nils-herrmann/clean_scirepeval huggingface dataset:
Graph Database Application using Neo4j - Railroad Planner Simulation

Such as relational databases, most graphs databases are OLTP databases (online transaction processing) of generic use and can be used to produce a wide range of solutions. That said, they shine particularly when the solution depends, first, on our understanding of how things are connected. This is more common than one may think. And in many cases it is not only how things are connected but often one wants to know something about the different relationships in our field their names, qualities, weight and so on. Briefly, connectivity is the key. The graphs are the best abstraction one has to model and query the connectivity; databases graphs in turn give developers and the data specialists the ability to apply this abstraction to their specific problems. For this purpose, in this paper one used this approach to simulate the route planner application, capable of querying connected data. Merely having keys and values is not enough; no more having data partially connected through joins semantically poor. We need both the connectivity and contextual richness to operate these solutions. The case study herein simulates a railway network railway stations connected with one another where each connection between two stations may have some properties. And one answers the question: how to find the optimized route (path) and know whether a station is reachable from one station or not and in which depth.
Learning Structured Text Representations

In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias (Cheng et al., 2016; Kim et al., 2017), we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluations across different tasks and datasets show that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.
Effnet: An Efficient Structure for Convolutional Neural Networks

With the ever increasing application of Convolutional Neural Networks to customer products the need emerges for models to efficiently run on embedded, mobile hardware. Slimmer models have therefore become a hot research topic with various approaches which vary from binary networks to revised convolution layers. We offer our contribution to the latter and propose a novel convolution block which significantly reduces the computational burden while surpassing the current state-of-the-art. Our model, dubbed EffNet, is optimised for models which are slim to begin with and is created to tackle issues in existing models such as MobileNet and ShuffleNet.
OptionGAN: Learning Joint Reward-Policy Options Using Generative Adversarial Inverse Reinforcement Learning

Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for intricate tasks. Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations. Yet in reality, the corpus of demonstrations may contain trajectories arising from a diverse set of underlying reward functions rather than a single one. Thus, in inverse reinforcement learning, it is useful to consider such a decomposition. The options framework in reinforcement learning is specifically designed to decompose policies in a similar light. We therefore extend the options framework and propose a method to simultaneously recover reward options in addition to policy options. We leverage adversarial methods to learn joint reward-policy options using only observed expert states. We show that this approach works well in both simple and complex continuous control tasks and shows significant performance increases in one-shot transfer learning.
Adversarial Collaborative Auto-encoder for Top-N Recommendation

During the past decade, model-based recommendation methods have evolved from latent factor models to neural network-based models. Most of these techniques mainly focus on improving the overall performance, such as the root mean square error for rating predictions and hit ratio for top-N recommendation, where the users’ feedback is considered as the ground-truth. However, in realworld applications, the users’ feedback is possibly contaminated by imperfect user behaviours, namely, careless preference selection. Such data contamination poses challenges on the design of robust recommendation methods. In this work, to address the above issue, we propose a general adversarial training framework for neural network-based recommendation models, which improves both the model robustness and the overall performance. We point out the tradeos between performance and robustness enhancement with detailed instructions on how to strike a balance. Specically, we implement our approach on the collaborative auto-encoder, followed by experiments on three public available datasets: MovieLens-1M, Ciao, and FilmTrust. We show that our approach outperforms highly competitive state-of-the-art recommendation methods. In addition, we carry out a thorough analysis on the noise impacts, as well as the complex interactions between model nonlinearity and noise levels. rough simple modications, our adversarial training framework can be applied to a host of neural network-based models whose robustness and performance are expected to be both enhanced.
The Psychology of Worldviews

A worldview (or “world view”) is a set of assumptions about physical and social reality that may have powerful effects on cognition and behavior. Lacking a comprehensive model or formal theory up to now, the construct has been underused. This article advances theory by addressing these gaps. Worldview is defined. Major approaches to worldview are critically reviewed. Lines of evidence are described regarding worldview as a justifiable construct in psychology. Worldviews are distinguished from schemas. A collated model of a worldview’s component dimensions is described. An integrated theory of worldview function is outlined, relating worldview to personality traits, motivation, affect, cognition, behavior, and culture. A worldview research agenda is outlined for personality and social psychology (including positive and peace psychology).
Architectural Decision Management for Digital Transformation of Products and Services

The digitization of our society changes the way we live, work, learn, communicate, and collaborate. The Internet of Things, Enterprise Social Networks, Adaptive Case Management, Mobility systems, Analytics for Big Data, and Cloud services environments are emerging to support smart connected products and services and the digital transformation. Biological metaphors of living and adaptable ecosystems provide the logical foundation for self-optimizing and resilient run-time environments for intelligent business services and service-oriented enterprise architectures. Our aim is to support flexibility and agile transformations for both business domains and related information technology. The present research paper investigates mechanisms for decision analytics in the context of multi-perspective explorations of enterprise services and their digital enterprise architectures by extending original architecture reference models with state of art elements for agile architectural engineering for the digitization and collaborative architectural decision support. The paper’s context focuses on digital transformations of business and IT and integrates fundamental mappings between adaptable digital enterprise architectures and service-oriented information systems. We are putting a spotlight on the example domain – Internet of Things.
Mining knowledge from text using information extraction

An important approach to text mining involves the use of natural-language information extraction. Information extraction (IE) distills structured data or knowledge from unstructured text by identifying references to named entities as well as stated relationships between such entities. IE systems can be used to directly extricate abstract knowledge from a text corpus, or to extract concrete data from a set of documents which can then be further analyzed with traditional data-mining techniques to discover more general patterns. We discuss methods and implemented systems for both of these approaches and summarize results on mining real text corpora of biomedical abstracts, job announcements, and product descriptions. We also discuss challenges that arise when employing current information extraction technology to discover knowledge in text.
Active Network Alignment: A Matching-Based Approach

Network alignment is the problem of matching the nodes of two graphs, maximizing the similarity of the matched nodes and the edges between them. This problem is encountered in a wide array of applications---from biological networks to social networks to ontologies---where multiple networked data sources need to be integrated. Due to the difficulty of the task, an accurate alignment can rarely be found without human assistance. Thus, it is of great practical importance to develop network alignment algorithms that can optimally leverage experts who are able to provide the correct alignment for a small number of nodes. Yet, only a handful of existing works address this active network alignment setting.
 The majority of the existing active methods focus on absolute queries ("are nodes a and b the same or not?"), whereas we argue that it is generally easier for a human expert to answer relative queries ("which node in the set b1,...,bn is the most similar to node a?"). This paper introduces two novel relative-query strategies, TopMatchings and GibbsMatchings, which can be applied on top of any network alignment method that constructs and solves a bipartite matching problem. Our methods identify the most informative nodes to query by sampling the matchings of the bipartite graph associated to the network-alignment instance.
 We compare the proposed approaches to several commonly-used query strategies and perform experiments on both synthetic and real-world datasets. Our sampling-based strategies yield the highest overall performance, outperforming all the baseline methods by more than 15 percentage points in some cases. In terms of accuracy, TopMatchings and GibbsMatchings perform comparably. However, GibbsMatchings is significantly more scalable, but it also requires hyperparameter tuning for a temperature parameter.
Design Patterns for Container-based Distributed Systems

In the late 1980s and early 1990s, object-oriented programming revolutionized software development, popularizing the approach of building of applications as collections of modular components. Today we are seeing a similar revolution in distributed system development, with the increasing popularity of microservice architectures built from containerized software components. Containers     are particularly well-suited as the fundamental “object” in distributed systems by virtue of the walls they erect at the container boundary. As this architectural style matures, we are seeing the emergence of design patterns, much as we did for objectoriented programs, and for the same reason – thinking in terms of objects (or containers) abstracts away the lowlevel details of code, eventually revealing higher-level patterns that are common to a variety of applications and algorithms. This paper describes three types of design patterns that we have observed emerging in container-based distributed systems: single-container patterns for container management, single-node patterns of closely cooperating containers, and multi-node patterns for distributed algorithms. Like object-oriented patterns before them, these patterns for distributed computation encode best practices, simplify development, and make the systems where they are used more reliable.
Time Domain Passivity Control of Haptic Interface

A patent-pending, energy-based method is presented for controlling a haptic interface system to ensure stable contact under a wide variety of operating conditions. System stability is analyzed in terms of the time-domain definition of passivity. We define a “Passivity Observer” (PO) which measures energy flow in and out of one or more subsystems in real-time software. Active behavior is indicated by a negative value of the PO at any time. We also define the “Passivity Controller” (PC), an adaptive dissipative element which, at each time sample, absorbs exactly the net energy output (if any) measured by the PO. The method is tested with simulation and implementation in the Excalibur haptic interface system. Totally stable operation was achieved under conditions such as stiffness 100 N/mm or time delays of 15 ms. The PO/PC method requires very little additional computation and does not require a dynamical model to be identified.
Reputation and Corporate Tax Planning : A Moral Licensing View Prepared for the 2017 Lone Star Accounting Research Conference

This study examines how a firm’s overall reputation status (reputation hereafter) affects its tax planning. Drawing on the moral licensing theory, we posit that managers’ and other stakeholders’ perception of a firm’s questionable behavior may be affected by the firm’s reputation and that a good reputation may help a firm to justify, or “license”, such behavior. This licensing effect may reduce a firm’s concerns about its tax avoidance behavior and incentivize reputable firms to engage in more tax reduction activities that have ambiguities in transgression. The empirical findings support our conjecture. Specifically, we test the association between a firm’s established reputation and its tax planning using multiple tax avoidance measures, which capture different tax reduction technologies that either fall into the gray area or violate tax and financial reporting rules. Relative to less reputable firms, more reputable firms on average avoid more taxes by using tax reduction technologies that have ambiguity in transgression, but are less likely to engage in tax-related activities that are blatant transgressions. We further investigate whether the licensing effect of reputation is more pronouced under the more principles-based or rules-based standards. Our findings suggest that the licensing effect is more pronounced under the more principle-based standards.
Traffic modelling in cities – validation of space syntax at an urban scale Dr

To understand and facilitate modal shift to more sustainable modes of transport there is a need to model accessibility and connectivity at an urban scale using data collection and modelling procedures that require less data and specialist input than traditional transport models. The research described in this paper uses spatial analysis modelling procedures based on space syntax to investigate the potential to model aggregate traffic flows at an urban scale. The research has demonstrated that space syntax modelling is an effective means of representing an urban scale motor traffic network, however, modifications to th e original model were required to achieve a correlation between modelled and measured motor traffic flow comparable to other modelling procedures. Weighting methods were tested with ‘boundary weighting’ found to be effective at representing traffic crossing the boundary of an isolated urban sub-area, but not so effective at an urban scale. ‘Road weighting’ was found to be effective in improving model performance by representing traffic flows along routes according to a national classification scheme. The modelling approach has the potential to be extremely useful at an early planning stage to represent changes to flows across the network and to be useful for different modes.
Deep convolutional neural fields for depth estimation from a single image

We consider the problem of depth estimation from a single monocular image in this work. It is a challenging task as no reliable depth cues are available, e.g., stereo correspondences, motions etc. Previous efforts have been focusing on exploiting geometric priors or additional sources of information, with all using hand-crafted features. Recently, there is mounting evidence that features from deep convolutional neural networks (CNN) are setting new records for various vision applications. On the other hand, considering the continuous characteristic of the depth values, depth estimations can be naturally formulated into a continuous conditional random field (CRF) learning problem. Therefore, we in this paper present a deep convolutional neural field model for estimating depths from a single image, aiming to jointly explore the capacity of deep CNN and continuous CRF. Specifically, we propose a deep structured learning scheme which learns the unary and pairwise potentials of continuous CRF in a unified deep CNN framework. The proposed method can be used for depth estimations of general scenes with no geometric priors nor any extra information injected. In our case, the integral of the partition function can be analytically calculated, thus we can exactly solve the log-likelihood optimization. Moreover, solving the MAP problem for predicting depths of a new image is highly efficient as closed-form solutions exist. We experimentally demonstrate that the proposed method outperforms state-of-the-art depth estimation methods on both indoor and outdoor scene datasets.
Enabling mutation testing for Android apps

Mutation testing has been widely used to assess the fault-detection effectiveness of a test suite, as well as to guide test case generation or prioritization. Empirical studies have shown that, while mutants are generally representative of real faults, an effective application of mutation testing requires “traditional” operators designed for programming languages to be augmented with operators specific to an application domain and/or technology. This paper proposes MDroid+, a framework for effective mutation testing of Android apps. First, we systematically devise a taxonomy of 262 types of Android faults grouped in 14 categories by manually analyzing 2,023 so ware artifacts from different sources (e.g., bug reports, commits). Then, we identified a set of 38 mutation operators, and implemented an infrastructure to automatically seed mutations in Android apps with 35 of the identified operators. The taxonomy and the proposed operators have been evaluated in terms of stillborn/trivial mutants generated as compared to well know mutation tools, and their capacity to represent real faults in Android apps
Computational Power and the Social Impact of Artificial Intelligence

Machine learning is a computational process. To that end, it is inextricably tied to computational power the tangible material of chips and semiconductors that the algorithms of machine intelligence operate on. Most obviously, computational power and computing architectures shape the speed of training and inference in machine learning, and therefore influence the rate of progress in the technology. But, these relationships are more nuanced than that: hardware shapes the methods used by researchers and engineers in the design and development of machine learning models. Characteristics such as the power consumption of chips also define where and how machine learning can be used in the real world.
Deep Text Mining of Instagram Data without Strong Supervision

With the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. This textual data can be analyzed for the purpose of improving user recommendations and detecting trends. Instagram is one of the largest social media platforms, containing both text and images. However, most of the prior research on text processing in social media is focused on analyzing Twitter data, and little attention has been paid to text mining of Instagram data. Moreover, many text mining methods rely on annotated training data, which in practice is both difficult and expensive to obtain. In this paper, we present methods for unsupervised mining of fashion attributes from Instagram text, which can enable a new kind of user recommendation in the fashion domain. In this context, we analyze a corpora of Instagram posts from the fashion domain, introduce a system for extracting fashion attributes from Instagram, and train a deep clothing classifier with weak supervision to classify Instagram posts based on the associated text. With our experiments, we confirm that word embeddings are a useful asset for information extraction. Experimental results show that information extraction using word embeddings outperforms a baseline that uses Levenshtein distance. The results also show the benefit of combining weak supervision signals using generative models instead of majority voting. Using weak supervision and generative modeling, an F1 score of 0.61 is achieved on the task of classifying the image contents of Instagram posts based solely on the associated text, which is on level with human performance. Finally, our empirical study provides one of the few available studies on Instagram text and shows that the text is noisy, that the text distribution exhibits the long-tail phenomenon, and that comment sections on Instagram are multi-lingual.
De-convolutional auto-encoder for enhancement of fingerprint samples

Reliability and accuracy of the features extracted from fingerprints are essential for the performance of any fingerprint comparison algorithm. Image Enhancement as a pre-processing step allows to extract features more accurately by enhancing the quality of the fingerprint signal. This work proposes to use De-Convolutional Auto-Encoders for fingerprint image enhancement. Its performance is compared to seven state-of-the-art methods regarding their improvements for recognitions of the biometric system. Biometric performance is tested with MINDTCT and FingerJetFX for feature extraction and BOZORTH3 for biometric comparison. Critical comparisons are determined from 14 datasets. Those are used for evaluation of the methods. The impact of a method on biometric performance varies significantly. No single image enhancement can be found, which works best for all combinations. However, the proposed method ConvEnhance achieves highest count of best improvements among the evaluated methods.
Effectiveness of an integrated handwriting program for first-grade students: a pilot study.

We developed and piloted a program for first-grade students to promote development of legible handwriting and writing fluency. The Write Start program uses a coteaching model in which occupational therapists and teachers collaborate to develop and implement a handwriting-writing program. The small-group format with embedded individualized supports allows the therapist to guide and monitor student performance and provide immediate feedback. The 12-wk program was implemented with 1 class of 19 students. We administered the Evaluation of Children's Handwriting Test, Minnesota Handwriting Assessment, and Woodcock-Johnson Fluency and Writing Samples test at baseline, immediately after the Write Start program, and at the end of the school year. Students made large, significant gains in handwriting legibility and speed and in writing fluency that were maintained at 6-mo follow-up. The Write Start program appears to promote handwriting and writing skills in first-grade students and is ready for further study in controlled trials.