nils-herrmann/clean_scirepeval huggingface dataset:
Design and Basic Experiments of a Shape-shifting Mobile Robot for Urban Search and Rescue

The recent natural and man-made devastations have urged the research on the urban search and rescue (USAR) robot systems. This paper presents a novel shape-shifting mobile robot system named as Amoeba II (A-II) for the urban search and rescue application. It has been designed with three degrees of freedom (DOFs) and two tracked drive systems. This robot consists of two modular mobile units and a joint unit. The mobile unit is a tracked mechanism to enforce the propulsion of robot. The joint unit can transform the robot shape for getting high mobility. A-II robot not only can adapt to the environment but also can change its body corresponding to locus space. It behaves two states including the parallel state (named as II state) and the linear state (named as I state). The parallel state enables the robot with high mobility on rough ground. With the linear state the robot can climb upstairs and go through narrow space such as the pipe, cave etc. Also, the joint unit can propel the robot to roll in sidewise direction. Especially, two modular A-II robots can be connected through jointing common interfaces on the joint unit to compose a stronger shape-shifting robot, which can transform the body into four wheels-driven vehicle. Finally, the elementary experimental results validate the adaptation and its mobility
Hashing Algorithms for Large-Scale Learning

Minwise hashing is a standard technique in the context of sea rch for efficiently computing set similarities. The recent development of b-bit minwise hashing provides a substantial improvement by storing only the lowest b bi s of each hashed value. In this paper, we demonstrate that b-bit minwise hashing can be naturally integrated with linear learning algorithms such as li near SVM and logistic regression, to solve large-scale and high-dimensional sta tistic l learning tasks, especially when the data do not fit in memory. We compare b-bit minwise hashing with the Count-Min (CM) and Vowpal Wabbit (VW) algorithms, w hich have essentially the same variances as random projections. Our the o etical and empirical comparisons illustrate that b-bit minwise hashing is significantly more accurate (at the same storage cost) than VW (and random projections) for b ina y data.
Special Issue: Information Technology and Organizational Governance: The IT Governance Cube

Amrit tiwAnA is a professor of MIS in the terry College of Business at the university of Georgia. He serves as a senior editor at Information Systems Research and on the boards of Journal of Management Information Systems, Strategic Management Journal, and  He has also served on MIS Quarterly’s editorial board. His work on It governance has appeared in Information Systems Research, Journal of Management Information Systems, Strategic Management Journal, Decision Sciences, ACM Transactions on Software Engineering, California Management Review, and other journals.
Stuxnet worm impact on industrial cyber-physical system security

Industrial systems consider only partially security, mostly relying on the basis of “isolated” networks, and controlled access environments. Monitoring and control systems such as SCADA/DCS are responsible for managing critical infrastructures operate in these environments, where a false sense of security assumptions is usually made. The Stuxnet worm attack demonstrated widely in mid 2010 that many of the security assumptions made about the operating environment, technological capabilities and potential threat risk analysis are far away from the reality and challenges modern industrial systems face. We investigate in this work the highly sophisticated aspects of Stuxnet, the impact that it may have on existing security considerations and pose some thoughts on the next generation SCADA/DCS systems from a security perspective.
Information-centric network function virtualization over 5g mobile wireless networks

Wireless network virtualization and information-centric networking (ICN) are two promising techniques in software-defined 5G mobile wireless networks. Traditionally, these two technologies have been addressed separately. In this paper we show that integrating wireless network virtualization with ICN techniques can significantly improve the end-to-end network performance. In particular, we propose an information- centric wireless network virtualization architecture for integrating wireless network virtualization with ICN. We develop the key components of this architecture: radio spectrum resource, wireless network infrastructure, virtual resources (including content-level slicing, network-level slicing, and flow-level slicing), and informationcentric wireless virtualization controller. Then we formulate the virtual resource allocation and in-network caching strategy as an optimization problem, considering the gain of not only virtualization but also in-network caching in our proposed information-centric wireless network virtualization architecture. The obtained simulation results show that our proposed information-centric wireless network virtualization architecture and the related schemes significantly outperform the other existing schemes.
A survey on vehicular cloud computing

Vehicular networking has become a significant research area due to its specific features and applications such as standardization, efficient traffic management, road safety and infotainment. Vehicles are expected to carry relatively more communication systems, on board computing facilities, storage and increased sensing power. Hence, several technologies have been deployed to maintain and promote Intelligent Transportation Systems (ITS). Recently, a number of solutions were proposed to address the challenges and issues of vehicular networks.  is one of the solutions. VCC is a new hybrid technology that has a remarkable impact on traffic management and road safety by instantly using vehicular resources, such as computing, storage and internet for decision making. This paper presents the state-of-the-art survey of vehicular cloud computing. Moreover, we present a taxonomy for vehicular cloud in which special attention has been devoted to the extensive applications, cloud formations, key management, inter cloud communication systems, and broad aspects of privacy and security issues. Through an extensive review of the literature, we design an architecture for VCC, itemize the properties required in vehicular cloud that support this model. We compare this mechanism with normal Cloud Computing (CC) and discuss open research issues and future directions. By reviewing and analyzing literature, we found that VCC is a technologically feasible and economically viable technological shifting paradigm for converging intelligent vehicular networks towards autonomous traffic, vehicle control and perception systems. & 2013
Fully Convolutional Neural Networks with Full-Scale-Features for Semantic Segmentation

In this work, we propose a novel method to involve full-scale-features into the fully convolutional neural networks (FCNs) for Semantic Segmentation. Current works on FCN has brought great advances in the task of semantic segmentation, but the receptive field, which represents region areas of input volume connected to any output neuron, limits the available information of output neuron’s prediction accuracy. We investigate how to involve the full-scale or full-image features into FCNs to enrich the receptive field. Specially, the fullscale feature network (FFN) extends the full-connected network and makes an end-to-end unified training structure. It has two appealing properties. First, the introduction of full-scale-features is beneficial for prediction. We build a unified extracting network and explore several fusion functions for concatenating features. Amounts of experiments have been carried out to prove that full-scale-features makes fair accuracy raising. Second, FFN is applicable to many variants of FCN which could be regarded as a general strategy to improve the segmentation accuracy. Our proposed method is evaluated on PASCAL VOC 2012, and achieves a state-of-art result.
CSTBT (III) as the next generation IGBT

Recently, the performance of Si power devices gradually approaches the physical limit, and the latest SiC device seemingly has the ability to substitute the Si insulated gate bipolar transistor (IGBT) in 1200 V class. In this paper, we demonstrate the feasibility of further improving the Si IGBT based on the new concept of CSTBTtrade. In point of view of low turn-off loss and high uniformity in device characteristics, we employ the techniques of fine-pattern and retro grade doping in the design of new device structures, resulting in significant reduction on the turn-off loss and the VGE(th) distribution, respectively.
Deconstructing Mindfulness and Constructing Mental Health : Understanding Mindfulness and its Mechanisms of Action

Research on mindfulness indicates that it is associated with improved mental health, but the use of multiple different definitions of mindfulness prevents a clear understanding of the construct. In particular, the boundaries between different conceptualizations of mindfulness and emotion regulation are unclear. Furthermore, the mechanisms by which any of these conceptualizations of mindfulness might influence mental health are not well-understood. The two studies presented here addressed these questions using correlational, self-report data from a non-clinical sample of undergraduate students. The first study used a combination of exploratory and confirmatory factor analyses to better understand the factor structure of mindfulness and emotion regulation measures. Results indicated that these measures assess heterogeneous and overlapping constructs, and may be most accurately thought of as measuring four factors: presentcentered attention, acceptance of experience, clarity about one’s internal experience, and the ability to manage negative emotions. A path analysis supported the hypothesis that mindfulness (defined by a two-factor construct including present-centered attention and acceptance of experience) contributed to clarity about one’s experience, which improved the ability to manage negative emotions. The second study developed these findings by exploring the mediating roles of clarity about one’s internal life, the ability to manage negative emotions, non-attachment (or the extent to which one’s happiness is independent of specific outcomes and events), and rumination in the relationship between mindfulness and two aspects of mental health, psychological distress and flourishing mental health. Results confirmed the importance of these mediators in the relationship between mindfulness and mental health.
Learning One-hidden-layer Neural Networks with Landscape Design

We consider the problem of learning a one-hidden-layer neural network: we assume the input x ∈ R is from Gaussian distribution and the label y = a>σ(Bx) + ξ, where a is a nonnegative vector in R with m ≤ d, B ∈ Rm×d is a full-rank weight matrix, and ξ is a noise vector. We first give an analytic formula for the population risk of the standard squared loss and demonstrate that it implicitly attempts to decompose a sequence of low-rank tensors simultaneously. Inspired by the formula, we design a non-convex objective function G(·) whose landscape is guaranteed to have the following properties: 1. All local minima of G are also global minima. 2. All global minima of G correspond to the ground truth parameters. 3. The value and gradient of G can be estimated using samples. With these properties, stochastic gradient descent on G provably converges to the global minimum and learn the ground-truth parameters. We also prove finite sample complexity results and validate the results by simulations.
Chinese sentence segmentation as comma classification

We describe a method for disambiguating Chinese commas that is central to Chinese sentence segmentation. Chinese sentence segmentation is viewed as the detection of loosely coordinated clauses separated by commas. Trained and tested on data derived from the Chinese Treebank, our model achieves a classification accuracy of close to 90% overall, which translates to an F1 score of 70% for detecting commas that signal sentence boundaries.
Chinese Whispers - An Efficient Graph Clustering Algorithm And Its Application To Natural Language Processing Problems

We introduce Chinese Whispers, a randomized graph-clustering algorithm, which is time-linear in the number of edges. After a detailed definition of the algorithm and a discussion of its strengths and weaknesses, the performance of Chinese Whispers is measured on Natural Language Processing (NLP) problems as diverse as language separation, acquisition of syntactic word classes and word sense disambiguation. At this, the fact is employed that the small-world property holds for many graphs in NLP.
Object Recognition Using Deep Neural Networks: A Survey

Recognition of objects using Deep Neural Networks is an active area of research and many breakthroughs have been made in the last few years. The paper attempts to indicate how far this field has progressed. The paper briefly describes the history of research in Neural Networks and describe several of the recent advances in this field. The performances of recently developed Neural Network Algorithm over benchmark datasets have been tabulated. Finally, some the applications of this field have been provided.
Text detection in scene images using stroke width and nearest-neighbor constraints

Text in scene images can provide very useful as well as vital information and hence, its detection and recognition is an important task. We propose an adaptive edge-based connected-component method for text-detection in natural scene images. The approach is based on three reasonable assumptions - (i) characters of a particular word are locally aligned in a certain direction (ii) each character is of uniform color ( iii) stroke width is almost constant for most of the characters in a particular word. We apply color quantization and use the luminance to obtain the intensity values. An improved edge-detection technique that performs adaptive thresholding is used to capture all possible text components with some non-text components initially. Then, we remove obvious non-text component based on a few heuristics. Further, we classify those components as text for which we successfully obtain two consecutive nearest-neighbors that are aligned in a direction and satisfy certain constraints based on size and inter component distance. Finally, we estimate stroke width and foreground color for each component and those having a fairly uniform value of the same are classified as text.  The main advantage of our method is that it is robust to font-size, degraded intensities and complex backgrounds. Also, the use of stroke width and color in this manner for text detection is novel to the best of the authorpsilas knowledge.
Analysis of Firewall Policy Rules Using Data Mining Techniques

Firewall is the de facto core technology of today's network security and defense. However, the management of firewall rules has been proven to be complex, error-prone, costly and inefficient for many large-networked organizations. These firewall rules are mostly custom-designed and hand-written thus in constant need for tuning and validation, due to the dynamic nature of the traffic characteristics, ever-changing network environment and its market demands. One of the main problems that we address in this paper is that how much the firewall rules are useful, up-to-dated, well-organized or efficient to reflect the current characteristics of network traffics. In this paper, we present a set of techniques and algorithms to analysis and manage firewall policy rules: (1) data mining technique to deduce efficient firewall policy rules by mining its network traffic log based on its frequency, (2) filtering-rule generalization (FRG) to reduce the number of policy rules by generalization, and (3) a technique to identify any decaying rule and a set of few dominant rules, to generate a new set of efficient firewall policy rules. The anomaly detection based on the mining exposes many hidden but not detectable by analyzing only the firewall policy rules, resulting in two new types of the anomalies. As a result of these mechanisms, network security administrators can automatically review and update the rules. We have developed a prototype system and demonstrated usefulness of our approaches
Towards a deployable satellite gripper based on multisegment dielectric elastomer minimum energy structures

Dielectric Elastomer Actuators (DEAs) are an emerging actuation technology which are inherent lightweight and compliant in nature, enabling the development of unique and versatile devices, such as the Dielectric Elastomer Minimum Energy Structure (DEMES). We present the development of a multisegment DEMES actuator for use in a deployable microsatellite gripper. The satellite, called CleanSpace One, will demonstrate active debris removal (ADR) in space using a small cost effective system. The inherent flexibility and lightweight nature of the DEMES actuator enables space efficient storage (e.g. in a rolled configuration) of the gripper prior to deployment. Multisegment DEMES have multiple open sections and are an effective way of amplifying bending deformation. We present the evolution of our DEMES actuator design from initial concepts up until the final design, describing briefly the trade-offs associated with each method. We describe the optimization of our chosen design concept and characterize this design in terms on bending angle as a function of input voltage and gripping force. Prior to the characterization the actuator was stored and subsequently deployed from a rolled state, a capability made possible thanks to the fabrication methodology and materials used. A tip angle change of approximately 60o and a gripping force of 0.8 mN (for small deflections from the actuator tip) were achieved. The prototype actuators (approximately 10 cm in length) weigh a maximum of 0.65 g and are robust and mechanically resilient, demonstrating over 80,000 activation cycles.
Deep learning for sentiment analysis: successful approaches and future challenges

Sentiment analysis (also known as opinion mining) is an active research area in natural language processing. It aims at identifying, extracting and organizing sentiments from user generated texts in social networks, blogs or product reviews. A lot of studies in literature exploit machine learning approaches to solve sentiment analysis tasks from different perspectives in the past 15 years. Since the performance of a machine learner heavily depends on the choices of data representation, many studies devote to building powerful feature extractor with domain expert and careful engineering. Recently, deep learning approaches emerge as powerful computational models that discover intricate semantic representations of texts automatically from data without feature engineering. These approaches have improved the state-of-the-art in many sentiment analysis tasks including sentiment classification of sentences/documents, sentiment extraction and sentiment lexicon learning. In this paper, we provide an overview of the successful deep learning approaches for sentiment analysis tasks, lay out the remaining challenges and provide some suggestions to address these challenges.
Sequential Testing for Early Stopping of Online Experiments

Online evaluation methods, such as A/B and interleaving experiments, are widely used for search engine evaluation. Since they rely on noisy implicit user feedback, running each experiment takes a considerable time. Recently, the problem of reducing the duration of online experiments has received substantial attention from the research community. However, the possibility of using sequential statistical testing procedures for reducing the time required for the evaluation experiments remains less studied. Such sequential testing procedures allow an experiment to stop early, once the data collected is sufficient to make a conclusion. In this work, we study the usefulness of sequential testing procedures for both interleaving and A/B testing. We propose modified versions of the O'Brien & Fleming and MaxSPRT sequential tests that are applicable for testing in the interleaving scenario. Similarly, for A/B experiments, we assess the usefulness of the O'Brien & Fleming test, as well as that of our proposed MaxSPRT-based sequential testing procedure. In our experiments on datasets containing 115 interleaving and 41 A/B testing experiments, we observe that considerable reductions in the average experiment duration can be achieved by using our proposed tests. In particular, for A/B experiments, the average experiment durations can be reduced by up to 66% in comparison with a single step test procedure, and by up to 44% in comparison with the O'Brien & Fleming test. Similarly, a marked relative reduction of 63% in the duration of the interleaving experiments can be achieved.
High-resolution electrohydrodynamic jet printing.

Efforts to adapt and extend graphic arts printing techniques for demanding device applications in electronics, biotechnology and microelectromechanical systems have grown rapidly in recent years. Here, we describe the use of electrohydrodynamically induced fluid flows through fine microcapillary nozzles for jet printing of patterns and functional devices with submicrometre resolution. Key aspects of the physics of this approach, which has some features in common with related but comparatively low-resolution techniques for graphic arts, are revealed through direct high-speed imaging of the droplet formation processes. Printing of complex patterns of inks, ranging from insulating and conducting polymers, to solution suspensions of silicon nanoparticles and rods, to single-walled carbon nanotubes, using integrated computer-controlled printer systems illustrates some of the capabilities. High-resolution printed metal interconnects, electrodes and probing pads for representative circuit patterns and functional transistors with critical dimensions as small as 1 mum demonstrate potential applications in printed electronics.
A behavioral theory of insider-threat risks: A system dynamics approach

The authors describe a behavioral theory of the dynamics of insider-threat risks. Drawing on data related to information technology security violations and on a case study created to explain the dynamics observed in that data, the authors constructed a system dynamics model of a theory of the development of insider-threat risks and conducted numerical simulations to explore the parameter and response spaces of the model. By examining several scenarios in which attention to events, increased judging capabilities, better information, and training activities are simulated, the authors theorize about why information technology security effectiveness changes over time. The simulation results argue against the common presumption that increased security comes at the cost of reduced production.
Classroom Emotional Climate , Student Engagement , and Academic Achievement

The emotional connections students foster in their classrooms are likely to impact their success in school. Using a multimethod, multilevel approach, this study examined the link between classroom emotional climate and academic achievement, including the role of student engagement as a mediator. Data were collected from 63 fifthand sixth-grade classrooms (N 1,399 students) and included classroom observations, student reports, and report card grades. As predicted, multilevel mediation analyses showed that the positive relationship between classroom emotional climate and grades was mediated by engagement, while controlling for teacher characteristics and observations of both the organizational and instructional climates of the classrooms. Effects were robust across grade level and student gender. The discussion highlights the role of classroom-based, emotion-related interactions to promote academic achievement.