Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Design of Super Twisting Control with PSO Algorithm for Robotic Manipulator

Robotic manipulators are nonlinear and coupling systems exposing to external disturbance. They are used in wide industrial applications; the suitable selection of a nonlinear robust controller is required. Sliding Mode Controller (SMC) was designed to achieve these requirements, but unfortunately the chattering phenomenon was the main drawback of the conventional SMC. It leads to destructive of some components of a real system and subsequent loss in its accuracy. Hence, the design of Super-Twisting Controller (STC) is suggested for chattering elimination. In previous literatures, the accomplishment of the manual adjustment for the parameters of STC was a large burden and time consuming process. Therefore, a new combination of Particle Swarm Optimization (PSO) algorithm with STC is proposed for optimal tuning of STC parameters. The simulation results demonstrate the superiority of the super twisting technique for chattering mitigation comparing to the conventional SMC. Also, STC tuned via PSO proves its effectiveness and robustness to different types of external disturbances without the needs for the knowledge of their upper boundary values. Besides, the performance of the controlled system is faster and more accurate in the criteria of overshoot, settling time and rise time compared to the manual adjusting of super twisting controllers.
Strengthening the decentralisation of collective bargaining in Spain Between the legal changes and real developments

Controversy about the adequacy of the structure of collective bargaining to the changes in the economic cycle in Spain is not certainly new. In fact, it can be traced back to the early 1990s (Cruz 2015; Fernández et al. 2016). However, this debate has gained momentum since the onset of the Great Recession in 2008. The renewed interest in this topic can be explained by the larger scale of the impact of the current crisis in Spain compared with other European countries, especially in terms of employment. The most important consequence of this shock was the sharp rise in the unemployment rate, which still remains at dramatic levels: 18.6 per cent in the fourth quarter of 2016, the second highest in the European Union (EU), after Greece.1
Research Collaboration among Organizations: Structure and its Impact on Research Outcome in Interdisciplinary Fields

Emerging concern exists in the global common issues like sustainability and aging society, which must be tackled by academia by integrating diverse knowledge and skill. These complex issues we face cannot be solved within single academic discipline and therefore interdisciplinary research and collaboration among organizations and researchers are essential. In this paper, we analyze the current status of academic research collaboration among universities and research institutes in sustainability and gerontology and detect collaboration gaps. We found that collaboration community extracted by clustering of co-authorship network tends to be located in physically proximate regions. We also analyzed the association between topological position of organizations in collaboration network and scientific impact in citedness of those organizations, and found that boundary spanning organization has positive impact on research outcome while the correlation is weak. Our results encourage scholars to collaborate more with the others in different communities, and suggest policy makers and managers to support them.
Genet ic analysi s reveals four Irish oak genoty pes

Oak woods cover a very small proportion of the land area in Ireland (approximately 0.1%). The coverage has diminished since 5000 BC due primarily to human influence in the form of woodland utilisation and converting land use to livestock grazing and crop production. The woodland remnants thus represent a scarce and valuable resource. However, very little is known about the genetic diversity and composition of woodlands in Ireland. A COFORD-funded study obtained data on genetic characteristics of oak woods in Ireland (Kelleher et al. 2002). This study revealed an underlying geographical genetic structure in Irish oak populations and presented estimates of diversity from both nuclear and chloroplast DNA analyses (Kelleher et al. 2002, Kelleher et al. 2004a, Kelleher et al. 2005). A follow-up study funded by NPWS sampled more populations in an attempt to investigate further geographic patterns. A synopsis of the combined analysis of these projects is presented within.
A Survey on Data Clustering Approaches

The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. Clustering is the unsupervised classification of patterns into groups. However, clustering differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms. <br><br>
A Lower Bound Study on Software Development Effort

This paper depicts a study on the lower bound of software development effort. The work begins with the transformation model which is with the popular software development lifecycle. In general, a combination of properly handled transformations can ultimately produce the software, and the transformations form the path of software development. Each transformation is associated with its effort, or the weight, in a path. There can be a number of these paths since many different methods and tools can be used or reused for a specific software development case. Then, the Shortest Path algorithm is applied to find a shortest path which is with a minimal total effort among all the paths. However, from time to time, when advanced methods and tools are introduced, the new paths and efforts will change the previously identified shortest path. Hence, the continued work is to discuss the minimal total effort of a potential shortest path, although it may be currently unavailable. Convergence analysis is firstly provided for the discussion of whether this shortest path exists, and lower bound analysis is then provided for the discussion of completeness and soundness. This lower bound study is significant, since an optimal software development effort is determinable.
A Novel Data Mining Algorithm for Semantic Web Based Data Cloud

By a cloud, we mean an infrastructure that provides resources and/or services over the Internet. A storage cloud provides storage services, while a compute cloud provides compute services. We describe the design of the Sector storage cloud and how it provides the storage services required by the Sphere compute cloud [14]. Different efforts have been made to address the problem of data mining in the cloud framework. In this paper we propose an algorithm to mine the data from the cloud using sector/sphere framework and association rules. We also describe the programming paradigm supported by the Sphere compute cloud and Association rules. Sector and Sphere are discussed for analyzing large data sets using computer clusters connected with wide area high performance networks
Convolutional neural networks for image classification

This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculation of recognition error and training accuracy are presented.
Palynological and Paleoenvironmental Study of the Tertiary Formations of the Audouin-Begretto Tertiary and the Bay of ‘Milliardaires’: South-West of The Lagoons Fault (Côte d’Ivoire)

The palynological and paleoenvironmental study of tertiary age deposits are crossed by two hydraulic boreholes located at southwest of the "lagoon fault" on the coastal basin of Abidjan (Côte d’Ivoire) where fifty (50) pairs of thin palynological blades were examined. This study revealed an exceptional richness of these samples in spores and pollen grains associated with rare dinokysts that revealed an Association with Danea californica (Danien), an Association to Apectodinium quinquelatum and Apectodinium homomurphum (Selandian-Thanétian), an Association to Kallosphaeridium yorubaense, Adnatosphaeridium multispinosum, (Lutetian-Bartonian), an Association to Proxapertites operculatus (Priabonian) and an Association to Laevigatosporites ovatu (Aquitanian-Burdigalian). Paleoenvironmentally, sediments have evolved in an internal coastal nerine environment throughout the wells, depending on eustatic movements.
Linguistic Adoption in Online Citizen Science: A Structurational Perspective

For peer-production projects to be successful, members must develop a specific and universal language that enables them to cooperate. Complicating the development of language in some projects is the lack of formalized structures (e.g., roles) that communicate to members the norms and practices around language. We address the question of how do role differences among participants interact with the adoption and dissemination of new terminologies in open peer production communities? Answering this question is crucial because we want communities to be productive even when selfmanaged, which requires understanding how shared language emerges. We examine this question using a structurational lens in the setting of a citizen science project. Exploring the use of words in the Gravity Spy citizen science project, we find that many words are reused and that most new words that are introduced are not picked up, showing a reproduction of structure. However, some novel words are used by others, showing an evolution of the structure. Participants with roles closer to the science are more likely to have their words reused, showing the mutually reinforcing nature of structures of signification, legitimation, and domination.
The effects of work stress and trust in managers on employee turnover intentions

Employee turnover is detrimental for the general performance of any company, thus the factors that enhance employees’ intentions to leave their company should be identified and addressed. The present study examined the impact of work stress and employees’ trust in managers in this respect. Work stress was conceptualized through three inter-related dimensions, workload, low rewards and overcommitment, which we expected to affect also employees’ trust in their managers. The study was performed on a sample of Romanian employees from several work-sectors. Results show that all three dimensions of work stress are positively related to turnover intentions, and that employees’ perceptions of low rewards and their overcommitment negatively affect their trust in managers. Moreover, the latter emerged as a protective-factor against turnover intentions. Overall, this pattern of findings indicates a significant influence of work stress on turnover intentions, which’s partially mediated by its undermining effect on employees’ trust in their managers.
Towards a dendrochronologically refined date of the Laacher See Eruption

The precise date of the Laacher See eruption (LSE), central Europe’s largest Late Pleistocene volcanic event that occurred around 12,900 years ago, is still unknown. Here, we outline the potential of combined high-resolution dendrochronological, wood anatomical and radiocarbon (14C) measurements, to refine the age of this major Plinian eruption. Based on excavated trees that were killed during the explosive LSE and buried under its pyroclastic deposits, we describe how a firm date of the eruption might be achieved, and how the resulting temporal precision would further advance our understanding of the environmental and societal impacts of this event. Moreover, we discuss the relevance of an accurate LSE date for improving the synchronization of European terrestrial and lacustrine Late Glacial to Holocene archives, and outline how the proposed, interdisciplinary dating approach can be applied to other large, yet undated, volcanic eruptions.
An Image Steganography Algorithm Using Huffman and Interpixel Difference Encoding

Steganography is an art of hiding secret information on a cover medium through imperceptible methodology. The three pillars on which a steganography algorithm should be erected are: Embedding capacity, Imperceptibility and Robustness. It is fortunate that all these goals are interdependent on one another. The state of art is finding an optimum solution that keep up all the steganography goals. It is believed that there is no productivity if the size of cover medium gets extended to meet in housing the secret data on it. This happens due to lack in refinement of embedding algorithm and failing in analyzing the data structure of secret data. In this paper, an attempt has made to improve embedding capacity and bring very less distortion to the cover medium by analyzing the data structure of the payload. A residual coding is carried on the pay load before it is submitted to Huffman encoding which is a lossless compression technique. As a result, the representation of payload had shrink. Further, the variable bit encoding (Huffman) do a lossless compression and finally the payload get housed on the cover medium. This ended with high embedding capacity and less imperceptibility. Peak signal to noise ratio confirms that the residual coding had given improvised results than few existing embedding algorithm.
Poster: Automated, Context-Sensitive Analysis of iOS Applications

The security of mobile phones and the surrounding ecosystem has attracted a lot of research in the last years. While lots of work has been performed for the Android platform, the security of iOS apps has yet not been explored as much. In this paper, we present an automated analysis method for iOS applications with a focus on code coverage. More specifically, we introduce an analysis method that enables a context-sensitive analysis of input fields in order to bypass registration forms and similar user interface elements with the goal of covering more code paths. For this purpose, static and dynamic program analysis methods are used to automatically detect and handle such UI elements. We built a prototype of the proposed method based on the analysis platform DiOS [1]. Our preliminary results based on an analysis of 25 apps from the official Apple App Store suggest that our approach enables on average a 16% increase in code coverage.
Video Event Speciication using Programmatic Composition

Many real-world video analysis applications require the ability to identify domain-speciic events, such as interviews and commercials in TV news broadcasts, or action sequences in ilm. Unfortunately, pre-trained models to detect all the events of interest in video may not exist, and training new models from scratch can be costly and labor-intensive. In this paper, we explore the utility of specifying new events in video in a more traditional manner: by writing queries that compose the outputs of existing, pre-trained models. To write these queries, we have developed Rekall, a library that exposes a data model and programming model for compositional video event speciication. Rekall represents video annotations from diferent sources (object detectors, transcripts, etc.) as spatiotemporal labels associated with continuous volumes of spacetime in a video, and provides operators for composing labels into queries that model new video events. We demonstrate the use of Rekall in analyzing video from cable TV news broadcasts and ilms. In these eforts, analysts were able to quickly (in a few hours to a day) author queries to detect new events. These queries were often on par with or more accurate than learned approaches (6.5 F1 points more accurate on average).
REAL-TIME MODELLING OF THE HEART USING THE PROPER ORTHOGONAL DECOMPOSITION WITH INTERPOLATION

In this research, a reduced order method (ROM) called the Proper Orthogonal Decomposition with Interpolation (PODI) is used to drastically reduce computation time of highly complex and non-linear problems as encountered in simulating the heart. The idea behind the method is to first construct a database of pre-computed full-scale solutions using the Element Free Galerkin method (EFG), project the set of solutions to a low dimensional space, use the Moving Least Square method to carry out the interpolation for the problem at hand and project it back to the original high dimensional solution space. Calculations are carried out on a bi-ventricle model to investigate the performance and accuracy while varying one of the material parameters.
Novel Navigation Strategy Study on Autonomous Mobile Robots

Potential field method has been widely used in obstacle avoidance for mobile robots because of its elegance and simplicity. However, this method has inherent drawbacks. Considering this, this paper introduces a new behaviour-based navigation strategy. Aiming at a mobile robot SDLG-1 developed by the authors, the kinematics model is built based on its motion structure. Using twelve sonar sensors, the strategy algorithm of behaviour-based navigation control is brought forth. Based on the algorithm, software simulations and experimental evaluations have been conducted. Both results indicate the navigation strategy proposed in this paper is effective.
The Mental Health Effects of Retirement

We study the retirement effects on mental health using a fuzzy regression discontinuity design based on the eligibility age to the state pension in the Netherlands. We find that the mental effects are heterogeneous by gender and marital status. Retirement of partnered men positively affects mental health of both themselves and their partners. Single men retiring experience a drop in mental health. Female retirement has hardly any effect on their own mental health or the mental health of their partners. Part of the effects seem to be driven by loneliness after retirement.
C-3PR: A Bot for Fixing Static Analysis Violations via Pull Requests

Static analysis tools are frequently used to detect common programming mistakes or bad practices. Yet, the existing literature reports that these tools are still underused in the industry, which is partly due to (1) the frequent high number of false positives generated, (2) the lack of automated repairing solutions, and (3) the possible mismatches between tools and workflows of development teams. In this study we explored the question: “How could a bot-based approach allow seamless integration of static analysis tools into developers' workflows? ” To this end we introduce C-3PR, an event-based bot infrastructure that automatically proposes fixes to static analysis violations through pull requests (PRs). We have been using C-3PR in an industrial setting for a period of eight months. To evaluate C-3PR usefulness, we monitored its operation in response to 2179 commits to the code base of the tracked projects. The bot autonomously executed 201346 analyses, yielding 610 pull requests. Among them, 346 (57%) were merged into the projects' code bases. We observed that, on average, these PRs are evaluated faster than general-purpose PRs (2.58 and 5.78 business days, respectively). Accepted transformations take even shorter time (1.56 days). Among the reasons for rejection, bugs in C-3PR and in the tools it uses are the most common ones. PRs that require the resolution of a merge conflict are almost always rejected as well. We also conducted a focus group to assess how C-3PR affected the development workflow. We observed that developers perceived C-3PR as efficient, reliable, and useful. For instance, the participants mentioned that, given the chance, they would keep using C-3PR. Our findings bring new evidence that a bot-based infrastructure could mitigate some challenges that hinder the wide adoption of static analysis tools.
MVDR BEAMFORMING WITH INVERSE UPDATING

A stable alternative is described for the `standard' systolic MVDR beamforming algorithm of McWhirter and Shepherd [4], which is known to su er from linear round-o error build-up. The algorithm combines a so-called inverse QR-updating algorithm, with (part of) the McWhirter and Shepherd procedure. Similar to the McWhirter and Shepherd algorithm, it is amenable to parallel (pipelined) implementation. Unlike the McWhirter and Shepherd algorithm, it is stable numerically, and hence does not need repeated reinitializations.
Scaling up Psychology via Scientific Regret Minimization: A Case Study in Moral Decisions

Do large datasets provide value to psychologists? Without a systematic methodology for working with such datasets, there is a valid concern that analyses will produce noise artifacts rather than true effects. In this paper, we offer a way to enable researchers to systematically build models and identify novel phenomena in large datasets. One traditional approach is to analyze the residuals of models---the biggest errors they make in predicting the data---to discover what might be missing from those models. However, once a dataset is sufficiently large, machine learning algorithms approximate the true underlying function better than the data, suggesting instead that the predictions of these data-driven models should be used to guide model-building. We call this approach"Scientific Regret Minimization"(SRM) as it focuses on minimizing errors for cases that we know should have been predictable. We demonstrate this methodology on a subset of the Moral Machine dataset, a public collection of roughly forty million moral decisions. Using SRM, we found that incorporating a set of deontological principles that capture dimensions along which groups of agents can vary (e.g. sex and age) improves a computational model of human moral judgment. Furthermore, we were able to identify and independently validate three interesting moral phenomena: criminal dehumanization, age of responsibility, and asymmetric notions of responsibility.
THE CONSEQUENCES OF STATE TAX PREFERENTIAL TREATMENT OF THE ELDERLY

Every state offers some type of income tax break for older individuals. These often sizable tax breaks are typically justified as necessary for recruiting new retirees or preventing current residents from fleeing to lower tax states; equity considerations are also sometimes given. These policies are currently in flux; for the first time, some states have made meaningful reductions while other states continue to expand them. This research presents multi-faceted evidence on the consequences of these tax breaks. We begin by reporting the current status of and recent changes to these tax breaks. Using data from the American Community Survey and income tax liabilities generated via TAXSIM, we investigate the fiscal consequences of these tax breaks – for both the taxpayers who receive them and the states that offer them. We explore the geographic and income distribution of these tax expenditures as well and simulate the effect of several recently enacted and proposed changes. The magnitude of these consequences are then juxtaposed against recent evidence that elderly interstate migration is fairly rare and unresponsive to policy changes. The paper concludes with an overall evaluation of these policies and discusses possible reasons why these tax breaks persist and their likelihood of survival into the future.