Reviewerly/exHarmony huggingface dataset:
A Composite Dispatching Rule-Based Method for Multi-Objective Aircraft Landing Problem

The high air traffic demand and scarce supply of resources have imposed intense pressure on the air transportation system. In this study, we propose a new method to solve the multi-objective aircraft landing problem quickly. Firstly, we comb the different criteria of the aircraft landing problem, and we choose the average scheduled time, maximum flow time, and maximum delay time as multiple objectives. Secondly, we formulate the model of the multi-objective aircraft landing problem and present the appropriate algorithms to solve the problem. In addition, a new composite dispatching rule is developed to solve the multi-objective aircraft landing problem with high computational efficiency. Finally, the performance of the proposed method is evaluated by a set of benchmark instances and in a real case scenario. The computational results illustrate the efficiency of our approach, which could simultaneously enhance the runway capacity, maximize the cost-effectiveness of airlines, and reduce the workloads of air traffic controllers.
The Information Infrastructure for Analyzing and Visualizing Learning Logs in Ubiquitous Learning Environments

In this paper, we describe about an information infrastructure and data flow in SCROLL Data Tracking Module for analyzing and visualizing students’ learning logs in ubiquitous learning environments. There are lots of behaviors log in SCROLL, which not be used to analyzed and visualized yet. To build the information infrastructure in SCROLL, we send learning behaviors data to xAPI (Experience API) for analyzing and visualizing and share students’ learning logs with LTI (Learning Tools Interoperability).
Investigation of the Effect of Letter Labeling Positions on Consecutive Typing on Mobile Devices

Using soft keyboard is the main text entry method on mobile devices. However, texting on small mobile devices, such as smartphones remains challenging and inconvenient. It’s because of the small size of keys, the difference between view angle and touch point, the tilt of the device etc. When entering text, the human eye conducted visual search for the target key, then casted their attention on the symbol on the button. In this research we would like to understand how the touch points would be affected by the symbol positions on the button. A click-based App has been designed to observe, record, and analyze the data and behaviors of the subjects when they clicked on the keypad buttons. In the experiments the subjects have been asked to perform a series of clicking on the target points under five conditions that the symbols appeared at the center of the buttons, the upper right portions, the lower right portions, the upper left portions and the lower left portions. According to analyses we fund that under the circumstances of different symbol positions, the subjects tended to click on the positions where the symbols appeared instead of the centers of the buttons.
A Proposal of Video Evaluation Method Using Facial Expression for Video Recommendation System

Recently, video sharing services and video on demand services like YouTube, Netflix and Amazon Prime Video have come to prominence worldwide. These services try to get the users’ evaluation of videos for the better recommendation. For example, some of them prepare the thumbs-up/down buttons and star buttons in their interface to get users’ explicit evaluation. However, the effect of this method is doubts. The reason is these method needs user’s conscious operation. Therefore, we need efficient method for getting users’ implicit evaluation of videos. In this study, we focus on facial expressions especially smile of users watching videos. In this paper, we have developed the system that can get user’s expression of users watching videos by some APIs, and investigated the relation between evaluation of videos by scoring and facial expression by using the proposed system. As a result of the experiment, positive correlations between the ranking by the user’s scoring and the ranking based on the amount of smile of users watching videos are shown in 22 of the 28 participants. In addition, strong positive correlations were found among 13 of them. This result suggests possibility of using the implicit evaluation like smiling as well as the explicit evaluation like a scoring. So, it is suggested that recommendation method based on facial expression is effective.
Cultivation of Computational Thinking in College Computer Basic Teaching

Abstract Cultivating college students’ computational thinking is an important teaching plan in computer basic teaching in university stage. Teachers should integrate the teaching content according to the level of computer basic knowledge and ability of college students, classify the basic teaching contents of college computer scientifically and increase the part of experimental teaching in the college computer teaching classroom. Using scientific computer teaching methods can improve students’ computational thinking effectively, so that the comprehensive ability of college students can be improved.
Privacy Preserving System for Real-Time Enriched-Integrated Service with Feedback to Providers

We have developed a secure data-providing system for an enriched-integrated service with feedback to providers featuring a verifiable attribute-based keyword search (VABKS). One potential application of the system is the Integrated Broadcast-Broadband (IBB) service, which acquires information related to broadcast programs via broadband networks. One of the services IBB provides is a recommendation service that delivers recommended information matching user preferences (such as TV programs) based on user viewing history. Another application is in mobile environments featuring smart-phone usage, where services based on user location can be suggested. In this study, we propose a secure system that adds the functions of privacy preservation and feedback to providers. Thereby the functions provide increased business benefit to users of the IBB service for mobile usage, and feedback property provides another benefit to the providers of IBB services.
The Interaction Design Principles about Page Layout of Over-the-counter Blockchain Transaction

Objective: To study the user"s visual behavioral habits of Over-the-counter Blockchain transaction webpages.Background: With the continuous development of the blockchain, a large number of users conduct off-exchange transactions on the exchange"s website, and the user experience is not very well.Method: Through the eye tracker experiment test and the software (BeGaze) analysis data, as well as the post-experimental questionnaire survey analysis and user interviews, the data is finally integrated and analyzed, and the user"s over-the-counter transaction in the blockchain exchange is obtained. Visual behavioral habits on the web. (1) The scope of the study included the user"s visual behavioral habits on the web side of the blockchain exchange. (2) Research methods include: quantitative research, qualitative research.Results: (1) Total eye data were analyzed by independent sample t test, as shown in total eye data. There were significant differences in the number of fixation points and average fixation time between the two pictures; (2) The average number of fixation points of adversary"s information area of both tests is obviously higher than the standard deviation, and the standard value of average fixation time is also significantly higher than the standard deviation; (3) The user"s demand for the adversary"s information area is very low; (4) Users think data is their most urgent need; (5) The heat map shape presents the shape of the waterfall; (6) The eye movement trajectory of most users shows that the user first scans in the vertical direction, then scans in the horizontal direction, and decreases from the top to the bottom.Conclusion: Five results were obtained through research and analysis, weakening adversary"s information, data emphasis, important rules for arrangement of different areas, the arrangement of sequence numbers, waterfall shape visual glance behavior.Application: The results of this study can be used for the layout guidance of the network side of the OTC transaction of the blockchain exchange, which can improve the service experience of the user on the exchange.
Intelligent Construction and Management of Thermal Power Plant Based on Internet + Mode

How to respond to the national call, the establishment and management of intelligent thermal power plants under the Internet + mode is a problem that the current thermal power plant management staff needs to think about. Throughout the domestic large-scale thermal power companies have gradually entered the era of information and intelligent management. Based on this, the paper first analyzes the concept and characteristics of intelligent thermal power plants, analyzes its construction foundation, discusses the system architecture and business direction of intelligent thermal power plant construction, and combines new technologies such as artificial intelligence and new products to analyze intelligent thermal power plants. Prospects for development, thereby maximizing the economic benefits of the power plant and the social benefits.
Examination of physical pattern evidence

Physical fit, also called direct fit, matching is the simplest, yet, often one of the most persuasive types of evidence that can be obtained. A physical direct and fit match is a match between or among pieces of a randomly fractured, torn, or cut object. The process of recognition is the first step of physical pattern comparison. Direct physical fit and matching is jigsaw fit matching and is applicable to solid materials that break into pieces in a random way. Indirect physical matching is fitting, or attempting to fit, severed, broken, or torn pieces of an item or object together in a way that convincingly demonstrates that the pieces were once part of the same structure. Pattern evidence can be seen as one of the four major categories of physical evidence in terms of the scope of criminalistics. It is usually thought of as physical matching exercises, imprints, striations, shapes and forms, and reconstruction patterns.
QianLi: A Modular System for Connecting Distant Family Members Through Tacit Interaction

Many families are now geographically separated for various reasons, while most of current communication technologies call for active participation and are intrusive. In this paper, we propose the concept of tacit interaction that supports nonintrusive communication. The concept is then applied to the design of the QianLi system comprising a central base and several modular devices that help distant family members to tacitly stay in touch without excessive intrusion. Such tacit interaction in the QianLi system is realized using artificial intelligence and the Internet of Things technology. When the user fulfils a predetermined condition, the QianLi system shows the corresponding customized reaction to other distant users. This paper also demonstrates the implementation methods, technical components, and interaction possibilities of the QianLi system. An evaluation reveals that our system assists users to share their lives with distant family members in a nonintrusive, customized and tacit way. In the future, QianLi may also serve as a potential research material that can be used to obtain more insights into tacit interaction in practical applications.
Person Re-Identification Using Pose-Driven Body Parts

The topic of Person Re-Identification (Re-ID) is currently attracting much interest from researchers due to the various possible applications such as behavior recognition, person tracking and safety purposes at public places. General approach is to extract discriminative color and texture features from images and calculate their distances as a measure of similarity. Most of the work consider whole body to extract descriptors. However, human body maybe occluded or seen from different views that prevent correct matching between persons. We propose in this paper to use a reliable pose estimation algorithm to extract meaningful body parts. Then, we extract descriptors from each part separately using LOcal Maximal Occurrence (LOMO) algorithm and Cross-view Quadratic Discriminant Analysis (XQDA) metric learning algorithm to compute the similarity. A comparison between state-of-the-art Re-ID methods in most commonly used benchmark Re-ID datasets will be also presented in this work.
Remote Monitoring and Control of Electrical Systems with Augmented Reality and Digital Twins

In this paper an attempt is made to bring in an interactive laboratory experience in the working of an electric circuit. The circuit operation can be controlled remotely. The augmented reality system is implemented through an app developed on a smart mobile phone. The whole system involves a physical electric circuit, its digital counterpart and a smart mobile phone app. The mobile app developed is capable of identifying the individual circuit elements and overlaying it on the circuit. It is capable of implementing changes in circuit parameters in real time through the app and the display of the waveforms from the operation of the circuit in real time. The paper also highlights the successful working prototype of an electrical load such as an electric bulb whose on-off and brightness control operation is accomplished remotely through the mobile phone and state of the lamp is visualized, its electrical parameters such as voltage, overlaid and controlled in real time through the app on the mobile phone. The results indicate the reliable and stable operation of such systems in an uninterrupted internet facilitated environment.
Interaction Design in the Active and Assistive Living Field of Practice

The design process of interactive systems is a multifaceted process that can lead to various forms of manifestations. Designers can draw on different interaction design styles to realize the intended, to-be designed interactive system and the corresponding HCI artifacts. This work identifies and investigates different interaction design styles based on concrete prototypes that have been designed, developed and evaluated in co-funded research project in the Active and Assisted Living domain. In total, the work presents 12 concrete prototypes which have been implemented between 2011 and 2018 and list 8 designable interaction design styles. The work lists identifies main characteristics and elaborates their relationships and dependencies which serve as basis for the future extensive research work that questions the impact of different interaction design styles on the targeted end users supposed to use the intended, to-be designed interactive system.
A VRLE Design Scheme for the Learning of Film Making

Education of film making has made great progress since the earliest film and television departments were established in American universities in early 1960s. Nowadays, students can learn theories and techniques of film making either online or in a classroom. But the cost of actually shooting a film for practice purposes is still too high and keeps many learners from getting enough training. In recent years, with the development of science and technology, virtual reality (VR) has been applied to various fields, including medicine, entertainment, education, military, and aerospace. The application of VR technology will promisingly become the next big leap in the future development of education. This paper focuses on how to effectively design a virtual reality learning system to help future film makers solve the problem of insufficient practice in resource-constrained situations. A prototype of the system was developed based on prior research conclusions, and 12 participants were recruited to experience the system and answer questionnaires followed by in-depth interviews. The qualitative research results shew that the practice carried out in a virtual reality environment was certainly usable and effective. In conclusion, virtual reality technology may very likely play an important role in the education of film makers.
Tractable Learning of Probability Distributions Using the Contrastive Hebbian Algorithm

In some tasks (e.g., assigning meanings to ambiguous words) humans produce multiple distinct alternatives in response to a particular stimulus, apparently mirroring the environmental probabilities associated with each alternative. For this purpose, a network architecture is needed that can produce a distribution of outcomes, and a learning algorithm is needed that can lead to the discovery of ensembles of connection weights that reproduce the environmentally specified probabilities. Stochastic symmetric networks such as Boltzmann machines and networks that use graded activations perturbed with Gaussian noise can exhibit such distributions at equilibrium, and they can be trained to match environmentally specified probabilities using Contrastive Hebbian Leaning, the generalized form of the Boltzmann Learning algorithm. Learning distributions exacts a considerable computational cost as processing time is used both in settling to equilibrium and in sampling equilibrium statistics. The work presented here examines the extent of this cost and how it may be minimized, and produces speed-ups of roughly a factor of 5 compared to previously published results.
Design of Single Patient Care Monitoring System and Robot

The advent of VLSI technology brings in its wake huge benefits to patients, doctors and designers, undreamt before, by bringing in automation and raising the level of care nurses are able to provide. Instead of walking to each patient’s bedside to collect vital information, nurses can collect it in one location and concentrate their efforts on caring for those who need them the most. As an outcome of these requirements, a prospect arose to design a highly efficient patient care and monitoring system which can handle patients and multiple parametric measurements from every single patient in real time. Patient care and monitoring embedded systems are proposed in this work to acquire, store, display physiological information obtained from multi patients and are vital in operating rooms, emergency rooms, intensive care units, ambulances as well as at homes. The design exploits development of Novel, Fast Algorithms, Design of Architectures using labVIEW implementations.
Chapter 13. Telling the Story

In this chapter, we will take a look back at the various issues discussed in the book and attempt to bring together the various threads to consider some of the unresolved questions that may change the way that science is recorded in the future. Although the issues may seem unrelated initially, they may in fact converge when we think about the potential role of digital tools in capturing and making use of the scientific record. First, we will take a look at what belongs in the scientific record and expand upon our definition from Chapter 1. We will take a look at the nature of Electronic Laboratory Notebooks (ELNs) and consider whether there is a need to development digital research management platforms or ELN-type tools that can do everything to support the needs of researchers. Next, we will examine the arguments for and against paper, and consider whether the move to paperless technology is essential for progress in science. Finally, we will consider storytelling and serendipity in science and different ways that stories about research and researchers might be told using digital tools and other methods.
Lexicon-Based Sentiment Analysis of Online Customer Ratings as a Quinary Classification Problem

Online customer reviews are not only an important decision-making tool for customers, they are also used by e-commerce providers as a source of information to analyze customer satisfaction. In order to reduce the complexity of evaluation comments, written reviews are additionally represented by evaluation stars in many evaluation systems. Numerous studies address the sentiment recognition of written reviews and view polarity recognition as a binary or ternary problem. This study presents the first results of a holistic approach, which takes up the combination of customer reviews with evaluation points realized in platform-dependent evaluation systems. Sentiment analysis is regarded as a quinary classification problem. In this study, 5,000 customer evaluations are analyzed with lexicon-based sentiment analysis at document level with the target to predict the evaluation points based on the determined polarity. For sentiment analysis the data mining tool RapidMiner is used and the categorization of the sentiment polarity is realized by using different NLP techniques in combination with the sentiment dictionary SentiWordNet. The supervised learning algorithms k-Nearest Neighbor, Naïve Bayes and Random Forest are used for classification and their classification quality is compared. Random Forest achieves the most accurate results in conjunction with NLP techniques, while the other two classifiers provide worse results. The results suggest that a stronger scaling of polarity requires a stronger differentiation between classes and thus a more intensive lexical preprocessing.
Learning Basal Cell Carcinoma Patterns by Fusing Pathologists’ WSI Navigations and Graph-Based Centrality Features

This article introduces a model that automatically detects suspicious basal cell carcinoma regions in whole slide images (WSI) by integrating nuclear architectural features and information captured from WSI pathologists’ navigations during diagnostic tasks. In such an approach, manual annotations are not needed since high-level expert knowledge is implicitly captured when the pathologist is exploring the WSI. The method was tested on a set of 10 cases of patients diagnosed with basal cell carcinoma using a leave-one-out cross-validation technique. At each iteration, a quadratic discriminant analysis classifier was trained to identify cancerous nuclei using architectural features of nuclei belonging to the regions that were highly or little visited by pathologists when rendering a diagnosis. Experimental results showed an average accuracy of 86% and an F-score of 76%, thereby demonstrating the potential of this approach to be included in actual clinical scenarios.
Power Conservation in Cloud-Assisted Real-Time Context Learning System

Contextual information can be learned at the mobile devices, such as smartphones, in real-time from the sensors to provide better services to the user. The sensor data collection process, where the data is collected by various internal sensors or autonomous external sensors, incurs greater power consumption, depending upon the type of sensor and data capturing rate in the mobile devices. On the other hand, the learning process itself drains the battery and at the same time affects the accuracy of the learning due to the limited computational power of the mobile devices. These problems can be addressed by shifting the learning process to the cloud, which is however achieved at the cost of reducing the accuracy of the real-time solutions and incurs heavy bandwidth usage depending upon the context of the user. Therefore, we propose a cloud-based real-time context-learning system where the user of the system will get the predetermined service in real-time according to the user-determined context, which is learned from the related sensors while conserving a maximum amount of power compared to the standalone system or the cloud-based system. We have produced experimental results using a smartphone that illustrates that our system conserves 96.36&#x0025; of power compared to the mobile-learning system while at the same time, the network data usage is 80&#x0025; lower when compared to the cloud-based system. We have also showed that the proposed system works 76.17&#x0025; and 94.81&#x0025; faster compared to the mobile-learning and cloud-based system respectively.