Reviewerly/exHarmony huggingface dataset:
Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.
Correction to: Adaptive random forests for evolving data stream classification

The Publisher regrets an error in the spelling of the family name of the sixth author. The correct spelling is Bernhard Pfahringer, as it appears in the author list above.
Non Normal Logics: Semantic Analysis and Proof Theory

We introduce proper display calculi for basic monotonic modal logic, the conditional logic CK and a number of their axiomatic extensions. These calculi are sound, complete, conservative and enjoy cut elimination and subformula property. Our proposal applies the multi-type methodology in the design of display calculi, starting from a semantic analysis based on the translation from monotonic modal logic to normal bi-modal logic.
Collaborative Filtering Recommender System

Recently, with the presence of a lot of information and the emergence of many programs, sites and companies that provide items to customers like Amazon for products or Netflix for movies …, it was necessary to exploit this data to achieve a quantum leap in the world of technology and specially do not leave the customer confused in the item to be chosen among other huge options, so many of sciences that are interested in the field of Big data and using the large information to meet the needs of users intervened to improve the area of recommendation such as data science, machine learning…. however there is one solution to give suggestions for customers is recommender systems. Recommender systems is a useful information filtering tool for guiding users in a personalized way of discovering products or services they might be interested in from a large space of possible options. It predicts interests of users and makes recommendation according to the interest model of users. On one hand, there is a traditional recommender systems recommend items based on different criteria of users or items like item price, user profile …on another hand we have recommender systems using deep learning techniques even if not been well explored yet. In this article, we first introduce different kinds of the most famous category of recommender systems and focus on one type to do movies recommendations and then make a quantitative comparison.
Multitask Metric Learning: Theory and Algorithm

In this paper, we study the problem of multitask metric learning (mtML). We first examine the generalization bound of the regularized mtML formulation based on the notion of algorithmic stability, proving the convergence rate of mtML and revealing the trade-off between the tasks. Moreover, we also establish the theoretical connection between the mtML, single-task learning and pooling-task learning approaches. In addition, we present a novel boosting-based mtML (mt-BML) algorithm, which scales well with the feature dimension of the data. Finally, we also devise an efficient second-order Riemannian retraction operator which is tailored specifically to our mt-BML algorithm. It produces a low-rank solution of mtML to reduce the model complexity, and may also improve generalization performances. Extensive evaluations on several benchmark data sets verify the effectiveness of our learning algorithm.
Disorder-assisted graph coloring on quantum annealers

We are at the verge of a new era, which will be dominated by noisy intermediate-scale quantum devices. Prototypical examples for these new technologies are present-day quantum annealers. In the present work, we investigate to what extent static disorder generated by an external source of noise does not have to be detrimental, but can actually assist quantum annealers in achieving better performance. In particular, we analyze the graph coloring problem that can be solved on a sparse topology (i.e., chimera graph) via suitable embedding. We show that specifically tailored disorder can enhance the fidelity of the annealing process and thus increase the overall performance of the annealer.
A classification algorithm with Linear Discriminant Analysis and Axiomatic Fuzzy Sets

In exploratory data mining, most classifiers pay more attention on the accuracy and speed of learned models, but they are lacking of the interpretability. In this paper, an interpretable and comprehensible classifier is proposed based on Linear Discriminant Analysis (LDA) and Axiomatic Fuzzy Sets (AFS). The algorithm utilizes LDA to extract features with the largest inter-class variance. Besides, the proposed approach aims to explore a transformation from the selected feature space to a semantic space where the samples in the same class are made as close as possible to one another, whereas the samples in the different class are as far as possible from one another. Moreover, the descriptions of each class can be obtained by the proposed approach. When compared with well-known classifiers such as LogisticR, C4.5Tree, SVM and KNN, the proposed method not only can achieve better performance in terms of accuracy but also has the capability of interpretability and comprehension.
An effective approach to develop location-based augmented reality information support

Using location-based augmented reality (AR) for pedestrian navigation can greatly improve user action to reduce the travel time. Pedestrian navigation differs in many ways from the conventional navigation system used in a car or other vehicles. A major issue with using location-based AR for navigation to a specific landmark is their quality of usability, especially if the active screen is overcrowded with the augmented POI markers which were overlap each other at the same time. This paper describes the user journey map approach that led to new insights about how users were using location-based AR for navigation. These insights led to a deep understanding of challenges that user must face when using location-based AR application for pedestrian navigation purpose, and more generally, they helped the development team to appreciate the variety of user experience in software requirement specification phase. To prove our concept, a prototype of intuitive location-based AR was built to be compared with existing standard-location based AR. The user evaluation results reveal that the overall functional requirements which are gathered from user journey have same level of success rate criteria when compared with standard location-based AR. Nevertheless, the field study participants highlighted the extended features in our prototype could significantly enhance the user action on locating the right object in particular place when compared with standard location-based AR application (proved with the required time).
A Framework for Evaluation and Analysis on Infection Countermeasures Against Fault Attacks

Infection is a fault attack countermeasure, which aims to destroy the dependency of the faulty ciphertexts on the secret key. However, current security evaluations on infection countermeasures are either tailored for the specific attack scenario or not general enough to apply to various infection instances. They cannot come to convincing results, let alone make comparisons between different countermeasures. Based on information theory, this paper presents a generic evaluation framework that is feasible for various infection countermeasures and attack scenarios. The framework is constructed with the idea to separate the infection function from the unprotected cipher yet consider the fault injection effect on the unprotected cipher in the infection function evaluation. First, the security judging criteria for the infection function under different attack scenarios are personalized according to the injection-caused security loss of the unprotected cipher. Then, a universal method of security quantitative analysis on infection function is proposed with two important steps: the prior knowledge collection and the infection operation decomposition analysis. Because the analysis results of the simple infection operation can be reused within the infection function under various attack scenarios, the security quantifications are efficient. Based on this framework, the paper also reviews some existing infection countermeasures for their fault attack resistances. The result shows that our analysis can expose more infection vulnerabilities than the previous works. Besides, the security quantification and judgment on these countermeasures give us a new insight into their security applicable scopes. They are instructive for the countermeasure selection when the implementation costs are very close. Furthermore, the framework provides an efficient way to evaluate future infection countermeasures.
Discrimination, Harassment and Racism in Finnish Lower Secondary Schools

In this article, we examine the experiences of students in the ninth grade (aged 15–16 years) in relation to harassment, discrimination and racism. The study was carried out at eight Finnish lower secondary schools with a high proportion of students with an immigrant background in 2015. The sample consisted of survey data (n = 445) and thematic interviews (n = 112) conducted with young people of Finnish and immigrant origins. Approximately one-quarter of the respondents said that they had been harassed or discriminated against at schools, and one-tenth had experienced this behaviour in their free time. In addition, almost half of the young people thought that discrimination is widespread in Finland. The more often a student experienced discrimination, the less they liked school. Experiences of harassment, discrimination and racism were especially downplayed when the respondent was the target.
Open-Source Tool of Vector Map for Path Planning in Autoware Autonomous Driving Software

In this paper, we develop a tool to generate vector map necessary for path planning in Autoware, an open-source autonomous driving software. Vector map defines road network structure, from which the global path is produced in planning module. Although Autoware is open-source, an approach to create customized vector map is considered as business nature. The free available vector mapper tool for Autoware is also not reliable and requires point cloud data file as input. However, this proposed tool takes only waypoints, locations at which a car drives, as input and produces vector map.
Engineering Multi-Agent Systems

The continuous integration of software-intensive systems together with the ever-increasing computing power offer a breeding ground for intelligent agents and multi-agent systems (MAS) more than ever before. Over the past two decades, a wide variety of languages, models, techniques and methodologies have been proposed to engineer agents and MAS. Despite this substantial body of knowledge and expertise, the systematic engineering of large-scale and open MAS still poses many challenges. Researchers and engineers still face fundamental questions regarding theories, architectures, languages, processes, and platforms for designing, implementing, running, maintaining, and evolving MAS. This paper reports on the results of the 6th International Workshop on Engineering Multi-Agent Systems (EMAS 2018, 14th-15th of July, 2018, Stockholm, Sweden), where participants discussed the issues above focusing on the state of affairs and the road ahead for researchers and engineers in this area.
SL-COMP: Competition of Solvers for Separation Logic

SL-COMP aims at bringing together researchers interested on improving the state of the art of the automated deduction methods for Separation Logic (SL). The event took place twice until now and collected more than 1K problems for different fragments of SL. The input format of problems is based on the SMT-LIB format and therefore fully typed; only one new command is added to SMT-LIB’s list, the command for the declaration of the heap’s type. The SMT-LIB theory of SL comes with ten logics, some of them being combinations of SL with linear arithmetics. The competition’s divisions are defined by the logic fragment, the kind of decision problem (satisfiability or entailment) and the presence of quantifiers. Until now, SL-COMP has been run on the StarExec platform, where the benchmark set and the binaries of participant solvers are freely available. The benchmark set is also available with the competition’s documentation on a public repository in GitHub.
Handwritten Hindi Character Recognition using Deep Learning Techniques

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Extendibility of Bosonic Gaussian States

Extendibility of bosonic Gaussian states is a key issue in continuous-variable quantum information. We show that a bosonic Gaussian state is k-extendible if and only if it has a Gaussian k-extension, and we derive a simple semidefinite program, whose size scales linearly with the number of local modes, to efficiently decide k-extendibility of any given bosonic Gaussian state. When the system to be extended comprises one mode only, we provide a closed-form solution. Implications of these results for the steerability of quantum states and for the extendibility of bosonic Gaussian channels are discussed. We then derive upper bounds on the distance of a k-extendible bosonic Gaussian state to the set of all separable states, in terms of trace norm and Rényi relative entropies. These bounds, which can be seen as "Gaussian de Finetti theorems," exhibit a universal scaling in the total number of modes, independently of the mean energy of the state. Finally, we establish an upper bound on the entanglement of formation of Gaussian k-extendible states, which has no analogue in the finite-dimensional setting.
A new computer-controlled platform for ADC-based true random number generator and its applications

The basis of encryption techniques is random number generators (RNGs). The application areas of cryptology are increasing in number due to continuously developing technology, so the need for RNGs is increasing rapidly, too. RNGs can be divided into two categories as pseudorandom number generator (PRNGs) and true random number generator (TRNGs). TRNGs are systems that use unpredictable and uncontrollable entropy sources and generate random numbers. During the design of TRNGs, while analog signals belonging to the used entropy sources are being converted to digital data, generally comparators, flip-flops, Schmitt triggers, and ADCs are used. In this study, a computer-controlled new and flexible platform to find the most appropriate system parameters in ADC-based TRNG designs is designed and realized. As a sample application with this new platform, six different TRNGs that use three different outputs of Zhongtang, which is a continuous time chaotic system, as an entropy source are designed. Random number series generated with the six designed TRNGs are put through the NIST800–22 test, which has the internationally highest standards, and they pass all tests. With the help of the new platform designed, ADC-based high-quality TRNGs can be developed fast and also without the need for expertise. The platform has been designed to decide which entropy source and parameter are better by comparing them before complex embedded TRNG designs. In addition, this platform can be used for educational purposes to explain how to work an ADC-based TRNG. That is why it can be utilized as an experiment set in engineering education, as well.
Arabia Felix 2.0: a cross-linguistic Twitter analysis of happiness patterns in the United Arab Emirates

The global popularity of social media platforms has given rise to unprecedented amounts of data, much of which reflects the thoughts, opinions and affective states of individual users. Systematic explorations of these large datasets can yield valuable information about a variety of psychological and sociocultural variables. The global nature of these platforms makes it important to extend this type of exploration across cultures and languages as each situation is likely to present unique methodological challenges and yield findings particular to the specific sociocultural context. To date, very few studies exploring large social media datasets have focused on the Arab world. This study examined social media use in Arabic and English across the United Arab Emirates (UAE), looking specifically at indicators of subjective wellbeing (happiness) across both languages. A large social media dataset, spanning 2013 to 2017, was extracted from Twitter. More than 17 million Twitter messages (tweets), written in Arabic and English and posted by users based in the UAE, were analyzed. Numerous differences were observed between individuals posting messages (tweeting) in English compared with those posting in Arabic. These differences included significant variations in the mean number of tweets posted, and the mean size of users networks (e.g. the number of followers). Additionally, using lexicon-based sentiment analytic tools (Hedonometer and Valence Shift Word Graphs), temporal patterns of happiness (expressions of positive sentiment) were explored in both languages across all seven regions (Emirates) of the UAE. Findings indicate that 7:00 am was the happiest hour, and Friday was the happiest day for both languages (the least happy day varied by language). The happiest months differed based on language, and there were also significant variations in sentiment patterns, peaks and troughs in happiness, associated with events of sociopolitical and religio-cultural significance for the UAE.
A Novel Dynamic KCi - Slice Publishing Prototype for Retaining Privacy and Utility of Multiple Sensitive Attributes

Data publishing plays a major role to establish a path between current world scenarios and next generation requirements and it is desirable to keep the individuals privacy on the released content without reducing the utility rate.Existing KC and KC i models concentrate on multiple categorical sensitive attributes.Both these models have their own merits and demerits.This paper proposes a new method named as novel KC islice model, to enhance the existing KC i approach with better utility levels and required privacy levels.The proposed model uses two rounds to publish the data.Anatomization approach is used to separate the sensitive attributes and quasi attributes.The first round uses a novel approach called as enhanced semantic l-diversity technique to bucketize the tuples and also determine the correlation of the sensitive attributes to build different sensitive tables.The second round generates multiple quasi tables by performing slicing operation on concatenated correlated quasi attributes.It concatenate the attributes of the quasi tables with the ID's of the buckets from the different sensitive tables and perform random permutations on the buckets of quasi tables.Proposed model publishes the data with more privacy and high utility levels when compared to the existing models.
A Systematic Approach for Identifying Requirement Change Management Challenges

Requirement Change is one of the most challenging tasks in software development lifecycle, particularly in the complex context of Global Software Development (GSD). During the last decade, many studies are carried out to address these problems, however, careful examination of these works suggests that there's a potential research gap. This paper has performed a Systematic Literature Review (SLR) to identify the most significant/commonly studied challenges of requirement change management process and furthermore this process under GSD context. We identified ten challenges such as impact analysis, cost estimation, artifacts documents management, requirement traceability, requirements dependency, conflicts with existing requirements, time estimation, change prioritization, user involvement, and system destabilizing. Furthermore, three challenges such as communication and coordination, knowledge sharing, management, and Change Control Board (CCB) management are identified for globally distributed projects. We also mapped these identified challenges to Requirement Change Management Process (RCMP) outcomes proposed in our previous study. We believe that mapping between RCM challenges and RCMP outcomes will enhance the practical significance of this study results. Considering the systematic literature review results, we suggest that there is a need to develop a framework for requirement change management for quality software systems development.