nils-herrmann/clean_scirepeval huggingface dataset:
FuzzerGym: A Competitive Framework for Fuzzing and Learning

Fuzzing is a commonly used technique designed to test software by automatically crafting program inputs. Currently, the most successful fuzzing algorithms emphasize simple, low-overhead strategies with the ability to efficiently monitor program state during execution. Through compile-time instrumentation, these approaches have access to numerous aspects of program state including coverage, data flow, and heterogeneous fault detection and classification. However, existing approaches utilize blind random mutation strategies when generating test inputs. We present a different approach that uses this state information to optimize mutation operators using reinforcement learning (RL). By integrating OpenAI Gym with libFuzzer we are able to simultaneously leverage advancements in reinforcement learning as well as fuzzing to achieve deeper coverage across several varied benchmarks. Our technique connects the rich, efficient program monitors provided by LLVM Santizers with a deep neural net to learn mutation selection strategies directly from the input data. The cross-language, asynchronous architecture we developed enables us to apply any OpenAI Gym compatible deep reinforcement learning algorithm to any fuzzing problem with minimal slowdown.
Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing

Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc. To this end, models need to comprehensively perceive the semantic information and the differences between instances in a multi-human image, which is recently defined as the multi-human parsing task. In this paper, we present a new large-scale database “Multi-Human Parsing (MHP)” for algorithm development and evaluation, and advances the state-of-the-art in understanding humans in crowded scenes. MHP contains 25,403 elaborately annotated images with 58 fine-grained semantic category labels, involving 2-26 persons per image and captured in real-world scenes from various viewpoints, poses, occlusion, interactions and background. We further propose a novel deep Nested Adversarial Network (NAN) model for multi-human parsing. NAN consists of three Generative Adversarial Network (GAN)-like sub-nets, respectively performing semantic saliency prediction, instance-agnostic parsing and instance-aware clustering. These sub-nets form a nested structure and are carefully designed to learn jointly in an end-to-end way. NAN consistently outperforms existing state-of-the-art solutions on our MHP and several other datasets, and serves as a strong baseline to drive the future research for multi-human parsing.
A systematic review of the effectiveness of health promotion aimed at improving oral health.

To examine the quality of oral health promotion research evidence and to assess the effectiveness of health promotion, aimed at improving oral health using a systematic and scientifically defensible methodology.


BASIC RESEARCH DESIGN
Systematic review of oral health promotion research evidence using electronic searching, iterative hand-searching, critical appraisal and data synthesis.



The settings of the primary research reviewed were clinical, community, schools or other institutions. The participants were children, the elderly, adults and people with handicaps and disabilities.



Only studies which reported an evaluative component were included. Theoretical and purely descriptive papers were excluded.



The review examined the evidence of effectiveness of oral health promotion on caries, oral hygiene, oral health related knowledge, attitudes and behaviours.



Very few definitive conclusions about the effectiveness of oral health promotion can be drawn from the currently available evidence. Caries and periodontal disease can be controlled by regular toothbrushing with a fluoride toothpaste but a cost-effective method for reliably promoting such behaviour has not yet been established. Knowledge levels can almost always be improved by oral health promotion initiatives but whether these shifts in knowledge and attitudes can be causally related to changes in behaviour or clinical indices of disease has also not been established.



Oral health promotion which brings about the use of fluoride is effective for reducing caries. Chairside oral health promotion has been shown to be effective more consistently than other methods of health promotion. Mass media programmes have not been shown to be effective. The quality of oral health promotion evaluation research needs to be improved.
Variational Option Discovery Algorithms

We explore methods for option discovery based on variational inference and make two algorithmic contributions. First: we highlight a tight connection between variational option discovery methods and variational autoencoders, and introduce Variational Autoencoding Learning of Options by Reinforcement (VALOR), a new method derived from the connection. In VALOR, the policy encodes contexts from a noise distribution into trajectories, and the decoder recovers the contexts from the complete trajectories. Second: we propose a curriculum learning approach where the number of contexts seen by the agent increases whenever the agent’s performance is strong enough (as measured by the decoder) on the current set of contexts. We show that this simple trick stabilizes training for VALOR and prior variational option discovery methods, allowing a single agent to learn many more modes of behavior than it could with a fixed context distribution. Finally, we investigate other topics related to variational option discovery, including fundamental limitations of the general approach and the applicability of learned options to downstream tasks.
Unsupervised segmentation of natural images via lossy data compression

In this paper, we cast natural-image segmentation as a problem of clustering texure features as multivariate mixed data. We model the distribution of the texture features using a mixture of Gaussian distributions. However, unlike most existing clustering methods, we allow the mixture components to be degenerate or nearly-degenerate. We contend that this assumption is particularly important for mid-level image segmentation, where degeneracy is typically introduced by using a common feature representation for different textures. We show that such a mixture distribution can be effectively segmented by a simple agglomerative clustering algorithm derived from a lossy data compression approach. Using simple fixed-size Gaussian windows as texture features, the algorithm segments an image by minimizing the overall coding length of all the feature vectors. In terms of a variety of performance indices, our algorithm compares favorably against other well-known image segmentation methods on the Berkeley image database.
Psychology of Violence The Effect of Video Game Competition and Violence on Aggressive Behavior : Which Characteristic Has the Greatest Influence ?

This study is the first to our knowledge to isolate the effect of video game violence and competitiveness on aggressive behavior.  In Pilot Study 1, a violent and nonviolent video game were matched on competitiveness, difficulty, and pace of action, and the effect of each game on aggressive behavior was then compared using an unambiguous measure of aggressive behavior (i.e., the Hot Sauce Paradigm) in Experiment 1. In Pilot Study 2, competitiveness was isolated by matching games on difficulty and pace of action, and systematically controlling for violence. The effect of video game competition on aggressive behavior was then examined in Experiment 2.  We found that video game violence was not sufficient to elevate aggressive behavior compared with a nonviolent video game, and that more competitive games produced greater levels of aggressive behavior, irrespective of the amount of violence in the games.  It appears that competition, not violence, may be the video game characteristic that has the greatest influence on aggressive behavior. Future research is needed to explore the mechanisms through which video game competitiveness influences aggressive behavior, as well as whether this relation holds in the long-term.
Multimodal Learning and Reasoning for Visual Question Answering

Reasoning about entities and their relationships from multimodal data is a key goal of Artificial General Intelligence. The visual question answering (VQA) problem is an excellent way to test such reasoning capabilities of an AI model and its multimodal representation learning. However, the current VQA models are oversimplified deep neural networks, comprised of a long short-term memory (LSTM) unit for question comprehension and a convolutional neural network (CNN) for learning single image representation. We argue that the single visual representation contains a limited and general information about the image contents and thus limits the model reasoning capabilities. In this work we introduce a modular neural network model that learns a multimodal and multifaceted representation of the image and the question. The proposed model learns to use the multimodal representation to reason about the image entities and achieves a new state-of-the-art performance on both VQA benchmark datasets, VQA v1.0 and v2.0, by a wide margin.
Automated Static Analysis of Unit Test Code

Automated unit tests are an essential software quality assurance measure that is widely used in practice. In many projects, thus, large volumes of test code have co-evolved with the production code throughout development. Like any other code, test code too may contain faults, affecting the effectiveness, reliability and usefulness of the tests. Furthermore, throughout the software system's ongoing development and maintenance phase, the test code too has to be constantly adapted and maintained. To support detecting problems in test code and improving its quality, we implemented 42 static checks for analyzing JUnit tests. These checks encompass best practices for writing unit tests, common issues observed in using xUnit frameworks, and our experiences collected from several years of providing trainings and reviews of test code for industry and in teaching. The checks can be run using the open source analysis tool PMD. In addition to a description of the implemented checks and their rationale, we demonstrate the applicability of using static analysis for test code by analyzing the unit tests of the open source project JFreeChart.
Designing a health data management system based hadoop-agent

Nowadays the amount of data that is being generated every day is increasing in a high level for various sectors. In fact, this volume and diversity of data push us to think wisely for a better solution to store, process and analyze it in the right way. Taking into consideration the healthcare industry, there is a great benefit for using the concept of big data, due to the diversity of data that we are dealing with, the extant, and the velocity which lead us to think about providing the best care for the patients. In this paper, we aim to present a new architecture model for health data. The framework supports the storage and the management of unstructured medical data in a distributed environment based on multi-agent paradigm. The integration of the mobile agent model into hadoop ecosystem will give us the opportunity to enable instant communication process between multiple health repositories.
Successor Features for Transfer in Reinforcement Learning

Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. We propose a transfer framework for the scenario where the reward function changes between tasks but the environment’s dynamics remain the same. Our approach rests on two key ideas: successor features, a value function representation that decouples the dynamics of the environment from the rewards, and generalized policy improvement, a generalization of dynamic programming’s policy improvement operation that considers a set of policies rather than a single one. Put together, the two ideas lead to an approach that integrates seamlessly within the reinforcement learning framework and allows the free exchange of information across tasks. The proposed method also provides performance guarantees for the transferred policy even before any learning has taken place. We derive two theorems that set our approach in firm theoretical ground and present experiments that show that it successfully promotes transfer in practice, significantly outperforming alternative methods in a sequence of navigation tasks and in the control of a simulated robotic arm.
Accountability for Data Governance in Cloud Ecosystems

Accountability has emerged as a critical concept related to data protection in cloud ecosystems. It is necessary to maintain chains of accountability across cloud ecosystems. This is to enhance the confidence in the trust that cloud actors have while operating in the cloud. This paper is concerned with accountability in the cloud. It presents a conceptual model, consisting of attributes, practices and mechanisms for accountability in the cloud. The proposed model allows us to explain, in terms of accountability attributes, cloud-mediated interactions between actors. This forms the basis for characterizing accountability relationships between cloud actors, and hence chains of accountability in cloud ecosystems.
Additive Manufacturing Technologies : 3 D Printing , Rapid Prototyping , and Direct Digital Manufacturing ”

“Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing” is authored by Ian Gibson, David Rosen and Brent Stucker, who collectively possess 60 years’ experience in the fi eld of additive manufacturing (AM). This is the second edition of the book which aims to include current developments and innovations in a rapidly changing fi eld. Its primary aim is to serve as a teaching aid for developing and established curricula, therefore becoming an all-encompassing introductory text for this purpose. It is also noted that researchers may fi nd the text useful as a guide to the ‘state-of-the-art’ and to identify research opportunities. The book is structured to provide justifi cation and information for the use and development of AM by using standardised terminology to conform to standards (American Society for Testing and Materials (ASTM) F42) introduced since the fi rst edition. The basic principles and historical developments for AM are introduced in summary in the fi rst three chapters of the book and this serves as an excellent introduction for the uninitiated. Chapters 4–11 focus on the core technologies of AM individually and, in most cases, in comprehensive detail which gives those interested in the technical application and development of the technologies a solid footing. The remaining chapters provide guidelines and examples for various stages of the process including machine and/or materials selection, design considerations and software limitations, applications and post-processing considerations.
2Q: A Low Overhead High Performance Buffer Management Replacement Algorithm

In a path-breaking paper last year Pat and Betty O’Neil and Gerhard Weikum pro posed a self-tuning improvement to the Least Recently Used (LRU) buffer management algorithm[l5]. Their improvement is called LRU/k and advocates giving priority to buffer pages baaed on the kth most recent access. (The standard LRU algorithm is denoted LRU/l according to this terminology.) If Pl’s kth most recent access is more more recent than P2’s, then Pl will be replaced after P2. Intuitively, LRU/k for k > 1 is a good strategy, because it gives low priority to pages that have been scanned or to pages that belong to a big randomly accessed file (e.g., the account file in TPC/A). They found that LRU/S achieves most of the advantage of their method. The one problem of LRU/S is the processor *Supported by U.S. Office of Naval Research #N00014-91-E 1472 and #N99914-92-J-1719, U.S. National Science Foundation grants #CC%9103953 and IFlI-9224691, and USBA #5555-19. Part of this work was performed while Theodore Johnson was a 1993 ASEE Summer Faculty Fellow at the National Space Science Data Center of NASA Goddard Space Flight Center.
Starting over: Gm/Id-based MOSFET modeling as a basis for modernized analog design methodologies

1 Gilgamesh Associates, Fletcher, Vermont, USA; dfoty@sover.net. 2 University of North Carolina/Charlotte, Charlotte, North Carolina, USA; dmbinkle@uncc.edu. 3 National Technological University of Athens, Zagraphou, Athens, Greece; mbucher@elab.ntua.gr.  A method of interpreting MOSFET behavior is described which is more coherent for modern analog CMOS circuit design. This method supercedes the use of simple but antiquated equations in design, and replaces them with an approach based on the inversion coefficient of the individual transistors in the design. Measurements and modeling confirm that this method can be used directly to arbitrate among the various countervailing requirements of demanding analog designs.
An Introduction to Social Network Data Analytics

The advent of online social networks has been one of the most exciting events in this decade. Many popular online social networks such as Twitter, LinkedIn, and Facebook have become increasingly popular. In addition, a number of multimedia networks such as Flickr have also seen an increasing level of popularity in recent years. Many such social networks are extremely rich in content, and they typically contain a tremendous amount of content and linkage data which can be leveraged for analysis. The linkage data is essentially the graph structure of the social network and the communications between entities; whereas the content data contains the text, images and other multimedia data in the network. The richness of this network provides unprecedented opportunities for data analytics in the context of social networks. This book provides a data-centric view of online social networks; a topic which has been missing from much of the literature. This chapter provides an overview of the key topics in this field, and their coverage in this book.
Symmetric Nonnegative Matrix Factorization for Graph Clustering

Nonnegative matrix factorization (NMF) provides a lower rank approximation of a nonnegative matrix, and has been successfully used as a clustering method. In this paper, we offer some conceptual understanding for the capabilities and shortcomings of NMF as a clustering method. Then, we propose Symmetric NMF (SymNMF) as a general framework for graph clustering, which inherits the advantages of NMF by enforcing nonnegativity on the clustering assignment matrix. Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative). We compare SymNMF with the widely-used spectral clustering methods, and give an intuitive explanation of why SymNMF captures the cluster structure embedded in the graph representation more naturally. In addition, we develop a Newton-like algorithm that exploits second-order information efficiently, so as to show the feasibility of SymNMF as a practical framework for graph clustering. Our experiments on artificial graph data, text data, and image data demonstrate the substantially enhanced clustering quality of SymNMF over spectral clustering and NMF. Therefore, SymNMF is able to achieve better clustering results on both linear and nonlinear manifolds, and serves as a potential basis for many extensions
One size fits all ? Towards a differentiated policy approach with respect to regional innovation systems

The knowledge economy, learning and innovation have moved to the foreground both in regional and industrial policies in the past decade. Concrete policies were shaped in the past by the linear innovation model (focus on R&D and technology diffusion), and more recently, by “best practice models” of interactive innovation derived from high techand well performing regions. These were often applied in a similar way across many types of regions. In this paper an attempt was made to show that there is no “ideal model“ for innovation policy. Empirical investigations demonstrate that preconditions for innovation, innovation activities and processes, as well networks differ strongly between central, peripheral and old industrial regions. The RIS approach allows to take such differences into account by analysing the strengths and weaknesses of the various subsystems, clusters and the interdependencies within the respective region and beyond. In the present paper we are going to analyse different types of regional innovation systems with respect to their preconditions for innovation, networking and innovation barriers. Based on this classification different policy options and strategies are developed and discussed. Besides high performing regional innovation systems with well developed and dynamic clusters we will deal with situations which are characterised by low levels of clustering, a weak endowment with relevant institutions, fragmentation and “lock in“. We argue that policy approaches will differ – amongst other dimensions – with respect to the weight given to the stimulation of incremental/radical innovations, the orientation on endogenous/exogenous companies and knowledge sources, and the fostering of internal/external networking.
Applications of artificial neural networks for ECG signal detection and classification.

The authors have investigated potential applications of artificial neural networks for electrocardiographic QRS detection and beat classification. For the task of QRS detection, the authors used an adaptive multilayer perceptron structure to model the nonlinear background noise so as to enhance the QRS complex. This provided more reliable detection of QRS complexes even in a noisy environment. For electrocardiographic QRS complex pattern classification, an artificial neural network adaptive multilayer perceptron was used as a pattern classifier to distinguish between normal and abnormal beat patterns, as well as to classify 12 different abnormal beat morphologies. Preliminary results using the MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital, Cambridge, MA) arrhythmia database are encouraging.
0 SleepMonitor : Monitoring Respiratory Rate and Body Position During Sleep Using

Respiratory rate and body position are two major physiological parameters in sleep study, and monitoring them during sleep can provide helpful information for health care. In this paper, we present SleepMonitor, a smartwatch based system which leverages the built-in accelerometer to monitor the respiratory rate and body position. To calculate respiratory rate, we design a filter to extract the weak respiratory signal from the noisy accelerometer data collected on the wrist, and use frequency analysis to estimate the respiratory rate from the data along each axis. Further, we design a multi-axis fusion approach which can adaptively adjust the estimates from the three axes and then significantly improve the estimation accuracy. To detect the body position, we apply machine learning techniques based on the features extracted from the accelerometer data. We have implemented our system on Android Wear based smartwatches and evaluated its performance in real experiments. The results show that our system can monitor respiratory rate and body position during sleep with high accuracy under various conditions.