huggingface dataset:
Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN) has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO), ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast OPEN ACCESS
Compact Printed MIMO Antenna for UWB Applications

A compact multiple-input-multiple-output (MIMO) antenna is presented for ultrawideband (UWB) applications. The antenna consists of two open L-shaped slot (LS) antenna elements and a narrow slot on the ground plane. The antenna elements are placed perpendicularly to each other to obtain high isolation, and the narrow slot is added to reduce the mutual coupling of antenna elements in the low frequency band (3-4.5 GHz). The proposed MIMO antenna has a compact size of 32 ×32 mm2, and the antenna prototype is fabricated and measured. The measured results show that the proposed antenna design achieves an impedance bandwidth of larger than 3.1-10.6 GHz, low mutual coupling of less than 15 dB, and a low envelope correlation coefficient of better than 0.02 across the frequency band, which are suitable for portable UWB applications.
Device Mismatch: An Analog Design Perspective

Device variability is receiving a lot of interest recently due to its important impact on the design of digital integrated systems. In analog integrated circuits, the variability of identically designed devices has long been a concern since it directly affects the attainable precision. This paper reviews the mismatch device models that are widely used in analog design as well as the fundamental impact of device mismatch on the trade-off between different performance parameters.
3.6 W/mm high power density W-band InAlGaN/GaN HEMT MMIC power amplifier

We demonstrated a W-band high-power-density MMIC power amplifier with 80 nm InAlGaN/GaN HEMTs. The MMIC consists of two-stage cascade units, each of which has two transistors with the same gate periphery for a high gain and low-loss matching circuit. The MMIC achieved a maximum output power of 1.15 W and maximum PAE of 12.3 % at 86 GHz under CW operation. Its power density reached 3.6 W/mm, representing the highest performance of the W-band GaN HEMT MMIC power amplifier.
Reinforcement Learning with a Hierarchy of Abstract Models

Reinforcement learning (RL) algorithms have traditionally been thought of as trial and error learning methods that use actual control experience to incrementally improve a control policy. Sutton's DYNA architecture demonstrated that RL algorithms can work as well using simulated experience from an environment model, and that the resulting computation was similar to doing one-step lookahead planning. Inspired by the literature on hierarchical planning, I propose learning a hierarchy of models of the environment that abstract temporal detail as a means of improving the scalability of RL algorithms. I present H-DYNA (Hierarchical DYNA), an extension to Sutton's DYNA architecture that is able to learn such a hierarchy of abstract models. H-DYNA di ers from hierarchical planners in two ways: rst, the abstract models are learned using experience gained while learning to solve other tasks in the same environment, and second, the abstract models can be used to solve stochastic control tasks. Simulations on a set of compositionally-structured navigation tasks show that H-DYNA can learn to solve them faster than conventional RL algorithms. The abstract models also serve as mechanisms for achieving transfer of learning across multiple tasks.
Learning Topics in Short Texts by Non-negative Matrix Factorization on Term Correlation Matrix

Nowadays, short texts are very prevalent in various web applications, such as microblogs, instant messages. The severe sparsity of short texts hinders existing topic models to learn reliable topics. In this paper, we propose a novel way to tackle this problem. The key idea is to learn topics by exploring term correlation data, rather than the high-dimensional and sparse term occurrence information in documents. Such term correlation data is less sparse and more stable with the increase of the collection size, and can well capture the necessary information for topic learning. To obtain reliable topics from term correlation data, we first introduce a novel way to compute term correlation in short texts by representing each term with its co-occurred terms. Then we formulated the topic learning problem as symmetric non-negative matrix factorization on the term correlation matrix. After learning the topics, we can easily infer the topics of documents. Experimental results on three data sets show that our method provides substantially better performance than the baseline methods.
At all Costs: A Comparison of Robust Cost Functions for Camera Correspondence Outliers

Camera-based localization techniques must be robust to correspondence errors, i.e., when visual features (landmarks)are matched incorrectly. The two primary techniques to address this issue are RANSAC and robust M-estimation -- each more appropriate for different applications. This paper investigates the use of different robust cost functions for M-estimation to deal with correspondence outliers, and assesses their performance under varying degrees of data corruption. Experimental results show that using an aggressive red ascending cost function (e.g., Dynamic Covariance Scaling (DCS) or Geman-McClure (G-M)) best improves accuracy by excluding outliers almost entirely. Additionally, adjusting an error-scaling parameter for the robust cost function over the course of the optimization improves convergence with poor initial conditions.
The Graph Structure in the Web - Analyzed on Different Aggregation Levels

Knowledge about the general graph structure of the World Wide Web is important for understanding the social mechanisms that govern its growth, for designing ranking methods, for devising better crawling algorithms, and for creating accurate models of its structure. In this paper, we analyze a large web graph. The graph was extracted from a large publicly accessible web crawl that was gathered by the Common Crawl Foundation in 2012. The graph covers over 3.5 billion web pages and 128.7 billion hyperlinks. We analyze and compare, among other features, degree distributions, connectivity, average distances, and the structure of weakly/strongly connected components. We conduct our analysis on three different levels of aggregation: page, host, and pay-level domain (PLD) (one “dot level” above public suffixes). Our analysis shows that, as evidenced by previous research (Serrano et al., 2007), some of the features previously observed by Broder et al., 2000 are very dependent on artifacts of the crawling process, whereas other appear to be more structural. We confirm the existence of a giant strongly connected component; we however find, as observed by other researchers (Donato et al., 2005; Boldi et al., 2002; Baeza-Yates and Poblete, 2003), very different proportions of nodes that can reach or that can be reached from the giant component, suggesting that the “bow-tie structure” as described by Broder et al. is strongly dependent on the crawling process, and to the best of our current knowledge is not a structural property of the Web. More importantly, statistical testing and visual inspection of size-rank plots show that the distributions of indegree, outdegree and sizes of strongly connected components of the page and host graph are not power laws, contrarily to what was previously reported for much smaller crawls, although they might be heavy tailed. If we aggregate at pay-level domain, however, a power law emerges. We also provide for the first time accurate measurement of distance-based features, using recently introduced algorithms that scale to the size of our crawl (Boldi and Vigna, 2013).
Language Style Transfer from Sentences with Arbitrary Unknown Styles

Language style transfer is the problem of migrating the content of a source sentence to a target style. In many of its applications, parallel training data are not available and source sentences to be transferred may have arbitrary and unknown styles. Under this problem setting, we propose an encoder-decoder framework. First, each sentence is encoded into its content and style latent representations. Then, by recombining the content with the target style, we decode a sentence aligned in the target domain. To adequately constrain the encoding and decoding functions, we couple them with two loss functions. The first is a style discrepancy loss, enforcing that the style representation accurately encodes the style information guided by the discrepancy between the sentence style and the target style. The second is a cycle consistency loss, which ensures that the transferred sentence should preserve the content of the original sentence disentangled from its style. We validate the effectiveness of our model in three tasks: sentiment modification of restaurant reviews, dialog response revision with a romantic style, and sentence rewriting with a Shakespearean style.
The Birth of Prolog

The programming language, Prolog, was born of a project aimed not at producing a programming language but at processing natural languages; in this case, French. The project gave rise to a preliminary version of Prolog at the end of 1971 and a more definitive version at the end of 1972. This article gives the history of this project and describes in detail the preliminary and then the final versions of Prolog. The authors also felt it appropriate to describe the Q-systems since it was a language which played a prominent part in Prolog's genesis.
Automatic Detection and Prevention of Cyberbullying

The recent development of social media poses new challenges to the research community in analyzing online interactions between people. Social networking sites offer great opportunities for connecting with others, but also increase the vulnerability of young people to undesirable phenomena, such as cybervictimization. Recent research reports that on average, 20% to 40% of all teenagers have been victimized online. In this paper, we focus on cyberbullying as a particular form of cybervictimization. Successful prevention depends on the adequate detection of potentially harmful messages. However, given the massive information overload on the Web, there is a need for intelligent systems to identify potential risks automatically. We present the construction and annotation of a corpus of Dutch social media posts annotated with fine-grained cyberbullying-related text categories, such as insults and threats. Also, the specific participants (harasser, victim or bystander) in a cyberbullying conversation are identified to enhance the analysis of human interactions involving cyberbullying. Apart from describing our dataset construction and annotation, we present proof-of-concept experiments on the automatic identification of cyberbullying events and fine-grained cyberbullying categories.
A fully data parallel WFST-based large vocabulary continuous speech recognition on a graphics processing unit

Tremendous compute throughput is becoming available in personal desktop and laptop systems through the use of graphics processing units (GPUs). However, exploiting this resource requires re-architecting an application to fit a data parallel programming model. The complex graph traversal routines in the inference process for large vocabulary continuous speech recognition (LVCSR) have been considered by many as unsuitable for extensive parallelization. We explore and demonstrate a fully data parallel implementation of a speech inference engine on NVIDIA’s GTX280 GPU. Our implementation consists of two phases compute-intensive observation probability computation phase and communication-intensive graph traversal phase. We take advantage of dynamic elimination of redundant computation in the compute-intensive phase while maintaining close-to-peak execution efficiency. We also demonstrate the importance of exploring application-level trade-offs in the communication-intensive graph traversal phase to adapt the algorithm to data parallel execution on GPUs. On 3.1 hours of speech data set, we achieve more than 11× speedup compared to a highly optimized sequential implementation on Intel Core i7 without sacrificing accuracy.
Large-Signal Analysis of MOS Varactors in CMOS Gm LC VCOs

MOS varactors are used extensively as tunable elements in the tank circuits of RF voltage-controlled oscillators (VCOs) based on submicrometer CMOS technologies. MOS varactor topologies include conventionalD=S=B connected, inversion-mode (I-MOS), and accumulation-mode (A-MOS) structures. When incorporated into the VCO tank circuit, the large-signal swing of the VCO output oscillation modulates the varactor capacitance in time, resulting in a VCO tuning curve that deviates from the dc tuning curve of the particular varactor structure. This paper presents a detailed analysis of this large-signal effect. Simulated results are compared to measurements for an example 2.5-GHz complementary LC VCO using I-MOS varactors implemented in 0.35m CMOS technology.
Mood Classification of Hindi Songs based on Lyrics

Digitization of music has led to easier access to different forms music across the globe. Increasing work pressure denies the necessary time to listen and evaluate music for a creation of a personal music library. One solution might be developing a music search engine or recommendation system based on different moods. In fact mood label is considered as an emerging metadata in the digital music libraries and online music repositories. In this paper, we proposed mood taxonomy for Hindi songs and prepared a mood annotated lyrics corpus based on this taxonomy. We also annotated lyrics with positive and negative polarity. Instead of adopting a traditional approach to music mood classification based solely on audio features, the present study describes a mood classification system from lyrics as well by combining a wide range of semantic and stylistic features extracted from textual lyrics. We also developed a supervised system to identify the sentiment of the Hindi song lyrics based on the above features. We achieved the maximum average F-measure of 68.30% and 38.49% for classifying the polarities and moods of the Hindi lyrics, respectively.
DIGITAL CAMERA CALIBRATION METHODS : CONSIDERATIONS AND COMPARISONS

Camera calibration has always been an essential component of photogrammetric measurement, with self-calibration nowadays being an integral and routinely applied operation within photogrammetric triangulation, especially in high-accuracy close-range measurement. With the very rapid growth in adoption of off-the-shelf digital cameras for a host of new 3D measurement applications, however, there are many situations where the geometry of the image network will not support robust recovery of camera parameters via on-the-job calibration. For this reason, stand-alone camera calibration has again emerged as an important issue in close-range photogrammetry, and it also remains a topic of research interest in computer vision. This paper overviews the current approaches adopted for camera calibration in close-range photogrammetry and computer vision, and discusses operational aspects for self-calibration. Also, the results of camera calibrations using different algorithms are summarized. Finally, the impact of chromatic aberration on modelled radial distortion is touched upon to highlight the fact that there are still issues of research interest in the photogrammetric calibration of consumer-grade digital cameras.
The Firm ’ s Management of Social Interactions

Consumer choice is influenced in a direct and meaningful way by the actions taken by others. These “actions” range from face-to-face recommendations from a friend to the passive observation of what a stranger is wearing. We refer to the set of such contexts as “social interactions” (SI). We believe that at least some of the SI effects are partially within the firm’s control and that this represents an exciting research opportunity. We present an agenda that identifies a list of unanswered questions of potential interest to both researchers and managers. In order to appreciate the firm’s choices with respect to its management of SI, it is important to first evaluate where we are in terms of understanding the phenomena themselves. We highlight five questions in this regard: (1) What are the antecedents of word of mouth (WOM)? (2) How does the transmission of positive WOM differ from that of negative WOM? (3) How does online WOM differ from offline WOM? (4) What is the impact of WOM? (5) How can we measure WOM? Finally, we identify and discuss four principal, non-mutually exclusive, roles that the firm might play: (1) observer, (2) moderator, (3) mediator, and (4) participant.
Three-dimensional flow analysis of a mechanical bileaflet mitral prosthesis

The Jyros (JR) and the Advancing The Standard (ATS) valves were compared with the St. Jude Medical (SJM) valve in the mitral position to study the effects of design differences, installed valve orientation to the flow, and closing sounds using particle tracking velocimetry and particle image velocimetry methods utilizing a high-speed video flow visualization technique to map the velocity field. Sound measurements were made to confirm the claims of the manufacturers. Based on the experimental data, the following general conclusions can be made: On the vertical measuring plane which passes through the centers of the aortic and the mitral valves, the SJM valve shows a distinct circulatory flow pattern when the valve is installed in the antianatomical orientation; the SJM valve maintains the flow through the central orifice quite well; the newer curved leaflet JR valve and the ATS valve, which does not fully open during the peak flow phase, generates a higher but divergent flow close to the valve location when the valve was installed anatomically. The antianatomically installed JR valve showed diverse and less distinctive flow patterns and slower velocity on the central measuring plane than the SJM valve did, with noticeably lower valve closing noise. On the velocity field directly below the mitral valve that is normal to the previous measuring plane, the three valves show symmetrical twin circulations due to the divergent nature of the flow generated by the two inclined half discs; the SJM valve with centrally downward circulation is contrasted by the two other valves with peripherally downward circulation. These differences may have an important role in generation of the valve closing sound.
Econometric Analysis Of Cross Section And Panel Data The Mit Press

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The winner decision model of tic tac toe game by using multi-tape turing machine

The Tic tac toe is very popular game having a 3 × 3 grid board and 2 players. A Special Symbol (X or O) is assigned to each player to indicate the slot is covered by the respective player. The winner of the game is the player who first cover a horizontal, vertical and diagonal row of the board having only player's own symbols. This paper presents the design model of Tic tac toe Game using Multi-Tape Turing Machine in which both player choose input randomly and result of the game is declared. The computational Model of Tic tac toe is used to describe it in a formal manner.
Functional architecture of basal ganglia circuits: neural substrates of parallel processing

Concepts of basal ganglia organization have changed markedly over the past decade, due to significant advances in our understanding of the anatomy, physiology and pharmacology of these structures. Independent evidence from each of these fields has reinforced a growing perception that the functional architecture of the basal ganglia is essentially parallel in nature, regardless of the perspective from which these structures are viewed. This represents a significant departure from earlier concepts of basal ganglia organization, which generally emphasized the serial aspects of their connectivity. Current evidence suggests that the basal ganglia are organized into several structurally and functionally distinct 'circuits' that link cortex, basal ganglia and thalamus, with each circuit focused on a different portion of the frontal lobe. In this review, Garrett Alexander and Michael Crutcher, using the basal ganglia 'motor' circuit as the principal example, discuss recent evidence indicating that a parallel functional architecture may also be characteristic of the organization within each individual circuit.
Supply Chain Object Discovery with Semantic-enhanced Blockchain

Supply chains can be seen as cyber-physical networks grounded on object identification and tracking. Conventional trust models featuring centralized information management architectures and simplistic things classification lend two of the most relevant limitations to current solutions. Blockchain introduces novel and a valuable trust approaches while semantic technologies better permit a things description. This paper introduces a semantic-enhanced blockchain platform allowing a flexible object discovery. It is based on validation by consensus of smart contracts and adopt a semantic matchmaking between queries and object annotations expressed w.r.t. ontology models. Early experiments assess the good behaviour of the proposed framework.
Using the gray wolf optimizer for solving optimal reactive power dispatch problem

This paper presents the use of a new meta-heuristic technique namely gray wolf optimizer (GWO) which is inspired from gray wolves’ leadership and hunting behaviors to solve optimal reactive power dispatch (ORPD) problem. ORPD problem is a well-known nonlinear optimization problem in power system. GWO is utilized to find the best combination of control variables such as generator voltages, tap changing transformers’ ratios as well as the amount of reactive compensation devices so that the loss and voltage deviation minimizations can be achieved. In this paper, two case studies of IEEE 30bus system and IEEE 118-bus system are used to show the effectiveness of GWO technique compared to other techniques available in literature. The results of this research show that GWO is able to achieve less power loss and voltage deviation than those determined by other techniques.