Reviewerly/exHarmony huggingface dataset:
The Moderating Effects of Learning Flow on the Influence of Workplace Learning Perceived by Hotel Culinary Employees on Dual Commitment

In this context, with the employees especially in the cooking department of hotel enterprises, this study elucidated the effects of workplace learning that they perceive on dual commitment with organization and career, and verified not only the moderating effects of learning commitment from the relationship between two factors. From the empirical analysis, the results were derived as follows. Firstly, the dual commitment exhibited to be influenced mostly from formal learning, and followed by incidental learning and informal learning among workplace learning. Secondly, it was observed that organization commitment increases more when both the informal learning among workplace learning and the learning commitment are perceived higher simultaneously, on the other hand, when the formal learning among workplace learning and the learning commitment are perceived higher simultaneously, career commitment decreases. Thus, it is necessary for the hotel enterprises to prepare the opportunity and environment that a learner by oneself can commit to learning and to make them have own objectives.
SMVS: A Web-based Application for Graphical Visualization of Malay Text Corpus

Information visualization is an interesting field nowadays. A good information visualization ensures distraction of misleading information is not included in the visualization. Many studies have been conducted on the Quranic corpus. The advancement technology coupled with modern approach of the computer technology can support the learners to understand Qur'an easily. Smart Malay Visualization System (SMVS) is a Python Flask framework web application which help users efficiently to produce the most basic data visualization from a big data. This web application displayed information from the state-of-the-art corpus which is identified through text. Agile development has been adapted to prepare this web application. Six phases of the methodology have been implemented in this study which are requirements, analysis, planning, design, implementation, testing, and deployment. Natural Language Processing approach has been used to visualize the data. Twenty most informative word from each verse has been visualized using Frequency Distribution and has been embedded to the web application. This work focuses on the Malay translation of the Qur'an corpus.
Feature Points Extraction Based on Line Fitting for Power Distribution Switch

During inspection and operation tasks in the power distribution room, it is necessary for unattended robots to have the ability to acquire high-precision position and attitude information of each distribution switch. For the common square distribution switch, this paper proposes a highly versatile and high-precision feature points extraction method, which can be used for positioning the switch in three-dimensional space. This method combines the classification advantages of machine learning with the positioning advantages of traditional image algorithms, and obtains feature points for matching with a series of methods such as Hough transform and least squares. The matching feature points obtained by the algorithm are fitted by a large number of edge contour points, thus offering high precision and high robustness. The experiment shows that the precision of calculated switch position based on our method can reach 1.5 mm in translation and 1.5 degrees in rotation, which meets the requirements of unattended robot operation.
A Competitive Memory Paradigm for Multimodal Optimization Driven by Clustering and Chaos

Evolutionary Computation Methods (ECMs) are proposed as stochastic search methods to solve complex optimization problems where classical optimization methods are not suitable. Most of the proposed ECMs aim to find the global optimum for a given function. However, from a practical point of view, in engineering, finding the global optimum may not always be useful, since it may represent solutions that are not physically, mechanically or even structurally realizable. Commonly, the evolutionary operators of ECMs are not designed to efficiently register multiple optima by executing them a single run. Under such circumstances, there is a need to incorporate certain mechanisms to allow ECMs to maintain and register multiple optima at each generation executed in a single run. On the other hand, the concept of dominance found in animal behavior indicates the level of social interaction among two animals in terms of aggressiveness. Such aggressiveness keeps two or more individuals as distant as possible from one another, where the most dominant individual prevails as the other withdraws. In this paper, the concept of dominance is computationally abstracted in terms of a data structure called “competitive memory” to incorporate multimodal capabilities into the evolutionary operators of the recently proposed Cluster-Chaotic-Optimization (CCO). Under CCO, the competitive memory is implemented as a memory mechanism to efficiently register and maintain all possible optimal values within a single execution of the algorithm. The performance of the proposed method is numerically compared against several multimodal schemes over a set of benchmark functions. The experimental study suggests that the proposed approach outperforms its competitors in terms of robustness, quality, and precision.
Collision-free Navigation of Flying Robots in Outdoor Environments with Dynamic Obstacles

In this paper, a collision-free navigation algorithm for flying robots is proposed. The algorithm is applied to three-dimensional environments with dynamic obstacles. Shapes of moving obstacles may be non-convex, motion of the obstacles is variable. The algorithm is a sliding mode control law, switching between two modes, the target approaching mode and the obstacle avoidance mode. Mathematical analysis of the proposed algorithm is given. Simulation results are also provided in order to demonstrate its performance. Compared with other approaches, the algorithm is a really practical one as the constraints of the obstacles are not too hard to be satisfied. Moreover, the proposed algorithm is computationally efficient.
Visual Comparator: An Interactive Tool for Dynamic Spatiotemporal Comparative Visualizations

Abstract Visualizations enable us to detect patterns, time-evolving features, and trends in complex datasets that might not be obvious by looking at the raw data. The visual exploration process often requires comparisons between multiple visualizations, either from the same dataset or a different one, to identify relationships and patterns. This visualization process, referred to as comparative visualization, is valuable for analyzing multivariate, multispectral, or multidimensional data. The existing tools that facilitate visual comparisons do this by three means: juxtaposition (placing visuals side by side), superposition (overlaying visuals), and explicit encoding (visualizing a derived quantity corresponding to the relationship being studied). While superposition is ideal for static, geospatial datasets, where spatialization is a key component of the data, the spatiotemporal nature of Earth science datasets presents a challenge with comparative visualizations. Visual Comparator is an interactive, cross-platform (desktops, kiosks, and web), open-source application, developed to address this shortcoming. The application is used to superimpose and compare up to three synchronized, animated visualizations, and a transition between the visualizations is provided through a slider-based interface. This form of visualization has the advantage of drawing the viewers’ attention to changes between the datasets, enabling comparisons of scale, and reducing the clutter caused by having multiple variables in one visual. This article provides an overview of the project, a brief review of literature pertaining to human perception research and comparative visualizations, and a guide to accessing this application.
Evolving Adaptation Rules at Runtime for Multi-cloud Applications

Cloud computing is the most prevailing computing paradigm as it has led to a proliferation of cloud-based applications, either through the migration of existing legacy or the development of novel ones from scratch. In fact, nowadays, there is also a move towards adopting multi-clouds due to the main benefits they introduce, including vendor lock-in avoidance and optimal application design and provisioning via different cloud services. Unfortunately, multi-cloud applications face the challenge that even a single cloud environment exhibits a certain level of dynamicity and uncertainty. As such, a suitable service level cannot be handed over to their customers, which leads to SLA penalty costs and application provider reputation reduction. To address this challenge, we have previously proposed a cross-level and multi-cloud application adaptation architecture. Towards realising such an architecture, this paper focuses on supporting cross-level application adaptation through the modelling of adaptation rules that enact adaptation workflows but also on evolving such an adaptation to address both the application and exploited cloud services evolution as well as the provisioning environment’s dynamicity. The modelling of such rules and their execution history is accommodated through corresponding extensions to a state-of-the-art cloud modelling language called CAMEL. Further, a specific selection algorithm for those alternative adaptation rules able to address the current problematic situation is suggested which takes into account their execution history and especially their performance.
Video Dehazing Based on Convolutional Neural Network Driven Using Our Collected Dataset

With the fast development of automatic driving and video monitoring applications, video dehazing is a vital problem in computer vision because turbid images can have a large impact on their performance. As one of the remarkable techniques, convolutional neural networks have been greatly developed and shown great effectiveness in video dehazing at present. Inspired by deep learning, this paper introduces one of the convolutional neural networks named AOD-Net and collects a dataset to evaluate its dehazing performance. By comparison, the proposed algorithm achieves high SSIM and high PSNR on both our collected dataset and NYU Depth V2 dataset. AOD-Net not only generates well-dehazed images, but also learns very quickly during the training process. This All-in-One Dehazing Network improves visual quality of hazy images to a large extent.
Linear dynamical systems of dimension two over the ring of integers modulo pt

In this paper, we investigate the linear dynamical system [Formula: see text], where [Formula: see text] is the ring of integers modulo [Formula: see text] ([Formula: see text] is a prime). In order to facilitate the visualization of this system, we associate a graph [Formula: see text] on it, whose nodes are the points of [Formula: see text], and for which there is an arrow from [Formula: see text] to [Formula: see text], when [Formula: see text] for a fixed [Formula: see text] matrix [Formula: see text]. In this paper, the in-degree of each node in [Formula: see text] is obtained, and a complete description of [Formula: see text] is given, when [Formula: see text] is an idempotent matrix, or a nilpotent matrix, or a diagonal matrix. The results in this paper generalize Elspas’ [1959] and Toledo’s [2005].
The Global-Local Transformation

The Global-Local transformation, a shape representation technique for manifolds of multiple dimensions is presented in this chapter. Useful properties of the transform space are examined and through experiments, unique advantages of the GL-transform are revealed. Applications in shape matching and radar shadow analysis demonstrate its effectiveness in real life scenarios.
Inverse Kinematics and Probabilistic Constrained Motion Planning for Modular Redundant Manipulators

This paper designs a modular redundant manipulator to solve the safety problem in complex working environment, and the research on the obstacle avoidance motion planning method of the manipulator is carried out. In order to prove the rationality of the design of the manipulator and simplify the development of the controller, we first analyze the forward and inverse kinematics of the manipulator, determine its D-H parameters, and solve the inverse kinematics of the manipulator using the IKFast algorithm based on the analytical solution. Then we propose a manipulator motion planning method based on probability-constrained GB-RRT* (Goal-Bias RRT*). Finally, the inverse kinematics algorithm and improved motion planning algorithm of the manipulator are simulated in ROS.
LinkZoo

This article presents LinkZoo, a web-based, linked data enabled tool that supports collaborative management of information resources. LinkZoo addresses the modern needs of information-intensive collaboration environments to publish, manage, and share heterogeneous resources within user-driven contexts. Users create and manage diverse types of resources into common spaces such as files, web documents, people, datasets, and calendar events. They can interlink them, annotate them, and share them with other users, thus enabling collaborative editing, as well as enrich them with links to externally linked data resources. Resources are inherently modeled and published as resource description framework (RDF) and can be explicitly interlinked and dereferenced by external applications. LinkZoo supports creation of dynamic communities that enable web-based collaboration through resource sharing and annotating, exposing objects on the linked data Cloud under controlled vocabularies and permissions. The authors demonstrate the applicability of the tool on a popular collaboration use case scenario for sharing and organizing research resources.
"Sport Direct" - Mobile Application for Sports Events

Smartphone is a common computational device that influenced by the most of people nowadays, which is the motivation to create an android application that its information can be easily reached anywhere, any time. [PROF.POONAM KUMARI, 2015]. With a view increasing popularity of sports event nowadays, we propose to create a brand new smart-phone app, Sport Direct.The prime objective of Sport Direct is to create a fully, fledged android application which would discover the sports events happening in and around the Malaysia so that no one will miss out being notified on interested sports events. The user can check all the sports events happening around Malaysia. The effort to search for sports events around their current location is made easier and the system can help navigate them to the event’s location. User can follow their favourite sports and get notified when the organizers announce new events.
A Comparative Study of Parametric Versus Non-Parametric Text Classification Algorithms

Evolution of modern technologies allowed to store the text in various digital formats such as e-mails, e-documents, libraries, etc. The amount of text data that is produced daily is increasing dramatically. Discovering useful patterns in text that can be represented in unstructured, semi-structured or structured format is a difficult task that requires a good understanding of machine learning algorithms. Finding a suitable algorithm for text mining tasks such as classification, clustering or natural language processing is a demanding situation that tests researchers’ abilities. This paper provides an overview of the text mining process also, presents a comparison of the performance and limitations of two predictive models generated using the parametric Naïve Bayes algorithm and nonparametric Deep Learning neural network. RapidMiner data science software platform has been used for models’ implementations and e-mail classification.
An Uncertain Tuple Density Clustering (UTDC) Algorithm for Uncertain Data

Abstract The massive uncertain data generated by network applications has potential value, and it is of great significance to carry out clustering analysis of uncertain data. However, the uncertainty of data brings a serious challenge to traditional clustering algorithms. There are some problems in the existing clustering algorithms for uncertain data. (1) Some algorithms have a lot of meaningless distance calculations when calculating the distance of uncertain objects, and then the calculation complexity of algorithms increases. (2) The data model in clustering algorithms results in the loss of data distribution information, and then the accuracy of the algorithms decreases. In this paper, we propose an uncertain tuple density clustering (UTDC) algorithm for uncertain data. Firstly, we extract the data distribution features of uncertain instances and construct uncertain tuples by introducing the cloud model, which realizes the pruning of clustering objects. Secondly, we apply the EW distance to the traditional density clustering algorithm DBSCAN, which completes the density clustering for uncertain data. The experimental results show that comparing with UK-Means and FDBSCAN, UTDC algorithm effectively reduces the computational complexity and improves the accuracy.
Blockchain Technology and Cryptocurrencies: An Alternative to Central Banking?

Blockchain technology and its application to cryptocurrencies is gaining increasing importance in many sectors. Current events, including the hotly-debated announcement of the launch of Facebook’s digital currency ‘Libra’ illustrate that the topic is now as relevant as ever. In the present article, we investigate whether blockchain technology and cryptocurrencies have the potential to one day replace or offer a serious alternative to central banking and the traditional financial sector by illustrating how blockchain technology functions and offering a brief review of the 2008 global financial crisis in the light of blockchain technology. Furthermore, stablecoins and current events relating to central banking and cryptocurrencies are discussed, along with whether gold-backed cryptocurrencies could serve as a capital asset. Finally, the compliance risks of cryptocurrencies are demonstrated, including a re-enactment of the concrete steps that criminals take to launder their incriminated funds, finance terrorist groups or bribe corrupt foreign officials.
Cloud Storage Reliability

This chapter introduces the data integrity verification technique for cloud storage systems. First, the basic paradigms and principles of the data integrity verification technique are presented. Then, a comprehensive survey on the data integrity verification technique is provided, where existing data integrity verification schemes are introduced, and their pros and cons are analyzed. Finally, the latest advances in the data integrity verification technique are studied.
Improved Graph Autoencoder for Network Reconstruction

Network embedding technology transforms network structure into node vectors, which reduces the complexity of representation and can be effectively applied to tasks such as classification, network reconstruction and link prediction. The most important concern of network embedding is how to retain the local structural features while effectively capturing the global features of the network. In view of the shortcomings of SDNE in weighted and directed network, this paper proposes an improved SDNE model based on node degree. The experimental results show that the improved SDNE model has better effect than the original algorithm with higher computational efficiency.
MASC-Net: Multi-scale Anisotropic Sparse Convolutional Network for Sparse Depth Densification

Irregular sparse depth densification has attracted a significant amount of recent interests in computer vision, robotics, autonomous driving and augmented reality applications. Low-cost 3D sensors available on the market today produce sparse and irregular depth data resulting in sometimes inaccurate or inconsistent results. This paper proposes a novel approach for sparse depth densification called the Multi-scale Anisotropic Sparse Convolutional Network (MASC-Net). Conventional sparse convolutional approaches face two main challenges in deep neural networks: 1) blurry depth and fattening artifacts near object boundaries due to isotropic validity masks, and 2) the validity mask mismatch problem across different layers. To address these problems, we propose anisotropic sparse convolutional layers based on spatially varying validity masks with guidance features. We also propose Validity-Aware Modules (VAMs) to resolve the validity mask mismatch problem, which enables many modern deep learning components to be applied to sparse data. Further, a multi-scale completion module is proposed to utilize multiple scales of context to fill the missing information. Experimental results show that MASC-Net outperforms the state-of-the-art depth densification methods on the KITTI depth completion benchmark in both quantitative and qualitative measures.
Gaming in Microwave Engineering Education [Educator's Corner]

Playing games is not just for children. In fact, playing games can make the learning process easier and more fun for adult students as well, for example, in language courses. Despite this success in teaching other subjects, microwave engineering education rarely employs games for learning. However, during the past few years, we have designed and optimized a game that is suitable for senior undergraduates or first-year graduate students in an introductory microwave engineering course. The game is welcomed by our students and indeed makes both teaching and learning more interesting. In this article, I introduce the design philosophy of the game and explain how to play it.
Vector Based Object Identification in Spherical Images

3D virtual environment of the real world library is built using the spherical images. Nowadays identifying an object in a spherical image is a challenging task in itself, a technique is introduced to identify a particular object in a spherical image. Our case study is towards identifying a particular book in a book-shelf image which is spherical in nature. The proposed work is divided into 3 modules, the first module includes Speeded Up Robust Feature (SURF) detection and extraction. Second module incorporates application of vector based mismatch feature removal algorithm on detected SURF features. Finally the boundary box or a marker is drawn on the book detected. Proposed technique examines which format of spherical image yields better result. Experimental results show that SURF on cubic image yield better results than SURF on equirectangular image.
Kitsune: Structurally Aware and Adaptable Plagiarism Detection

Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. Many source code plagiarism tools do not support a high number of languages because doing so requires maintaining too large of a codebase. It is also difficult to add support for new languages because each language can be vastly different syntactically. Tools that are more extensible often do so by reducing the features of a language that are encoded and end up closer to text comparison tools than structurally aware program analysis tools [27]. This paper introduces a new tool called Kitsune, a plagiarism detection tool, focused on syntactically and structurally aware yet adaptable plagiarism detection. Kitsune has been evaluated for 10 of the languages in the Antlr4 grammar repository with success and could easily be extended to support all the grammars currently developed by Antlr4 or future grammars which are developed as new languages are written.