nils-herrmann/clean_scirepeval huggingface dataset:
Dc-bus voltage regulation and power compensation with bi-directional inverter in dc-microgrid applications

This paper presents a single-phase bi-directional inverter with dc-bus voltage regulation and power compensation in dc-microgrid applications. In dc-microgrid applications, a power distribution system requires a bi-directional inverter to control the power flow between dc bus and ac grid, and to regulate the dc bus to a certain range of voltages, in which dc load may change abruptly. This will result in high dc-bus voltage variation. In this paper, we take into account this variation and propose an on-line regulation mechanism according to the inductor current levels to balance power flow and enhance the dynamic performance. Additionally, for power compensation and islanding protection, the bi-directional inverter can shift its current commands according to the specified power factor at ac grid side. Simulated and Experimental results obtained from a 5 kW single-phase bi-directional inverter have verified the analysis and discussion.
Performance Comparison of VxWorks, Linux, RTAI, and Xenomai in a Hard Real-Time Application

We report on a set of performance measurements executed on VMEbus MVME5500 boards equipped with MPC7455 PowerPC processor, running four different operating systems: Wind River VxWorks, Linux, RTAI, and Xenomai. Some components of RTAI and Xenomai have been ported to the target architecture. Interrupt latency, rescheduling and inter-process communication times are compared in the framework of a sample real-time application. Performance measurements on Gigabit Ethernet network communication have also been carried out on the target boards. To this purpose, we have considered the Linux IP stack and RTnet, an open-source hard real-time network protocol stack for Xenomai and RTAI, which was ported to the considered architecture. Performance measurements show that the tested open-source software is suitable for hard real-time applications.
Beyond the technical challenges for deploying Machine Learning solutions in a software company

Recently software development companies started to embrace Machine Learning (ML) techniques for introducing a series of advanced functionality in their products such as personalisation of the user experience, improved search, content recommendation and automation. The technical challenges for tackling these problems are heavily researched in literature. A less studied area is a pragmatic approach to the role of humans in a complex modern industrial environment where ML based systems are developed. Key stakeholders affect the system from inception and up to operation and maintenance. Product managers want to embed “smart” experiences for their users and drive the decisions on what should be built next; software engineers are challenged to build or utilise ML software tools that require skills that are well outside of their comfort zone; legal and risk departments may influence design choices and data access; operations teams are requested to maintain ML systems which are non-stationary in their nature and change behaviour over time; and finally ML practitioners should communicate with all these stakeholders to successfully build a reliable system. This paper discusses some of the challenges we faced in Atlassian as we started investing more in the ML space.
Minimally Supervised Event Causality Identification

This paper develops a minimally supervised approach, based on focused distributional similarity methods and discourse connectives, for identifying of causality relations between events in context. While it has been shown that distributional similarity can help identifying causality, we observe that discourse connectives and the particular discourse relation they evoke in context provide additional information towards determining causality between events. We show that combining discourse relation predictions and distributional similarity methods in a global inference procedure provides additional improvements towards determining event causality.
Intelligent multi-agent system for smart home energy management

Smart grid literature shows a potential way to implement smart grid using Multi-Agent System (MAS) which is a distributed computational intelligence technique that comprises of multiple interactive intelligent agents in an environment. This paper presents an application of MAS technique for improving the efficiency and optimizing the energy usage of smart homes. The smart appliances in a smart home are modeled as agents and optimization algorithms are used in decision making of the agents. These agents work together to reduce energy consumption while striking a balance between consumers comfort, energy cost and peak energy saving in the distribution grid. Such a way, a Home Energy Management System (HEMS) was designed and developed using MAS. This research enables smart homes to communicate, interact and negotiate with energy sources and devices in smart homes that achieve maximum overall energy efficiency and minimum electricity bill. Simulation studies carried out on the developed MAS have shown that it has the potential to provide the optimum solutions. How MAS communication influences on decision making of the agents and getting the optimum solution is also demonstrated in this paper.
Intrusion Detection : Support Vector Machines and Neural Networks

This paper concerns intrusion detection and audit trail reduction. We describe approaches to intrusion detection and audit data reduction using support vector machines and neural networks. Using a set of benchmark data from the KDD (Knowledge Discovery and Data Mining) competition designed by DARPA, we demonstrate that efficient and highly accurate classifiers can be built using either support vector machines (SVMs) or neural networks for intrusion detection. Further, we present SVMs and neural networks that use only the (13 of 41) most significant features of the data and deliver only-slightly-lower detection accuracy in the binary attack/normal classification. We also compare the performance of neural networks and SVMs.
A Systematic Literature Review on Fault Prediction Performance in Software Engineering

The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software.  We investigate how the context of models, the independent variables used, and the modeling techniques applied influence the performance of fault prediction models.  We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2010. We synthesize the quantitative and qualitative results of 36 studies which report sufficient contextual and methodological information according to the criteria we develop and apply.  The models that perform well tend to be based on simple modeling techniques such as Naive Bayes or Logistic Regression. Combinations of independent variables have been used by models that perform well. Feature selection has been applied to these combinations when models are performing particularly well.  The methodology used to build models seems to be influential to predictive performance. Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively.
Calculating Completeness of Agile Scope in Scaled Agile Development

Flexible nature of scope definition in agile makes it difficult or impossible to measure its completeness and quality. The aim of this paper is to highlight the important ingredients of scope definition for agile projects and to present a method for agile projects in order to measure the quality and completeness of their scope definitions. The proposed method considers the elements that are retrieved as a result of the systematic literature review. An industrial survey is conducted to validate and prioritize these elements. Elements are then assigned weights according to their importance in scope definition to build a scorecard for calculating the score of user stories present in the product backlog. The proposed method is able to identify the clear and complete user stories that can better be implemented in the coming iteration. Formal experiments are performed for the evaluation of the proposed method, and it suggests that the method is useful for experts in order to quantify the completeness and quality of scope definition of an agile software project.
Lane-Level Integrity Provision for Navigation and Map Matching With GNSS, Dead Reckoning, and Enhanced Maps

Lane-level positioning and map matching are some of the biggest challenges for navigation systems. Additionally, in safety applications or in those with critical performance requirements (such as satellite-based electronic fee collection), integrity becomes a key word for the navigation community. In this scenario, it is clear that a navigation system that can operate at the lane level while providing integrity parameters that are capable of monitoring the quality of the solution can bring important benefits to these applications. This paper presents a pioneering novel solution to the problem of combined positioning and map matching with integrity provision at the lane level. The system under consideration hybridizes measurements from a global navigation satellite system (GNSS) receiver, an odometer, and a gyroscope, along with the road information stored in enhanced digital maps, by means of a multiple-hypothesis particle-filter-based algorithm. A set of experiments in real environments in France and Germany shows the very good results obtained in terms of positioning, map matching, and integrity consistency, proving the feasibility of our proposal.
Band-pass shielding enclosure for 2.4/5 GHz WLAN applications

This paper presents a design of band-pass shielding enclosure for two frequency bands of WLAN. The proposed design employs a compact frequency selective surface to highlight the advantages of these miniaturized structures for the selective shielding of such enclosures that are electrically small. The results indicate that the proposed shielding enclosure has high transmittance in both the pass bands for horizontal and vertical polarizations, while offers a good amount of electromagnetic shielding outside these frequency bands.
Class Activation Map Generation by Representative Class Selection and Multi-Layer Feature Fusion

Existing method generates class activation map (CAM) by a set of fixed classes (i.e., using all the classes), while the discriminative cues between class pairs are not considered. Note that activation maps by considering different class pair are complementary, and therefore can provide more discriminative cues to overcome the shortcoming of the existing CAM generation that the highlighted regions are usually local part regions rather than global object regions due to the lack of object cues. In this paper, we generate CAM by using a few of representative classes, with aim of extracting more discriminative cues by considering each class pair to obtain CAM more globally. The advantages are twofold. Firstly, the representative classes are able to obtain activation regions that are complementary to each other, and therefore leads to generating activation map more accurately. Secondly, we only need to consider a small number of representative classes, making the CAM generation suitable for small networks. We propose a clustering based method to select the representative classes. Multiple binary classification models rather than a multiple class classification model are used to generate the CAM. Moreover, we propose a multi-layer fusion based CAM generation method to simultaneously combine high-level semantic features and low-level detail features. We validate the proposed method on the PASCAL VOC and COCO database in terms of segmentation groundtruth. Various networks such as classical network (Resnet-50, Resent-101 and Resnet-152) and small network (VGG-19, Resnet-18 and Mobilenet) are considered. Experimental results show that the proposed method improves the CAM generation obviously.
Personality Based E-Recruitment System

Nodes in Mobile Ad Hoc Networks (MANETs) are limited battery powered. That’s why energy efficient routing has become an important optimization criterion in MANETs. The conventional routing protocols do not consider energy of the nodes while selecting routes which leads to early exhaustion of nodes and partitioning of the network. This paper attempts to provide an energy aware routing algorithm. The proposed algorithm finds the transmission energy between the nodes relative to the distance and the performance of the algorithm is analyzed between two metrics Total Transmission energy of a route and Maximum Number of Hops. The proposed algorithm shows efficient energy utilization and increased network lifetime with total transmission energy metric.
Emergence in Nascent Online Communities: An Affordance Perspective

Online communities like Wikipedia have the potential to transform our global society. Despite their growing importance, however, the extant literature has theorized only little about how member contribution behavior in a nascent online community emerges in interaction with the materiality of the technology platform. Studying this is critical for understanding the important early stage evolution of online communities. Without a rich history, the platform’s materiality is almost the only thing that members can use to make sense of the online community. We investigate this question by applying a mixedmethod approach to a longitudinal case study of AshokaHub, a nascent global online community of social entrepreneurs. We use an affordance perspective and our findings to date suggest that the materiality of the technology platform plays an important role in the emergence of member contribution behavior in the nascent online community. Moreover, they suggest extensions to the concepts of affordances and imbrication.
SPOT Poachers in Action: Augmenting Conservation Drones With Automatic Detection in Near Real Time

The unrelenting threat of poaching has led to increased development of new technologies to combat it. One such example is the use of long wave thermal infrared cameras mounted on unmanned aerial vehicles (UAVs or drones) to spot poachers at night and report them to park rangers before they are able to harm animals. However, monitoring the live video stream from these conservation UAVs all night is an arduous task. Therefore, we build SPOT (Systematic POacher deTector), a novel application that augments conservation drones with the ability to automatically detect poachers and animals in near real time. SPOT illustrates the feasibility of building upon state-of-the-art AI techniques, such as Faster RCNN, to address the challenges of automatically detecting animals and poachers in infrared images. This paper reports (i) the design and architecture of SPOT, (ii) a series of efforts towards more robust and faster processing to make SPOT usable in the field and provide detections in near real time, and (iii) evaluation of SPOT based on both historical videos and a real-world test run by the end users in the field. The promising results from the test in the field have led to a plan for larger-scale deployment in a national park in Botswana. While SPOT is developed for conservation drones, its design and novel techniques have wider application for automated detection from UAV videos.
Agile development with security engineering activities

Agile software development has been used by industry to create a more flexible and lean software development process, i.e making it possible to develop software at a faster rate and with more agility during development. There are however concerns that the higher development pace and lack of documentation are creating less secure software. We have therefore looked at three known Security Engineering processes, Microsoft SDL, Cigatel touchpoints and Common Criteria and identified what specific security activities they performed. We then compared these activities with an Agile development process that is used in industry. Developers, from a large telecommunication manufacturer, were interviewed to learn their impressions on using these security activities in an agile development process. We produced a security enhanced Agile development process that we present in this paper. This new Agile process use activities from already established security engineering processes that provide the benefit the developers wanted but did not hinder or obstruct the Agile process in a significant way.
White matter lesion extension to automatic brain tissue segmentation on MRI

A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.
Technology , globalization , and international competitiveness : Challenges for developing countries

This paper traces the role of technology in economic growth and competitiveness, summarizes the strategies of the fastest growing economies over the last 50 years from the perspective of their technology strategy, summarizes some of the key global trends which are making it more difficult for developing countries to replicate the fast growth experience of the countries mentioned, and traces the impact of the rise of China on developing countries. The main argument of this paper is that technology is an increasingly important element of globalisation and of competitiveness and that the acceleration in the rate of technological change and the pre-requisites necessary to participate effectively in globalisation are making it more difficult for many developing countries to compete. Section 2 gives a long-term perspective on technology and economic growth. Section 3 presents a global overview of changes in regional competitiveness as revealed by economic growth. Section 4 identifies some of the high performers in the last 50 years and reviews the strategies of the high performing East Asian economies comprising the well known “gang of four”, plus three South East Asian countries. Section 5 reviews the strategies of the BRICM countries, the largest developing country economies (Brazil, Russia, India, China and Mexico). It also argues that it is harder for developing countries to replicate the success of the high performing East Asian countries for two main reasons. One relates to new elements in the global competitive environment. These are summarized in section 6. The other is the rapid rise of China (and to a lesser extent India). This is covered in Section 7, which also includes a preliminary analysis of the effects of the rapid rise of China on the rest of the world. Finally, Section 8 draws some conclusions. Developing countries must develop more technological capability and greater flexibility to succeed in the more demanding and asymmetric global environment. It is likely that the pressures of globalisation and greater international competition generate strong protectionist retrenchment in both developed and developing countries. These should be resisted. The world as a whole will be better off if developed countries focus on increasing their flexibility to adjust to changing comparative advantage resulting from rapid technical change, and developing countries focus on increasing their education, infrastructure, and technological capability. There remain however large asymmetries in the global system and greater efforts need to be made to provide some global balancing and transfer mechanisms.
Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors

The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring. Most current devices generate so many false-positive alerts that devices cannot be used for routine clinical practice. This paper explores principled machine learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. We adopt a one-class support vector machine formulation, proposing a formulation for determining the free parameters of the model using partial area under the ROC curve, a method arising from the unique requirements of performing online analysis with data from patient-worn sensors. There are few clinical evaluations of machine learning techniques in the literature, so we present results from a study at the Oxford University Hospitals NHS Trust devised to investigate the large-scale clinical use of patient-worn sensors for predictive monitoring in a ward with a high incidence of patient mortality. We show that our system can combine routine manual observations made by clinical staff with the continuous data acquired from wearable sensors. Practical considerations and recommendations based on our experiences of this clinical study are discussed, in the context of a framework for personalized monitoring.
Decision making using fuzzy logic for stock trading

This paper present the use of fuzzy if-then rules for a decision support system in stock trading. The three following linguistic variables well be the input for the rule : view from the expert, earning — per-share and price to earnings ratio. The purpose of this rule is to assist investors making a decision on their shares. The investors need to make a right decision to gain a high profit in stock trading. Stock market is a complex environment. Therefore by using this Artificial Intelligence (AI) application which is Fuzzy Logic (FL) can make it simpler as well as giving benefits to investors. Most of the earlier research works show that FL can work within stocks environment.