nils-herrmann/clean_scirepeval huggingface dataset:
Hotspot Patterns: The Formal Definition and Automatic Detection of Architecture Smells

In this paper, we propose and empirically validate a suite of hotspot patterns: recurring architecture problems that occur in most complex systems and incur high maintenance costs. In particular, we introduce two novel hotspot patterns, Unstable Interface and Implicit Cross-module Dependency. These patterns are defined based on Baldwin and Clark's design rule theory, and detected by the combination of history and architecture information. Through our tool-supported evaluations, we show that these patterns not only identify the most error-prone and change-prone files, they also pinpoint specific architecture problems that may be the root causes of bug-proneness and change-proneness. Significantly, we show that 1) these structure-history integrated patterns contribute more to error- and change-proneness than other hotspot patterns, and 2) the more hotspot patterns a file is involved in, the more error- and change-prone it is. Finally, we report on an industrial case study to demonstrate the practicality of these hotspot patterns. The architect and developers confirmed that our hotspot detector discovered the majority of the architecture problems causing maintenance pain, and they have started to improve the system's maintainability by refactoring and fixing the identified architecture issues.
Theory of Collective Intelligence

In this chapter an analysis of the behavior of an arbitrary (perhaps massive) collective of computational processes in terms of an associated “world” utility function is presented We concentrate on the situation where each process in the collective can be viewed as though it were striving to maximize its own private utility function. For such situations the central design issue is how to initialize/update the collective’s structure, and in particular the private utility functions, so as to induce the overall collective to behave in a way that has large values of the world utility. Traditional “team game” approaches to this problem simply set each private utility function equal to the world utility function. The “Collective Intelligence” (COIN) framework is a semi-formal set of heuristics that recently have been used to construct private utility functions that in many experiments have resulted in world utility values up to orders of magnitude superior to that ensuing from use of the team game utility. In this paper we introduce a formal mathematics for analyzing and designing collectives. We also use this mathematics to suggest new private utilities that should outperform the COIN heuristics in certain kinds of domains. In accompanying work we use that mathematics to explain previous experimental results concerning the superiority of COIN heuristics. In that accompanying work we also use the mathematics to make numerical predictions, some of which we then test. In this way these two papers establish the study of collectives as a proper science, involving theory, explanation of old experiments, prediction concerning new experiments, and engineering insights.
General Video Game Playing

One of the grand challenges of AI is to create general intelligence: an agent that can excel at many tasks, not just one. In the area of games, this has given rise to the challenge of General Game Playing (GGP). In GGP, the game (typically a turn-taking board game) is defined declaratively in terms of the logic of the game (what happens when a move is made, how the scoring system works, how the winner is declared, and so on). The AI player then has to work out how to play the game and how to win. In this work, we seek to extend the idea of General Game Playing into the realm of video games, thus forming the area of General Video Game Playing (GVGP). In GVGP, computational agents will be asked to play video games that they have not seen before. At the minimum, the agent will be given the current state of the world and told what actions are applicable. Every game tick the agent will have to decide on its action, and the state will be updated, taking into account the actions of the other agents in the game and the game physics. We envisage running a competition based on GVGP playing, using arcadestyle (e.g. similar to Atari 2600) games as our starting point. These games are rich enough to be a formidable challenge to a GVGP agent, without introducing unnecessary complexity. The competition that we envisage could have a number of tracks, based on the form of the state (frame buffer or object model) and whether or not a forward model of action execution is available. We propose that the existing Physical Travelling Salesman (PTSP) software could be extended for our purposes and that a variety of GVGP games could be created in this framework by AI and Games students and other developers. Beyond this, we envisage the development of a Video Game Description Language (VGDL) as a way of concisely specifying video games. For the competition, we see this as being an interesting challenge in terms of deliberative search, machine learning and transfer of existing knowledge into new domains.
Elementary School Science and Math Tests as a Driver for AI: Take the Aristo Challenge!

While there has been an explosion of impressive, datadriven AI applications in recent years, machines still largely lack a deeper understanding of the world to answer questions that go beyond information explicitly stated in text, and to explain and discuss those answers. To reach this next generation of AI applications, it is imperative to make faster progress in areas of knowledge, modeling, reasoning, and language. Standardized tests have often been proposed as a driver for such progress, with good reason: Many of the questions require sophisticated understanding of both language and the world, pushing the boundaries of AI, while other questions are easier, supporting incremental progress. In Project Aristo at the Allen Institute for AI, we are working on a specific version of this challenge, namely having the computer pass Elementary School Science and Math exams. Even at this level there is a rich variety of problems and question types, the most difficult requiring significant progress in AI. Here we propose this task as a challenge problem for the community, and are providing supporting datasets. Solutions to many of these problems would have a major impact on the field so we encourage you: Take the Aristo Challenge!
Integrated risk reduction framework to improve railway hazardous materials transportation safety.

Rail transportation plays a critical role to safely and efficiently transport hazardous materials. A number of strategies have been implemented or are being developed to reduce the risk of hazardous materials release from train accidents. Each of these risk reduction strategies has its safety benefit and corresponding implementation cost. However, the cost effectiveness of the integration of different risk reduction strategies is not well understood. Meanwhile, there has been growing interest in the U.S. rail industry and government to best allocate resources for improving hazardous materials transportation safety. This paper presents an optimization model that considers the combination of two types of risk reduction strategies, broken rail prevention and tank car safety design enhancement. A Pareto-optimality technique is used to maximize risk reduction at a given level of investment. The framework presented in this paper can be adapted to address a broader set of risk reduction strategies and is intended to assist decision makers for local, regional and system-wide risk management of rail hazardous materials transportation.
Deep Depth Completion of a Single RGB-D Image

The goal of our work is to complete the depth channel of an RGB-D image. Commodity-grade depth cameras often fail to sense depth for shiny, bright, transparent, and distant surfaces. To address this problem, we train a deep network that takes an RGB image as input and predicts dense surface normals and occlusion boundaries. Those predictions are then combined with raw depth observations provided by the RGB-D camera to solve for depths for all pixels, including those missing in the original observation. This method was chosen over others (e.g., inpainting depths directly) as the result of extensive experiments with a new depth completion benchmark dataset, where holes are filled in training data through the rendering of surface reconstructions created from multiview RGB-D scans. Experiments with different network inputs, depth representations, loss functions, optimization methods, inpainting methods, and deep depth estimation networks show that our proposed approach provides better depth completions than these alternatives.
Database resources of the National Center for Biotechnology Information

In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's website. NCBI resources include Entrez, PubMed, PubMed Central, LocusLink, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, SARS Coronavirus Resource, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD) and the Conserved Domain Architecture Retrieval Tool (CDART). Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih.gov.
A Survey of Research into Legacy System Migration

Legacy information systems typically form the backbone of the information flow within an organisation and are the main vehicle for consolidating information about the business. As a solution to the problems these systems pose brittleness, inflexibility, isolation, non-extensibility, lack of openness etc. many companies are migrating their legacy systems to new environments which allow the information system to more easily adapt to new business requirements. This paper presents a survey of research into Migration of Legacy Information Systems. The main problems that companies with legacy systems must face are analysed, and the challenges possible solutions must solve discussed. The paper provides an overview of the most important currently available solutions, and their main downsides are Jesus Bisbal, Deirdre Lawless, Ray Richardson, Donie O’Sulli van, Bing Wu, Jane Grimson, Vincent Wade, Broadcom Éireann Research, Trinity College, Dublin, Ireland. Dublin, Ireland.
PSYCHOLINGUISTIC APPROACHES TO SLA

These are exciting times for research into the psychological processes underlying second language acquisition (SLA). In the 1970s, SLA emerged as a field of inquiry in its own right (Brown 1980), and in the 1980s, a number of different approaches to central questions in the field began to develop in parallel and in relative isolation (McLaughlin and Harrington 1990). In the 1990s, however, these different approaches began to confront one another directly. Now we are entering a period reminiscent, in many ways, of the intellectually turbulent times following the Chomskyan revolution (Chomsky 1957; 1965). Now, as then, researchers are debating basic premises of a science of mind, language, and learning. Some might complain, not entirely without reason, that we are still debating the same issues after 30-40 years. However, there are now new conceptual and research tools available to test hypotheses in ways previously thought impossible. Because of this, many psychologists believe there will soon be significant advancement on some SLA issues that have resisted closure for decades. We outline some of these developments and explore where the field may be heading. More than ever, it appears possible that psychological theory and SLA theory are converging on solutions to common issues.
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation

In this work we introduce a time- and memory-efficient method for structured prediction that couples neuron decisions across both space at time. We show that we are able to perform exact and efficient inference on a densely-connected spatio-temporal graph by capitalizing on recent advances on deep Gaussian Conditional Random Fields (GCRFs). Our method, called VideoGCRF is (a) efficient, (b) has a unique global minimum, and (c) can be trained end-to-end alongside contemporary deep networks for video understanding. We experiment with multiple connectivity patterns in the temporal domain, and present empirical improvements over strong baselines on the tasks of both semantic and instance segmentation of videos. Our implementation is based on the Caffe2 framework and will be available at https://github.com/siddharthachandra/gcrf-v3.0.
Electrical generation and distribution for the more electric aircraft

The aircraft industry is developing the more electric aircraft (MEA) with an ultimate goal of distributing only electrical power across the airframe. The replacement of existing systems with electric equivalents has, and will continue to, significantly increase the electrical power requirement. This has created a need for the enhancement of generation capacity and changes to distribution systems. The higher powers will push distribution voltages higher in order to limit conduction losses and reduce cable size, and hence weight. A power electronic interface may be required to regulate generator output into the distributed power form.
An NLP-based cognitive system for disease status identification in electronic health records

This paper presents a natural language processing (NLP) based cognitive decision support system that automatically identifies the status of a disease from the clinical notes of a patient record. The system relies on IBM Watson Patient Record NLP analytics and supervised or semi-supervised learning techniques. It uses unstructured text in clinical notes, data from the structured part of a patient record, and disease control targets from the clinical guidelines. We evaluated the system using de-identified patient records of 414 hypertensive patients from a multi-specialty hospital system in the U.S. The experimental results show that, using supervised learning methods, our system can achieve an average 0.86 F1-score in identifying disease status passages and average accuracy of 0.77 in classifying the status as controlled or not. To the best of our knowledge, this is the first system to automatically identify disease control status from clinical notes.
Learning Discriminative Features with Multiple Granularities for Person Re-Identification

The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large variances. In this paper, we propose an end-to-end feature learning strategy integrating discriminative information with various granularities. We carefully design the Multiple Granularity Network (MGN), a multi-branch deep network architecture consisting of one branch for global feature representations and two branches for local feature representations. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that our method robustly achieves state-of-the-art performances and outperforms any existing approaches by a large margin. For example, on Market-1501 dataset in single query mode, we obtain a top result of Rank-1/mAP=96.6%/94.2% with this method after re-ranking.
Publicly Verifiable Lotteries: Applications of Delaying Functions

This paper uses delaying functions, functions that require signiicant calculation time, in the development of a one-pass lottery scheme in which winners are chosen fairly using only internal information. Since all this information may be published (even before the lottery closes), anyone can do the calculation and therefore verify that the winner was chosen correctly. Since the calculation uses a delaying function, ticket purchasers cannot take advantage of this information. Fraud on the part of the lottery agent is detectable and no single ticket purchaser needs to be trusted. Coalitions of purchasers attempting to control the winning ticket calculation are either unsuccessful or are detected. The scheme can be made resistant to coalitions of arbitrary size. Since we assume that coalitions of larger size are harder to assemble, the probability that the lottery is fair can be made arbitrarily high. The paper deenes delaying functions and contrasts them with pricing functions 8] and time-lock puzzles 16].
Homography Estimation from Image Pairs with Hierarchical Convolutional Networks

In this paper, we introduce a hierarchy of twin convolutional regression networks to estimate the homography between a pair of images. In this framework, networks are stacked sequentially in order to reduce error bounds of the estimate. At every convolutional network module, features from each image are extracted independently, given a shared set of kernels, also known as Siamese network model. Later on in the process, they are merged together to estimate the homography. Further, we evaluate and compare effects of various training parameters in this context. We show that given the iterative nature of the framework, highly complicated models are not necessarily required, and high performance is achieved via hierarchical arrangement of simple models. Effectiveness of the proposed method is shown through experiments on MSCOCO dataset, in which it significantly outperforms the state-of-the-art.
The assessment of school engagement: examining dimensionality and measurement invariance by gender and race/ethnicity.

The construct of school engagement has attracted growing interest as a way to ameliorate the decline in academic achievement and increase in dropout rates. The current study tested the fit of a second-order multidimensional factor model of school engagement, using large-scale representative data on 1103 students in middle school. In order to make valid model comparisons by group, we evaluated the extent to which the measurement structure of this model was invariant by gender and by race/ethnicity (European-American vs. African-American students). Finally, we examined differences in latent factor means by these same groups. From our confirmatory factor analyses, we concluded that school engagement was a multidimensional construct, with evidence to support the hypothesized second-order engagement factor structure with behavioral, emotional, and cognitive dimensions. In this sample, boys and girls did not substantially differ, nor did European-American and African-American students, in terms of the underlying constructs of engagement and the composition of these constructs. Finally, there were substantial differences in behavioral and emotional engagement by gender and by racial/ethnic groups in terms of second-order factor mean differences.
Automatic Mass Balancing of Air-Bearing-Based Three-Axis Rotational Spacecraft Simulator

Air-bearing-based spacecraft simulators are widely used to develop and verify spacecraft control techniques required by modern spacecraft applications. To create a spacelike environment with ground spacecraft simulators, the effects of gravity should be minimized. For a spherical air-bearing system with 3 rotational degrees of freedom, the center of rotation of the spacecraft simulator should be exactly aligned with the center of gravity. This paper presents the automatic mass balancingmethod, which compensates for the center of gravity offset from the center of rotation by actuating three proof masses on linear motion stages. Adaptive control of the automatic mass balancing system is usedwhile the balancingmasses are actuated in real time. The proposed techniques are implemented on the ground-based three-axis spacecraft simulator for the bifocal relay mirror spacecraft.
Computer Self-Efficacy, Computer Anxiety, and Attitudes toward the Internet: A Study among Undergraduates in Unimas

Eighty-one female and sixty-seven male undergraduates at a Malaysian university, from seven faculties and a Center for Language Studies completed a Computer Self-Efficacy Scale, Computer Anxiety Scale, and an Attitudes toward the Internet Scale and give information about their use of the Internet. This survey research investigated undergraduates’ computer anxiety, computer self-efficacy, and reported use of and attitudes toward the Internet. This study also examined differences in computer anxiety, computer selfefficacy, attitudes toward the Internet and reported use of the Internet for undergraduates with different demographic variables. The findings suggest that the undergraduates had moderate computer anxiousness, medium attitudes toward the Internet, and high computer self-efficacy and used the Internet extensively for educational purposes such as doing research, downloading electronic resources and e-mail communications. This study challenges the long perceived male bias in the computer environment and supports recent studies that have identified greater gender equivalence in interest, use, and skills levels. However, there were differences in undergraduates’ Internet usage levels based on the discipline of study. Furthermore, higher levels of Internet usage did not necessarily translate into better computer self-efficacy among the undergraduates. A more important factor in determining computer self-efficacy could be the discipline of study and undergraduates studying computer related disciplines appeared to have higher self-efficacy towards computers and the Internet. Undergraduates who used the Internet more often may not necessarily feel more comfortable using them. Possibly, other factors such as the types of application used, the purpose for using, and individual satisfaction could also influence computer self-efficacy and computer anxiety. However, although Internet usage levels may not have any impact on computer self-efficacy, higher usage of the Internet does seem to decrease the levels of computer anxiety among the undergraduates. Undergraduates with lower computer anxiousness demonstrated more positive attitudes toward the Internet in this study.