nils-herrmann/clean_scirepeval huggingface dataset:
Animation: can it facilitate?

Graphics have been used since ancient times to portray things that are inherently spatiovisual, like maps and building plans. More recently, graphics have been used to portray things that are metaphorically spatiovisual, like graphs and organizational charts. The assumption is that graphics can facilitate comprehension, learning, memory, communication and inference. Assumptions aside, research on static graphics has shown that only carefully designed and appropriate graphics prove to be beneficial for conveying complex systems. Effective graphics conform to the Congruence Principle according to which the content and format of the graphic should correspond to the content and format of the concepts to be conveyed. From this, it follows that animated graphics should be effective in portraying change over time. Yet the research on the efficacy of animated over static graphics is not encouraging. In cases where animated graphics seem superior to static ones, scrutiny reveals lack of equivalence between animated and static graphics in content or procedures; the animated graphics convey more information or involve interactivity. Animations of events may be ineffective because animations violate the second principle of good graphics, the Apprehension Principle, according to which graphics should be accurately perceived and appropriately conceived. Animations are often too complex or too fast to be accurately perceived. Moreover, many continuous events are conceived of as sequences of discrete steps. Judicious use of interactivity may overcome both these disadvantages. Animations may be more effective than comparable static graphics in situations other than conveying complex systems, for example, for real time reorientations in time and space.
Optimal design of formation tracking control for a tractor-trailer robotic system with omni-directional wheels

This paper addresses the formation tracking control and optimal selection of control design parameters for a tractor-trailer omni-directional type robotic system with on-axle hitching. We design a sliding mode controller incorporating the dynamics of the system to drive the tractor robot to track a desired trajectory and the trailer robot to maintain a desired orientation with respect to the tractor. We also define an optimization problem to find the control design parameters that maximize the control performance and solve it using meta-heuristics optimization techniques. The stability of the developed controller is studied using Lyapunov theorem. We demonstrate the effectiveness of our proposed methodology using simulation results. It is concluded that the proposed control methodology can be adopted in real-time experiments and is applicable in real-world applications for good formation tracking performance under large disturbances.
Adaptive middleware for context-aware applications in smart-homes

We propose an adaptive middleware design for context-aware applications that abstracts the applications from the sensors that provide context. Further, we use application-specific utility functions to choose, given multiple alternatives for providing a specific context, one alternative at any time that provides the context for all applications, whilst maximising the applications' total "satisfaction" with the quality of context from the chosen provider. Our middleware also implements autonomic properties, such as self-configuration and resilience to failures, in the provision of context information to context-aware applications.
Attacking DDoS at the Source

Distributed denial-of-service (DDoS) attacks present an Internet-wide threat. We propose D-WARD, a DDoS defense system deployed at source-end networks that autonomously detects and stops attacks originating from these networks. Attacks are detected by the constant monitoring of two-way traffic flows between the network and the rest of the Internet and periodic comparison with normal flow models. Mismatching flows are rate-limited in proportion to their aggressiveness. D-WARD offers good service to legitimate traffic even during an attack, while effectively reducing DDoS traffic to a negligible level. A prototype of the system has been built in a Linux router. We show its effectiveness in various attack scenarios, discuss motivations for deployment, and describe associated costs.
IBM Mastor: Multilingual Automatic Speech-To-Speech Translator

In this paper, we describe the IBM MASTOR systems which handle spontaneous free-form speech-to-speech translation on both laptop and hand-held PDAs. Challenges include speech recognition and machine translation in adverse environments, lack of data and linguistic resources for under-studied languages, and the need to rapidly develop capabilities for new languages. Importantly, the code and models must fit within the limited memory and computational resources of hand-held devices. We describe our approaches, experience, and success in building working free-form S2S systems that can handle two language pairs (including a low-resource language)
Acoustical Sound Database in Real Environments for Sound Scene Understanding and Hands-Free Speech Recognition

This paper reports on a project for collection of the sound scene data. The sound scene data is necessary for studies such as sound source localization, sound retrieval, sound recognition and hands-free speech recognition in real acoustical environments. There are many kinds of sound scenes in real environments. The sound scene is denoted by sound sources and room acoustics. The number of combination of the sound sources, source positions and rooms is huge in real acoustical environments. However, the sound in the environments can be simulated by convolution of the isolated sound sources and impulse responses. As an isolated sound source, a hundred kinds of non-speech sounds and speech sounds are collected. The impulse responses are collected in various acoustical environments. In this paper, progress of our sound scene database project and application to environment sound recognition are described.
Design of bipolar pulse generator topology based on Marx supplied by double power

Pulsed power technology has been used for the ablation tumor. Considerable research shows that a high-strength unipolar pulse electric field can induce irreversible electroporation (IRE) on the cell membrane, which can effectively kill cells. But some scholars and doctors have found that muscle contractions occur during treatment, which are associated with the delivery of electric pulses. Confirmed by further studies that bipolar pulses have been proven more advanced in the treatment of tumor because of the elimination of muscle contractions and the effect for ablating non-uniform tissue. So the bipolar pulse generator is needed for the research on the tumor ablation with bipolar pulses. In this paper, a new type of modular bipolar pulsed-power generator base on Marx generator with double power is proposed. The concept of this generator is charging two series of capacitors in parallel by two power sources respectively, and then connecting the capacitors in series through solid-state switches with different control strategy. Utilizing a number of fast solid-state switches, the capacitors can be connected in series with different polarities, so that a positive or negative polarity pulse will be delivered to the load. A laboratory prototype has been implemented in laboratory. The development of this pulse generator can provide the hardware foundation for the research on biological effect without muscle contraction when the tumors are applied with bipolar pulse electric field.
Probabilistic neural networks

-By replacing the sigmoid activation function often used in neural networks with an exponential function, a probabilistic neural network ( PNN) that can compute nonlinear decision boundaries which approach the Bayes optimal is formed. Alternate activation functions having similar properties are also discussed. A fourlayer neural network of the type proposed can map any input pattern to any number of classifications. The decision boundaries can be modified in real-time using new data as they become available, and can be implemented using artificial hardware "'neurons" that operate entirely in parallel. Provision is also made for estimating the probability and reliability of a classification as well as making the decision. The technique offers a tremendous speed advantage for problems in which the incremental adaptation time of back propagation is a significant fraction of the total computation time. For one application, the PNN paradigm was 200,000 times faster than back-propagation. Neural network, Probability density function, Parallel processor, "Neuron", Pattern recognition, Parzen window, Bayes strategy, Associative memory.
Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding

In this paper, we introduce an application of matrix factorization to produce corpus-derived, distributional models of semantics that demonstrate cognitive plausibility. We find that word representations learned by Non-Negative Sparse Embedding (NNSE), a variant of matrix factorization, are sparse, effective, and highly interpretable. To the best of our knowledge, this is the first approach which yields semantic representation of words satisfying these three desirable properties. Though extensive experimental evaluations on multiple real-world tasks and datasets, we demonstrate the superiority of semantic models learned by NNSE over other state-of-the-art baselines.
Artificial Neural Networks architectures for stock price prediction: comparisons and applications

We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief résumé of the existing literature on the subject, we consider the Multi-layer Perceptron (MLP), the Convolutional Neural Networks (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks techniques. We focus on the importance of choosing the correct input features, along with their preprocessing, for the specific learning algorithm one wants to use. Eventually, we consider the S&P500 historical time series, predicting trend on the basis of data from the past days, and proposing a novel approach based on combination of wavelets and CNN, which outperforms the basic neural networks ones. We show, that neural networks are able to predict financial time series movements even trained only on plain time series data and propose more ways to improve results.  Artificial neural networks, Multi-layer neural network, Convolutional neural network, Long shortterm memory, Recurrent neural network, Deep Learning, Stock markets analysis, Time series analysis, financial forecasting
Echo State Queueing Network: A new reservoir computing learning tool

In the last decade, a new computational paradigm was introduced in the field of Machine Learning, under the name of Reservoir Computing (RC). RC models are neural networks which a recurrent part (the reservoir) that does not participate in the learning process, and the rest of the system where no recurrence (no neural circuit) occurs. This approach has grown rapidly due to its success in solving learning tasks and other computational applications. Some success was also observed with another recently proposed neural network designed using Queueing Theory, the Random Neural Network (RandNN). Both approaches have good properties and identified drawbacks. In this paper, we propose a new RC model called Echo State Queueing Network (ESQN), where we use ideas coming from RandNNs for the design of the reservoir. ESQNs consist in ESNs where the reservoir has a new dynamics inspired by recurrent RandNNs. The paper positions ESQNs in the global Machine Learning area, and provides examples of their use and performances. We show on largely used benchmarks that ESQNs are very accurate tools, and we illustrate how they compare with standard ESNs.
Automatic crop irrigation system

India is agriculture based nation. It is necessary to improve the productivity and quality of agro based products. The proposed design is an automatic system that aids the famers in irrigation process. It keeps notifying the farmer through an on-board LCD display and messages that is sent on the farmer's cellular number. This proposed design is also helpful for the farmers who are facing power failure issues to maintain a uniform water supply due to power failure or inadequate and non-uniform water supply. The automatic irrigation system also keeps the farmer updated with all the background activities through a SIM900 Module that sends messages on the registered number. This device can be a turning point for our society. The device is easily affordable by the farmers of the country. This proposed design is helpful for reducing the human labour. This is a low budget system with an essential social application.
IDENTIFICATION OF FAILURE MECHANISMS TO ENHANCE PROGNOSTIC OUTCOMES

Predicting the reliability of a system in its actual life-cycle conditions and estimating its time to failure is helpful in decision making to mitigate system risks. There are three approaches to prognostics: the physics-of-failure approach, the data-driven approach, and the fusion approach. A key requirement in all these approaches is identification of the appropriate parameter(s) to monitor to gather data that can be used to assess impending failure. This paper presents a physics-of-failure approach, which uses failure modes, mechanisms and effects analysis (FMMEA) to enhance prognostics planning and implementation. This paper also presents the fusion approach to prognostics and the applicability of FMMEA to this approach. An example case of generating FMMEA information and using that to identify appropriate parameters to monitor is presented.
Video summarisation: A conceptual framework and survey of the state of the art

Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users.
A novel axial flux segmented SRM for electric vehicle application

A novel electric machine is proposed for electric vehicles (EV) to circumvent permanent magnet (PM) machine and to achieve the same performance by using the advantages of both axial flux topology and radial flux segmented switched reluctance motor (SRM). A 3-phase, 12/8 pole toroidal wound axial flux segmented SRM (AFSSRM) for EV application is proposed. Outer diameter and axial length of the motor are decided based on the wheel rim size of a typical scooter. Sizing equation is derived for AFSSRM. Linearized 2-D model is developed to evaluate the performance of the machine. Aligned phase inductance is calculated analytically and compared with that obtained from finite element analysis (FEA) based simulation.
Channel estimation and precoder design for millimeter-wave communications: The sparse way

We propose strategies for mmWave communications that exploit the inherent sparsity of mmWave channels in the angle and delay domains. In particular, we propose the use of aperture shaping to ensure a sparse virtual-domain MIMO channel representation; fast FFT-based modulation and demodulation schemes to expose the virtual-channel coefficients; a pilot design that facilitates fast LASSO-based sparse-channel estimation; and spectrally efficient precoding and decoding, via the Lanczos algorithm and waterfilling over both frequency and angle. Numerical experiments suggest that our approach comes close to achieving the perfect-CSI capacity of the mmWave channel.
Using Anchors to Estimate Clinical State without Labeled Data

We present a novel framework for learning to estimate and predict clinical state variables without labeled data. The resulting models can used for electronic phenotyping, triggering clinical decision support, and cohort selection. The framework relies on key observations which we characterize and term "anchor variables". By specifying anchor variables, an expert encodes a certain amount of domain knowledge about the problem while the rest of learning proceeds in an unsupervised manner. The ability to build anchors upon standardized ontologies and the framework's ability to learn from unlabeled data promote generalizability across institutions. We additionally develop a user interface to enable experts to choose anchor variables in an informed manner. The framework is applied to electronic medical record-based phenotyping to enable real-time decision support in the emergency department. We validate the learned models using a prospectively gathered set of gold-standard responses from emergency physicians for nine clinically relevant variables.
Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks—With a Focus on Propagation Models

This paper provides an overview of the features of fifth generation (5G) wireless communication systems now being developed for use in the millimeter wave (mmWave) frequency bands. Early results and key concepts of 5G networks are presented, and the channel modeling efforts of many international groups for both licensed and unlicensed applications are described here. Propagation parameters and channel models for understanding mmWave propagation, such as line-of-sight (LOS) probabilities, large-scale path loss, and building penetration loss, as modeled by various standardization bodies, are compared over the 0.5–100 GHz range.
A Reservoir Computing Approach for Human Gesture Recognition from Kinect Data

This paper describes a novel approach for human gesture recognition from motion data captured by a Kinect camera. The proposed method is based on encoding the temporal history of input data using bidirectional Echo State Networks, whereas the output is computed by means of a multi-layer perceptron with softmax. Results achieved at the time-series classification challenge organized within the 2016 ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data show the potentiality of the approach.
Encrypted key exchange: password-based protocols secure against dictionary attacks

Classical cryptographic protocols based on user chosen keys allow an attacker to mount password guessing attacks We introduce a novel combination of asymmetric public key and symmetric secret key cryptography that allow two parties sharing a common password to exchange con dential and authenticated information over an insecure network These proto cols are secure against active attacks and have the property that the password is protected against o line dictionary attacks There are a number of other useful applications as well including secure public tele phones
Have I really met timing? - validating primetime timing reports with SPICE

At sign-off everybody is wondering about how good the accuracy of the static timing analysis timing reports generated with PrimeTime¯ really is. Errors can be introduced by STA setup, interconnect modeling, library characterization etc. The claims that path timingcalculated by PrimeTime usually is within a few percent of Spice don't help to ease your uncertainty.When the Signal Integrity features were introduced to PrimeTime there was also a feature added that was hardly announced: PrimeTime can write out timing paths for simulation with Spice that can be used to validate the timing numbers calculated by PrimeTime. By comparingthe numbers calculated by PrimeTime to a simulation with Spice for selected paths the designers can verify the timing and build up confidence or identify errors.This paper will describe a validation flow for PrimeTime timing reports that is based on extraction of the Spice paths, starting the Spice simulation, parsing the simulation results, and creating a report comparing PrimeTime and Spice timing. All these steps are done inside the TCL environment of PrimeTime. It will describe this flow, what is needed for the Spice simulation, how it can be set up, what can go wrong, and what kind of problems in the STA can be identified.
A view on Internet of Things driving Cloud Federation

Cloud Federation offers Cloud Service Providers (CSPs) the ability to lease excess capacity to other CSPs in need of temporary additional resources such as large amounts of computational and networking power, or storage capacity to meet consumer's insatiable demand requirements. This promise, not only directly contradicts the Cloud Computing principles of scalability and elasticity, but also challenges and undermines the main business interest of CSPs in competition for unrivaled market positioning and industry dominance. In this paper, we provide a new view on Cloud Federation through analyzing its technological and organizational integration challenges and identifying the business incentives required for CSPs to combine their aggregated infrastructures to meet greater business outcomes and increased sustainable profits. Our position is that the Internet of Things (IoT) will be the main business driver and catalyst for Cloud Federation.
Tagging YouTube-A Classification of Tagging Practice on YouTube

A problem exists of how to categorise the abundance of user generated content being uploaded to social sites. One method of categorisation being applied is tagging, user generated keywords that are assigned to the content. This research presents a study into the tagging practice of YouTube users. A classification scheme was applied to a dataset of 768 tags, assigning the tags to different categories of tag type. Analysis reveals how useful the tagging method on YouTube is at improving the categorisation of user generated video content in contrast to collaborative tagging systems.