nils-herrmann/clean_scirepeval huggingface dataset:
A Polynomial-Time Algorithm for Learning Noisy Linear Threshold Functions

In this paper we consider the problem of learning a linear threshold function (a half-space in n dimensions, also called a ``perceptron''). Methods for solving this problem generally fall into two categories. In the absence of noise, this problem can be formulated as a Linear Program and solved in polynomial time with the Ellipsoid Algorithm or Interior Point methods. Alternatively, simple greedy algorithms such as the Perceptron Algorithm are often used in practice and have certain provable noise-tolerance properties; but their running time depends on a separation parameter, which quantifies the amount of ``wiggle room'' available for a solution, and can be exponential in the description length of the input. In this paper we show how simple greedy methods can be used to find weak hypotheses (hypotheses that correctly classify noticeably more than half of the examples) in polynomial time, without dependence on any separation parameter. Suitably combining these hypotheses results in a polynomial-time algorithm for learning linear threshold functions in the PAC model in the presence of random classification noise. (Also, a polynomial-time algorithm for learning linear threshold functions in the Statistical Query model of Kearns.) Our algorithm is based on a new method for removing outliers in data. Specifically, for any set S of points in R n , each given to b bits of precision, we show that one can remove only a small fraction of S so that in the remaining set T , for every vector v , max x ∈ T (v . x) 2 ≤ poly(n,b) E x ∈ T (v . x) 2 ; i.e., for any hyperplane through the origin, the maximum distance (squared) from a point in T to the plane is at most polynomially larger than the average. After removing these outliers, we are able to show that a modified version of the Perceptron Algorithm finds a weak hypothesis in polynomial time, even in the presence of random classification noise.
Implementation of Recurrent Neural Network Algorithm for Shortest Path Calculation in Network Routing

This paper describes the architecture and implementation of a shortest path processor, both in reconfigurable hardware and VLSI. This processor is based on the principles of recurrent spatiotemporal neural network. The processor’s operation is similar to Dijkstra’s algorithm and it can be used for network routing calculations. The objective of the processor is to find the least cost path in a weighted graph between a given node and one or more destinations. The digital implementation exhibits a regular interconnect structure and uses simple processing elements, which is well suited for VLSI implementation and reconfigurable hardware.
A Criticism to Society (As Seen by Twitter Analytics)

Analytic tools are beginning to be largely employed, given their ability to rank, e.g., the visibility of social media users. Visibility that, in turns, can have a monetary value, since social media popular people usually either anticipate or establish trends that could impact the real world (at least, from a consumer point of view). The above rationale has fostered the flourishing of private companies providing statistical results for social media analysis. These results have been accepted, and largely diffused, by media without any apparent scrutiny, while Academia has moderately focused its attention on this phenomenon. In this paper, we provide evidence that analytic results provided by field-flagship companies are questionable (at least). In particular, we focus on Twitter and its "fake followers". We survey popular Twitter analytics that count the fake followers of some target account. We perform a series of experiments aimed at verifying the trustworthiness of their results. We compare the results of such tools with a machine-learning classifier whose methodology bases on scientific basis and on a sound sampling scheme. The findings of this work call for a serious re-thinking of the methodology currently used by companies providing analytic results, whose present deliveries seem to lack on any reliability.
A Partitioning Algorithm for Markov Decision Processes with Applications to Market Microstructure

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Phonetic Bengali Input Method for Computer and Mobile Devices

Current mobile devices do not support Bangla (or Bengali) Input method. Due to this many Bangla language speakers have to write Bangla in mobile phone using English alphabets. During this time they used to write English foreign words using English spelling. This tendency also exists when writing in computer using phonetically input methods, which cause many typing mistakes. In this scenario, computer transliteration input method need to correct the foreign words written using English spelling. In this paper, we proposed a transliteration input method for Bangla language. For English foreign words, the system used International-PhoneticAlphabet(IPA)-based transliteration method for Bangla language. Our proposed approach improved the quality of Bangla transliteration input method by 14 points.
Adult Image Classification by a Local-Context Aware Network

To build a healthy online environment, adult image recognition is a crucial and challenging task. Recent deep learning based methods have brought great advances to this task. However, the recognition accuracy and generalization ability need to be further improved. In this paper, a local-context aware network is proposed to improve the recognition accuracy and a corresponding curriculum learning strategy is proposed to guarantee a good generalization ability. The main idea is to integrate the global classification and the local sensitive region detection into one network and optimize them simulatenously. Such strategy helps the classification networks focus more on suspicious regions and thus provide better recognition performance. Two datasets containing over 150,000 images have been collected to evaluate the performance of the proposed approach. From the experiment results, it is observed that our approach can always achieve the best classification accuracy compared with several state-of-the-art approaches investigated.
BG: A Benchmark to Evaluate Interactive Social Networking Actions

BG is a benchmarkthat ratesa datastorefor processinginteracti ve socialnetworking actionsusing a pre-specifiedservicelevel agreement, SLA. An exampleSLA mayrequire95%of issuedrequeststo observe a responsetime fasterthan100milliseconds.BG computestwo differentratingsnamedSoAR andSocialites. In addition,it elevatestheamountof unpredictabledataproducedby adatastoreto afirst classmetric,includingit asakey component of theSLA andquantifyingit asapartof thebenchmarking process. Onemay useBG for a variety of purposesrangingfrom comparingdifferentdatastoreswith one another , evaluatingalternati ve physicaldataorganizationtechniquesgivena datastore,quantifyingthe performancecharacteristicsof adatastorein thepresenceof failures(eitherCPor AP in CAPtheorem), amongothers.This studyillustratesBG’s first usecase,comparinga documentstorewith anindustrial strengthrelationaldatabasemanagement system(RDBMS)deployedeitherin standalonemodeor augmentedwith memcached.No onesystemis superiorfor all BG actions.However, whenconsideringa mix of actions,thememcachedaugmentedRDBMS produceshigherratings.
Towards intelligent caring agents for Aging-In-Place: Issues and challenges

The aging of the world's population presents vast societal and individual challenges. The relatively shrinking workforce to support the growing population of the elderly leads to a rapidly increasing amount of technological innovations in the field of elderly care. In this paper, we present an integrated framework consisting of various intelligent agents with their own expertise and responsibilities working in a holistic manner to assist, care, and accompany the elderly around the clock in the home environment. To support the independence of the elderly for Aging-In-Place (AIP), the intelligent agents must well understand the elderly, be fully aware of the home environment, possess high-level reasoning and learning capabilities, and provide appropriate tender care in the physical, cognitive, emotional, and social aspects. The intelligent agents sense in non-intrusive ways from different sources and provide wellness monitoring, recommendations, and services across diverse platforms and locations. They collaborate together and interact with the elderly in a natural and holistic manner to provide all-around tender care reactively and proactively. We present our implementation of the collaboration framework with a number of realized functionalities of the intelligent agents, highlighting its feasibility and importance in addressing various challenges in AIP.
Javelin: Internet-based Parallel Computing using Java

Java offers the basic infrastructure needed to integrate computers connected to the Internet into a seamless parallel computational resource: a flexible, easily-installed infrastructure for running coarsegrained parallel applications on numerous, anonymous machines. Ease of participation is seen as a key property for such a resource to realize the vision of a multiprocessing environment comprising thousands of computers. We present Javelin, a Java-based infrastructure for global computing. The system is based on Internet software technology that is essentially ubiquitous: Web technology. Its architecture and implementation require participants to have access only to a Java-enabled Web browser. The security constraints implied by this, the resulting architecture, and current implementation are presented. The Javelin architecture is intended to be a substrate on which various programming models may be implemented. Several such models are presented: A Linda Tuple Space, an SPMD programming model with barriers, as well as support for message passing. Experimental results are given in the form of micro-benchmarks and a Mersenne Prime application that runs on a heterogeneous network of several parallel machines, workstations, and PCs.
Solving the symbol grounding problem: a critical review of fifteen years of research

This article reviews eight proposed strategies for solving the Symbol Grounding Problem (SGP), which was given its classic formulation in Harnad (1990). After a concise introduction, we provide an analysis of the requirement that must be satisfied by any hypothesis seeking to solve the SGP, the zero semantical commitment condition. We then use it to assess the eight strategies, which are organised into three main approaches: representationalism, semi-representationalism and nonrepresentationalism. The conclusion is that all the strategies are semantically committed and hence that none of them provides a valid solution to the SGP, which remains an open problem.
Language-independent Techniques for Automated Text Summarization

Text summarization is the process of distilling the most important information from source/sources to produce an abridged version for a particular user/users and task/tasks. Automatically generated summaries can significantly reduce the information overload on intelligence analysts in their daily work. Moreover, automated text summarization can be utilized for automated classification and filtering of text documents, information search over the Internet, content recommendation systems, online social networks, etc. The increasing trend of cross-border globalization accompanied by the growing multi-linguality of the Internet requires text summarization techniques to work equally well on multiple languages. However, only some of the automated summarization methods proposed in the literature can be defined as “multi-lingual" or “language-independent," as they are not based on any morphological analysis of the summarized text. In this chapter, we present a novel approach called MUSE (MUltilingual Sentence Extractor) to “language-independent" extractive summarization, which represents the summary as a collection of the most informative fragments of the summarized document without any language-specific text analysis. We use a Genetic Algorithm to find the best linear combination of 31 sentence scoring metrics based on vector and graph representations of text documents. Our summarization methodology is evaluated on two monolingual corpora of English and Hebrew documents, and, in addition, on a bilingual collection of English and Hebrew documents. The results are compared to 15 statistical sentence scoring methods for extractive single-document summarization found in the literature and to several stateof-the-art summarization tools. These bilingual experiments show that the MUSE methodology significantly outperforms the existing approaches and tools in both languages.
Circulating Tumor Cell Clusters Are Oligoclonal Precursors of Breast Cancer Metastasis

Circulating tumor cell clusters (CTC clusters) are present in the blood of patients with cancer but their contribution to metastasis is not well defined. Using mouse models with tagged mammary tumors, we demonstrate that CTC clusters arise from oligoclonal tumor cell groupings and not from intravascular aggregation events. Although rare in the circulation compared with single CTCs, CTC clusters have 23- to 50-fold increased metastatic potential. In patients with breast cancer, single-cell resolution RNA sequencing of CTC clusters and single CTCs, matched within individual blood samples, identifies the cell junction component plakoglobin as highly differentially expressed. In mouse models, knockdown of plakoglobin abrogates CTC cluster formation and suppresses lung metastases. In breast cancer patients, both abundance of CTC clusters and high tumor plakoglobin levels denote adverse outcomes. Thus, CTC clusters are derived from multicellular groupings of primary tumor cells held together through plakoglobin-dependent intercellular adhesion, and though rare, they greatly contribute to the metastatic spread of cancer.
Multiple Solutions of Optimal PMU Placement Using Exponential Binary PSO Algorithm for Smart Grid Applications

For smart grid execution, one of the most important requirements is fast, precise, and efficient synchronized measurements, which are possible by phasor measurement unit (PMU). To achieve fully observable network with the least number of PMUs, optimal placement of PMU (OPP) is crucial. In trying to achieve OPP, priority may be given at critical buses, generator buses, or buses that are meant for future extension. Also, different applications will have to be kept in view while prioritizing PMU placement. Hence, OPP with multiple solutions (MSs) can offer better flexibility for different placement strategies as it can meet the best solution based on the requirements. To provide MSs, an effective exponential binary particle swarm optimization (EBPSO) algorithm is developed. In this algorithm, a nonlinear inertia-weight-coefficient is used to improve the searching capability. To incorporate previous position of particle, two innovative mathematical equations that can update particle's position are formulated. For quick and reliable convergence, two useful filtration techniques that can facilitate MSs are applied. Single mutation operator is conditionally applied to avoid stagnation. The EBPSO algorithm is so developed that it can provide MSs for various practical contingencies, such as single PMU outage and single line outage for different systems.
Gene Expression Analysis during Interaction of Tomato and Related Wild Species with Clavibacter michiganensis subsp. michiganensis

Clavibacter michiganensis subsp. michiganensis is a Gram-positive bacterial pathogen causing bacterial wilt and canker of tomato (Solanum lycopersicum), producing economic losses worldwide. In this study, gene expression analysis was conducted using several resistant tomato-related wild species, including Solanum peruvianum LA2157, S. peruvianum LA2172, and Solanum habrochaites LA2128, and a tomato susceptible species, to identify genes involved in disease response. Using cDNA-amplified fragment length polymorphism (AFLP), 403 differentially expressed transcripts were identified. Among those, several genes showed contrasting expression patterns among resistant and susceptible species, including genes involved in the ubiquitin-mediated protein degradation pathway and secretory peroxidase. These genes were up-regulated in resistant species, but down-regulated in susceptible species, suggesting their likely involvement in early plant defense responses following C. michiganensis subsp. michiganensis infection. These identified genes would serve as new candidate bacterial wilt disease resistance genes and should be subjected to further functional analyses to determine the molecular basis of incompatibility between wild species of tomato and C. michiganensis subsp. michiganensis. This would then contribute to the development of more effective and sustainable C. michiganensis subsp. michiganensis control methods.
Increased cerebral response during a divided attention task following sleep deprivation.

We recently reported that the brain showed greater responsiveness to some cognitive demands following total sleep deprivation (TSD). Specifically, verbal learning led to increased cerebral activation following TSD while arithmetic resulted in decreased activation. Here we report data from a divided attention task that combined verbal learning and arithmetic. Thirteen normal control subjects performed the task while undergoing functional magnetic resonance imaging (FMRI) scans after a normal night of sleep and following 35 h TSD. Behaviourally, subjects showed only modest impairments following TSD. With respect to cerebral activation, the results showed (a) increased activation in the prefrontal cortex and parietal lobes, particularly in the right hemisphere, following TSD, (b) activation in left inferior frontal gyrus correlated with increased subjective sleepiness after TSD, and (c) activation in bilateral parietal lobes correlated with the extent of intact memory performance after TSD. Many of the brain regions showing a greater response after TSD compared with normal sleep are thought to be involved in control of attention. These data imply that the divided attention task required more attentional resources (specifically, performance monitoring and sustained attention) following TSD than after normal sleep. Other neuroimaging results may relate to the verbal learning and/or arithmetic demands of the task. This is the first study to examine divided attention performance after TSD with neuroimaging and supports our previous suggestion that the brain may be more plastic during cognitive performance following TSD than previously thought.
That is your evidence?: Classifying stance in online political debate

A growing body of work has highlighted the challenges of identifying the stance that a speaker holds towards a particular topic, a task that involves identifying a holistic subjective disposition. We examine stance classification on a corpus of 4873 posts from the debate website ConvinceMe.net, for 14 topics ranging from the playful to the ideological. We show that ideological debates feature a greater share of rebuttal posts, and that rebuttal posts are significantly harder to classify for stance, for both humans and trained classifiers. We also demonstrate that the number of subjective expressions varies across debates, a fact correlated with the performance of systems sensitive to sentiment-bearing terms. We present results for classifying stance on a per topic basis that range from 60% to 75%, as compared to unigram baselines that vary between 47% and 66%. Our results suggest that features and methods that take into account the dialogic context of such posts improve accuracy.
A Two-Dimensional Bandwidth Extrapolation Technique for Polarimetric Synthetic Aperture Radar Images

The resolution of a synthetic aperture radar (SAR) image, in range and azimuth, is determined by the transmitted bandwidth and the synthetic aperture length, respectively. Various superresolution techniques for improving resolution have been proposed, and we have proposed an algorithm that we call polarimetric bandwidth extrapolation (PBWE). To apply PBWE to a radar image, one needs to first apply PBWE in the range direction and then in the azimuth direction, or vice versa . In this paper, PBWE is further extended to the 2-D case. This extended case (2D-PBWE) utilizes a 2-D polarimetric linear prediction model and expands the spatial frequency bandwidth in range and azimuth directions simultaneously. The performance of the 2D-PBWE is shown through a simulated radar image and a real polarimetric SAR image
Evaluating Criticism of Smart Growth 28 May 2015

This paper evaluates various criticisms of smart growth. It defines the concept of smart growth, contrasts it with sprawl, and describes common smart growth strategies. It examines various criticisms of smart growth including the claims that it harms consumers, infringes on freedom, increases traffic congestion and air pollution, reduces housing affordability, causes social problems, increases public service costs, requires wasteful transit subsidies and is unjustified. Some specific critics’ papers are examined. This analysis indicates that many claims by critics reflect an incomplete understanding of smart growth or inaccurate analysis. Critics identify some legitimate problems that must be addressed to optimize smart growth, but present no convincing evidence to diminish overall justifications for smart growth. Evaluating Criticism of Smart Growth Victoria Transport Policy Institute
RISKCALC FOR PRIVATE COMPANIES

This report describes and documents Moody's version of its RiskCalc default model for private firms. RiskCalc analyzes financial statement data to produce default probability predictions for corporate obligors — particularly those in the middle market. We discuss the model's derivation in detail, analyze its accuracy, and provide context for its application. The model's key advantage derives from Moody's unique and proprietary middle market private firm financial statement and default database (Credit Research Database), which comprises 28,104 companies and 1,604 defaults. Our main insights and conclusions are: