Reviewerly/exHarmony huggingface dataset:
Proposed Risk Management Model to Handle Changing Requirements

The change in requirements while construction of a software may bring into several risks like over budget and extra schedule.The changes in requirements are considered as a high risk to fail the software projects.A good project manager always incorporates risk management paradigm to manage the risks of changing requirements.This research uses available statistical techniques to estimate the cost of risk management with respect to the changing requirement.In addition, a hybrid cost estimation model is proposed using action strategy model to counteract, mitigate and manage the risks of changing requirements.The proposed model is validated using an industrial case study in Saudi Electricity Company (SEC) to conclude the results.The results are found supportive because the proposed model shows significant improvement to estimate the costs of changing requirements as compared to the existing cost estimation models.
Increasing Students’ Motivation to Learn at Tertiary Educational Institutions

The article is devoted to the study of the main purpose and features of increasing the students’ educational motivation atthe tertiary educational institution (TEI) in times of urgent need for a total overhaul of the educational paradigm and principles of teaching not only at higher educational establishment but also in the Ukrainian education system as a whole. During the investigation the methods of interdisciplinary approach, system analysis, social-cultural method, social-activity and concrete-historical approach have been applied. An important practical conclusion from the results obtained lies in the fact that the effectiveness of the educational process largely depends on the strategies of educational behavior, used by students. Accordingly, educators (and parents) should not only deliver educational material, but also teach students more effective ways and techniques for capturing it. For instance, the educator may recommend students to make a plan (or scheme) of the material studied or lend assistance to students in developing an individual plan for its mastering. Knowledge about the formation peculiarities of educational motivation at the tertiary educational institutions and personality’s value systems in the process of its professionalization can be used in the educational process to optimize the educational activities of students and improve training of specialists. The obtained results make it possible to clarify the subjective criteria for the effectiveness of students’ learning. Knowledge about the content of the value system of the modern student’s personality and its manifestation in the motivation of educational activity is of practical importance both in terms of self-knowledge and human development, and in terms of organizing professional university and postgraduate training. In the course of investigationit was possible to clarify and specify the theoretical ideas about motivation as a structural-level education, torepresentideas about the values that reflect the attitude to activity as a tool of meeting individual development needs, and their impact on the motivation of educational activities. The motives’ conceptof personal educational activity and values of students at tertiary educational institutions as psychological parameters of professional activity, requiring correlation with life objectives, have been highlighted.
„oh isch FIND_s nich;“

This paper adds to the growing field of conversation analytical research on smartphone-use in face-to-face interactions. Whenever smartphones are used in mobile-supported sharing activities – e.g. to show a picture to co-present others – the smartphone user needs to search for and find the "searchable object" in the World Wide Web, an App or on the device's local memory. Analyzing audio-recordings of naturally-occurring conversations, this paper identifies two types of practices of speech that explicitly orient to ongoing smartphone-supported searches: Collaborative search (cf. Brown/McGregor/McMillan 2015) and search-accompanying commentary by the smartphone-user. Both practices verbally provide for the accountability of the otherwise opaque device use. They differ in the way they produce opportunities for co-present others to substantively contribute to the progression of the search as well as the degree to which they produce the search as an interactionally public event.
Automating Mashup Service Recommendation via Semantic and Structural Features

Increasing physical objects connected to the Internet make it possible for smart things to access all kinds of cloud services. Mashup has been an effective way to the rapid IoT (Internet of Things) application development. It remains a big challenge to bridge the semantic gap between user expectations and application functionality with the development of mashup services. This paper proposes a mashup service recommendation approach via merging semantic features from API descriptions and structural features from the mashup-API network. To validate our approach, large-scale experiments are conducted based on a real-world accessible service repository, ProgrammableWeb. The results show the effectiveness of our proposed approach.
Fuzzy-PID based control scheme for PMDC series motor speed control

Background/Objectives: PID controllers are widely used in different industrial appliations especially in achieving stable speed control of DC motors.Hower, due to their limitations, most often other methods like Sliding Mode controller, or fuzzy based controller are used.Since the Fuzzy controlers are more adaptive to non-linearity.This study is to analyse a fuzzy-based speed control scheme for tuning/adusting the performance of PID controller for the purpose of speed control of DC motor under the load condition.Methods/Statistical Analysis: Because of low precision and slow response in different control schemes, a performance comparison is shown between three different control approaches for the DC motor speed control.MATLAB's Simulink platform is used to realize DC Motor and to implement the PID, Fuzzy and Fuzzy-based PID controllers to run the DC motor on the desired speed.Fuzzy controller is based on very few rule & performance is analyzed considering the load.Findings: The performance of three controllers is evaluated in terms of transient domain characteristics like Percentage overshoot, settling and rise times and percentage error under load (2000 rpm).The PID controller has the highest overshoot and hence a faster rise time while FLC has significantly reduced the overshoot, therefore causing rise time to increase.For the Fuzzy-PID controller the percentage overshoot has almost vanished.The rise time also decreased as compared to the FLC.The simulated controller's responses confirm that Fuzzy-based PID controller has better performance comparing to independent PID and FLC controllers.Novel/Applications: In this work, the design of an intelligent Fuzzy-PID controller for the speed control of the DC motor with reduced complexity and a faster response using with minimum number of Fuzzy rules producing more optimized performance is presented.The design of a control strategy that has capability to control nonlinear behaviors and to stabilize the performance of linear systems specially to provide optimized performance for speed tracking system which is an important aspect in real time system design.
MACHINE LEARNING FOR APPROXIMATING UNKNOWN FACE

Abstract. The problem of facial appearance reconstruction (or facial approximation) basing on a skull is very important as for anthropology and archaeology as for forensics. Recent progress in optical 3D measurements allowed to substitute manual facial reconstruction techniques with computer-aided ones based on digital skull 3D models. Growing amount of data and developing methods for data processing provide a background for creating fully automated technique of face approximation.The performed study addressed to a problem of facial approximation based on skull digital 3D model with deep learning techniques. The skull 3D models used for appearance reconstruction are generated by the original photogrammetric system in automated mode. These 3D models are then used as input for the algorithm for face appearance reconstruction. The paper presents a deep learning approach for facial approximation basing on a skull. It exploits the generative adversarial learning for transition data from one modality (skull) to another modality (face) using digital skull 3D models and face 3D models. A special dataset containing skull 3D models and face 3D models has been collected and adapted for convolutional neural network training and testing. Evaluation results on testing part of the dataset demonstrates high potential of the developed approach in facial approximation.
Approximate optimization of the MaxCut problem with a local spin algorithm

Local tensor methods are a class of optimization algorithms introduced in [Hastings, arXiv:1905.07047v2] as a classical analogue of the quantum approximate optimization algorithm. These algorithms treat the cost function as a Hamiltonian on spin degrees of freedom and simulate the relaxation of the system to a low-energy configuration using local update rules on the spins. Whereas the emphasis in Hastings' work was on theoretical worst-case analysis, we here investigate the underlying dynamics of the algorithm, and find that the local tensor method closely follows discretized, imaginary-time dynamics of the system under the problem Hamiltonian. Then, we study practical performance through benchmarking experiments on instances of the MaxCut problem. Through heuristic arguments we propose formulas for choosing the hyperparameters of the algorithm which are found to be in good agreement with the optimal choices determined from experiment. We observe that the local tensor method is closely related to gradient descent on a relaxation of MaxCut to continuous variables but consistently outperforms gradient descent in all instances tested. We find that time to solution achieved by the local tensor method is highly uncorrelated with that achieved by a widely used commercial optimization package; on some MaxCut instances the local tensor method beats the commercial solver in time to solution by up to two orders of magnitude and vice versa.
The Impact of Leadership on Employee Innovative Work Behaviour in Facilities Management Service Providers in Oman

Employee innovative work behavior (EIWB) within organizations plays a key role in achieving the goals of the organization. The current study describes the level of employee innovative work behavior among FacilityManagement (FM) service providers in Oman and the effect of leadership on employee innovative work behavior before then after an employee motivation has been included as mediator.It focuses on mid-level managers, technicians and supervisors of FM service providers in Oman.Self-structured questionnaire that utilized a set of questions which measure leadership, employees' motivation besides employee innovative work behaviour used as a data collection instrument.Sample size was 320 employees from four FM service providers.The regression analysis identified employees' motivation partially mediates the relationship among leadership and employees innovative work behavior.Additionally, the mean values show that the levels of employee innovative work behavior among employees are moderately high.The outcomes of this paper recommend that leadership, presence an influential-motivation instrument for depicting the innovative work.The current paper offers an insight into workers ' creative change in work behavior arising from leadership and enthusiasm for their career and organization.Besides drawbacks, the paper also addressed the implications.
An Improved Combination of Image Denoisers Using Spatial Local Fusion Strategy

Image denoising is a well-researched problem in the image processing field. Numerous image denoising algorithms have been proposed in the past. Although researchers have continually focused on improving the denoising algorithm performance regarding denoising effect and outstanding results have been achieved, the improvement amplitude of a single denoising algorithm has decreased over time. Recently, the consensus neural network (CsNet), which combines multiple image denoisers to produce an overall better result compared to single algorithms, was proposed. However, the denoising process of CsNet is time-consuming owing to its MSE optimal weight setting and iterative boosting stages. Therefore, we propose an improved combination of nonlocally centralized sparse representation (NCSR) with a fast and flexible denoising convolutional neural network (FFDNet) using a spatial local fusion strategy (ICID). ICID uses a structural-based patch to decompose their denoised images into the strength, structure, and mean intensity components. Thereafter, an image patch is reconstructed and placed back into the fused image after fusing the three components separately. Experimental results verified that our algorithm is superior to CsNet, and it is faster. The combination of NCSR and FFDNet can harmonize the complementary denoising capabilities of different denoising algorithms. NCSR can preserve as many details as possible in natural images with numerous repeated structures, whereas FFDNet can achieve state-of-the-art results with a sufficiently large training set of images. Moreover, ICID uses the structural-based method, which considers more local details and preserves more textures, resulting in superior performance.
SUPERPIXEL SEGMENTATION FOR POLSAR IMAGES BASED ON HEXAGON INITIALIZATION AND EDGE REFINEMENT

Abstract. Superpixel segmentation for PolSAR images can heavily decrease the number of primitives for subsequent interpretation while reducing the impact of speckle noise. However, traditional superpixel segmentation methods for PolSAR images only focus on the boundary adherence, the significance of superpixel segmentation will be lost when the accuracy is improved at the expense of computation efficiency. To solve this problem, this paper proposes a novel superpixel segmentation algorithm for PolSAR images based on hexagon initialization and edge refinement. First, the PolSAR image is initialized as hexagonal distribution, where the complexity of searching pixels for relabelling in the local regions can be reduced by 30% theoretically. Second, all pixels in the PolSAR image are initialized as unstable pixels based on the hexagonal superpixels, which can boost the segmentation performance in the heterogeneous regions and effectively maintain all the potential edge pixels. Third, the revised Wishart distance and the spatial distance are integrated as a distance measure to relabel all unstable pixels. Finally, the postprocessing procedure based on a dissimilarity measure is applied to generate the final superpixels. Extensive experiments conducted on both the simulated and real-world PolSAR images demonstrate the superiority and effectiveness of our proposed algorithm in terms of computation efficiency and segmentation accuracy, compared to three other state-of-the-art methods.
Cellists’ sound quality is shaped by their primary postural behavior

Abstract During the last 20 years, the role of musicians’ body movements has emerged as a central question in instrument practice: Why do musicians make so many postural movements, for instance, with their torsos and heads, while playing musical instruments? The musical significance of such ancillary gestures is still an enigma and therefore remains a major pedagogical challenge, since one does not know if these movements should be considered essential embodied skills that improve musical expressivity. Although previous studies established clear connections between musicians’ body movements and musical structures (particularly for clarinet, piano or violin performances), no evidence of direct relationships between body movements and the quality of the produced timbre has ever been found. In this study, focusing on the area of bowed-string instruments, we address the problem by showing that cellists use a set of primary postural directions to develop fluid kinematic bow features (velocity, acceleration) that prevent the production of poor quality (i.e., harsh, shrill, whistling) sounds. By comparing the body-related angles between normal and posturally constrained playing situations, our results reveal that the chest rotation and vertical inclination made by cellists act as coordinative support for the kinematics of the bowing gesture. These findings support the experimental works of Alexander, especially those that showed the role of head movements with respect to the upper torso (the so-called primary control) in ensuring the smooth transmission of fine motor control in musicians all the way to the produced sound. More generally, our research highlights the importance of focusing on this fundamental postural sense to improve the quality of human activities across different domains (music, dance, sports, rehabilitation, working positions, etc.).
Analysis of Local Spatial Data Infrastructure to Support Volcanic Mudflow Mitigation along Putih River, Magelang Regency, Central Java Province, Indonesia

One of the most devastating disasters in Indonesia was the Mount Merapi eruption in 2010. After the eruption there still exists the secondary hazard of volcanic mudflow, which has caused damage and casualties. Volcanic mudflow is a mixture of pyroclastic material and rainwater, meaning that in the rainy season the area along rivers becomes a high volcanic mudflow hazard, including the area along Putih River in Magelang Regency, Central Java Province. The development of Spatial Data Infrastructure (SDI) plays an important role in disaster management, especially in disaster mitigation efforts. Building an SDI which shares information on spatial conditions in the area along the Putih River could save many lives and reduce the risk from volcanic mudflow. This research was conducted employing interview surveys, field surveys and secondary data collection at government institutions. The results of the analysis have provided a geoportal prototype as an information gateway for the mitigation of volcanic mudflow along the Putih River and the reduction of disaster risk both for the government and community.
Test them all, is it worth it?

This is an extended abstract of the article: Axel Halin, Alexandre Nuttinck, Mathieu Acher, Xavier Devroey, Gilles Perrouin, and Benoit Baudry. 2018. Test them all, is it worth it? Assessing configuration sampling on the JHipster Web development stack. In Empirical Software Engineering (17 Jul 2018). https://doi.org/10.1007/s10664-018-9635-4.
The development of Schoology web-based learning media with GeoGebra to improve the ICT literacy on quadratic functions

The use of technology in learning is essential for developing students' ICT literacy. However, the application of technology as a learning media remains limited. The purpose of this study is to develop a learning media to enhance students' ICT literacy. This research is conducted using a 4-D model, which consists of four stages: defining, designing, developing, and disseminating. This learning media is developed based on ICT literacy indicators. The study participants are 33 of 10th-grade students at one of the public vocational schools in Jember, East Java. Data collection methods consist of validation sheets, tests, questionnaires, interviews, and observations. The expert validation is used to validate the media, while the students' response questionnaire determines the practicality. The validity result shows that the media can be used to measure ICT literacy. Moreover, the practicality of media also shows that it can be easily applied. Furthermore, the effectiveness of learning media is obtained from the N-Gain average of test and ICT literacy questionnaire. The observation data also support the effectiveness of the media during the implementation. The result shows that the media confirmed to be effective as it can improve the students' ICT literacy. To sum up, the Schoology web-based learning media with GeoGebra is useful to improve ICT literacy on quadratic functions
The Performance of Penalty Methods on Tree-Seed Algorithm for Numerical Constrained Optimization Problems

The constraints are the most important part of many optimization problems. The metaheuristic algorithms are designed for solving continuous unconstrained optimization problems initially. The constraint handling methods are integrated into these algorithms for solving constrained optimization problems. Penalty approaches are not only the simplest way but also as effective as other constraint handling techniques. In literature, there are many penalty approaches and these are grouped as static, dynamic and adaptive. In this study, we collect them and discuss the key benefits and drawbacks of these techniques. Tree-Seed Algorithm (TSA) is a recently developed metaheuristic algorithm, and in this study, nine different penalty approaches are integrated with the TSA. The performance of these approaches is analyzed on well-known thirteen constrained benchmark functions. The obtained results are compared with state-of-art algorithms like Differential Evolution (DE), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Genetic Algorithm (GA). The experimental results and comparisons show that TSA outperformed all of them on these benchmark functions
AR With Cloud Anchors

Despite all the potential of augmented reality to improve the human-computer interface (HCI) and the user experience, it's still below the expected usage. The reason may be related to the fact that until recently the AR implementation was mostly marker-based or GPS-based to trigger additional content (video, 3D, or other) to the reality identified with the camera. The research in this paper is focused on AR marker-less solutions that allows sharing AR content between users across the Cloud, based on the anchor identification. With this technological paradigm shift, the potential for use of new functional environments and an unprecedented status of HCI enrichment is achieved. In addition to the operations related to the applications functionality, the door opens for media-art artists to create AR models that can be shared in a multiple user environment across the Cloud.
Uncertainty about Uncertainty: Optimal Adaptive Algorithms for Estimating Mixtures of Unknown Coins

Given a mixture between two populations of coins, “positive” coins that each have|unknown and potentially different—bias ≥ ½ + Δ and “negative” coins with bias ≤ ½ – Δ, we consider the task of estimating the fraction ρ of positive coins to within additive error ∊. We achieve an upper and lower bound of samples for a 1 – δ probability of success, where crucially, our lower bound applies to all fully-adaptive algorithms. Thus, our sample complexity bounds have tight dependence for every relevant problem parameter. A crucial component of our lower bound proof is a decomposition lemma (Lemma 5.2) showing how to assemble partially-adaptive bounds into a fully-adaptive bound, which may be of independent interest: though we invoke it for the special case of Bernoulli random variables (coins), it applies to general distributions. We present simulation results to demonstrate the practical efficacy of our approach for realistic problem parameters for crowdsourcing applications, focusing on the “rare events” regime where ρ is small. The fine-grained adaptive avor of both our algorithm and lower bound contrasts with much previous work in distributional testing and learning.