nils-herrmann/clean_scirepeval huggingface dataset:
Valid population inference for information-based imaging: From the second-level <ce:italic>t</ce:italic>-test to prevalence inference

In multivariate pattern analysis of neuroimaging data, 'second-level' inference is often performed by entering classification accuracies into a t-test vs chance level across subjects. We argue that while the random-effects analysis implemented by the t-test does provide population inference if applied to activation differences, it fails to do so in the case of classification accuracy or other 'information-like' measures, because the true value of such measures can never be below chance level. This constraint changes the meaning of the population-level null hypothesis being tested, which becomes equivalent to the global null hypothesis that there is no effect in any subject in the population. Consequently, rejecting it only allows to infer that there are some subjects in which there is an information effect, but not that it generalizes, rendering it effectively equivalent to fixed-effects analysis. This statement is supported by theoretical arguments as well as simulations. We review possible alternative approaches to population inference for information-based imaging, converging on the idea that it should not target the mean, but the prevalence of the effect in the population. One method to do so, 'permutation-based information prevalence inference using the minimum statistic', is described in detail and applied to empirical data.
A mixture model for contextual text mining

Contextual text mining is concerned with extracting topical themes from a text collection with context information (e.g., time and location) and comparing/analyzing the variations of themes over different contexts. Since the topics covered in a document are usually related to the context of the document, analyzing topical themes within context can potentially reveal many interesting theme patterns. In this paper, we generalize some of these models proposed in the previous work and we propose a new general probabilistic model for contextual text mining that can cover several existing models as special cases. Specifically, we extend the probabilistic latent semantic analysis (PLSA) model by introducing context variables to model the context of a document. The proposed mixture model, called contextual probabilistic latent semantic analysis (CPLSA) model, can be applied to many interesting mining tasks, such as temporal text mining, spatiotemporal text mining, author-topic analysis, and cross-collection comparative analysis. Empirical experiments show that the proposed mixture model can discover themes and their contextual variations effectively.
A Chat-bot based Multimodal Virtual Guide for Cultural Heritage Tours

The use of a PDA with ad-hoc built-in information retrieval functionalities can help people in visiting an historical site in a natural manner instead of traditional audio/visual pre-recorded guides. The aim of this work is to build a versatile virtual-guide system adaptable to the user needs of mobility and therefore usable on different devices (e.g. PDAs, Smartphones). An information retrieval service is included and is easily accessible through a spoken language interaction. The system takes the advantages of chat-bot and speech recognition technologies, allowing a natural interaction with the user. The system has been implemented on a Qtek PDA with Windows Mobile 2003 with the aim of assisting an user during a visit to a cultural heritage institution.
Convex incremental extreme learning machine

Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes,  (2006) 879–892] have recently proposed a new theory to show that single-hidden-layer feedforward networks (SLFNs) with randomly generated additive or radial basis function (RBF) hidden nodes (according to any continuous sampling distribution) can work as universal approximators and the resulting incremental extreme learning machine (I-ELM) outperforms many popular learning algorithms. I-ELM randomly generates the hidden nodes and analytically calculates the output weights of SLFNs, however, I-ELM does not recalculate the output weights of all the existing nodes when a new node is added. This paper shows that while retaining the same simplicity, the convergence rate of I-ELM can be further improved by recalculating the output weights of the existing nodes based on a convex optimization method when a new hidden node is randomly added. Furthermore, we show that given a type of piecewise continuous computational hidden nodes (possibly not neural alike nodes), if SLFNs f nðxÞ 1⁄4 Pn i1⁄41 biGðx; ai; biÞ can work as universal approximators with adjustable hidden node parameters, from a function approximation point of view the hidden node parameters of such ‘‘generalized’’ SLFNs (including sigmoid networks, RBF networks, trigonometric networks, threshold networks, fuzzy inference systems, fully complex neural networks, high-order networks, ridge polynomial networks, wavelet networks, etc.) can actually be randomly generated according to any continuous sampling distribution. In theory, the parameters of these SLFNs can be analytically determined by ELM instead of being tuned. r 2007 Elsevier B.V. All rights reserved.
Protecting Moving Trajectories with Dummies

Dummy-based anonymization techniques for protecting location privacy of mobile users have been proposed in the literature. By generating dummies that move in humanlike trajectories, shows that location privacy of mobile users can be preserved. However, by monitoring long-term movement patterns of users, the trajectories of mobile users can still be exposed. We argue that, once the trajectory of a user is identified, locations of the user is exposed. Thus, it's critical to protect the moving trajectories of mobile users in order to preserve user location privacy. We propose two schemes that generate consistent movement patterns in a long run. Guided by three parameters in user specified privacy profile, namely, short- term disclosure, long-term disclosure and distance deviation, the proposed schemes derive movement trajectories for dummies. A preliminary performance study shows that our approach is more effective than existing work in protecting moving trajectories of mobile users and their location privacy.
CARSKit: A Java-Based Context-Aware Recommendation Engine

Recommender system has been demonstrated as one of the most useful tools to assist users' decision makings. Several recommendation algorithms have been developed and implemented by both commercial and open-source recommendation libraries. Context-aware recommender system (CARS) emerged as a novel research direction during the past decade and many contextual recommendation algorithms have been proposed. Unfortunately, no recommendation engines start to embed those algorithms in their kits, due to the special characteristics of the data format and processing methods in the domain of CARS. This paper introduces an open-source Java-based context-aware recommendation engine named as CARSKit which is recognized as the 1st open source recommendation library specifically designed for CARS. It implements the state-of-the-art context-aware recommendation algorithms, and we will showcase the ease with which CARSKit allows recommenders to be configured and evaluated in this demo.
32-Bit Cyclic Redundancy Codes for Internet Applications

Standardized 32-bit Cyclic Redundancy Codes provide fewer bits of guaranteed error detection than they could, achieving a Hamming Distance (HD) of only 4 for maximum-length Ethernet messages, whereas HD=6 is possible. Although research has revealed improved codes, exploring the entire design space has previously been computationally intractable, even for special-purpose hardware. Moreover, no CRC polynomial has yet been found that satisfies an emerging need to attain both HD=6 for 12K bit messages and HD=4 for message lengths beyond 64K bits. This paper presents results from the first exhaustive search of the 32-bit CRC design space. Results from previous research are validated and extended to include identifying all polynomials achieving a better HD than the IEEE 802.3 CRC-32 polynomial. A new class of polynomials is identified that provides HD=6 up to nearly 16K bit and HD=4 up to 114K bit message lengths, providing the best achievable design point that maximizes error detection for both legacy and new applications, including potentially iSCSI and application-implemented error checks.
Rapid understanding of scientific paper collections: Integrating statistics, text analytics, and visualization

Keeping up with rapidly growing research fields, especially when there are multiple interdisciplinary sources, requires substantial effort for researchers, program managers, or venture capital investors. Current theories and tools are directed at finding a paper or website, not gaining an understanding of the key papers, authors, controversies, and hypotheses. This report presents an effort to integrate statistics, text analytics, and visualization in a multiple coordinated window environment that supports exploration. Our prototype system, Action Science Explorer (ASE), provides an environment for demonstrating principles of coordination and conducting iterative usability tests of them with interested and knowledgeable users. We developed an understanding of the value of reference management, statistics, citation context extraction, natural language summarization for single and multiple documents, filters to interactively select key papers, and network visualization to see citation patterns and identify clusters. The three-phase usability study guided our revisions to ASE and led us to improve the testing methods.
Municipal solid waste management in Indian cities - A review.

Municipal solid waste management (MSWM) is one of the major environmental problems of Indian cities. Improper management of municipal solid waste (MSW) causes hazards to inhabitants. Various studies reveal that about 90% of MSW is disposed of unscientifically in open dumps and landfills, creating problems to public health and the environment. In the present study, an attempt has been made to provide a comprehensive review of the characteristics, generation, collection and transportation, disposal and treatment technologies of MSW practiced in India. The study pertaining to MSWM for Indian cities has been carried out to evaluate the current status and identify the major problems. Various adopted treatment technologies for MSW are critically reviewed, along with their advantages and limitations. The study is concluded with a few fruitful suggestions, which may be beneficial to encourage the competent authorities/researchers to work towards further improvement of the present system.
Genetic Programming II: Automatic Discovery of Reusable Programs.

Reading Genetic Programming IE Automatic Discovery ofReusable Programs (GPII) in its entirety is not a task for the weak-willed because the book without appendices is about 650 pages. An entire previous book by the same author  is devoted to describing Genetic Programming (GP), while this book is a sequel extolling an extension called Automatically Defined Functions (ADFs). The author, John R. Koza, argues that ADFs can be used in conjunction with GP to improve its efficacy on large problems. "An automatically defined function (ADF) is a function (i.e., subroutine, procedure, module) that is dynamically evolved during a run of genetic programming and which may be called by a calling program (e.g., a main program) that is simultaneously being evolved" (p. 1). Dr. Koza recommends adding the ADF technique to the "GP toolkit." The book presents evidence that it is possible to interpret GP with ADFs as performing either a top-down process of problem decomposition or a bottom-up process of representational change to exploit identified regularities. This is stated as Main Point 1. Main Point 2 states that ADFs work by exploiting inherent regularities, symmetries, patterns, modularities, and homogeneities within a problem, though perhaps in ways that are very different from the style of programmers.  3 to 7 are appropriately qualified statements to the effect that, with a variety of problems, ADFs pay off be-
The road to SDN: an intellectual history of programmable networks

Software Defined Networking (SDN) is an exciting technology that enables innovation in how we design and manage networks. Although this technology seems to have appeared suddenly, SDN is part of a long history of efforts to make computer networks more programmable. In this paper, we trace the intellectual history of programmable networks, including active networks, early efforts to separate the control and data plane, and more recent work on OpenFlow and network operating systems. We highlight key concepts, as well as the technology pushes and application pulls that spurred each innovation. Along the way, we debunk common myths and misconceptions about the technologies and clarify the relationship between SDN and related technologies such as network virtualization.
Stereopsis via deep learning

Estimation of binocular disparity in vision systems is typically based on a matching pipeline and rectification. Estimation of disparity in the brain, in contrast, is widely assumed to be based on the comparison of local phase information from binocular receptive fields. The classic binocular energy model shows that this requires the presence of local quadrature pairs within the eye which show phaseor position-shifts across the eyes. While numerous theoretical accounts of stereopsis have been based on these observations, there has been little work on how energy models and depth inference may emerge through learning from the statistics of image pairs. Here, we describe a probabilistic, deep learning approach to modeling disparity and a methodology for generating binocular training data to estimate model parameters. We show that within-eye quadrature filters occur as a result of fitting the model to data, and we demonstrate how a three-layer network can learn to infer depth entirely from training data. We also show how training energy models can provide depth cues that are useful for recognition. We also show that pooling over more than two filters leads to richer dependencies between the learned filters.
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis

Genetic algorithms play a significant role, as search techniques forhandling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the naturalevolution principles of populations. These algorithms process apopulation of chromosomes, which represent search space solutions,with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.
Human Activity Recognition on Smart Phones

This paper analyzes the performance of different classification methods for online activity recognition on smart phones using the built-in accelerometers. First, we evaluate the performance of activity recognition using the Naïve Bayes classifier and next we utilize an improvement of Minimum Distance and K-Nearest Neighbor (KNN) classification algorithms, called Clustered KNN. For the purpose of online recognition, clustered KNN eliminates the computational complexity of KNN by creating clusters, i.e., smaller training sets for each activity and classification is performed based on these compact, reduced sets. We evaluate the performance of these classifiers on five test subjects for activities of walking, running, sitting and standing, and find that Naïve Bayes provides not satisfactory results whereas Clustered KNN gives promising results compared to the previous studies and even with the ones which consider offline classification.
Named-Entity Recognition from Greek and English Texts

Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The timeconsuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.
The design of low power 11.6mW high speed 1.8Gb/s stand-alone LVDS driver in 0.18-µm CMOS

This paper presents a design of power efficient and high speed transmitter implemented in CMOS 180nm UMC technology, fully compatible with low voltage differential signaling (LVDS) standard specified by IEEE. The main stand-alone driver's functional blocks: LVDS core, common mode feedback (CMFB), control buffer and band-gap reference source are described in detail. Three architectures of LVDS transmitter cores: bridged-switched current source (BSCS), double current source (DCS) and open drain (OD) are compared and finally the BSCS architecture is selected due to project's requirements. The designed LVDS driver characterizes a very low level of static and dynamic power dissipation, Pstat = 7.5mW and Pdyn = 11.6mW respectively, at data speed transmission 1.8Gb/s with receiver's input capacitance CR = 1pF and Pdyn = 8.5mW, data rates equals 400Mb/s at CR = 5pF.
Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI

We present our 11-layers deep, double-pathway, 3D Convolutional Neural Network, developed for the segmentation of brain lesions. The developed system segments pathology voxel-wise after processing a corresponding multi-modal 3D patch at multiple scales. We demonstrate that it is possible to train such a deep and wide 3D CNN on a small dataset of 28 cases. Our network yields promising results on the task of segmenting ischemic stroke lesions, accomplishing a mean Dice of 64% (66% after postprocessing) on the ISLES 2015 training dataset, ranking among the top entries. Regardless its size, our network is capable of processing a 3D brain volume in 3 minutes, making it applicable to the automated analysis of larger study cohorts.
I2R: Three Systems for Word Sense Discrimination, Chinese Word Sense Disambiguation, and English Word Sense Disambiguation

This paper describes the implementation of our three systems at SemEval-2007, for task 2 (word sense discrimination), task 5 (Chinese word sense disambiguation), and the first subtask in task 17 (English word sense disambiguation). For task 2, we applied a cluster validation method to estimate the number of senses of a target word in untagged data, and then grouped the instances of this target word into the estimated number of clusters. For both task 5 and task 17, We used the label propagation algorithm as the classifier for sense disambiguation. Our system at task 2 achieved 63.9% F-score under unsupervised evaluation, and 71.9% supervised recall with supervised evaluation. For task 5, our system obtained 71.2% micro-average precision and 74.7% macro-average precision. For the lexical sample subtask for task 17, our system achieved 86.4% coarsegrained precision and recall.
Photosensitive graphene transistors.

High performance photodetectors play important roles in the development of innovative technologies in many fields, including medicine, display and imaging, military, optical communication, environment monitoring, security check, scientific research and industrial processing control. Graphene, the most fascinating two-dimensional material, has demonstrated promising applications in various types of photodetectors from terahertz to ultraviolet, due to its ultrahigh carrier mobility and light absorption in broad wavelength range. Graphene field effect transistors are recognized as a type of excellent transducers for photodetection thanks to the inherent amplification function of the transistors, the feasibility of miniaturization and the unique properties of graphene. In this review, we will introduce the applications of graphene transistors as photodetectors in different wavelength ranges including terahertz, infrared, visible, and ultraviolet, focusing on the device design, physics and photosensitive performance. Since the device properties are closely related to the quality of graphene, the devices based on graphene prepared with different methods will be addressed separately with a view to demonstrating more clearly their advantages and shortcomings in practical applications. It is expected that highly sensitive photodetectors based on graphene transistors will find important applications in many emerging areas especially flexible, wearable, printable or transparent electronics and high frequency communications.
6-Element 28/38 GHz dual-band MIMO PIFA for future 5G cellular systems

This paper presents a 6-element 28/38 GHz dualband multiple-input multiple-output (MIMO) printed inverted F-antenna (PIFA) for the future millimeter-wave (mm-Wave) fifth generation (5G) cellular communication systems. The MIMO antenna system consists of six dual-band PIFA antenna elements in a certain arrangement. This arrangement has been chosen based on an extensive study to select the best one for good matching impedance, better isolation and reasonable radiation pattern and gain.
Anomaly Detection in Time Series of Graphs using ARMA Processes

There are many situations in which indicators of changes or anomalies in communication networks can be helpful, e.g. in the identification of faults. A dynamic communication network is characterised as a series of graphs with vertices representing IP addresses and edges representing information exchange between these entities weighted by packets sent. Ten graph distance metrics are used to create time series of network changes by sequentially comparing graphs from adjacent periods. These time series are individually modelled as univariate autoregressive moving average (ARMA) processes. Each time series is assessed on the ability of the best ARMA model of it to identify anomalies through residual thresholding.
Position based fluids

In fluid simulation, enforcing incompressibility is crucial for realism; it is also computationally expensive. Recent work has improved efficiency, but still requires time-steps that are impractical for real-time applications. In this work we present an iterative density solver integrated into the Position Based Dynamics framework (PBD). By formulating and solving a set of positional constraints that enforce constant density, our method allows similar incompressibility and convergence to modern smoothed particle hydro-dynamic (SPH) solvers, but inherits the stability of the geometric, position based dynamics method, allowing large time steps suitable for real-time applications. We incorporate an artificial pressure term that improves particle distribution, creates surface tension, and lowers the neighborhood requirements of traditional SPH. Finally, we address the issue of energy loss by applying vorticity confinement as a velocity post process.