nils-herrmann/clean_scirepeval huggingface dataset:
State Representation Learning in Robotics: Using Prior Knowledge about Physical Interaction

State representations critically affect the effectiveness of learning in robots. In this paper, we propose a roboticsspecific approach to learning such state representations. Robots accomplish tasks by interacting with the physical world. Physics in turn imposes structure on both the changes in the world and on the way robots can effect these changes. Using prior knowledge about interacting with the physical world, robots can learn state representations that are consistent with physics. We identify five robotic priors and explain how they can be used for representation learning. We demonstrate the effectiveness of this approach in a simulated slot car racing task and a simulated navigation task with distracting moving objects. We show that our method extracts task-relevant state representations from highdimensional observations, even in the presence of task-irrelevant distractions. We also show that the state representations learned by our method greatly improve generalization in reinforcement learning.
Photovoltaic modeling and its characteristics

A photovoltaic cell converts the solar energy into the electrical energy by the photovoltaic effect. Solar cells are widely used in terrestrial and space applications. The photovoltaic cells must be operated at their maximum power point. The maximum power point varies with illumination, temperature, radiation dose and other ageing effects. In this paper, mathematical modeling, V-I and P-V characteristics of PV cells are studied. Different modeling techniques like empirical model and ANFIS model are proposed and developed. The result obtained by empirical models will be compared with ANFIS model and it will be proved that ANFIS gives better result. The simulated V-I and P-V characteristics of Photovoltaic cell for various temperature and irradiance are presented. Also ANFIS model outputs are presented. This can be used for sizing of PV system.
Comparative Analysis of Software Engineering Models from Traditional to Modern Methodologies

Software Engineering aims to produce a quality software product that is delivered on time, within the allocated budget, and with the requirements expected by the customer but unfortunately maximum of the times this goal is rarely achieved. A software life cycle is the series of identifiable stages that a software product undergoes during its lifetime. However, a properly managed project in a matured software engineering environment can consistently achieve this goal. This research is concerned with the methodologies that examine the life cycle of software through the development models, which are known as software development life cycle. Hereby, we are representing traditional i.e. waterfall, Iteration, Spiral models as well as modern development methodologies like Agile methodologies that includes Extreme programming, Scrum, Feature Driven Development, Component based software development methodologies etc. All of these models have advantages and disadvantages as well. Therefore, the main objective of this research is to represent different models of software development by showing the good and bad practices of each model. A comparative analysis of traditional as well as modern methodologies is made.
Metrics for Software Adaptability

This paper discusses our initial work in developing metrics for software adaptability. In this paper we have developed several metrics for software adaptability. One of the advantages of the metrics that we have developed is that they are applicable at the architectural level. Since architecture development is the first stage of the design process, the extent to which the architecture is adaptable will determine the adaptability of the final software. Hence the metrics in this paper will help determine the extent to which the final software will be adaptable as well.
A Quantum-Theoretic Approach to Distributional Semantics

In this paper we explore the potential of quantum theory as a formal framework for capturing lexical meaning. We present a novel semantic space model that is syntactically aware, takes word order into account, and features key quantum aspects such as superposition and entanglement. We define a dependency-based Hilbert space and show how to represent the meaning of words by density matrices that encode dependency neighborhoods. Experiments on word similarity and association reveal that our model achieves results competitive with a variety of classical models.
EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild

Research in face perception and emotion theory requires very large annotated databases of images of facial expressions of emotion. Annotations should include Action Units (AUs) and their intensities as well as emotion category. This goal cannot be readily achieved manually. Herein, we present a novel computer vision algorithm to annotate a large database of one million images of facial expressions of emotion in the wild (i.e., face images downloaded from the Internet). First, we show that this newly proposed algorithm can recognize AUs and their intensities reliably across databases. To our knowledge, this is the first published algorithm to achieve highly-accurate results in the recognition of AUs and their intensities across multiple databases. Our algorithm also runs in real-time (>30 images/second), allowing it to work with large numbers of images and video sequences. Second, we use WordNet to download 1,000,000 images of facial expressions with associated emotion keywords from the Internet. These images are then automatically annotated with AUs, AU intensities and emotion categories by our algorithm. The result is a highly useful database that can be readily queried using semantic descriptions for applications in computer vision, affective computing, social and cognitive psychology and neuroscience, e.g., "show me all the images with happy faces" or "all images with AU 1 at intensity c".
Rule-Based Problem Classification in IT Service Management

Problem management is a critical and expensive element for delivering IT service management and touches various levels of managed IT infrastructure. While problem management has been mostly reactive, recent work is studying how to leverage large problem ticket information from similar IT infrastructures to probatively predict the onset of problems. Because of the sheer size and complexity of problem tickets, supervised learning algorithms have been the method of choice for problem ticket classification, relying on labeled (or pre-classified) tickets from one managed infrastructure to automatically create signatures for similar infrastructures. However, where there are insufficient preclassified data, leveraging human expertise to develop classification rules can be more efficient. In this paper, we describe a rule-based crowdsourcing approach, where experts can author classification rules and a social networkingbased platform (called xPad) is used to socialize and execute these rules by large practitioner communities. Using real data sets from several large IT delivery centers, we demonstrate that this approach balances between two key criteria: accuracy and cost effectiveness.
High resolution multispectral video capture with a hybrid camera system

We present a new approach to capture video at high spatial and spectral resolutions using a hybrid camera system. Composed of an RGB video camera, a grayscale video camera and several optical elements, the hybrid camera system simultaneously records two video streams: an RGB video with high spatial resolution, and a multispectral video with low spatial resolution. After registration of the two video streams, our system propagates the multispectral information into the RGB video to produce a video with both high spectral and spatial resolution. This propagation between videos is guided by color similarity of pixels in the spectral domain, proximity in the spatial domain, and the consistent color of each scene point in the temporal domain. The propagation algorithm is designed for rapid computation to allow real-time video generation at the original frame rate, and can thus facilitate real-time video analysis tasks such as tracking and surveillance. Hardware implementation details and design tradeoffs are discussed. We evaluate the proposed system using both simulations with ground truth data and on real-world scenes. The utility of this high resolution multispectral video data is demonstrated in dynamic white balance adjustment and tracking.
Marginal Release Under Local Differential Privacy

Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subjects. Applications for these statistics range from finding correlations in the data to fitting sophisticated prediction models. In this paper, we provide a set of algorithms for materializing marginal statistics under the strong model of local differential privacy. We prove the first tight theoretical bounds on the accuracy of marginals compiled under each approach, perform empirical evaluation to confirm these bounds, and evaluate them for tasks such as modeling and correlation testing. Our results show that releasing information based on (local) Fourier transformations of the input is preferable to alternatives based directly on (local) marginals.
Optimum Design of Asymmetrical Multisection Two-Way Power Dividers With Arbitrary Power Division and Impedance Matching

A general analysis and design procedure is developed for the asymmetrical multisection power divider with arbitrary power division ratio and arbitrary specifications of input and output impedance matching over any desired frequency bandwidth. The even- and odd-mode analysis, which was previously applied to the design of multisection Gysel power dividers, required that the unequal power division ratios be accompanied with appropriately proportional output impedances. This requirement is relaxed here. The equivalent circuits are first obtained for the divider and then their scattering parameters are determined. Some error functions are then constructed by the method of least squares. Their minimization determines the geometrical dimensions of the optimum divider. An approximate method based on the even and odd modes is developed for its initial design of the divider. Two examples of single- and double-section dividers are designed. Their frequency responses of isolation and transmission coefficients are obtained by the proposed method, HFSS software, fabrication, and measurement. They agree within the approximate assumptions. A two-section and two-way power divider is designed and fabricated by the proposed method for the case of unequal port impedances in the L-band. The measured isolation between the outputs is better than -22 dB in 44% of the band.
A Survey of Classification Techniques in the Area of Big Data

Big Data concern large-volume, growing data sets that are complex and have multiple autonomous sources. Earlier technologies were not able to handle storage and processing of huge data thus Big Data concept comes into existence. This is a tedious job for users to identify accurate data from huge unstructured data. So, there should be some mechanism which classify unstructured data into organized form which helps user to easily access required data. Classification techniques over big transactional database provide required data to the users from large datasets more simple way. There are two main classification techniques, supervised and unsupervised. In this paper we focused on to study of different supervised classification techniques. Further this paper shows application of each technique and their advantages and limitations.
Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation

Due to the low-dimensional property of clean hyperspectral images (HSIs), many low-rank-based methods have been proposed to denoise HSIs. However, in an HSI, the noise intensity in different bands is often different, and most of the existing methods do not take this fact into consideration. In this paper, a noise-adjusted iterative low-rank matrix approximation (NAILRMA) method is proposed for HSI denoising. Based on the low-rank property of HSIs, the patchwise low-rank matrix approximation (LRMA) is established. To further separate the noise from the signal subspaces, an iterative regularization framework is proposed. Considering that the noise intensity in different bands is different, an adaptive iteration factor selection based on the noise variance of each HSI band is adopted. This noise-adjusted iteration strategy can effectively preserve the high-SNR bands and denoise the low-SNR bands. The randomized singular value decomposition (RSVD) method is then utilized to solve the NAILRMA optimization problem. A number of experiments were conducted in both simulated and real data conditions to illustrate the performance of the proposed NAILRMA method for HSI denoising.
Trotting, pacing and bounding by a quadruped robot.

This paper explores the quadruped running gaits that use the legs in pairs: the trot (diagonal pairs), the pace (lateral pairs), and the bound (front and rear pairs). Rather than study these gaits in quadruped animals, we studied them in a quadruped robot. We found that each of the gaits that use the legs in pairs can be transformed into a common underlying gait, a virtual biped gait. Once transformed, a single set of control algorithms produce all three gaits, with modest parameter variations between them. The control algorithms manipulated rebound height, running speed, and body attitude, while a low-level mechanism coordinated the behavior of the legs in each pair. The approach was tested with laboratory experiments on a four-legged robot. Data are presented that show the details of the running motion for the three gaits and for transitions from one gait to another.
A Deep Learning Approach to Link Prediction in Dynamic Networks

Time varying problems usually have complex underlying structures represented as dynamic networks where entities and relationships appear and disappear over time. The problem of efficiently performing dynamic link inference is extremely challenging due to the dynamic nature in massive evolving networks especially when there exist sparse connectivities and nonlinear transitional patterns. In this paper, we propose a novel deep learning framework, i.e., Conditional Temporal Restricted Boltzmann Machine (ctRBM), which predicts links based on individual transition variance as well as influence introduced by local neighbors. The proposed model is robust to noise and have the exponential capability to capture nonlinear variance. We tackle the computational challenges by developing an efficient algorithm for learning and inference of the proposed model. To improve the efficiency of the approach, we give a faster approximated implementation based on a proposed Neighbor Influence Clustering algorithm. Extensive experiments on simulated as well as real-world dynamic networks show that the proposed method outperforms existing algorithms in link inference on dynamic networks.
Neural bases of social anxiety disorder: emotional reactivity and cognitive regulation during social and physical threat.

Social anxiety disorder is thought to involve emotional hyperreactivity, cognitive distortions, and ineffective emotion regulation. While the neural bases of emotional reactivity to social stimuli have been described, the neural bases of emotional reactivity and cognitive regulation during social and physical threat, and their relationship to social anxiety symptom severity, have yet to be investigated.



To investigate behavioral and neural correlates of emotional reactivity and cognitive regulation in patients and controls during processing of social and physical threat stimuli.



Participants were trained to implement cognitive-linguistic regulation of emotional reactivity induced by social (harsh facial expressions) and physical (violent scenes) threat while undergoing functional magnetic resonance imaging and providing behavioral ratings of negative emotion experience.



Academic psychology department.



Fifteen adults with social anxiety disorder and 17 demographically matched healthy controls.



Blood oxygen level-dependent signal and negative emotion ratings.



Behaviorally, patients reported greater negative emotion than controls during social and physical threat but showed equivalent reduction in negative emotion following cognitive regulation. Neurally, viewing social threat resulted in greater emotion-related neural responses in patients than controls, with social anxiety symptom severity related to activity in a network of emotion- and attention-processing regions in patients only. Viewing physical threat produced no between-group differences. Regulation during social threat resulted in greater cognitive and attention regulation-related brain activation in controls compared with patients. Regulation during physical threat produced greater cognitive control-related response (ie, right dorsolateral prefrontal cortex) in patients compared with controls.



Compared with controls, patients demonstrated exaggerated negative emotion reactivity and reduced cognitive regulation-related neural activation, specifically for social threat stimuli. These findings help to elucidate potential neural mechanisms of emotion regulation that might serve as biomarkers for interventions for social anxiety disorder.
Low Power Wide Area Networks: An Overview

Low power wide area (LPWA) networks are attracting a lot of attention primarily because of their ability to offer affordable connectivity to the low-power devices distributed over very large geographical areas. In realizing the vision of the Internet of Things, LPWA technologies complement and sometimes supersede the conventional cellular and short range wireless technologies in performance for various emerging smart city and machine-to-machine applications. This review paper presents the design goals and the techniques, which different LPWA technologies exploit to offer wide-area coverage to low-power devices at the expense of low data rates. We survey several emerging LPWA technologies and the standardization activities carried out by different standards development organizations (e.g., IEEE, IETF, 3GPP, ETSI) as well as the industrial consortia built around individual LPWA technologies (e.g., LoRa Alliance, Weightless-SIG, and Dash7 alliance). We further note that LPWA technologies adopt similar approaches, thus sharing similar limitations and challenges. This paper expands on these research challenges and identifies potential directions to address them. While the proprietary LPWA technologies are already hitting the market with large nationwide roll-outs, this paper encourages an active engagement of the research community in solving problems that will shape the connectivity of tens of billions of devices in the next decade.
A Naive Bayesian Multi-label Classication Algorithm With Application to Visualize Text Search Results

Search results visualization has emerged as an important research topic due to its application on search engine amelioration. From the perspective of machine learning, the text search results visualization task fits to the multi-label learning framework that a document is usually related to multiple category labels. In this paper, a Näıve Bayesian (NB) multi-label classification algorithm is proposed by incorporating a two-step feature selection strategy which aims to satisfy the assumption of conditional independency in NB classification theory. The experiments over public data set demonstrate that the proposed method has highly competitive performance with several well-established multi-label classification algorithms. We implement a prototype system named TJ-MLWC based on the proposed algorithm, which acts as an intermediate layer between users and a commercial Internet Search Engine, allowing the search results of a query displaying by one or multiple categories. Testing results indicate that our prototype improves search experience by adding the function of browsing search results by category.
Active Learning with Support Vector Machines in the Drug Discovery Process

We investigate the following data mining problem from computer-aided drug design: From a large collection of compounds, find those that bind to a target molecule in as few iterations of biochemical testing as possible. In each iteration a comparatively small batch of compounds is screened for binding activity toward this target. We employed the so-called "active learning paradigm" from Machine Learning for selecting the successive batches. Our main selection strategy is based on the maximum margin hyperplane-generated by "Support Vector Machines". This hyperplane separates the current set of active from the inactive compounds and has the largest possible distance from any labeled compound. We perform a thorough comparative study of various other selection strategies on data sets provided by DuPont Pharmaceuticals and show that the strategies based on the maximum margin hyperplane clearly outperform the simpler ones.
Predicting Active Users' Personality Based on Micro-Blogging Behaviors

Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 839 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory [corrected]. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors.
Document-level Entity Linking: CMCRC at TAC 2010

This paper describes the CMCRC systems entered in the TAC 2010 entity linking challenge. The best performing system we describe implements the document-level entity linking system from Cucerzan (2007), with several additions that exploit global information. Our implementation of Cucerzan’s method achieved a score of 74.9% in development experiments. Additional global information improves performance to 78.4%. On the TAC 2010 test data, our best system achieves a score of 84.4%, which is second in the overall rankings of submitted systems.
Chapter 2 Electric Vehicle Battery Technologies

As discussed in the previous chapter, electrification is the most viable way to achieve clean and efficient transportation that is crucial to the sustainable development of the whole world. In the near future, electric vehicles (EVs) including hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and pure battery electric vehicles (BEVs) will dominate the clean vehicle market [1, 2]. By 2020, it is expected that more than half of new vehicle sales will likely be EV models. The key and the enabling technology to this revolutionary change is battery. The importance of batteries to EVs has been verified in the history. The first EV was seen on the road shortly after the invention of rechargeable lead–acid batteries and electric motors in the late 1800s . In the early years of 1900s, there was a golden period of EVs. At that time, the number of EVs was almost double that of gasoline power cars. However, EVs almost disappeared and gave the wholemarket to