Reviewerly/exHarmony huggingface dataset:
A Robot Object Recognition Method Based on Scene Text Reading in Home Environments

With the aim to solve issues of robot object recognition in complex scenes, this paper proposes an object recognition method based on scene text reading. The proposed method simulates human-like behavior and accurately identifies objects with texts through careful reading. First, deep learning models with high accuracy are adopted to detect and recognize text in multi-view. Second, datasets including 102,000 Chinese and English scene text images and their inverse are generated. The F-measure of text detection is improved by 0.4% and the recognition accuracy is improved by 1.26% because the model is trained by these two datasets. Finally, a robot object recognition method is proposed based on the scene text reading. The robot detects and recognizes texts in the image and then stores the recognition results in a text file. When the user gives the robot a fetching instruction, the robot searches for corresponding keywords from the text files and achieves the confidence of multiple objects in the scene image. Then, the object with the maximum confidence is selected as the target. The results show that the robot can accurately distinguish objects with arbitrary shape and category, and it can effectively solve the problem of object recognition in home environments.
The Enlightenment of "AR / VR" Technical University Course Education in Taiwan, China

The development of computers and information technology, especially the emergence of the Internet, big data, artificial intelligence, virtual reality, and mental enhancement, has created a new field of educational technology and entered a new chapter in the development of educational information. Augmented reality technology will be the most promising technology in the field of education after multimedia and computer networks. The most common teaching application of AR/VR technology in mainland China is to integrate digital learning resources and carry out relevant theoretical and experimental courses in multiple universities; China's Taiwan AR/VR technology mainly focuses on image processing related fields and education, and combines multiple teaching methods. This article summarizes and compares the application of augmented reality technology in university curriculum education on both sides of the Taiwan Straits from mainland China and Taiwan, and draws some enlightenment.
Research on Nonuniform Sampling of Sector-Scan Mode for a Mechanical Scanning Radar

In this paper, two methods are proposed to solve the problem that the target is easy to be lost and has low accuracy caused by non-uniform sampling during target tracking by sector scanning in mechanical scanning radar. One is the track fusion method based on Hough transformation, which converts the nonuniform sampling problem of sector scanning into two parallel uniform samples for processing, and outputs the track fusion through Hough transformation. The other method solves the problem of non-uniform sampling by analyzing the sampling time interval change of different positions of the sector and making a rough estimation of the target prediction time. Finally, the two methods are compared by experimental analysis.
Information-Bottleneck Approach to Salient Region Discovery

We propose a new method for learning image attention masks in a semi-supervised setting based on the Information Bottleneck principle. Provided with a set of labeled images, the mask generation model is minimizing mutual information between the input and the masked image while maximizing the mutual information between the same masked image and the image label. In contrast with other approaches, our attention model produces a Boolean rather than a continuous mask, entirely concealing the information in masked-out pixels. Using a set of synthetic datasets based on MNIST and CIFAR10 and the SVHN datasets, we demonstrate that our method can successfully attend to features known to define the image class.
Ontology-Driven Mental Healthcare Applications: A Case Study on Cognitive Rehabilitation with Serious Games

The emergence of Semantic Web offered an opportunity for developing new approaches regarding the integration, deployment and operation of healthcare applications. By combining network connectivity, contemporary software architectural styles and semantic-based technologies efficient representation and communication of data and knowledge is feasible between different system components. In this context, standards such as Resource Description Framework (RDF) and Web Ontology Language (OWL) are underpinning the definition of ontologies in the healthcare domain. The aim of this work is to provide insights on the design and implementation of ontology-based healthcare applications. The focus is on the domain of mental healthcare and in particular the development of a game platform for mild cognitive impairment rehabilitation is examined closely as a case study. The positive impact of the synergy between healthcare applications and ontologies is discussed and some of the major challenges and obstacles in this scope are identified.
Audrey- Flower-like Social Assistive Robot

Audrey is a flower-like socially assistive robot (SAR) that aims to support older adults living alone in times of social isolation. Audrey is aimed to: First, reduce boredom and overcome loneliness by intellectual stimulation and games according to the user's hobbies, preferences, and interests. Second, facilitate staying in touch with your loved ones: using video chats and active reminders for family members and friends to interact. Third, promote healthy living- encourage a healthy lifestyle and early detection of stroke or fallings for emergency aid.
Reliable Sensing Data Fusion Through Robust Multiview Prototype Learning

Due to emerging development of intelligent sensing technologies in Internet of Things, multisensor cooperation has been widely deployed in applications. Although multisensor information fusion can be addressed by multiview learning, its performance tends to degrade if any one sensor is disturbed with annoying noises by the environment or other factors. Therefore, fusing these cross-sensor data in a reliable and secure manner while removing those noises is crucial. Although there have been outlier-against multiview works proposed, most of them suffer from redundant parameters or performance degradation. Even worse, few of them have considered the complementary information across the sensors. In this article, we argue that in multiview information fusion, not only the clean data, but also those outliers share the same prototypes in a common space, except that the outliers are disturbed with noises. To this end, we propose a type of robust multiview prototype (RMVP) learning to fuse the sensing data while removing the noises automatically in the learning process. Specifically, in RMVP, projection matrices are designed for each sensing view to sketch the data prototypes. In addition, one auxiliary margin matrix is modeled for each sensing view to capture its data noises through penalizing a sparsity regularization on it. Afterwards, an alternating algorithm is presented to solve the proposed model. Finally, extensive experiments on intelligent sensing data sets are conducted to testify the effectiveness of the proposed method.
Revealing best practices in legal regulation in the sphere of blockchain technology

The paper discusses the issues of legal regulation in the sphere of the blockchain technology use. Development of this technology has significantly changed the global cyberspace. Governments of all the world countries have to face the need to regulate this sphere, but as the blockchain technology is characterized by unprecedented abilities, most states remain at least alert to it. In this context, the issues of studying international experience in legal regulation of the blockchain technology become relevant for research. The goal of this study is to consider experience of different countries and international organizations in regulation of the blockchain technology. The paper presents the results of a study of the attitude towards blockchain technologies and their application by various international organizations, such as the Organization for Economic Cooperation and Development, the International Monetary Fund, the Eurasian Economic Union.The authors have developed a comprehensive methodology for revealing best practices of legal regulation of the blackchain technology use based on experience of the leading international organizations engaged in evaluation of the current practices of legal regulation in selected fields of economy. This methodology consists of four stages and is carried out in five directions: a general assessment of the effectiveness of legal norms, industry specificity, solving problems of using blockchain, assessing the risks of using blockchain, calculating the dynamics of indicators. Application of this methodology made it possible to evaluate the available experience of 47 countries and reveal 12 leaders of legal regulation of the blockchain technology. The obtained results may be used by concerned researchers from different countries for studying the world experience and building, on this basis, their own legal frameworks for spreading contemporary financial technologies.
Hyperparameter adjustment in regression neural networks for predicting support case durations

Regression is a powerful technique for predicting a single scalar value with a high degree of certainty. A regression model requires a dataset that consists of only numeric features. However, datasets frequently contain both numeric and categorical features. This paper sets out to study several different text encoding techniques, which can solve this problem by transforming a certain text value into a corresponding numerical value that is statistically sane in relation to the dataset. The scenario we study is a neural network regression model predicting support case durations. Many of the dataset's features are indeed categorical such as issue description, team, username, severity and impact among others. The most challenging aspect of the encoding is the resulting change of the dimensionality of the dataset. Every encoding method affects the dimensionality in various degrees depending on the feature cardinality. This result is the main challenge for the tuning of the neural network hyperparameters. We must make such a setup that can robustly handle the altered dataset. The paper compares five different approaches for encoding: one-hot, hashing, binary, target and entity embeddings. The hyperparameter settings for each approach are presented by using common neural network performance metrics and a baseline neural network setup. We can conclude that a moderately increased dimensionality can enhance the model's predictive power as observed in the case of the binary and the hashing encoder
Medical Image Compression Based on SPIHT-BAT Algorithms

Abstract The main aim of this study is to decrease the amount of storage as much as possible and the decoded image seen on the monitor should be as close as possible to the original image. The main goal of this study is to design a fully hybrid system for medical image compression. For this purpose, a hybrid techniques were used to enhance the compression performance, decreasing the computational complexity level and raising the CR (Compression Ratio, the proposed system is adopted on these tools: to design a new fully hybrid image compression system to compress a medical image (Brain Tumour disease type). Furthermore, a new reliable algorithm was proposed in order to identify the ROI (Region of Interest) and NROI (Non-Region of Interest) before compression process. In addition, this algorithm has less computational complexity and efficient, also develop new algorithms to compress the ROI and NROI regions. The first region, ROI, is compressed by cascading of SPIHT and BAT algorithms. Meanwhile, the second region (NROI) is compressed by the 2D-DWT algorithm, finally to design a new coding system by mixed the RLE (Run-Length Encoding) and Huffman coding algorithms to improve the CR. The results indicate that the SPIHT-BAT algorithm has increase the compression ratio better than SPIHT. Furthermore, the result of ROI region better than the result of NROI region. While the result of coding when used (RLE- Huffman) algorithm better than the result when used (RLE) alone or Huffman algorithm. The different parameters of compression process indicate that the proposed system is better than that of Traditional systems that described in literature.
Research on power-communication coordination recovery strategy based on grid dividing after extreme disasters

Abstract Extreme natural disasters will cause great damage to the power and communication systems of the distribution network. During the restoration process of the distribution network, the power and communication systems interact and restrict each other. The coordination of the two can make the distribution network restore effectively and quickly. To improve the recovery speed and efficiency of the distribution network after disasters, power-communication coordination recovery strategies based on gridding method after disasters are proposed. Firstly, according to the corresponding relationship between mobile base stations and power nodes, multi-level grids of distribution networks are divided, and power-communication coordinated recovery models of distribution networks are respectively established aiming at short rush repair time and small power outage loss. On this basis, a coordinated recovery strategy of power-communication is proposed in combination with the gridding method. Finally, the IEEE33-node distribution network system is taken as an example to verify the necessity of the gridding method and coordinated power-communication recovery and the effectiveness of the proposed method.
Improving diffusion-based recommendation in online rating systems

Network diffusion processes play an important role in solving the information overload problem. It has been shown that the diffusion-based recommendation methods have the advantage to generate both accurate and diverse recommendation items for online users. Despite that, numerous existing works consider the rating information as link weight or threshold to retain the useful links, few studies use the rating information to evaluate the recommendation results. In this paper, we measure the average rating of the recommended products, finding that diffusion-based recommendation methods have the risk of recommending low-rated products to users. In addition, we use the rating information to improve the network-based recommendation algorithms. The idea is to aggregate the diffusion results on multiple user-item bipartite networks each of which contains only links of certain ratings. By tuning the parameters, we find that the new method can sacrifice slightly the recommendation accuracy for improving the average rating of the recommended products.
Emotion Regulation in Intelligent Tutoring Systems: A Systematic Literature Review

Emotion regulation (ER) is a process responsible for altering emotions' reactions and expressions as well as situations that simulate such feelings to better respond to environmental issues.Hence, ER is actually a mere mental and behavioral process that many of us already perform consciously and unconsciously. In this issue, with the imperious role of emotions in learning process which they crucially influence student's cognitive performances and academic outcomes, several researchers proposed different methods and tools for regulating learners' negative emotions while learning in online environment. In the present paper, we discuss the current and original research works that apply different strategies and methods for emotion regulation in different smart environments (i.e., Intelligent Tutoring Systems) by adopting a Systematic Literature Review (SLR) approach.
Face Authentication for E-testing Using Sequential Reference Update

In an e-testing system, spoofing may occur if the teacher is unable to see examinees during tests. We examined variations in facial images captured by a webcam during e-testing with the aim of detecting impersonation. In previous work, we proposed an authentication method based on updating an examinee's facial images, which is limited to VOD-based e-learning. We thus optimized its application to e-testing in this paper. Our findings indicate that the proposed method improves authentication accuracy.
A Novel Blockchain Approach for Improve the Performance of Network Security Using Polynomial Ephemeral Blockchain-Based Secure Routing in Wireless Sensor Network

Wireless sensor networks are networks that include hundreds to thousands of autonomous distributed devices. These sensors are used to monitor physical and environmental conditions. Reliable routing protocols are designed to build a reliable and secure network with power management and control overhead and congestion. The wireless medium allows malicious user?s wireless sensor networks (WSN) to take part in actions to get a damaging attack on network control nodes. Due to the proprietary nature of such networks, they are more complex and lack tamper-resistant hardware. Security mechanisms are more wit due to the wireless and transient computing or communication complexity of communication relationships between nodes in a network. The existing method doesn?t provide security in inconvenient network performance, and the attackers modify the data and damage it. The proposed blockchain method is fixed at the same level of security, such as Polynomial Ephemeral Blockchain-based Secure Routing (PEBSR) encryption, to create a secure network. In this method, first, to authenticate the neighbor node validate using a Polynomial Ephemeral key-based Neighbor node authentication method. It evaluates the node location trust node and distributes the Ephemeral key in this key established between the nodes to communicate securely. The second method provides a cryptography chain network, and it maintains each node information in the sink database in this process if any attacker or anyone modifies the data is stored on each individual database. Finally, to find the route between each sink and participate nodes in the network. In this proposed PEBSR method, simulation results in providing efficient security, throughput, and less response time; they are compared to the existing method.
Timely detection of Seismic waves in Ground motion data using Improved S-Transform

In Seismology, exact recognition of arrival times of seismic wave has been a key challenge for a geologist. The arrival time of body waves provides significant information about the hypocenter location, source systems and tomography analysis. In this paper, Linear Gaussian kernel window method is incorporated with S-transform for time frequency decomposition of seismic signals. It progressively controls the window width to increase the concentration of energy and reduce the smearing effect in time-frequency region and minimize the spreading of wave. The IST is applied for time identification body wave components called Primary and Secondary waves. The accelerograph of famous earthquakes of San Francisco in 2014 and California in 2012 regions have been used for the analysis. The superiority of the results obtained from IST is compared with benchmark techniques continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and S-Transform (ST) in terms of time detection.
Research on Computer Assisted English Teaching in Foreign Language Teaching in Colleges

Abstract The continuous development of network technology in the information age has provided hardware guarantees for networked teaching, and the deepening of computer-assisted teaching research has promoted the development of networked computer-assisted teaching. With the popularization and application of informatization in colleges and universities, the use of modern information technology to change the traditional teaching mode has become the focus of attention. The C++ network-assisted teaching system uses computers and networks as the main means to develop network-based teaching and learning auxiliary teaching methods. This system is a mixed mode of network teaching and traditional teaching, which can enhance the communication between teachers and students, and can also play the role of the student-based teaching mode in network teaching.
Joint Bilateral Filter for Signal Recovery from Phase Preserved Curvelet Coefficients for Image Denoising

Thresholding of Curvelet Coefficients, for image denoising, drains out subtle signal component in noise subspace. In effect, it also produces ringing artifacts near edges. We found that the noise sensitivity of Curvelet phases — in contrast to their magnitude — reduces with higher noise level. Thus, we preserved the phase of the coefficients below threshold at coarser scale and estimated the corresponding magnitude by Joint Bilateral Filtering (JBF) technique. In contrast to the traditional hard thresholding, the coefficients in the finest scale is estimated using Bilateral Filtering (BF). The proposed filtering approach in the finest scale exhibits better connectedness among the edges, while removing the granular artifacts in the denoised image due to hard thresholding. Finally, the use of Guided Image Filter (GIF) on the Curvelet-based reconstructed image (initial denoised image in spatial domain) ensures the preservation of small image information with sharper edges and textures detail in the final denoised image. The lower noise sensitivity of Curvelet phase at higher noise strength accelerates the performance of proposed method over several state-of-the-art techniques and provides comparable outcome at lower noise levels.
WITHDRAWN: Realtime analysis and visualization of data for instant decisions: A futuristic requirement of the digital world

The speed of data transactions on Internet-connected devices has become faster nowadays. In the real-time analysis, the speedy data availability and an appropriate statistical analysis are the enablers of instant decision making. Instant decision-making based on a massive dataset helps in any verticals of life. The objective is to highlight the applications and tools used for real-time analysis. That works for both types of datasets, data-at-rest on magnetic disks and in-memory-data. Real-time Data Analytics (RTDA) will take a lead into instant decision-making and become empirical support for human intelligence.
A preliminary study on visibility improvement of turbid underwater images for dismantling of nuclear facilities

Video monitoring environment of underwater cutting objects is rapidly deteriorated due to the increasing water turbidity in proportion to cutting operation. Precise monitoring of the cutting area is one of keys to improve work efficiency. In this paper, we studied a visibility improvement technique based on an artificial neural network for monitoring objects in a turbid underwater cutting site. In order to improve visibility in turbid water, a deep learning neural network that learned two types of visibility enhancement process was adopted. The first type is to train to restore an ideal underwater image with the best visibility from a turbid underwater image, and the second type is to train to restore an image with improved visibility using the conventional visibility improvement technique. We adopted two types of training real images based on the GAN (generative adversarial networks) model for the corresponding turbid input images. The first training image is the histogram-equalized image of a clear underwater image. If the first training image does not exist, an image with improved visibility by using histogram equalization for the turbid input image itself was used as the second training image. Experiments demonstrated that the trained neural network provided significantly improved clarity in turbid images compared to that of the conventional improving technique.