nils-herrmann/clean_scirepeval huggingface dataset:
Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues Knowle

Knowledge is a broad and abstract notion that has defined epistemological debate in western philosophy since the classical Greek era. In the past few years, however, there has been a raging interest in treating knowledge as a significant organizational resource. The heightened interest in organizational knowledge and knowledge management stems from the transition into the knowledge economy, where knowledge is viewed as the principle source of value creation and sustainable competitive advantage. Consistent with the growing interest in organizational knowledge and knowledge management (KM), recently IS researchers have been promoting a class of information systems, referred to as knowledge management systems (KMS). The objective of KMS is to support construction, sharing and application of knowledge in organizations. Knowledge and knowledge management are complex and multi-faceted concepts. Thus, effective development and implementation of KMS requires a foundation in several rich literatures. We believe that to be credible, KMS research and development should preserve and built upon the significant literature that exists in different but related fields. We have promoted this view in this paper by providing a review and interpretation of knowledge management literatures in different fields with an eye towards identifying the important areas for future research. Next, we have presented a detailed process-view of organizational knowledge management with a focus on the potential role of IT in this process. The paper concludes with a discussion of major research questions that emerge from the review of literature as well as the process-view of KM. It is our contention that in large and global firms information technologies (in form of KMS) will be interlaced with organizational knowledge management strategies and processes. We therefore believe that the KMS should and will receive considerable scholarly attention and will become a focal point of inquiry. It is our hope that the ideas, discussion, and the broad research issues set forth in this paper contributes to future work in the knowledge management area by IS researchers. Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues "In post-capitalism, power comes from transmitting information to make it productive, not from hiding it." (Drucker, 1995)
Segmenting cardiac MRI tagging lines using Gabor filter banks

t—This paper describes a new method for the segmentation and extraction of cardiac MRI s. Our method is based on the novel use of a 2D bank. By convolving the tagged input image with ilters, the tagging lines are automatically enhanced ted out. We design the Gabor filter bank based on age’s spatial and frequency characteristics. The is a combination of each filter’s response in the bank. We demonstrate that compared to bandpass ds such as HARP, this method results in robust and mentation of the tagging lines.
Bringing PageRank to the citation analysis

The paper attempts to provide an alternative method for measuring the importance of scientific papers based on the Google’s PageRank. The method is a meaningful extension of the common integer counting of citations and is then experimented for bringing PageRank to the citation analysis in a large citation network. It offers a more integrated picture of the publications’ influence in a specific field. We firstly calculate the PageRanks of scientific papers. The distributional characteristics and comparison with the traditionally used number of citations are then analyzed in detail. Furthermore, the PageRank is implemented in the evaluation of research influence for several countries in the field of Biochemistry and Molecular Biology during the time period of 2000–2005. Finally, some advantages of bringing PageRank to the citation analysis are concluded.
Who falls for phish?: a demographic analysis of phishing susceptibility and effectiveness of interventions

In this paper we present the results of a roleplay survey instrument administered to 1001 online survey respondents to study both the relationship between demographics and phishing susceptibility and the effectiveness of several anti-phishing educational materials. Our results suggest that women are more susceptible than men to phishing and participants between the ages of 18 and 25 are more susceptible to phishing than other age groups. We explain these demographic factors through a mediation analysis. Educational materials reduced users' tendency to enter information into phishing webpages by 40% percent; however, some of the educational materials we tested also slightly decreased participants' tendency to click on legitimate links.
Continuous-time Gaussian process motion planning via probabilistic inference

We introduce a novel formulation of motion planning, for continuous-time trajectories, as probabilistic inference. We first show how smooth continuous-time trajectories can be represented by a small number of states using sparse Gaussian process (GP) models. We next develop an efficient gradient-based optimization algorithm that exploits this sparsity and Gaussian process interpolation. We call this algorithm the Gaussian Process Motion Planner (GPMP). We then detail how motion planning problems can be formulated as probabilistic inference on a factor graph. This forms the basis for GPMP2, a very efficient algorithm that combines GP representations of trajectories with fast, structure-exploiting inference via numerical optimization. Finally, we extend GPMP2 to an incremental algorithm, iGPMP2, that can efficiently replan when conditions change. We benchmark our algorithms against several samplingbased and trajectory optimization-based motion planning algorithms on planning problems in multiple environments. Our evaluation reveals that GPMP2 is several times faster than previous algorithms while retaining robustness. We also benchmark iGPMP2 on replanning problems, and show that it can find successful solutions in a fraction of the time required by GPMP2 to replan from scratch.
SmartDroid: an automatic system for revealing UI-based trigger conditions in android applications

User interface (UI) interactions are essential to Android applications, as many Activities require UI interactions to be triggered. This kind of UI interactions could also help malicious apps to hide their sensitive behaviors (e.g., sending SMS or getting the user's device ID) from being detected by dynamic analysis tools such as TaintDroid, because simply running the app, but without proper UI interactions, will not lead to the exposure of sensitive behaviors. In this paper we focus on the challenging task of triggering a certain behavior through automated UI interactions. In particular, we propose a hybrid static and dynamic analysis method to reveal UI-based trigger conditions in Android applications. Our method first uses static analysis to extract expected activity switch paths by analyzing both Activity and Function Call Graphs, and then uses dynamic analysis to traverse each UI elements and explore the UI interaction paths towards the sensitive APIs. We implement a prototype system SmartDroid and show that it can automatically and efficiently detect the UI-based trigger conditions required to expose the sensitive behavior of several Android malwares, which otherwise cannot be detected with existing techniques such as TaintDroid.
Learning Latent Personas of Film Characters

We present two latent variable models for learning character types, or personas, in film, in which a persona is defined as a set of mixtures over latent lexical classes. These lexical classes capture the stereotypical actions of which a character is the agent and patient, as well as attributes by which they are described. As the first attempt to solve this problem explicitly, we also present a new dataset for the text-driven analysis of film, along with a benchmark testbed to help drive future work in this area.
Deep Contrast Learning for Salient Object Detection

Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). However, existing CNN-based methods operate at the patch level instead of the pixel level. Resulting saliency maps are typically blurry, especially near the boundary of salient objects. Furthermore, image patches are treated as independent samples even when they are overlapping, giving rise to significant redundancy in computation and storage. In this paper, we propose an end-to-end deep contrast network to overcome the aforementioned limitations. Our deep network consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image. The second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in the fused result from these two streams. Experimental results demonstrate that our deep model significantly improves the state of the art.
Discovering Graph Temporal Association Rules

Detecting regularities between complex events in temporal graphs is critical for emerging applications. This paper proposes graph temporal association rules (GTAR). A GTAR extends traditional association rules to discover temporal associations for complex events captured by a class of temporal pattern queries. We introduce notions of support and confidence for GTARS and formalize the discovery problem for GTARS. We show that despite the enhanced expressive power, GTARS discovery is feasible over large temporal graphs. We develop an effective rule discovery algorithm, which integrates event mining and rule discovery as a single process, and reduces the redundant computation by leveraging their interaction. Using real-life and synthetic data, we experimentally verify the effectiveness and scalability of the algorithms. Our case study also verifies that GTARS demonstrate highly interpretable associations in real-world networks.
A Multimodal, Full-Surround Vehicular Testbed for Naturalistic Studies and Benchmarking: Design, Calibration and Deployment

Recent progress in autonomous and semiautonomous driving has been made possible in part through an assortment of sensors that provide the intelligent agent with an enhanced perception of its surroundings. It has been clear for quite some while now that for intelligent vehicles to function effectively in all situations and conditions, a fusion of different sensor technologies is essential. Consequently, the availability of synchronized multi-sensory data streams are necessary to promote the development of fusion based algorithms for low, mid and high level semantic tasks. In this paper, we provide a comprehensive description of our heavily sensorized, panoramic testbed capable of providing high quality data from a slew of synchronized and calibrated sensors such as cameras, LIDARs, radars, and the IMU/GPS. The vehicle has recorded over 100 hours of real world data for a very diverse set of weather, traffic and daylight conditions. All captured data is accurately calibrated and synchronized using timestamps, and stored safely in high performance servers mounted inside the vehicle itself. Details on the testbed instrumentation, sensor layout, sensor outputs, calibration and synchronization are described in this paper.
Parents' roles in shaping early adolescents' occupational aspirations.

Relations among dimensions of parenting and adolescents' occupational aspirations were examined in two specific domains: academics and sports. The sample consisted of 444 seventh graders, with approximately equal numbers of African American and European American males and females, from two-parent nondivorced families. Multiple measures were used as indicators of parents' values and behaviors, youths' values and beliefs, positive identification with parents, and adolescents' occupational aspirations. In the academic domain, parents' values predicted youths' values directly rather than indirectly through their behaviors. In contrast, fathers' behaviors mediated the relation between parents' and youths' values in the sports domain. Positive identification was directly related to adolescents' values (especially about academics); however, positive identification did not moderate the transmission of values from parent to child in either domain. Parents' values predicted adolescents' occupational aspirations via both direct and indirect pathways. Similar results were obtained for African American and European American males and females. These findings highlight the potential role of parents as socializers of achievement-related values, and, ultimately, adolescents' occupational visions of themselves in the future.
Dynamo: amazon's highly available key-value store

Reliability at massive scale is one of the biggest challenges we face at Amazon.com, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The Amazon.com platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components fail continuously and the way persistent state is managed in the face of these failures drives the reliability and scalability of the software systems.
 This paper presents the design and implementation of Dynamo, a highly available key-value storage system that some of Amazon's core services use to provide an "always-on" experience. To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.
Multistory Floor Plan Generation and Room Labeling of Building Interiors from Laser Range Data

Automatic generation of building floor plans is useful in many emerging applications, including indoor navigation, augmented and virtual reality, as well as building energy simulation software. These applications require watertight models with limited complexity. In this paper, we present an approach that produces 2.5D extruded watertight models of building interiors from either 2D particle filter grid maps or full 3D point-clouds captured by mobile mapping systems. Our approach is to triangulate a 2D sampling of wall positions and separate these triangles into interior and exterior sets. We partition the interior volume of the building model by rooms, then simplify the model to reduce noise. Such labels are useful for building energy simulations involving thermal models, as well as for ensuring geometric accuracy of the resulting 3D model. We experimentally verify the performance of our proposed approach on a wide variety of buildings. Our approach is efficient enough to be used in real-time in conjunction with Simultaneous Localization and Mapping (SLAM) applications.
Control Flow Analysis for Reverse Engineering of Sequence Diagrams

Reverse engineering of UML sequence diagrams plays an important role in iterative development and in software maintenance. In static analysis for such reverse engineering, an open question is how to map control-flow graphs to the control-flow primitives of the recently-introduced new generation of UML. Our work presents the first algorithm that solves this problem. We also propose transformations of the reverse-engineered diagrams in order to improve diagram comprehension. Our extensive experiments show that these techniques are efficient and effective. We also describe a test coverage tool based on the sequence diagrams, and discuss its use for the evaluation of a real-world test suite.
High Performance Emulation of Quantum Circuits

As quantum computers of non-trivial size become available in the near future, it is imperative to develop tools to emulate small quantum computers. This allows for validation and debugging of algorithms as well as exploring hardware-software co-design to guide the development of quantum hardware and architectures. The simulation of quantum computers entails multiplications of sparse matrices with very large dense vectors of dimension 2n, where n denotes the number of qubits, making this a memory-bound and network bandwidth-limited application. We introduce the concept of a quantum computer emulator as a component of a software framework for quantum computing, enabling a significant performance advantage over simulators by emulating quantum algorithms at a high level rather than simulating individual gate operations. We describe various optimization approaches and present benchmarking results, establishing the superiority of quantum computer emulators in terms of performance.
A Dynamic Method to Forecast the Wheel Slip for Antilock Braking System and Its Experimental Evaluation

The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems.
A stable multi-scale kernel for topological machine learning

Topological data analysis offers a rich source of valuable information to study vision problems. Yet, so far we lack a theoretically sound connection to popular kernel-based learning techniques, such as kernel SVMs or kernel PCA. In this work, we establish such a connection by designing a multi-scale kernel for persistence diagrams, a stable summary representation of topological features in data. We show that this kernel is positive definite and prove its stability with respect to the 1-Wasserstein distance. Experiments on two benchmark datasets for 3D shape classification/retrieval and texture recognition show considerable performance gains of the proposed method compared to an alternative approach that is based on the recently introduced persistence landscapes.
End-to-End Goal-Driven Web Navigation

We propose a goal-driven web navigation as a benchmark task for evaluating an agent with abilities to understand natural language and plan on partially observed environments. In this challenging task, an agent navigates through a website, which is represented as a graph consisting of web pages as nodes and hyperlinks as directed edges, to find a web page in which a query appears. The agent is required to have sophisticated high-level reasoning based on natural languages and efficient sequential decision-making capability to succeed. We release a software tool, called WebNav, that automatically transforms a website into this goal-driven web navigation task, and as an example, we make WikiNav, a dataset constructed from the English Wikipedia. We extensively evaluate different variants of neural net based artificial agents on WikiNav and observe that the proposed goal-driven web navigation well reflects the advances in models, making it a suitable benchmark for evaluating future progress. Furthermore, we extend the WikiNav with questionanswer pairs from Jeopardy! and test the proposed agent based on recurrent neural networks against strong inverted index based search engines. The artificial agents trained on WikiNav outperforms the engined based approaches, demonstrating the capability of the proposed goal-driven navigation as a good proxy for measuring the progress in real-world tasks such as focused crawling and question-answering.
Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding

We present a novel vision-based grasp point detection algorithm that can reliably detect the corners of a piece of cloth, using only geometric cues that are robust to variation in texture. Furthermore, we demonstrate the effectiveness of our algorithm in the context of folding a towel using a general-purpose two-armed mobile robotic platform without the use of specialized end-effectors or tools. The robot begins by picking up a randomly dropped towel from a table, goes through a sequence of vision-based re-grasps and manipulations—partially in the air, partially on the table—and finally stacks the folded towel in a target location. The reliability and robustness of our algorithm enables for the first time a robot with general purpose manipulators to reliably and fully-autonomously fold previously unseen towels, demonstrating success on all 50 out of 50 single-towel trials as well as on a pile of 5 towels.
A Novel Four-DOF Parallel Manipulator Mechanism and Its Kinematics

A novel 4-UPU parallel manipulator mechanism that can perform three-dimensional translations and rotation about Z axis is presented. The principle that the mechanism can perform the above motions is analyzed based on the screw theory, the mobility of the mechanism is calculated, and the rationality of the chosen input joints is discussed. The forward and inverse position kinematics solutions of the mechanism and corresponding numerical examples are given, the workspace and the singularity of the parallel mechanism are discussed. The mechanism having the advantages of simple symmetric structure and large stiffness can be applied to the developments of NC positioning platforms, parallel machine tools, four-dimensional force sensors and micro-positional parallel manipulators
Automatic quantification of crack patterns by image processing

Image processing technologies are proposed to quantify crack patterns. On the basis of the technologies, a software “Crack Image Analysis System” (CIAS) has been developed. An image of soil crack network is used as an example to illustrate the image processing technologies and the operations of the CIAS. The quantification of the crack image involves the following three steps: image segmentation, crack identification and measurement. First, the image is converted to a binary image using a cluster analysis method; noise in the binary image is removed; and crack spaces are fused. Then, the medial axis of the crack network is extracted from the binary image, with which nodes and crack segments can be identified. Finally, various geometric parameters of the crack network can be calculated automatically, such as node number, crack number, clod area, clod perimeter, crack area, width, length, and direction. The thresholds used in the operations are specified by cluster analysis and other innovative methods. As a result, the objects (nodes, cracks and clods) in the crack network can be quantified automatically. The software may be used to study the generation and development of soil crack patterns and rock fractures. & 2013 Elsevier Ltd. All rights reserved.