nils-herrmann/clean_scirepeval huggingface dataset:
A Framework for Blockchain-Based Applications

Blockchains have recently generated explosive interest from both academia and industry, with many proposed applications. But descriptions of many these proposals are more visionary projections than realizable proposals, and even basic definitions are often missing. We define “blockchain” and “blockchain network”, and then discuss two very different, well known classes of blockchain networks: cryptocurrencies and Git repositories. We identify common primitive elements of both and use them to construct a framework for explicitly articulating what characterizes blockchain networks. The framework consists of a set of questions that every blockchain initiative should address at the very outset. It is intended to help one decide whether or not blockchain is an appropriate approach to a particular application, and if it is, to assist in its initial design stage.
Jump-oriented programming: a new class of code-reuse attack

Return-oriented programming is an effective code-reuse attack in which short code sequences ending in a ret instruction are found within existing binaries and executed in arbitrary order by taking control of the stack. This allows for Turing-complete behavior in the target program without the need for injecting attack code, thus significantly negating current code injection defense efforts (e.g., W⊕X). On the other hand, its inherent characteristics, such as the reliance on the stack and the consecutive execution of return-oriented gadgets, have prompted a variety of defenses to detect or prevent it from happening.
 In this paper, we introduce a new class of code-reuse attack, called jump-oriented programming. This new attack eliminates the reliance on the stack and ret instructions (including ret-like instructions such as pop+jmp) seen in return-oriented programming without sacrificing expressive power. This attack still builds and chains functional gadgets, each performing certain primitive operations, except these gadgets end in an indirect branch rather than ret. Without the convenience of using ret to unify them, the attack relies on a dispatcher gadget to dispatch and execute the functional gadgets. We have successfully identified the availability of these jump-oriented gadgets in the GNU libc library. Our experience with an example shellcode attack demonstrates the practicality and effectiveness of this technique.
Titian: Data Provenance Support in Spark

Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today's DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence (e.g., from log files) and performing trial and error debugging. To aid this effort, we built Titian, a library that enables data provenance-tracking data through transformations-in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds-orders-of-magnitude faster than alternative solutions-while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.
Natural Logic for Textual Inference

This paper presents the first use of a computational model of natural logic—a system of logical inference which operates over natural language—for textual inference. Most current approaches to the PASCAL RTE textual inference task achieve robustness by sacrificing semantic precision; while broadly effective, they are easily confounded by ubiquitous inferences involving monotonicity. At the other extreme, systems which rely on first-order logic and theorem proving are precise, but excessively brittle. This work aims at a middle way. Our system finds a low-cost edit sequence which transforms the premise into the hypothesis; learns to classify entailment relations across atomic edits; and composes atomic entailments into a top-level entailment judgment. We provide the first reported results for any system on the FraCaS test suite. We also evaluate on RTE3 data, and show that hybridizing an existing RTE system with our natural logic system yields significant performance gains.
Evaluating link prediction methods

Link prediction is a popular research area with important applications in a variety of disciplines, including biology, social science, security, and medicine. The fundamental requirement of link prediction is the accurate and effective prediction of new links in networks. While there are many different methods proposed for link prediction, we argue that the practical performance potential of these methods is often unknown because of challenges in the evaluation of link prediction, which impact the reliability and reproducibility of results. We describe these challenges, provide theoretical proofs and empirical examples demonstrating how current methods lead to questionable conclusions, show how the fallacy of these conclusions is illuminated by methods we propose, and develop recommendations for consistent, standard, and applicable evaluation metrics. We also recommend the use of precision-recall threshold curves and associated areas in lieu of receiver operating characteristic curves due to complications that arise from extreme imbalance in the link prediction classification problem.
Misbehavior in Bitcoin: A Study of Double-Spending and Accountability

Bitcoin is a decentralized payment system that relies on Proof-of-Work (PoW) to resist double-spending through a distributed timestamping service. To ensure the operation and security of Bitcoin, it is essential that all transactions and their order of execution are available to all Bitcoin users.
 Unavoidably, in such a setting, the security of transactions comes at odds with transaction privacy. Motivated by the fact that transaction confirmation in Bitcoin requires tens of minutes, we analyze the conditions for performing successful double-spending attacks against fast payments in Bitcoin, where the time between the exchange of currency and goods is short (in the order of a minute). We show that unless new detection techniques are integrated in the Bitcoin implementation, double-spending attacks on fast payments succeed with considerable probability and can be mounted at low cost. We propose a new and lightweight countermeasure that enables the detection of double-spending attacks in fast transactions.
 In light of such misbehavior, accountability becomes crucial. We show that in the specific case of Bitcoin, accountability complements privacy. To illustrate this tension, we provide accountability and privacy definition for Bitcoin, and we investigate analytically and empirically the privacy and accountability provisions in Bitcoin.
Zero-Shot Learning Across Heterogeneous Overlapping Domains

We present a zero-shot learning approach for text classification, predicting which natural language understanding domain can handle a given utterance. Our approach can predict domains at runtime that did not exist at training time. We achieve this extensibility by learning to project utterances and domains into the same embedding space while generating each domain-specific embedding from a set of attributes that characterize the domain. Our model is a neural network trained via ranking loss. We evaluate the performance of this zero-shot approach on a subset of a virtual assistant’s third-party domains and show the effectiveness of the technique on new domains not observed during training. We compare to generative baselines and show that our approach requires less storage and performs better on new domains.
RIS-Based Compact Circularly Polarized Microstrip Antennas

Compact, asymmetric\symmetric-slotted\slit-microstrip patch antennas on reactive impedance surface (RIS) are proposed and studied for circularly polarized (CP) radiation. The antennas consist of a slotted-slit-microstrip patch on a RIS substrate. The CP radiation with compact size is achieved by asymmetric\symmetric-slot-slit cut along the orthogonal\diagonal directions of the patch radiator. The asymmetric\symmetric-slotted\slit microstrip patches on the RIS structure are used for further miniaturization of the antenna with improvement in CP radiation. The measured results of the compact asymmetric-cross slotted square patch antenna are 1.6% (2.51-2.55 GHz) for 3-dB axial ratio bandwidth, 5.2% (2.47-2.60 GHz) for 10-dB return loss bandwidth, and 3.41 dBic for gain over 3-dB axial ratio bandwidth. The overall antenna volume is 0.292λo × 0.292λo × 0.0308λo on a low cost FR4 substrate at 2.5 GHz.
SentiBank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content

A picture is worth one thousand words, but what words should be used to describe the sentiment and emotions conveyed in the increasingly popular social multimedia? We demonstrate a novel system which combines sound structures from psychology and the folksonomy extracted from social multimedia to develop a large visual sentiment ontology consisting of 1,200 concepts and associated classifiers called SentiBank. Each concept, defined as an Adjective Noun Pair (ANP), is made of an adjective strongly indicating emotions and a noun corresponding to objects or scenes that have a reasonable prospect of automatic detection. We believe such large-scale visual classifiers offer a powerful mid-level semantic representation enabling high-level sentiment analysis of social multimedia. We demonstrate novel applications made possible by SentiBank including live sentiment prediction of social media and visualization of visual content in a rich intuitive semantic space.
End-to-end attention-based large vocabulary speech recognition

Many state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) Systems are hybrids of neural networks and Hidden Markov Models (HMMs). Recently, more direct end-to-end methods have been investigated, in which neural architectures were trained to model sequences of characters . To our knowledge, all these approaches relied on Connectionist Temporal Classification  modules. We investigate an alternative method for sequence modelling based on an attention mechanism that allows a Recurrent Neural Network (RNN) to learn alignments between sequences of input frames and output labels. We show how this setup can be applied to LVCSR by integrating the decoding RNN with an n-gram language model and by speeding up its operation by constraining selections made by the attention mechanism and by reducing the source sequence lengths by pooling information over time. Recognition accuracies similar to other HMM-free RNN-based approaches are reported for the Wall Street Journal corpus.
Generating Flexible Workloads for Graph Databases

Graph data management tools are nowadays evolving at a great pace. Key drivers of progress in the design and study of data intensive systems are solutions for synthetic generation of data and workloads, for use in empirical studies. Current graph generators, however, provide limited or no support for workload generation or are limited to fixed use-cases. Towards addressing these limitations, we demonstrate gMark, the first domainand query languageindependent framework for synthetic graph and query workload generation. Its novel features are: (i) fine-grained control of graph instance and query workload generation via expressive user-defined schemas; (ii) the support of expressive graph query languages, including recursion among other features; and, (iii) selectivity estimation of the generated queries. During the demonstration, we will showcase the highly tunable generation of graphs and queries through various user-defined schemas and targeted selectivities, and the variety of supported practical graph query languages. We will also show a performance comparison of four state-ofthe-art graph database engines, which helps us understand their current strengths and desirable future extensions.
Neighborhoods and violent crime: a multilevel study of collective efficacy.

It is hypothesized that collective efficacy, defined as social cohesion among neighbors combined with their willingness to intervene on behalf of the common good, is linked to reduced violence. This hypothesis was tested on a 1995 survey of 8782 residents of 343 neighborhoods in Chicago, Illinois. Multilevel analyses showed that a measure of collective efficacy yields a high between-neighborhood reliability and is negatively associated with variations in violence, when individual-level characteristics, measurement error, and prior violence are controlled. Associations of concentrated disadvantage and residential instability with violence are largely mediated by collective efficacy.
Kinematics and interactive simulation system modeling for robot manipulators

In this paper, three convenient, efficient and accurate virtual scene modeling methods are proposed for robot manipulators. 3D modeling softwares in terms of SolidWorks and UG are exploited to construct the 3D models of the virtual scene. The 3D models are established and the data are used and shared by OpenGL and VRML. The virtual scene is displayed on Visual C++, LabVIEW and MATLAB platform, which can be controlled as requested. The forward and inverse kinematics of a robot manipulator named Katana450 are analyzed by using the exponential product method, then the interactive simulation system is established based on the three mentioned platforms, finally the simulation and experimental results are provided base on MATLAB/Simulink. The methods are expected to be widely used in virtual scene modeling of robot manipulators and extended to other complex mechanical systems as well.
Flexible shaping: How learning in small steps helps

Humans and animals can perform much more complex tasks than they can acquire using pure trial and error learning. This gap is filled by teaching. One important method of instruction is shaping, in which a teacher decomposes a complete task into sub-components, thereby providing an easier path to learning. Despite its importance, shaping has not been substantially studied in the context of computational modeling of cognitive learning. Here we study the shaping of a hierarchical working memory task using an abstract neural network model as the target learner. Shaping significantly boosts the speed of acquisition of the task compared with conventional training, to a degree that increases with the temporal complexity of the task. Further, it leads to internal representations that are more robust to task manipulations such as reversals. We use the model to investigate some of the elements of successful shaping.
Meta-Path-Based Search and Mining in Heterogeneous Information Networks

Information networks that can be extracted from many domains are widely studied recently. Different functions for mining these networks are proposed and developed, such as ranking, community detection, and link prediction. Most existing network studies are on homogeneous networks, where nodes and links are assumed from one single type. In reality, however, heterogeneous information networks can better model the real-world systems, which are typically semi-structured and typed, following a network schema. In order to mine these heterogeneous information networks directly, we propose to explore the meta structure of the information network, i.e., the network schema. The concepts of meta-paths are proposed to systematically capture numerous semantic relationships across multiple types of objects, which are defined as a path over the graph of network schema. Meta-paths can provide guidance for search and mining of the network and help analyze and understand the semantic meaning of the objects and relations in the network. Under this framework, similarity search and other mining tasks such as relationship prediction and clustering can be addressed by systematic exploration of the network meta structure. Moreover, with user’s guidance or feedback, we can select the best meta-path or their weighted combination for a specific mining task.
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). Over the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Then, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we analyze the spear-and-shield relation between Byzantine attack and defense from an interactive game-theoretical perspective. Moreover, we highlight the unsolved research challenges and depict the future research directions.
Denoising based Vector Approximate Message Passing

The D-AMP methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D into the AMP algorithm to achieve state-of-the-art compressive image recovery. But AMP diverges with small deviations from the i.i.d.-Gaussian assumption on the measurement matrix. Recently, the VAMP algorithm has been proposed to fix this problem. In this work, we show that the benefits of VAMP extend to D-VAMP. Consider the problem of recovering a (vectorized) image x0 ∈ R from compressive (i.e., M ≪ N ) noisy linear measurements y = Φx0 +w ∈ R M , (1) known as “compressive imaging.” The “sparse” approach to this problem exploits sparsity in the coefficients v0 , Ψx0 ∈ R N of an orthonormal wavelet transform Ψ. The idea is to rewrite (1) as y = Av0 +w for A , ΦΨ , (2) recover an estimate v̂ of v0 from y, and then construct the image estimate as x̂ = Ψ v̂. Although many algorithms have been proposed for sparse recovery of v0, a notable one is the approximate message passing (AMP) algorithm from . It is computationally efficient (i.e., one multiplication by A and A per iteration and relatively few iterations) and its performance, when M and N are large and Φ is zero-mean i.i.d. Gaussian, is rigorously characterized by a scalar state evolution. A variant called “denoising-based AMP” (D-AMP) was recently proposed [2] for direct recovery of x0 from (1). It exploits the fact that, at iteration t, AMP constructs a pseudo-measurement of the form v0 + N (0, σ t I) with known σ t , which is amenable to any image denoising algorithm. By plugging in a state-of-the-art image denoiser like BM3D , D-AMP yields state-of-the-art compressive imaging. AMP and D-AMP, however, have a serious weakness: they diverge under small deviations from the zero-mean i.i.d. Gaussian assumption on Φ, such as non-zero mean or mild ill-conditioning. A robust alternative called “vector AMP” (VAMP) was recently proposed [4]. VAMP has similar complexity to AMP and a rigorous state evolution November 7, 2016 DRAFT
Advanced Topics in Workflow Management: Issues, Requirements, and solutions

This paper surveys and investigates the strengths and weaknesses of a number of recent approaches to advanced workflow modelling. Rather than inventing just another workflow language, we briefly describe recent workflow languages, and we analyse them with respect to their support for advanced workflow topics. Object Coordination Nets, Workflow Graphs, WorkFlow Nets, and an approach based on Workflow Evolution are described as dedicated workflow modelling approaches. In addition, the Unified Modelling Language as the de facto standard in objectoriented modelling is also investigated. These approaches are discussed with respect to coverage of workflow perspectives and support for flexibility and analysis issues in workflow management, which are today seen as two major areas for advanced workflow support. Given the different goals and backgrounds of the approaches mentioned, it is not surprising that each approach has its specific strengths and weaknesses. We clearly identify these strengths and weaknesses, and we conclude with ideas for combining their best features.
Impossible Spaces: Maximizing Natural Walking in Virtual Environments with Self-Overlapping Architecture

Walking is only possible within immersive virtual environments that fit inside the boundaries of the user's physical workspace. To reduce the severity of the restrictions imposed by limited physical area, we introduce "impossible spaces," a new design mechanic for virtual environments that wish to maximize the size of the virtual environment that can be explored with natural locomotion. Such environments make use of self-overlapping architectural layouts, effectively compressing comparatively large interior environments into smaller physical areas. We conducted two formal user studies to explore the perception and experience of impossible spaces. In the first experiment, we showed that reasonably small virtual rooms may overlap by as much as 56% before users begin to detect that they are in an impossible space, and that the larger virtual rooms that expanded to maximally fill our available 9.14m × 9.14m workspace may overlap by up to 31%. Our results also demonstrate that users perceive distances to objects in adjacent overlapping rooms as if the overall space was uncompressed, even at overlap levels that were overtly noticeable. In our second experiment, we combined several well-known redirection techniques to string together a chain of impossible spaces in an expansive outdoor scene. We then conducted an exploratory analysis of users' verbal feedback during exploration, which indicated that impossible spaces provide an even more powerful illusion when users are naive to the manipulation.
An Enhanced Visualization Process Model for Incremental Visualization

With today's technical possibilities, a stable visualization scenario can no longer be assumed as a matter of course, as underlying data and targeted display setup are much more in flux than in traditional scenarios. Incremental visualization approaches are a means to address this challenge, as they permit the user to interact with, steer, and change the visualization at intermediate time points and not just after it has been completed. In this paper, we put forward a model for incremental visualizations that is based on the established Data State Reference Model, but extends it in ways to also represent partitioned data and visualization operators to facilitate intermediate visualization updates. In combination, partitioned data and operators can be used independently and in combination to strike tailored compromises between output quality, shown data quantity, and responsiveness-i.e., frame rates. We showcase the new expressive power of this model by discussing the opportunities and challenges of incremental visualization in general and its usage in a real world scenario in particular.