nils-herrmann/clean_scirepeval huggingface dataset:
A minimum spanning tree algorithm with Inverse-Ackermann type complexity

A deterministic algorithm for computing a minimum spanning tree of a connected graph is presented. Its running time is <italic>0</italic>(<italic>m</italic> α(<italic>m, n</italic>)), where  α is the classical functional inverse of Ackermann's function and <italic>n</italic> (respectively, <italic>m</italic>) is the number of vertices (respectively, edges). The algorithm is comparison-based : it uses pointers, not arrays, and it makes no numeric assumptions on the edge costs.
Measuring the effects of business intelligence systems: The relationship between business process and organizational performance

Received 1 July 2007 Received in revised form 15 March 2008 Accepted 20 March 2008 Business intelligence (BI) systems provide the ability to analyse business information in order to support and improve management decision making across a broad range of business activities. They leverage the large data infrastructure investments (e.g. ERP systems) made by firms, and have the potential to realise the substantial value locked up in a firm's data resources. While substantial business investment in BI systems is continuing to accelerate, there is a complete absence of a specific and rigorous method to measure the realised business value, if any. By exploiting the lessons learned from prior attempts to measure business valueof IT-intensive systems,wedevelopa newmeasure that is based on an understanding of the characteristics of BI systems in a process-oriented framework. We then employ the measure in an examination of the relationship between the business process performance and organizational performance, finding significant differences in the strength of the relationship between industry sectors. This study reinforces the need to consider the specific context of use when designing performance measurement for IT-intensive systems, and highlights the need for further research examining contextual moderators to the realisation of such performance benefits.
Components of executive control with advantages for bilingual children in two cultures

The present study used a behavioral version of an anti-saccade task, called the 'faces task', developed by [Bialystok, E., Craik, F. I. M., & Ryan, J. (2006). Executive control in a modified anti-saccade task: Effects of aging and bilingualism.  Learning, Memory, and Cognition, 32, 1341-1354] to isolate the components of executive functioning responsible for previously reported differences between monolingual and bilingual children and to determine the generality of these differences by comparing bilinguals in two cultures. Three components of executive control were investigated: response suppression, inhibitory control, and cognitive flexibility. Ninety children, 8-years old, belonged to one of three groups: monolinguals in Canada, bilinguals in Canada, and bilinguals in India. The bilingual children in both settings were faster than monolinguals in conditions based on inhibitory control and cognitive flexibility but there was no significant difference between groups in response suppression or on a control condition that did not involve executive control. The children in the two bilingual groups performed equivalently to each other and differently from the monolinguals on all measures in which there were group differences, consistent with the interpretation that bilingualism is responsible for the enhanced executive control. These results contribute to understanding the mechanism responsible for the reported bilingual advantages by identifying the processes that are modified by bilingualism and establishing the generality of these findings across bilingual experiences. They also contribute to theoretical conceptions of the components of executive control and their development.
Learning by Tracking: Siamese CNN for Robust Target Association

This paper introduces a novel approach to the task of data association within the context of pedestrian tracking, by introducing a two-stage learning scheme to match pairs of detections. First, a Siamese convolutional neural network (CNN) is trained to learn descriptors encoding local spatio-temporal structures between the two input image patches, aggregating pixel values and optical flow information. Second, a set of contextual features derived from the position and size of the compared input patches are combined with the CNN output by means of a gradient boosting classifier to generate the final matching probability. This learning approach is validated by using a linear programming based multi-person tracker showing that even a simple and efficient tracker may outperform much more complex models when fed with our learned matching probabilities. Results on publicly available sequences show that our method meets state-of-the-art standards in multiple people tracking.
XQuery on SQL Hosts

Relational database systems may be turned into efficient XML and XPath processors if the system is provided with a suitable relational tree encoding. This paper extends this relational XML processing stack and shows that an RDBMS can also serve as a highly efficient XQuery runtime environment. Our approach is purely relational: XQuery expressions are compiled into SQL code which operates on the tree encoding. The core of the compilation procedure trades XQuery’s notions of variable scopes and nested iteration (FLWOR blocks) for equi-joins. The resulting relational XQuery processor closely adheres to the language semantics, e.g., it obeys node identity as well as document and sequence order, and can support XQuery’s full axis feature. The system exhibits quite promising performance figures in experiments. Somewhat unexpectedly, we will also see that the XQuery compiler can make good use of SQL’s OLAP functionality.
Probabilistic multi-class segmentation for the Amazon Picking Challenge

We present a method for multi-class segmentation from RGB-D data in a realistic warehouse picking setting. The method computes pixel-wise probabilities and combines them to find a coherent object segmentation. It reliably segments objects in cluttered scenarios, even when objects are translucent, reflective, highly deformable, have fuzzy surfaces, or consist of loosely coupled components. The robust performance results from the exploitation of problem structure inherent to the warehouse setting. The proposed method proved its capabilities as part of our winning entry to the 2015  We present a detailed experimental analysis of the contribution of different information sources, compare our method to standard segmentation techniques, and assess possible extensions that further enhance the algorithm's capabilities. We release our software and data sets as open source.
ROP is Still Dangerous: Breaking Modern Defenses

Return Oriented Programming (ROP) has become the exploitation technique of choice for modern memory-safety vulnerability attacks. Recently, there have been multiple attempts at defenses to prevent ROP attacks. In this paper, we introduce three new attack methods that break many existing ROP defenses. Then we show how to break kBouncer and ROPecker, two recent low-overhead defenses that can be applied to legacy software on existing hardware. We examine several recent ROP attacks seen in the wild and demonstrate that our techniques successfully cloak them so they are not detected by these defenses. Our attacks apply to many CFI-based defenses which we argue are weaker than previously thought. Future defenses will need to take our attacks into account.
The Forget-me-not Process

We introduce the Forget-me-not Process, an efficient, non-parametric metaalgorithm for online probabilistic sequence prediction for piecewise stationary, repeating sources. Our method works by taking a Bayesian approach to partitioning a stream of data into postulated task-specific segments, while simultaneously building a model for each task. We provide regret guarantees with respect to piecewise stationary data sources under the logarithmic loss, and validate the method empirically across a range of sequence prediction and task identification problems.
You Are How You Touch: User Verification on Smartphones via Tapping Behaviors

Smartphone users have their own unique behavioral patterns when tapping on the touch screens. These personal patterns are reflected on the different rhythm, strength, and angle preferences of the applied force. Since smart phones are equipped with various sensors like accelerometer, gyroscope, and touch screen sensors, capturing a user's tapping behaviors can be done seamlessly. Exploiting the combination of four features (acceleration, pressure, size, and time) extracted from smart phone sensors, we propose a non-intrusive user verification mechanism to substantiate whether an authenticating user is the true owner of the smart phone or an impostor who happens to know the pass code. Based on the tapping data collected from over 80 users, we conduct a series of experiments to validate the efficacy of our proposed system. Our experimental results show that our verification system achieves high accuracy with averaged equal error rates of down to 3.65%. As our verification system can be seamlessly integrated with the existing user authentication mechanisms on smart phones, its deployment and usage are transparent to users and do not require any extra hardware support.
Scalable testing of file system checkers

File system checkers (like e2fsck) are critical, complex, and hard to develop, and developers today rely on hand-written tests to exercise this intricate code. Test suites for file system checkers take a lot of effort to develop and require careful reasoning to cover a sufficiently comprehensive set of inputs and recovery mechanisms. We present a tool and methodology for testing file system checkers that reduces the need for a specification of the recovery process and the development of a test suite. Our methodology splits the correctness of the checker into two objectives: consistency and completeness of recovery. For each objective, we leverage either the file system checker code itself or a comparison among the outputs of multiple checkers to extract an implicit specification of correct behavior. Our methodology is embodied in a testing tool called SWIFT, which uses a mix of symbolic and concrete execution; it introduces two new techniques: a specific concretization strategy and a corruption model that leverages test suites of file system checkers. We used SWIFT to test the file system checkers of ext2, ext3, ext4, ReiserFS, and Minix; we found bugs in all checkers, including cases leading to data loss. Additionally, we automatically generated test suites achieving code coverage on par with manually constructed test suites shipped with the checkers.
Learning to Classify Documents According to Formal and Informal Style

This paper discusses an important issue in computational linguistics: classifying texts as formal or informal style. Our work describes a genreindependent methodology for building classifiers for formal and informal texts. We used machine learning techniques to do the automatic classification, and performed the classification experiments at both the document level and the sentence level. First, we studied the main characteristics of each style, in order to train a system that can distinguish between them. We then built two datasets: the first dataset represents general-domain documents of formal and informal style, and the second represents medical texts. We tested on the second dataset at the document level, to determine if our model is sufficiently general, and that it works on any type of text. The datasets are built by collecting documents for both styles from different sources. After collecting the data, we extracted features from each text. The features that we designed represent the main characteristics of both styles. Finally, we tested several classification algorithms, namely Decision Trees, Naïve Bayes, and Support Vector Machines, in order to choose the classifier that generates the best classification results. 1 LiLT Volume 8, Issue 1, March 2012. Learning to Classify Documents According to Formal and Informal Style.
Fast and High Resolution 3D Face Scanning

In this work, we present a framework to capture 3D models of faces in high resolutions with low computational load. The system captures only two pictures of the face, one illuminated with a colored stripe pattern and one with regular white light. The former is needed for the depth calculation, the latter is used as texture. Having these two images a combination of specialized algorithms is applied to generate a 3D model. The results are shown in different views: simple surface, wire grid respective polygon mesh or textured 3D surface.
Topic Classification in Social Media Using Metadata from Hyperlinked Objects

Social media presents unique challenges for topic classification, including the brevity of posts, the informal nature of conversations, and the frequent reliance on external hyperlinks to give context to a conversation. In this paper we investigate the usefulness of these external hyperlinks for determining the topic of an individual post. We focus specifically on hyperlinks to objects which have related metadata available on the Web, including Amazon products and YouTube videos. Our experiments show that the inclusion of metadata from hyperlinked objects in addition to the original post content improved classifier performance measured with the F-score from 84% to 90%. Further, even classification based on object metadata alone outperforms classification based on the original post content.
Overview of data quality challenges in the context of Big Data

Data quality management systems are thoroughly researched topics and have resulted in many tools and techniques developed by both academia and industry. However, the advent of Big Data might pose some serious questions pertaining to the applicability of existing data quality concepts. There is a debate concerning the importance of data quality for Big Data; one school of thought argues that high data quality methods are essential for deriving higher level analytics while another school of thought argues that data quality level will not be so important as the volume of Big Data would be used to produce patterns and some amount of dirty data will not mask the analytic results which might be derived. This paper aims to investigate various components and activities forming part of data quality management such as dimensions, metrics, data quality rules, data profiling and data cleansing. The result list existing challenges and future research areas associated with Big Data for data quality management.
Spectral Signatures in Backdoor Attacks

A recent line of work has uncovered a new form of data poisoning: so-called backdoor attacks. These attacks are particularly dangerous because they do not affect a network’s behavior on typical, benign data. Rather, the network only deviates from its expected output when triggered by a perturbation planted by an adversary. In this paper, we identify a new property of all known backdoor attacks, which we call spectral signatures. This property allows us to utilize tools from robust statistics to thwart the attacks. We demonstrate the efficacy of these signatures in detecting and removing poisoned examples on real image sets and state of the art neural network architectures. We believe that understanding spectral signatures is a crucial first step towards designing ML systems secure against such backdoor attacks.
Elicitation Technique Selection: How Do Experts Do It?

Requirements elicitation techniques are methods used by analysts to determine the needs of customers and users, so that systems can be built with a high probability of satisfying those needs. Analysts with extensive experience seem to be more successful than less experienced analysts in uncovering the user needs. Less experienced analysts often select a technique based on one of two reasons: (a) it is the only one they know, or (b) they think that a technique that worked well last time must surely be appropriate this time. This paper presents the results of in-depth interviews with some of the world's most experienced analysts. These results demonstrate how they select elicitation techniques based on a variety of situational assessments.
On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing

This paper introduces dual decomposition as a framework for deriving inference algorithms for NLP problems. The approach relies on standard dynamic-programming algorithms as oracle solvers for sub-problems, together with a simple method for forcing agreement between the different oracles. The approach provably solves a linear programming (LP) relaxation of the global inference problem. It leads to algorithms that are simple, in that they use existing decoding algorithms; efficient, in that they avoid exact algorithms for the full model; and often exact, in that empirically they often recover the correct solution in spite of using an LP relaxation. We give experimental results on two problems: 1) the combination of two lexicalized parsing models; and 2) the combination of a lexicalized parsing model and a trigram part-of-speech tagger.
Real-time imitation of human whole-body motions by humanoids

In this paper, we present a system that enables humanoid robots to imitate complex whole-body motions of humans in real time. In our approach, we use a compact human model and consider the positions of the end-effectors as well as the center of mass as the most important aspects to imitate. Our system actively balances the center of mass over the support polygon to avoid falls of the robot, which would occur when using direct imitation. For every point in time, our approach generates a statically stable pose. Hereby, we do not constrain the configurations to be in double support. Instead, we allow for changes of the support mode according to the motions to imitate. To achieve safe imitation, we use retargeting of the robot's feet if necessary and find statically stable configurations by inverse kinematics. We present experiments using human data captured with an Xsens MVN motion capture system. The results show that a Nao humanoid is able to reliably imitate complex whole-body motions in real time, which also include extended periods of time in single support mode, in which the robot has to balance on one foot.
Deep Neural Network Optimized to Resistive Memory with Nonlinear Current-Voltage Characteristics

Artificial Neural Network computation relies on intensive vector-matrix multiplications. Recently, the emerging nonvolatile memory (NVM) crossbar array showed a feasibility of implementing such operations with high energy efficiency. Thus, there have been many works on efficiently utilizing emerging NVM crossbar arrays as analog vector-matrix multipliers. However, nonlinear I-V characteristics of NVM restrain critical design parameters, such as the read voltage and weight range, resulting in substantial accuracy loss. In this article, instead of optimizing hardware parameters to a given neural network, we propose a methodology of reconstructing the neural network itself to be optimized to resistive memory crossbar arrays. To verify the validity of the proposed method, we simulated various neural networks with MNIST and CIFAR-10 dataset using two different Resistive Random Access Memory models. Simulation results show that our proposed neural network produces inference accuracies significantly higher than conventional neural network when the network is mapped to synapse devices with nonlinear I-V characteristics.
Application of K-Nearest Neighbor ( KNN ) Approach for Predicting Economic Events : Theoretical Background

In the present study k-Nearest Neighbor classification method, have been studied for economic forecasting. Due to the effects of companies’ financial distress on stakeholders, financial distress prediction models have been one of the most attractive areas in financial research. In recent years, after the global financial crisis, the number of bankrupt companies has risen. Since companies' financial distress is the first stage of bankruptcy, using financial ratios for predicting financial distress have attracted too much attention of the academics as well as economic and financial institutions. Although in recent years studies on predicting companies’ financial distress in Iran have been increased, most efforts have exploited traditional statistical methods; and just a few studies have used nonparametric methods. Recent studies demonstrate this method is more capable than other methods.
MSU Tailbot: Controlling Aerial Maneuver of a Miniature-Tailed Jumping Robot

Inspired by the aerial maneuvering ability of lizards, we present the design and control of MSU (Michigan State University) tailbot - a miniature-tailed jumping robot. The robot can not only wheel on the ground, but also jump up to overcome obstacles. Moreover, once leaping into the air, it can control its body angle using an active tail to dynamically maneuver in midair for safe landings. We derive the midair dynamics equation and design controllers, such as a sliding mode controller, to stabilize the body at desired angles. To the best of our knowledge, this is the first miniature (maximum size 7.5 cm) and lightweight (26.5 g) robot that can wheel on the ground, jump to overcome obstacles, and maneuver in midair. Furthermore, tailbot is equipped with on-board energy, sensing, control, and wireless communication capabilities, enabling tetherless or autonomous operations. The robot in this paper exemplifies the integration of mechanical design, embedded system, and advanced control methods that will inspire the next-generation agile robots mimicking their biological counterparts. Moreover, it can serve as mobile sensor platforms for wireless sensor networks with many field applications.
Follow My Recommendations: A Personalized Privacy Assistant for Mobile App Permissions

Modern smartphone platforms have millions of apps, many of which request permissions to access private data and resources, like user accounts or location. While these smartphone platforms provide varying degrees of control over these permissions, the sheer number of decisions that users are expected to manage has been shown to be unrealistically high. Prior research has shown that users are often unaware of, if not uncomfortable with, many of their permission settings. Prior work also suggests that it is theoretically possible to predict many of the privacy settings a user would want by asking the user a small number of questions. However, this approach has neither been operationalized nor evaluated with actual users before. We report on a field study (n=72) in which we implemented and evaluated a Personalized Privacy Assistant (PPA) with participants using their own Android devices. The results of our study are encouraging. We find that 78.7% of the recommendations made by the PPA were adopted by users. Following initial recommendations on permission settings, participants were motivated to further review and modify their settings with daily “privacy nudges.” Despite showing substantial engagement with these nudges, participants only changed 5.1% of the settings previously adopted based on the PPA’s recommendations. The PPA and its recommendations were perceived as useful and usable. We discuss the implications of our results for mobile permission management and the design of personalized privacy assistant solutions.