nils-herrmann/clean_scirepeval huggingface dataset:
Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks

Effective training of neural networks requires a lot of data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation alleviates this by using existing data more effectively. However standard data augmentation produces only limited plausible alternative data. Given there is potential to generate a much broader set of augmentations, we design and train a generative model to do data augmentation. The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and augment it by generating other within-class data items. As this generative process does not depend on the classes themselves, it can be applied to novel unseen classes of data. We demonstrate that a Data Augmentation Generative Adversarial Network (DAGAN) augments classifiers very well on Omniglot, EMNIST and VGG-Face.
Evaluation of Conducting Capital Structure Arbitrage Using the Multi-Period Extended Geske-Johnson Model

This study uses a multi-period structural model developed by Chen and Yeh (2006), which extends the Geske-Johnson (1987) compound option model to evaluate the performance of capital structure arbitrage under a multi-period debt structure. Previous studies exploring capital structure arbitrage have typically employed single-period structural models, which have very limited empirical scopes. In this paper, we predict the default situations of a firm using the multi-period Geske-Johnson model that assumes endogenous default barriers. The Geske-Johnson model is the only model that accounts for the entire debt structure and imputes the default barrier to the asset value of the firm. This study also establishes trading strategies and analyzes the arbitrage performance of 369 North American obligators from 2004 to 2008. Comparing the performance of capital structure arbitrage between the Geske-Johnson and CreditGrades models, we find that the extended Geske-Johnson model is more suitable than the CreditGrades model for exploiting the mispricing between equity prices and credit default swap spreads.
Privacy-Preserving Personal Model Training

Many current Internet services rely on inferences from models trained on user data. Commonly, both the training and inference tasks are carried out using cloud resources fed by personal data collected at scale from users. Holding and using such large collections of personal data in the cloud creates privacy risks to the data subjects, but is currently required for users to benefit from such services. We explore how to provide for model training and inference in a system where computation is pushed to the data in preference to moving data to the cloud, obviating many current privacy risks. Specifically, we take an initial model learnt from a small set of users and retrain it locally using data from a single user. We evaluate on two tasks: one supervised learning task, using a neural network to recognise users' current activity from accelerometer traces; and one unsupervised learning task, identifying topics in a large set of documents. In both cases the accuracy is improved. We also analyse the robustness of our approach against adversarial attacks, as well as its feasibility by presenting a performance evaluation on a representative resource-constrained device (a Raspberry Pi).
Interactive Instance-based Evaluation of Knowledge Base Question Answering

Most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we present a tool that aids in debugging of question answering systems that construct a structured semantic representation for the input question. Previous work has largely focused on building question answering interfaces or evaluation frameworks that unify multiple data sets. The primary objective of our system is to enable interactive debugging of model predictions on individual instances (questions) and to simplify manual error analysis. Our interactive interface helps researchers to understand the shortcomings of a particular model, qualitatively analyze the complete pipeline and compare different models. A set of sit-by sessions was used to validate our interface design.
Understanding Password Choices: How Frequently Entered Passwords Are Re-used across Websites

From email to online banking, passwords are an essential component of modern internet use. Yet, users do not always have good password security practices, leaving their accounts vulnerable to attack. We conducted a study which combines self-report survey responses with measures of actual online behavior gathered from 134 participants over the course of six weeks. We find that people do tend to re-use each password on 1.7–3.4 different websites, they reuse passwords that are more complex, and mostly they tend to re-use passwords that they have to enter frequently. We also investigated whether self-report measures are accurate indicators of actual behavior, finding that though people understand password security, their self-reported intentions have only a weak correlation with reality. These findings suggest that users manage the challenge of having many passwords by choosing a complex password on a website where they have to enter it frequently in order to memorize that password, and then re-using that strong password across other websites.
Ten simple rules for responsible big data research

1 Department of Geography, University of Kentucky, Lexington, Kentucky, United States of America, 2 Microsoft Research, New York, New York, United States of America, 3 Data & Society, New York, New York, United States of America, 4 Information Law Institute, New York University, New York, New York, United States of America, 5 Department of Media and Communications, London School of Economics, London, United Kingdom, 6 Harvard-Smithsonian Center for Astrophysics, Harvard University, Cambridge, Massachusetts, United States of America, 7 Center for Engineering Ethics and Society, National Academy of Engineering, Washington, DC, United States of America, 8 Institute for Health Aging, University of California-San Francisco, San Francisco, California, United States of America, 9 Ethical Resolve, Santa Cruz, California, United States of America, 10 Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America, 11 Department of Sociology, Columbia University, New York, New York, United States of America, 12 Carey School of Law, University of Maryland, Baltimore, Maryland, United States of America
An overview of clustering methods

Data clustering is the process of identifying natural groupings or clusters within multidimensional data based on some similarity measure. Clustering is a fundamental process in many different disciplines. Hence, researchers from different fields are actively working on the clustering problem. This paper provides an overview of the different representative clustering methods. In addition, several clustering validations indices are shown. Furthermore, approaches to automatically determine the number of clusters are presented. Finally, application of different heuristic approaches to the clustering problem is also investigated.
Explaining Deep Learning Models - A Bayesian Non-parametric Approach

Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide some clues as to how an ML model makes individual predictions, they cannot provide users with an ability to inspect a model as a complete entity. In this work, we propose a novel technical approach that augments a Bayesian non-parametric regression mixture model with multiple elastic nets. Using the enhanced mixture model, we can extract generalizable insights for a target model through a global approximation. To demonstrate the utility of our approach, we evaluate it on different ML models in the context of image recognition. The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.
3D Traffic Scene Understanding From Movable Platforms

In this paper, we present a novel probabilistic generative model for multi-object traffic scene understanding from movable platforms which reasons jointly about the 3D scene layout as well as the location and orientation of objects in the scene. In particular, the scene topology, geometry, and traffic activities are inferred from short video sequences. Inspired by the impressive driving capabilities of humans, our model does not rely on GPS, lidar, or map knowledge. Instead, it takes advantage of a diverse set of visual cues in the form of vehicle tracklets, vanishing points, semantic scene labels, scene flow, and occupancy grids. For each of these cues, we propose likelihood functions that are integrated into a probabilistic generative model. We learn all model parameters from training data using contrastive divergence. Experiments conducted on videos of 113 representative intersections show that our approach successfully infers the correct layout in a variety of very challenging scenarios. To evaluate the importance of each feature cue, experiments using different feature combinations are conducted. Furthermore, we show how by employing context derived from the proposed method we are able to improve over the state-of-the-art in terms of object detection and object orientation estimation in challenging and cluttered urban environments.
Joint Panel Sizing and Appointment Scheduling in Outpatient Care

Patients nationwide experience difficulties in accessing medical appointments in a timely manner due to long backlogs. Meanwhile, patients do not always show up for their scheduled services, with significant no-show rates. Unattended appointments result in under-utilization of a clinic’s valuable resources, and limit the access for other patients who could have filled the missed slots. Medical practices aim to utilize their valuable resources efficiently, provide timely access to care, and at the same time they strive to provide short waits for patients present at the medical facility. We study the joint problem of determining the panel size of a medical practice and the number of offered appointment slots per day, so that patients do not face long backlogs and the clinic is not overcrowded. We explicitly model the two separate time scales involved in accessing medical care: appointment delay (order of days, weeks) and clinic delay (order of minutes, hours). We analyze the two queueing systems associated with each type of delay, and provide explicit expressions for the performance measures of interest based on diffusion approximations. In our analysis we capture many features of the complex reality of outpatient care, including patients’ non-punctuality, no-shows, balking behavior, and stochastic service times. Two additional distinctive characteristics of this study are the balking behavior of the patients who face long appointment backlogs, and the transient-state analysis of the clinic delay, which allow the study of a system with traffic intensity greater than one. Concerning the panel sizing and appointment scheduling decisions, our analysis provides theoretical and numerical support that the two-variable optimization problem reduces to a single variable-one, and either an “Open Access” policy is optimal, or supply and demand are perfectly matched and are both very small (“Limited Access” regime). Under our Open Access regime, the clinic offers as many appointment slots as possible per day, and the optimal panel size depends on the clinic’s characteristics. A solution within the Limited Access regime arises when the service times are long, and the patients are very sensitive to the appointment delay.
State-of-the-art of Business Process Compliance Approaches: A Survey (Extended Abstract)

Compliance means to ensure that business practice and processes are aligned at commonly accepted norms. Companies are expected to comply with an increasing number of regulations of different origin and complexity. Regulations that are required by law are given the highest priority. Other regulations can be derived from best practice frameworks, company internal standards and inter-company business contracts. Both are necessary to form a company’s compliance policy which meets particular business requirements. Business Process Compliance (BPC) approaches seek to discover methods and techniques to ease the implementation of these compliance regulations. The approaches reveal different process modelling languages and scenarios for mapping relevant compliance rules and the detection of compliance violations. This contribution provides an overview on the state-ofthe-art of business process compliance approaches by conducting a literature review. The literature will be analysed and divided into four dimensions. The first dimension includes variables of general business process modelling, such as order, information, location and resource. The second dimension deals with temporal aspects of process modelling. The third dimension differentiates between BPC and verification/validation approaches. The last dimension specifies whether a method or a technical artefact was developed. The findings will be used to visualize the state-of-the-art of BPC.
WIDM at the NTCIR-13 STC-2 Task

In this paper, we describe our contribution for the NTCIR-13 Short Text Conversation (STC) Chinese task. Short text conversation remains an important part on social media gathering much attention recently. The task aims to retrieve or generate a relevant comment given a post. We consider both closed and open domain STC for retrieval–based and generation-based track. To be more specific, the former applies a retrieval-based approach from the given corpus, while the later utilizes the Web to fulfill the generation-based track. Evaluation results show that our retrieval–based approach performs better than the generation-based one.
What can we learn from Facebook activity?: using social learning analytics to observe new media literacy skills

Social media platforms such as Facebook are now a ubiquitous part of everyday life for many people. New media scholars posit that the participatory culture encouraged by social media gives rise to new forms of literacy skills that are vital to learning. However, there have been few attempts to use analytics to understand the new media literacy skills that may be embedded in an individual's participation in social media. In this paper, I collect raw activity data that was shared by an exploratory sample of Facebook users. I then utilize factor analysis and regression models to show how (a) Facebook members' online activity coalesce into distinct categories of social media behavior and (b) how these participatory behaviors correlate with and predict measures of new media literacy skills. The study demonstrates the use of analytics to understand the literacies embedded in people's social media activity. The implications speak to the potential of social learning analytics to identify and predict new media literacy skills from data streams in social media platforms.
Big Data: A Survey

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.
Demystifying ResNet

The Residual Network (ResNet), proposed in He et al. (2015a), utilized shortcut connections to significantly reduce the difficulty of training, which resulted in great performance boosts in terms of both training and generalization error. It was empirically observed in He et al. (2015a) that stacking more layers of residual blocks with shortcut 2 results in smaller training error, while it is not true for shortcut of length 1 or 3. We provide a theoretical explanation for the uniqueness of shortcut 2. We show that with or without nonlinearities, by adding shortcuts that have depth two, the condition number of the Hessian of the loss function at the zero initial point is depth-invariant, which makes training very deep models no more difficult than shallow ones. Shortcuts of higher depth result in an extremely flat (high-order) stationary point initially, from which the optimization algorithm is hard to escape. The shortcut 1, however, is essentially equivalent to no shortcuts, which has a condition number exploding to infinity as the number of layers grows. We further argue that as the number of layers tends to infinity, it suffices to only look at the loss function at the zero initial point. Extensive experiments are provided accompanying our theoretical results. We show that initializing the network to small weights with shortcut 2 achieves significantly better results than random Gaussian (Xavier) initialization, orthogonal initialization, and shortcuts of deeper depth, from various perspectives ranging from final loss, learning dynamics and stability, to the behavior of the Hessian along the learning process.
The effect of Kahoot, Quizizz and Google Forms on the student's perception in the classrooms response system

Kahoot, Quizizz, and Google Forms are learning technology opens for new ways of teaching in the classroom. The teachers' laptops connected to a video projector, access to wireless network and the students smartphones, tablets or laptops can be utilized to enhance the interaction between the teacher and students, as well as boost the students motivation, engagement and learning. This paper shows the results from investigating the effect of using Kahoot, Quizizz, and Google Forms in classroom on how the students' perception of concentration, engagement, enjoyment, perceived learning, motivation, and satisfaction. The results show that students learned something from doing the quiz via Kahoot, Quizizz and Google Forms. But, there are significant differences in the concentration, engagement, enjoyment, motivation, and satisfaction. Kahoot and Quizizz has presented a lot of positives over Google forms when used in the classroom.
The Labeled Segmentation of Printed Books

We introduce the task of book structure labeling: segmenting and assigning a fixed category (such as TABLE OF CONTENTS, PREFACE, INDEX) to the document structure of printed books. We manually annotate the page-level structural categories for a large dataset totaling 294,816 pages in 1,055 books evenly sampled from 1750– 1922, and present empirical results comparing the performance of several classes of models. The best-performing model, a bidirectional LSTM with rich features, achieves an overall accuracy of 95.8 and a class-balanced macro F-score of 71.4.
APPRAISING PUBLIC VALUE : PAST , PRESENT AND FUTURES

Despite the increasing popularity of the concept of ‘public value’ within both academic and practice settings, there has to date been no formal review of the literature on its provenance, empirical basis, and application. This paper seeks to fill this gap. It provides a critical introduction to public value and its conceptual development before presenting the main elements of the published literature. Following this, a series of key areas of disagreement are discussed and implications for future research and practice put forward. The authors argue that if the espoused aspirations for the public value framework are to be realized, a concerted process of research, debate and application is required. Although some criticisms of public value are argued to be unwarranted, the authors acknowledge ongoing concerns over the apparent silence of public value on questions of power and heterogeneity, and the difficulties in empirically testing the framework’s propositions.
Face Demorphing

The morphing attack proved to be a serious threat for modern automated border control systems where face recognition is used to link the identity of a passenger to his/her e-document. In this paper, we show that by exploiting the live face image acquired at the gate, the morphed face image stored in the document can be reverted (or demorphed) enough to reveal the identity of the legitimate document owner, thus allowing the system to issue a warning. A number of practical experiments on two data sets proves the efficacy of our approach.
Learning Semantic Patterns for Question Generation and Question Answering

In recent years we have been watching an increase of educational research. A huge effort was made to push online courses, or Massive Open Online Courses (MOOCs), and Intelligent Tutoring Systems (ITSs). These paradigms have different pros and cons, but they share the same limitation: the content has to be provided manually by experts. This creates a huge burden on professors and instructors, who need to spend more time creating simple questions to automatically assess the students. In this thesis, we propose a example-based system to automatically perform the task of creating Question/Answer pairs given an information source, through Question Answering (QA) and Question Generation (QG). QA systems try to answer questions posed in natural language, in contrast to search engines that just return a set of related documents given some keywords, while QG systems, on the other hand, try to do the inverse, that is, to generate questions from raw text. Although each task has its own characteristics, some steps can be shared or reversed. Many works in different areas use example-based strategies. Regarding QA and QG, pattern-based approaches have been widely applied; however, most of these methods only go to the lexical and syntactic level, and do not use past interactions to learn new knowledge that can be adapted and reused. In this work, we intend to add a semantic layer to this kind of approach, creating a system that automatically creates Question/Answer pairs from an information source, while being able to learn from past examples how to perform better. Our proposed QG system aims at providing a tool for many scenarios. First, it can be used as an authoring tool to help professors create content for their courses. The relevance and usefulness of the generated questions will be evaluated, as well as the time saved by the teacher. Secondly, it can prove to be an useful tool to create a large dataset of Question/Answer pairs that can be used by real external QA and QG systems. This will be evaluated by qualitative measures, like grammatical correctness and semantic soundness, through the crowd (Amazon Mechanical Turk), and extrinsically, as a supporting data source for real systems in their tasks (for instance, to train neural network systems).
Study on stock price prediction based on BP Neural Network

In this paper, two kinds of methods, namely additional momentum method and self-adaptive learning rate adjustment method, are used to improve the BP algorithm. Considering the diversity of factors which affect stock prices, Single-input and Multi-input Prediction Model (SIPM and MIPM) are established respectively to implement short-term forecasts for SDIC Electric Power (600886) shares and Bank of China (601988) shares in 2009. Experiments indicate that the improved BP model has superior performance to the basic BP model, and MIPM is also better than SIPM. However, the best performance is obtained by using MIPM and improved prediction model cohesively.