nils-herrmann/clean_scirepeval huggingface dataset:
Learning from the past: answering new questions with past answers

Community-based Question Answering sites, such as Yahoo! Answers or Baidu Zhidao, allow users to get answers to complex, detailed and personal questions from other users. However, since answering a question depends on the ability and willingness of users to address the asker's needs, a significant fraction of the questions remain unanswered. We measured that in Yahoo! Answers, this fraction represents 15% of all incoming English questions. At the same time, we discovered that around 25% of questions in certain categories are recurrent, at least at the question-title level, over a period of one year.
 We attempt to reduce the rate of unanswered questions in Yahoo! Answers by reusing the large repository of past resolved questions, openly available on the site. More specifically, we estimate the probability whether certain new questions can be satisfactorily answered by a best answer from the past, using a statistical model specifically trained for this task. We leverage concepts and methods from query-performance prediction and natural language processing in order to extract a wide range of features for our model. The key challenge here is to achieve a level of quality similar to the one provided by the best human answerers.
 We evaluated our algorithm on offline data extracted from Yahoo! Answers, but more interestingly, also on online data by using three "live" answering robots that automatically provide past answers to new questions when a certain degree of confidence is reached. We report the success rate of these robots in three active Yahoo! Answers categories in terms of both accuracy, coverage and askers' satisfaction. This work presents a first attempt, to the best of our knowledge, of automatic question answering to questions of social nature, by reusing past answers of high quality.
The Language of Mathematics

Mohan Ganesalingam. The Language of Mathematics: A Linguistic and Philosophical Investigation. FoLLI Publications on Logic, Language and Information. Springer, 2013.  978-3-642-37011-3 (hbk); 978-3-642-37012-0 (ebook). Pp. xx + 260. This ambitious book sets out to provide a linguistic analysis of the language used in written mathematics, both textual and symbolic. It is a revised version of the author’s Cambridge Ph.D. thesis, a worthy recipient of FoLLI’s E. W. Beth dissertation award for 2011. Mohan Ganesalingam is a linguist with a Ph.D. in computer science, and his work combines insights from these disciplines with a substantial grasp of mathematics. However, there is much in the book that should interest philosophers of mathematics. Firstly, Ganesalingam’s project leads him to confront some significant issues in the foundations of mathematics, for which he proposes a response that is, in part, novel. Secondly, and perhaps more importantly, he demonstrates something which is often discussed but seldom attempted: he shows how his account of mathematics can be applied to a significant body of actual mathematical practice. The book is very clearly structured. Chapter 1 begins with a defence of Ganesalingam’s methodological presuppositions. Critically, and in distinction from earlier projects of more modest scope, notably the work of Aarne Ranta (Ranta, 1997), he insists on sufficient breadth to encompass all of pure mathematics and on what he calls ‘full adaptivity’, that any mathematical content be extracted from the text under analysis, and not baked into the analytic system (p. 3). The latter constraint prevents him from, for example, building set theory into his linguistic model. Although his account is intended to provide an analysis of the content of ‘rigorous, careful textbooks’ he confines it to what he calls the ‘formal mode’ of the language found therein: the statements exclusively concerning mathematical objects and mathematical properties (p. 7). Characteristically, such textbooks also contain much that is in an informal mode—remarks about the context, or value, or interest, of the mathematical results, say—but, as Ganesalingam notes, analysis of these comments would require a full analysis of natural language (p. 8). Conversely, one of the attractions mathematics in the formal mode holds for the linguist is its comparative simplicity. In Chapter 2, Ganesalingam surveys the problem that he has set himself, identifying some of the distinctive linguistic features of mathematics that his analysis, or any comparable rival, should account for. These include the interdependency of text and symbols, the extensive use of stipulative definition to expand the language as it is being used, and some idiomatic features of symbolic notation that lack counterparts in natural language. In particular, Ganesalingam observes that the syntax of mathematics is type-dependent: for example, an expression such as α → (β)n may be syntactically well-defined only if α and β are ordinals and
False data injection attacks against state estimation in electric power grids

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.
 In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks, against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks, which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.
Differential quasi self-complimentary (QSC) ultra-wideband (UWB) MIMO antenna

In this paper, a differentially excited ultra-wideband (UWB) MIMO antenna is designed for pattern diversity applications, utilizing quasi self-complimentary (QSC) antenna elements. Four QSC elements are realized using half- octagon shaped monopoles, and their complementary cuts from the ground plane. Due to the QSC property, wide impedance bandwidth covering the UWB frequency range of 3–11 GHz is achieved within very less antenna footprint. The QSC elements are placed symmetrically about the four edges of a square substrate. Two oppositely positioned elements are fed with 180o phase difference, in order to form a single differential pair. Two such differential pairs are placed perpendicular to each other for realizing pattern diversity performance with high value of differential isolation.
Design of RKE System Based on KEELOQ Encryption Technology

KEELOQ code hopping technology is a nonlinear anti-encryption algorithm designed for secure Remote Keyless Entry (RKE) systems. It combines a 66-bits transmission length, so as to prevent the system from code predicting, code grabbing and code scanning. Because of its feature of high-security, it effectively overcomes the disadvantages of the traditional fixed code technology. This paper researched on KEELOQ encryption algorithm and presented a designed RKE system based on KEELOQ technology. When the software and hardware were designed, the whole RKE system was afterwards made as a prototype for the functionality tests. The experimental results showed that the design achieved the anticipative effects.
Model Driven Architecture with OOHDM

A model-driven architecture (MDA) transforms a platformindependent model (PIM) into a platform-specific model (PSM) before it transforms the PSM into code. OOHDM together with a recently proposed business process extension allows to model hypermedia-based and advanced Web applications by an object model which is a platformindependent. But an OOHDM model cannot directly be used as PIM since it is not an executable model. A behavioral semantics definition of the OOHDM core features and business process makes an OOHDM model executable on a platform-independent Web Application virtual Machine (WAM). As a consequence, an OOHDM behavioral semantics model can serve as a PIM for a MDA. We present two different aspects of the PIM, and give two concrete examples for platform-specific models (PSM) into which the PIM is transformed.
Safer smart contracts through type-driven development

We show how dependent and polymorphic types can make smart contract development safer. This is demonstrated by using the functional language Idris to describe smart contracts on the Ethereum platform. In particular, we show how one class of common errors can be captured at compile time using dependent types and algebraic side effects. We also bring type annotations to the realm of smart contracts, helping developers to circumvent another class of common errors. To demonstrate the feasibility of our solutions, we have extended the Idris compiler with a backend for the Ethereum Virtual Machine. While we find that the functional paradigm might not be the most suitable for the domain, our approach solves the identified problems and provides advantages over the languages in current use.
What would other programmers do: suggesting solutions to error messages

Interpreting compiler errors and exception messages is challenging for novice programmers. Presenting examples of how other programmers have corrected similar errors may help novices understand and correct such errors. This paper introduces HelpMeOut, a social recommender system that aids the debugging of error messages by suggesting solutions that peers have applied in the past. HelpMeOut comprises IDE instrumentation to collect examples of code changes that fix errors; a central database that stores fix reports from many users; and a suggestion interface that, given an error, queries the database for a list of relevant fixes and presents these to the programmer. We report on implementations of this architecture for two programming languages. An evaluation with novice programmers found that the technique can suggest useful fixes for 47% of errors after 39 person-hours of programming in an instrumented environment.
Revisiting Cryptographic Accumulators, Additional Properties and Relations to Other Primitives

Cryptographic accumulators allow to accumulate a finite set of values into a single succinct accumulator. For every accumulated value, one can efficiently compute a witness, which certifies its membership in the accumulator. However, it is computationally infeasible to find a witness for any nonaccumulated value. Since their introduction, various accumulator schemes for numerous practical applications and with different features have been proposed. Unfortunately, to date there is no unifying model capturing all existing features. Such a model can turn out to be valuable as it allows to use accumulators in a black-box fashion. To this end, we propose a unified formal model for (randomized) cryptographic accumulators which covers static and dynamic accumulators, their universal features and includes the notions of undeniability and indistinguishability. Additionally, we provide an exhaustive classification of all existing schemes. In doing so, it turns out that most accumulators are distinguishable. Fortunately, a simple, light-weight generic transformation allows to make many existing dynamic accumulator schemes indistinguishable. As this transformation, however, comes at the cost of reduced collision freeness, we additionally propose the first indistinguishable scheme that does not suffer from this shortcoming. Finally, we employ our unified model for presenting a black-box construction of commitments from indistinguishable accumulators as well as a black-box construction of indistinguishable, undeniable universal accumulators from zero-knowledge sets. Latter yields the first universal accumulator construction that provides indistinguishability.
Chianti: a tool for change impact analysis of java programs

This paper reports on the design and implementation of Chianti, a change impact analysis tool for Java that is implemented in the context of the Eclipse environment. Chianti analyzes two versions of an application and decomposes their difference into a set of atomic changes. Change impact is then reported in terms of affected (regression or unit) tests whose execution behavior may have been modified by the applied changes. For each affected test, Chianti also determines a set of affecting changes that were responsible for the test's modified behavior. This latter step of isolating the changes that induce the failure of one specific test from those changes that only affect other tests can be used as a debugging technique in situations where a test fails unexpectedly after a long editing session. We evaluated Chianti on a year (2002) of CVS data from M. Ernst's Daikon system, and found that, on average, 52% of Daikon's unit tests are affected. Furthermore, each affected unit test, on average, is affected by only 3.95% of the atomic changes. These findings suggest that our change impact analysis is a promising technique for assisting developers with program understanding and debugging.
A synthesis of automated planning and reinforcement learning for efficient, robust decision-making

Automated planning and reinforcement learning are characterized by complementary views on decision making: the former relies on previous knowledge and computation, while the latter on interaction with the world, and experience. Planning allows robots to carry out different tasks in the same domain, without the need to acquire knowledge about each one of them, but relies strongly on the accuracy of the model. Reinforcement learning, on the other hand, does not require previous knowledge, and allows robots to robustly adapt to the environment, but often necessitates an infeasible amount of experience. We present Domain Approximation for Reinforcement LearnING (DARLING), a method that takes advantage of planning to constrain the behavior of the agent to reasonable choices, and of reinforcement learning to adapt to the environment, and increase the reliability of the decision making process. We demonstrate the effectiveness of the proposed method on a service robot, carrying out a variety of tasks in an office building. We find that when the robot makes decisions by planning alone on a given model it often fails, and when it makes decisions by reinforcement learning alone it often cannot complete its tasks in a reasonable amount of time. When employing DARLING, even when seeded with the same model that was used for planning alone, however, the robot can quickly learn a behavior to carry out all the tasks, improves over time, and adapts to the environment as it changes.
Learning Latent Vector Spaces for Product Search

We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations. The power of the model lies in its ability to directly model the discriminative relation between products and a particular word. We compare our method to existing latent vector space models (LSI, LDA and word2vec) and evaluate it as a feature in a learning to rank setting. Our latent vector space model achieves its enhanced performance as it learns better product representations. Furthermore, the mapping from words to products and the representations of words benefit directly from the errors propagated back from the product representations during parameter estimation. We provide an in-depth analysis of the performance of our model and analyze the structure of the learned representations.
Sparse PCA via Bipartite Matchings

We consider the following multi-component sparse PCA problem: given a set of data points, we seek to extract a small number of sparse components with disjoint supports that jointly capture the maximum possible variance. These components can be computed one by one, repeatedly solving the single-component problem and deflating the input data matrix, but as we show this greedy procedure is suboptimal. We present a novel algorithm for sparse PCA that jointly optimizes multiple disjoint components. The extracted features capture variance that lies within a multiplicative factor arbitrarily close to 1 from the optimal. Our algorithm is combinatorial and computes the desired components by solving multiple instances of the bipartite maximum weight matching problem. Its complexity grows as a low order polynomial in the ambient dimension of the input data matrix, but exponentially in its rank. However, it can be effectively applied on a low-dimensional sketch of the data; this allows us to obtain polynomial-time approximation guarantees via spectral bounds. We evaluate our algorithm on real data-sets and empirically demonstrate that in many cases it outperforms existing, deflationbased approaches.
Load identification and compensation for a Cable-Driven parallel robot

In Cable-Driven parallel robots, elastic cables are used to control the movement of a platform in the workspace. The accuracy of a robot system is one of the most important comparison criteria for various applications. In this paper, we present a new approach to increase the relative accuracy of a cable robot under changing payload, by compensating the error induced by elasticity. In the first step, the load is identified based on cable force sensors and the acceleration of the platform. Second, for the actual pose the stiffness matrix is computed and the expected displacement of the platform in Cartesian space is determined. Lastly, the displacement is compensated by adapting the cable lengths. Furthermore, the influence of the calibration of cable force sensors and their parameters' inaccuracies are discussed. The algorithms are implemented and evaluated on a 6-DOF cable robot. We could experimentally prove an improvement of over 50% in the relative accuracy under changing load.
Exploring Topic Models on Short Texts: A Case Study with Crisis Data

In recent years, social media platforms like Twitter and Facebook have become one of the crucial sources of information for a wide spectrum of users. As a result, these platforms have also become great resources to provide support for emergency management. During any crisis, it is necessary to sieve through a huge amount of social media texts within a short span of time to extract meaningful information from them. Extraction of topic keywords from these unstructured social media texts play a significant role in building any application for emergency management. Topic models have the ability to discover latent topics and latent feature representations of words in a document collection and can be used for this purpose. The main aim of this paper is twofold: to explore topic model implementations and look at its effectiveness on short messages (as short as 140 characters); and to perform an exploratory data analysis on short texts related to crises collected from Twitter, and look at different visualizations to understand the commonality and differences between topics and different crisis related data.
Entity Recommendations Using Hierarchical Knowledge Bases

Recent developments in recommendation algorithms have focused on integrating Linked Open Data to augment traditional algorithms with background knowledge. These developments recognize that the integration of Linked Open Data may offer better performance, particularly in cold start cases. In this paper, we explore if and how a specific type of Linked Open Data, namely hierarchical knowledge, may be utilized for recommendation systems. We propose a content-based recommendation approaches that adapts a spreading activation algorithm over the DBpedia category structure to identify entities of interest to the user. Evaluation of the algorithm over the Movielens dataset demonstrates that our method yields more accurate recommendations compared to a previously proposed taxonomy driven approach for recommendations.
Cryptonite: a programmable crypto processor architecture for high bandwidth applications

Cryptographic methods are widely used within networking and digital rights management. Numerous algorithms exist, e.g. spanning VPNs or distributing sensitive data over a shared network infrastructure. While these algorithms can be run with moderate performance on general purpose processors, such processors do not meet typical embedded systems requirements (e.g. area, cost and power consumption). Instead, specialized cores dedicated to one or a combination of algorithms are typically used. These cores provide very high bandwidth data transmission and meet the needs of embedded systems. However, with such cores changing the algorithm is not possible without replacing the hardware. This paper describes a fully programmable processor architecture which has been tailored for the needs of a spectrum of cryptographic algorithms and has been explicitly designed to run at high clock rates while maintaining a significantly better performance/area/power tradeoff than general purpose processors. Both the architecture and instruction set have been developed to achieve a bits-per-clock rate of greater than one, even with complex algorithms. This performance will be demonstrated with standard cryptographic algorithms (AES and DES) and a widely used hash algorithm (MD5).
Abstractions for network update

Configuration changes are a common source of instability in networks, leading to outages, performance disruptions, and security vulnerabilities. Even when the initial and final configurations are correct, the update process itself often steps through intermediate configurations that exhibit incorrect behaviors. This paper introduces the notion of consistent network updates---updates that are guaranteed to preserve well-defined behaviors when transitioning mbetween configurations. We identify two distinct consistency levels, per-packet and per-flow, and we present general mechanisms for implementing them in Software-Defined Networks using switch APIs like OpenFlow. We develop a formal model of OpenFlow networks, and prove that consistent updates preserve a large class of properties. We describe our prototype implementation, including several optimizations that reduce the overhead required to perform consistent updates. We present a verification tool that leverages consistent updates to significantly reduce the complexity of checking the correctness of network control software. Finally, we describe the results of some simple experiments demonstrating the effectiveness of these optimizations on example applications.