nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Taxi and Ridesharing: A Framework and Heuristics for the Optimization Problem

In this paper we study a dynamic problem of ridesharing and taxi sharing with time windows. We consider a scenario where people needing a taxi or interested in getting a ride use a phone app to designate their source and destination points in a city, as well others restrictions (such as maximum allowable time to be at the destination). On the other hand, we have taxis and people interested in giving a ride, with their current positions and also some constraints (vehicle capacity, destination, maximum time to destination). We want to maximize the number of shared trips: in the case of taxis, people going to close locations can share the costs of the trip, and in case of rides, the driver and passengers can share costs as well. This problem is dynamic since new calls for taxis or calls for rides arrive on demand. This gives rise to an optimization problem which we prove to be NP-Hard. We then propose heuristics to deal with it. We focus on the taxi sharing problem, but we show that our model is easily extendable to model the ridesharing situation or even a situation where there are both taxis and car owners. In addition, we present a framework that consists basically of a client application and a server. The last one processes all incoming information in order to match vehicles to passengers requests. The entire system can be used by taxi companies and riders in a way to reduce the traffic in the cities and to reduce the emission of greenhouse gases.
Machine Learning Approaches for Mood Classification of Songs toward Music Search Engine

Human often wants to listen to music that fits best his current emotion. A grasp of emotions in songs might be a great help for us to effectively discover music. In this paper, we aimed at automatically classifying moods of songs based on lyrics and metadata, and proposed several methods for supervised learning of classifiers. In future, we plan to use automatically identified moods of songs as metadata in our music search engine. Mood categories in a famous contest about Audio Music Mood Classification (MIREX 2007) are applied for our system. The training data is collected from a LiveJournal blog site in which each blog entry is tagged with a mood and a song. Then three kinds of machine learning algorithms are applied for training classifiers: SVM, Naive Bayes and Graph-based methods. The experiments showed that artist, sentiment words, putting more weight for words in chorus and title parts are effective for mood classification. Graph-based method promises a good improvement if we have rich relationship information among songs.
Learning Character-level Representations for Part-of-Speech Tagging

Distributed word representations have recently been proven to be an invaluable resource for NLP. These representations are normally learned using neural networks and capture syntactic and semantic information about words. Information about word morphology and shape is normally ignored when learning word representations. However, for tasks like part-of-speech tagging, intra-word information is extremely useful, specially when dealing with morphologically rich languages. In this paper, we propose a deep neural network that learns character-level representation of words and associate them with usual word representations to perform POS tagging. Using the proposed approach, while avoiding the use of any handcrafted feature, we produce stateof-the-art POS taggers for two languages: English, with 97.32% accuracy on the Penn Treebank WSJ corpus; and Portuguese, with 97.47% accuracy on the Mac-Morpho corpus, where the latter represents an error reduction of 12.2% on the best previous known result.
Processor Design Space Exploration via Statistical Sampling and Semi-Supervised Ensemble Learning

The design space exploration (DSE) has become a major challenge in microprocessors design due to the increasing complexity of microprocessor architecture and the extremely time-consuming software simulation technology. To more effectively and efficiently perform DSE, recently machine learning techniques are widely explored to build predictive models with a small set of simulations. However, most previous models are supervised models and the training samples are randomly selected. Thus they still suffered from high simulation cost or low prediction accuracy. In order to minimize the simulation overhead for DSE, this paper proposes an efficient DSE method which combines Latin hypercube sampling and semi-supervised ensemble learning technique. Latin hypercube sampling is first employed to select a small set of representative design points for simulation. Then a semi-supervised learning based AdaBoost model (SemiBoost) is proposed to predict the responses of the configurations that have not been simulated. We conduct extensive evaluations on the benchmarks of SPEC CPU2006 suite, and the experimental results demonstrate that the proposed SemiBoost model is superior to existing state-of-the-art models in terms of both efficiency and effectiveness.
Generalization by Weight-Elimination with Application to Forecasting

Inspired by the information theoretic idea of minimum description length, we add a term to the back propagation cost function that penalizes network complexity. We give the details of the procedure, called weight-elimination, describe its dynamics, and clarify the meaning of the parameters involved. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. We use this procedure to predict the sunspot time series and the notoriously noisy series of currency exchange rates.
Analyzing CyberCrimes Strategies: The Case of Phishing Attack

The growth in technology usage came along with many risks and increase in cybercrimes incidents. Phishing attacks are a form of cybercrimes by which attackers trick victims in order to obtain personal and sensitive information. Phishers motivations include gaining unauthorized information and access, cause financial loss and many more negative impacts. While cybercriminals keep developing their techniques, investigating different attack styles and methodologies are important steps toward developing effective protection mechanisms. This paper contributes to this direction by presenting and analyzing various phishing attack styles including Nigerian, Ghanaian, Chinese and Russian cybercrime styles. Due to richness of learning resources Russians and Chinese were found to be using more advanced techniques than Ghanaians and Nigerians who has limited resources.
Tree ensembles for predicting structured outputs

In this paper, we address the task of learning models for predicting structured outputs. We consider both global and local predictions of structured outputs, the former based on a single model that predicts the entire output structure and the latter based on a collection of models, each predicting a component of the output structure. We use ensemble methods and apply them in the context of predicting structured outputs. We propose to build ensemble models consisting of predictive clustering trees, which generalize classification trees: these have been used for predicting different types of structured outputs, both locally and globally. More specifically, we develop methods for learning two types of ensembles (bagging and random forests) of predictive clustering trees for global and local predictions of different types of structured outputs. The types of outputs considered correspond to different predictive modeling tasks: multi-target regression, multi-target classification, and hierarchical multi-label classification. Each of the combinations can be applied both in the context of global prediction (producing a single ensemble) or local prediction (producing a collection of ensembles). We conduct an extensive experimental evaluation across a range of benchmark datasets for each of the three types of structured outputs. We compare ensembles for global and local prediction, as well as single trees for global prediction and tree collections for local prediction, both in terms of predictive performance and in terms of efficiency (running times and model complexity). The results show that both global and local tree ensembles perform better than the single model counterparts in terms of predictive power. Global and local tree ensembles perform equally well, with global ensembles being more efficient and producing smaller models, as well as needing fewer trees in the ensemble to achieve the maximal performance. & 2012 Elsevier Ltd. All rights reserved.
Single-Image Noise Level Estimation for Blind Denoising

Noise level is an important parameter to many image processing applications. For example, the performance of an image denoising algorithm can be much degraded due to the poor noise level estimation. Most existing denoising algorithms simply assume the noise level is known that largely prevents them from practical use. Moreover, even with the given true noise level, these denoising algorithms still cannot achieve the best performance, especially for scenes with rich texture. In this paper, we propose a patch-based noise level estimation algorithm and suggest that the noise level parameter should be tuned according to the scene complexity. Our approach includes the process of selecting low-rank patches without high frequency components from a single noisy image. The selection is based on the gradients of the patches and their statistics. Then, the noise level is estimated from the selected patches using principal component analysis. Because the true noise level does not always provide the best performance for nonblind denoising algorithms, we further tune the noise level parameter for nonblind denoising. Experiments demonstrate that both the accuracy and stability are superior to the state of the art noise level estimation algorithm for various scenes and noise levels.
SVM-BDT PNN and Fourier Moment Technique for Classification of Leaf Shape 1

This paper presents three techniques of plants classification based on their leaf shape the SVM-BDT, PNN and Fourier moment technique for solving multiclass problems. All the three techniques have been applied to a database of 1600 leaf shapes from 32 different classes, where most of the classes have 50 leaf samples of similar kind. In the proposed work three techniques are used for comparing the performance of classification of leaves. Probabilistic Neural Network with principal component analysis, Support Vector Machine utilizing Binary Decision Tree and Fourier Moment. The proposed SVM based Binary Decision Tree architecture takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. This can lead to a dramatic improvement in recognition speed when addressing problems with large number of classes. Classification results from all the three techniques were compared and it was observed that SVM-BDT performs better than Fourier and PNN technique. Key  Neural Network, Support vector machine, Binary Decisions
Achievements : Re ̄ ections on the Measurement of Women ' s Empowerment

This paper begins from the understanding that women's empowerment is about the process by which those who have been denied the ability to make strategic life choices acquire such an ability. Awide gap separates this processual understanding of empowerment from the more instrumentalist forms of advocacy which have required the measurement and quanti®cation of empowerment. The ability to exercise choice incorporates three inter-related dimensions: resources (de®ned broadly to include not only access, but also future claims, to both material and human and social resources); agency (including processes of decision making, as well as less measurable manifestations of agency such as negotiation, deception and manipulation); and achievements (well-being outcomes). A number of studies of women's empowerment are analysed to make some important methodological points about the measurement of empowerment. The paper argues that these three dimensions of choice are indivisible in determining the meaning of an indicator and hence its validity as a measure of empowerment. The notion of choice is further quali®ed by referring to the conditions of choice, its content and consequences. These quali®cations represent an attempt to incorporate the structural parameters of individual choice in the analysis of women's empowerment. CONCEPTUALIZING EMPOWERMENT
Convolutional Neural Networks for Real-Time Beat Tracking: A Dancing Robot Application

In this paper a novel approach that adopts Convolutional Neural Networks (CNN) for the Beat Tracking task is proposed. The proposed architecture involves 2 convolutional layers with the CNN filter dimensions corresponding to time and band frequencies, in order to learn a Beat Activation Function (BAF) from a time-frequency representation. The output of each convolutional layer is computed only over the past values of the previous layer, to enable the computation of the BAF in an online fashion. The output of the CNN is post-processed by a dynamic programming algorithm in combination with a bank of resonators for calculating the salient rhythmic periodicities. The proposed method has been designed to be computational efficient in order to be embedded on a dancing NAO robot application, where the dance moves of the choreography are synchronized with the beat tracking output. The proposed system was submitted to the Signal Processing Cup Challenge 2017 and ranked among the top third algorithms.
MIMO for DVB-NGH, the next generation mobile TV broadcasting [Accepted From Open Call]

DVB-NGH (Digital Video Broadcasting - Next Generation Handheld) is the next generation technology for mobile TV broadcasting, which has been developed by the DVB project with the most advanced transmission technologies. DVB-NGH is the first broadcasting standard to incorporate multiple-input multiple-output (MIMO) as the key technology to overcome the Shannon limit of single antenna communications. MIMO techniques can be used to improve the robustness of the transmitted signal by exploiting the spatial diversity of the MIMO channel, but also to achieve increased data rates through spatial multiplexing. This article describes the benefits of MIMO that motivated its incorporation in DVB-NGH, reviews the MIMO schemes adopted, and discusses some aspects related to the deployment of MIMO networks in DVB-NGH. The article also provides a feature comparison with the multi-antenna techniques for 3GGP's LTE/LTE-Advanced for cellular networks. Finally, physical layer simulation results calibrated within the DVB-NGH standardization process are provided to illustrate the gain of MIMO for the next generation of mobile TV broadcasting.
Enabling packet fan-out in the libpcap library for parallel traffic processing

The large availability of multi-gigabit network cards for commodity PCs requires network applications to potentially cope with high volumes of traffic. However, computation intensive operations may not catch up with high traffic rates and need to be run in parallel over multiple processing cores. As of today, the vast majority of network applications are still based on the use of the pcap library interface which, unfortunately, does not provide a native multi-core support, even though the underlying capture technologies do. This paper introduces a novel version of the pcap library for the Linux operating-system that allows application level parallelism. The new library natively supports fanout operations for both multi-threaded and multi-process applications, by means of extended API as well as by a declarative grammar configuration suitable for legacy applications. In addition, the library can transparently run on top of the standard Linux socket and other accelerated capture engines. Performance evaluation has been carried out on a multi-core architecture in pure capture tests and in more realistic use cases involving monitoring applications such as Tstat and Bro, with standard Linux socket and the PFQ accelerated engine.
Universal Planning Networks

A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via imagebased goals. Visit https://sites.google. com/view/upn-public/home for video highlights.
Visualization of oceanographic applications using a common data model

Due to the rapid development of ocean observation technology, the volume of ocean data is increasing exponentially. To better utilize these data, more and more computational methods have been applied to exploring, sharing, and processing such data. Although great efforts have been made, there is an urgent need for a single platform containing a set of visualization components for every kind of ocean data. This paper presents a distributed, scalable and service-oriented visualization platform. The development of the platform is based on a common data model, by which ocean data with different spatial and temporal distribution can be organized and accessed in a uniform fashion, which sets a solid foundation for designing and implementing all kinds of visualization components and facilitates collaborative oceanographic research. The platform is run in China Ocean Observation System (COOS), and enables easy and convenient development of all kinds of visualization components for professionals and the general public and greatly advances ocean data sharing and scientific research collaboration.
Realtime FPGA-based processing unit for a high-resolution automotive MIMO radar platform

Next generations of high-resolution automotive radar sensors rely on powerful, real-time capable processing units. High sampling rates, large antenna arrays as well as hard real-time constraints require the use of both parallel architectures and high-bandwidth memory interfaces. This paper presents a novel architecture of a FPGA-based multiple-input multiple-output (MIMO) radar sensor processing unit. Sampling rates up to 250 MSPS and a maximum of 16 parallel receiving channels can be used, resulting in a maximum data rate of 56GBit/s. The processing chain consists of a flexible FIR filter, a range-Doppler processing unit using windowed FFTs and an ordered statistics constant-false-alarm-rate (OS-CFAR) unit for optimal target detection and data reduction. The realized target system is composed of a Virtex7-FPGA and a 1GByte SDRAM memory. The resource usage of the FPGA implementation is analyzed in order to provide estimations for future system designs. Finally, the resulting performance of the system is verified in connection with a prototype MIMO radar front-end. High-resolution measurements of moving scenes have been carried out to validate the correct operation of the system.
Smart Parking System with Image Processing Facility

Smart Parking Systems obtain information about available parking spaces, process it and then place the car at a certain position. A prototype of the parking assistance system based on the proposed architecture was constructed here. The adopted hardware, software, and implementation solutions in this prototype construction are described in this paper. The effective circular design is introduced here having rack-pinion special mechanism which is used to lift and place the car in the certain position. The design of rack pinion mechanism is also simulated using AUTODESK INVENTOR and COMSOL software. Index Terms Car Parking, Multi-level, Design, Rackpinion mechanism, S imulation.
Color, Change and Control for Quantitative Data Display

Color is used widely and reliably to display the value of a single scalar variable. It is more rarely, and far less reliably, used to display multivariate data. Calico, a dynamic tool for the creation and manipulation of color mappings, adds the element of dynamic control over the color mapping to that of color itself for the more effective display and exploration of multivariate spatial data. Using Calico, a one-or two variable color mapping can be created using parametric equations and a variety of color models. This mapping can be manipulated by moving input devices referenced in the parametric expressions, by applying affine transforms, or by performing free-form deformations. As the user changes the mapping, an image showing the data displayed using the current mapping is updated in real time, along with the images describing the mapping. This paper presents an empirical study investigating the effects of user control and smooth change in the display of quantitative data on user accuracy, confidence, and preference. While the user control did not quite produce a significant effect on accuracy, it did produce significant increases in user preference and confidence.
AuditoryMotor Interaction Revealed by fMRI: Speech, Music, and Working Memory in Area Spt

The concept of auditorymotor interaction pervades speech science research, yet the cortical systems supporting this interface have not been elucidated. Drawing on experimental designs used in recent work in sensorymotor integration in the cortical visual system, we used fMRI in an effort to identify human auditory regions with both sensory and motor response properties, analogous to single-unit responses in known visuomotor integration areas. The sensory phase of the task involved listening to speech (nonsense sentences) or music (novel piano melodies); the motor phase of the task involved covert rehearsal/humming of the auditory stimuli. A small set of areas in the superior temporal and temporal parietal cortex responded both during the listening phase and the rehearsal/humming phase. A left lateralized region in the posterior Sylvian fissure at the parietaltemporal boundary, area Spt, showed particularly robust responses to both phases of the task. Frontal areas also showed combined auditory + rehearsal responsivity consistent with the claim that the posterior activations are part of a larger auditorymotor integration circuit. We hypothesize that this circuit plays an important role in speech development as part of the network that enables acousticphonetic input to guide the acquisition of language-specific articulatory-phonetic gestures; this circuit may play a role in analogous musical abilities. In the adult, this system continues to support aspects of speech production, and, we suggest, supports verbal working memory.