nils-herrmann/clean_scirepeval huggingface dataset:
FlexVec: auto-vectorization for irregular loops

Traditional vectorization techniques build a dependence graph with distance and direction information to determine whether a loop is vectorizable. Since vectorization reorders the execution of instructions across iterations, in general instructions involved in a strongly connected component (SCC) are deemed not vectorizable unless the SCC can be eliminated using techniques such as scalar expansion or privatization. Therefore, traditional vectorization techniques are limited in their ability to efficiently handle loops with dynamic cross-iteration dependencies or complex control flow interweaved within the dependence cycles. When potential dependencies do not occur very often, the end-result is under utilization of the SIMD hardware. In this paper, we propose FlexVec architecture that combines new vector instructions with novel code generation techniques to dynamically adjusts vector length for loop statements affected by cross-iteration dependencies that happen at runtime. We have designed and implemented FlexVec's new ISA as extensions to the recently released AVX-512 ISA. We have evaluated the performance improvements enabled by FlexVec vectorization for 11 C/C++ SPEC 2006 benchmarks and 7 real applications with AVX-512 vectorization as baseline. We show that FlexVec vectorization technique produces a Geomean speedup of 9% for SPEC 2006 and a Geomean speedup of 11% for 7 real applications.
Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph

There is high demand for automated tools that assign polarity to microblog content such as tweets (Twitter posts), but this is challenging due to the terseness and informality of tweets in addition to the wide variety and rapid evolution of language in Twitter. It is thus impractical to use standard supervised machine learning techniques dependent on annotated training examples. We do without such annotations by using label propagation to incorporate labels from a maximum entropy classifier trained on noisy labels and knowledge about word types encoded in a lexicon, in combination with the Twitter follower graph. Results on polarity classification for several datasets show that our label propagation approach rivals a model supervised with in-domain annotated tweets, and it outperforms the noisily supervised classifier it exploits as well as a lexicon-based polarity ratio classifier.
Search-Free DOD, DOA and Range Estimation for Bistatic FDA-MIMO Radar

Frequency diverse array (FDA) is an emerging technology, the hybrid of FDA and multiple-input-multiple-output (FDA-MIMO) under monostatic scenarios has received much attention in recent years. However, little work have been done for bistatic FDA-MIMO radar. In this paper, we investigate strategies on estimating direction-of-departure (DOD), direction-of-arrival and range for bistatic FDA-MIMO radar. Our strategies have two aspects. First, nonlinear frequency increments including both subarray and random modes are employed to overcome the problem that the DOD and range parameters of FDA transmitting steering vectors are coupled. Second, in order to reduce the computational complexity associated with the 3-D spectral peak searching algorithms, estimation of signal parameters via rotational invariance technique and parallel factor algorithms with their corresponding phase ambiguity resolving methods, are proposed for subarray and random modes, respectively. Both of the two algorithms perform well while the range parameter of targets satisfy a range constraint criterion. This criterion can also be used for designing frequency increments of bistatic FDA-MIMO radar. Additionally, the Cramér-Rao bound of bistatic FDA-MIMO radar and the algorithm performance analysis consist of identifiability and complexity are derived. All the proposed methods are verified by both theoretical analysis and numerical simulations. And satisfactory results are achieved.
Taxonomy and Survey of Collaborative Intrusion Detection

The dependency of our society on networked computers has become frightening: In the economy, all-digital networks have turned from facilitators to drivers; as cyber-physical systems are coming of age, computer networks are now becoming the central nervous systems of our physical world—even of highly critical infrastructures such as the power grid. At the same time, the 24/7 availability and correct functioning of networked computers has become much more threatened: The number of sophisticated and highly tailored attacks on IT systems has significantly increased. Intrusion Detection Systems (IDSs) are a key component of the corresponding defense measures; they have been extensively studied and utilized in the past. Since conventional IDSs are not scalable to big company networks and beyond, nor to massively parallel attacks, Collaborative IDSs (CIDSs) have emerged. They consist of several monitoring components that collect and exchange data. Depending on the specific CIDS architecture, central or distributed analysis components mine the gathered data to identify attacks. Resulting alerts are correlated among multiple monitors in order to create a holistic view of the network monitored. This article first determines relevant requirements for CIDSs; it then differentiates distinct building blocks as a basis for introducing a CIDS design space and for discussing it with respect to requirements. Based on this design space, attacks that evade CIDSs and attacks on the availability of the CIDSs themselves are discussed. The entire framework of requirements, building blocks, and attacks as introduced is then used for a comprehensive analysis of the state of the art in collaborative intrusion detection, including a detailed survey and comparison of specific CIDS approaches.
An Inference-Based Approach to Recognizing Entailment

For this year's RTE challenge we have continued to pursue a (somewhat) "logical" approach to recognizing entailment, in which our system, called BLUE (Boeing Language Understanding Engine) first creates a logic-based representation of a text T and then performs simple inference (using WordNet and the DIRT inference rule database) to try and infer a hypothesis H. The overall system can be viewed as comprising of three main elements: parsing, WordNet, and DIRT, built on top of a simple baseline of bag-of-words comparison. Ablation studies suggest that WordNet substantially improves the accuracy scores, while, somewhat suprisingly, parsing and DIRT only marginally improve the accuracy scores. We illustrate and discuss these results. Overall, BLUE's reasoning is sometimes insightful but sometimes nonsensical, the primary challenges being noise in the knowledge sources, lack of world knowledge, and the difficulty of accurate syntactic and semantic analysis. Despite these challenges, we argue that forming semantic representations is a necessary first step towards the larger goal of machine reading, and worthy of further exploration. Our best scores were 61.5% (2 way), 54.7% (3 way), and F=0.29 (Search Pilot).
OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles

We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.
Learned Optimizers that Scale and Generalize

Learning to learn has emerged as an important direction for achieving artificial intelligence. Two of the primary barriers to its adoption are an inability to scale to larger problems and a limited ability to generalize to new tasks. We introduce a learned gradient descent optimizer that generalizes well to new tasks, and which has significantly reduced memory and computation overhead. We achieve this by introducing a novel hierarchical RNN architecture, with minimal perparameter overhead, augmented with additional architectural features that mirror the known structure of optimization tasks. We also develop a meta-training ensemble of small, diverse optimization tasks capturing common properties of loss landscapes. The optimizer learns to outperform RMSProp/ADAM on problems in this corpus. More importantly, it performs comparably or better when applied to small convolutional neural networks, despite seeing no neural networks in its meta-training set. Finally, it generalizes to train Inception V3 and ResNet V2 architectures on the ImageNet dataset for thousands of steps, optimization problems that are of a vastly different scale than those it was trained on. We release an open source implementation of the meta-training algorithm.
Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images

The three main tools in the single image restoration theory are the maximum likelihood (ML) estimator, the maximum a posteriori probability (MAP) estimator, and the set theoretic approach using projection onto convex sets (POCS). This paper utilizes the above known tools to propose a unified methodology toward the more complicated problem of superresolution restoration. In the superresolution restoration problem, an improved resolution image is restored from several geometrically warped, blurred, noisy and downsampled measured images. The superresolution restoration problem is modeled and analyzed from the ML, the MAP, and POCS points of view, yielding a generalization of the known superresolution restoration methods. The proposed restoration approach is general but assumes explicit knowledge of the linear space- and time-variant blur, the (additive Gaussian) noise, the different measured resolutions, and the (smooth) motion characteristics. A hybrid method combining the simplicity of the ML and the incorporation of nonellipsoid constraints is presented, giving improved restoration performance, compared with the ML and the POCS approaches. The hybrid method is shown to converge to the unique optimal solution of a new definition of the optimization problem. Superresolution restoration from motionless measurements is also discussed. Simulations demonstrate the power of the proposed methodology.
Survey of failures and fault tolerance in cloud

Cloud computing provides support for hosting client's application. Cloud is a distributed platform that provides hardware, software and network resources to both execute consumer's application and also to store and mange user's data. Cloud is also used to execute scientific workflow applications that are in general complex in nature when compared to other applications. Since cloud is a distributed platform, it is more prone to errors and failures. In such an environment, avoiding a failure is difficult and identifying the source of failure is also complex. Because of this, fault tolerance mechanisms are implemented on the cloud platform. This ensures that even if there are failures in the environment, critical data of the client is not lost and user's application running on cloud is not affected in any manner. Fault tolerance mechanisms also help in improving the cloud's performance by proving the services to the users as required on demand. In this paper a survey of existing fault tolerance mechanisms for the cloud platform are discussed. This paper also discusses the failures, fault tolerant clustering methods and fault tolerant models that are specific for scientific workflow applications.
Crime Data Analysis and Prediction of Perpetrator Identity Using Machine Learning Approach

Crime is one of the most predominant and alarming aspects in our society and its prevention is a vital task. Crime analysis is a systematic way of detecting and investigating patterns and trends in crime. The aim of this model is to increase the efficiency of crime investigation systems. This model detects crime patterns from inferences collected from the crime scene and predicts the description of the perpetrator who is likely suspected to commit the crime. This work has two major aspects: Crime Analysis and Prediction of perpetrator identity. The Crime Analysis phase identifies the number of unsolved crimes, and analyses the influence of various factors like year, month, and weapon on the unsolved crimes. The prediction phase estimates the description of the perpetrators like, their age, sex and relationship with the victim. These predictions are done based on the evidences collected from the crime scene. The system predicts the description of the perpetrator using algorithms like, Multilinear Regression, K- Neighbors Classifier and Neural Networks. It was trained and tested using San Francisco Homicide dataset (1981–2014) and implemented using python.
Moving object surveillance using object proposals and background prior prediction

In this paper, a moving object detection algorithm is combined with a background estimate and a Bing (Binary Norm Gradient) object is proposed in video surveillance. A simple background estimation method is used to detect rough images of a group of moving foreground objects. The foreground setting in the foreground will estimate another set of candidate object windows, and the target (pedestrian / vehicle) from the intersection area comes from the first two steps. In addition, the time cost is reduced by the estimated area. Experiments on outdoor datasets show that the proposed method can not only achieve high detection rate (DR), but also reduce false alarm rate (FAR) and time cost.
Improved Plagiarism Detection Algorithm Based on Abstract Syntax Tree

Statements with conditionals are widely used in C, C++ and java, such as if and while statements and they are easy to plagiarize by adjusting the logical structure of the corresponding statements. However, the existing relative algorithms and tools cannot effectively detect code plagiarism of these statements. This paper puts forward an improved code plagiarism detection algorithm based on abstract syntax tree. The algorithm calculates the hash value for each node of the abstract syntax tree, and compares the hash values node by node. Based on this, it analyzes the if-statement plagiarism, as if-statements are representative in the statements with conditionals, and puts forward the corresponding detection schemes in order to detect plagiarism effectively. After that, with the results of many experiments, the algorithm is proved effective on detecting if-statement plagiarisms.
Beyond Play : A New Approach to Games

would also like to thank those who commented on a post on this subject that I made to the blog Terra Nova (http://terranova.blogs.com); they were instrumental in giving me the opportunity to hone my ability to convey these ideas. At an even earlier stage, a conversation that originated in an online gaming guild first prompted me to attempt to develop these ideas from prior work as a contribution to the present state of games scholarship; I thank all of those who chimed in. Of course, all shortcomings are mine alone. Games have intruded into popular, academic, and policy-maker awareness to an unprecedented level, and this creates new opportunities for advancing our understanding of the relationship of games to society. The author offers a new approach to games that stresses them as characterized by process. Games, the author argues, are domains of contrived contingency, capable of generating emergent practices and interpretations, and are intimately connected with everyday life to a degree heretofore poorly understood. This approach is both consistent with a range of existing social theory and avoids many of the limitations that have characterized much games scholarship to date, in particular its tendency toward unsustainable formalism and exceptionalism. Rather than seeing gaming as a subset of play, and therefore as an activity that is inherently separable , safe, and pleasurable, the author offers a pragmatic rethinking of games as social artifacts in their own right that are always in the process of becoming. This view both better accords with the experience of games by participants cross-culturally and bears the weight of the new questions being asked about games and about society. W ith games' vastly increased prominence, game scholars are now (finally, many might say) being asked to illuminate policy makers, interested media, and the public at large on what games mean. The range of uses to which online games are put by their users, the development of games as spaces for learning, and the sheer growth of the computer game industry cannot but prompt these new questions. This is an important opportunity for game scholarship to make a contribution at a scope that exceeds its previous ambitions. Whereas until recently the study of games contented itself with the limited purview suggested by the play concept, we are now at a point where a reconceptualization of what games are reveals a whole new vista for social theory and research. In …
Determinants of Tax Avoidance

I model the determinants of corporate tax avoidance. Prior research hypothesizes that firms forgo profitable tax-saving strategies due to non-tax costs, financial reporting incentives, and tax authority scrutiny. Because many of these frictions are not directly observable, I quantify their effects using a dynamic structural model in which the firm chooses a set of tax-saving projects in each period in order to maximize share price. I find that average non-tax costs decrease pre-tax earnings by 14.7%. Existing tax risk disclosures decrease tax avoidance by 6.9% but only capture 63% of the expected repayment amount under the audit assumptions specified in the disclosure. The cross-sectional dispersion in effective tax rates primarily arises from differences in the ability to avoid taxes rather than differences in non-tax costs. Finally, I also consider the impact of hypothetical reporting requirements on firms’ decisions to avoid taxes. 1I am grateful for the support of my dissertation committee, David Larcker (chair), Lisa De Simone, Wesley Hartmann, and Peter Reiss. I thank Anne Beyer, Elizabeth Blankespoor, Rebecca Lester, and participants of the 2017 CMU Mini Conference and Stanford Accounting Workshop for helpful suggestions. I have also benefited greatly from Lil Mills, Bridget Stomberg, and several tax directors for providing institutional insight. Finally, I thank my fellow PhD students for their encouragement and support. All errors are my own.
Machine Learning in Medical Applications

Research in Machine Learning methods to-date remains centered on technological issues and is mostly application driven. This letter summarizes successful applications of machine learning methods that were presented at the Workshop on Machine Learning in Medical Applications. The goals of the workshop were to foster fundamental and applied research in the application of machine learning methods to medical problem solving and to medical research, to provide a forum for reporting significant results, to determine whether Machine Learning methods are able to underpin the research and development on intelligent systems for medical applications, and to identify those areas where increased research is likely to yield advances. A number of recommendations for a research agenda were produced, including both technical and human-centered issues.
Distant Supervision for Relation Extraction Using Tree Kernels

In this paper we define a simple Relation Extraction system based on SVMs using tree kernels and employing a weakly supervised approach, known as Distant Supervision (DS). Our method uses the simple one-versus-all strategy to handle overlapping relations, i.e., defined on the same pair of entities. The DS data is defined over the New York Times corpus by means of Freebase as an external knowledge base, which indicates the relations of some of the entities of the NYT text. Our experiments show that our simple approach performs well in this domain with respect to the current state of the art.
Forecasting Intraday stock price trends with text mining techniques

In this paper, we describe NewsCATS (news categorization and trading system), a system implemented to predict stock price trends for the time immediately after the publication of press releases. NewsCATS consists mainly of three components. The first component retrieves relevant information from press releases through the application of text preprocessing techniques. The second component sorts the press releases into predefined categories. Finally, appropriate trading strategies are derived by the third component by means of the earlier categorization. The findings indicate that a categorization of press releases is able to provide additional information that can be used to forecast stock price trends, but that an adequate trading strategy is essential for the results of the categorization to be fully exploited.
Markov Blanket based Feature Selection: A Review of Past Decade

feature selection via the induction of Markov blanket which can be traced back to 1996, and the concept of Markov blanket itself firstly appeared even earlier in 1988. but why they were invented and their relative advantage as well as disadvantages, from both theoretical and practical viewpoint. Besides, it is noticed that all of these mentioned works are all constraint learning which depends on conditional independence test to induce the target, instead of via score-and-search, another mainstream manner as applied in the structure learning of one closely related concept, Bayesian network. Bing the first one, we discuss the cause which uncovers that this choice is not accidental, though not in a formal way. The discussion covered here is believed a valuable reference for academic researchers as well as applicants.
Towards Social-Aware Ridesharing Group Query Services

With the deep penetration of smartphones and geo-locating devices, ridesharing is envisioned as a promising solution to transportation-related problems in metropolitan cities, such as traffic congestion and air pollution. Despite the potential to provide significant societal and environmental benefits, ridesharing has not so far been as popular as expected. Notable barriers include social discomfort and safety concerns when traveling with strangers. To overcome these barriers, in this paper, we propose a new type of Social-aware Ridesharing Group (SaRG) queries which retrieve a group of riders by taking into account their social connections and spatial proximities. While SaRG queries are of practical usefulness, we prove that, however, the SaRG query problem is NP-hard. Thus, we design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. We also present several incremental strategies to accelerate the search speed by reducing repeated computations. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Experimental results on real datasets show that our proposed algorithms achieve desirable performance.
Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques

Peer-to-peer (P2P) lending is a global trend of financial markets that allow individuals to obtain and concede loans without having financial institutions as a strong proxy. As many real-world applications, P2P lending presents an imbalanced characteristic, where the number of creditworthy loan requests is much larger than the number of non-creditworthy ones. In this work, we wrangle a real-world P2P lending data set from Lending Club, containing a large amount of data gathered from 2007 up to 2016. We analyze how supervised classification models and techniques to handle class imbalance impact creditworthiness prediction rates. Ensembles, cost-sensitive and sampling methods are combined and evaluated along logistic regression, decision tree, and bayesian learning schemes. Results show that, in average, sampling techniques outperform ensembles and cost sensitive approaches.