nils-herrmann/clean_scirepeval huggingface dataset:
Models of Semantic Representation with Visual Attributes

We consider the problem of grounding the meaning of words in the physical world and focus on the visual modality which we represent by visual attributes. We create a new large-scale taxonomy of visual attributes covering more than 500 concepts and their corresponding 688K images. We use this dataset to train attribute classifiers and integrate their predictions with text-based distributional models of word meaning. We show that these bimodal models give a better fit to human word association data compared to amodal models and word representations based on handcrafted norming data.
Efficient market hypothesis and forecasting

The efficient market hypothesis gives rise to forecasting tests that mirror those adopted when testing the optimality of a forecast in the context of a given information set. However, there are also important differences arising from the fact that market efficiency tests rely on establishing profitable trading opportunities in ‘real time’. Forecasters constantly search for predictable patterns and affect prices when they attempt to exploit trading opportunities. Stable forecasting patterns are therefore unlikely to persist for long periods of time and will self-destruct when discovered by a large number of investors. This gives rise to nonstationarities in the time series of financial returns and complicates both formal tests of market efficiency and the search for successful forecasting approaches. D 2003 International Institute of Forecasters.
New Well-being Measures : Short Scales to Assess Flourishing and Positive and Negative Feelings

Measures of well-being were created to assess psychological flourishing and feelings—positive feelings, negative feelings, and the difference between the two. The scales were evaluated in a sample of 689 college students from six locations. The Flourishing Scale is a brief 8-item summary measure of the respondent’s self-perceived success in important areas such as relationships, self-esteem, purpose, and optimism. The scale provides a single psychological well-being score. The measure has good psychometric properties, and is strongly associated with other psychological well-being scales. The Scale of Positive and Negative Experience produces a score for positive feelings (6 items), a score for negative feelings (6 items), and the two can be combined to create a balance score. This 12-item brief scale has a number of desirable features compared to earlier measures of positive and negative emotions. In particular, the scale assesses with a few E. Diener (&) Department of Psychology, University of Illinois, 603 E. Daniel Street, Champaign, IL 61820, USA e-mail: ediener@cyrus.psych.uiuc.edu E. Diener The Gallup Organization, Omaha, NE, USA D. Wirtz East Carolina University, Greenville, NC, USA W. Tov Singapore Management University, Bras Basah, Singapore C. Kim-Prieto College of New Jersey, Ewing, NJ, USA D. Choi California State University, East Bay, Hayward, CA, USA S. Oishi University of Virginia, Charlottesville, VA, USA R. Biswas-Diener Center for Applied Positive Psychology, Milwaukie, OR, USA 123 Soc Indic Res (2010) 97:143–156 DOI 10.1007/s11205-009-9493-y
Anomaly detection in online social networks

Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, and as being nline social networks labelled or unlabelled, and survey methods for detecting these different types of anomalies. We suggest that the detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this feature space. In addition, this paper provides an overview of the types of problems that anomaly detection can address and identifies key areas for future research.
Three Factors in Language Design

The biolinguistic perspective regards the language faculty as an organ of the body, along with other cognitive systems. Adopting it, we expect to find three factors that interact to determine (I-) languages attained: genetic endowment (the topic of Universal Grammar), experience, and principles that are language- or even organism-independent. Research has naturally focused on I-languages and UG, the problems of descriptive and explanatory adequacy. The Principles-and-Parameters approach opened the possibility for serious investigation of the third factor, and the attempt to account for properties of language in terms of general considerations of computational efficiency, eliminating some of the technology postulated as specific to language and providing more principled explanation of linguistic phenomena
A Maximum Entropy Approach to Natural Language Processing

The concept of maximum entropy can be traced back along multiple threads to Biblical times Only recently however have computers become powerful enough to permit the widescale application of this concept to real world problems in statistical estimation and pattern recognition In this paper we describe a method for statistical modeling based on maximum entropy We present a maximum likelihood approach for automatically con structing maximum entropy models and describe how to implement this approach e ciently using as examples several problems in natural language processing
Chapter 25 – MULTIPLE SYSTEMS OF PERCEPTUAL CATEGORY LEARNING: THEORY AND COGNITIVE TESTS

The first neurobiologically detailed theory of multiple systems in category learning, called COVIS, was originally conceived in 1998 (Ashby, Alfonso-Reese, Turken, & Waldron, 1998). COVIS, which is now well established, postulates two systems that compete throughout learning – a frontal-based declarative system that uses logical reasoning and depends on working memory and executive attention, and a basal ganglia-mediated system that uses procedural learning. The procedural system can learn a wide variety of category structures, but it learns in a slow incremental fashion and is highly dependent on reliable and immediate feedback. In contrast, the declarative rule-based system can learn a fairly small set of category structures quickly – specifically, those structures that can be learned via a logical reasoning process. These two systems learn simultaneously, but as long as rule-based strategies lead to successful performance, the declarative system inhibits the procedural system. This theory is described in detail and a variety of cognitive-behavioral and cognitive-neuroscience experiments are reviewed that test some parameter-free a priori predictions made by COVIS.
Power and empowerment in nursing: three theoretical approaches.

Definitions and uses of the concept of empowerment are wide-ranging: the term has been used to describe the essence of human existence and development, but also aspects of organizational effectiveness and quality. The empowerment ideology is rooted in social action where empowerment was associated with community interests and with attempts to increase the power and influence of oppressed groups (such as workers, women and ethnic minorities). Later, there was also growing recognition of the importance of the individual's characteristics and actions. Based on a review of the literature, this paper explores the uses of the empowerment concept as a framework for nurses' professional growth and development. Given the complexity of the concept, it is vital to understand the underlying philosophy before moving on to define its substance. The articles reviewed were classified into three groups on the basis of their theoretical orientation: critical social theory, organization theory and social psychological theory. Empowerment seems likely to provide for an umbrella concept of professional development in nursing.
A Proposed Architecture for Big Data Driven Supply Chain Analytics

Advancement in information and communication technology (ICT) has given rise to explosion of data in every field of operations. Working with the enormous volume of data (or Big Data, as it is popularly known as) for extraction of useful information to support decision making is one of the sources of competitive advantage for organizations today. Enterprises are leveraging the power of analytics in formulating business strategy in every facet of their operations to mitigate business risk. Volatile global market scenario has compelled the organizations to redefine their supply chain management (SCM). In this paper, we have delineated the relevance of Big Data and its importance in managing end to end supply chains for achieving business excellence. A Big Data-centric architecture for SCM has been proposed that exploits the current state of the art technology of data management, analytics and visualization. The security and privacy requirements of a Big Data system have also been highlighted and several mechanisms have been discussed to implement these features in a real world Big Data system deployment in the context of SCM. Some future scope of work has also been pointed out.  Big Data, Analytics, Cloud, Architecture, Protocols, Supply Chain Management, Security, Privacy.
3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation

Segmentation of colorectal cancerous regions from 3D Magnetic Resonance (MR) images is a crucial procedure for radiotherapy which conventionally requires accurate delineation of tumour boundaries at an expense of labor, time and reproducibility. While deep learning based methods serve good baselines in 3D image segmentation tasks, small applicable patch size limits effective receptive field and degrades segmentation performance. In addition, Regions of interest (RoIs) localization from large whole volume 3D images serves as a preceding operation that brings about multiple benefits in terms of speed, target completeness, reduction of false positives. Distinct from sliding window or non-joint localizationsegmentation based models, we propose a novel multitask framework referred to as 3D RoI-aware U-Net (3D RU-Net), for RoI localization and in-region segmentation where the two tasks share one backbone encoder network. With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detailpreserving segmentation and therefore enlarged applicable volume size and effective receptive field. To effectively train the model, we designed a Dice formulated loss function for the global-to-local multi-task learning procedure. Based on the efficiency gains demonstrated by the proposed method, we went on to ensemble models with different receptive fields to achieve even higher performance costing minor extra computational expensiveness. Extensive experiments were subsequently conducted on 64 cancerous cases with a four-fold cross-validation, and the results showed significant superiority in terms of accuracy and efficiency over conventional state-of-the art frameworks. In conclusion, the proposed method has a huge potential for extension to other 3D object segmentation tasks from medical images due to its inherent generalizability. The code for the proposed method is publicly available. 3D CNN, region of interest, multi-task learning, tumor segmentation, colorectal cancer.
Forecasting Volatility in Indian Stock Market using Artificial Neural Network with Multiple Inputs and Outputs

Volatility in stock markets has been extensively studied in the applied finance literature. In this paper, Artificial Neural Network models based on various back propagation algorithms have been constructed to predict volatility in the Indian stock market through volatility of NIFTY returns and volatility of gold returns. This model considers India VIX, CBOE VIX, volatility of crude oil returns (CRUDESDR), volatility of DJIA returns (DJIASDR), volatility of DAX returns (DAXSDR), volatility of Hang Seng returns (HANGSDR) and volatility of Nikkei returns (NIKKEISDR) as predictor variables. Three sets of experiments have been performed over three time periods to judge the effectiveness of the approach.
The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms

Online feedback mechanisms harness the bidirectional communication capabilities of the Internet to engineer large-scale, word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. Their growing popularity has potentially important implications for a wide range of management activities such as brand building, customer acquisition and retention, product development, and quality assurance. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based feedback mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, management science, sociology, and psychology to take into consideration the special properties of online environments. Finally, it identifies opportunities that this new area presents for operations research/management science (OR/MS) research.
SGXKernel: A Library Operating System Optimized for Intel SGX

Intel Software Guard Extensions (SGX) is an emerging trusted hardware technology. SGX enables user-level code to allocate regions of trusted memory, called enclaves, where the confidentiality and integrity of code and data are guaranteed. While SGX offers strong security for applications, one limitation of SGX is the lack of system call support inside enclaves, which leads to a non-trivial, refactoring effort when protecting existing applications with SGX. To address this issue, previous works have ported existing library OSes to SGX. However, these library OSes are suboptimal in terms of security and performance since they are designed without taking into account the characteristics of SGX.
 In this paper, we revisit the library OS approach in a new setting---Intel SGX. We first quantitatively evaluate the performance impact of enclave transitions on SGX programs, identifying it as a performance bottleneck for any library OSes that aim to support system-intensive SGX applications. We then present the design and implementation of SGXKernel, an in-enclave library OS, with highlight on its switchless design, which obviates the needs for enclave transitions. This switchless design is achieved by incorporating two novel ideas: asynchronous cross-enclave communication and preemptible in-enclave multi-threading. We intensively evaluate the performance of SGXKernel on microbenchmarks and application benchmarks. The results show that SGXKernel significantly outperforms a state-of-the-art library OS that has been ported to SGX.
Event Extraction as Dependency Parsing

Nested event structures are a common occurrence in both open domain and domain specific extraction tasks, e.g., a “crime” event can cause a “investigation” event, which can lead to an “arrest” event. However, most current approaches address event extraction with highly local models that extract each event and argument independently. We propose a simple approach for the extraction of such structures by taking the tree of event-argument relations and using it directly as the representation in a reranking dependency parser. This provides a simple framework that captures global properties of both nested and flat event structures. We explore a rich feature space that models both the events to be parsed and context from the original supporting text. Our approach obtains competitive results in the extraction of biomedical events from the BioNLP’09 shared task with a F1 score of 53.5% in development and 48.6% in testing.
Impact of Social Media and Web 2.0 on Decision-Making

Information technology continues to provide opportunities to alter the decisionmaking behavior of individuals, groups and organizations. Two related changes that are emerging are social media and Web 2.0 technologies. These technologies can positively and negatively impact the rationality and effectiveness of decision-making. For example, changes that help marketing managers alter consumer decision behavior may result in poorer decisions by consumers. Also, managers who heavily rely on a social network rather than expert opinion and facts may make biased decisions. A number of theories can help explain how social media may impact decision-making and the consequences.
Islamic Microfinance Supply : Providers and Products Providers

Sharia-compliant financial inclusion represents the confluence of two rapidly growing sectors: microfinance1 and Islamic finance. With an estimated 650 million Muslims living on less than $2 a day (Obaidullah and Tariqullay 2008), finding sustainable Islamic models could be the key to providing financial access to millions of Muslim poor who strive to avoid financial products that do not comply with Sharia (Islamic law). Consequently, Sharia-compliant financial inclusion has recently galvanized considerable interest among regulators, financial service providers, and other financial inclusion stakeholders. However, despite a four-fold increase in recent years in the number of poor clients using Sharia-compliant products (estimated at 1.28 million) and a doubling in the number of providers, the nascent sector continues to struggle to find sustainable business models with a broad array of products that can meet the diverse financial needs of religiously observant poor Muslims.
Adversarial Multi-task Learning for Text Classification

Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features. However, in most existing approaches, the extracted shared features are prone to be contaminated by task-specific features or the noise brought by other tasks. In this paper, we propose an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other. We conduct extensive experiments on 16 different text classification tasks, which demonstrates the benefits of our approach. Besides, we show that the shared knowledge learned by our proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks. The datasets of all 16 tasks are publicly available at http://nlp.fudan.
Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning

This paper presents a new method - adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts. Then, we incorporate the discriminator as another critic into the advantage actor-critic (A2C) framework, to encourage the dialogue agent to explore state-action within the regions where the agent takes actions similar to those of the experts. Experimental results in a movie-ticket booking domain show that the proposed Adversarial A2C can accelerate policy exploration efficiently.
Recorded Behavior as a Valuable Resource for Diagnostics in Mobile Phone Addiction: Evidence from Psychoinformatics

Psychologists and psychiatrists commonly rely on self-reports or interviews to diagnose or treat behavioral addictions. The present study introduces a novel source of data: recordings of the actual problem behavior under investigation. A total of N = 58 participants were asked to fill in a questionnaire measuring problematic mobile phone behavior featuring several questions on weekly phone usage. After filling in the questionnaire, all participants received an application to be installed on their smartphones, which recorded their phone usage for five weeks. The analyses revealed that weekly phone usage in hours was overestimated; in contrast, numbers of call and text message related variables were underestimated. Importantly, several associations between actual usage and being addicted to mobile phones could be derived exclusively from the recorded behavior, but not from self-report variables. The study demonstrates the potential benefit to include methods of psychoinformatics in the diagnosis and treatment of problematic mobile phone use.
Addressing vital sign alarm fatigue using personalized alarm thresholds

Alarm fatigue, a condition in which clinical staff become desensitized to alarms due to the high frequency of unnecessary alarms, is a major patient safety concern. Alarm fatigue is particularly prevalent in the pediatric setting, due to the high level of variation in vital signs with patient age. Existing studies have shown that the current default pediatric vital sign alarm thresholds are inappropriate, and lead to a larger than necessary alarm load. This study leverages a large database containing over 190 patient-years of heart rate data to accurately identify the 1st and 99th percentiles of an individual's heart rate on their first day of vital sign monitoring. These percentiles are then used as personalized vital sign thresholds, which are evaluated by comparing to non-default alarm thresholds used in practice, and by using the presence of major clinical events to infer alarm labels. Using the proposed personalized thresholds would decrease low and high heart rate alarms by up to 50% and 44% respectively, while maintaining sensitivity of 62% and increasing specificity to 49%. The proposed personalized vital sign alarm thresholds will reduce alarm fatigue, thus contributing to improved patient outcomes, shorter hospital stays, and reduced hospital costs.
RAM: Residual Attention Module for Single Image Super-Resolution

Attention mechanisms are a design trend of deep neural networks that stands out in various computer vision tasks. Recently, some works have attempted to apply attention mechanisms to single image super-resolution (SR) tasks. However, they apply the mechanisms to SR in the same or similar ways used for high-level computer vision problems without much consideration of the different nature between SR and other problems. In this paper, we propose a new attention method, which is composed of new channelwise and spatial attention mechanisms optimized for SR and a new fused attention to combine them. Based on this, we propose a new residual attention module (RAM) and a SR network using RAM (SRRAM). We provide in-depth experimental analysis of different attention mechanisms in SR. It is shown that the proposed method can construct both deep and lightweight SR networks showing improved performance in comparison to existing state-of-the-art methods.