Reviewerly/exHarmony huggingface dataset:
A Computationally Intelligent Hierarchical Authentication and Key Establishment Framework for the Internet of Things

Our high expectations for the Internet of Things (IoT) and how it will positively influence our lifestyles depend on a secure and trusted implementation of it, especially in sensitive sectors such as health and finance. IoT platforms and solutions must provide confidentiality, integrity, and availability (CIA) in a secure and transparent way. Due to the extremely large scales of IoT, traditional centralized solutions for security provisioning cannot be employed in their original form. This article discusses the authentication problem in IoT, which is fundamental to providing CIA. We propose a hierarchical security architecture for this problem and focus on computationally lightweight authentication protocols that can intelligently distribute the computational load across multiple levels and effectively push the load toward upper layers.
COVID‐19 field instruction: Bringing the forests of British Columbia to students 8,000 km away

Abstract Field instruction is a crucial component of natural sciences education. The COVID‐19 pandemic has shifted many university courses to an online format, significantly impacting field instruction. FRST 350, Foundational Field School , is an 8‐day University of British Columbia Forestry field course taught to incoming transfer students from partner universities in China. In August 2020, I taught this course online to students studying remotely. In re‐developing the course, I spent 9 days in the field filming high definition (HD) video, 360° video, and 360° photography to best recreate the course in a short time‐frame. A 360° video records omnidirectionally, allowing viewers to “look around” in all directions, resulting in a highly immersive experience. Students expressed favorable opinions of the course, especially traditional HD and 360° video. Students generally preferred HD videos over 360°, though this was due mostly to the high bandwidth needed for 360° video and the fact that core course content was primarily conveyed as HD videos (in recognition of bandwidth challenges), with supplementary 360° videos. Students favorably noted the interactivity and immersive feel of 360° videos and photographs. This technology is financially and logistically feasible for use in a natural sciences course. Instructors engaged in online field instruction should weigh the strengths and weaknesses of various technologies, including 360° video, when determining how to best meet their learning objectives.
Occlusion aware unsupervised learning of optical flow from video

In this paper, we proposed an unsupervised learning method for estimating the optical flow between video frames, especially to solve the occlusion problem. Occlusion is caused by the movement of an object or the movement of the camera, defined as when certain pixels are visible in one video frame but not in adjacent frames. Due to the lack of pixel correspondence between frames in the occluded area, incorrect photometric loss calculation can mislead the optical flow training process. In the video sequence, we found that the occlusion in the forward (t&rarr;t+1) and backward (t&rarr;t-1) frame pairs are usually complementary. That is, pixels that are occluded in subsequent frames are often not occluded in the previous frame and vice versa. Therefore, by using this complementarity, a new weighted loss is proposed to solve the occlusion problem. Our method achieves competitive optical flow accuracy compared to the baseline and some supervised methods on KITTI and Sintel benchmarks.
Optimisation of automatic web services composition using genetic algorithm

In recent years, with the expansion of organisations, service-oriented architecture is known as an effective tool for creating applications. Hence, the need to use web services in organisations to reduce costs is felt more than ever. The purpose of web service composition is to determine a proper mix of user requests that cannot be met by a simple web service. In this paper, a genetic-based algorithm is proposed for combining cloud services that ensures multiple clouds work efficiently. The proposed method also provides an overview of the weaknesses of other available methods in terms of computational complexity in automated selection of web services and makes it possible to fulfil the demands of the composition of web services in a more optimal way. It is worth noting that the simulation results show the superiority of the proposed method compared to other methods analysed in the paper.
The application of k-nearest neighbors classifier for sentiment analysis of PT PLN (Persero) twitter account service quality

Abstract Social media has an important role in human life. In its implementation social media is used as a media for opinion and self-expression. One of the social media that is often used in Indonesia is Twitter. PT PLN (Persero) as a State-Owned Enterprise that is engaged in providing electricity always tries to provide optimal services. The text mining method can be used to control PT PLN (Persero) service quality by classifying Twitter data with the k -Nearest Neighbors algorithm. Text mining is used to extract information from unstructured textual data to produce useful information. Data classification is a text mining application for information retrieval. In this study the data collected will pass the preprocessing stage, using the k -Nearest Neighbors algorithm to classify data into negative, neutral, or positive classes. The data used in this study was sourced from Twitter. Data is taken from 1 December 2019 to 1 February 2020 using the Twitter API with the keyword ‘@ pln_123’. We obtained 3,000 tweet data successfully. The results are implemented in web-based applications that are built using the Python programming language. Evaluation of the k -Nearest Neighbors model produces an accuracy value of 87.41%. The classification prediction results also show that there is a tendency of the positive sentiment of 35%, the neutral sentiment of 28%, and the negative sentiment of 37%.
Optimised fertiliser suggestion in smart agriculture system based on fuzzy inference rule

Rice crop is one of the major cultivating crops in India, especially in south India. Fertiliser plays an important role to achieve the highest rice yield. Each crop needs an adequate amount of fertiliser for its growth. Inadequate and overdose of fertiliser leads to nutrient deficiency and lesser yield of crops. Rice has a unique fertiliser recommendation based on climatic conditions and soil types but the issue is the random use of fertilisers by the farmer, without knowing their own land soil nutrition. Soil consists of primary nutrients and secondary nutrients. This supplement is used for crop growth. This paper discusses a proposed methodology of an optimised fertiliser simulation model for Cuddalore and Salem district and its sub talukas for rice and maize crops. In this research work, the fuzzy logic technique is used because this is one of the best ways for problem-solving techniques. In this fuzzy logic, Mamdani fuzzy logic-based tool is used. The Mamdani fuzzy logic tool is used for the suggestion of the fertilisers required according to the minerals present in their soil. In result total 45 rules are created for recommending the fertiliser use for paddy and maize crops.
Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.
Explicit Duration Recurrent Networks

Recurrent neural networks (RNNs) can be used to operate over sequences of vectors and have been successfully applied to a variety of problems. However, it is hard to use RNNs to model the variable dwell time of the hidden state underlying an input sequence. In this article, we interpret the typical RNNs, including original RNN, standard long short-term memory (LSTM), peephole LSTM, projected LSTM, and gated recurrent unit (GRU), using a slightly extended hidden Markov model (HMM). Based on this interpretation, we are motivated to propose a novel RNN, called explicit duration recurrent network (EDRN), analog to a hidden semi-Markov model (HSMM). It has a better performance than conventional LSTMs and can explicitly model any duration distribution function of the hidden state. The model parameters become interpretable and can be used to infer many other quantities that the conventional RNNs cannot obtain. Therefore, EDRN is expected to extend and enrich the applications of RNNs. The interpretation also suggests that the conventional RNNs, including LSTM and GRU, can be made small modifications to improve their performance without increasing the parameters of the networks.
Spatio-Temporal Data Augmentation for Visual Surveillance

Visual surveillance aims to detect a foreground object using a continuous image acquired from a fixed camera. Recent deep learning methods based on supervised learning show superior performance compared to classical background subtraction algorithms. However, there is still room for improvement in the static foreground, dynamic background, hard shadow, illumination changes, camouflage, etc. In addition, most of the deep learning-based methods operate well in environments similar to training. If the testing environments are different from training ones, their performance degrades. As a result, additional training in those operating environments is required to ensure good performance. Our previous work, which uses spatio-temporal input data consisting of several past images, background images, and the current image, showed promising results in different environments from training. However, it uses a simple U-NET structure. This paper proposes a data augmentation technique suitable for visual surveillance for additional performance improvement using the same network used in our previous work. In deep learning, most data augmentation techniques deal with spatial-level data augmentation techniques used in image classification and object detection. We propose two data augmentation methods of adjusting background model images and past images. The proposed algorithm improves performance in complex areas such as static foreground and ghost objects compared to previous studies. Through quantitative and qualitative evaluation using SBI, LASIESTA, and our dataset, we show superior performance compared to deep learning-based algorithms and background subtraction algorithms. In addition, it has a 30.2% and 27.9% reduction of false detection rate in the LASIESTA and SBI dataset, respectively, compared to our previous study.
Artificial Intelligence Brain

A computer-implemented method of generating a response based on a physical signal, a non-transitory memory and a system to implement the method is described, The method includes detecting a physical signal by an image sensor and a sound sensor; processing the detected physical signal by a corresponding first deep neural network; storing the processed signal as processed data in individual corresponding memory units; connecting the individual corresponding memory units with a second deep neural network to form one or more cognition units; generating, by the one more cognitions units, an expression, from a signal produced by the second deep neural network; and converting, by a third deep neural network, the expression into an output for an output device.
Journalism Education in the Context of COVID-19 Pandemic: Pros and Cons

The article discusses journalism education affected by the increased information scope, media convergence and introduction of new online teaching forms and face-to-screen communication during the coronavirus pandemic. Since the introduction of distance learning at universities, the quality of teaching and learning has been debated by students, teaching staff and officials. By having to overcome the difficulties, the students majoring in Journalism develop a necessary skill of gathering information and issuing school newspapers, radio and TV programs in the new informational reality of the crisis. The study included a survey aimed at finding out how students and faculty assess effectiveness of distance learning in COVID-19 context. Using a method of case study to make a complex analysis, the authors examine the academic process at the RUDN’s Department of Mass Communications as empirical object. The survey results reveal the key problems the respondents had to face when organizing and participating in the online learning process. The survey also helped to indicate the most convenient applications for online lecturing and the most effective teaching techniques, and to analyze the performance of the university’s internal telecommunication learning and information system.
Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain*

To solve the problem that the magnetic resonance (MR) image has weak boundaries, large amount of information, and low signal-to-noise ratio, we propose an image segmentation method based on the multi-resolution Markov random field (MRMRF) model. The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales. The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm, and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation. The results are then segmented by the improved MRMRF model. In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model, it is proposed to introduce variable weight parameters in the segmentation process of each scale. Furthermore, the final segmentation results are optimized. We name this algorithm the variable-weight multi-resolution Markov random field (VWMRMRF). The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness, and can accurately and stably achieve low signal-to-noise ratio, weak boundary MR image segmentation.
Comparison of Feature Extraction for Speaker Identification System

This paper compares the performance of speaker identification systems based on feature extraction methods. Fast Fourier Transform (FFT), Mel-Frequency Cepstral Coefficient (MFCC) and Discrete Wavelet Transform (DWT) are three of chosen feature extraction techniques used to test. These methods are applied to identify speakers by a word spoken. The system used Dynamic Time Warping (DTW) as classifier. Programming is done on MATLAB for training and testing. In this experiment, the combination of DWT and DTW gives better accuracy result than the other methods.
Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

Abstract There have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks either specialized in specific cohorts or an aggregate of all cohorts when there are only a few images available for the target cohort. A trade-off between decreasing the number of models and their cancer detection performance was evident in our experiments with The Cancer Genomic Atlas dataset, with the former approach achieving higher performance at the cost of having to acquire large datasets from the cohort of interest. Constructing annotated datasets for individual cohorts is extremely time-consuming, with the acquisition cost of such datasets growing linearly with the number of cohorts. Another issue associated with developing cohort-specific models is the difficulty of maintenance: all cohort-specific models may need to be adjusted when a new DL algorithm is to be used, where training even a single model may require a non-negligible amount of computation, or when more data is added to some cohorts. In resolving the sub-optimal behavior of a universal cancer detection model trained on an aggregate of cohorts, we investigated how cohorts can be grouped to augment a dataset without increasing the number of models linearly with the number of cohorts. This study introduces several metrics which measure the morphological similarities between cohort pairs and demonstrates how the metrics can be used to control the trade-off between performance and the number of models.
A New Method and Implementation of Blind Restoration Algorithm for Moving Fuzzy License Plate Image Based on Frequency-Domain Characteristics

In order to solve the problem of blurred license plate image in road monitoring, the PSF estimation method based on spectrum is often used to restore the blurred license plate image. Due to the large error of motion blur information estimation, a blind license plate restoration algorithm based on image spectrum and cepstrum is proposed in this paper. First, Radon transform is used to detect the parallel line features in the spectrum of fuzzy license plate image to estimate the fuzzy angle. Then, the cepstrum data of the blurred image is used to measure the peak value and estimate the fuzzy length of the image. Finally, blind restoration of fuzzy license plate image is carried out by Wiener filter of autocorrelation function. The experimental results show that the PSF estimation result of this algorithm is more accurate, and it can achieve better restoration effect of fuzzy image for license plate images under different fuzzy conditions. It is verified that the blind restoration algorithm based on fusion of image spectrum and cepstrum has certain feasibility and adaptability.
O ENSINO DE ENFERMAGEM E OS DESAFIOS DO USO DE TECNOLOGIAS REMOTAS EM TEMPOS DE PANDEMIA DO CORONAVÍRUS (COVID-19) / TEACHING AND THE CHALLENGES OF USE OF REMOTE TECHNOLOGIES IN CORONAVIRUS PANDEMY TIME (COVID-19)

This essay aims to reflect on nursing education and the challenges for the use of remote technologies in the context of the pandemic caused by the new Coronavirus (COVID-19). We opted for a study based on secondary sources in the relevant literature. The various facets of the training of nurses in the context of distance higher education in Brazil were analyzed, based on a brief historical analysis of the evolution of this activity. In a second step, the current situation of the Coronavirus pandemic (COVID-19) was addressed, which caused in an emergency the need for managers of higher education institutions worldwide to enable new ways to promote education, especially from the use of digital and informational technologies for remote education. Finally, the possible consequences of this modality of teaching-learning practices were analyzed. The conclusions point out that although distance learning applied to the training of nursing nurses is a viable alternative, especially in the pandemic context, this modality should serve as a complement to the face-to-face activities, to guarantee the quality of the training of nursing professionals.
On the Use and Denoising of the Temporal Geometric Mean for SAR Time Series

The increasing availability of synthetic aperture radar (SAR) time series creates many opportunities for remote sensing applications, but it can be challenging in terms of amount of data to process. This letter discusses the interest of the geometric mean to average SAR time series. First, the properties of the geometric mean and the arithmetic mean are compared. Then, a speckle-reduction method specifically designed to improve images obtained with the geometric mean is presented. This method is based on an adaptation of the MuLoG framework to take into account the specific distribution of the geometric mean. Finally, applications of this denoised geometric-mean image are presented.
An Oil and Gas Big Data Sharing Model Based on Blockchain Technology

Abstract With the continuous in-depth application of new technologies such as big data and Internet of Things in the oil and gas field, the scale and value of oil and gas data continue to rise, laying the foundation for oil and gas big data sharing. Oil and gas big data sharing can effectively integrate different data sources and break the dilemma of “data islands” where data in the oil and gas field are separated from each other. However, there are problems in actual practice such as (1) difficulties in trust among different government departments, scientific research institutions and enterprises, (2) insufficient data privacy protection, and (3) weak data traceability. In response to the above problems, this article proposes a model of oil and gas big data sharing based on blockchain technology. We build a blockchain for oil and gas big data sharing alliances to secure oil and gas big data by utilizing the decentralization and non-tampering characteristics. We also provide supports for oil and gas big data sharing by controlling data access strictly based on the smart contracts. The evaluation and analysis results show that this model makes oil and gas big data more secure and authoritative compared with the traditional centralized sharing method.
NoSQL distributed database for DICOM objects

Every year, the amount of data produced in health-care institutions in the field of medical imaging is increasing. The easy accessibility to digital modalities and the proliferation of diagnosis centers working in network of services, creates the need for very large repositories of data. Moreover, the institutions have redundant storage across the network and load balancing mechanism to improve access performance. NoSQL databases provide horizontal scalability which allows the system to grow easily and effortlessly. Document databases are particularly suitable to store the DICOM metadata and the JSON format is already supported by the standard. This paper, we discuss the use of document database model in the medical imaging field, trying to focus on both clinical and research environments. Moreover, we focused on the proposal of an architecture based on MongoDB to distribute the load between multiple nodes. The proposal was integrated with an open-source PACS and validated by simulating multiple operations over data distributed across multiple virtual locations, and the results are promising.