nils-herrmann/clean_scirepeval huggingface dataset:
Hybrid cryptography mechanism for securing self-organized wireless networks

Nowadays use of wireless technologies tremendously increased due to the continuous development of these technologies in various applications. One of the development in wireless technologies leads to deployment of the networks in which all mobile nodes will communicate via wireless links directly without use of any base station or predefined infrastructural support. These networks are self-organized networks called mobile ad hoc networks (MANETs) and architecture used for these networks will be either stand alone with one or few nodes communicating with base station or distributed. As communication is done through wireless links, these types of networks are more prone to several kinds of attacks. Authentication and encryption acts as first line of defense while an intrusion detection system (IDS) acts as a second line of defense. One of the intrusion detection system used in MANET is Enhanced Adaptive Acknowledgement (EAACK) which is based on acknowledgement packets for detection of malicious activities. This system increases the overhead of network significantly relative to other system such as watchdog. So, in this paper we adopt the hybrid cryptography to reduce the network overhead and enhance the security of networks significantly.
A Matlab-based home energy management algorithm development toolbox

This paper presents the architecture and modeling approach of a Matlab-based toolbox for developing and testing home energy management (HEM) algorithms under a number of typical operation conditions. This toolbox serves as a developer platform that includes a graphical user interface, a model database, a computational engine, and an input-output database. The model database consists of home appliance models, energy storage models, baseload models, roof-top photovoltaic models, as well as typical weather profiles. The graphical interface allows the users to select different inputs such as utility rate structure, customer comfort settings, solar profiles, and outdoor temperature profiles. The HEM algorithms serve as the computational engines to power the virtual house so that the performance of those algorithms can be evaluated fairly using the same set of inputs and models. Simulation results have demonstrated the effectiveness of the modeling platform when developing HEM algorithms.
Heart rate variability and psychometric responses to overload and tapering in collegiate sprint-swimmers.

The purpose of this study was to evaluate cardiac-parasympathetic and psychometric responses to competition preparation in collegiate sprint-swimmers. Additionally, we aimed to determine the relationship between average vagal activity and its daily fluctuation during each training phase.



Observational.



Ten Division-1 collegiate sprint-swimmers performed heart rate variability recordings (i.e., log transformed root mean square of successive RR intervals, lnRMSSD) and completed a brief wellness questionnaire with a smartphone application daily after waking. Mean values for psychometrics and lnRMSSD (lnRMSSDmean) as well as the coefficient of variation (lnRMSSDcv) were calculated from 1 week of baseline (BL) followed by 2 weeks of overload (OL) and 2 weeks of tapering (TP) leading up to a championship competition.



Competition preparation resulted in improved race times (p<0.01). Moderate decreases in lnRMSSDmean, and Large to Very Large increases in lnRMSSDcv, perceived fatigue and soreness were observed during the OL and returned to BL levels or peaked during TP (p<0.05). Inverse correlations between lnRMSSDmean and lnRMSSDcv were Very Large at BL and OL (p<0.05) but only Moderate at TP (p>0.05).



OL training is associated with a reduction and greater daily fluctuation in vagal activity compared with BL, concurrent with decrements in perceived fatigue and muscle soreness. These effects are reversed during TP where these values returned to baseline or peaked leading into successful competition. The strong inverse relationship between average vagal activity and its daily fluctuation weakened during TP.
A Reconfigurable Quadri-Polarization Diversity Aperture-Coupled Patch Antenna

We present a novel reconfigurable quadri-polarization diversity aperture-coupled patch antenna which can provide four polarization states. By controlling the dc bias voltage of the pin-diodes on the feeding network, we can reconfigure the proposed antenna to provide a pair of orthogonal linear polarizations and a pair of orthogonal circular polarizations. Numerical and experimental results validate our design. This novel antenna provides potential polarization diversity features for wireless local area networks and multiple-input multiple-output systems
Development of Autonomous Car—Part II: A Case Study on the Implementation of an Autonomous Driving System Based on Distributed Architecture

Part I of this paper proposed a development process and a system platform for the development of autonomous cars based on a distributed system architecture. The proposed development methodology enabled the design and development of an autonomous car with benefits such as a reduction in computational complexity, fault-tolerant characteristics, and system modularity. In this paper (Part II), a case study of the proposed development methodology is addressed by showing the implementation process of an autonomous driving system. In order to describe the implementation process intuitively, core autonomous driving algorithms (localization, perception, planning, vehicle control, and system management) are briefly introduced and applied to the implementation of an autonomous driving system. We are able to examine the advantages of a distributed system architecture and the proposed development process by conducting a case study on the autonomous system implementation. The validity of the proposed methodology is proved through the autonomous car A1 that won the 2012 Autonomous Vehicle Competition in Korea with all missions completed.
Assessment of cognitive bias in decision-making and leadership styles among critical care nurses: a mixed methods study.

To raise awareness of critical care nurses' cognitive bias in decision-making, its relationship with leadership styles and its impact on care delivery.



The relationship between critical care nurses' decision-making and leadership styles in hospitals has been widely studied, but the influence of cognitive bias on decision-making and leadership styles in critical care environments remains poorly understood, particularly in Jordan.



Two-phase mixed methods sequential explanatory design and grounded theory.



critical care unit, Prince Hamza Hospital, Jordan. Participant sampling: convenience sampling Phase 1 (quantitative, n = 96), purposive sampling Phase 2 (qualitative, n = 20).



Pilot tested quantitative survey of 96 critical care nurses in 2012. Qualitative in-depth interviews, informed by quantitative results, with 20 critical care nurses in 2013. Descriptive and simple linear regression quantitative data analyses. Thematic (constant comparative) qualitative data analysis.



Quantitative - correlations found between rationality and cognitive bias, rationality and task-oriented leadership styles, cognitive bias and democratic communication styles and cognitive bias and task-oriented leadership styles. Qualitative - 'being competent', 'organizational structures', 'feeling self-confident' and 'being supported' in the work environment identified as key factors influencing critical care nurses' cognitive bias in decision-making and leadership styles. Two-way impact (strengthening and weakening) of cognitive bias in decision-making and leadership styles on critical care nurses' practice performance.



There is a need to heighten critical care nurses' consciousness of cognitive bias in decision-making and leadership styles and its impact and to develop organization-level strategies to increase non-biased decision-making.
Process Mining and the ProM Framework: An Exploratory Survey

In the last decade the field of process mining gained attention from research and practice. There is, however, not much known about the use and the appreciation of the involved techniques and tools, many of which are integrated into the well-known ProM framework. Therefore a questionnaire was sent out to ask people’s opinions about process mining and the ProM framework. This paper reports on the answers and tries to link them to existing knowledge from academic literature and popular articles. It must be seen as a first, exploratory attempt to reveal the adoption of process mining and the actual use of the ProM framework.
Whole-body aerial manipulation by transformable multirotor with two-dimensional multilinks

In this paper, we introduce the achievement of the aerial manipulation by using the whole body of a transformable aerial robot, instead of attaching an additional manipulator. The aerial robot in our work is composed by two-dimensional multilinks which enable a stable aerial transformation and can be employed as an entire gripper. We propose a planning method to find the optimized grasping form for the multilinks while they are on the air, which is based on the original planar enveloping algorithm, along with the optimization of the internal force and joint torque for the force-closure. We then propose the aerial approach and grasp motion strategy, which is devoted to the determination of the form and position of the aerial robot to approach and grasp effectively the object from the air. Finally we present the experimental results of the aerial manipulation which involves grasping, carrying and dropping different types of object. These results validate the performance of aerial grasping based on our proposed whole-body grasp planning and motion control method.
Assessing Test Suite E ↵ ectiveness Using Static Metrics

With the increasing amount of automated tests, we need ways to measure the test e↵ectiveness. The state-of-the-art technique for assessing test e↵ectiveness, mutation testing, is too slow and cumbersome to be used in large scale evolution studies or code audits by external companies. In this paper we investigated two alternatives, namely code coverage and assertion count. We discovered that code coverage outperforms assertion count by showing a relation with test suite e↵ectiveness for all analysed project. Assertion count only displays such a relation in only one of the analysed projects. Further analysing this relationship between assertion count coverage and test e↵ectiveness would allow to circumvent some of the problems of mutation testing.
Face/Off: Preventing Privacy Leakage From Photos in Social Networks

The capabilities of modern devices, coupled with the almost ubiquitous availability of Internet connectivity, have resulted in photos being shared online at an unprecedented scale. This is further amplified by the popularity of social networks and the immediacy they offer in content sharing. Existing access control mechanisms are too coarse-grained to handle cases of conflicting interests between the users associated with a photo; stories of embarrassing or inappropriate photos being widely accessible have become quite common. In this paper, we propose to rethink access control when applied to photos, in a way that allows us to effectively prevent unwanted individuals from recognizing users in a photo. The core concept behind our approach is to change the granularity of access control from the level of the photo to that of a user's personally identifiable information (PII). In this work, we consider the face as the PII. When another user attempts to access a photo, the system determines which faces the user does not have the permission to view, and presents the photo with the restricted faces blurred out. Our system takes advantage of the existing face recognition functionality of social networks, and can interoperate with the current photo-level access control mechanisms. We implement a proof-of-concept application for Facebook, and demonstrate that the performance overhead of our approach is minimal. We also conduct a user study to evaluate the privacy offered by our approach, and find that it effectively prevents users from identifying their contacts in 87.35% of the restricted photos. Finally, our study reveals the misconceptions about the privacy offered by existing mechanisms, and demonstrates that users are positive towards the adoption of an intuitive, straightforward access control mechanism that allows them to manage the visibility of their face in published photos.
Technology infusion for complex systems: A framework and case study

Manufacturing companies in today’s competitive environment constantly need to develop new technologies and infuse them into their line of products to stay ahead of the competition. Most new technologies only deliver value once they are successfully infused into a parent system. However, there has been little research done to develop formal methods to assess the impact of new technology infusion into existing products and systems. In this paper, a systematic framework to quantify and assess the impact of technology infusion early in the product planning cycle is proposed. The proposed methodology quantitatively estimates the impact of technology infusion through the use of a Design Structure Matrix (DSM) and the creation of a Delta DSM (∆DSM) describing the changes to the original system due to the infused technology. The cost for technology infusion is then estimated from the ∆DSM, and the potential market impact of the technology is calculated based on customer value, expressed through utility curves for system technical performance measures. Finally, the probabilistic ∆NPV of a newly infused technology is obtained using Monte Carlo simulation. The proposed methodology was demonstrated on an actual complex printing system, represented as an 84 element DSM with a density of 3.7%, where a newly developed value-enhancing technology was infused into the existing product. The result shows that a positive marginal net present value ∆NPV can be expected, despite the new technology causing an invasiveness of 8.5% to the existing design. The methodology can be applied in a rigorous and repeatable manner, opening up possibilities for further implementation of the proposed framework, including analysis of the interactions amongst multiple technologies.
HoMonit: Monitoring Smart Home Apps from Encrypted Traffic

Smart home is an emerging technology for intelligently connecting a large variety of smart sensors and devices to facilitate automation of home appliances, lighting, heating and cooling systems, and security and safety systems. Our research revolves around Samsung SmartThings, a smart home platform with the largest number of apps among currently available smart home platforms. The previous research has revealed several security flaws in the design of SmartThings, which allow malicious smart home apps (or SmartApps) to possess more privileges than they were designed and to eavesdrop or spoof events in the SmartThings platform. To address these problems, this paper leverages side-channel inference capabilities to design and develop a system, dubbed HoMonit, to monitor SmartApps from encrypted wireless traffic. To detect anomaly, HoMonit compares the SmartApps activities inferred from the encrypted traffic with their expected behaviors dictated in their source code or UI interfaces. To evaluate the effectiveness of HoMonit, we analyzed 181 official SmartApps and performed evaluation on 60 malicious SmartApps, which either performed over-privileged accesses to smart devices or conducted event-spoofing attacks. The evaluation results suggest that HoMonit can effectively validate the working logic of SmartApps and achieve a high accuracy in the detection of SmartApp misbehaviors.
Research on high-frequency induction heating load-matched based on LLC resonant circuit

Modern high-frequency induction heating technology puts forward higher requirements for load matching of the induction heating power supply. LLC resonant load circuit can replace the traditional induction heating load matching transformer, and increase power density. This paper analyzes the load matching characteristics of LLC load resonant circuit in high frequency induction heating power supply, and gives the parameter selection method of matching components. At last, combining with phase-shifting pulse-width modulation (PWM) control method, the phase-shifting induction heating experiment platform based on LLC load resonant circuit is established. Experimental results show that the performance of LLC circuit in high frequency induction heating power supply load matching is excellent.
A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification

Screening mammography is an important front-line tool for the early detection of breast cancer, and some 39 million exams are conducted each year in the United States alone. Here, we describe a multi-scale convolutional neural network (CNN) trained with a curriculum learning strategy that achieves high levels of accuracy in classifying mammograms. Specifically, we first train CNN-based patch classifiers on segmentation masks of lesions in mammograms, and then use the learned features to initialize a scanning-based model that renders a decision on the whole image, trained end-to-end on outcome data. We demonstrate that our approach effectively handles the “needle in a haystack” nature of full-image mammogram classification, achieving 0.92 AUROC on the DDSM dataset.
A Privacy Preserving Method Using Privacy Enhancing Techniques for Location Based Services

The move towards service-oriented architectures and the increasing usage of mobile devices to access such services are two of the major changes in modern computing. Information about the user, their location and their trajectory can provide additional context information to a service, leading to useful applications such as directing a user to the nearest bus stop and displaying which buses are due to arrive in the next minutes. While this type of information can be useful, when the offered services are trusted, it also introduces privacy issues relating to gathering of location information for non-trusted applications like locationbased marketing or user behaviour profiling. Users can limit their location information provided to a service but these controls are simple, making it important for the user to understand how their location information is being used by services. This paper reviews some of the methods currently being proposed to reduce the impact of location tracking on user privacy, and presents a novel encryption method for preserving the location and trajectory path of a user using Privacy-Enhancing Technologies.
A Survey: Smart agriculture IoT with cloud computing

IoT is a revolutionary technology that represents the future of computing and communications. Most of the people over all worlds depend on agriculture. Because of this reason smart IT technologies are needed to migrate with traditional agriculture methods. Using modern technologies can control the cost, maintenance and monitoring performance. Satellite and aerial imagery play a vital role in modern agriculture. Precision agriculture sensor monitoring network is used greatly to measure agri-related information like temperature, humidity, soil PH, soil nutrition levels, water level etc. so, with IoT farmers can remotely monitor their crop and equipment by phones and computers. In this paper, we surveyed some typical applications of Agriculture IoT Sensor Monitoring Network technologies using Cloud computing as the backbone. This survey is used to understand the different technologies and to build sustainable smart agriculture. Simple IoT agriculture model is addressed with a wireless network.
A piezoelectric energy-harvesting shoe system for podiatric sensing

This paper provides an energy-harvesting, shoe-mounted system for medical sensing using piezoelectric transducers for generating power. The electronics are integrated inside a conventional consumer shoe, measuring the pressure of the wearer's foot exerted on the sole at six locations. The electronics are completely powered by the harvested energy from walking or running, generating 10-20 μJ of energy per step that is then consumed by capturing and storing the force sensor data. The overall shoe system demonstrates that wearable sensor electronics can be adequately powered through piezoelectric energy-harvesting.
An ontological framework for knowledge modeling and decision support in cyber-physical systems

Our work is concerned with the development of knowledge structures to support correct-by-design cyber-physical systems (CPS). This class of systems is defined by a tight integration of software and physical processes, the need to satisfy stringent constraints on performance and safety, and a reliance on automation for the management of system functionality and decision making. To assure correctness of functionality with respect to requirements, there is a strong need for system models to account for semantics of the domains involved. This paper introduces a new ontological-based knowledge and reasoning framework for decision support for CPS. It enables the development of determinate, provable and executable CPS models supported by sound semantics strengthening the model-driven approach to CPS design. An investigation into the structure of basic description logics (DL) has identified the needed semantic extensions to enable the web ontology language (OWL) as the ontological language for our framework. The SROIQ DL has been found to be the most appropriate logic-based knowledge formalism as it maps to OWL 2 and ensures its decidability. Thus, correct, stable, complete and terminating reasoning algorithms are guaranteed with this SROIQ-backed language. The framework takes advantage of the commonality of data and information processing in the different domains involved to overcome the barrier of heterogeneity of domains and physics in CPS. Rules-based reasoning processes are employed. The framework provides interfaces for semantic extensions and computational support, including the ability to handle quantities for which dimensions and units are semantic parameters in the physical world. Together, these capabilities enable the conversion of data to knowledge and their effective use for efficient decision making and the study of system-level properties, especially safety. We exercise these concepts in a traffic light time-based reasoning system.
Enabling Temporal Reasoning for Fact Statements : A Web-based Approad ( Technical Report )

There exists a precise time period during which a given fact such as an event or a status is valid. In this paper, we propose a new approach to determine the validity time of a fact statement by leveraging unstructured and noisy data from the Web, while overcoming the limitations of existing natural language processing technologies designed for the same task. Given a statement of a fact, the proposed solution first constructs a Semantic Bayesian Network based on its relevant snippets retrieved from Web, then estimates the validity probabilities of time points using the constructed network. In the interest of dealing with the semantic complexity of keywords on the Web, we also present a technique based on relative standard deviation to estimate distortion risks of keywords and incorporate their risk estimation into the process of probability computation. Finally, we show by extensive experiments on real data that the proposed approach can achieve considerable improvements in performance over 4 state-of-the-art alternatives for reasoning. In addition, the proposed risk reduction technique can effectively improve validity time reasoning’s precision.