nils-herrmann/clean_scirepeval huggingface dataset:
A new look at survey propagation and its generalizations

We study the survey propagation algorithm [19, 5, 4], which is an iterative technique that appears to be very effective in solving random k-SAT problems even with densities close to threshold. We first describe how any SAT formula can be associated with a novel family of Markov random fields (MRFs), parameterized by a real number ρ. We then show that applying belief propagation---a well-known "message-passing technique---to this family of MRFs recovers various algorithms, ranging from pure survey propagation at one extreme (ρ = 1) to standard belief propagation on the uniform distribution over SAT assignments at the other extreme (ρ = 0). Configurations in these MRFs have a natural interpretation as generalized satisfiability assignments, on which a partial order can be defined. We isolate cores as minimal elements in this partial ordering, and prove that any core is a fixed point of survey propagation. We investigate the associated lattice structure, and prove a weight-preserving identity that shows how any MRF with p > 0 can be viewed as a "smoothed" version of the naive factor graph representation of the k-SAT problem (p = 0). Our experimental results show that message-passing on our family of MRFs is most effective for values of ρ ≠ 1 (i.e., distinct from survey propagation); moreover, they suggest that random formulas may not typically possess non-trivial cores. Finally, we isolate properties of Gibbs sampling and message-passing algorithms that are typical for an ensemble of k-SAT problems. We prove that the space of cores for random formulas is highly disconnected, and show that for values of p sufficiently close to one, either the associated MRF is highly concentrated around the all-star assignment, or it has exponentially small conductance. Similarly, we prove that for p sufficiently close to one, the all-star assignment is attractive for message-passing when analyzed in the density-evolution setting.
Customer support ticket escalation prediction using feature engineering

Understanding and keeping the customer happy is a central tenet of requirements engineering. Strategies to gather, analyze, and negotiate requirements are complemented by efforts to manage customer input after products have been deployed. For the latter, support tickets are key in allowing customers to submit their issues, bug reports, and feature requests. If insufficient attention is given to support issues, however, their escalation to management becomes time-consuming and expensive, especially for large organizations managing hundreds of customers and thousands of support tickets. Our work provides a step toward simplifying the job of support analysts and managers, particularly in predicting the risk of escalating support tickets. In a field study at our large industrial partner, IBM, we used a design science research methodology to characterize the support process and data available to IBM analysts in managing escalations. In a design science methodology, we used feature engineering to translate our understanding of support analysts’ expert knowledge of their customers into features of a support ticket model. We then implemented these features into a machine learning model to predict support ticket escalations. We trained and evaluated our machine learning model on over 2.5 million support tickets and 10,000 escalations, obtaining a recall of 87.36% and an 88.23% reduction in the workload for support analysts looking to identify support tickets at risk of escalation. Further on-site evaluations, through a prototype tool we developed to implement our machine learning techniques in practice, showed more efficient weekly support ticket management meetings. Finally, in addition to these research evaluation activities, we compared the performance of our support ticket model with that of a model developed with no feature engineering; the support ticket model features outperformed the non-engineered model. The artifacts created in this research are designed to serve as a starting place for organizations interested in predicting support ticket escalations, and for future researchers to build on to advance research in escalation prediction.
Discovering Structure in the Universe of Attribute Names

Recently, search engines have invested significant effort to answering entity–attribute queries from structured data, but have focused mostly on queries for frequent attributes. In parallel, several research efforts have demonstrated that there is a long tail of attributes, often thousands per class of entities, that are of interest to users. Researchers are beginning to leverage these new collections of attributes to expand the ontologies that power search engines and to recognize entity– attribute queries. Because of the sheer number of potential attributes, such tasks require us to impose some structure on this long and heavy tail of attributes. This paper introduces the problem of organizing the attributes by expressing the compositional structure of their names as a rule-based grammar. These rules offer a compact and rich semantic interpretation of multi-word attributes, while generalizing from the observed attributes to new unseen ones. The paper describes an unsupervised learning method to generate such a grammar automatically from a large set of attribute names. Experiments show that our method can discover a precise grammar over 100,000 attributes of Countries while providing a 40-fold compaction over the attribute names. Furthermore, our grammar enables us to increase the precision of attributes from 47% to more than 90% with only a minimal curation effort. Thus, our approach provides an efficient and scalable way to expand ontologies with attributes of user interest.
Predicting the Structure of Cooking Recipes

Cooking recipes exist in abundance; but due to their unstructured text format, they are hard to study quantitatively beyond treating them as simple bags of words. In this paper, we propose an ingredientinstruction dependency tree data structure to represent recipes. The proposed representation allows for more refined comparison of recipes and recipe-parts, and is a step towards semantic representation of recipes. Furthermore, we build a parser that maps recipes into the proposed representation. The parser’s edge prediction accuracy of 93.5% improves over a strong baseline of 85.7% (54.5% error reduction).
Completed Local Binary Count for Rotation Invariant Texture Classification

In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.
Control Flow Integrity for COTS Binaries

Control-Flow Integrity (CFI) has been recognized as an important low-level security property. Its enforcement can defeat most injected and existing code attacks, including those based on Return-Oriented Programming (ROP). Previous implementations of CFI have required compiler support or the presence of relocation or debug information in the binary. In contrast, we present a technique for applying CFI to stripped binaries on x86/Linux. Ours is the first work to apply CFI to complex shared libraries such as glibc. Through experimental evaluation, we demonstrate that our CFI implementation is effective against control-flow hijack attacks, and eliminates the vast majority of ROP gadgets. To achieve this result, we have developed robust techniques for disassembly, static analysis, and transformation of large binaries. Our techniques have been tested on over 300MB of binaries (executables and shared libraries).
Effectiveness of heuristic evaluation in usability evaluation of e-learning applications in higher education

The Internet, World Wide Web (WWW) and e-learning are contributing to new forms of teaching and learning. Such environments should be designed and evaluated in effective ways, considering both usabilityand pedagogical issues. The selection of usability evaluation methods (UEMs) is influenced by the cost of a methods and its effectiveness in addressing users’ issues. The issue of usability is vital in e-learning, where students cannot begin to learn unless they can first use the application. Heuristic evaluation (HE) remains the most widely-used usability evaluation method. This paper describes meta-evaluation research that investigated an HE of a web-based learning (WBL) application. The evaluations were based on a synthesised framework of criteria, related to usability and learning within WBL environments. HE was found to be effective in terms of the number and nature of problems identified in the target application by a complementary team of experienced experts. The findings correspond closely with those of a survey among learners. CATEGORIES AND SUBJECT DESCRIPTORS H.5.1 [Information Interfaces and Presentation]: Multimedia Information Systems – evaluation/methodology; K.3.1 [Computers and Education]: Computer Uses in Education – computer-assisted instruction (CAI).
Pathologies in information bottleneck for deterministic supervised learning

Information bottleneck (IB) is a method for extracting information from one random variable X that is relevant for predicting another random variable Y . To do so, IB identifies an intermediate “bottleneck” variable T that has low mutual information I(X;T ) and high mutual information I(Y ;T ). The IB curve characterizes the set of bottleneck variables that achieve maximal I(Y ;T ) for a given I(X;T ), and is typically explored by optimizing the IB Lagrangian, I(Y ;T )− βI(X;T ). Recently, there has been interest in applying IB to supervised learning, particularly for classification problems that use neural networks. In most classification problems, the output class Y is a deterministic function of the input X , which we refer to as “deterministic supervised learning”. We demonstrate three pathologies that arise when IB is used in any scenario where Y is a deterministic function of X: (1) the IB curve cannot be recovered by optimizing the IB Lagrangian for different values of β; (2) there are “uninteresting” solutions at all points of the IB curve; and (3) for classifiers that achieve low error rates, the activity of different hidden layers will not exhibit a strict trade-off between compression and prediction, contrary to a recent proposal. To address problem (1), we propose a functional that, unlike the IB Lagrangian, can recover the IB curve in all cases. We finish by demonstrating these issues on the MNIST dataset.
High lithium anodic performance of highly nitrogen-doped porous carbon prepared from a metal-organic framework.

Theoretical and experimental results have revealed that the lithium-ion storage capacity for nitrogen-doped graphene largely depends on the nitrogen-doping level. However, most nitrogen-doped carbon materials used for lithium-ion batteries are reported to have a nitrogen content of approximately 10 wt% because a higher number of nitrogen atoms in the two-dimensional honeycomb lattice can result in structural instability. Here we report nitrogen-doped graphene particle analogues with a nitrogen content of up to 17.72 wt% that are prepared by the pyrolysis of a nitrogen-containing zeolitic imidazolate framework at 800 °C under a nitrogen atmosphere. As an anode material for lithium-ion batteries, these particles retain a capacity of 2,132 mA h g(-1) after 50 cycles at a current density of 100 mA g(-1), and 785 mAh g(-1) after 1,000 cycles at 5 A g(-1). The remarkable performance results from the graphene analogous particles doped with nitrogen within the hexagonal lattice and edges.
Intrinsic Motivation and the Process of Learning : Beneficial Effects of Contextualization , Personalization , and Choice

This experiment examined the effects on the learning process of 3 complementary strategies—contextualization, personalization, and provision of choices—for enhancing students' intrinsic motivation. Elementary school children in 1 control and 4 experimental conditions worked with educational computer activities designed to teach arithmetical order-ofoperations rules. In the control condition, this material was presented abstractly. In the experimental conditions, identical material was presented in meaningful and appealing learning contexts, in either generic or individually personalized form. Half of the students in each group were also offered choices concerning instructionally incidental aspects of the learning contexts; the remainder were not. Contextualization, personalization, and choice all produced dramatic increases, not only in students' motivation but also in their depth of engagement in learning, the amount they learned in a fixed time period, and their perceived competence and levels of aspiration.
Budgeted Bandit Problems with Continuous Random Costs

We study the budgeted bandit problem, where each arm is associated with both a reward and a cost. In a budgeted bandit problem, the objective is to design an arm pulling algorithm in order to maximize the total reward before the budget runs out. In this work, we study both multi-armed bandits and linear bandits, and focus on the setting with continuous random costs. We propose an upper confidence bound based algorithm for multi-armed bandits and a confidence ball based algorithm for linear bandits, and prove logarithmic regret bounds for both algorithms. We conduct simulations on the proposed algorithms, which verify the effectiveness of our proposed algorithms.
Focused Crawling: A New Approach to Topic-Specific Web Resource Discovery

The rapid growth of the World-Wide Web poses unprecedented scaling challenges for general-purpose crawlers and search engines. In this paper we describe a new hypertext resource discovery system called a Focused Crawler. The goal of a focused crawler is to selectively seek out pages that are relevant to a pre-defined set of topics. The topics are specified not using keywords, but using exemplary documents. Rather than collecting and indexing all accessible Web documents to be able to answer all possible ad-hoc queries, a focused crawler analyzes its crawl boundary to find the links that are likely to be most relevant for the crawl, and avoids irrelevant regions of the Web. This leads to significant savings in hardware and network resources, and helps keep the crawl more up-to-date. To achieve such goal-directed crawling, we designed two hypertext mining programs that guide our crawler: a classifier that evaluates the relevance of a hypertext document with respect to the focus topics, and a distiller that identifies hypertext nodes that are great access points to many relevant pages within a few links. We report on extensive focused-crawling experiments using several topics at different levels of specificity. Focused crawling acquires relevant pages steadily while standard crawling quickly loses its way, even though they are started from the same root set. Focused crawling is robust against large perturbations in the starting set of URLs. It discovers largely overlapping sets of resources in spite of these perturbations. It is also capable of exploring out and discovering valuable resources that are dozens of links away from the start set, while carefully pruning the millions of pages that may lie within this same radius. Our anecdotes suggest that focused crawling is very effective for building high-quality collections of Web documents on specific topics, using modest desktop hardware.
A comparative study of differential evolution variants for global optimization

In this paper, we present an empirical comparison of some Differential Evolution variants to solve global optimization problems. The aim is to identify which one of them is more suitable to solve an optimization problem, depending on the problem's features and also to identify the variant with the best performance, regardless of the features of the problem to be solved. Eight variants were implemented and tested on 13 benchmark problems taken from the specialized literature. These variants vary in the type of recombination operator used and also in the way in which the mutation is computed. A set of statistical tests were performed in order to obtain more confidence on the validity of the results and to reinforce our discussion. The main aim is that this study can help both researchers and practitioners interested in using differential evolution as a global optimizer, since we expect that our conclusions can provide some insights regarding the advantages or limitations of each of the variants studied.
Effects of prosocial video games on prosocial behavior.

Previous research has documented that playing violent video games has various negative effects on social behavior in that it causes an increase in aggressive behavior and a decrease in prosocial behavior. In contrast, there has been much less evidence on the effects of prosocial video games. In the present research, 4 experiments examined the hypothesis that playing a prosocial (relative to a neutral) video game increases helping behavior. In fact, participants who had played a prosocial video game were more likely to help after a mishap, were more willing (and devoted more time) to assist in further experiments, and intervened more often in a harassment situation. Results further showed that exposure to prosocial video games activated the accessibility of prosocial thoughts, which in turn promoted prosocial behavior. Thus, depending on the content of the video game, playing video games not only has negative effects on social behavior but has positive effects as well.
Amortized Efficiency of List Update and Paging Rules

In this article we study the amortized efficiency of the “move-to-front” and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes &thgr;(i) time, we show that move-to-front is within a constant factor of optimum among a wide class of list maintenance rules. Other natural heuristics, such as the transpose and frequency count rules, do not share this property. We generalize our results to show that move-to-front is within a constant factor of optimum as long as the access cost is a convex function. We also study paging, a setting in which the access cost is not convex. The paging rule corresponding to move-to-front is the “least recently used” (LRU) replacement rule. We analyze the amortized complexity of LRU, showing that its efficiency differs from that of the off-line paging rule (Belady's MIN algorithm) by a factor that depends on the size of fast memory. No on-line paging algorithm has better amortized performance.
The Potential of Unmanned Aerial Vehicles for Mapping

Unmanned aerial vehicles (UAVs) can be used for mapping in the close range domain, combining aerial and terrestrial photogrammetry and as alternative for aerial mapping technologies in small scale areas. At the current state mainly lowcost UAVs are used in mapping projects with low budgets. However, in the last years low-cost UAVs reached a level of practical reliability and professionalism which allow the use of these systems as mapping platforms. UAV based mapping provides not only the required accuracy with respect to cadastral laws and policies as well as requirements for the generation of elevation models in small-scale areas such as gravel pits, UAVs are also competitive to other measurement technologies in terms of economic aspects. In the following an overview on various UAVs will be given and their classification with respect to mapping task will be introduced. In addition, a generic workflow for the photogrammetric UAV flight planning, image acquisition, quality control and data processing will be explained. Complementary to the workflow two applications will be presented which focus on the practicability of UAVs in cadastre applications and generation of elevation models of small-scale areas.
Synchronous Distributed Generation Islanding Protection Using Intelligent Relays

Islanding protection is an essential and one of the more challenging aspect of distributed generation interconnection protection. This article demonstrates how intelligent relays, employing multivariate analysis and data mining techniques, can be used for the islanding protection of synchronous distributed generation in the presence of high-speed reclosing. The developed methodology is outlined and its application is illustrated for a number of diverse system operating states. The performance of the intelligent relays is assessed and compared against currently used islanding protection devices.
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank

This article presents an algorithm for translating the Penn Treebank into a corpus of Combinatory Categorial Grammar (CCG) derivations augmented with local and long-range word-word dependencies. The resulting corpus, CCGbank, includes 99.4% of the sentences in the Penn Treebank. It is available from the Linguistic Data Consortium, and has been used to train wide-coverage statistical parsers that obtain state-of-the-art rates of dependency recovery. In order to obtain linguistically adequate CCG analyses, and to eliminate noise and inconsistencies in the original annotation, an extensive analysis of the constructions and annotations in the Penn Treebank was called for, and a substantial number of changes to the Treebank were necessary. We discuss the implications of our findings for the extraction of other linguistically expressive grammars from the Treebank, and for the design of future treebanks.
Variational Autoencoders for Collaborative Filtering

We extend variational autoencoders (vaes) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation. Despite widespread use in language modeling and economics, the multinomial likelihood receives less attention in the recommender systems literature. We introduce a different regularization parameter for the learning objective, which proves to be crucial for achieving competitive performance. Remarkably, there is an efficient way to tune the parameter using annealing. The resulting model and learning algorithm has information-theoretic connections to maximum entropy discrimination and the information bottleneck principle. Empirically, we show that the proposed approach significantly outperforms several state-of-the-art baselines, including two recently-proposed neural network approaches, on several real-world datasets. We also provide extended experiments comparing the multinomial likelihood with other commonly used likelihood functions in the latent factor collaborative filtering literature and show favorable results. Finally, we identify the pros and cons of employing a principled Bayesian inference approach and characterize settings where it provides the most significant improvements.