Reviewerly/exHarmony huggingface dataset:
The Extraction of Hidden Fault Diagnostic Knowledge in Equipment Technology Manual Based on Semantic Annotation

Due to small quantities, lack of service experience, and poor fault diagnosis knowledge of new-type equipment, it is often difficult to determine the exact location of a trouble. To address this problem, a knowledge capitalization and fault diagnosis method based on semantic annotation was proposed, which can extract deep fault knowledge implied in the technical publications. Firstly, the unstructured nature of deep fault knowledge in the technical publications is outlined. And the role of semantic annotation in the process of knowledge acquisition is highlighted. Secondly, an ontology model for deep fault diagnosis knowledge extraction is developed to annotate the technical publications semantically. And the annotation method is presented to translate the unstructured and implicit knowledge into formal-defined and computer readable semantic net. Then, a fault diagnostic algorithm is proposed to use the annotation results based on hierarchical diagnosis algorithm of directed graph. Finally, an application case of VE-type fuel-injection pump verifies the feasibility and effectiveness of this method.
Target Based Speech Act Classification in Political Campaign Text

We study pragmatics in political campaign text, through analysis of speech acts and the target of each utterance.We propose a new annotation schema incorporating domainspecific speech acts, such as commissiveaction, and present a novel annotated corpus of media releases and speech transcripts from the 2016 Australian election cycle.We show how speech acts and target referents can be modeled as sequential classification, and evaluate several techniques, exploiting contextualized word representations, semi-supervised learning, task dependencies and speaker meta-data.
Generalized net models of queueing disciplines in finite buffer queueing systems with intuitionistic fuzzy evaluations of the tasks

Generalized net models of different queueing disciplines in queueing systems are proposed in [11].In the present paper, we propose modifications of these models including Intuitionistic Fuzzy Pairs (IFP) and Interval-Valued Intuitionistic Fuzzy Pairs (IVIFP) which determine the way in which the requests are serviced.In each of the models, the buffer has finite capacity and is represented by two Generalized net transitions.The buffer cells are represented by places of the net.The two simple queueing disciplines considered are FIFO and LIFO.A more general model with IFP (or IVIFP) in which the requests can change their parameters and position within the buffer is also proposed.
The impact of internet of things on customer service: A preliminary study

The study discuss about internet of Things on serving passengers at Queen Alia International Airport in Amman, Jordan, by knowing its importance and how to minimize its shortcomings in order to improve customer service at the airport. This research allowed us to understand the fears that travelers had, which, if provided, would ensure greater service improvement. Search results show that there is a great impact on Internet things on customer service. The results showed that the variables (safety and scalability) have a statistically significant impact on customer service. While the cost has a clear positive effect, it is not statistically significant. The study also showed that there is a significant impact on the cost of customer service. Security plays an important role in customer service, meaning that there is a significant security impact on customer service. It shows that the most important factor in customer service is security. Extensibility is the key to a successful Internet object system and it is important to focus on it.
The Impact of Job Burnout on the Performance of Staff Member at King Abdul-Aziz University

Job burnouts have been associated with reduced employee satisfaction and performance.Establishing the causes of job burnout and its effect on staff members of the King Abdul-Aziz University, Jeddah, Saudi Arabia, was the intention of the present investigation.Three dimensions of burnout were assessed: exhaustion, depersonalization, and personal accomplishment.It was hypothesized that each of the three had direct impacts on workplace satisfaction and performance.The research assumed a quantitative approach using a descriptive design.Data from 500 individuals who work at the institution were randomly sampled and analyzed; and information obtained through questionnaires.The mean exhaustion level of the sample was 2.95 (std, dev.= 0.77) while that for depersonalization and personal accomplishment were 2. 09 (std, dev.= 0.77) and 3.83 (std, dev.= 0.53) respectively.The average staff performance was 3.36 (std, dev.= 0.60).Significant associations were established between performance and each of the dimensions.
RotateView: A Video Composition System for Interactive Product Display

Product display in e-commerce commonly uses static images or videos. In this paper, we design a novel video composition system “RotateView” which displays real-world products videos on the mobile phone in an interactive and 3D-like way. The system estimates the rotation direction of products using optical flow, segments products using an improved version of online adaptive video object segmentation, and adjusts motion and color for composition. The rotation direction estimation methods are evaluated on a video dataset of 1500 rotated videos that are uploaded by sellers, and on our collected real-world video object segmentation (RVOS) dataset. On our RVOS dataset, experimental results show an improvement in segmentation over the state of the art. Moreover, the proposed algorithms for motion and color adjustment are evaluated using extensive user studies. The RVOS dataset can be downloaded via <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://realvos.github.io/</uri> for further studies and code will be made available.
KLASIFIKASI DOKUMEN TUGAS AKHIR (SKRIPSI) MENGGUNAKAN K-NEAREST NEIGHBOR

Various scientific works from academicians such as theses, research reports, practical work reports and so forth are available in the digital version. However, in general this phenomenon is not accompanied by a growth in the amount of information or knowledge that can be extracted from these electronic documents. This study aims to classify the abstract data of informatics engineering thesis. The algorithm used in this study is K-Nearest Neighbor. Amount of data used 50 abstract data of Indonesian language, 454 data of English abstract and 504 title data. Each data is divided into training data and test data. Test data will be classified automatically with the classifier model that has been made. Based on the research conducted, the classification of the Indonesian essential data resulted in greater accuracy without going through a stemming process that had a 9: 1 ratio of 100.0% compared to an 8: 2 ratio of 90.0%, 7: 3 which was 80.0%, 6: 4 which is 60.0% and the data distribution using Kfold cross validation is 80.0%.
Systematic Mapping Study Protocol For Secure Software Engineering

In todays world, software security has become essential for protecting the overall organizations operations. Mostly software development companies are adopting various strategies to build secure software to cope with the challenges of the client organization. Information become threatened due to connection with the cyber world, and it requires better security mechanisms. Software development organizations are receiving pressure from their clients to focus on the enhancement of security during the whole software development life cycle (SDLC). This protocol aims to review the literature in a systematic way to identify the state-of-the-art of software security to be considered by Global Software Development (GSD) vendor organizations during the development of a secure software as it evolves from requirements engineering to its final disposal. In order to improve security processes in the context of software development, our current research developed a Systematic Mapping Study (SMS) protocol. Presently, we are in the implementation phase of the protocol for the development of secure software. The expected outcomes of this SMS will be a list of security measurements and their solutions to be incorporated by GSD vendor organizations in each phase of the SDLC. This will also give a direction for new research in this area.
Clay Minerals Mapping from Imaging Spectroscopy

Mapping subsurface clay minerals is an important issue because they have particular behaviors in terms of mechanics and hydrology that directly affects assets laid at the surface such as buildings, houses, etc. They have a direct impact in ground stability due to their swelling capacities, constraining infiltration processes during flooding, especially when moisture is important. So detecting and characterizing clay mineral in soils serve urban planning issues and improve the risk reduction by predicting impacts of subsidence on houses and infrastructures. High-resolution clay maps are thus needed with accurate indications on mineral species and abundances. Clay minerals, known as phyllosilicates, are divided in three main species: smectite, illite, and kaolinite. The smectite group highly contributes to the swelling behavior of soils, and because geotechnical soil analyses are expensive and time-consuming, it is urgent to develop new approaches for mapping clays’ spatial distribution by using new technologies, e.g., ground spectrometer or remote hyperspectral cameras [0.4–2.5 μm]. These technics constitute efficient alternatives to conventional methods. We present in this chapter some recent results we got for characterizing clay species and their abundances from spectrometry, used either from a ground spectrometer or from hyperspectral cameras.
Dynamic Scheduling Strategy for Block Parallel Cholesky Factorization Based on Activity on Edge Network

The efficient development of system software and design applications in parallel architecture is a notable challenge considering various aspects, such as load balancing, memory spaces, communication, and synchronization. This paper presents a block parallel Cholesky factorization algorithm for a multicore system, which is developed based on activity on edge network. First, the basic block computing tasks and their dependencies are taken as vertices and edges, respectively, and a directed acyclic graph corresponding to the specific block parallel Cholesky factorization is generated. Next, each edge of the directed acyclic graph is assigned to a weight equal to the processing time of the initial vertex of the edge, and the directed acyclic graph becomes an activity on edge network with only one starting and one ending vertex. Finally, a queuing algorithm is designed for the basic block computing tasks according to the edge activity on edge network, and a dynamic scheduling strategy is developed for block parallel Cholesky factorization. The results of the experiments concerning the parallel execution time of the algorithm in multicore systems with different configurations demonstrate that the proposed algorithm has notable advantages compared with the traditional static scheduling algorithm, and it exhibits satisfactory load balancing, parallelism, and scalability capacities.
PARNet: A Joint Loss Function and Dynamic Weights Network for Pedestrian Semantic Attributes Recognition of Smart Surveillance Image

The capability for recognizing pedestrian semantic attributes, such as gender, clothes color and other semantic attributes is of practical significance in bank smart surveillance, intelligent transportation and so on. In order to recognize the key multi attributes of pedestrians in indoor and outdoor scenes, this paper proposes a deep network with dynamic weights and joint loss function for pedestrian key attribute recognition. First, a new multi-label and multi-attribute pedestrian dataset, which is named NEU-dataset, is built. Second, we propose a new deep model based on DeepMAR model. The new network develops a loss function, which joins the sigmoid function and the softmax loss to solve the multi-label and multi-attribute problem. Furthermore, the dynamic weight in the loss function is adopted to solve the unbalanced samples problem. The experiment results show that the new attribute recognition method has good generalization performance.
The Gap between NMT and Professional Translation from the Perspective of Discourse

Neural Machine Translation (NMT) attracts an intensification of research efforts in recent years. The claim that the quality of machine translation is matching human translator requires extraordinary evidences. It is almost certain that machine translation so far cannot replace or exceed human translator, but what is the gap between NMT and professional translator? We truly investigate the quality difference between the well-known Google Neural Machine Translation (GNMT) and professional translators at the document level. We build an evaluation model based on the discourse cohesion theory to compare the usage of grammatical and semantical devices in Chinese-to-English translations including reference, substitution, ellipsis, conjunction and lexical cohesion. The evaluation results show that there is a significant gap between NMT and professional translator in document translations. In a comprehensive point of view, for the professional translator, translation is a kind of arts; while for the computers, it is still a kind of technologies.
Implementation of Dormand-Prince based chaotic oscillator designs in different IQ-Math number standards on FPGA

Chaos and chaotic systems, one of the most important work areas in recent years, are used in areas such as cryptology and secure communication, industrial control, artificial neural networks, random number generators and image processing. The most basic structure used in these studies is a chaotic oscillator design that produces a chaotic signal. Chaotic oscillators are expressed by using differential equations. Numerical algorithms such as Euler, Heun, fourth order Runge-Kutta-4 (RK4), fifth order RK5-Butcher and Dormand-Prince are used for solving these differential equations. When the current literature is searched, chaotic oscillator designs are found by Euler, Heun, RK4 and RK5- Butcher method. However, FPGA-based chaotic oscillator design studies have not been found using the Dormand-Prince method, which produces more accurate solutions than other methods. In this work, self-excited attractor chaotic system was first designed in 16I-16Q, 14I-14Q, 12I-12Q, 10I_10Q, 8I-8Q IQ-Math number standards on FPGA using Dormand-Prince numerical algorithm and encoded in VHDL language. Xilinx ISE Design Tools were used to design the chaotic system. The design was synthesized and tested for the Xilinx Virtex-6 FPGA chip. Using the Xilinx ISE design tool, the chip statistics and maximum operating frequency obtained after the "Route-Place" operation are presented. In future work, safe communication and real random number generator applications can be realized by using the Dormand-Prince based oscillator design presented in this study.
Diffusion of Innovation: Case of Co-Design of Cabins in Mobile Work Machine Industry

This paper describes the development of using virtual reality for work content in one application area over a decade. Virtual reality technology has developed rapidly; from walk-in CAVE-like virtual environments to head-mounted displays within a decade. In this paper, the development is studied through the lens of diffusion of innovation theory, which focuses not only on innovation itself, but also on the social system. The development of virtual technology is studied by one case, which is cabin design in the mobile work machine industry. This design process has been especially suitable for using virtual reality technology.
Editorial for the Special Issue “Solar Radiation, Modeling, and Remote Sensing”

Surface-solar radiation is of vital importance for life on Earth, radiation–energy balance, photosynthesis, and photochemical reactions, meteorological and climatic conditions, and the water cycle. Solar radiation measurements are growing in quality and density but they are still scarce enough to properly explain the spatial and temporal variability. As a consequence, great efforts are still being devoted to improving modeling and retrievals of solar radiation data. This Special Issue reviews techniques for solar radiation modeling and remote sensing using satellite and advanced statistical techniques for solar radiation. Satellite remote sensing of solar radiation provides better spatial coverage, and various methods have been presented on this issue covering several aspects: updated models for solar radiation modeling under clear sky conditions, new approaches for retrieving solar radiation from satellite imagery and validation against ground data, forecasting solar radiation, and modeling photosynthetically active radiation.
Peningkatan Keterampilan Menulis Teks Eksposisi Menggunakan Model Problem Based Learning

The aim of this research is improving expositional text writing skill using PBL for students eighth grade at SMPN 3 Surakarta. PBL is a learning model where the students are asked to solve the problems based on the information given, later they are asked to describe the problems in real life. This Classroom Action Research used the data collection technique with test. The instruments of learning process consisted of 3 items of activity and 20 indicators that were divided into 3 initial stages, core stage, and closing stage. It is shown from the data analysis that the teacher’s teaching activity in the first cycle scored 73, in the second cycle scored 77, and the third cycle reached 80, and all those three results achieved ‘good’ category. The percentage of students' passing level in the first cycle was 43.75%, the second cycle was 68.75%, and the third cycle was 100%. The implementation of PBL was better than in the previous cycle. It was indicated by the improvement in the passing score from 73% (Classification C) in the second cycle, to 82% (Classification B)in the third cycle.
Crowd Counting using DMCNN

To estimate the crowd density map and count the crowd from a single image accurately is always a challenging task. With arbitrary perspective and random crowd density, occlusions, appearance variations and perspective distortions may occur. Some of current crowd counting methods are based on image cropping. And some popular deep learning models are difficult to optimize. In this paper, we propose a Dilated Multi-column Convolutional Neural Network architecture for crowd density estimation in still images improved from the MCNN model [1]. We also use the dilated layer and optimize the loss function to get better accuracy. The DMCNN model is lightweight, easy to train and has better fitting ability. Meanwhile the architecture is an end-to-end system and robust for images with different perspective or crowd density. Furthermore, the ground truth (density map) is generated based on our Perspective-Adaptive Gaussian Kernels which can better represent the heads of pedestrians. We conduct experiments on the WorldExpo'10 dataset, the ShanghaiTech dataset, the UCF_CC_50 dataset, and the mall dataset. The results show that our method achieves better estimation and is convenient to utilize. Our DMCNN model has a good practical application prospect.
A novel pedestrian detection algorithm based on data fusion of face images

In order to facilitate effective crime prevention and to issue timely warnings for the sake of public security, it is important to pinpoint the accurate position of particular pedestrians in crowded areas. Face recognition is the most popular method to detect and track pedestrian movement. During the face recognition process, feature classification ability and reliability are determined by the feature extraction methods. The primary challenge for researchers is to obtain a stable result while the targeted face is subject to varying conditions—particularly of illumination. To address this issue, we propose a novel pedestrian detection algorithm with multisource face images, which involves a face recognition algorithm based on the conjugate orthonormalized partial least-squares regression analysis under a complex lighting environment. Statistical learning theory is a research specialization of machine learning, especially applicable to small samples. Building upon the theoretical principles used to solve small-sample statistical problems, a new hypothesis has been developed; using this concept, we integrate the conjugate orthonormalized partial least-squares regression with the revised support vector machine algorithm to undertake the solution of the facial recognition problem. The experimental result proves that our algorithm achieves better performance when compared with other state-of-the-art methodologies, both numerically and visually.
Addressing the Cold-Start Problem in Personalized Flight Ticket Recommendation

With the rapid development of the tourist industry, an increasing number of passengers book flight tickets through online travel agencies.When searching for a flight ticket online, users are overwhelmed by the choice on offer.Even though flight ticket recommendation has been widely investigated, the current approaches are unable to recommend flight tickets that meet an individual's preference efficiently because of the severe cold-start problem.This paper provides strategies to address the cold-start problem for flight ticket recommendation.We conduct an exploratory study over the real-world flight ticket recommendation scenario and classify the cold-start problem of flight ticket recommendation into two categories, namely route cold-start and user cold-start.We propose methods based on route similarity and social relationships between passengers to improve user models.Finally, we map an enhanced user preference model and flight features to latent factor spaces to generate the recommendation results.The experimental results on a realworld data set demonstrate the effectiveness of the proposed approaches.
Blockchain Enabled E-Voting System

The Blockchain-Enabled E-Voting uses a digital-currency analogy where in eligible voters can cast a ballot anonymously using a computing environment.BEV employs an encrypted key, smart biometrics and tamperproof realtime personal ID verification.Blockchain enable the creation of tamper-proof audit trails for voting.The idea of adapting digital voting systems to make the public electoral process cheaper, faster and easier, is a compelling one in modern society which normalizes it in the eyes of the voters, removes a certain power barrier between the voter and the elected candidate, thus making it an effective way for casting vote in this generation of technology.
Game Edukasi: Apakah membuat belajar lebih menarik?

Era Industri 4.0 has prompted academics to be able to apply a technology in a learning. However, this condition is still not supported by the availability of learning media used in schools. Therefore, this study aims to describe the response of academics, to educational game in learning. Two hundred twenty one students and fifty-three teachers from Probolinggo were randomly allocated to be the subject of this study. The responses collected with non-test techniques, were analyzed quantitatively and qualitatively. The results indicated educational game gain a positive response and greatly needed by academics. Furthermore, this result can be followed up for media developers and researchers to test the educational game in learning.