nils-herrmann/clean_scirepeval huggingface dataset:
Exposed ! A Survey of Attacks on Private Data

Privacy-preserving statistical data analysis addresses the general question of protecting privacy when publicly releasing information about a sensitive dataset. A privacy attack takes seemingly innocuous released information and uses it to discern the private details of individuals, thus demonstrating that such information compromises privacy. For example, re-identification attacks have shown that it is easy to link supposedly de-identified records to the identity of the individual concerned. This survey focuses on attacking aggregate data, such as statistics about how many individuals have a certain disease, genetic trait, or combination thereof. We consider two types of attacks: reconstruction attacks, which approximately determine a sensitive feature of all the individuals covered by the dataset, and tracing attacks, which determine whether or not a target individual’s data is included in the dataset. We also discuss techniques from the differential privacy literature for releasing approximate aggregate statistics while provably thwarting any privacy attack. 12.1 Review in Advance first posted online on December 21, 2016. (Changes may still occur before final publication online and in print.) Changes may still occur before final publication online and in print A nn u. R ev . S ta t. A pp l. 20 17 .4 . D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y U ni ve rs ity o f Sh er br oo ke o n 12 /2 5/ 16 . F or p er so na l u se o nl y. ST04CH12-Dwork ARI 14 December 2016 14:35
Opportunity Discovery , Entrepreneurial Action , and Economic Organization

This paper reviews and critiques the “opportunity discovery” approach to entrepreneurship and argues that entrepreneurship can be more thoroughly grounded, and more closely linked to more general problems of economic organization, by adopting the Cantillon-KnightMises understanding of entrepreneurship as judgment. I begin by distinguishing among occupational, structural, and functional approaches to entrepreneurship and distinguishing among two influential interpretations of the entrepreneurial function, discovery and judgment. I turn next to the contemporary literature on opportunity identification and argue that this literature misinterprets Kirzner’s instrumental use of the discovery metaphor and mistakenly makes “opportunities” the unit of analysis. I then describe an alternative approach in which investment is the unit of analysis and link this approach to Austrian capital theory. I close with some applications to organizational form and entrepreneurial teams. I thank Jay Barney, Nicolai Foss, Jeffrey Herbener, Joseph Mahoney, Mario Mondelli, Joseph Salerno, Sharon Alvarez (guest editor), two anonymous referees, and participants at the 2007 Washington University Conference on Opportunity Discovery and 2007 Academy of Management meetings for comments on previous versions.
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks

Click prediction is one of the fundamental problems in sponsored search. Most of existing studies took advantage of machine learning approaches to predict ad click for each event of ad view independently. However, as observed in the real-world sponsored search system, user’s behaviors on ads yield high dependency on how the user behaved along with the past time, especially in terms of what queries she submitted, what ads she clicked or ignored, and how long she spent on the landing pages of clicked ads, etc. Inspired by these observations, we introduce a novel framework based on Recurrent Neural Networks (RNN). Compared to traditional methods, this framework directly models the dependency on user’s sequential behaviors into the click prediction process through the recurrent structure in RNN. Large scale evaluations on the click-through logs from a commercial search engine demonstrate that our approach can significantly improve the click prediction accuracy, compared to sequence-independent approaches.
Language control in the bilingual brain.

How does the bilingual brain distinguish and control which language is in use? Previous functional imaging experiments have not been able to answer this question because proficient bilinguals activate the same brain regions irrespective of the language being tested. Here, we reveal that neuronal responses within the left caudate are sensitive to changes in the language or the meaning of words. By demonstrating this effect in populations of German-English and Japanese-English bilinguals, we suggest that the left caudate plays a universal role in monitoring and controlling the language in use.
Deep Reinforcement Learning using Capsules in Advanced Game Environments

Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to vast capabilities of Convolutional Neural Networks (ConvNet), enabling algorithms to extract useful information from noisy environments. Capsule Network (CapsNet) is a recent introduction to the Deep Learning algorithm group and has only barely begun to be explored. The network is an architecture for image classification, with superior performance for classification of the MNIST dataset. CapsNets have not been explored beyond image classification. This thesis introduces the use of CapsNet for Q-Learning based game algorithms. To successfully apply CapsNet in advanced game play, three main contributions follow. First, the introduction of four new game environments as frameworks for RL research with increasing complexity, namely Flash RL, Deep Line Wars, Deep RTS, and Deep Maze. These environments fill the gap between relatively simple and more complex game environments available for RL research and are in the thesis used to test and explore the CapsNet behavior. Second, the thesis introduces a generative modeling approach to produce artificial training data for use in Deep Learning models including CapsNets. We empirically show that conditional generative modeling can successfully generate game data of sufficient quality to train a Deep Q-Network well. Third, we show that CapsNet is a reliable architecture for Deep Q-Learning based algorithms for game AI. A capsule is a group of neurons that determine the presence of objects in the data and is in the literature shown to increase the robustness of training and predictions while lowering the amount training data needed. It should, therefore, be ideally suited for game plays. We conclusively show that capsules can be applied to Deep Q-Learning, and present experimental results of this method in the environments introduced. We further show that capsules do not scale as well as convolutions, indicating that CapsNet-based algorithms alone will not be able to play even more advanced games without improved scalability.
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the relevant driving series to make predictions. In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. In the first stage, we introduce an input attention mechanism to adaptively extract relevant driving series (a.k.a., input features) at each time step by referring to the previous encoder hidden state. In the second stage, we use a temporal attention mechanism to select relevant encoder hidden states across all time steps. With this dual-stage attention scheme, our model can not only make predictions effectively, but can also be easily interpreted. Thorough empirical studies based upon the SML 2010 dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can outperform state-of-the-art methods for time series prediction.
A Qualitative Framework for Collecting and Analyzing Data in Focus Group Research

Despite the abundance of published material on conducting focus groups, scant specific information exists on how to analyze focus group data in social science research. Thus, the authors provide a new qualitative framework for collecting and analyzing focus group data. First, they identify types of data that can be collected during focus groups. Second, they identify the qualitative data analysis techniques best suited for analyzing these data. Third, they introduce what they term as a micro-interlocutor analysis, wherein meticulous information about which participant responds to each question, the order in which each participant responds, response characteristics, the nonverbal communication used, and the like is collected, analyzed, and interpreted. They conceptualize how conversation analysis offers great potential for analyzing focus group data. They believe that their framework goes far beyond analyzing only the verbal communication of focus group participants, thereby increasing the rigor of focus group analyses in social science research.  2009, 8(3) 2
A Study of Machine Learning in Wireless Sensor Network

Within this Paper, a concept of machine learning strategies suggested. In this investigation to address the design issues in WSNs is introduced. As can be viewed within this paper, countless endeavors have induced up to now; several layout issues in wireless sensor networks have been remedied employing numerous machine learning strategies. Utilizing machine learning based algorithms in WSNs need to deem numerous constraints, for instance, minimal sources of the network application that really needs distinct events to be tracked as well as other operational and non-operational aspects. Index Terms – Wireless Sensor Network, Machine Learning, Supervised Machine Learning, Unsupervised Machine Learning.
Exploring Markov Logic Networks for Question Answering

Elementary-level science exams pose significant knowledge acquisition and reasoning challenges for automatic question answering. We develop a system that reasons with knowledge derived from textbooks, represented in a subset of firstorder logic. Automatic extraction, while scalable, often results in knowledge that is incomplete and noisy, motivating use of reasoning mechanisms that handle uncertainty. Markov Logic Networks (MLNs) seem a natural model for expressing such knowledge, but the exact way of leveraging MLNs is by no means obvious. We investigate three ways of applying MLNs to our task. First, we simply use the extracted science rules directly as MLN clauses and exploit the structure present in hard constraints to improve tractability. Second, we interpret science rules as describing prototypical entities, resulting in a drastically simplified but brittle network. Our third approach, called Praline, uses MLNs to align lexical elements as well as define and control how inference should be performed in this task. Praline demonstrates a 15% accuracy boost and a 10x reduction in runtime as compared to other MLNbased methods, and comparable accuracy to word-based baseline approaches.
Efficient Learning of Selective Bayesian Network Classifiers

In this paper, we empirically evaluate algorithms for learning four Bayesian network (BN) classifiers: Naïve-Bayes, tree augmented Naïve-Bayes (TANs), BN augmented NaïveBayes (BANs) and general BNs (GBNs), where the GBNs and BANs are learned using two variants of a conditional independence based BN-learning algorithm. Experimental results show the GBNs and BANs learned using the proposing learning algorithms are competitive with (or superior to) the best classifiers based on both Bayesian networks and other formalisms, and that the computational time for learning and using these classifiers is relatively small. These results argue that BN classifiers deserve more attention in machine learning and data mining communities.
On the impact of phoneme alignment in DNN-based speech synthesis

Recently, deep neural networks (DNNs) have significantly improved the performance of acoustic modeling in statistical parametric speech synthesis (SPSS). However, in current implementations, when training a DNN-based speech synthesis system, phonetic transcripts are required to be aligned with the corresponding speech frames to obtain the phonetic segmentation, called phoneme alignment. Such an alignment is usually obtained by forced alignment based on hidden Markov models (HMMs) since manual alignment is labor-intensive and timeconsuming. In this work, we study the impact of phoneme alignment on the DNN-based speech synthesis system. Specifically, we compare the performances of different DNN-based speech synthesis systems, which use manual alignment and HMM-based forced alignment from three types of labels: HMM mono-phone, tri-phone and full-context. Objective and subjective evaluations are conducted in term of the naturalness of synthesized speech to compare the performances of different alignments.
Climate change , sea-level rise , and conservation : keeping island biodiversity afloat

Island conservation in the longer term Conservation of biodiversity on islands is important globally because islands are home to more than 20% of the terrestrial plant and vertebrate species in the world, within less than 5% of the global terrestrial area. Endemism on islands is a magnitude higher than on continents ; ten of the 35 biodiversity hotspots in the world are entirely, or largely consist of, islands . Yet this diversity is threatened: over half of all recent extinctions have occurred on islands, which currently harbor over one-third of all terrestrial species facing imminent extinction  . In response to the biodiversity crisis, island conservation has been an active field of research and action. Hundreds of invasive species eradications and endangered species translocations have been successfully completed [4–6]. However, despite climate change being an increasing research focus generally, its impacts on island biodiversity are only just beginning to be investigated. For example, invasive species eradications on islands have been prioritized largely by threats to native biodiversity, eradication feasibility, economic cost, and reinvasion potential, but have never considered the threat of sea-level rise. Yet, the probability and extent of island submersion would provide a relevant metric for the longevity of long-term benefits of such eradications.
Deep learning approach to link weight prediction

Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to link weight prediction problem. This model extracts knowledge of nodes from known links' weights and uses this knowledge to predict unknown links' weights. We demonstrate the power of Model R through experiments and compare it with stochastic block model and its derivatives. Model R shows that deep learning can be successfully applied to link weight prediction and it outperforms stochastic block model and its derivatives by up to 73% in terms of prediction accuracy. We anticipate this new approach to provide effective solutions to more graph mining tasks.
Cross-lingual Models of Word Embeddings: An Empirical Comparison

Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature. We perform an extensive evaluation of four popular approaches of inducing cross-lingual embeddings, each requiring a different form of supervision, on four typologically different language pairs. Our evaluation setup spans four different tasks, including intrinsic evaluation on mono-lingual and cross-lingual similarity, and extrinsic evaluation on downstream semantic and syntactic applications. We show that models which require expensive cross-lingual knowledge almost always perform better, but cheaply supervised models often prove competitive on certain tasks.
POS-originated transaction traces as a source of contextual information for risk management systems in EFT transactions

Transaction traces analysis is a key utility for marketing, trend monitoring, and fraud detection purposes. However, they can also be used for designing and verification of contextual risk management systems for card-present transactions. In this paper, we presented a novel approach to collect detailed transaction traces directly from payment terminal. Thanks to that, it is possible to analyze each transaction step precisely, including its frequency and timing. We also demonstrated our approach to analyze such data based on real-life experiment. Finally, we concluded this paper with important findings for designers of such a system.
Effective Deep Memory Networks for Distant Supervised Relation Extraction

Distant supervised relation extraction (RE) has been an effective way of finding novel relational facts from text without labeled training data. Typically it can be formalized as a multi-instance multilabel problem. In this paper, we introduce a novel neural approach for distant supervised RE with special focus on attention mechanisms. Unlike the feature-based logistic regression model and compositional neural models such as CNN, our approach includes two major attention-based memory components, which are capable of explicitly capturing the importance of each context word for modeling the representation of the entity pair, as well as the intrinsic dependencies between relations. Such importance degree and dependency relationship are calculated with multiple computational layers, each of which is a neural attention model over an external memory. Experiment on real-world datasets shows that our approach performs significantly and consistently better than various baselines.
Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications

Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al. this issue).
Encoding Relation Requirements for Relation Extraction via Joint Inference

Most existing relation extraction models make predictions for each entity pair locally and individually, while ignoring implicit global clues available in the knowledge base, sometimes leading to conflicts among local predictions from different entity pairs. In this paper, we propose a joint inference framework that utilizes these global clues to resolve disagreements among local predictions. We exploit two kinds of clues to generate constraints which can capture the implicit type and cardinality requirements of a relation. Experimental results on three datasets, in both English and Chinese, show that our framework outperforms the state-of-theart relation extraction models when such clues are applicable to the datasets. And, we find that the clues learnt automatically from existing knowledge bases perform comparably to those refined by human.
A pathways model of problem and pathological gambling.

At the moment, there is no single conceptual theoretical model of gambling that adequately accounts for the multiple biological, psychological and ecological variables contributing to the development of pathological gambling. Advances in this area are hampered by imprecise definitions of pathological gambling, failure to distinguish between gambling problems and problem gamblers and a tendency to assume that pathological gamblers form one, homogeneous population with similar psychological principles applying equally to all members of the class. The purpose of this paper is to advance a pathways model that integrates the complex array of biological, personality, developmental, cognitive, learning theory and ecological determinants of problem and pathological gambling. It is proposed that three distinct subgroups of gamblers manifesting impaired control over their behaviour can be identified. These groups include (a) behaviourally conditioned problem gamblers, (b) emotionally vulnerable problem gamblers and (c) antisocial, impulsivist problem gamblers. The implications for clinical management are discussed.
Understanding, scoping and defining user experience: a survey approach

Despite the growing interest in user experience (UX), it has been hard to gain a common agreement on the nature and scope of UX. In this paper, we report a survey that gathered the views on UX of 275 researchers and practitioners from academia and industry. Most respondents agree that UX is dynamic, context-dependent, and subjective. With respect to the more controversial issues, the authors propose to delineate UX as something individual (instead of social) that emerges from interacting with a product, system, service or an object. The draft ISO definition on UX seems to be in line with the survey findings, although the issues of experiencing anticipated use and the object of UX will require further explication. The outcome of this survey lays ground for understanding, scoping, and defining the concept of user experience.
DaDianNao: A Machine-Learning Supercomputer

Many companies are deploying services, either for consumers or industry, which are largely based on machine-learning algorithms for sophisticated processing of large amounts of data. The state-of-the-art and most popular such machine-learning algorithms are Convolutional and Deep Neural Networks (CNNs and DNNs), which are known to be both computationally and memory intensive. A number of neural network accelerators have been recently proposed which can offer high computational capacity/area ratio, but which remain hampered by memory accesses.
 However, unlike the memory wall faced by processors on general-purpose workloads, the CNNs and DNNs memory footprint, while large, is not beyond the capability of the on-chip storage of a multi-chip system. This property, combined with the CNN/DNN algorithmic characteristics, can lead to high internal bandwidth and low external communications, which can in turn enable high-degree parallelism at a reasonable area cost. In this article, we introduce a custom multi-chip machine-learning architecture along those lines. We show that, on a subset of the largest known neural network layers, it is possible to achieve a speedup of 450.65x over a GPU, and reduce the energy by 150.31x on average for a 64-chip system. We implement the node down to the place and route at 28nm, containing a combination of custom storage and computational units, with industry-grade interconnects.