nils-herrmann/clean_scirepeval huggingface dataset:
Business Process Design by Reusing Business Process Fragments from the Cloud

The constant development of technologies forces companies to be more innovative in order to stay competitive. In fact, designing a process from scratch is time consuming, error prone and costly. In this context, companies are heading to reuse process fragments when designing a new process to ensure a high degree of efficiency, with respect to delivery deadlines. However, reusing these fragments may disclose sensitive business activities, especially if these latter are deployed in an untrusted environment. In addition, companies are concerned about their user's privacy. To address these issues, we investigate how to build a new business process by reusing the safest existing fragments coming from various cloud servers, i.e. The ones that comply at best with company's preferences and policies, and offer an appropriate level of safety.
Capacitated clustering problem in computational biology: Combinatorial and statistical approach for sibling reconstruction

The capacitated clustering problem (CCP) has been studied in a wide range of applications. In this study, we investigate a challenging CCP in computational biology, namely, sibling reconstruction problem (SRP). The goal of SRP is to establish the sibling relationship (i.e., groups of siblings) of a population from genetic data. The SRP has gained more and more interests from computational biologists over the past decade as it is an important and necessary keystone for studies in genetic and population biology. We propose a large-scale mixed-integer formulation of the CCP for SRP that is based on both combinatorial and statistical genetic concepts. The objective is not only to find the minimum number of sibling groups, but also to maximize the degree of similarity of individuals in the same sibling groups while each sibling group is subject to genetic constraints derived from Mendel’s laws. We develop a new randomized greedy optimization algorithm to effectively and efficiently solve this SRP. The algorithm consists of two key phases: construction and enhancement. In the construction phase, a greedy approach with randomized perturbation is applied to construct multiple sibling groups iteratively. In the enhancement phase, a two-stage local search with a memory function is used to improve the solution quality with respect to the similarity measure. We demonstrate the effectiveness of the proposed algorithm using real biological data sets and compare it with state-of-the-art approaches in the literature. We also test it on larger simulated data sets. The experimental results show that the proposed algorithm provide the best reconstruction solutions. & 2011 Elsevier Ltd. All rights reserved.
Structured Prediction of Sequences and Trees using Infinite Contexts

Linguistic structures exhibit a rich array of global phenomena, however commonly used Markov models are unable to adequately describe these phenomena due to their strong locality assumptions. We propose a novel hierarchical model for structured prediction over sequences and trees which exploits global context by conditioning each generation decision on an unbounded context of prior decisions. This builds on the success of Markov models but without imposing a fixed bound in order to better represent global phenomena. To facilitate learning of this large and unbounded model, we use a hierarchical PitmanYor process prior which provides a recursive form of smoothing. We propose prediction algorithms based on A* and Markov Chain Monte Carlo sampling. Empirical results demonstrate the potential of our model compared to baseline finite-context Markov models on part-of-speech tagging and syntactic parsing.
Inflammatory reaction to hyaluronic acid: A newly described complication in vocal fold augmentation.

To establish the rate of inflammatory reaction to hyaluronic acid (HA) in vocal fold injection augmentation, determine the most common presenting signs and symptoms, and propose an etiology.



Retrospective chart review.



Patients injected with HA over a 5-year period were reviewed to identify those who had a postoperative inflammatory reaction. Medical records were reviewed for patient demographic information, subjective complaints, Voice Handicap Index-10 (VHI-10) scores, medical intervention, and resolution time. Videolaryngostroboscopy examinations were also evaluated.



A total of 186 patients (245 vocal folds) were injected with HA over a 5-year period, with a postoperative inflammatory reaction rate of 3.8%. The most common complaints in these patients were odynophagia, dysphonia, and dyspnea with vocal fold erythema, edema, and loss of pliability on videolaryngostroboscopy. All patients were treated with corticosteroids. Return of vocal fold vibration ranged from 3 weeks to 26 months, with VHI-10 scores normalizing in 50% of patients.



This reaction may be a form of hypersensitivity related to small amounts of protein linked to HA. Alternatively, extravascular compression from the HA could lead to venous congestion of the vocal fold. The possibility of equipment contamination is also being investigated. Further studies are needed to determine the etiology and best treatment.
Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

Genetic screens help infer gene function in mammalian cells, but it has remained difficult to assay complex phenotypes-such as transcriptional profiles-at scale. Here, we develop Perturb-seq, combining single-cell RNA sequencing (RNA-seq) and clustered regularly interspaced short palindromic repeats (CRISPR)-based perturbations to perform many such assays in a pool. We demonstrate Perturb-seq by analyzing 200,000 cells in immune cells and cell lines, focusing on transcription factors regulating the response of dendritic cells to lipopolysaccharide (LPS). Perturb-seq accurately identifies individual gene targets, gene signatures, and cell states affected by individual perturbations and their genetic interactions. We posit new functions for regulators of differentiation, the anti-viral response, and mitochondrial function during immune activation. By decomposing many high content measurements into the effects of perturbations, their interactions, and diverse cell metadata, Perturb-seq dramatically increases the scope of pooled genomic assays.
Centrally Banked Cryptocurrencies

Current cryptocurrencies, starting with Bitcoin, build a decentralized blockchain-based transaction ledger, maintained through proofs-of-work that also serve to generate a monetary supply. Such decentralization has benefits, such as independence from national political control, but also significant limitations in terms of computational costs and scalability. We introduce RSCoin, a cryptocurrency framework in which central banks maintain complete control over the monetary supply, but rely on a distributed set of authorities, or mintettes, to prevent double-spending. While monetary policy is centralized, RSCoin still provides strong transparency and auditability guarantees. We demonstrate, both theoretically and experimentally, the benefits of a modest degree of centralization, such as the elimination of wasteful hashing and a scalable system for avoiding doublespending attacks.
Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription

We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our approach outperforms many traditional models of polyphonic music on a variety of realistic datasets. We show how our musical language model can serve as a symbolic prior to improve the accuracy of polyphonic transcription.
High-Performance Quasi-Monte Carlo Financial Simulation: FPGA vs. GPP vs. GPU

Quasi-Monte Carlo simulation is a special Monte Carlo simulation method that uses quasi-random or low-discrepancy numbers as random sample sets. In many applications, this method has proved advantageous compared to the traditional Monte Carlo simulation method, which uses pseudo-random numbers, thanks to its faster convergence and higher level of accuracy. This article presents the design and implementation of a massively parallelized Quasi-Monte Carlo simulation engine on an FPGA-based supercomputer, called Maxwell. It also compares this implementation with equivalent graphics processing units (GPUs) and general purpose processors (GPP)-based implementations. The detailed comparison between these three implementations (FPGA vs. GPP vs. GPU) is done in the context of financial derivatives pricing based on our Quasi-Monte Carlo simulation engine. Real hardware implementations on the Maxwell machine show that FPGAs outperform equivalent GPP-based software implementations by 2 orders of magnitude, with the speed-up figure scaling linearly with the number of processing nodes used (FPGAs/GPPs). The same implementations show that FPGAs achieve a ~ 3x speedup compared to equivalent GPU-based implementations. Power consumption measurements also show FPGAs to be 336x more energy efficient than CPUs, and 16x more energy efficient than GPUs.
Place recognition based on matching of planar surfaces and line segments

This paper considers the potential of using 3D planar surfaces and line segments detected in depth images for place recognition. A place recognition method is presented which is based on matching sets of surface and line features extracted from depth images provided by a 3D camera to features of the same type contained in a previously created environment model. The considered environment model consists of a set of local models representing particular locations in the modeled environment. Each local model consists of planar surface segments and line segments representing the edges of objects in the environment. The presented method is designed for indoor and urban environments. A computationally efficient pose hypothesis generation approach is proposed which ranks the features according to their potential contribution to the pose information, thereby reducing the time needed for obtaining accurate pose estimation. Furthermore, a robust probabilistic method for selecting the best pose hypothesis is proposed which allows matching of partially overlapping point clouds with gross outliers. The proposed approach is experimentally tested on a benchmark dataset containing depth images acquired in indoor environment with changes in lighting conditions and presence of moving objects. A comparison of the proposed method to FAB-MAP and DLoopDetector is reported.
Fast correspondence search for 3 D surface matching

An algorithm for least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized Gauss-Markoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surfaces simultaneously, without using explicit tie points. Besides the mathematical model and execution aspects we pay particular interest to the reduction of the computational expenses. An efficient space partitioning method is implemented in order to speed up the correspondence search, which is the main portion of the computational efforts. The simultaneous matching of sub-surface patches is given as another strategy. It provides a computationally effective solution, since it matches only relevant multi-subpatches rather then the whole overlapping area. A practical example including computation times is given for the demonstration of the method.  Corresponding author.
Measuring academic influence: Not all citations are equal

The importance of a research article is routinely measured by counting how many times it has been cited. However, treating all citations with equal weight ignores the wide variety of functions that citations perform. We want to automatically identify the subset of references in a bibliography that have a central academic influence on the citing paper. For this purpose, we examine the effectiveness of a variety of features for determining the academic influence of a citation. By asking authors to identify the key references in their own work, we created a dataset in which citations were labeled according to their academic influence. Using automatic feature selection with supervised machine learning, we found a model for predicting academic influence that achieves good performance on this dataset using only four features. The best features, among those we evaluated, were features based on the number of times a reference is mentioned in the body of a citing paper. The performance of these features inspired us to design an influence-primed h-index (the hip-index). Unlike the conventional h-index, it weights citations by how many times a reference is mentioned. According to our experiments, the hip-index is a better indicator of researcher performance than the conventional h-index.
SQLrand: Preventing SQL Injection Attacks

We present a practical protection mechanism against SQL injection attacks. Such attacks target databases that are accessible through a web frontend, and take advantage of flaws in the input validation logic of Web components such as CGI scripts. We apply the concept of instruction-set randomization to SQL, creating instances of the language that are unpredictable to the attacker. Queries injected by the attacker will be caught and terminated by the database parser. We show how to use this technique with the MySQL database using an intermediary proxy that translates the random SQL to its standard language. Our mechanism imposes negligible performance overhead to query processing and can be easily retrofitted to existing systems.
A Scalable Multiphysics Network Simulation Using PETSc DMNetwork

A scientific framework for simulations of large-scale networks, such as is required for the analysis of critical infrastructure interaction and interdependencies, is needed for applications on exascale computers. Such a framework must be able to manage heterogeneous physics and unstructured topology, and must be reusable. To this end we have developed DMNetwork, a class in PETSc that provides data and topology management and migration for network problems, along with multiphysics solvers to exploit the problem structure. It eases the application development cycle by providing the necessary infrastructure through simple abstractions to define and query the network. This paper presents the design of the DMNetwork, illustrates its user interface, and demonstrates its ability to solve large network problems through the numerical simulation of a water pipe network with more than 2 billion variables on extreme-scale computers using up to 30,000 processor cores.
The SeaLion has Landed: An IDE for Answer-Set Programming---Preliminary Report

We report about the current state and designated features of the tool SeaLion, aimed to serve as an integrated development environment (IDE) for answer-set programming (ASP). A main goal of SeaLion is to provide a user-friendly environment for supporting a developer to write, evaluate, debug, and test answer-set programs. To this end, new support techniques have to be developed that suit the requirements of the answer-set semantics and meet the constraints of practical applicability. In this respect, SeaLion benefits from the research results of a project on methods and methodologies for answer-set program development in whose context SeaLion is realised. Currently, the tool provides source-code editors for the languages of Gringo and DLV that offer syntax highlighting, syntax checking, and a visual program outline. Further implemented features are support for external solvers and visualisation as well as visual editing of answer sets. SeaLion comes as a plugin of the popular Eclipse platform and provides itself interfaces for future extensions of the IDE.
Single-ISA heterogeneous multi-core architectures for multithreaded workload performance

A single-ISA heterogeneous multi-core architecture is achip multiprocessor composed of cores of varying size, performance,and complexity. This paper demonstrates that thisarchitecture can provide significantly higher performance inthe same area than a conventional chip multiprocessor. It doesso by matching the various jobs of a diverse workload to thevarious cores. This type of architecture covers a spectrum ofworkloads particularly well, providing high single-thread performancewhen thread parallelism is low, and high throughputwhen thread parallelism is high.This paper examines two such architectures in detail,demonstrating dynamic core assignment policies that providesignificant performance gains over naive assignment, andeven outperform the best static assignment. It examines policiesfor heterogeneous architectures both with and withoutmultithreading cores. One heterogeneous architecture we examineoutperforms the comparable-area homogeneous architectureby up to 63%, and our best core assignment strategyachieves up to 31% speedup over a naive policy.
Performance Modeling and Scalability Optimization of Distributed Deep Learning Systems

Big deep neural network (DNN) models trained on large amounts of data have recently achieved the best accuracy on hard tasks, such as image and speech recognition. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. To enable training of extremely large DNNs, models are partitioned across machines. To expedite training on very large data sets, multiple model replicas are trained in parallel on different subsets of the training examples with a global parameter server maintaining shared weights across these replicas. The correct choice for model and data partitioning and overall system provisioning is highly dependent on the DNN and distributed system hardware characteristics. These decisions currently require significant domain expertise and time consuming empirical state space exploration.
 This paper develops performance models that quantify the impact of these partitioning and provisioning decisions on overall distributed system performance and scalability. Also, we use these performance models to build a scalability optimizer that efficiently determines the optimal system configuration that minimizes DNN training time. We evaluate our performance models and scalability optimizer using a state-of-the-art distributed DNN training framework on two benchmark applications. The results show our performance models estimate DNN training time with high estimation accuracy and our scalability optimizer correctly chooses the best configurations, minimizing the training time of distributed DNNs.
Business Intelligence and Analytics Education, and Program Development: A Unique Opportunity for the Information Systems Discipline

“Big Data,” huge volumes of data in both structured and unstructured forms generated by the Internet, social media, and computerized transactions, is straining our technical capacity to manage it. More importantly, the new challenge is to develop the capability to understand and interpret the burgeoning volume of data to take advantage of the opportunities it provides in many human endeavors, ranging from science to business. Data Science, and in business schools, Business Intelligence and Analytics (BI&A) are emerging disciplines that seek to address the demands of this new era. Big Data and BI&A present unique challenges and opportunities not only for the research community, but also for Information Systems (IS) programs at business schools. In this essay, we provide a brief overview of BI&A, speculate on the role of BI&A education in business schools, present the challenges facing IS departments, and discuss the role of IS curricula and program development, in delivering BI&A education. We contend that a new vision for the IS discipline should address these challenges.
A Measurement-Driven Anti-Jamming System for 802.11 Networks

Dense, unmanaged IEEE 802.11 deployments tempt saboteurs into launching jamming attacks by injecting malicious interference. Nowadays, jammers can be portable devices that transmit intermittently at low power in order to conserve energy. In this paper, we first conduct extensive experiments on an indoor 802.11 network to assess the ability of two physical-layer functions, rate adaptation and power control, in mitigating jamming. In the presence of a jammer, we find that: 1) the use of popular rate adaptation algorithms can significantly degrade network performance; and 2) appropriate tuning of the carrier sensing threshold allows a transmitter to send packets even when being jammed and enables a receiver to capture the desired signal. Based on our findings, we build ARES, an Anti-jamming REinforcement System, which tunes the parameters of rate adaptation and power control to improve the performance in the presence of jammers. ARES ensures that operations under benign conditions are unaffected. To demonstrate the effectiveness and generality of ARES, we evaluate it in three wireless test-beds: 1) an 802.11n WLAN with MIMO nodes; 2) an 802.11a/g mesh network with mobile jammers; and 3) an 802.11a WLAN with TCP traffic. We observe that ARES improves the network throughput across all testbeds by up to 150%.
Multicamera People Tracking with a Probabilistic Occupancy Map

Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes. In addition, we also derive metrically accurate trajectories for each of them. Our contribution is twofold. First, we demonstrate that our generative model can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori. Second, we show that multiperson tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and that we avoid confusing them with one another.
Virtual Reality on a WIM: Interactive Worlds in Miniature

This paper explores a user interface technique which augments an immersive head tracked display with a hand-held miniature copy of the virtual environment. We call this interface technique the Worlds in Miniature (WIM) metaphor. In addition to the first-person perspective offered by a virtual reality system, a World in Miniature offers a second dynamic viewport onto the virtual environment. Objects may be directly manipulated either through the immersive viewport or through the three-dimensional viewport offered by the WIM. In addition to describing object manipulation, this paper explores ways in which Worlds in Miniature can act as a single unifying metaphor for such application independent interaction techniques as object selection, navigation, path planning, and visualization. The WIM metaphor offers multiple points of view and multiple scales at which the user can operate, without requiring explicit modes or commands. Informal user observation indicates that users adapt to the Worlds in Miniature metaphor quickly and that physical props are helpful in manipulating the WIM and other objects in the environment.