nils-herrmann/clean_scirepeval huggingface dataset:
Horse Racing Prediction Using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been applied to predict many complex problems. In this paper ANNs are applied to horse racing prediction. We employed Back-Propagation, Back-Propagation with Momentum, QuasiNewton, Levenberg-Marquardt and Conjugate Gradient Descent learning algorithms for real horse racing data and the performances of five supervised NN algorithms were analyzed. Data collected from AQUEDUCT Race Track in NY, include 100 actual races from 1 January to 29 January 2010. The experimental results demonstrate that NNs are appropriate methods in horse racing prediction context. Results show that BP algorithm performs slightly better than other algorithms but it needs a longer training time and more parameter selection. Furthermore, LM algorithm is the fastest.  Artificial Neural Networks, Time Series Analysis, Horse Racing Prediction, Learning Algorithms, BackPropagation
Mediators , Moderators , and Tests for Mediation

The following points are developed. First, mediation relations are generally thought of in causal terms. Influences of an antecedent are transmitted to a consequence through an intervening mediator. Second, mediation relations may assume a number of functional forms, including nonadditive, nonlinear, and nonrecursive forms Special attention is given to nonadditive forms, or moderated mediation, where it is shown that, although mediation and moderation are distinguishable processes, a particular variable may be both a mediator and a moderator within a single set of functional relations Third, current procedures for testing mediation relations m industrial and organizational psychology need to be updated because these procedures often involve a dubious interplay between exploratory (correlational) statistical tests and causal inference It is suggested that no middle ground exists between exploratory and confirmatory (causal) analysis and that attempts to explain how mediation processes occur require well-specified causal models. Given such models, confirmatory analytic techniques furnish the more informative tests of mediation.
A brief survey on sequence classification

Sequence classification has a broad range of applications such as genomic analysis, information retrieval, health informatics, finance, and abnormal detection. Different from the classification task on feature vectors, sequences do not have explicit features. Even with sophisticated feature selection techniques, the dimensionality of potential features may still be very high and the sequential nature of features is difficult to capture. This makes sequence classification a more challenging task than classification on feature vectors. In this paper, we present a brief review of the existing work on sequence classification. We summarize the sequence classification in terms of methodologies and application domains. We also provide a review on several extensions of the sequence classification problem, such as early classification on sequences and semi-supervised learning on sequences.
Emerging adulthood. A theory of development from the late teens through the twenties.

Emerging adulthood is proposed as a new conception of development for the period from the late teens through the twenties, with a focus on ages 18-25. A theoretical background is presented. Then evidence is provided to support the idea that emerging adulthood is a distinct period demographically, subjectively, and in terms of identity explorations. How emerging adulthood differs from adolescence and young adulthood is explained. Finally, a cultural context for the idea of emerging adulthood is outlined, and it is specified that emerging adulthood exists only in cultures that allow young people a prolonged period of independent role exploration during the late teens and twenties.
Metal Additive Manufacturing : A Review

This paper reviews the state-of-the-art of an important, rapidly emerging, manufacturing technology that is alternatively called additive manufacturing (AM), direct digital manufacturing, free form fabrication, or 3D printing, etc. A broad contextual overview of metallic AM is provided. AM has the potential to revolutionize the global parts manufacturing and logistics landscape. It enables distributed manufacturing and the productions of parts-on-demand while offering the potential to reduce cost, energy consumption, and carbon footprint. This paper explores the material science, processes, and business consideration associated with achieving these performance gains. It is concluded that a paradigm shift is required in order to fully exploit AM potential.
Stochastic Neighbor Embedding

We describe a probabilistic approach to the task of placing objects, described by high-dimensional vectors or by pairwise dissimilarities, in a low-dimensional space in a way that preserves neighbor identities. A Gaussian is centered on each object in the high-dimensional space and the densities under this Gaussian (or the given dissimilarities) are used to define a probability distribution over all the potential neighbors of the object. The aim of the embedding is to approximate this distribution as well as possible when the same operation is performed on the low-dimensional “images” of the objects. A natural cost function is a sum of Kullback-Leibler divergences, one per object, which leads to a simple gradient for adjusting the positions of the low-dimensional images. Unlike other dimensionality reduction methods, this probabilistic framework makes it easy to represent each object by a mixture of widely separated low-dimensional images. This allows ambiguous objects, like the document count vector for the word “bank”, to have versions close to the images of both “river” and “finance” without forcing the images of outdoor concepts to be located close to those of corporate concepts.
S2RDF: RDF Querying with SPARQL on Spark

RDF has become very popular for semantic data publishing due to its flexible and universal graph-like data model. Yet, the ever-increasing size of RDF data collections makes it more and more infeasible to store and process them on a single machine, raising the need for distributed approaches. Instead of building a standalone but closed distributed RDF store, we endorse the usage of existing infrastructures for Big Data processing, e.g. Hadoop. However, SPARQL query performance is a major challenge as these platforms are not designed for RDF processing from ground. Thus, existing Hadoop-based approaches often favor certain query pattern shape while performance drops significantly for other shapes. In this paper, we describe a novel relational partitioning schema for RDF data called ExtVP that uses a semi-join based preprocessing, akin to the concept of Join Indices in relational databases, to efficiently minimize query input size regardless of its pattern shape and diameter. Our prototype system S2RDF is built on top of Spark and uses its relational interface to execute SPARQL queries over ExtVP. We demonstrate its superior performance in comparison to state of the art SPARQL-on-Hadoop approaches using the recent WatDiv test suite. S2RDF achieves sub-second runtimes for majority of queries on a billion triples RDF graph.
Sensor Network Localization via Received Signal Strength Measurements with Directional Antennas

In wireless sensor networks position awareness is necessary to exploit the communication benefits of directional antennas and for sensors to provide meaningful information about their surroundings. In this paper we evaluate the feasibility and quality of self-localization that can be obtained using received signal strength (RSS) measurements from arrays of directional antennas on each sensor node. We compare the performance of an optimal estimator utilizing all RSS data with a suboptimal one using angles of arrival as an intermediate statistic. Further, we compare the performance bounds of our approach with others based on distance measurements potentially obtained through time of arrival or RSS observations. We demonstrate that sub-meter location accuracy is possible using 802.11 radio frequency communication signals and no assumed model for propagation loss. Results from an outdoor field experiment and our proposed printed circuit board quasi-Yagi directional antenna are also presented.
Location Privacy in Mobile Systems: A Personalized Anonymization Model

This paper describes a personalized k-anonymity model for protecting location privacy against various privacy threats through location information sharing. Our model has two unique features. First, we provide a unified privacy personalization framework to support location k-anonymity for a wide range of users with context-sensitive personalized privacy requirements. This framework enables each mobile node to specify the minimum level of anonymity it desires as well as the maximum temporal and spatial resolutions it is willing to tolerate when requesting for k-anonymity preserving location-based services (LBSs). Second, we devise an efficient message perturbation engine which runs by the location protection broker on a trusted server and performs location anonymization on mobile users' LBS request messages, such as identity removal and spatio-temporal cloaking of location information. We develop a suite of scalable and yet efficient spatio-temporal cloaking algorithms, called CliqueCloak algorithms, to provide high quality personalized location k-anonymity, aiming at avoiding or reducing known location privacy threats before forwarding requests to LBS provider(s). The effectiveness of our CliqueCloak algorithms is studied under various conditions using realistic location data synthetically generated using real road maps and traffic volume data
Five disruptive technology directions for 5G

New research directions will lead to fundamental changes in the design of future fifth generation (5G) cellular networks. This article describes five technologies that could lead to both architectural and component disruptive design changes: device-centric architectures, millimeter wave, massive MIMO, smarter devices, and native support for machine-to-machine communications. The key ideas for each technology are described, along with their potential impact on 5G and the research challenges that remain.
Hybrid Neural Networks for Learning the Trend in Time Series

Trend of time series characterizes the intermediate upward and downward behaviour of time series. Learning and forecasting the trend in time series data play an important role in many real applications, ranging from resource allocation in data centers, load schedule in smart grid, and so on. Inspired by the recent successes of neural networks, in this paper we propose TreNet, a novel end-toend hybrid neural network to learn local and global contextual features for predicting the trend of time series. TreNet leverages convolutional neural networks (CNNs) to extract salient features from local raw data of time series. Meanwhile, considering the long-range dependency existing in the sequence of historical trends, TreNet uses a long-short term memory recurrent neural network (LSTM) to capture such dependency. Then, a feature fusion layer is to learn joint representation for predicting the trend. TreNet demonstrates its effectiveness by outperforming CNN, LSTM, the cascade of CNN and LSTM, Hidden Markov Model based method and various kernel based baselines on real datasets.
Increased prefrontal and parietal activity after training of working memory

Working memory capacity has traditionally been thought to be constant. Recent studies, however, suggest that working memory can be improved by training. In this study, we have investigated the changes in brain activity that are induced by working memory training. Two experiments were carried out in which healthy, adult human subjects practiced working memory tasks for 5 weeks. Brain activity was measured with functional magnetic resonance imaging (fMRI) before, during and after training. After training, brain activity that was related to working memory increased in the middle frontal gyrus and superior and inferior parietal cortices. The changes in cortical activity could be evidence of training-induced plasticity in the neural systems that underlie working memory.
An overview of 3D object grasp synthesis algorithms

This overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands. Robotic grasping has been an active research subject for decades, and a great deal of effort has been spent on grasp synthesis algorithms. Existing papers focus on reviewing the mechanics of grasping and the finger-object contact interactions [7] or robot hand design and their control [1]. Robot grasp synthesis algorithms have been reviewed in [63], but since then an important progress has been made toward applying learning techniques to the grasping problem. This overview focuses on analytical as well as empirical grasp synthesis approaches.
Design of a Data-Driven PID Controller

Since most processes have nonlinearities, controller design schemes to deal with such systems are required. On the other hand, proportional-integral-derivative (PID) controllers have been widely used for process systems. Therefore, in this paper, a new design scheme of PID controllers based on a data-driven (DD) technique is proposed for nonlinear systems. According to the DD technique, a suitable set of PID parameters is automatically generated based on input/output data pairs of the controlled object stored in the database. This scheme can adjust the PID parameters in an online manner even if the system has nonlinear properties and/or time-variant system parameters. Finally, the effectiveness of the newly proposed control scheme is evaluated on some simulation examples, and a pilot-scale temperature control system.
A ripple voltage sensing MPPT circuit for ultra-low power microsystems

We propose a maximum power point tracking (MPPT) circuit for micro-scale sensor systems that measures ripple voltages in a switched capacitor energy harvester. Compared to conventional current mirror type MPPT circuits, this design incurs no voltage drop and does not require high bandwidth amplifiers. Using correlated double sampling, high accuracy is achieved with a power overhead of 5%, even at low harvested currents of 1.4uA based on measured results in 180nm CMOS.
Printed Multi-Band MIMO Antenna Systems and Their Performance Metrics [Wireless Corner]

Multiple-input-multiple-output (MIMO) antenna systems are a key enabling technology for modern fourth-generation (4G) wireless systems. The need for higher data rates for multimedia applications within the limited bandwidth and power levels led the way to the use of multiple antennas at the receiver and transmitter ends. Printed MIMO antenna systems, supporting multiple bands, including the lower band of the 4G wireless standard (LTE), pose a challenge in terms of available size. In this work, we start by defining the new required metrics to characterize MIMO antenna systems. We then present several recent printed multi-band MIMO antenna systems, along with some isolation-enhancement mechanisms that are used in these closely packed antennas.
Algorithms for Mining Distance-Based Outliers in Large Datasets

This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The identification of outliers can lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of professional athletes. Existing methods that we have seen for finding outliers in large datasets can only deal efficiently with two dimensions/attributes of a dataset. Here, we study the notion of DB(DistanceBased) outliers. While we provide formal and empirical evidence showing the usefulness of DB-outliers, we focus on the development of algorithms for computing such outliers. First, we present two simple algorithms, both having a complexity of O(k N’), k being the dimensionality and N being the number of objects in the dataset. These algorithms readily support datasets with many more than two attributes. Second, we present an optimized cell-based algorithm that has a complexity that is linear wrt N, but exponential wrt k. Third, for datasets that are mainly disk-resident, we present another version of the cell-based algorithm that guarantees at most 3 passes over a dataset. We provide Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed fOT direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, OT to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 24th VLDB Conference New York, USA, 1998 experimental results showing that these cellbased algorithms are by far the best for k 5 4.
Coupling Matrix Extraction and Reconfiguration Using a Generalized Isospectral Flow Method

The coupling matrix model is a helpful tool during synthesis and analysis of coupled resonator filters. Application of the coupling matrix for filter synthesis often requires the designer to reconfigure the coupling matrix to yield a specific matrix structure matching the topology of the filter. Currently, there is no closed-form solution known to this matrix reconfiguration problem. This paper proposes a new approach to solve this problem for coupling matrix representations of general lossy filters. The method is based on infinitesimal matrix rotations and can be considered as a continuous analog to the well-known coupling matrix similarity transforms. The results shown indicate good convergence behavior of the reconfiguration algorithm, as well as the ability of the method to always keep the desired scattering parameter goal. Based on this algorithm, a method for coupling matrix extraction of lossy filters is presented and successfully evaluated using simulated and measured scattering parameter data.
Predicting purchase behaviors from social media

In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users' social media profiles and their e-commerce behaviors. This paper presents a system for predicting a user's purchase behaviors on e-commerce websites from the user's social media profile. We specifically aim at understanding if the user's profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). The paper provides an extensive analysis on how users' Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction.
A Synthesis of Business Role Models: Conceptual Tool for Business Innovations

This paper aims to clarify the concept of business models, its usages, and its roles in the Information Systems domain. A review of the literature shows a broad diversity of understandings, usages, and places in the firm. The paper identifies the terminology or ontology used to describe a business model, and compares this terminology with previous work. Then the general usages, roles and potential of the concept are outlined. Finally, the connection between the business model concept and Information Systems is described in the form of eight propositions to be analyzed in future work.
A Fuzzy Logic based Defense Mechanism against Distributed Denial of Service Attack in Cloud Computing Environment

Cloud defines a new age of computing solution that provides services to customers with its unique feat ures of agility and multi-tenancy. As the critical resources are ho sted at cloud provider’s end, security is a big challenge in clou d computing. If the cloud environment is compromised and attackers get the access of core data centers, the availability of the critical resources becomes a big concern for the service consumers. Denial of Se rvic and Distributed Denial of Service kind of attacks are l aunched towards cloud environment to make the resources unavailable for legitimate users. In this paper we propose a fuzzy logic based d fense mechanism that can be set with predefined rules by which it can detect the malicious packets and takes proper count er measures to mitigate the DDoS attack. Also a detailed study of different kind of DDoS attack and existing defense strategies has bee n carried out.
Parssec: A Parallel Simulation Environment for Complex Systems

S ystems are now being designed that can scale to very large configurations. Examples include parallel architectures with thousands of processors, wireless networks connecting tens of thousands of computers, and parallel database systems capable of processing millions of transactions a second. In some cases, systems have grown considerably larger than envisaged by their designers. Design and development costs for such systems could be significantly reduced if only there were efficient techniques for evaluating design alternatives and predicting their impact on overall system performance metrics. Due to the systems' analytical intractability, simulation is the most common performance evaluation technique for such systems. However, the long execution times needed for sequential simulation models often hamper evaluation. For instance, it can easily take weeks to simulate wireless networks with thousands of mobile nodes that are subject to the interference and signal-fading effects of a realistic environment. Also, the simulation of parallel programs typically suffers from slowdown factors of 30 to 50 per processor. 1 This means that simulation of a parallel program executing for five to 15 minutes on a 128-node multi-processor can take anywhere from a few weeks to a few months—even on state-of-the-art sequential workstations. As the size of the physical system increases, the models' memory requirements can easily exceed the workstations' capacity. The limitations of sequential model execution have led to growing interest in the use of parallel execution for simulating large-scale systems. Widespread use of parallel simulation, however, has been significantly hindered by a lack of tools for integrating parallel model execution into the overall framework of system simulation. Although a number of algorithmic alternatives exist for parallel execution of discrete-event simulation models, performance analysts not expert in parallel simulation have relatively few tools giving them flexibility to experiment with multiple algorithmic or architectural alternatives for model execution. Another drawback to widespread use of simulations is the cost of model design and maintenance. The design and development costs for detailed simulation models for complex systems can easily rival the costs for the physical systems themselves. The simulation environment we developed at UCLA attempts to address some of these issues by providing these features: • An easy path for the migration of simulation models to operational software prototypes. • Implementation on both distributed-and shared-memory platforms and support for a diverse set of parallel simulation protocols. • Support for visual and hierarchical model design. Our environment consists of three primary components: …