nils-herrmann/clean_scirepeval huggingface dataset:
Neural Program Synthesis with Priority Queue Training

We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far. Then, we synthesize new programs and add them to the priority queue by sampling from the RNN. We benchmark our algorithm, called priority queue training (or PQT), against genetic algorithm and reinforcement learning baselines on a simple but expressive Turing complete programming language called BF. Our experimental results show that our simple PQT algorithm significantly outperforms the baselines. By adding a program length penalty to the reward function, we are able to synthesize short, human readable programs.
Marijuana and Coronary Heart Disease

Marijuana is the most commonly abused drug in the United States.1 It mainly acts on cannabinoid receptors. There are two types of cannabinoid receptors in humans: cannabinoid receptor type 1 (CB1) and cannabinoid receptor type 2 (CB2). CB1 receptor activation is pro-atherogenic, and CB2 receptor activation is largely anti-atherogenic. Marijuana use is also implicated as a trigger for myocardial infarction (MI) in patients with stable coronary artery disease (CAD).
Information rules - a strategic guide to the network economy

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An Integrated Approach to Filling Holes in Meshes

Data obtained by scanning 3D models typically contains missing pieces and holes. This paper describes an integrated approach for filling holes in triangular meshes obtained during the process of surface reconstruction, which consists of three steps: triangulation, refinement and smoothing. The approach first generates a minimal triangulation to the hole, and then iteratively subdivides the patching meshes making it assort with the density of surrounding meshes. Finally, geometric optimization is enforced to the patching meshes for improving regularity. The approach can deal with arbitrary holes in oriented connected manifold meshes. Experimental results on several triangular meshes are presented.
Adversarial Examples for Generative Models

We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. Deep generative models have recently become popular due to their ability to model input data distributions and generate realistic examples from those distributions. We present three classes of attacks on the VAE and VAE-GAN architectures and demonstrate them against networks trained on MNIST, SVHN and CelebA. Our first attack leverages classification-based adversaries by attaching a classifier to the trained encoder of the target generative model, which can then be used to indirectly manipulate the latent representation. Our second attack directly uses the VAE loss function to generate a target reconstruction image from the adversarial example. Our third attack moves beyond relying on classification or the standard loss for the gradient and directly optimizes against differences in source and target latent representations. We also motivate why an attacker might be interested in deploying such techniques against a target generative network.
Bidirectional LSTM-CRF for Clinical Concept Extraction

Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.e., treatments, tests and problems). State-of-the-art concept extraction approaches heavily rely on handcrafted features and domain-specific resources which are hard to collect and define. For this reason, this paper proposes an alternative, streamlined approach: a recurrent neural network (the bidirectional LSTM with CRF decoding) initialized with general-purpose, off-the-shelf word embeddings. The experimental results achieved on the 2010 i2b2/VA reference corpora using the proposed framework outperform all recent methods and ranks closely to the best submission from the original 2010 i2b2/VA challenge.
Tracing cyber attacks from the practical perspective

The integrity of the Internet is severely impaired by rampant denial of service and distributed DoS attacks. It is by no means trivial to devise a countermeasure to address these attacks because of their anonymous and distributed nature. This article presents a brief survey of the most promising recently proposed schemes for tracing cyber attacks: IP traceback. Since IP traceback technology is evolving rapidly, for the community to better comprehend and capture the properties of disparate traceback approaches, we first classify these schemes from multiple aspects. From the perspective of practicality and feasibility, we then analyze and explore the advantages and disadvantages of these schemes in depth so that shortcomings and possible enhancements of each scheme are highlighted. Finally, open problems and future work are discussed, and concluding remarks are drawn.
TESLA: a formally defined event specification language

The need for timely processing large amounts of information, flowing from the peripheral to the center of a system, is common to different application domains, and it has justified the development of several languages to describe how such information has to be processed. In this paper, we analyze such languages showing how most approaches lack the expressiveness required for the applications we target, or do not provide the precise semantics required to clearly state how the system should behave. Moving from these premises, we present TESLA, a complex event specification language. Each TESLA rule considers incoming data items as notifications of events and defines how certain patterns of events cause the occurrence of others, said to be "complex". TESLA has a simple syntax and a formal semantics, given in terms of a first order, metric temporal logic. It provides high expressiveness and flexibility in a rigorous framework, by offering content and temporal filters, negations, timers, aggregates, and fully customizable policies for event selection and consumption. The paper ends by showing how TESLA rules can be interpreted by a processing system, introducing an efficient event detection algorithm based on automata.
Automatic query-based keyword and keyphrase extraction

Extracting keywords and keyphrases mainly for identifying content of a document, has an importance role in text processing tasks such as text summarization, information retrieval, and query expansion. In this research, we introduce a new keyword/keyphrase extraction approach in which both single and multi-document keyword/keyphrase extraction techniques are considered. The proposed approach is specifically practical when a user is interested in additional data such as keywords/keyphrases related to a topic or query. In the proposed approach, first a set of documents are retrieved based on user's query, then a single document keyword extraction method is applied to extract candidate keyword/keyphrases from each retrieved document. Finally, a new re-scoring scheme is introduced to extract final keywords/keyphrases. We have evaluated the proposed method based on the relationship between the final keyword/keyphrases with the initial user query, and based user's satisfaction. Our experimental results show how much the extracted keywords/keyphrases are relevant and wellmatched with user's need.
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and backoff n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger questionanswer pair corpus and from pretrained word embeddings.
Work Stress as a Risk Factor for Cardiovascular Disease

The role of psychosocial work stress as a risk factor for chronic disease has been the subject of considerable debate. Many researchers argue in support of a causal connection while others remain skeptical and have argued that the effect on specific health conditions is either negligible or confounded. This review of evidence from over 600,000 men and women from 27 cohort studies in Europe, the USA and Japan suggests that work stressors, such as job strain and long working hours, are associated with a moderately elevated risk of incident coronary heart disease and stroke. The excess risk for exposed individuals is 10-40 % compared with those free of such stressors. Differences between men and women, younger versus older employees and workers from different socioeconomic backgrounds appear to be small, indicating that the association is robust. Meta-analyses of a wider range of health outcomes show additionally an association between work stress and type 2 diabetes, though not with common cancers or chronic obstructive pulmonary disease, suggesting outcome specificity. Few studies have addressed whether mitigation of work stressors would reduce the risk of cardiovascular disease. In view of the limited interventional evidence on benefits, harms and cost-effectiveness, definitive recommendations have not been made (e.g. by the US Preventive Services Taskforce) for the primary prevention of cardiovascular disease via workplace stress reduction. Nevertheless, governments are already launching healthy workplace campaigns, and preventing excessive work stress is a legal obligation in several countries. Promoting awareness of the link between stress and health among both employers and workers is an important component of workplace health promotion.
Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge

We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning. A logic formalism called Real Logic is defined on a first-order language whereby formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers. Logical constants are interpreted as feature vectors of real numbers. Real Logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational machine learning. We show how Real Logic can be implemented in deep Tensor Neural Networks with the use of Google’s TENSORFLOW primitives. The paper concludes with experiments applying Logic Tensor Networks on a simple but representative example of knowledge comple-
Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network

In order to increase the diagnostic accuracy of ground-source heat pump (GSHP) system, especially for multiple-simultaneous faults, the paper proposes a multi-source information fusion based fault diagnosis methodology by using Bayesian network, due to the fact that it is considered to be one of the most useful models in the filed of probabilistic knowledge representation and reasoning, and can deal with the uncertainty problem of fault diagnosis well. The Bayesian networks based on sensor data and observed information of human being are established, respectively. Each Bayesian network consists of two layers: fault layer and fault symptom layer. The Bayesian network structure is established according to the cause and effect sequence of faults and symptoms, and the parameters are studied by using Noisy-OR and NoisyMAX model. The entire fault diagnosis model is established by combining the two proposed Bayesian networks. Six fault diagnosis cases of GSHP system are studied, and the results show that the fault diagnosis model using evidences from only sensor data is accurate for single fault, while it is not accurate enough for multiple-simultaneous faults. By adding the observed information as evidences, the probability of fault present for single fault of ‘‘Refrigerant overcharge’’ increases to 100% from 99.69%, and the probabilities of fault present for multiple-simultaneous faults of ‘‘Non-condensable gas’’ and ‘‘Expansion valve port largen’’ increases to almost 100% from 61.1% and 52.3%, respectively. In addition, the observed information can correct the wrong fault diagnostic results, such as ‘‘Evaporator fouling’’. Therefore, the multi-source information fusion based fault diagnosis model using Bayesian network can increase the fault diagnostic accuracy greatly.
Calibrating Noise to Sensitivity in Private Data Analysis

We continue a line of research initiated in [10, 11] on privacypreserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f = P i g(xi), where xi denotes the ith row of the database and g maps database rows to [0, 1]. We extend the study to general functions f , proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f . Roughly speaking, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation results showing the increased value of interactive sanitization mechanisms over non-interactive.
Transfer Feature Learning with Joint Distribution Adaptation

Transfer learning is established as an effective technology in computer vision for leveraging rich labeled data in the source domain to build an accurate classifier for the target domain. However, most prior methods have not simultaneously reduced the difference in both the marginal distribution and conditional distribution between domains. In this paper, we put forward a novel transfer learning approach, referred to as Joint Distribution Adaptation (JDA). Specifically, JDA aims to jointly adapt both the marginal distribution and conditional distribution in a principled dimensionality reduction procedure, and construct new feature representation that is effective and robust for substantial distribution difference. Extensive experiments verify that JDA can significantly outperform several state-of-the-art methods on four types of cross-domain image classification problems.
Human facial expressions as adaptations: Evolutionary questions in facial expression research.

The importance of the face in social interaction and social intelligence is widely recognized in anthropology. Yet the adaptive functions of human facial expression remain largely unknown. An evolutionary model of human facial expression as behavioral adaptation can be constructed, given the current knowledge of the phenotypic variation, ecological contexts, and fitness consequences of facial behavior. Studies of facial expression are available, but results are not typically framed in an evolutionary perspective. This review identifies the relevant physical phenomena of facial expression and integrates the study of this behavior with the anthropological study of communication and sociality in general. Anthropological issues with relevance to the evolutionary study of facial expression include: facial expressions as coordinated, stereotyped behavioral phenotypes, the unique contexts and functions of different facial expressions, the relationship of facial expression to speech, the value of facial expressions as signals, and the relationship of facial expression to social intelligence in humans and in nonhuman primates. Human smiling is used as an example of adaptation, and testable hypotheses concerning the human smile, as well as other expressions, are proposed.
Clustering Uncertain Data Based on Probability Distribution Similarity

Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts significant challenges on both modeling similarity between uncertain objects and developing efficient computational methods. The previous methods extend traditional partitioning clustering methods like $(k)$-means and density-based clustering methods like DBSCAN to uncertain data, thus rely on geometric distances between objects. Such methods cannot handle uncertain objects that are geometrically indistinguishable, such as products with the same mean but very different variances in customer ratings. Surprisingly, probability distributions, which are essential characteristics of uncertain objects, have not been considered in measuring similarity between uncertain objects. In this paper, we systematically model uncertain objects in both continuous and discrete domains, where an uncertain object is modeled as a continuous and discrete random variable, respectively. We use the well-known Kullback-Leibler divergence to measure similarity between uncertain objects in both the continuous and discrete cases, and integrate it into partitioning and density-based clustering methods to cluster uncertain objects. Nevertheless, a naïve implementation is very costly. Particularly, computing exact KL divergence in the continuous case is very costly or even infeasible. To tackle the problem, we estimate KL divergence in the continuous case by kernel density estimation and employ the fast Gauss transform technique to further speed up the computation. Our extensive experiment results verify the effectiveness, efficiency, and scalability of our approaches.
Quality and Trustworthiness in Qualitative Research in Counseling Psychology

This article examines concepts of the trustworthiness, or credibility, of qualitative research. Following a “researcher-as-instrument,” or self-reflective, statement, the paradigmatic underpinnings of various criteria for judging the quality of qualitative research are explored, setting the stage for a discussion of more transcendent standards (those not associated with specific paradigms) for conducting quality research: social validity, subjectivity and reflexivity, adequacy of data, and adequacy of interpretation. Finally, current guidelines for writing and publishing qualitative research are reviewed, and strategies for conducting and writing qualitative research reports are suggested.
When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?

We interpret non-negative matrix factorization geometrically, as the problem of finding a simplicial cone which contains a cloud of data points and which is contained in the positive orthant. We show that under certain conditions, basically requiring that some of the data are spread across the faces of the positive orthant, there is a unique such simplicial cone. We give examples of synthetic image articulation databases which obey these conditions; these require separated support and factorial sampling. For such databases there is a generative model in terms of “parts” and NMF correctly identifies the ”parts”. We show that our theoretical results are predictive of the performance of published NMF code, by running the published algorithms on one of our synthetic image articulation databases.
Role-Based Access Control

This set of slides gives our definition for Role-Based Access Control, gives some historical background, and describes our current NIST RBAC projects. These projects include an NSA R23 sponsored project whose tasks include the development of an RBAC formal model and an implementation of RBAC on R23’s Mach microkernel-based distributed operating system called Synergy. This set of slides also gives the motivation for RBAC and the preferred environment of use. As opposed to other access control policies, RBAC actually models how an enterprise functionally allows access by those in various job positions to the objects that the enterprise owns, and RBAC greatly eases system administration. We show these principals in the model through our definitions for a role, role actions, role types, job positions, organization units, and resource sets, and by defining system administration actions. Also, in this set of slides the list of model components is given and various model properties are highlighted. These properties include the Role Type Inheritance property which shows how role types form a lattice and shows multiple inheritance of attributes, the Separation of Duty properties, and the Conflict of Interest properties, all of which are fundamental to our definition of Role- This set of slides also includes a description of the possible cooperative agreements that NIST can have with industry. Since RBAC is particularly suited for the non-classified environment, we here at NIST are very interest in forming alliances with industry to create a standard definition for RBAC and for furthering this work. Finally, the advantages of RBAC are summarized. Role-Based Access Control Janet Cugini, David Ferraiolo Security Division, Computer Systems Lab, NIST Building 224/A24, Gaithersburg, MD 20899 Phone: (301) 975-4524 Fax: (301) 926-2733 cugini@csmes.ncsl.nist.gov National Institute of Standards and Technology Gaithersburg Maryland 20899 2
Plummer Autoencoders

Estimating the true density in high-dimensional feature spaces is a well-known problem in machine learning. This work shows that it is possible to formulate the optimization problem as a minimization and use the representational power of neural networks to learn very complex densities. A theoretical bound on the estimation error is given when dealing with finite number of samples. The proposed theory is corroborated by extensive experiments on different datasets and compared against several existing approaches from the families of generative adversarial networks and autoencoder-based models.