nils-herrmann/clean_scirepeval huggingface dataset:
Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications

The future smart grid is envisioned as a large scale cyberphysical system encompassing advanced power, communications, control, and computing technologies. To accommodate these technologies, it will have to build on solid mathematical tools that can ensure an efficient and robust operation of such heterogeneous and large-scale cyberphysical systems. In this context, this article is an overview on the potential of applying game theory for addressing relevant and timely open problems in three emerging areas that pertain to the smart grid: microgrid systems, demand-side management, and communications. In each area, the state-of-the-art contributions are gathered and a systematic treatment, using game theory, of some of the most relevant problems for future power systems is provided. Future opportunities for adopting game-theoretic methodologies in the transition from legacy systems toward smart and intelligent grids are also discussed. In a nutshell, this article provides a comprehensive account of the application of game theory in smart grid systems tailored to the interdisciplinary characteristics of these systems that integrate components from power systems, networking, communications, and control.
Metacognition and Consciousness 1 Metacognition and Consciousness

The study of metacognition can shed light on some fundamental issues about consciousness and its role in behavior. Metacognition research concerns the processes by which people self reflect on their own cognitive and memory processes (monitoring), and how they put their metaknowledge to use in regulating their information processing and behavior (control). Experimental research on metacognition has addressed the following questions: First, what are the bases of metacognitive judgments that people make in monitoring their learning, remembering, and performance? Second, how valid are such judgments and what are the factors that affect the correspondence between subjective and objective indexes of knowing? Third, what are the processes that underlie the accuracy and inaccuracy of metacognitive judgments? Fourth, how does the output of metacognitive monitoring contribute to the strategic regulation of learning and remembering? Finally, how do the metacognitive processes of monitoring and control affect actual performance? Research addressing these questions is reviewed, emphasizing its implication for issues concerning consciousness, in particular, the genesis of subjective experience, the function of self-reflective consciousness, and the cause-and-effect relation between subjective experience and behavior.
Manual motor performance in a deafferented man.

We have studied manual motor function in a man deafferented by a severe peripheral sensory neuropathy. Motor power was almost unaffected. Our patients could produce a very wide range of preprogrammed finger movements with remarkable accuracy, involving complex muscle synergies of the hand and forearm muscles. He could perform individual finger movements and outline figures in the air with high eyes closed. He had normal pre- and postmovement EEG potentials, and showed the normal bi/triphasic pattern of muscle activation in agonist and antagonist muscles during fast limb movements. He could also move his thumb accurately through three different distances at three different speeds, and could produce three different levels of force at his thumb pad when required. Although he could not judge the weights of objects placed in his hands without vision, he was able to match forces applied by the experimenter to the pad of each thumb if he was given a minimal indication of thumb movement. Despite his success with these laboratory tasks, his hands were relatively useless to him in daily life. He was unable to grasp a pen and write, to fasten his shirt buttons or to hold a cup in one hand. Part of hist difficulty lay in the absence of any automatic reflex correction in his voluntary movements, and also to an inability to sustain constant levels of muscle contraction without visual feedback over periods of more than one or two seconds. He was also unable to maintain long sequences of simple motor programmes without vision.
Initializing Convolutional Filters with Semantic Features for Text Classification

The crux of our initialization technique is n-gram selection, which assists neural networks to extract important n-gram features at the beginning of the training process. In the following tables, we illustrate those selected n-grams of different classes and datasets to understand our technique intuitively. Since all of MR, SST-1, SST-2, CR, and MPQA are sentiment classification datasets, we only report the selected n-grams of SST-1 (Table 1). N-grams selected by our method in SUBJ and TREC are shown in Table 2 and Table 3.
Transfer Learning in Brain-Computer Interfaces

The performance of brain-computer interfaces (BCIs) improves with the amount of available training data; the statistical distribution of this data, however, varies across subjects as well as across sessions within individual subjects, limiting the transferability of training data or trained models between them. In this article, we review current transfer learning techniques in BCIs that exploit shared structure between training data of multiple subjects and/or sessions to increase performance. We then present a framework for transfer learning in the context of BCIs that can be applied to any arbitrary feature space, as well as a novel regression estimation method that is specifically designed for the structure of a system based on the electroencephalogram (EEG). We demonstrate the utility of our framework and method on subject-to-subject transfer in a motor-imagery paradigm as well as on session-to-session transfer in one patient diagnosed with amyotrophic lateral sclerosis (ALS), showing that it is able to outperform other comparable methods on an identical dataset.
Process Equivalence: Comparing Two Process Models Based on Observed Behavior

In various application domains there is a desire to compare process models, e.g., to relate an organization-specific process model to a reference model, to find a web service matching some desired service description, or to compare some normative process model with a process model discovered using process mining techniques. Although many researchers have worked on different notions of equivalence (e.g., trace equivalence, bisimulation, branching bisimulation, etc.), most of the existing notions are not very useful in this context. First of all, most equivalence notions result in a binary answer (i.e., two processes are equivalent or not). This is not very helpful, because, in real-life applications, one needs to differentiate between slightly different models and completely different models. Second, not all parts of a process model are equally important. There may be parts of the process model that are rarely activated while other parts are executed for most process instances. Clearly, these should be considered differently. To address these problems, this paper proposes a completely new way of comparing process models. Rather than directly comparing two models, the process models are compared with respect to some typical behavior. This way we are able to avoid the two problems. Although the results are presented in the context of Petri nets, the approach can be applied to any process modeling language with executable semantics.
Music Composition using Recurrent Neural Networks

Music artists have composed pieces that are both creative and precise. For example, classical music is well-known for its meticulous structure and emotional effect. Recurrent Neural Networks (RNNs) are powerful models that have achieved excellent performance on difficult learning tasks having temporal dependencies. We propose generative RNN models that create sheet music with well-formed structure and stylistic conventions without predefining music composition rules to the models. We see that Character RNNs are able to learn some patterns but not create structurally accurate music, with a test accuracy of 60% and fooling only upto 35% of the human listeners to believe that the music was created by a human. Generative Adversarial Networks (GANs) were tried, with various training techniques, but produced no meaningful results due to instable training. On the other hand, Seq2Seq models do very well in producing both structurally correct and human-pleasing music, with a test accuracy of 65% and some of its generated music fooling ⇠ 70% of the human listeners.
Geometry-Contrastive Generative Adversarial Network for Facial Expression Synthesis

In this paper, we propose a geometry-contrastive generative adversarial network GC-GAN for generating facial expression images conditioned on geometry information. Specifically, given an input face and a target expression designated by a set of facial landmarks, an identity-preserving face can be generated guided by the target expression. In order to embed facial geometry onto a semantic manifold, we incorporate contrastive learning into conditional GANs. Experiment results demonstrate that the manifold is sensitive to the changes of facial geometry both globally and locally. Benefited from the semantic manifold, dynamic smooth transitions between different facial expressions are exhibited via geometry interpolation. Furthermore, our method can also be applied in facial expression transfer even there exist big differences in face shape between target faces and driving faces.
Highly Accurate Debye Models for Normal and Malignant Breast Tissue Dielectric Properties at Microwave Frequencies

The finite difference time domain (FDTD) method is widely used as a computational tool for development, validation, and optimization of emerging microwave breast cancer detection and treatment techniques. When expressed in terms of Debye parameters, dispersive breast tissue dielectric properties can be efficiently incorporated into FDTD codes. Previously, we experimentally characterized the dielectric properties of a large number of excised normal and malignant breast tissue samples from 0.5 to 20 GHz. We subdivided the large database of normal tissue data into three groups based on the percent adipose tissue present in a particular sample. In addition, we formed a group of all cancer samples that contained at least 30% malignant tissue. We summarized the data using one-pole Cole-Cole models that were rigorously fit to the median dielectric properties of the three normal tissue groups and one malignant tissue group. In this letter, we present computationally simpler one- and two-pole Debye models that retain the high accuracy of the Cole-Cole models. Model parameters are derived for two sets of frequency ranges: the entire measurement frequency range from 0.5 to 20 GHz, and the 3.1-10.6 GHz FCC band allocated for ultrawideband medical applications. The proposed Debye models provide a means for creating computationally efficient FDTD breast models with realistic wideband dielectric properties derived from the largest and most comprehensive experimental study conducted to date on human breast tissue.
Hydrogels for tissue engineering: scaffold design variables and applications.

Polymer scaffolds have many different functions in the field of tissue engineering. They are applied as space filling agents, as delivery vehicles for bioactive molecules, and as three-dimensional structures that organize cells and present stimuli to direct the formation of a desired tissue. Much of the success of scaffolds in these roles hinges on finding an appropriate material to address the critical physical, mass transport, and biological design variables inherent to each application. Hydrogels are an appealing scaffold material because they are structurally similar to the extracellular matrix of many tissues, can often be processed under relatively mild conditions, and may be delivered in a minimally invasive manner. Consequently, hydrogels have been utilized as scaffold materials for drug and growth factor delivery, engineering tissue replacements, and a variety of other applications.
Dual-polarized microstrip array antenna with orthogonal feed circuit

In this paper, a dual-polarized microstrip array antenna with orthogonal feed circuit is proposed. The proposed microstrip array antenna consists of a single substrate layer. The proposed array antenna has microstrip antenna elements, microstrip lines, air-bridges and cross slot lines. For dual polarization, an orthogonal feed circuit uses the Both-Sided MIC Technology including air-bridges. The Both-Sided MIC Technology is one of the useful MIC technologies for realizing a simple feed circuit. The air-bridges are often used for MMICs because it is possible to reduce the circuit complexity. The characteristics of proposed array antenna are investigated by both the simulation and the experiment. Consequently, it is confirmed that the proposed array antenna with the orthogonal feed circuit has dual polarization performance with very simple structure. The proposed array antenna will be a basic technology to realize high performance and attractive multifunction antennas.
Automatic generation of release notes

This paper introduces ARENA (Automatic RElease Notes generAtor), an approach for the automatic generation of release notes. ARENA extracts changes from the source code, summarizes them, and integrates them with information from versioning systems and issue trackers. It was designed based on the manual analysis of 1,000 existing release notes. To evaluate the quality of the ARENA release notes, we performed three empirical studies involving a total of 53 participants (45 professional developers and 8 students). The results indicate that the ARENA release notes are very good approximations of those produced by developers and often include important information that is missing in the manually produced release notes.
A simple and efficient control scheme to reverse a tractor-trailer system on a trajectory

Trailer reversing is a problem frequently considered in the literature, usually with fairly complex non-linear control theory based approaches. In this paper, we present a simple method for stabilizing a tractor-trailer system to a trajectory based on the notion of controlling the hitch-angle of the trailer rather than the steering angle of the tractor. The method is intuitive, provably stable, and shown to be viable through various experimental results conducted on our test platform, the CSIRO autonomous tractor.
The detection of disease clustering and a generalized regression approach.

The problem of identifying subtle time-space clustering of dis ease, as may be occurring in leukemia, is described and reviewed. Published approaches, generally associated with studies of leuke mia, not dependent on knowledge of the underlying population for their validity, are directed towards identifying clustering by establishing a relationship between the temporal and the spatial separations for the n(n â€”l)/2 possible pairs which can be formed from the n observed cases of disease. Here it is proposed that statistical power can be improved by applying a reciprocal trans form to these separations. While a permutational approach can give valid probability levels for any observed association, for reasons of practicability, it is suggested that the observed associa tion be tested relative to its permutational variance. Formulas and computational procedures for doing so are given. While the distance measures between points represent sym metric relationships subject to mathematical and geometric regu larities, the variance formula developed is appropriate for ar bitrary relationships. Simplified procedures are given for the ease of symmetric and skew-symmetric relationships. The general pro cedure is indicated as being potentially useful in other situations as, for example, the study of interpersonal relationships. Viewing the procedure as a regression approach, the possibility for extend ing it to nonlinear and mult Â¡variatesituations is suggested. Other aspects of the problem and of the procedure developed are discussed.
Real-Time Human Pose Tracking from Range Data

Tracking human pose in real-time is a difficult problem with many interesting applications. Existing solutions suffer from a variety of problems, especially when confronted with unusual human poses. In this paper, we derive an algorithm for tracking human pose in real-time from depth sequences based on MAP inference in a probabilistic temporal model. The key idea is to extend the iterative closest points (ICP) objective by modeling the constraint that the observed subject cannot enter free space, the area of space in front of the true range measurements. Our primary contribution is an extension to the articulated ICP algorithm that can efficiently enforce this constraint. Our experiments show that including this term improves tracking accuracy significantly. The resulting filter runs at 125 frames per second using a single desktop CPU core. We provide extensive experimental results on challenging real-world data, which show that the algorithm outperforms the previous state-of-the-art trackers both in computational efficiency and accuracy.
Observation-Level Interaction with Clustering and Dimension Reduction Algorithms

Observation-Level Interaction (OLI) is a sensemaking technique relying upon the interactive semantic exploration of data. By manipulating data items within a visualization, users provide feedback to an underlying mathematical model that projects multidimensional data into a meaningful two-dimensional representation. In this work, we propose, implement, and evaluate an OLI model which explicitly defines clusters within this data projection. These clusters provide targets against which data values can be manipulated. The result is a cooperative framework in which the layout of the data affects the clusters, while user-driven interactions with the clusters affect the layout of the data points. Additionally, this model addresses the OLI "with respect to what" problem by providing a clear set of clusters against which interaction targets are judged and computed.
Mechanical design of the humanoid robot platform, HUBO

The Korea Advanced Institute of Science and Technology (KAIST) humanoid robot 1 (KHR-1) was developed for the purpose of researching the walking action of bipeds. KHR-1, which has no hands or head, has 21 degrees of freedom (DOF): 12 DOF in the legs, 1 DOF in the torso, and 8 DOF in the arms. The second version of this humanoid robot, KHR-2, (which has 41 DOF) can walk on a living-room floor; it also moves and looks like a human. The third version, KHR-3 (HUBO), has more human-like features, a greater variety of movements, and a more human-friendly character. We present the mechanical design of HUBO, including the design concept, the lower body design, the upper body design, and the actuator selection of joints. Previously we developed and published details of KHR-1 and KHR-2. The HUBO platform, which is based on KHR-2, has 41 DOF, stands 125 cm tall, and weighs 55 kg. From a mechanical point of view, HUBO has greater mechanical stiffness and a more detailed frame design than KHR-2. The stiffness of the frame was increased and the detailed design around the joints and link frame were either modified or fully redesigned. We initially introduced an exterior art design concept for KHR-2, and that concept was implemented in HUBO at the mechanical design stage.
Online extremism and the communities that sustain it: Detecting the ISIS supporting community on Twitter

The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS' unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and "lone wolf" attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS' sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group's propaganda dissemination through retweets.
Can Collective Sentiment Expressed on Twitter Predict Political Elections?

Research examining the predictive power of social media (especially Twitter) displays conflicting results, particularly in the domain of political elections. This paper applies methods used in studies that have shown a direct correlation between volume/sentiment of Twitter chatter and future electoral results in a new dataset about political elections. We show that these methods display a series of shortcomings, that make them inadequate for determining whether social media messages can predict the outcome of elections.
A Steganography Implementation based on LSB & DCT

In recent years, Steganography and Steganalysis are two important areas of research that involve a number of applications. These two areas of research are important especially when reliable and secure information exchange is required. Steganography is an art of embedding information in a cover image without causing statistically significant variations to the cover image. Steganalysis is the technology that attempts to defeat Steganography by detecting the hidden information and extracting. In this paper a comparative analysis is made to demonstrate the effectiveness of the proposed methods. The effectiveness of the proposed methods has been estimated by computing Mean square error (MSE) and Peak Signal to Noise Ratio (PSNR), Processing time, security.The analysis shows that the BER and PSNR is improved in the LSB Method but security sake DCT is the best method.
Novel Multi-Bit Non-Volatile SRAM Cells for Runtime Reconfigurable Computing

We discuss non-volatile SRAM cells capable of storing multiple bits and their applications as multi-context configuration memory. The cells are based on the standard 6T SRAM with multiple pairs of programmable resistors such as magnetic tunnel junction or resistive memory elements. In one of the cell designs the active state of the SRAM can be switched in one clock cycle by the use of an additional equalizer transistor, without the need to turn off the power to the cell, allowing real-time and low energy switching between different contexts in reconfigurable circuits. Other variations of the multistate non-volatile SRAM cells are also discussed.