nils-herrmann/clean_scirepeval huggingface dataset:
On Loan Sales , Loan Contracting , and Lending Relationships

Banks are increasingly using secondary loan sales to manage credit risk and diversify their portfolios. However, loan sales fundamentally alter the lending process by separating loan origination from servicing and funding. How do banks reduce agency problems that arise from selling loans? How do loan sales affect lending relationships and borrower’s access to loans? Our analysis suggests that sold loans are structured to reduce buyer-seller agency problems. Sold loans contain additional, more restrictive covenants, particularly when agency problems between buyers and sellers are likely to be more severe, such as when low reputation lenders originate the loan. When loans are sold, borrowers benefit from increased access to private debt capital, both in the present and in the future. This potentially balances the costs of more restrictive covenants and borrowing from additional lenders. Contrary to concerns that that loan selling negatively impacts lending relationships, borrowers whose loans are sold are more likely to retain their lending relationships. We also provide large-sample evidence on the characteristics of borrowers and lenders that affect salability and find evidence consistent with information asymmetry placing limitations on loan selling. * Drucker is from the Graduate School of Business, Columbia University.  212-854-4151. Puri is from the Fuqua School of Business, Duke University, and NBER.  919-660-7657. We thank Ken Ayotte, Chris Mayer, Anil Shivdasani, and seminar participants at Ohio University and the Washington University in St. Louis Corporate Finance Conference for helpful comments. We acknowledge funding from the FDIC Center for Financial Research.
Challenges in Security for Cyber-Physical Systems

The design of security for cyber-physical systems must take into account several characteristics common to such systems. Among these are feedback between the cyber and physical environment, distributed management and control, uncertainty, real-time requirements, and geographic distribution. This paper discusses these characteristics and suggests a design approach that better integrates security into the core design of the system. A research roadmap is presented that highlights some of the missing pieces needed to enable such an approach. 1. What is a Cyber-Physical-System? The term cyber-physical system has been applied to many problems, ranging from robotics, through SCADA, and distributed control systems. Not all cyber-physical systems involve critical infrastructure, but there are common elements that change the nature of the solutions that must be considered when securing cyber-physical systems. First, the extremely critical nature of activities performed by some cyber-physical systems means that we need security that works, and that by itself means we need something different. All kidding aside, there are fundamental system differences in cyber-physical systems that will force us to look at security in ways more closely tied to the physical application. It is my position that by focusing on these differences we can see where new (or rediscovered) approaches are needed, and that by building systems that support the inclusion of security as part of the application architecture, we can improve the security of both cyber-physical systems, where such an approach is most clearly warranted, as well as improve the security of cyber-only systems, where such an approach is more easily ignored. In this position paper I explain the characteristics of cyber-physical systems that must drive new research in security. I discuss the security problem areas that need attention because of these characteristics and I describe a design methodology for security that provides for better integration of security design with application design. Finally, I suggest some of the components of future systems that can help us include security as a focusing issue in the architectural design of critical applications.
Towards A Process View on Critical Success Factors in Big Data Analytics Projects

The research tries to identify factors that are critical for a Big Data project’s success. In total 27 success factors could be identified throughout the analysis of these published case studies. Subsequently, to the identification the success factors were categorized according to their importance for the project’s success. During the categorization process 6 out of the 27 success factors were declared mission critical. Besides this identification of success factors, this thesis provides a process model, as a suggested way to approach Big Data projects. The process model is divided into separate phases. In addition to a description of the tasks to fulfil, the identified success factors are assigned to the individual phases of the analysis process. Finally, this thesis provides a process model for Big Data projects and also assigns success factors to individual process stages, which are categorized according to their importance for the success of the entire project.
Entropy-based Subspace Clustering for Mining Numerical Data

Mining numerical data is a relatively difficult problem in data mining. Clustering is one of the techniques. We consider a database with numerical attributes, in which each transaction is viewed as a multi-dimensional vector. By studying the clusters formed by these vectors, we can discover certain behaviors hidden in the data. Traditional clustering algorithms find clusters in the full space of the data sets. This results in high dimensional clusters, which are poorly comprehensible to human. One important task in this setting is the ability to discover clusters embedded in the subspaces of a high-dimensional data set. This problem is known as subspace clustering. We follow the basic assumptions of previous work CLIQUE. It is found that the number of subspaces with clustering is very large, and a criterion called the coverage is proposed in CLIQUE for the pruning. In addition to coverage, we identify new useful criteria for this problem and propose an entropybased algorithm called ENCLUS to handle the criteria. Our major contributions are: (1) identify new meaningful criteria of high density and correlation of dimensions for goodness of clustering in subspaces, (2) introduce the use of entropy and provide evidence to support its use, (3) make use of two closure properties based on entropy to prune away uninteresting subspaces efficiently, (4) propose a mechanism to mine non-minimally correlated subspaces which are of interest because of strong clustering, (5) experiments are carried out to show the effectiveness of the proposed method.
Micro Phase Shifting

We consider the problem of shape recovery for real world scenes, where a variety of global illumination (inter-reflections, subsurface scattering, etc.) and illumination defocus effects are present. These effects introduce systematic and often significant errors in the recovered shape. We introduce a structured light technique called Micro Phase Shifting, which overcomes these problems. The key idea is to project sinusoidal patterns with frequencies limited to a narrow, high-frequency band. These patterns produce a set of images over which global illumination and defocus effects remain constant for each point in the scene. This enables high quality reconstructions of scenes which have traditionally been considered hard, using only a small number of images. We also derive theoretical lower bounds on the number of input images needed for phase shifting and show that Micro PS achieves the bound.
Pebbles: Fine-Grained Data Management Abstractions for Modern Operating Systems

Support for fine-grained data management has all but disappeared from modern operating systems such as Android and iOS. Instead, we must rely on each individual application to manage our data properly – e.g., to delete our emails, documents, and photos in full upon request; to not collect more data than required for its function; and to back up our data to reliable backends. Yet, research studies and media articles constantly remind us of the poor data management practices applied by our applications. We have developed Pebbles, a fine-grained data management system that enables management at a powerful new level of abstraction: application-level data objects, such as emails, documents, notes, notebooks, bank accounts, etc. The key contribution is Pebbles’s ability to discover such high-level objects in arbitrary applications without requiring any input from or modifications to these applications. Intuitively, it seems impossible for an OS-level service to understand object structures in unmodified applications, however we observe that the high-level storage abstractions embedded in modern OSes – relational databases and object-relational mappers – bear significant structural information that makes object recognition possible and accurate.
A Simple Algorithm for Automatic Generation of Polyphonic Piano Fingerings

We present a novel method for assigning fingers to notes in a polyphonic piano score. Such a mapping (called a “fingering”) is of great use to performers. To accommodate performers’ unique hand sha our method relies on a simple, user function. We use dynamic programming to search the space of all possible fingerings for the optimal fi ngering under this cost function. Despite the simplicity of the algorithm we achieve re asonable and useful results.
On Non-Functional Requirements in Software Engineering

Essentially a software system’s utility is determined by both its functionality and its non-functional characteristics, such as usability, flexibility, performance, interoperability and security. Nonetheless, there has been a lop-sided emphasis in the functionality of the software, even though the functionality is not useful or usable without the necessary non-functional characteristics. In this chapter, we review the state of the art on the treatment of non-functional requirements (hereafter, NFRs), while providing some prospects for future
On Paraphrase Identification Corpora

We analyze in this paper a number of data sets proposed over the last decade or so for the task of paraphrase identification. The goal of the analysis is to identify the advantages as well as shortcomings of the previously proposed data sets. Based on the analysis, we then make recommendations about how to improve the process of creating and using such data sets for evaluating in the future approaches to the task of paraphrase identification or the more general task of semantic similarity. The recommendations are meant to improve our understanding of what a paraphrase is, offer a more fair ground for comparing approaches, increase the diversity of actual linguistic phenomena that future data sets will cover, and offer ways to improve our understanding of the contributions of various modules or approaches proposed for solving the task of paraphrase identification or similar tasks. We also developed a data collection tool, called Data Collector, that proactively targets the collection of paraphrase instances covering linguistic phenomena important to paraphrasing.
Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations

For applications such as Amazon warehouse order fulfillment, robots must grasp a desired object amid clutter: other objects that block direct access. This can be difficult to program explicitly due to uncertainty in friction and push mechanics and the variety of objects that can be encountered. Deep Learning networks combined with Online Learning from Demonstration (LfD) algorithms such as DAgger and SHIV have potential to learn robot control policies for such tasks where the input is a camera image and system dynamics and the cost function are unknown. To explore this idea, we introduce a version of the grasping in clutter problem where a yellow cylinder must be grasped by a planar robot arm amid extruded objects in a variety of shapes and positions. To reduce the burden on human experts to provide demonstrations, we propose using a hierarchy of three levels of supervisors: a fast motion planner that ignores obstacles, crowd-sourced human workers who provide appropriate robot control values remotely via online videos, and a local human expert. Physical experiments suggest that with 160 expert demonstrations, using the hierarchy of supervisors can increase the probability of a successful grasp (reliability) from 55% to 90%.
Adversarial Network Compression

Neural network compression has recently received much attention due to the computational requirements of modern deep models. In this work, our objective is to transfer knowledge from a deep and accurate model to a smaller one. Our contributions are threefold: (i) we propose an adversarial network compression approach to train the small student network to mimic the large teacher, without the need for labels during training; (ii) we introduce a regularization scheme to prevent a trivially-strong discriminator without reducing the network capacity and (iii) our approach generalizes on different teacher-student models. In an extensive evaluation on five standard datasets, we show that our student has small accuracy drop, achieves better performance than other knowledge transfer approaches and it surpasses the performance of the same network trained with labels. In addition, we demonstrate state-ofthe-art results compared to other compression strategies.
Fitting 3D Morphable Face Models using local features

In this paper, we propose a novel fitting method that uses local image features to fit a 3D Morphable Face Model to 2D images. To overcome the obstacle of optimising a cost function that contains a non-differentiable feature extraction operator, we use a learning-based cascaded regression method that learns the gradient direction from data. The method allows to simultaneously solve for shape and pose parameters. Our method is thoroughly evaluated on Morphable Model generated data and first results on real data are presented. Compared to traditional fitting methods, which use simple raw features like pixel colour or edge maps, local features have been shown to be much more robust against variations in imaging conditions. Our approach is unique in that we are the first to use local features to fit a 3D Morphable Model. Because of the speed of our method, it is applicable for realtime applications. Our cascaded regression framework is available as an open source library at github.com/patrikhuber/superviseddescent.
Benchmarking Derivative-Free Optimization Algorithms

We propose data profiles as a tool for analyzing the performance of derivative-free optimization solvers when there are constraints on the computational budget. We use performance and data profiles, together with a convergence test that measures the decrease in function value, to analyze the performance of three solvers on sets of smooth, noisy, and piecewise-smooth problems. Our results provide estimates for the performance difference between these solvers, and show that on these problems, the model-based solver tested performs better than the two direct search solvers tested.
Optimal energy management policies for energy harvesting sensor nodes

We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable sub-optimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.
BLEWS: Using Blogs to Provide Context for News Articles

An overwhelming number of news articles are available every day via the internet. Unfortunately, it is impossible for us to peruse more than a handful; furthermore it is difficult to ascertain an article’s social context, i.e., is it popular, what sorts of people are reading it, etc. In this paper, we develop a system to address this problem in the restricted domain of political news by harnessing implicit and explicit contextual information from the blogosphere. Specifically, we track thousands of blogs and the news articles they cite, collapsing news articles that have highly overlapping content. We then tag each article with the number of blogs citing it, the political orientation of those blogs, and the level of emotional charge expressed in the blog posts that link to the news article. We summarize and present the results to the user via a novel visualization which displays this contextual information; the user can then find the most popular articles, the articles most cited by liberals, the articles most emotionally discussed in the political blogosphere, etc.
Visual tracking decomposition

We propose a novel tracking algorithm that can work robustly in a challenging scenario such that several kinds of appearance and motion changes of an object occur at the same time. Our algorithm is based on a visual tracking decomposition scheme for the efficient design of observation and motion models as well as trackers. In our scheme, the observation model is decomposed into multiple basic observation models that are constructed by sparse principal component analysis (SPCA) of a set of feature templates. Each basic observation model covers a specific appearance of the object. The motion model is also represented by the combination of multiple basic motion models, each of which covers a different type of motion. Then the multiple basic trackers are designed by associating the basic observation models and the basic motion models, so that each specific tracker takes charge of a certain change in the object. All basic trackers are then integrated into one compound tracker through an interactive Markov Chain Monte Carlo (IMCMC) framework in which the basic trackers communicate with one another interactively while run in parallel. By exchanging information with others, each tracker further improves its performance, which results in increasing the whole performance of tracking. Experimental results show that our method tracks the object accurately and reliably in realistic videos where the appearance and motion are drastically changing over time.
Regionally accelerated batch informed trees (RABIT*): A framework to integrate local information into optimal path planning

Sampling-based optimal planners, such as RRT*, almost-surely converge asymptotically to the optimal solution, but have provably slow convergence rates in high dimensions. This is because their commitment to finding the global optimum compels them to prioritize exploration of the entire problem domain even as its size grows exponentially. Optimization techniques, such as CHOMP, have fast convergence on these problems but only to local optima. This is because they are exploitative, prioritizing the immediate improvement of a path even though this may not find the global optimum of nonconvex cost functions. In this paper, we present a hybrid technique that integrates the benefits of both methods into a single search. A key insight is that applying local optimization to a subset of edges likely to improve the solution avoids the prohibitive cost of optimizing every edge in a global search. This is made possible by Batch Informed Trees (BIT*), an informed global technique that orders its search by potential solution quality. In our algorithm, Regionally Accelerated BIT* (RABIT*), we extend BIT* by using optimization to exploit local domain information and find alternative connections for edges in collision and accelerate the search. This improves search performance in problems with difficult-to-sample homotopy classes (e.g., narrow passages) while maintaining almost-sure asymptotic convergence to the global optimum. Our experiments on simulated random worlds and real data from an autonomous helicopter show that on certain difficult problems, RABIT* converges 1.8 times faster than BIT*. Qualitatively, in problems with difficult-to-sample homotopy classes, we show that RABIT* is able to efficiently transform paths to avoid obstacles.
Fast directional chamfer matching

We study the object localization problem in images given a single hand-drawn example or a gallery of shapes as the object model. Although many shape matching algorithms have been proposed for the problem over the decades, chamfer matching remains to be the preferred method when speed and robustness are considered. In this paper, we significantly improve the accuracy of chamfer matching while reducing the computational time from linear to sublinear (shown empirically). Specifically, we incorporate edge orientation information in the matching algorithm such that the resulting cost function is piecewise smooth and the cost variation is tightly bounded. Moreover, we present a sublinear time algorithm for exact computation of the directional chamfer matching score using techniques from 3D distance transforms and directional integral images. In addition, the smooth cost function allows to bound the cost distribution of large neighborhoods and skip the bad hypotheses within. Experiments show that the proposed approach improves the speed of the original chamfer matching upto an order of 45x, and it is much faster than many state of art techniques while the accuracy is comparable.
Quantifying the impact of human mobility on malaria.

Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies importation routes that contribute to malaria epidemiology on regional spatial scales.
Cryptanalysis and improvement of an elliptic curve Diffie-Hellman key agreement protocol

In SAC'05, Strangio proposed protocol ECKE- 1 as an efficient elliptic curve Diffie-Hellman two-party key agreement protocol using public key authentication. In this letter, we show that protocol ECKE-1 is vulnerable to key-compromise impersonation attacks. We also present an improved protocol - ECKE-1N, which can withstand such attacks. The new protocol's performance is comparable to the well-known MQV protocol and maintains the same remarkable list of security properties.
Application of Deep Recurrent Neural Networks for Prediction of User Behavior in Tor Networks

Due to the amount of anonymity afforded to users of the Tor infrastructure, Tor has become a useful tool for malicious users. With Tor, the users are able to compromise the non-repudiation principle of computer security. Also, the potentially hackers may launch attacks such as DDoS or identity theft behind Tor. For this reason, there are needed new systems and models to detect the intrusion in Tor networks. In this paper, we present the application of Deep Recurrent Neural Networks (DRNNs) for prediction of user behavior in Tor networks. We constructed a Tor server and a Deep Web browser (Tor client) in our laboratory. Then, the client sends the data browsing to the Tor server using the Tor network. We used Wireshark Network Analyzer to get the data and thenused the DRNNs to make the prediction. The simulation results show that our simulation system has a good prediction of user behavior in Tor networks.