nils-herrmann/clean_scirepeval huggingface dataset:
Design and Analysis of a Low-Profile and Broadband Microstrip Monopolar Patch Antenna

A new microstrip monopolar patch antenna is proposed and analyzed. The antenna has a wide bandwidth and a monopole like radiation pattern. Such antenna is constructed on a circular patch antenna that is shorted concentrically with a set of conductive vias. The antenna is analyzed using a cavity model. The cavity model analysis not only distinguishes each resonating mode and gives a physical insight into each mode of the antenna, but also provides a guideline to design a broadband monopolar patch antenna that utilizes two modes (TM01 and TM02 modes). Both modes provide a monopole like radiation pattern. The proposed antenna has a simple structure with a low profile of 0.024 wavelengths, and yields a wide impedance bandwidth of 18% and a maximum gain of 6 dBi.
Deep Reinforcement Learning for Robotic Manipulation

Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on offpolicy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.
Biofeedback of heart rate variability and related physiology: a critical review.

Low heart rate variability (HRV) characterizes several medical and psychological diseases. HRV biofeedback is a newly developed approach that may have some use for treating the array of disorders in which HRV is relatively low. This review critically appraises evidence for the effectiveness of HRV and related biofeedback across 14 studies in improving (1) HRV and baroreflex outcomes and (2) clinical outcomes. Results revealed that HRV biofeedback consistently effectuates acute improvements during biofeedback practice, whereas the presence of short-term and long-term carry-over effects is less clear. Some evidence suggests HRV biofeedback may result in long-term carry-over effects on baroreflex gain, which is an area most promising for future investigations. On the other hand, concerning clinical outcomes, there is ample evidence attesting to efficacy of HRV biofeedback. However, because clinical and physiological outcomes do not improve concurrently in all cases, the mechanism by which HRV biofeedback results in salutary effects in unclear. Considerations for the field in addressing shortcomings of the reviewed studies and advancing understanding of the way in which HRV biofeedback may improve physiological and clinical outcomes are offered in light of the reviewed evidence.
Deep Representation Learning with Target Coding

We consider the problem of learning deep representation when target labels are available. In this paper, we show that there exists intrinsic relationship between target coding and feature representation learning in deep networks. Specifically, we found that distributed binary code with error correcting capability is more capable of encouraging discriminative features, in comparison to the 1-of-K coding that is typically used in supervised deep learning. This new finding reveals additional benefit of using error-correcting code for deep model learning, apart from its well-known error correcting property. Extensive experiments are conducted on popular visual benchmark datasets.
Pseudogen: A Tool to Automatically Generate Pseudo-Code from Source Code

Understanding the behavior of source code written in an unfamiliar programming language is difficult. One way to aid understanding of difficult code is to add corresponding pseudo-code, which describes in detail the workings of the code in a natural language such as English. In spite of its usefulness, most source code does not have corresponding pseudo-code because it is tedious to create. This paper demonstrates a tool Pseudogen that makes it possible to automatically generate pseudo-code from source code using statistical machine translation (SMT). Pseudogen currently supports generation of English or Japanese pseudo-code from Python source code, and the SMT framework makes it easy for users to create new generators for their preferred source code/pseudo-code pairs.
Autonomous indoor 3D exploration with a micro-aerial vehicle

In this paper, we propose a stochastic differential equation-based exploration algorithm to enable exploration in three-dimensional indoor environments with a payload constrained micro-aerial vehicle (MAV). We are able to address computation, memory, and sensor limitations by considering only the known occupied space in the current map. We determine regions for further exploration based on the evolution of a stochastic differential equation that simulates the expansion of a system of particles with Newtonian dynamics. The regions of most significant particle expansion correlate to unexplored space. After identifying and processing these regions, the autonomous MAV navigates to these locations to enable fully autonomous exploration. The performance of the approach is demonstrated through numerical simulations and experimental results in single and multi-floor indoor experiments.
Sentic patterns: Dependency-based rules for concept-level sentiment analysis

The Web is evolving through an era where the opinions of users are getting increasingly important and valuable. The distillation of knowledge from the huge amount of unstructured information on the Web can be a key factor for tasks such as social media marketing, branding, product positioning, and corporate reputation management. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions involves a deep understanding of natural language text by machines, from which we are still very far. To this end, concept-level sentiment analysis aims to go beyond a mere word-level analysis of text and provide novel approaches to opinion mining and sentiment analysis that enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data. A recent knowledge-based technology in this context is sentic computing, which relies on the ensemble application of commonsense computing and the psychology of emotions to infer the conceptual and affective information associated with natural language. Sentic computing, however, is limited by the richness of the knowledge base and by the fact that the bag-of-concepts model, despite more sophisticated than bag-of-words, misses out important discourse structure information that is key for properly detecting the polarity conveyed by natural language opinions. In this work, we introduce a novel paradigm to concept-level sentiment analysis that merges linguistics, common-sense computing, and machine learning for improving the accuracy of tasks such as polarity detection. By allowing sentiments to flow from concept to concept based on the dependency relation of the input sentence, in particular, we achieve a better understanding of the contextual role of each concept within the sentence and, hence, obtain a polarity detection engine that outperforms state-of-the-art statistical methods.
High speed operation of electrical machines, a review on technology, benefits and challenges

The High Speed (HS) technology, in electrical machine based drive systems, is reviewed in this paper by investigating around 200 different machine's data that are reported in the literature or open sources. This study includes information regarding clarifying the maximum power vs. speed capability, potential applications, different machine types, design limitation issues consisting of thermal, electro-magnetic, mechanical, control, inverter and power electronics. The limitations of electrical machines in HS technology are classified by introducing HS-index, which is the product of the machine nominal power and nominal speed (= n × Pout). It is shown that HS-index behavior changes by speed, because limitation factors for HS power capability take different physics/nature, as speed increases. It is found that in relatively low speeds, n <; 20 krpm, maximum mechanical stress in the rotor, and in high speeds, n > 20 krpm, additional losses in the machine dictates the maximum power capability. The survey shows that Induction Machine (IM) and Permanent Magnet (PM) machine have relatively superior performance. The maximum rotor surface speed, vmax, is limited to 250-300 m/s (can reach 550 m/s) and is independent of n and output power. The HS-index is a function of vmax2 and almost independent of n for n <; 10-20 krpm. However, for n > 20 krpm is proportional to vmax3/n2 due to thermal limitations. The maximum power is almost cubically (Pout ∝ d2.6) proportional to the rotor diameter.
Babinet principle applied to the design of metasurfaces and metamaterials.

The electromagnetic theory of diffraction and the Babinet principle are applied to the design of artificial metasurfaces and metamaterials. A new particle, the complementary split rings resonator, is proposed for the design of metasurfaces with high frequency selectivity and planar metamaterials with a negative dielectric permittivity. Applications in the fields of frequency selective surfaces and polarizers, as well as in microwave antennas and filter design, can be envisaged. The tunability of all these devices by an applied dc voltage is also achievable if these particles are etched on the appropriate substrate.
Positive Youth Development , Participation in Community Youth Development Programs , and Community Contributions of Fifth-Grade Adolescents : Findings From the First Wave Of the 4-H Study of Positive Youth Development

The 4-H Study of Positive Youth Development (PYD), a longitudinal investigation of a diverse sample of 1,700 fifth graders and 1,117 of their parents, tests developmental contextual ideas linking PYD, youth contributions, and participation in community youth development (YD) programs, representing a key ecological asset. Using data from Wave 1 of the study, structural equation modeling procedures provided evidence for five first-order latent factors representing the “Five Cs” of PYD (competence, confidence, connection, character, and caring), and for their convergence on a second-order, PYD latent construct. A theoretical construct, youth “contribution,” was also created and examined. Both PYD and YD program participation independently related to contribution. The importance of longitudinal analyses for extending the present results is discussed.
Machine morality: bottom-up and top-down approaches for modelling human moral faculties

The implementation of moral decision making abilities in artificial intelligence (AI) is a natural and necessary extension to the social mechanisms of autonomous software agents and robots. Engineers exploring design strategies for systems sensitive to moral considerations in their choices and actions will need to determine what role ethical theory should play in defining control architectures for such systems. The architectures for morally intelligent agents fall within two broad approaches: the top-down imposition of ethical theories, and the bottom-up building of systems that aim at goals or standards which may or may not be specified in explicitly theoretical terms. In this paper we wish to provide some direction for continued research by outlining the value and limitations inherent in each of these approaches.
Opinion Mining in Online Reviews About Distance Education Programs

The popularity of distance education programs is increasing at a fast pace. En par with this development, online communication in fora, social media and reviewing platforms between students is increasing as well. Exploiting this information to support fellow students or institutions requires to extract the relevant opinions in order to automatically generate reports providing an overview of pros and cons of different distance education programs. We report on an experiment involving distance education experts with the goal to develop a dataset of reviews annotated with relevant categories and aspects in each category discussed in the specific review together with an indication of the sentiment. Based on this experiment, we present an approach to extract general categories and specific aspects under discussion in a review together with their sentiment. We frame this task as a multi-label hierarchical text classification problem and empirically investigate the performance of different classification architectures to couple the prediction of a category with the prediction of particular aspects in this category. We evaluate different architectures and show that a hierarchical approach leads to superior results in comparison to a flat model which makes decisions independently. This work has been performed while the first and last authors were at Bielefeld University.
Fully Parallel Stochastic LDPC Decoders

Stochastic decoding is a new approach to iterative decoding on graphs. This paper presents a hardware architecture for fully parallel stochastic low-density parity-check (LDPC) decoders. To obtain the characteristics of the proposed architecture, we apply this architecture to decode an irregular state-of-the-art (1056,528) LDPC code on a Xilinx Virtex-4 LX200 field-programmable gate-array (FPGA) device. The implemented decoder achieves a clock frequency of 222 MHz and a throughput of about 1.66 Gb/s at Eb/N0=4.25 dB (a bit error rate of 10-8). It provides decoding performance within 0.5 and 0.25 dB of the floating-point sum-product algorithm with 32 and 16 iterations, respectively, and similar error-floor behavior. The decoder uses less than 40% of the lookup tables, flip-flops, and IO ports available on the FPGA device. The results provided in this paper validate the potential of stochastic LDPC decoding as a practical and competitive fully parallel decoding approach.
Leveraging the schema in latent factor models for knowledge graph completion

We focus on the problem of predicting missing links in large Knowledge Graphs (KGs), so to discover new facts. Over the last years, latent factor models for link prediction have been receiving an increasing interest: they achieve state-of-the-art accuracy in link prediction tasks, while scaling to very large KGs. However, KGs are often endowed with additional schema knowledge, describing entity classes, their sub-class relationships, and the domain and range of each predicate: the schema is actually not used by latent factor models proposed in the literature. In this work, we propose an unified method for leveraging additional schema knowledge in latent factor models, with the aim of learning more accurate link prediction models. Our experimental evaluations show the effectiveness of the proposed method on several KGs.
There's no 'Count or Predict' but task-based \\selection for distributional models

In this paper, we investigate the differences between prediction-based (word2vec), dense countbased (GloVe) and sparse count-based (JoBimText) semantic models. We evaluate the models, which were selected because they can all be computed efficiently on large data, based on word similarity tasks and a semantic ranking task both for verbs and nouns. We demonstrate that prediction-based models yield higher scores than the other two models at determining a similarity score between two words. To the contrary, sparse count-based methods perform best in the ranking task. Further, sparse count-based methods benefit more from linguistically informed contexts, such as dependency relations. In summary, we highlight differences of popular distributional semantic representations and derive recommendations for their usage.
Initial Beam Association in Millimeter Wave Cellular Systems: Analysis and Design Insights

Enabling the high data rates of millimeter wave (mmWave) cellular systems requires deploying large antenna arrays at both the basestations and mobile users. Prior work on coverage and rate of mmWave cellular networks focused on the case when basestations and mobile beamforming vectors are predesigned for maximum beamforming gains. Designing beamforming/combining vectors, though, requires training, which may impact both the SINR coverage and rate of mmWave systems. This paper evaluates mmWave cellular network performance while accounting for the beam training/association overhead. First, a model for the initial beam association is developed based on beam sweeping and downlink control pilot reuse. To incorporate the impact of beam training, a new metric, called the effective reliable rate, is defined and adopted. Using stochastic geometry, the effective rate of mmWave cellular networks is derived for two special cases: near-orthogonal pilots and full pilot reuse. Analytical and simulation results provide insights into the answers of two important questions. First, what is the impact of beam association on mmWave network performance? Then, should orthogonal or reused pilots be employed? The results show that unless the employed beams are very wide, initial beam training with full pilot reuse is nearly as good as perfect beam alignment.
Kafka : a Distributed Messaging System for Log Processing

Log processing has become a critical component of the data pipeline for consumer internet companies. We introduce Kafka, a distributed messaging system that we developed for collecting and delivering high volumes of log data with low latency. Our system incorporates ideas from existing log aggregators and messaging systems, and is suitable for both offline and online message consumption. We made quite a few unconventional yet practical design choices in Kafka to make our system efficient and scalable. Our experimental results show that Kafka has superior performance when compared to two popular messaging systems. We have been using Kafka in production for some time and it is processing hundreds of gigabytes of new data each day.
Endurance exercise performance: the physiology of champions.

Efforts to understand human physiology through the study of champion athletes and record performances have been ongoing for about a century. For endurance sports three main factors--maximal oxygen consumption (.VO(2,max)), the so-called 'lactate threshold' and efficiency (i.e. the oxygen cost to generate a given running speed or cycling power output)--appear to play key roles in endurance performance. and lactate threshold interact to determine the 'performance .VO(2)' which is the oxygen consumption that can be sustained for a given period of time. Efficiency interacts with the performance .VO(2) to establish the speed or power that can be generated at this oxygen consumption. This review focuses on what is currently known about how these factors interact, their utility as predictors of elite performance, and areas where there is relatively less information to guide current thinking. In this context, definitive ideas about the physiological determinants of running and cycling efficiency is relatively lacking in comparison with .VO(2,max) and the lactate threshold, and there is surprisingly limited and clear information about the genetic factors that might pre-dispose for elite performance. It should also be cautioned that complex motivational and sociological factors also play important roles in who does or does not become a champion and these factors go far beyond simple physiological explanations. Therefore, the performance of elite athletes is likely to defy the types of easy explanations sought by scientific reductionism and remain an important puzzle for those interested in physiological integration well into the future.
FactoMineR: An R Package for Multivariate Analysis

In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals) and finally supplementary information (supplementary individuals and variables). Moreover, the dimensions issued from the different exploratory data analyses can be automatically described by quantitative and/or categorical variables. Numerous graphics are also available with various options. Finally, a graphical user interface is implemented within the Rcmdr environment in order to propose an user friendly package.
The use of Web analytics for digital marketing performance measurement ☆

a r t i c l e i n f o Keywords: Case study Digital marketing Industrial business Performance measurement Web analytics This study proposes that the benefits gained from marketing performance measurement are determined by how an organization exploits the metrics system under specific circumstances. For this purpose, the authors review performance measurement literature and apply it to the use of Web analytics, which offers companies a metrics system to measure digital marketing performance. By performing an in-depth investigation of the use of Web an-alytics in industrial companies, the study shows that an organization's efforts to use marketing metrics systems and the resulting outcomes cannot be understood without considering the reasoning behind the chosen metrics, the processing of metrics data, and the organizational context surrounding the use of the system. Given the continuously growing importance of digital marketing in the industrial sector, this study illustrates how industrial companies characterized by complex selling processes can harness Web analytics to demonstrate how digital marketing activities benefit their businesses. The role of digital marketing 2 (DM) in a firm's marketing strategy has been expanding in the industrial sector, as evidenced by industrial firms' increasing investments in DM activities, which currently account for approximately one-quarter (26%) of industrial firms' total marketing budgets (Gartner, 2013). In addition to cost effectiveness and changes in customer behavior, investments in DM are motivated by its results being more easily measured compared with those of traditional marketing (Hennig-Thurau et al. As customers are increasingly interacting with companies through digital channels, marketers have realized the need to track these interactions and to measure their performance (Chaffey & Patron, 2012). For this purpose, firms must adopt Web analytics (WA), defined as " the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage " (Web Analytics Association, 2008, p. 3). In this study, WA refers to a tool that collects clickstream data regarding the source of website traffic (e.g., e-mail, search engines, display ads, social links), navigation paths, and the behavior of visitors during their website visits and that presents the data in a meaningful format. The WA data are used to understand online customer behavior, to measure online customers' responses to DM stimuli, and to optimize DM elements and actions that foster customer behavior that benefits the business (Nakatani & Chuang, 2011). Although it is limited to the digital environment, the use …