nils-herrmann/clean_scirepeval huggingface dataset:
Discriminative probabilistic models for passage based retrieval

The approach of using passage-level evidence for document retrieval has shown mixed results when it is applied to a variety of test beds with different characteristics. One main reason of the inconsistent performance is that there exists no unified framework to model the evidence of individual passages within a document. This paper proposes two probabilistic models to formally model the evidence of a set of top ranked passages in a document. The first probabilistic model follows the retrieval criterion that a document is relevant if any passage in the document is relevant, and models each passage independently. The second probabilistic model goes a step further and incorporates the similarity correlations among the passages. Both models are trained in a discriminative manner. Furthermore, we present a combination approach to combine the ranked lists of document retrieval and passage-based retrieval.
 An extensive set of experiments have been conducted on four different TREC test beds to show the effectiveness of the proposed discriminative probabilistic models for passage-based retrieval. The proposed algorithms are compared with a state-of-the-art document retrieval algorithm and a language model approach for passage-based retrieval. Furthermore, our combined approach has been shown to provide better results than both document retrieval and passage-based retrieval approaches.
Parallel simulation of UAV swarm scenarios

The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. This "emergent behavior" is the aggregate result of many simple interactions occurring within the flock, school, or swarm. Exploration of this emergent behavior in a swarm is accomplished through a high performance computing parallel discrete event simulation. After design of the system, several experiments are designed, tested, and analyzed for efficiency and effectiveness.
Enhancing the Data Capacity of QR Codes by Compressing the Data before Generation

Quick Response (QR) Codes helps us in encoding the data in an efficient manner. The data capacity is limited according to the various data formats used. For increasing the data capacity, data to be encoded can first be compressed using any of the data compression techniques. Then, the data can be encoded. This paper suggests a technique for data compression which in turn helped to increase the data capacity of QR Codes. Results are compared with the normal QR Codes to find the efficiency of the new technique of encoding followed by compression.
Modelling pedestrian trajectory patterns with Gaussian processes

We propose a non-parametric model for pedestrian motion based on Gaussian Process regression, in which trajectory data are modelled by regressing relative motion against current position. We show how the underlying model can be learned in an unsupervised fashion, demonstrating this on two databases collected from static surveillance cameras. We furthermore exemplify the use of model for prediction, comparing the recently proposed GP-Bayesfilters with a Monte Carlo method. We illustrate the benefit of this approach for long term motion prediction where parametric models such as Kalman Filters would perform poorly.
Text simplification for language learners: a corpus analysis

Simplified texts are commonly used by teachers and students in bilingual education and other language-learning contexts. These texts are usually manually adapted, and teachers say this is a timeconsuming and sometimes challenging task. Our goal is the development of tools to aid teachers by automatically proposing ways to simplify texts. As a first step, this paper presents a detailed analysis of a corpus of news articles and abridged versions written by a literacy organization in order to learn what kinds of changes people make when simplifying texts for language learners.
Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.
A generative sketch model for human hair analysis and synthesis

In this paper, we present a generative sketch model for human hair analysis and synthesis. We treat hair images as 2D piecewise smooth vector (flow) fields and, thus, our representation is view-based in contrast to the physically-based 3D hair models in graphics. The generative model has three levels. The bottom level is the high-frequency band of the hair image. The middle level is a piecewise smooth vector field for the hair orientation, gradient strength, and growth directions. The top level is an attribute sketch graph for representing the discontinuities in the vector field. A sketch graph typically has a number of sketch curves which are divided into 11 types of directed primitives. Each primitive is a small window (say 5 times 7 pixels) where the orientations and growth directions are defined in parametric forms, for example, hair boundaries, occluding lines between hair strands, dividing lines on top of the hair, etc. In addition to the three level representation, we model the shading effects, i.e., the low-frequency band of the hair image, by a linear superposition of some Gaussian image bases and we encode the hair color by a color map. The inference algorithm is divided into two stages: 1) We compute the undirected orientation field and sketch graph from an input image and 2) we compute the hair growth direction for the sketch curves and the orientation field using a Swendsen-Wang cut algorithm. Both steps maximize a joint Bayesian posterior probability. The generative model provides a straightforward way for synthesizing realistic hair images and stylistic drawings (rendering) from a sketch graph and a few Gaussian bases. The latter can be either inferred from a real hair image or input (edited) manually using a simple sketching interface. We test our algorithm on a large data set of hair images with diverse hair styles. Analysis, synthesis, and rendering results are reported in the experiments
Generating Quiz Questions from Knowledge Graphs

We propose an approach to generate natural language questions from knowledge graphs such as DBpedia and YAGO. We stage this in the setting of a quiz game. Our approach, though, is general enough to be applicable in other settings. Given a topic of interest (e.g., Soccer) and a difficulty (e.g., hard), our approach selects a query answer, generates a SPARQL query having the answer as its sole result, before verbalizing the question.
Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks

Influence Maximization (IM), that seeks a small set of key users who spread the influence widely into the network, is a core problem in multiple domains. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. Despite the huge amount of effort, IM in billion-scale networks such as Facebook, Twitter, and World Wide Web has not been satisfactorily solved. Even the state-of-the-art methods such as TIM+ and IMM may take days on those networks. In this paper, we propose SSA and D-SSA, two novel sampling frameworks for IM-based viral marketing problems. SSA and D-SSA are up to 1200 times faster than the SIGMOD'15 best method, IMM, while providing the same (1-1/e-ε) approximation guarantee. Underlying our frameworks is an innovative Stop-and-Stare strategy in which they stop at exponential check points to verify (stare) if there is adequate statistical evidence on the solution quality. Theoretically, we prove that SSA and D-SSA are the first approximation algorithms that use (asymptotically) minimum numbers of samples, meeting strict theoretical thresholds characterized for IM. The absolute superiority of SSA and D-SSA are confirmed through extensive experiments on real network data for IM and another topic-aware viral marketing problem, named TVM.
Image processing and analysis - variational, PDE, wavelet, and stochastic methods

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Role Discovery in Networks

Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes belong to the same role if they are structurally similar. Roles have been mainly of interest to sociologists, but more recently, roles have become increasingly useful in other domains. Traditionally, the notion of roles were defined based on graph equivalences such as structural, regular, and stochastic equivalences. We briefly revisit these early notions and instead propose a more general formulation of roles based on the similarity of a feature representation (in contrast to the graph representation). This leads us to propose a taxonomy of three general classes of techniques for discovering roles that includes (i) graph-based roles, (ii) feature-based roles, and (iii) hybrid roles. We also propose a flexible framework for discovering roles using the notion of similarity on a feature-based representation. The framework consists of two fundamental components: (a) role feature construction and (b) role assignment using the learned feature representation. We discuss the different possibilities for discovering feature-based roles and the tradeoffs of the many techniques for computing them. Finally, we discuss potential applications and future directions and challenges.
Parallel K-Means Clustering Based on MapReduce

Data clustering has been received considerable attention in many applications, such as data mining, document retrieval, image segmentation and pattern classification. The enlarging volumes of information emerging by the progress of technology, makes clustering of very large scale of data a challenging task. In order to deal with the problem, many researchers try to design efficient parallel clustering algorithms. In this paper, we propose a parallel k -means clustering algorithm based on MapReduce, which is a simple yet powerful parallel programming technique. The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware.
Boosting for transfer learning with multiple sources

Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. The effectiveness of the transfer is affected by the relationship between source and target. Rather than improving the learning, brute force leveraging of a source poorly related to the target may decrease the classifier performance. One strategy to reduce this negative transfer is to import knowledge from multiple sources to increase the chance of finding one source closely related to the target. This work extends the boosting framework for transferring knowledge from multiple sources. Two new algorithms, TrAdaBoost, and TaskTrAdaBoost, are introduced, analyzed, and applied for object category recognition and specific object detection. The experiments demonstrate their improved performance by greatly reducing the negative transfer as the number of sources increases. TaskTrAdaBoost is a fast algorithm enabling rapid retraining over new targets.
Unbounded verification, falsification, and characterization of security protocols by pattern refinement

We present a new verification algorithm for security protocols that allows for unbounded verification, falsification, and complete characterization. The algorithm provides a number of novel features, including: (1) Guaranteed termination, after which the result is either unbounded correctness, falsification, or bounded correctness. (2) Efficient generation of a finite representation of an infinite set of traces in terms of patterns, also known as a complete characterization. (3) State-of-the-art performance, which has made new types of protocol analysis feasible, such as multi-protocol analysis.
Extreme Point-Based Heuristics for Three-Dimensional Bin Packing

One of the main issues in addressing three-dimensional packing problems is finding an efficient and accurate definition of the points where to place the items inside the bins. The performance of exact and heuristic methods is actually strongly influenced by the choice of a placement rule. We introduce the Extreme Point concept and present a new Extreme Point-based rule for packing items inside a three dimensional container. The Extreme Point rule is independent from the particular packing problem addressed and can handle additional constraints, such as fixing the position of the items. The new Extreme Point rule is also used to derive new constructive heuristics for the Three-Dimensional Bin Packing problem. Extensive computational results show the effectiveness of the new heuristics compared to state-of-the-art results. Moreover, the same heuristics, when applied to the Two-Dimensional Bin Packing problem, outperforms those specifically designed for the problem.
Learning with Partially Absorbing Random Walks

We propose a novel stochastic process that is with probability αi being absorbed at current state i, and with probability 1 − αi follows a random edge out of it. We analyze its properties and show its potential for exploring graph structures. We prove that under proper absorption rates, a random walk starting from a set S of low conductance will be mostly absorbed in S. Moreover, the absorption probabilities vary slowly inside S, while dropping sharply outside, thus implementing the desirable cluster assumption for graph-based learning. Remarkably, the partially absorbing process unifies many popular models arising in a variety of contexts, provides new insights into them, and makes it possible for transferring findings from one paradigm to another. Simulation results demonstrate its promising applications in retrieval and classification.
AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms

In this paper, we present the first-of-its-kind machine learning (ML) system, called AI Programmer, that can automatically generate full software programs requiring only minimal human guidance. At its core, AI Programmer uses genetic algorithms (GA) coupled with a tightly constrained programming language that minimizes the overhead of its ML search space. Part of AI Programmer’s novelty stems from (i) its unique system design, including an embedded, hand-crafted interpreter for efficiency and security and (ii) its augmentation of GAs to include instruction-gene randomization bindings and programming language-specific genome construction and elimination techniques. We provide a detailed examination of AI Programmer’s system design, several examples detailing how the system works, and experimental data demonstrating its software generation capabilities and performance using only mainstream CPUs.
Overcoming the "Ideology of Openness": Probing the Affordances of Social Media for Organizational Knowledge Sharing

Much of the emerging literature on social media in the workplace is characterized by an “ideology of openness” that assumes social media use will increase knowledge sharing in organizations, and that open communication is effective and desirable. We argue that affordances of social media may in fact promote both overt and covert behavior, creating dialectical tensions for distributed workers that must be communicatively managed. Drawing on a case study of the engineering division of a distributed high tech start-up, we find our participants navigate tensions in visibility-invisibility, engagement-disengagement, and sharing-control and strategically manage these tensions to preserve both openness and ambiguity. These findings highlight ways in which organizational members limit as well as share knowledge through social media, and the productive role of tensions in enabling them to attend to multiple goals.
Bucket Elimination: A Unifying Framework for Reasoning

Bucket elimination is an algorithmic framework that generalizes dynamic programming to accommodate many problem-solving and reasoning tasks. Algorithms such as directional-resolution for propositional satis ability, adaptive-consistency for constraint satisfaction, Fourier and Gaussian elimination for solving linear equalities and inequalities, and dynamic programming for combinatorial optimization, can all be accommodated within the bucket elimination framework. Many probabilistic inference tasks can likewise be expressed as bucket-elimination algorithms. These include: belief updating, nding the most probable explanation, and expected utility maximization. These algorithms share the same performance guarantees; all are time and space exponential in the inducedwidth of the problem's interaction graph. While elimination strategies have extensive demands on memory, a contrasting class of algorithms called \conditioning search" require only linear space. Algorithms in this class split a problem into subproblems by instantiating a subset of variables, called a conditioning set, or a cutset. Typical examples of conditioning search algorithms are: backtracking (in constraint satisfaction), and branch and bound (for combinatorial optimization). The paper presents the bucket-elimination framework as a unifying theme across probabilistic and deterministic reasoning tasks and show how conditioning search can be augmented to systematically trade space for time.
The sweet-home project: Audio technology in smart homes to improve well-being and reliance

The Sweet-Home project aims at providing audio-based interaction technology that lets the user have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. This paper presents an overview of the project focusing on the multimodal sound corpus acquisition and labelling and on the investigated techniques for speech and sound recognition. The user study and the recognition performances show the interest of this audio technology.
Competitive recommendation systems

A recommendation system tracks past purchases of a group of users to make product recommendations to individual members of the group. In this paper we present a notion of competitive recommendation systems, building on recent theoretical work on this subject. We reduce the problem of achieving competitiveness to a problem in matrix reconstruction. We then present a matrix reconstruction scheme that is competitive: it requires a small overhead in the number of users and products to be sampled, delivering in the process a net utility that closely approximates the best possible with full knowledge of all user-product preferences.
Development of an Active dv/dt Control Algorithm for Reducing Inverter Conducted EMI with Minimal Impact on Switching Losses

This work investigates the use of an active gate control circuit to reduce the EMI produced by switching power converters. The active gate drive circuit makes it possible to adjust the voltage transition rate of a MOS-gated semiconductor switch on a pulse-by-pulse basis during PWM operation. This paper shows that tailored application of this circuit in a hard- switched inverter can be used to reduce the conducted common- mode EMI generated by the inverter while minimizing the incremental increase of the switching losses produced as a result of this control. An active dv/dt control algorithm that has been developed for a three-phase inverter to achieve this EMI reduction has been implemented for testing. Experimental results show that EMI can be reduced using this active dv/dt control algorithm with lower total switching losses than a scheme that achieves a similar EMI reduction using adjustment of the gate drive resistors alone.
Learning Significant Locations and Predicting User Movement with GPS

Wearable computers have the potential to act as intelligent agents in everyday life and assist the user in a variety of tasks, using context to determine how to act. Location is the most common form of context used by these agents to determine the user’s task. However, another potential use of location context is the creation of a predictive model of the user’s future movements. We present a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales. These locations are then incorporated into a Markov model that can be consulted for use with a variety of applications in both single–user and collaborative scenarios.
A sketch-based interface for detail-preserving mesh editing

In this paper we present a method for the intuitive editing of surface meshes by means of view-dependent sketching. In most existing shape deformation work, editing is carried out by selecting and moving a handle, usually a set of vertices. Our system lets the user easily determine the handle, either by silhouette selection and cropping, or by sketching directly onto the surface. Subsequently, an edit is carried out by sketching a new, view-dependent handle position or by indirectly influencing differential properties along the sketch. Combined, these editing and handle metaphors greatly simplify otherwise complex shape modeling tasks.
A Generative Model for Parsing Natural Language to Meaning Representations

In this paper, we present an algorithm for learning a generative model of natural language sentences together with their formal meaning representations with hierarchical structures. The model is applied to the task of mapping sentences to hierarchical representations of their underlying meaning. We introduce dynamic programming techniques for efficient training and decoding. In experiments, we demonstrate that the model, when coupled with a discriminative reranking technique, achieves state-of-the-art performance when tested on two publicly available corpora. The generative model degrades robustly when presented with instances that are different from those seen in training. This allows a notable improvement in recall compared to previous models.