Reviewerly/exHarmony huggingface dataset:
PROMOTION SYSTEM FOR PROMOTIONAL PRODUCTS AND TOURIST OBJECT IN PATUK GUNUNGKIDUL DISTRICT

Patuk District is a gateway to enter Gunungkidul Regency. In Patuk District there are several tourist attractions such as Kampoeng Emas, Purba Volcano and Embung Nglanggeran. In addition, there is also the potential for processed food products from natural and agricultural products in Patuk District. Leading potentials in Patuk District need to be promoted to the wider community so that they are known and can be an attraction for tourists. The purpose of this research is to design and create a system of promotion of superior products and Web-based tourism objects that can facilitate Patuk District in introducing superior products owned by each village and facilitate the general public to obtain superior product information in Patuk District. The method used in the analysis and system design uses Unified Modeling Language (UML). In this study, a system consisting of three users was produced, namely admin in the Inter-Village Coordination Board (BKAD), admin of business owners and general users. This system can be accessed at the address www.jelajahipatuk.com.
Cloud Computing: It’s Security and Introduction to Amazon Web Services

Cloud is a collection of computing and network resources served with an overlay of virtualization which provides one-stop solution for scalability, elasticity, security and agility for same. Virtualization layer allows the user to access these computing and networking power as infrastructure, platform, software and functionality served with an overlay. It also provides robustness and reliability and has on-demand access to compute and store resources lies within the cloud–service providers, but it has some disadvantageous too. Like all software, hardware and almost all application data are stored and deployed in the cloud platform, the distrust between cloud suppliers and users are often arrived. So, some enterprises do not support believing in cloud computing. Amazon Web Services (AWS) cloud computing is the trusted cloud service provider. It has benefits like easy to use, no capacity limits, reliable and provides speed. The main aim of this paper is to understand cloud computing, it’s security concerns and AWS.
A Policy-Based Access Control Model for Mobile Terminal in SG-eIoT

Unlike other Internet of Things, handheld mobile terminals are needed in many application scenarios of the Internet of Things, such as transmission line patrol, substation patrol and so on. In order to avoid uploading false data and revealing user privacy, it is necessary to control the access of terminals. In this paper, a policy-based access control model for mobile terminal in SG-eIoT is designed, and the key issues such as security policy definition, security policy decision, security policy implementation and other key problems are deeply studied and tested. The experimental results show that the model has good control effect on the malware behavior of the application layer, the local layer and the kernel layer of the mobile terminal.
Wide and Deep Learning for Video Summarization via Attention Mechanism and Independently Recurrent Neural Network

Video summarization considers the problem of selecting a concise set of frames or shots to preserve the most essential contents of the original video. Most of the current approaches apply Recurrent Neural Network (RNN) to learn the interdependencies among the video frames without considering the distinct information of particular frames. Other methods leverage the attention mechanism to explore the characteristics of some certain frames, while ignoring the systematic knowledge across the video sequence. In this paper, a novel video summarization methodology, Wide and Deep Summarization Network (WD-SN), is proposed based on attention mechanism and Independently Recurrent Neural Network(IndRNN)[1]. It captures both the wide independent characteristics and the deep interdependencies of a video sequence. Our proposed model has an encoder-decoder structure (as shown in Fig. 1). The encoder obtains a sequence of video features through CNN, and the decoder predicts frame-level importance scores from the wide and deep network. Specifically, the wide component yatt is implemented by a self-attention mechanism, and the deep component yrnn is implemented by IndRNN. Experimental results prove the effectiveness of our model.
Transport analysis of infinitely deep neural network

We investigated the feature map inside deep neural networks (DNNs) by tracking the transport map. We are interested in the role of depth--why do DNNs perform better than shallow models?--and the in...
Guest Editor's Introduction: Special Section on Emerging Technologies for Disaster Management

The papers in this special section examine new and emerging technologies for disaster management. We live in a world in which natural and man-made disasters (such as earthquakes, hurricanes, terrorist attacks, and industrial accidents) often occur. These disasters are so sudden in nature, so causing loss of human lives and interruption of essential public services (such as health care, electricity, water, transportation and communication). Disaster management aims to reduce or avoid potential losses from hazards, ensure prompt and appropriate assistance to victims and achieve a rapid and effective recovery. In recent years, new computing/communication technologies have emerged to improve the efficiency of disaster management; for example, crowdsourcing has been applied in the Nepal earthquake to collect the latest information from earthquake-affected areas and create a dynamic map that shows the locations in which aid and relief are needed. Although some preliminary attempts have been made to apply emerging technologies for disaster management, there are many open challenges that need to be addressed to fully exploit the potentials of these promising technologies.
Classificação de Espécies de Plantas Usando Extreme Learning Machine

Plants play an important role in nature, but correct plant species identification is still a challenging task for non-specialized people. Many works have been proposed towards the development of automatic plant species recognition systems through Machine Learning methods, but most of them lack the proper experimental analysis. In this work, we evaluate the performance of a general-purpose Artificial Neural Network to perform plant classification task: the Extreme Learning Machine (ELM).We compare ELM with several classifiers from plant recognition literature by means of three real-world data sets obtained from different image processing and feature extraction processes. A statistical hypothesis test is employed to perform proper experimental evaluation.
Desarrollo de una Aplicación Móvil para la eficiente gestión de sustancias Químicas con base en la Norma Oficial Mexicana NOM-018-STPS-2015

Nowadays the use of mobile applications has contributed in different areas of engineering, the following project presents the development of a mobile application for the chemistry area, which allowed the effective management of substances following the Official Mexican Norm NOM-018-STPS-2015. The utilized method for the development of the application is scrum; which is an agile framework that allows collaboration, self-organization, early delivery, transparency beyond others; the workgroup was formed by students and teachers of the divisions of technologies of the information and communication and pharmaceutical and industrial chemistry. The initiative of implementing a mobile application for devices with an android operating system, with an API greater or equal to 4.4 allowed a highly effective, optimizing the management of the control of chemical substances used by students and teachers, Streamlining the process of returning the laboratory supplies and guaranteeing the fulfillment of the learning process in the laboratories of the technological university of San Juan Del Rio, it was implemented with students of Industrial and pharmaceutical chemistry engineering, where 86% of the users affirmed to be satisfied with the mobile application.
Learning from Multiple Demonstrations with Different Modes of Operations

In this paper, teaching multiple types of complex trajectories at once to a robot in a robust, easy to train model using Learning from Demonstration is studied where the robot is expected to gain the capacity to differentiate between different types of demonstrated trajectories and be able to reproduce these trajectories correctly.Demonstrated trajectories are used to train a Hidden Markov Model (HMM) and a modified version of Gaussian Mixture Regression (GMR) -which utilizes state transition probabilities between states of the HMM, the most probable state the end effector of the robot belongs to in the current reproduction of the trajectory, and previous points in the current reproduction of the trajectory-is used to estimate the trajectory iteratively.A Proportional Derivative (PD) controller is employed for the reproduction.Starting points that are intended to correspond to different types of trajectories which the robot is expected to differentiate between are tested on numerical and simulation experiments.Multiple numerical experiments and simulation experiments showed that our modified algorithm produced comparable results to previous work, and in certain complex trajectories our algorithm was successful where previous work has failed to produce expected results.
The Impact of Interface Design on the Usability of Code Analyzers

Code-level vulnerabilities play a significant role in the number of data breaches that happen across the globe. There have been several efforts by researchers and developers alike to design tools that provide programmers with code scanning capabilities. However, many developers and businesses are skeptical of using some of these tools. One major reason for the lack of acceptance and use of code scanning tools is poor interface design and substandard presentation of bug/vulnerability warnings. In an effort to design a recommender system to help programmers write more secure code, we conducted a knowledge elicitation survey and usability study to provide insights about the expectations of programmers in regards to a usable interface. This article summarizes our findings.
Conversational strategies : impact on search performance in a goal-oriented task

Conversational search relies on an interactive, natural language exchange between a user, who has an information need, and a search system, which elicits and reveals information. Prior research posits that due to the non-persistent nature of speech, conversational agents (CAs) should support users in their search task by: (1) actively suggesting query reformulations, and (2) providing summaries of the available options. Currently, however, the majority of CAs are passive (i.e. lack interaction initiative) and respond by providing lists of results – consequently putting more cognitive strain on users. To investigate the potential benefit of active search support and summarising search results, we performed a lab-based user study, where twenty-four participants undertook four goal-oriented search tasks (booking a ﬂight). A 2x2 within subjects design was used where the CAs strategies varied with respect to elicitation (Passive vs Active) and revealment (Listing vs. Summarising). Results show that when the CA’s elicitation was Active, participant’s task performance improved significantly. Confirming speculations that Active elicitation can lead to improved outcomes for end-users. A similar trend, though to the lesser extent, was observed for revealment – where Summarising results led to better performance than Listing them. These findings are the beginning of, but also highlight the need for, research into design and evaluation of conversational strategies that active or pro-active CAs should employ to support better search performance.
Focal Loss for End-to-end Short Utterances Chinese Dialect Identification

Short utterances dialect identification is a challenging task because of the substantial similarity between dialects. The previous cross-entropy loss function does not consider the category and probability of prediction error, which result in insensitivity to easily misclassified and unbalanced samples. To solve this problem, we propose to use an improved cross-entropy loss function, namely focal loss, introducing category weights and tunable focusing parameter to improve the classification accuracy. Experiments are carried out on AI Dialect Contest database. The results demonstrate that our proposed end-to-end model trained with focal loss achieves better performance than the model trained with cross-entropy loss function.
An Effective and Efficient Approach for Single Image Dehazing and Defogging

Images captured by outdoor systems are highly degraded under bad weather conditions, which directly affects the performance of driving assistance, navigation and tracking systems. Thus, recovering the scene from hazy and foggy image is consequential. In this paper, a fast single image deweathering technique is proposed based on morphological operation. In foggy and hazy images, the unequal distribution of depth map is the primary cause of degradation. This unequal variation of depth is compensated by proper estimation of atmospheric light and transmission depth. To accurately estimate the atmospheric light and transmission depth, morphological operation is used. The obtained experimental results in terms of qualitative and quantitative comparisons with the state-of-the-art show the efficacy of the proposed method. Furthermore, the proposed method takes less execution time with little or no halo artifact.
THE RELATIONSHIP BETWEEN NURSES’ JOB SATISFACTION AND NURSES’ PERFORMANCE IN THE PUBLIC HEALTH CARE SECTOR IN YEMEN

Nurses play a prominent role in improving the quality of health services provided to patients and contributes to improving the performance of the health system. Thus, it becomes vital for health care institutions to pay attention to the level of satisfaction of nurses as a determinant of the level of performance of nurses working in it. Based on that, this study aims to determine the level of job satisfaction and performance among 220 nurses working in 13 public hospitals in the governorates of Aden and Abyan, Yemen. Moreover, examining the correlational relationship between nurses’ job satisfaction and nurses’ performance will be investigated. In addition to the correlational relationship, the effect of nurses’ job satisfaction with its dimensions on their performance will be examined. To fulfill the study objectives, the researcher used a quantitative method in which a self‐administered questionnaire was distributed and then analyzed using Pearson correlation and multiple regression in IBM SPSS Statistics ® (SPSS). The findings of the study show that the level of overall nurses’ job satisfaction is low, and nurses’ performance is poor.   The result also reveals that overall nurses’ job satisfaction positively correlated to nurses’ performance. Moreover, the results also showed that Pay and Benefits are the most influential factors among other job satisfaction dimensions affecting nurses’ performance.
Three-dimensional histo-morphometric features from light sheet microscopy images result in improved discrimination of benign from malignant glands in prostate cancer

Glandular features play an important role in the evaluation of prostate cancer. There has been significant interest in the use of 2D pathomics (feature extraction) approaches for detection, diagnosis, and characterization of prostate cancer on digitized tissue slide images. With the development of 3D microscopy techniques, such as open-top light-sheet (OTLS), there is an opportunity for rapid 3D imaging of large tissue specimens such as whole biopsies. In this study, we sought to investigate whether 3D features of gland morphology, namely volume and surface curvature, from OTLS images offer superior discrimination between malignant and benign glands compared to the traditional 2D gland features, namely area and curvature, alone. In this study, a cohort of 8 de-identified fresh prostate biopsies comprehensively imaged in 3D via the OTLS platform. A total of 367 glands were segmented from these images, of which 79 were identified as benign and 288 were identified as malignant. Glands were segmented using a 3D watershed algorithm followed by post-processing steps to filter out falsepositive regions. The 2D and 3D features were compared quantitatively and qualitatively. Our experiments demonstrated that a model using 3D features outperformed one using 2D features in differentiating benign and malignant glands. In 3D, both features, gland volume (p = 1.45 × 10<sup>−3</sup>) and surface curvature (p = 3.2 × 10<sup>−3</sup>), were found to be informative whereas in 2D, only gland area (p = 9 × 10<sup>−18</sup>) was found to be discriminating (p = 0.79 for 2D curvature). Notable visual and quantitative differences between 3D benign/malignant glands encourage the development of additional more sophisticated features in the future.
Spooky effect in optimal OSPA estimation and how GOSPA solves it

In this paper, we show the spooky effect at a distance that arises in optimal estimation of multiple targets with the optimal sub-pattern assignment (OSPA) metric. This effect refers to the fact that if we have several independent potential targets at distant locations, a change in the probability of existence of one of them can completely change the optimal estimation of the rest of the potential targets. As opposed to OSPA, the generalised OSPA (GOSPA) metric $(\alpha=2)$ penalises localisation errors for properly detected targets, false targets and missed targets. As a consequence, optimal GOSPA estimation aims to lower the number of false and missed targets, as well as the localisation error for properly detected targets, and avoids the spooky effect.
Research on Optimizing Parameters of Pitch Angle Controller Based on Genetic Algorithm

This paper takes the pitch angle control of the wind turbine as the research object, for the sake of optimizing the performance of the pitch angle controller. The three parameters of the quantization factor $$ K_{e} ,\,K_{ec} $$ and the scale factor $$ K_{u} $$ in the fuzzy controller have a significant impact on the pitch angle. In this paper, the wind turbine is modeled in Matlab/Simulink, and the optimal solution of $$ K_{e} ,K_{ec} $$ and $$ K_{u} $$ is sought by the genetic algorithm. The simulation reveal that the optimized fuzzy controller has faster response and less fluctuation than the fuzzy controller without optimization and PID controller, which effectively avoids the loss caused by frequent blade start.
Development of android-based apps for courier service management

E-commerce has grown exponentially over the years. The growth has been characterized by strong consumer demands and the increasing number of various products available online. This in turn creates a logistical problem and a high demand for an efficient courier service to support the growing markets. It is very important for courier service provider that the delivery of the parcel is being done as fast as possible. One of the courier service’s vital and most crucial business process is in the last mile parcel delivery. This is where an efficient delivery service will be of utmost importance. An efficient system needs to be developed in order to facilitate the interaction between the courier service provider and consumer to precisely determine the optimal route for the parcel delivery. In this paper, an Android-based application system for courier service management with last mile route tracking module is developed. It is a mobile application that eases the courier delivery personnel in finding their way to deliver the parcels to the customer’s doorstep. The application will guide the courier personnel to get a list of courier data such as address and contact information and then navigate them to the selected customers’ addresses based on traffic data retrieved from Google Maps API. It will choose the best route to the address and notify the customers before arriving so that the customers are ready to receive the parcel. This last mile route tracking for parcel delivery will provide the basis for an efficient courier service system.
Developing a New Model for Cyber Security Behavior of E-Hailing Services: A Conceptual Paper

E-hailing industry is a new concept in the taxi services whereby private cars can register with online transport. Despite the rapid popularity of e-hailing services, there are major public concerns pertaining to cyber security behavior issues both to the passengers and the drivers such as hit and run accident, robbery, rape, sexual harassment, murder, rude drivers and grab driver scams. Hence, this study intends to investigate the predictors of cyber security behaviors of e-hailing services in Malaysia. The prevailing factors to be examined are perceived threats, perceived privacy, fraud victimization, response efficacy, perceived vulnerability, security self-efficacy, perceived benefit and government policy. The research methods to be used are quantitative and qualitative research design. The sampling technique is to collect data passengers and drivers of e-hailing services operating in Malaysia. The measurement of the twelve main variables consists of 90 items. The data will be collected using self-administered survey and analyzed using SMART PLS 3.0. The expected finding is a new cyber security behavior model for Malaysia, provide new inputs for e-hailing regulation and policy improvement in line with Industrial Revolution 4, new suggestions for e-hailing market segmentation according gender and generation gaps, enhancement of the existing applications according to research findings, set new guidelines on the development of similar application in line with industrial Revolution 4 and the installation of panic button in e-hailing automobile. From the pilot study results, all scales were found to be consistent and reliable with Cronbach's alpha readings above 0.70.
Combining Fuzzy Signature and Rough Sets Approach for Predicting the Minimum Passing Level of Competency Achievement

This paper aims to investigate the important factors that affect the value of the minimum passing level (MPL) of competency achievement and find the best method to predict it. The MPL of competency achievement is the value that represents the minimum passing score of examination related to the competency. Different schools may have a different value of the MPL because the MPL is defined based expert opinion on several uncertainty aspects and conditions at each school. This paper proposes the combination of rough sets and fuzzy signature method to predict the category of the MPL. The rough sets method is applied to reduce unnecessary features for classification and find the important factors to predict the MPL. The fuzzy signature is employed to predict the category of MPL based on the selected features. The method proposed in this paper consists of several stages, namely data collection and pre-processing, features selection, predict the category of the MPL using the combination of rough sets and fuzzy signatures method, and performance evaluation. Fifteen headmasters and sixty teachers of elementary schools participated in the data collection process. Based on the experiment with 203 objects data we achieved 97% accuracy in the prediction of MPL. The proposed method succeeded to identify the important factors on predicting the MPL on the complexity of competency and resource capacity of the school aspect. We obtained the improvement for accuracy of the complexity of competency prediction of 8.5% from the best method in the previous research.