Reviewerly/exHarmony huggingface dataset:
Object Recognition with Machine Learning: Case Study of Demand-Responsive Service

To assist the disabled, a system was developed and designed to provide passenger transport services for disabled persons in wheelchairs. Machine-learning image-recognition technology was applied to provide accessible bus rides for the disabled. Based on the video footage at the bus stop, this system can judge whether there are wheelchair riders in the waiting area. YOLOv3, a real-time image-recognition model, was used for object recognition and notification. By combining LINE with the Flask Web application framework, it helps the disabled transmit messages and actively notifies bus drivers. The API server was established using the Flask framework. When the system at the bus stop detects the disabled, the images of the disabled waiting for the bus are transmitted to the server, and the images and related messages are transmitted in real time to the users through the LINE Message API. Passengers can also use the LINE chatbot to enter keywords to confirm the real-time location of the bus. The dynamic bus information comes from the Open Data of the Bureau of Transportation. The LINE BOT API program was developed using the PyCharm tool and then transmitted to the Heroku cloud platform for deployment. The LINE BOT API provides real-time wheelchair image recognition, and receives notifications and bus information queries through the LINE chatbot. The main contributions of this study are the use of artificial intelligence (AI) technology to provide solutions to facilitate bus rides for the disabled and to improve the quality of barrier-free transport services in modern society.
Semantic Marking Method of Video Scene Based on 3D Convolutional Neural Network

With the rapid development of multimedia technology, video data has become an important source big data. How to identify and segment different semantic objects in video scenes accurately has become a research hotspot in the field of computer vision. On the basis of studying the traditional video scene semantic segmentation method, this paper proposes a semantic marking method of video scene based on 3D convolutional neural network. This method uses 3D convolutional neural network to learn the high-level discriminant features of objects of different semantic categories to improve the accuracy of semantic segmentation. The experimental results show that the proposed method is comparable to the classical method in segmentation accuracy and can overcome the drag effect caused by the false optical flow field relations.
Imminent Prospects of Mobile Mediated Learning Interaction in Traditional Classroom: A Survey 2013-19

Phenomenal growth in mobile technology with affordability has created a pervasive device extension associated with students. This is blended with both desired and distracting components. These significant ramifications of rise in mobile device usage motivates to focus the study to evaluate the ongoing research activity pertinent to the pedagogical inclusion of mobile devices in classroom interaction. This study represents the review of refereed articles about mobile device utility in classroom interaction during the year 2013-19. The research articles selected for assessment confined in the areas of interaction design, the usefulness of mobile devices in the traditional education system, adaptive web-based educational systems and potential distraction of the devices. Most of the works tried to emphasis the positive response about the context-dependent use of mobile devices' integration in the classroom. On the contrary significant claims on the interference of portable devices and causal effects have also been highlighted by many researchers. The quantitative and qualitative findings of the survey help in identifying the outright spaces and regulated use of mobile devices in face to face classroom interaction. This will also help in designing active research informative interaction with an imminent effect to enhance device mediated classroom learning.
Abnormality Detection with Rényi Divergence for Univariate Gaussian Data

This paper investigates the abnormality detection based on the Rényi divergence with the divergence order between 0 and 1. It is assumed that univariate Gaussian data samples are independent and identically distributed (i.i.d.). The distance between the estimated distribution from observed data and that from historical data under the normal condition is quantified by the Rényi divergence, the proposed test statistic. The false alarm rate (FAR) and the missed alarm rate (MAR) are derived analytically based on the distribution of the Rényi divergence. Under the abnormal condition, constant bias and multiplicative faults are considered. Taking both the FAR and MAR into consideration, the proposed detection algorithm can adaptively optimize the divergence order and threshold according to observed data. In the simulation, the analytical FAR and MAR results are verified by the Monte Carlo simulations, and the proposed algorithm is shown to outperform the Kullback-Leibler divergence based method.
PPCA

Automated tasks play an important role in both consumer and industrial IoT environments. In many scenarios, the IoT tasks are performed given certain conditions. To facilitate the tasks, it is necessary to delegate a third party to listen to events that trigger the conditions and issue commands to the IoT resources accordingly. However, without restriction, the third party may be over-privileged and able to control the resources unconditionally. We define the third party's permission to act under some conditions as a conditional action. We propose PPCA, a privacy-preserving service that allows users to create conditional actions in a decentralized platform using smart contracts. PPCA can guarantee strict privilege at the third party that holds conditional actions. By generalizing a variety of conditions into simple forms of conditional logic, the conditions can be verified in a privacy-preserving manner. We build a prototype of PPCA on Ethereum. The performance shows the feasibility of PPCA in IoT environments.
Research on the Environment Temperature of 3D Printing Methacrylate based on Adaptive Fuzzy PID Algorithm

In the 3D printing process of methacrylate gelatin (GelMA), the temperature control system of inner chamber of the 3D bio printer is a non-linear time-delay system, and its control quality directly affects the quality of GelMA printing. An adaptive fuzzy PID temperature control strategy was put forward in this article which can effectively adjust the PID parameters, complete the purpose of parameter conversion in the process of temperature control, and perform real-time regulation on the entire ambient temperature of GelMA printing. Compared with the temperature system controlled by traditional PID algorithm, the sampled fuzzy adaptive PID algorithm has better stability and performance, uniform temperature field distribution and temperature difference control and the printed GelMA has better biological performance and more complete shape. The accuracy was high, the anti-interference ability was strong, and the printed GelMA material had better shape quality.
Ghosting Elimination Method Based on Target Location Information

Real-time stitching of UAV (Unmanned Aerial Vehicle) sensor images can effectively enhance the sensory ability of the ground station upon the task environment thereof. When UVA sensor images stitched at different time, if the moving object is located at the overlapping part of the adjacent images, the dislocation brought by the object movement may cause "ghosting" phenomenon of the moving object in the final stitched image and accordingly influences the final stitching effect. Therefore, a ghosting elimination method based on target location information was designed in this paper, specifically as follows: firstly, obtaining the location information of the moving object by the target detection method; then, adopting the improved Criminisi algorithm to automatically repair the image from which the dynamic object is eliminated; finally, stitching the images and intensively displaying the extracted target at the corresponding position in a form of three dimensional entity. In this paper, the proposed method was adopted for the real-time stitching test of UVA aerial images, and the experiment result has proved the effectiveness of the proposed method.
Path Planning of Lunar Robot Based on an Adaptive Ant Colony Algorithm

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm (A-APFACO) suitable for path planning of lunar robot was proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. The simulation experiment results show that the convergence speed of the improved adaptive ant colony algorithm is significantly faster, and the success rate of global searching for the shortest path can reach 96%.
Data Representation Models of Concept Systems

This paper first introduces concept representation model to represent the concept and relations among concepts. Then, in view of the existing problems such as numerous types of concept systems, cross-application and standard inconsistency, this paper proposes that concept systems can be distinguished into five types according to their formalization degree, namely glossary, subject heading schemes, taxonomies, thesauri and ontology. Then we summarize and compare the characteristics of them, and propose data models of each type of concept system.
An Overview of Ways of Discovering Cause-Effect Relations in Text by Using Natural Language Processing

Understanding of cause-effect relations is vital for constructing a valid model of a system under development. Discovering cause-effect relations in text is one of the difficult tasks in Natural Language Processing (NLP). This paper presents a survey on trends in this field related to understanding how linguistically causal dependencies can be expressed in the text, what patterns and models exist, which of them are more and less successful and why. The results show that causal dependencies in text can be described using plenty lexical expressions as well as linguistic and syntactic patterns. Moreover, the same constructs can be used for non-causal dependencies. Solutions that combine the patterns, ontologies, temporal models and a use of machine learning demonstrate more accurate results in extracting and selecting cause-effect pairs. However, not all lexical expressions are well studied. There are few researches on multi-cause and multi-effect domains. The results of the survey are to be used for construction of a Topological Functioning Model (TFM) of a system, where cause-effect relations are one of key elements. However, they can be used also for construction of other behavioral models.
Testing and Evaluating SNOMED CT Web Browsers' Textual Search Feature

SNOMED CT is the comprehensive medical and clinical vocabulary populated worldwide. With rapid increasing of the scale, it requires powerful and user-friendly tools for data retrieving. The browsers should not only provide accurate retrieval of concepts, but also support approximately searching on their textual search function. In this paper, we evaluate seven SNOMED CT web browsers on their textual search feature. Our evaluation process consists of three steps:1) designing test cases; 2) using test cases to examine browsers' textual search feature; and 3) analyzing the search results using Precision & Recall, F-score, and Normalized Discounted Cumulative Gain(nDCG). The performance report for these seven browsers is provided. In the paper, we also discuss the possibility of optimizing SNOMED CT browsers' search function by using ontology deep learning model -- Deep Knowledge Extraction from Text (DKET).
Suitability of Transport Techniques for Video Transmission in IP Networks

The paper discusses the problem of video transmission in an IP network.The authors consider the ability of using the most popular video codecs that use both the MPEG2 Transport Stream and Dynamic Adaptive Streaming over Hypertext Transfer Protocol (DASH).The main emphasis was given to ensuring the quality of service and quality assessment methods, taking into account not only the service-or network provider's point of view but also the end user's perspective.Two quality assessment approaches were presented, i.e. objective and subjective methods.The authors presented the results of the quality evaluation for H.264/MPEG-4, H.265/HEVC and VP9 codecs.The objective measurements, proved by statistical analysis of user opinion scores, confirmed the ability of using H.265 and VP9 codecs in both real time and streaming transmissions, while the quality of video streaming over HTTP with the H.264 codec proved inadequate.The authors also presented a connection between the dynamics of network bandwidth changing and MPEG-DASH mechanism operation and their influence on the quality experienced by users.
A New TDOA Parameter Estimation Algorithm Based on Lifting Scheme

Although more accurate TDOA (Time Difference of Arrival) parameters can be obtained by using wavelet threshold de-noising method in TDOA parameter estimation under low signal-to-noise ratio, the complexity of the algorithm is further improved, which is not conducive to the requirement of fast acquisition of active device location in reality. In order to reduce the complexity of TDOA parameter estimation algorithm, an improved wavelet threshold de-noising TDOA parameter estimation algorithm based on Lifting Scheme is proposed in this paper. Lifting Scheme algorithm can achieve in-situ calculation and integer lifting, which requires less computation in wavelet decomposition and reconstruction. The simulation results show that the complexity of the proposed algorithm is reduced without sacrificing the estimation accuracy.
The Self-Organizing Map: An Methodological Note

Nowadays, the Self-Organizing Map (SOM) is configured as one of the most widely used algorithms for visualising and exploring high-dimensional data: this visual formation is derived from the learning map process in which neighbouring neurons, on the input lattice surface, are connected to their respective neighbouring neurons on the output lattice surface. The present methodological note will introduce the Self-Organizing Map (SOM) algorithm, discussing, in the first part, its background, properties, applications and extensions and, in the second part, its evolution: the formation of new types of topographic maps, used for categorical data, such as time series and the tree structured data. In particular, this new type of map could be useful for further micro data analysis applications, such as document analysis or web navigation analysis, going beyond the limitation of the kernel-based topographic maps or creating new type of kernels, detailed in the Support Vector Machine literature.
Media Education as the Factor of Safety of the Individual

The purpose of the work is to show the basis of the analysis of media education history, and the transformation of understanding of its role in the learning process, its versatility in the modern education system, the opportunities it provides for solving the problems of the development of critical thinking of the individual, and the preservation of its security and personal space. The transformation of the concept of “media education” from learning technologies using media to technologies that form critical and analytical thinking for the adaptation of man to the conditions of life in the modern media space is shown. Modern media education is designed not to protect young people from the influence of the media, and to lead them to the best examples, but allows students to make informed decisions about their own protection, as well as intelligently present themselves in the media space, creating their own media product.
Application of Speech Recognition based Interaction in Augmented Reality Children’s Books

Traditional systems of speech recognition based interaction need to identify the accurate semantics and grammar in the speech to be recognized. This kind of systems always contain problems such as using a specific language, presetting voice template, they are not suitable for speech recognition interaction in augmented reality children's books. For these reasons, this paper studies the pronunciation characteristics and interactive needs of children, analyses the differences among template matching method, random model method and artificial neural network method. Then uses template matching method to design a speech recognition system for small vocabulary that can replace the voice template, and applies it to the speech recognition interaction in an augmented reality children's reading application. Finally, the effectiveness of the proposed method is verified through experiments, which provides a new direction for the application of speech recognition technology in augmented reality children's books.
Next steps in variability management due to autonomous behaviour and runtime learning

One of the basic principles in product lines is to delay design decisions related to offered functionality and quality to later phases of the life cycle [25]. Instead of deciding on what system to develop in advance, a set of assets and a common reference architecture are specified and implemented during the Domain Engineering process. Later on, during Application Engineering, specific systems are developed to satisfy the requirements reusing the assets and architecture [16]. Traditionally, this is during the Application Engineering when delayed design decisions are solved. The realization of this delay relies heavily on the use of variability in the development of product lines and systems. However, as systems become more interconnected and diverse, software architects cannot easily foresee the software variants and the interconnections between components. Consequently, a generic a priori model is conceived to specify the system's dynamic behaviour and architecture. The corresponding design decisions are left to be solved at runtime [13].
New Hybrid Approach Multi-agents System and Case Based Reasoning for Management of Common Renewable Resources

In this paper, we present a new hybrid approach multi-agents system and Case-Based Reasoning (CBR). We have designed a generic and scalable class diagram to develop complex multi-agent systems [3] for Decision Support System based on CBR to predict and anticipate an environmental risk. Our approach inherits from Model Driven Architecture (MDA [11]), which aims to design, develop and implement models of multi-agent systems that we build from AUML. The source code of the models is generated by an open source tool called AndroMDA [13]. The model and source code will be used to design and develop applications to implement and simulate multi-agent models for Management of Common Renewable Resources [4].
Varieties of Chinese Discrimination Using Hybrid Squeeze-and-Excitation Network

As linguistic differences among related languages are less obvious than those among different languages, automatic language identification in similar languages, varieties, and dialects is a much more challenging task. In this paper, we propose a hybrid SENet (Squeeze-and-Excitation network) based attention model to discriminate varieties of Chinese from six regions (i.e., Mandarin Chinese, Hong Kong, Taiwan, Macao, Malaysia, and Singapore). Firstly, we encode the word embeddings using Bi-LSTM, and automatically extract the discriminative words related to different varieties of Chinese based on SENet. Then, since the original word-embeddings can implicitly encode rich linguistic regularities and patterns, a max-pooling strategy is adopted to extract the most salient features. Finally, we successfully incorporate the SENet and max-pooling to conduct varieties of Chinese discrimination. Experiments on our 6-way varieties of Chinese corpus show the effectiveness of the proposed model, which significantly outperforms the state-of-the-art widely used methods by a large margin.
The Different Perspectives of Saudi Female Undergraduate Students Towards Linguistic Violence

This paper explored the relationship between linguistic violence and attitudes of 90 Saudi female undergraduate students in the English department of King Saud University. The study was conducted to identify the most frequent abusive types of phrases and investigate its effectiveness on students’ attitudes by recording their responses. Students were able to identify 270 phrases that evoked their emotions. Quantitative measures were used to analyze the student’s attitudes when being exposed to verbal violence. Results confirm that labeling the person negatively (n=90) was more likely to trigger anger (n=114) and less likely to elicit frustration (n=67), which was the opposite in phrases implied threats that evoked the emotion of frustration more than anger. Regarding the hidden struggle among women in an abusive environment, findings suggest enrolling students in classes that motivate them to take action towards any form of verbal abuse. In addition, this research would raise awareness and decrease the rate of linguistic violence.
Deep Tree Search Based Pilot Efficiency Semi-Blind Channel Estimation for Massive IoT

As the tremendous increasing of Internet of Thing (IoT) deceives, the channel estimation between devices and base station will be a critical problem due to the pilot contamination. To address this issue, in this work, we provide a new perspective to alleviate pilot contamination for massive IoT system by reducing the command of piolt. A deep tree search (DTS) method is a decision-making algorithm. The basic principle of proposed algorithm is based on DTS and semi-blind channel estimation, which can reduce the number of transmitting pilot sequence and improve transmission efficiency. We first covert the channel state information (CSI) into a ternary quantization form utilizing the sparse channel property of massive IoT system. Then the channel estimation can regarded as a Markov Decision Process (MDP) with the goal of finding the minimal Frobenius norm between estimated CSI and the transmitted signal. We propose a deep neural network (DNN) based tree search method to solve the MDP based CSI estimation problem. Compared with conventional methods, the proposed method exhibits higher estimation efficiency, which reduces by 10% the pilot sequence to achieve the same estimation performance. The experiments show that the SER performance of T-DTS can reach about 5.6 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> at SNR=6 dB in massive MIMO systems, which outperforms existing methods.