nils-herrmann/clean_scirepeval huggingface dataset:
Security and privacy for mobile healthcare networks: from a quality of protection perspective

With the flourishing of multi-functional wearable devices and the widespread use of smartphones, MHN becomes a promising paradigm of ubiquitous healthcare to continuously monitor our health conditions, remotely diagnose phenomena, and share health information in real time. However, MHNs raise critical security and privacy issues, since highly sensitive health information is collected, and users have diverse security and privacy requirements about such information. In this article, we investigate security and privacy protection in MHNs from the perspective of QoP, which offers users adjustable security protections at fine-grained levels. Specifically, we first introduce the architecture of MHN, and point out the security and privacy challenges from the perspective of QoP. We then present some countermeasures for security and privacy protection in MHNs, including privacy- preserving health data aggregation, secure health data processing, and misbehavior detection. Finally, we discuss some open problems and pose future research directions in MHNs.
An empirical evaluation of information metrics for low-rate and high-rate DDoS attack detection

Distributed Denial of Service (DDoS) attacks represent a major threat to uninterrupted and efficient Internet service. In this paper, we empirically evaluate several major information metrics, namely, Hartley entropy, Shannon entropy, Renyi’s entropy, generalized entropy, Kullback–Leibler divergence and generalized information distance measure in their ability to detect both low-rate and high-rate DDoS attacks. These metrics can be used to describe characteristics of network traffic data and an appropriate metric facilitates building an effective model to detect both low-rate and high-rate DDoS attacks. We use MIT Lincoln Laboratory, CAIDA and TUIDS DDoS datasets to illustrate the efficiency and effectiveness of each metric for DDoS detection.
Deciding advantageously before knowing the advantageous strategy.

Deciding advantageously in a complex situation is thought to require overt reasoning on declarative knowledge, namely, on facts pertaining to premises, options for action, and outcomes of actions that embody the pertinent previous experience. An alternative possibility was investigated: that overt reasoning is preceded by a nonconscious biasing step that uses neural systems other than those that support declarative knowledge. Normal participants and patients with prefrontal damage and decision-making defects performed a gambling task in which behavioral, psychophysiological, and self-account measures were obtained in parallel. Normals began to choose advantageously before they realized which strategy worked best, whereas prefrontal patients continued to choose disadvantageously even after they knew the correct strategy. Moreover, normals began to generate anticipatory skin conductance responses (SCRs) whenever they pondered a choice that turned out to be risky, before they knew explicitly that it was a risky choice, whereas patients never developed anticipatory SCRs, although some eventually realized which choices were risky. The results suggest that, in normal individuals, nonconscious biases guide behavior before conscious knowledge does. Without the help of such biases, overt knowledge may be insufficient to ensure advantageous behavior.
Text-level Discourse Dependency Parsing

Previous researches on Text-level discourse parsing mainly made use of constituency structure to parse the whole document into one discourse tree. In this paper, we present the limitations of constituency based discourse parsing and first propose to use dependency structure to directly represent the relations between elementary discourse units (EDUs). The state-of-the-art dependency parsing techniques, the Eisner algorithm and maximum spanning tree (MST) algorithm, are adopted to parse an optimal discourse dependency tree based on the arcfactored model and the large-margin learning techniques. Experiments show that our discourse dependency parsers achieve a competitive performance on text-level discourse parsing.
Comparison of Dynamic Characteristics Between Virtual Synchronous Generator and Droop Control in Inverter-Based Distributed Generators

In recent researches on inverter-based distributed generators, disadvantages of traditional grid-connected current control, such as no grid-forming ability and lack of inertia, have been pointed out. As a result, novel control methods like droop control and virtual synchronous generator (VSG) have been proposed. In both methods, droop characteristics are used to control active and reactive power, and the only difference between them is that VSG has virtual inertia with the emulation of swing equation, whereas droop control has no inertia. In this paper, dynamic characteristics of both control methods are studied, in both stand-alone mode and synchronous-generator-connected mode, to understand the differences caused by swing equation. Small-signal models are built to compare transient responses of frequency during a small loading transition, and state-space models are built to analyze oscillation of output active power. Effects of delays in both controls are also studied, and an inertial droop control method is proposed based on the comparison. The results are verified by simulations and experiments. It is suggested that VSG control and proposed inertial droop control inherits the advantages of droop control, and in addition, provides inertia support for the system.
High-Efficiency GaN Doherty Power Amplifier for 100-MHz LTE-Advanced Application Based on Modified Load Modulation Network

This paper presents a high-efficiency GaN Doherty power amplifier (PA) with 100-MHz instantaneous bandwidth for 3.5-GHz long-term-evolution (LTE)-advanced application. A modified load modulation network, employing an enlarged peaking amplifier to carrier amplifier power ratio and moderately increased load impedance of the carrier amplifier, is proposed for enhancing efficiency and achieving improved load modulation. To increase the power ratio and alleviate the influence of slight impedance mismatch, a proposed load impedance strategy and corresponding stepped-impedance matching network are adopted for high-efficiency and wideband operation. By tuning the carrier offset line, the inconsistency of efficiency, gain, and output power in the operation band can be alleviated. Measurement results show that the Doherty PA has a drain efficiency of approximately 40% with gain fluctuation less than 0.5 dB at 9-dB back-off power, and maximum efficiency of about 60% at saturation in the signal band of 3.4-3.5 GHz. By using the digital pre-distortion (DPD) technique, the Doherty PA achieves adjacent channel leakage ratio of about -48 dBc at an average output power of 40.4 dBm with efficiency of 42.5%, when driven by 100-MHz LTE-advanced signal. To the best of the authors' knowledge, this is the first high-performance result of linearization using conventional DPD technique with 100-MHz bandwidth signals for the GaN Doherty PA at 3.5-GHz frequency band thus far.
A Taxonomy and Survey of Cloud Computing Systems

The computational world is becoming very large and complex. Cloud Computing has emerged as a popular computing model to support processing large volumetric data using clusters of commodity computers. According to J.Dean and S. Ghemawat , Google currently processes over 20 terabytes of raw web data. It's some fascinating, large-scale processing of data that makes your head spin and appreciate the years of distributed computing fine-tuning applied to today's large problems. The evolution of cloud computing can handle such massive data as per on demand service. Nowadays the computational world is opting for pay-for-use models and Hype and discussion aside, there remains no concrete definition of cloud computing. In this paper, we first develop a comprehensive taxonomy for describing cloud computing architecture. Then we use this taxonomy to survey several existing cloud computing services developed by various projects world-wide such as Google, force.com, Amazon. We use the taxonomy and survey results not only to identify similarities and differences of the architectural approaches of cloud computing, but also to identify areas requiring further research.
A New Approach for Scientific Citation Classification Using Cue Phrases

This paper introduces a new method for the rapid development of complex rule bases involving cue phrases for the purpose of classifying text segments. The method is based on Ripple-Down Rules, a knowledge acquisition method that proved very successful in practice for building medical expert systems and does not require a knowledge engineer. We implemented our system KAFTAN and demonstrate the applicability of our method to the task of classifying scientific citations. Building cue phrase rules in KAFTAN is easy and efficient. We demonstrate the effectiveness of our approach by presenting experimental results where our resulting classifier clearly outperforms previously built classifiers in the recent literature.
“Neural” computation of decisions in optimization problems

Highly-interconnected networks of nonlinear analog neurons are shown to be extremely effective in computing. The networks can rapidly provide a collectively-computed solution (a digital output) to a problem on the basis of analog input information. The problems to be solved must be formulated in terms of desired optima, often subject to constraints. The general principles involved in constructing networks to solve specific problems are discussed. Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks. Good solutions to this problem are collectively computed within an elapsed time of only a few neural time constants. The effectiveness of the computation involves both the nonlinear analog response of the neurons and the large connectivity among them. Dedicated networks of biological or microelectronic neurons could provide the computational capabilities described for a wide class of problems having combinatorial complexity. The power and speed naturally displayed by such collective networks may contribute to the effectiveness of biological information processing.
Multi-layer depth peeling via fragment sort

We present an accelerated depth peeling algorithm for order-independent transparency rendering on graphics hardware. Unlike traditional depth peeling which only peels one layer of transparent pixels per rendering pass, our algorithm peels multiple layers simultaneously per rendering pass. Our acceleration is achieved via our fragment program which sorts and writes multiple fragment colors and depths via MRT. A notable feature of our algorithm is that it is robust against the unreliable parallel read-after-write behavior in current graphics hardware, guaranteeing correct transparency ordering. For ordinary scenes rendered under RGBA8 color precision, we achieve up to 8x speed-up over conventional depth peeling with current generation graphics hardware. Our algorithm is simple to implement on current GPU without any hardware modification. In addition, it does not require applications to perform any pre-sorting of transparent geometry.
Performance Analysis of Future Multigigabit Wireless Communication Systems at THz Frequencies With Highly Directive Antennas in Realistic Indoor Environments

In this paper, a simulation environment encompassing realistic propagation conditions and system parameters is employed in order to analyze the performance of future multigigabit indoor communication systems at tetrahertz frequencies. The influence of high-gain antennas on transmission aspects is investigated. Transmitter position for optimal signal coverage is also analyzed. Furthermore, signal coverage maps and achievable data rates are calculated for generic indoor scenarios with and without furniture for a variety of possible propagation conditions.
Revive: Rebalancing Off-Blockchain Payment Networks

Scaling the transaction throughput of decentralized blockchain ledgers such as Bitcoin and Ethereum has been an ongoing challenge. Two-party duplex payment channels have been designed and used as building blocks to construct linked payment networks, which allow atomic and trust-free payments between parties without exhausting the resources of the blockchain.
 Once a payment channel, however, is depleted (e.g., because transactions were mostly unidirectional) the channel would need to be closed and re-funded to allow for new transactions. Users are envisioned to entertain multiple payment channels with different entities, and as such, instead of refunding a channel (which incurs costly on-chain transactions), a user should be able to leverage his existing channels to rebalance a poorly funded channel.
 To the best of our knowledge, we present the first solution that allows an arbitrary set of users in a payment channel network to securely rebalance their channels, according to the preferences of the channel owners. Except in the case of disputes (similar to conventional payment channels), our solution does not require on-chain transactions and therefore increases the scalability of existing blockchains. In our security analysis, we show that an honest participant cannot lose any of its funds while rebalancing. We finally provide a proof of concept implementation and evaluation for the Ethereum network.
“Water-in-salt” electrolyte enables high-voltage aqueous lithium-ion chemistries

Lithium-ion batteries raise safety, environmental, and cost concerns, which mostly arise from their nonaqueous electrolytes. The use of aqueous alternatives is limited by their narrow electrochemical stability window (1.23 volts), which sets an intrinsic limit on the practical voltage and energy output. We report a highly concentrated aqueous electrolyte whose window was expanded to ~3.0 volts with the formation of an electrode-electrolyte interphase. A full lithium-ion battery of 2.3 volts using such an aqueous electrolyte was demonstrated to cycle up to 1000 times, with nearly 100% coulombic efficiency at both low (0.15 C) and high (4.5 C) discharge and charge rates.
Limiting the Impact of Stealthy Attacks on Industrial Control Systems

While attacks on information systems have for most practical purposes binary outcomes (information was manipulated/eavesdropped, or not), attacks manipulating the sensor or control signals of Industrial Control Systems (ICS) can be tuned by the attacker to cause a continuous spectrum in damages. Attackers that want to remain undetected can attempt to hide their manipulation of the system by following closely the expected behavior of the system, while injecting just enough false information at each time step to achieve their goals. In this work, we study if attack-detection can limit the impact of such stealthy attacks. We start with a comprehensive review of related work on attack detection schemes in the security and control systems community. We then show that many of those works use detection schemes that are not limiting the impact of stealthy attacks. We propose a new metric to measure the impact of stealthy attacks and how they relate to our selection on an upper bound on false alarms. We finally show that the impact of such attacks can be mitigated in several cases by the proper combination and configuration of detection schemes. We demonstrate the effectiveness of our algorithms through simulations and experiments using real ICS testbeds and real ICS systems.
OpenID 2.0: a platform for user-centric identity management

With the advancement in user-centric and URI-based identity systems over the past two years, it has become clear that a single specification will not be the solution to all problems. Rather, like the other layers of the Internet, developing small, interoperable specifications that are independently implementable and useful will ultimately lead to market adoption of these technologies. This is the intent of the OpenID framework. OpenID Authentication 1.0 began as a lightweight HTTP-based URL authentication protocol. OpenID Authentication 2.0 it is now turning into an open community-driven platform that allows and encourages innovation. It supports both URLs and XRIs as user identifiers, uses Yadis XRDS documents for identity service discovery, adds stronger security, and supports both public and private identifiers. With continuing convergence under this broad umbrella, the OpenID framework is emerging as a viable solution for Internet-scale user-centric identity infrastructure.
Avogadro: an advanced semantic chemical editor, visualization, and analysis platform

The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types.



The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here.



Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net.
Information systems research and Hofstede's culture's consequences: an uneasy and incomplete partnership

Information systems (IS) researchers have begun to investigate how national culture, as articulated by Hofstede, affects a wide variety of issues. A citation analysis of IS articles that cite Hofstede’s research on national culture suggests that most research is focused on issues related to IS management and to IS, while issues related to IS development and operations and to IS usage remain relatively unexamined. Within the dominant categories, research is concentrated in the IS management and types of information systems subcategories. Furthermore, the dimensions of national culture outlined by Hofstede have not been frequently used to develop and to build theory. Research opportunities and approaches to develop a stronger cumulative tradition and theory for international IS issues are proposed.
Three-dimensional location estimation of circular features for machine vision

AbstructEstimation of 3-D information from 2-D image coordinates is a fundamental problem both in machine vision and computer vision. Circular features are the most common quadratic-curved features that have been addressed for 3-D location estimation. In this paper, a closed-form analytical solution to the problem of 3-D location estimation of circular features is presented. Two different cases are considered: 1) 3-D orientation and 3-D position estimation of a circular feature when its radius is known, and 2) 3-D orientation and 3-D position estimation of a circular feature when its radius is not known. As well, extension of the developed method to 3-D quadratic features is addressed. Specifically, a closed-form analytical solution is derived for 3-D position estimation of spherical features. For experimentation purposes, simulated as well as real setups were employed. Simulated experimental results obtained for all three cases mentioned above verified the analytical method developed in this paper. In the case of real experiments, a set of circles located on a calibration plate, whose locations were known with respect to a reference frame, were used for camera calibration as well as for the application of the developed method. Since various distortion factors had to be compensated in order to obtain accurate estimates of the parameters of the imaged circle-an ellipse-with respect to the camera's image frame, a sequential compensation procedure was applied to the input grey-level image. The experimental results obtained once more showed the validity of the total process involved in the 3-D location estimation of circular features in general and the applicability of the analytical method developed in this paper in particular.
Applying a Naive Bayes Similarity Measure to Word Sense Disambiguation

We replace the overlap mechanism of the Lesk algorithm with a simple, generalpurpose Naive Bayes model that measures many-to-many association between two sets of random variables. Even with simple probability estimates such as maximum likelihood, the model gains significant improvement over the Lesk algorithm on word sense disambiguation tasks. With additional lexical knowledge from WordNet, performance is further improved to surpass the state-of-the-art results.
'Online recognition of Chinese characters: the state-of-the-art

Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.