nils-herrmann/clean_scirepeval huggingface dataset:
A Stochastic Power Network Calculus for Integrating Renewable Energy Sources into the Power Grid

Renewable energy such as solar and wind generation will constitute an important part of the future grid. As the availability of renewable sources may not match the load, energy storage is essential for grid stability. In this paper we investigate the feasibility of integrating solar photovoltaic (PV) panels and wind turbines into the grid by also accounting for energy storage. To deal with the fluctuation in both the power supply and demand, we extend and apply stochastic network calculus to analyze the power supply reliability with various renewable energy configurations. To illustrate the validity of the model, we conduct a case study for the integration of renewable energy sources into the power system of an island off the coast of Southern California. In particular, we asses the power supply reliability in terms of the average Fraction of Time that energy is Not-Served (FTNS).
Kinematics and dynamics analysis of a 2-DOF spherical parallel robot

In this paper, the kinematic and dynamic analysis of a 2-degree-of-freedom spherical parallel manipulator is presented. The mobility and motion pattern of the manipulator are analyzed using screw theory, which resulted in obtaining the direct and inverse Jacobian matrices. Position, velocity and acceleration relations between different parts of the manipulator and the actuated joint angles are obtained, which are prerequisite for dynamic analysis. Since the manipulator, belongs to a class of parallel mechanism known as over-constrained mechanism, thus to obtain a dynamical model, a modification should be applied in its kinematic arrangement by preserving the performed motion pattern. Finally, accuracy of the dynamical model is verified by comparing the results obtained from the formulated model with results obtained from a SimMechanics model of the under study manipulator.
Fast plane extraction in organized point clouds using agglomerative hierarchical clustering

Real-time plane extraction in 3D point clouds is crucial to many robotics applications. We present a novel algorithm for reliably detecting multiple planes in real time in organized point clouds obtained from devices such as Kinect sensors. By uniformly dividing such a point cloud into non-overlapping groups of points in the image space, we first construct a graph whose node and edge represent a group of points and their neighborhood respectively. We then perform an agglomerative hierarchical clustering on this graph to systematically merge nodes belonging to the same plane until the plane fitting mean squared error exceeds a threshold. Finally we refine the extracted planes using pixel-wise region growing. Our experiments demonstrate that the proposed algorithm can reliably detect all major planes in the scene at a frame rate of more than 35Hz for 640×480 point clouds, which to the best of our knowledge is much faster than state-of-the-art algorithms.
CrowdOTA: An Online Task Assignment System in Crowdsourcing

Crowdsourcing is widely accepted as a means for resolving tasks that are hard for computers, e.g., entity resolution. Unfortunately, Crowdsourcing may yield relatively low-quality results if there is no proper quality control. Although previous studies attempt to eliminate workers by estimating workers' qualities via qualification tests or hidden tests, the qualities estimated may not be accurate, because workers may have diverse qualities across tasks. Thus, the quality of the results could be further improved by wisely assigning tasks to the workers who are specialized in the tasks and online task assignment is an effective way to achieve this goal. However, existing crowdsourcing platforms either do not support online task assignment (e.g., CrowdFlower) or are not user-friendly because they require to write complicated codes (e.g., Amazon MTurk). To address these limitations, we develop an online task assignment system, which can on-the-fly assign workers with appropriate tasks. We have deployed our system on top of MTurk. We demonstrate the following scenarios using our system. Firstly, requesters can easily utilize our system to enable online task assignment in order to improve answer quality. Moreover, requesters do not need to write any code. Secondly, our system can infer the quality of workers, and requesters can design and test their own task assignment algorithms using our proposed information. Thirdly, our system can monitor tasks and workers in real time, and the requesters can eliminate bad workers or terminate the crowdsourcing process to reduce the unnecessary cost.
The effects of mobile phone use on pedestrian crossing behaviour at signalized and unsignalized intersections.

Research amongst drivers suggests that pedestrians using mobile telephones may behave riskily while crossing the road, and casual observation suggests concerning levels of pedestrian mobile-use. An observational field survey of 270 females and 276 males was conducted to compare the safety of crossing behaviours for pedestrians using, versus not using, a mobile phone. Amongst females, pedestrians who crossed while talking on a mobile phone crossed more slowly, and were less likely to look at traffic before starting to cross, to wait for traffic to stop, or to look at traffic while crossing, compared to matched controls. For males, pedestrians who crossed while talking on a mobile phone crossed more slowly at unsignalized crossings. These effects suggest that talking on a mobile phone is associated with cognitive distraction that may undermine pedestrian safety. Messages explicitly suggesting techniques for avoiding mobile-use while road crossing may benefit pedestrian safety.
Exploring commonalities across participants in the neural representation of objects.

The question of whether the neural encodings of objects are similar across different people is one of the key questions in cognitive neuroscience. This article examines the commonalities in the internal representation of objects, as measured with fMRI, across individuals in two complementary ways. First, we examine the commonalities in the internal representation of objects across people at the level of interobject distances, derived from whole brain fMRI data, and second, at the level of spatially localized anatomical brain regions that contain sufficient information for identification of object categories, without making the assumption that their voxel patterns are spatially matched in a common space. We examine the commonalities in internal representation of objects on 3T fMRI data collected while participants viewed line drawings depicting various tools and dwellings. This exploratory study revealed the extent to which the representation of individual concepts, and their mutual similarity, is shared across participants.
Volunteered Geographic Information and Crowdsourcing Disaster Relief : A Case Study of the Haitian Earthquake

This paper outlines the ways in which information technologies (ITs) were used in the Haiti relief effort, especially with respect to web-based mapping services. Although there were numerous ways in which this took place, this paper focuses on four in particular: CrisisCamp Haiti, OpenStreetMap, Ushahidi, and GeoCommons. This analysis demonstrates that ITs were a key means through which individuals could make a tangible difference in the work of relief and aid agencies without actually being physically present in Haiti. While not without problems, this effort nevertheless represents a remarkable example of the power and crowdsourced online mapping and the potential for new avenues of interaction between physically distant places that vary tremendously.
Advances in Variational Inference

Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully used in various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, (c) accurate VI, which includes variational models beyond the mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.
An Experimental Evaluation of Apple Siri and Google Speech Recognition

We perform an experimental evaluation of two popular cloud-based speech recognition systems. Cloudbased speech recognition systems enhances Web surfing, transportation, health care, etc. Using voice commands helps drivers stay connected to the Internet by avoiding traffic safety risks. The performance of these type of applications should be robust under difficult network conditions. User frustration with network traffic problems can affect the usability of these applications. We evaluate the performance of two popular cloud-based speech recognition applications, Apple Siri and Google Speech Recognition (GSR) under various network conditions. We evaluate transcription delay and accuracy of transcription of each application under different packet loss and jitter values. Results of our study show that performance of cloud-based speech recognition systems can be affected by jitter and packet loss; which are commonly occurring over WiFi and cellular network connections.  Cloud Speech Recognition, Quality of Experience, Software Measurement, Streaming Media, Real-time Systems.
End-to-End Speech Command Recognition with Capsule Network

In recent years, neural networks have become one of the common approaches used in speech recognition(SR), with SR systems based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) achieving the state-of-the-art results in various SR benchmarks. Especially, since CNNs are capable of capturing the local features effectively, they are applied to tasks which have relatively short-term dependencies, such as keyword spotting or phoneme-level sequence recognition. However, one limitation of CNNs is that, with maxpooling, they do not consider the pose relationship between low-level features. Motivated by this problem, we apply the capsule network to capture the spatial relationship and pose information of speech spectrogram features in both frequency and time axes. We show that our proposed end-to-end SR system with capsule networks on one-second speech commands dataset achieves better results on both clean and noise-added test than baseline CNN models.
Towards a lightweight embedded virtualization architecture exploiting ARM TrustZone

Virtualization has been used as the de facto technology to allow multiple operating systems (virtual machines) to run on top of the same hardware platform. In the embedded systems domain, virtualization research has focused on the coexistence of real-time requirements with non-real-time characteristics. However, existent standard software-based virtualization solutions have been shown to negatively impact the overall system, especially in performance, memory footprint and determinism. This work in progress paper presents the implementation of an embedded virtualization architecture through commodity hardware. ARM TrustZone technology is exploited to implement a lightweight virtualization solution with low overhead and high determinism, corroborated by promising preliminary results. Research roadmap is also pointed and discussed.
CSDSM: Cognitive switch-based DDoS sensing and mitigation in SDN-driven CDNi word

Content Delivery Networks (CDNs) are increasingly deployed for their efficient content delivery and are often integrated with Software Defined Networks (SDNs) to achieve centrality and programmability of the network. However, these networks are also an attractive target for network attackers whose main goal is to exhaust network resources. One attack approach is to over-flood the OpenFlow switch tables containing routing information. Due to the increasing number of different flooding attacks such as DDoS, it becomes difficult to distinguish these attacks from normal traffic when evaluated with traditional attack detection methods. This paper proposes an architectural method that classifies and defends all possible forms of DDoS attack and legitimate Flash Crowd traffic using a segregated dimension functioning cognitive process based in a controller module. Our results illustrate that the proposed model yields significantly enhanced performance with minimal false positives and false negatives when classified with optimal Support Vector Machine and Logistic Regression algorithms. The traffic classifications initiate deployment of security rules to the OpenFlow switches, preventing new forms of flooding attacks. To the best of our knowledge, this is the first work conducted on SDN-driven CDNi used to detect and defend against all possible DDoS attacks through traffic segregated dimension functioning coupled with cognitive classification.
Better Approximation of Betweenness Centrality

Centrality indices are used to classify a graph in important and unimportant vertices. A commonly used index is betweenness centrality which is based on shortest paths. For large graphs it is almost impossible to calculate exact betweenness centrality in appropriate time. The best known algorithm today is based on solving n SSSPs and requires O(nm+ n2 log(n)) time. But in most cases, e.g. on a home computer, resources are limited. Current approximation algorithms extrapolate values by solving only k ≪ n SSSPs. Vertices near the source of a SSSP are often overestimated. We introduce improvements which take the distance to the source into account. Euclidean distance between approximation and exact values is reduced by factor 4 with same runtime. Or runtime is 16 times faster with same Euclidean distance in a standard example, the movie actor network. Other real-world networks show similar promising results.
Targeting DNA double-strand breaks with TAL effector nucleases.

Engineered nucleases that cleave specific DNA sequences in vivo are valuable reagents for targeted mutagenesis. Here we report a new class of sequence-specific nucleases created by fusing transcription activator-like effectors (TALEs) to the catalytic domain of the FokI endonuclease. Both native and custom TALE-nuclease fusions direct DNA double-strand breaks to specific, targeted sites.
Scalable Honeypot Architecture for Identifying Malicious Network Activities

Server honey pots are computer systems that hide in a network capturing attack packets. As the name goes, server honey pots are installed in server machines running a set of services. Enterprises and government organisations deploy these honey pots to know the extent of attacks on their network. Since, most of the recent attacks are advanced persistent attacks there is much research work going on in building better peripheral security measures. In this paper, the authors have deployed several honey pots in a virtualized environment to gather traces of malicious activities. The network infrastructure is resilient and provides much information about hacker's activities. It is cost-effective and can be easily deployed in any organisation without specialized hardware.
Agile Flight Control Techniques for a Fixed-Wing Aircraft by Frantisek Michal Sobolic

As unmanned aerial vehicles (UAVs) become more involved in challenging mission objectives, the need for agility controlled flight becomes more of a necessity. The ability to navigate through constrained environments as well as quickly maneuver to each mission target is essential. Currently, individual vehicles are developed with a particular mission objective, whether it be persistent surveillance or fly-by reconnaissance. Fixed-wing vehicles with a high thrust-to-weight ratio are capable of performing maneuvers such as take-off or perch style landing and switch between hover and conventional flight modes. Agile flight controllers enable a single vehicle to achieve multiple mission objectives. By utilizing the knowledge of the flight dynamics through all flight regimes, nonlinear controllers can be developed that control the aircraft in a single design. This thesis develops a full six-degree-of-freedom model for a fixed-wing propellerdriven aircraft along with methods of control through nonconventional flight regimes. In particular, these controllers focus on transitioning into and out of hover to level flight modes. This maneuver poses hardships for conventional linear control architectures because these flights involve regions of the post-stall regime, which is highly nonlinear due to separation of flow over the lifting surfaces. Using Lyapunov backstepping control stability theory as well as quaternion-based control methods, control strategies are developed that stabilize the aircraft through these flight regimes without the need to switch control schemes. The effectiveness of each control strategy is demonstrated in both simulation and flight experiments.  Jonathan P.
A Short History of Cleaning Robots

The definition of the desired functions and the design of an ultimate versatile personal robot is an ongoing debate. Meanwhile, however, precursors of this yet to evolve species are well on their way to become commercial products. Cleaning robots for public environments as well as for private households seem to be able to provide the breakthrough which the designers of non-industrial robot systems have long awaited. This survey describes a selection of 30 different cleaning robots, with the first developments reaching back more than 15 years. With a few exceptions we have focused on floor cleaning, in particular indoor floor cleaning. We describe a variety of scrubbing and vacuuming robots which were developed for this task. The described systems range from heavy, large, and expensive industrial cleaning vehicles to small-size, light-weight, low-cost household devices. The survey does not include, however, systems for cleaning facades of buildings, or windows, or production tools. Although not all of the 30 cleaning robots abovementioned have yet reached the state of commercial products, their number alone certainly reflects the expectations regarding the economic value associated with the automation of cleaning tasks. In Europe only the estimates for the market for cleaning services range up to the order of US$ 100 billion per year. It is therefore not surprising that the cleaning industry and the manufacturers of cleaning devices are rather enthusiastic with respect to the automation of cleaning tasks using (semi-)autonomous mobile robot systems.
Conditional generative adversarial nets for convolutional face generation

We apply an extension of generative adversarial networks (GANs)  to a conditional setting. In the GAN framework, a “generator” network is tasked with fooling a “discriminator” network into believing that its own samples are real data. We add the capability for each network to condition on some arbitrary external data which describes the image being generated or discriminated. By varying the conditional information provided to this extended GAN, we can use the resulting generative model to generate faces with specific attributes from nothing but random noise. We evaluate the likelihood of real-world faces under the generative model, and examine how to deterministically control face attributes by modifying the conditional information provided to the model.
Bayesian Classification (AutoClass): Theory and Results

We describe AutoClass, an approach to unsupervised classiication based upon the classical mixture model, supplemented by a Bayesian method for determining the optimal classes. We include a moderately detailed exposition of the mathematics behind the AutoClass system. We emphasize that no current unsupervised classiication system can produce maximally useful results when operated alone. It is the interaction between domain experts and the machine searching over the model space, that generates new knowledge. Both bring unique information and abilities to the database analysis task, and each enhances the others' eeectiveness. We illustrate this point with several applications of AutoClass to complex real world databases, and describe the resulting successes and failures.  This chapter is a summary of our experience in using an automatic classiication program (AutoClass) to extract useful information from databases. It also gives an outline of the principles that underlie automatic classiication in general, and AutoClass in particular. We are concerned with the problem of automatic discovery of classes in data (sometimes called clustering, or unsupervised learning), rather than the generation of class descriptions from labeled examples (called supervised learning). In some sense, automatic classiication aims at discovering the \natural" classes in the data. These classes reeect basic causal mechanisms that makes some cases look more like each other than the rest of the cases. The causal mechanisms may be as boring as sample biases in the data, or could reeect some major new discovery in the domain. Sometimes, these classes were well known to experts in the eld, but unknown to AutoClass, and other times
Convolutional Recurrent Deep Learning Model for Sentence Classification

As the amount of unstructured text data that humanity produces overall and on the Internet grows, so does the need to intelligently to process it and extract different types of knowledge from it. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to natural language processing systems with comparative, remarkable results. The CNN is a noble approach to extract higher level features that are invariant to local translation. However, it requires stacking multiple convolutional layers in order to capture long-term dependencies, due to the locality of the convolutional and pooling layers. In this paper, we describe a joint CNN and RNN framework to overcome this problem. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning network, then, the pre-trained parameters of the network are used to initialize the model. At a final stage, the proposed framework combines former information with a set of feature maps learned by a convolutional layer with long-term dependencies learned via long-short-term memory. Empirically, we show that our approach, with slight hyperparameter tuning and static vectors, achieves outstanding results on multiple sentiment analysis benchmarks. Our approach outperforms several existing approaches in term of accuracy; our results are also competitive with the state-of-the-art results on the Stanford Large Movie Review data set with 93.3% accuracy, and the Stanford Sentiment Treebank data set with 48.8% fine-grained and 89.2% binary accuracy, respectively. Our approach has a significant role in reducing the number of parameters and constructing the convolutional layer followed by the recurrent layer as a substitute for the pooling layer. Our results show that we were able to reduce the loss of detailed, local information and capture long-term dependencies with an efficient framework that has fewer parameters and a high level of performance.