Reviewerly/exHarmony huggingface dataset:
Challenges of simulation training for future engineering seafarers - A qualitative case study

Maritime transportation is currently in a transitional period to an impending autonomous future. To that end, novel technologies are increasingly being introduced on-board ships and their engine rooms. At the same time, advancements in digitalization and automation are progressively replacing and reducing the number of marine engineers on-board. Consequently, with increasing automation in machinery spaces and unmanned engine rooms, the role of the marine engineers has been altered to that of monitoring and oversight. The substantial changes in the nature of tools and job description of the marine engineers necessitate the re-assessment and revision of their training and pedagogy. Currently, the simulator is a powerful tool in the training and development of marine operators. Although the literature review reveals some interest in marine engineering simulation training, however, there is a lack of attention to remote and cloud-based simulation training as part of blended learning. This study reveals that imparting marine engineering simulation training online is not free from challenges. This study reports the findings from a qualitative study of marine engineering simulation training, conducted as part of a larger ethnographic study on developing maritime competence. The study utilizes the socio-historical, context-dependent framework of the Activity System (AS) to analyze marine engineering simulation training. The study reveals issues with cloud-based marine engineering simulation training. Firstly, cloud-based training is not seamless to access. Secondly, not all features present in the desktop simulation are present in the cloud version. Thirdly the cloud-based platform affords limited feedback in comparison to the desktop version. Fourthly, cloud-based simulation training does not support peer learning. An understanding of the challenges of cloud-based marine engineering simulation training will help address these concerns. Furthermore, it will facilitate the competence development of marine engineers as they work in increasingly automated workspaces in the transition to autonomous ship operations.
Behavioral Logic Thinking of UI Design for Smartphone APP

Faced with the especially severe economic situation in our country, various advanced technical means are rapidly popularized in the daily life or work of modern social groups, provides new opportunities for the development of various industries, and puts forward development requirements at higher-level for the improvement of subsequent work.The UI (UI) design for smartphone App needs centralized design of certain software regarding to man-machine interaction, operation logic, visual aesthetics of interface and so on.While ensuring the software operation is comfortable, simple and free, the core features and basic performance positioning of the software could be presented to the maximum extent.In order to ensure the integration of design work, the analysis on thinking mode for behavioral logic design is required to be strengthened, which shall strive to present a visual effect satisfied by users.
An Emerging Local Annealing Method for Simultaneous Crystallization and Activation in Three-Dimensional Nand Flash

The high thermal budget of three-dimensional NAND Flash (3D NAND Flash) is the key issue limiting the increase in vertical integration. The main purpose of this article is to design a local and high temperature annealing function to reduce the thermal budget and gain a high activation rate without diffusion at a target depth to improve the performance of 3D NAND Flash devices by laser annealing with green wavelength. The method of nanosecond pulse laser annealing (ns-PLA) for amorphous silicon (a-Si) crystallization and activation has been proven, and this method has the benefits of a low thermal budget and controllable heating. According to previous experiments, one of the important results is that ns-PLA with high energy can provide a high temperature that reaches the melting point of a-Si and obtain a deep and monocrystal-like silicon channel through liquid phase crystallization in 3D NAND applications and obtain a higher activation rate. These results cast emerging light on low thermal budget processes based on ns-PLA in 3D NAND flash and have significant ramifications for impurity activation rates and reducing channel leakages.
Parallel Recurrent Module With Inter-Layer Attention for Capturing Long-Range Feature Relationships

Capturing long-range feature relationships is becoming a central issue with regard to convolutional neural networks (CNNs).In particular, several recent end-to-end trainable attention modules have attempted to model spatial-channel relationships within a given layer.In this work, we focus instead on modeling relationships among visual information captured in different layers and propose a novel module, referred to as a Parallel Recurrent Module with Inter-layer Attention (PI module).The PI module exhibits several unique characteristics, including the ability to memorize information from earlier layers and ameliorate gradient vanishing, both of which are issues not addressed by existing attention modules.Furthermore, due to its easy-to-adopt structure also incurring negligible computational overheads, the module successfully extends to not only CNNs on regular grids but also to graph convolution networks, and even other attention modules.We demonstrate by extensive experiments that the PI module is costefficient yet effectively provides additional performance gains on multiple benchmarks in classification, detection, and segmentation tasks in the image domain and a segmentation task in the point cloud domain.
Deep Value-Based Reinforcement Learning

The previous chapter introduced the field of classic reinforcement learning. We learned about agents and environments, and about states, actions, values, and policy functions. We also saw our first planning and learning algorithms: value iteration, SARSA, and Q-learning. The methods in the previous chapter were exact, tabular, methods, which work for problems of moderate size that fit in memory.
Integrated Smart IoT Infrastructure Management Using Window Blockchain and Whale LSTM Approaches

Internet of Things (IoT) is playing a vital role in the smart infrastructure environment. The IoT vendors are delivering their products in the market without any concern about the security of the devices, so it is an open number of security issues on the IoT devices and data. Security threats are growing high because existing techniques measures are inadequate; two of the most significant concerns in IoT are security and privacy. Due to the IoT devices limited CPU, storage, and energy resources, existing security architectures are unable to provide the key safety requirements, so deep learning and blockchain algorithms are used. These IoT devices give accurate results from heavy complex datasets. Furthermore, blockchain and deep learning model are very familiar to give secured devices to IoT. This proposed model is window blockchain (WBC). In proof-of-work, it leverages past (n − 1) hash to construct the next hash with minimal change; because of this quick block analysis time, we can easily prevent IoT devices from the attackers. WBC’s performance is evaluated using an actual data stream generated by one of the analyzed smart infrastructure devices. Another method using deep learning hybrid algorithms for LSTM networks with whale optimization algorithm is a new schema optimization technique that mimics humpback whales’ intelligent bubble-net fishing activity. It is an easy, powerful, and predator probabilistic optimization algorithm that can avoid local optima and find global optimal answer. The findings indicate that the proposed window blockchain model improves security and reduces memory utilization this employing limited resources. In the Whale +LSTM (WLSTM), a large number of the dataset were gathered using a real-time scenario using OMNET++IoT plugins, and a Python API is created to insert various malicious activity through networks. The proposed WLSTM model output of 99% has been tested and related to other deep learning utilizing benchmark datasets such as CIDDC-001, UNSWN15, and KDD datasets, as well as actual datasets; the prediction of unknown threats is used to tackle the computation complexity in big networks.
Expert system model for assistance to management in choosing optimal Web hosting

The paper discusses the model of a software solution in the form of an expert system that helps entrepreneurs and companies in choosing Web hosting and service packages for the distribution of their Web applications. The software, based on the parameters entered by the user, analyzes 10 factors that are important when deciding on the choice of hosting, and based on the answers received by the user recommends one of the alternatives that best suits his preferences and capabilities. The model of the expert system has an advisory character in making decisions on the choice of adequate hosting and can be upgraded by introducing new parameters, rules and conclusions as proposed solutions, to include new problem situations. The aim of this paper is to point out the possibility of creating artificial intelligence systems that can help in making adequate business decisions for the electronic market.
Research on the Classification Modeling for the Natural Language Texts with Subjectivity Characteristic

The methods of natural language text classification have the characteristic of diversification, and the text characteristics are the basis of the method effectiveness; this paper takes the car service complaint data as an example to study the classification modeling for the texts with subjectivity characteristic.The effective handling of car service complaints is important for improving user experience and maintaining brand reputation; manual classification commonly has the disadvantages of experience dependence, prone to error, heavy workload and so on; corresponding automatic classification modeling research is of great practical significance.The core links of the research method in this study include word segmentation, text vectorization, feature selection and dimensionality reduction based on correlation, classification modeling based on progressive method and random forest, and model reliability analysis; the research results show that the car service complaint texts could be effectively classified based on the method in this study, which could provide a reference for related further research and application.
Measurement of Circularity Error Using an Evolutionary Algorithm Interfaced with Voronoi Diagram

Accuracy of result of circularity measurement result depends on the power of the algorithm and the methodology employed to assess the circularity error. Minimum Zone Circle method (MZC) has proven to be more efficient than the other methods. Many advanced algorithms use least square centre to initialize the search process in order to locate the optimum centre of the circles inside the profile. The centre obtained using the Voronoi method is found to be more accurate than the least square centre. But, the problem with the Voronoi method is the methodology used to construct the inner convex hull provides different profile for different starting point. In this work, a new methodology is presented to overcome this drawback. Also, the centre of the circularity profile obtained using the Voronoi method is used as the initial centre in PSO-MZC algorithm and the outcomes are analysed in terms of accuracy, number of iterations and computational time required to provide minimum circularity error in comparison with PSO-MZC method that uses the least square centre.
Design of Intelligent Scheduling Algorithms for Cloud Computing

The humungous amount of fluctuating data is generated from every walk of life, such as cloud users, health care, IoT devices, high-performance computing data a daily basis. Therefore, it is of utmost essential to process those data within a determined span of time. Hence, traditional cluster computing or grid computing is unsuitable for processing those tremendous data, both parallelly and distributed fashion among multi-computing systems. The advent of cloud computing has paved the way to be a viable option for scheduling as well as balancing the loads. Since the nature of data is dynamic, independent and non-preemptive, cloud computing appears to be a prominent solution as it offers a virtualization technology for the dynamic scheduling of cloud requests. The cloud requests should get serviced within a satisfactory time in order to reduce the response time and completion time while effectively improving resource utilization. Since task scheduling is an NP-hard problem, it is thus required to be executed within a polynomial time so as to achieve overall performance. In order to make this possible, some effective mechanisms need to have in place. Due to the complexity of the tasks that can be performed in cloud computing, many metaheuristics, such as BSO, Modified PSO, chaotic JAYA and combined genetic Algorithm with JAYA and quantum-inspired binary chaotic salp swarm algorithm (QBCSSA) as hybrid algorithms are planned to address the various issues associated with the dynamic task scheduling in this paradigm. These algorithms are optimized to provide consistent and robust results. The continuous solutions are transformed into discrete solutions using binary algorithms for representing tasks–VMs assignment in cloud computing. Both task and resource heterogeneities are taken into account to assess the effectiveness of the implemented algorithms. The CloudSim is used as a simulation tool to experiment with the disparate test cases with the considered tasks set and heterogeneous resources. The conflicting quality of service (QoS) scheduling parameters are considered for appraising the efficacy of the proposed algorithms. The real-world benchmark datasets considering both dependent and independent tasks are considered to authenticate the diversifying nature of planned algorithms. This work is apparent as of the simulation outcome where chaotic JAYA (one of the variants of JAYA) and QBCSSA outperform among aforementioned metaheuristic and hybrid algorithms.
Attention Adaptive Chinese Named Entity Recognition Based on Vocabulary Enhancement

To deal with the lack of word information in character vector embedding and the problem of Out-of-Vocabulary in Named Entity Recognition, an attention adaptive chinese named entity recognition (CNER) model based on vocabulary enhancement (ACVE) is proposed. The mechanism of potential information embedding is designed, which acquires word-level potential information by constructing semantic vectors, and the fusion embedding of character information and word-level information realizes the enhancement of semantic features; We also propose an attention mechanism for adaptive distribution, which adaptively adjusts the position of attention by introducing a dynamic scaling factor to obtain the attention distribution suitable for NER tasks. Experiments on a special field dataset with a large number of out-of-vocabulary (OOV) words show that, compared with state-of-the-art methods, our method is more effective and achieves better results.
Evaluating Students Satisfaction in Online Postgraduate Courses Through a Fuzzy Linguistic Approach

Student satisfaction provides a students' perception of their educational experiences in an educational setting. As online learning is growing, institutions and instructors have become more interested in knowing what factors influence students' satisfaction in online learning setting. This study presents a novel approach for evaluating students' satisfaction in online postgraduate courses through a fuzzy linguistic approach for computing with words which addresses. Our approach models the students' satisfaction as a linguistic variable which reflects the perceptual nature of this concept; it models the satisfaction evaluation as a linguistic multi-attribute decision-making problem; it gathers perceptions of students according to 14 attributes for 18 online courses; it offers a methodological framework based on the 2-tuple linguistic model for computing with words through which performs a linguistic multi-step incremental aggregation process that outputs at each step partial or global self-contained and informative results expressed in a common linguistic domain.
DISASTER-PRONE SETTLEMENT ARRANGEMENT IN KAMPUNG BARU, SEMADAM SUBDISTRICT, SOUTHEAST ACEH REGENCY

Semadam Subdistrict in Aceh Tenggara District is the area where flash floods occur most frequently. One of the villages that is threatened with disaster is Kampung Baru Village. Flash floods occur every year but the government's steps have not been carried out completely. Therefore, it is very important to conduct a study of social, economic, physical and environmental aspects to determine the level of disaster vulnerability as a basis for conducting comprehensive arrangement of riverbanks and settlements in order to minimize losses. This research is a descriptive type of research, using a qualitative approach in describing the vulnerability and arrangement of flash floods- prone settlements. As a result, disaster vulnerability was obtained in the high category, so the arrangement carried out included rivers and settlements at the same time. The river is structured by: construction of a reservoir at the head of a river, widening of the riverbed, and reconstruction of a bridge. Meanwhile, settlements are arranged by providing green open space and engineering the transportation system
Setting the Stage

This section begins by more formally characterizing the text ranking problem, explicitly enumerating our assumptions about characteristics of the input and output, and more precisely circumscribing the scope of this book. In this exposition, we will adopt the perspective of information access, focusing specifically on the problem of ranking texts with respect to their relevance to a particular query—what we have characterized as the “core” text ranking problem (and what information retrieval researchers would refer to as ad hoc retrieval). However, most of our definitions and discussions carry straightforwardly to other ranking tasks, such as the diverse applications discussed in Section 1.1.
The Road Cold Chain Transportation Information Traceability Platform Based on “IOT+ Blockchain” Allows Cold Chain Transportation Management More Credible

Cold chain logistics is an important area related to people’s livelihood. China’s road cold chain transportation is in its infancy, and temperature exceeding standard is still relatively common. Road cold chain transportation management lacks information technology support, so there is a large management blind area. Based on the current application status and problems of Internet of Things (IOT) device and blockchain technology in the field of road cold chain transportation, this paper puts forward the construction of road cold chain transportation information traceability platform based on IOT+ Blockchain technology by transportation management departments, so as to improve the digital governance ability and credibility of cold chain transportation management. This paper analyzes the demands of all parties involved in the platform, the function, the application mode of IOT+ Blockchain technology, the platform architecture and puts forward relevant suggestions for the practical problems encountered in the popularization and application of the platform.
Computer Vision Analysis of BRIEF and ORB Feature Detection Algorithms

Feature detection is a process to detect the image's different features and describe the image properties. A highlight may be essential data for understanding the computational errand related to a specific application. Highlights may be particular structures within the picture, such as focuses, edges, or objects. Highlights may result from a typical neighborhood operation or include a location connected to the picture. Include discovery and coordinating is a critical assignment in numerous computer vision applications, such as structure-from-motion, picture recovery, question discovery, and more. We'll be talking around nearby highlight locations and coordinating in this arrangement. This paper aims to compare BREIF (Binary Robust Independent Elementary Features) and ORB (Oriented FAST and Rotated BRIEF) algorithms with the help of an image. This work will compare algorithms to determine which algorithm detects more critical points in less time and contains more matching vital points. This work is validated by checking the efficiency of the algorithms in terms of a matching parameter to evaluate its performance with other projects based on computer vision, self-driving cars, etc.
Fully Protected Image Algorithm for Transmitting HDR Images Over a WSN

The proposed Fully-Protected Image algorithm aims on providing a purely new algorithm so that the High Dynamic Range Images (HDRI) could be transmitted over a WSN without compromising the quality of the images transmitted. Since most of the gadgets support HDRI these days and WSN is finding its way into enormous applications each day, it becomes necessary to design an algorithm that not only transmits the data securely but also maintains the quality of the received image. The task is challenging as the size of HDRI is much more than the normal images and also they need to be scaled down to support the devices. The proposed work mainly centers upon secured transmission of HDRI using a newly designed Fully Protected Image (FPI) Algorithm for encryption. FPI generates new values for the pixels of the image without using any known algorithm which makes it tougher for the attackers to guess the encryption technique. As neither the size nor the technique is understood, linear modulated transmission of the image makes it difficult to be decrypted. To check the robustness of the FPI algorithm, it was important to check the quality of the image that was transmitted and obtained. The calculated value of the transmitted image’s and received image’s Mean Square Error(MSE) and Peak Signal-to-Noise Ratio(PSNR) was compared with the current Genetic Algorithm and Arnold Cat Map for this reason, and it was observed that the designed FPI algorithm performed twelve percent better than the Genetic Algorithm and eight percent better than the Arnold Cat Map.
Holistic Resource Scheduling for Data Center In-Network Computing

The recent trend towards more programmable switching hardware in data centers opens up new possibilities for distributed applications to leverage in-network computing (INC). Literature so far has largely focused on individual application scenarios of INC, leaving aside the problem of coordinating usage of potentially scarce and heterogeneous switch resources among multiple INC scenarios, applications, and users. Alas, the traditional model of resource pools of isolated compute containers does not fit an INC-enabled data center. This paper describes HIRE, a holistic INC-aware resource manager which allows for server-local and INC resources to be coordinated in unison. HIRE introduces a novel flexible resource (meta-)model to address heterogeneity and resource interchangeability, and includes two approaches for INC scheduling: (a) retrofitting existing schedulers; (b) designing a new one. For (a), HIRE presents a retrofitting API and demonstrates it with four state-of-the-art schedulers. For (b), HIRE proposes a flow-based scheduler, cast as a min-cost max-flow problem, where a unified cost model is used to integrate the different costs. Experiments with a workload trace of a 4000 machine cluster show that HIRE makes better use of INC resources by serving 8–30% more INC requests, while simultaneously reducing network detours by 20% and reducing tail placement latency by 50%.