nils-herrmann/clean_scirepeval huggingface dataset:
CTrigger: exposing atomicity violation bugs from their hiding places

Multicore hardware is making concurrent programs pervasive. Unfortunately, concurrent programs are prone to bugs. Among different types of concurrency bugs, atomicity violation bugs are common and important. Existing techniques to detect atomicity violation bugs suffer from one limitation: requiring bugs to manifest during monitored runs, which is an open problem in concurrent program testing.
 This paper makes two contributions. First, it studies the interleaving characteristics of the common practice in concurrent program testing (i.e., running a program over and over) to understand why atomicity violation bugs are hard to expose. Second, it proposes CTrigger to effectively and efficiently expose atomicity violation bugs in large programs. CTrigger focuses on a special type of interleavings (i.e., unserializable interleavings) that are inherently correlated to atomicity violation bugs, and uses trace analysis to systematically identify (likely) feasible unserializable interleavings with low occurrence-probability. CTrigger then uses minimum execution perturbation to exercise low-probability interleavings and expose difficult-to-catch atomicity violation.
 We evaluate CTrigger with real-world atomicity violation bugs from four sever/desktop applications (Apache, MySQL, Mozilla, and PBZIP2) and three SPLASH2 applications on 8-core machines. CTrigger efficiently exposes the tested bugs within 1--235 seconds, two to four orders of magnitude faster than stress testing. Without CTrigger, some of these bugs do not manifest even after 7 full days of stress testing. In addition, without deterministic replay support, once a bug is exposed, CTrigger can help programmers reliably reproduce it for diagnosis. Our tested bugs are reproduced by CTrigger mostly within 5 seconds, 300 to over 60000 times faster than stress testing.
Design and use of the Simple Event Model (SEM)

Events have become central elements in the representation of data from domains such as history, cultural heritage, multimedia and geography. The Simple Event Model (SEM) is created to model events in these various domains, without making assumptions about the domain-specific vocabularies used. SEM is designed with a minimum of semantic commitment to guarantee maximal interoperability. In this paper, we discuss the general requirements of an event model for web data and give examples from two use cases: historic events and events in the maritime safety and security domain. The advantages and disadvantages of several existing event models are discussed in the context of the historic example. We discuss the design decisions underlying SEM. SEM is coupled with a Prolog API that enables users to create instances of events without going into the details of the implementation of the model. By a tight coupling to existing Prolog packages, the API facilitates easy integration of event instances to Linked Open Data. We illustrate use of the API with examples from the maritime domain.
The Antecedents of Customer Loyalty : An Empirical Investigation in Life Insurance Context

The present paper seeks to offer the most decipherable and widely applicable antecedents of customer loyalty. It explores the extant literature on customer loyalty and brings out seven variables which are responsible for formation of customer loyalty. Further, the relative importance of these variables has been ascertained through Multiple Regression Analysis which revealed that service quality and commitment are the strongest predictors of customer loyalty in the Indian life insurance industry. The paper also attempts to assess the loyalty status of life insurance customers in India and draw a comparison between public and private sector life insurance companies in order to provide significant insights to the life insurance companies that may assist them in devising better loyalty practices. The findings suggest that Indian customers do care about the public sector status of a financial service provider as it entails a sense of security and stability and thus creates a difference between customer loyalty of public sector life insurer and that of private sector life insurer. The paper holds significant implications for academicians interested in dynamics of customer loyalty as well as the marketers of life insurance services who are concerned with customer relationships.
Ensemble of shape functions for 3D object classification

This work addresses the problem of real-time 3D shape based object class recognition, its scaling to many categories and the reliable perception of categories. A novel shape descriptor for partial point clouds based on shape functions is presented, capable of training on synthetic data and classifying objects from a depth sensor in a single partial view in a fast and robust manner. The classification task is stated as a 3D retrieval task finding the nearest neighbors from synthetically generated views of CAD-models to the sensed point cloud with a Kinect-style depth sensor. The presented shape descriptor shows that the combination of angle, point-distance and area shape functions gives a significant boost in recognition rate against the baseline descriptor and outperforms the state-of-the-art descriptors in our experimental evaluation on a publicly available dataset of real-world objects in table scene contexts with up to 200 categories.
Toward De-Anonymizing Bitcoin by Mapping Users Location

The Bitcoin system (https://bitcoin.org) is a pseudo-anonymous currency that can dissociate a user from any real-world identity. In that context, a successful breach of the virtual and physical divide represents a signficant aw in the Bit-coin system . In this project we demonstrate how to glean information about the real-world users behind Bitcoin transactions. We analyze publicly available data about the cryptocurrency. In particular, we focus on determining information about a Bitcoin user's physical location by examining that user's spending habits.
Review of Micro Thermoelectric Generator

Used for thermal energy harvesting, thermoelectric generator (TEG) can convert heat into electricity directly. Structurally, the main part of TEG is the thermopile, which consists of thermocouples connected in series electrically and in parallel thermally. Benefiting from massive progress achieved in a microelectromechanical systems technology, micro TEG (<inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG) with advantages of small volume and high output voltage has obtained attention in recent 20 years. The review gives a comprehensive survey of the development and current status of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG. First, the principle of operation is introduced and some key parameters used for characterizing the performance of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG are highlighted. Next, <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEGs are classified from the perspectives of structure, material, and fabrication technology. Then, almost all the relevant works are summarized for the convenience of comparison and reference. Summarized information includes the structure, material property, fabrication technology, output performance, and so on. This will provide readers with an overall evaluation of different studies and guide them in choosing the suitable <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEGs for their applications. In addition, the existing and potential applications of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG are shown, especially the applications in the Internet of things. Finally, we summarize the challenges encountered in improving the output power of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG and predicted that more researchers would focus their efforts on the flexible structure <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG, and combination of <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG and other energy harvestings. With the emergence of more low-power devices and the gradual improvement of <italic>ZT</italic> value of the thermoelectric material, <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula>-TEG is promising for applications in various fields.
The Language of Deceivers: Linguistic Features of Crowdfunding Scams

Crowdfunding sites with recent explosive growth are equally attractive platforms for swindlers or scammers. Though the growing number of articles on crowdfunding scams indicate that the fraud threats are accelerating, there has been little knowledge on the scamming practices and patterns. The key contribution of this research is to discover the hidden clues in the text by exploring linguistic features to distinguish scam campaigns from non-scams. Our results indicate that by providing less information and writing more carefully (and less informally), scammers deliberately try to deceive people; (i) they use less number of words, verbs, and sentences in their campaign pages. (ii) scammers make less typographical errors, 4.5-4.7 times lower than non-scammers.(iii) Expressivity of scams is 2.6-8.5 times lower as well.
Computers and Intractability: A Guide to the Theory of NP-Completeness

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Marketing campaign evaluation in targeted display advertising

In this paper, we develop an experimental analysis to estimate the causal effect of online marketing campaigns as a whole, and not just the media ad design. We analyze the causal effects on user conversion probability. We run experiments based on A/B testing to perform this evaluation. We also estimate the causal effect of the media ad design given this randomization approach. We discuss the framework of a marketing campaign in the context of targeted display advertising, and incorporate the main elements of this framework in the evaluation. We consider budget constraints, the auction process, and the targeting engine in the analysis and the experimental set up. For the effects of this evaluation, we assume the targeting engine to be a black box that incorporates the impression delivery policy, the budget constraints, and the bidding process. Our method to disaggregate the campaign causal analysis is inspired on randomized experiments with imperfect compliance and the intention-to-treat (ITT) analysis. In this framework, individuals assigned randomly to the study group might refuse to take the treatment. For estimation, we present a Bayesian approach and provide credible intervals for the causal estimates. We analyze the effects of 2 independent campaigns for different products from the Advertising.com ad network for 20M+ users each campaign.
Triage: diagnosing production run failures at the user's site

Diagnosing production run failures is a challenging yet importanttask. Most previous work focuses on offsite diagnosis, i.e.development site diagnosis with the programmers present. This is insufficient for production-run failures as: (1) it is difficult to reproduce failures offsite for diagnosis; (2) offsite diagnosis cannot provide timely guidance for recovery or security purposes; (3)it is infeasible to provide a programmer to diagnose every production run failure; and (4) privacy concerns limit the release of information(e.g. coredumps) to programmers.
 To address production-run failures, we propose a system, called Triage, that automatically performs onsite software failure diagnosis at the very moment of failure. It provides a detailed diagnosis report, including the failure nature, triggering conditions, related code and variables, the fault propagation chain, and potential fixes. Triage achieves this by leveraging lightweight reexecution support to efficiently capture the failure environment and repeatedly replay the moment of failure, and dynamically--using different diagnosis techniques--analyze an occurring failure. Triage employs afailure diagnosis protocol that mimics the steps a human takes in debugging. This extensible protocol provides a framework to enable the use of various existing and new diagnosis techniques. We also propose a new failure diagnosis technique, delta analysis, to identify failure related conditions, code, and variables.
 We evaluate these ideas in real system experiments with 10 real software failures from 9 open source applications including four servers. Triage accurately diagnoses the evaluated failures, providing likely root causes and even the fault propagation chain, while keeping normal-run overhead to under 5%. Finally, our user study of the diagnosis and repair of real bugs shows that Triagesaves time (99.99% confidence), reducing the total time to fix by almost half.
Grammatical Error Correction with Alternating Structure Optimization

We present a novel approach to grammatical error correction based on Alternating Structure Optimization. As part of our work, we introduce the NUS Corpus of Learner English (NUCLE), a fully annotated one million words corpus of learner English available for research purposes. We conduct an extensive evaluation for article and preposition errors using various feature sets. Our experiments show that our approach outperforms two baselines trained on non-learner text and learner text, respectively. Our approach also outperforms two commercial grammar checking software packages.
On Single Image Scale-Up Using Sparse-Representations

In this paper we use sparse-representation modeling for the single image scale-up problem. The goal is to recover an original image from its blurred and down-scaled noisy version. Since this problem is highly ill-posed, a prior is needed in order to solve it in a robust fashion. The literature offers various ways to address this problem, ranging from simple linear space-invariant interpolation schemes (e.g., bicubic interpolation), to spatially adaptive and non-linear filters of various sorts. In this paper, we embark from a recently-proposed algorithm by Yang et. al. [1, 2], and similarly assume a local Sparse-Land model on image patches, thus stabilizing the problem. We introduce several important modifications to the above-mentioned solution, and show that these lead to improved results. These modifications include a major simplification of the overall process both in terms of the computational complexity and the algorithm architecture, using a different training approach for the dictionary-pair, and operating without a training-set by boot-strapping the scale-up task from the given low-resolution image. We demonstrate the results on true images, showing both visual and PSNR improvements.
Graph Cut Algorithms in Vision , Graphics and Machine Learning An Integrative Paper

This integrative paper studies graph-cut and network flow algorithms on graphs and compares its applications towards solving diverse problems in Computer Vision, Computer Graphics and Machine Learning. The following three papers form the core of this comparative study. • ’An Experimental Comparison of Min-Cut/MaxFlow Algorithms for Energy Minimization in Vision’ by Boykov et.al.[1] reformulates a variety of problems in computer vision namely image restoration, stereo and segmentation into energy minimization problems and solves them using graph-cut algorithms. • ’Graph Cut Textures: Image and Video Synthesis Using Graph Cuts’ by Kwatra et. al.  proposes a new algorithm for image and video texture synthesis in the area of Computer Graphics. At the core of their method is a graph-cut technique for computing optimal seams across multiple image patches which are to be blended and merged together to generate the syn-
360° Video Stabilization

We present a hybrid 3D-2D algorithm for stabilizing 360° video using a deformable rotation motion model. Our algorithm uses 3D analysis to estimate the rotation between key frames that are appropriately spaced such that the right amount of motion has occurred to make that operation reliable. For the remaining frames, it uses 2D optimization to maximize the visual smoothness of feature point trajectories. A new low-dimensional flexible deformed rotation motion model enables handling small translational jitter, parallax, lens deformation, and rolling shutter wobble. Our 3D-2D architecture achieves better robustness, speed, and smoothing ability than either pure 2D or 3D methods can provide. Stabilizing a video with our method takes less time than playing it at normal speed. The results are sufficiently smooth to be played back at high speed-up factors; for this purpose we present a simple 360° hyperlapse algorithm that remaps the video frame time stamps to balance the apparent camera velocity.
Securing communication in 6LoWPAN with compressed IPsec

Real-world deployments of wireless sensor networks (WSNs) require secure communication. It is important that a receiver is able to verify that sensor data was generated by trusted nodes. It may also be necessary to encrypt sensor data in transit. Recently, WSNs and traditional IP networks are more tightly integrated using IPv6 and 6LoWPAN. Available IPv6 protocol stacks can use IPsec to secure data exchange. Thus, it is desirable to extend 6LoWPAN such that IPsec communication with IPv6 nodes is possible. It is beneficial to use IPsec because the existing end-points on the Internet do not need to be modified to communicate securely with the WSN. Moreover, using IPsec, true end-to-end security is implemented and the need for a trustworthy gateway is removed. In this paper we provide End-to-End (E2E) secure communication between IP enabled sensor networks and the traditional Internet. This is the first compressed lightweight design, implementation, and evaluation of 6LoWPAN extension for IPsec. Our extension supports both IPsec's Authentication Header (AH) and Encapsulation Security Payload (ESP). Thus, communication endpoints are able to authenticate, encrypt and check the integrity of messages using standardized and established IPv6 mechanisms.
Paragon: QoS-aware scheduling for heterogeneous datacenters

Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty to match applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees that many cloud workloads require. While previous work has identified the impact of heterogeneity and interference, existing solutions are computationally intensive, cannot be applied online and do not scale beyond few applications.
 We present Paragon, an online and scalable DC scheduler that is heterogeneity and interference-aware. Paragon is derived from robust analytical methods and instead of profiling each application in detail, it leverages information the system already has about applications it has previously seen. It uses collaborative filtering techniques to quickly and accurately classify an unknown, incoming workload with respect to heterogeneity and interference in multiple shared resources, by identifying similarities to previously scheduled applications. The classification allows Paragon to greedily schedule applications in a manner that minimizes interference and maximizes server utilization. Paragon scales to tens of thousands of servers with marginal scheduling overheads in terms of time or state.
 We evaluate Paragon with a wide range of workload scenarios, on both small and large-scale systems, including 1,000 servers on EC2. For a 2,500-workload scenario, Paragon enforces performance guarantees for 91% of applications, while significantly improving utilization. In comparison, heterogeneity-oblivious, interference-oblivious and least-loaded schedulers only provide similar guarantees for 14%, 11% and 3% of workloads. The differences are more striking in oversubscribed scenarios where resource efficiency is more critical.
Feature Selection for Unsupervised Learning

In this paper, we identify two issues involved in developing an automated feature subset selection algorithm for unlabeled data: the need for finding the number of clusters in conjunction with feature selection, and the need for normalizing the bias of feature selection criteria with respect to dimension. We explore the feature selection problem and these issues through FSSEM (Feature Subset Selection using Expectation-Maximization (EM) clustering) and through two different performance criteria for evaluating candidate feature subsets: scatter separability and maximum likelihood. We present proofs on the dimensionality biases of these feature criteria, and present a cross-projection normalization scheme that can be applied to any criterion to ameliorate these biases. Our experiments show the need for feature selection, the need for addressing these two issues, and the effectiveness of our proposed solutions.
Efficient Private ERM for Smooth Objectives

In this paper, we consider efficient differentially private empirical risk minimization from the viewpoint of optimization algorithms. For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running time of previous state-of-the-art private optimization algorithms, for both ǫ-DP and (ǫ, δ)-DP. For non-convex but smooth objectives, we propose an RRPSGD (Random Round Private Stochastic Gradient Descent) algorithm, which provably converges to a stationary point with privacy guarantee. Besides the expected utility bounds, we also provide guarantees in high probability form. Experiments demonstrate that our algorithm consistently outperforms existing method in both utility and running time.
101companies: A Community Project on Software Technologies and Software Languages

101companies is a community project in computer science (or software science) with the objective of developing a free, structured, wiki-accessible knowledge resource including an open-source repository for different stakeholders with interests in software technologies, software languages, and technological spaces; notably: teachers and learners in software engineering or software languages as well as software developers, software technologists, and ontologists. The present paper introduces the 101companies Project. In fact, the present paper is effectively a call for contributions to the project and a call for applications of the project in research and education.