Reviewerly/exHarmony huggingface dataset:
Predicted Seamless Human Positioning Algorithm based on M-RCNN in Obstacle Environment for Indoor Localization

Deep learning technology using machine learning based on computer vision is developing to the stage where it can recognize objects in an image or video and analyze the meaning of the image. In general, the position of the lower body region is very important in the human body pose estimation technique for estimating the human position. This paper proposes a predicted seamless human positioning algorithm based on M-RCNN in obstacle environment for indoor localization that can obtain continuous position estimation results even in blind spots and obstacles. The human detection of pre-test accuracy according to the amount of movement in the range of 0m-5m was confirmed to be 100%. It was confirmed that the M-RCNN PSHP method was 24.55% shorter than the M-RCNN B-box method, and the cumulative error distance of the M-RCNN PSHP method was 34.02% shorter than the M-RCNN B-box method, respectively. However, an error occurred in the left ankle coordinates when both ankles of the human in the image disappeared.
A study of the user experience when interacting with applications that work with sports armbands to monitor human activity

This paper concerns the study of user experience and focuses on two aspects i.e. usability and user satisfaction. Two mobile applications for monitoring human activity, Mi Fit and Google Fit, were tested. Both applications work with sports armbands. Two methods were used for the study: a questionnaire and eye tracking. The comparison of the applications was made on the basis of the collected results from questionnaires, measurements of task completion times and the number and type of errors detected. Nine respondents participated in the study. The Google Fit application received a higher average score for user satisfaction, fewer errors and shorter task completion times.
Spatio-Temporal Inference Transformer Network for Video Inpainting

Video inpainting aims to complete in a visually pleasing way the missing regions in video frames. Video inpainting is an exciting task due to the variety of motions across different frames. The existing methods usually use attention models to inpaint videos by seeking the damaged content from other frames. Nevertheless, these methods suffer due to irregular attention weight from spatio-temporal dimensions, thus giving rise to artifacts in the inpainted video. To overcome the above problem, Spatio-Temporal Inference Transformer Network (STITN) has been proposed. The STITN aligns the frames to be inpainted and concurrently inpaints all the frames, and a spatio-temporal adversarial loss function improves the STITN. Our method performs considerably better than the existing deep learning approaches in quantitative and qualitative evaluation.
Proficient Masked Face Recognition Method Using Deep Learning Convolution Neural Network in Covid-19 Pandemic

The COVID-19 pandemic is an incomparable disaster triggering massive fatalities and security glitches. Under the pressure of these black clouds public frequently wear masks as safeguard to their lives. Facial Recognition becomes a challenge because significant portion of human face is hidden behind mask. Primarily researchers focus to derive up with recommendations to tackle this problem through prompt and effective solution in this COVID-19 pandemic. This paper presents a trustworthy method to for the recognition of masked faces on un-occluded and deep learning-based features. The first stage is to capture the non-obstructed face region. Then we extract the most significant features from the attained regions (forehead and eye) through pre-trained deep learning CNN. Bag-of- word paradigm to has been applied to the feature maps to quantize them and to get a minor illustration comparing to the CNN’s fully connected layer. In the end a Multilayer Perceptron has been used for classification. High recognition performance with significant accuracy is seen in experimental results.
Secured Healthcare Data Analytics on the Cloud Using Blockchain-Based Techniques

Blockchain and the internet of things (IoT) are progressive technologies that are changing the world with additional special care within the healthcare system. In healthcare, IoT is a remote patient monitoring system that allows IoT devices to collect patient information such as remote monitoring, test results, pharmacy detailsm and medical insurance details, and allows doctors to provide excellent care. In order to facilitate data sharing among different hospitals and other organizations, it is necessary to secure data with caution. Blockchain is a decentralized, distributed, and an immutable digital ledger that records healthcare transactions using peer-to-peer technology in an extremely secure manner. It uses the cloud environment to store the huge amount of data on healthcare. The data generated from IoT devices uses blockchain technology to share medical information being analyzed by healthcare professionals in different hospitals in a secure manner. The objective is to benefit patient monitoring remotely and overcome the problem of information blocking.
Online Video Conferencing with Report Generation

Currently, many organizations started using conferencing apps to connect students and teachers, also to conduct online classes. In the current pandemic, applications like google meet, zoom became a necessity for educational institutions to conduct online lectures. An educational institute may need a customized video conferencing system for hassle free online classes, summary of classes and discussion forums. This research work develops a web application with mixed features of video conferencing and report generation. Students who miss their classes can see the reports/summary related to the classes conducted on a particular day and can learn easily. Teachers can login through, create links for their respective classes and share it with their students. Every user utilize their respective login id. Discussion forums are also provided for students to discuss among their peers. Students obtaining quality education is needed which shapes their future. The summary of the classes will help them to learn more along with the lectures. Inter department students can communicate in the discussion forums and support each other.
A New Robust Filter for Nonlinear System Based on Multiple Scaled Multivariate Skew-t Distribution

The accuracy of the state estimation for nonlinear system may be reduced if each dimension of the measurement noises has different heavy tail and skewness characteristic. In order to deal with the thorny problem, the multiple scaled multivariate skew-t distribution (MSMST), which is able to be formulated as a Gaussian, truncated Gaussian-Gamma hierarchical form, is first proposed to described such heavy-tailed and skew measurement noise (HTSMN). A new robust nonlinear filter is developed by utilizing variational Bayesian (VB) technique on the basis of hierarchical nonlinear Gaussian model (HNGM), which is constructed on the basis of the MSMST distribution. A typical example of maneuvering target tracking show that the superiorities of the proposed algorithm.
Enhanced Steganography Technique via Visual Cryptography and Deep Learning

Steganography is one kind of information hiding technique where a file is hidden within a transferable medium, such as an image, video, or file. Many steganography methods have been proposed and implemented over the decades among which image-steganography is very popular. In image steganography, one of the most popular techniques is the Least Significant Bit (LSB) technique. However, there are certain security drawbacks to this method, such as the fact that anyone who knows where the information is concealed may simply recover it. In this paper, a new approach is proposed by integrating steganographic technique with deep learning and visual cryptography to solve the problem where a secret image can be hidden in a cover picture using steganography but the content of the secret image is embedded by both deep learning and visual cryptography first. The secret picture is initially sent into the autoencoder, which consists of an encoder and a decoder. It compresses the image and renders it unrecognizable. The picture is then subjected to visual cryptography by conducting an exclusive OR (XOR) operation on it with a randomly generated image named mask1. Using the LSB technique, the encrypted secret picture is then concealed within the carrier (cover) image. All of the encoding stages are reversed for the decryption of the secret picture. The consistency of the stego picture was assessed using image quality matrices, putting the experimental findings to test. The values of the image quality metrics indicate the enhancement of security. A comparison study was also conducted with various current tools, and our technique was shown to be superior to the majority of them.
Implementation of a technique for obstacle detection applied to Resistive Grid Path Planning Methodology

Path planning has been considered an import part of solving problems focused on collision-free movements for robotics system. Resistive Grid Path Planning Methodology (RGPPM) is a novel and reduced CPU time modelling procedure for robotic motions planning. In this paper, RGPPM is complements with a monocular vision system to generate autonomy in the planning of the paths with geometric resistive networks.
Towards an Extrinsic Formalization of Featherweight Java in Agda

Featherweight Java is one of the most popular calculi which specify object-oriented programming features. It has been used as the basis for investigating novel language functionalities, as well as to specify and understand the formal properties of existing features for languages in this paradigm. However, when considering mechanized formalization, it is hard to find an implementation for languages with complex structures and binding mechanisms as Featherweight Java. In this paper we formalize Featherweight Java, implementing the static and dynamic semantics in Agda, and proving the main safety properties for this calculus.
A Survey of Dynamic Replication Strategies in Cloud Computing Systems

Cloud Computing is another type of paradigm in IT, which makes things easier in terms of storage, computing power, development ready platforms, etc. Cloud computing is an implementation system to provide the right quality of service parameters. Replication is a technique applied in various cloud systems, which increases the performance of a system by improving its availability and therefore minimizes the response time to services. In this paper, some existing replication strategies are surveyed and inspected for various parameters such as availability, consumption of storage space, response time, etc. This study aims to verify whether these techniques meet the expectations of a replication strategy. We also propose a comparative study of the different approaches presented in this article. According to this study, we can conclude that there is no universal strategy of data replication in the cloud computing. It is an area where too many approaches can be experienced.
Robust human instance segmentation in a challenging forest environment

A key requirement of safe robotic operation in environments where a mobile robot shares a space with people is that it is able to recognise and avoid situations where it may collide with a human. This is particularly important in uncontrolled environments or rugged terrain where navigation options dynamically change and are usually constrained in some way. We propose a sensor system that will form part of such a safe navigation system that is able to robustly identify and localise people in a densely vegetated forest environment even when they are occluded by vegetation. It accomplished this by combining sensor data from thermal and colour cameras, and generating segmentation masks using a modified version of Mask R-CNN.
Proof‐theoretic semantics of natural deduction based on inversion

Abstract The article presents a full proof‐theoretic semantics for natural deduction based on an extended inversion principle: the elimination rule for an operator q may invert the introduction rule for q, but also vice versa, the introduction rule for a connective q may invert the elimination rule for q. Such an inversion—extending Prawitz' concept of inversion—gives the following theorem: Inversion for two rules of operator q (intro rule, elim rule) exists iff a reduction of a maximum formula for q exists. The inversion theorem is specified to two logics, Lambek calculus (LC) and intuitionistic linear logic (ILL), with four propositional connectives: two multiplicatives (implication and conjunction) and two additives (conjunction and disjunction), with two quantifiers and two modals. LC is defined by using elimination rules by composition. ILL is defined by using usual general elimination rules. Elimination rules by composition are an exciting alternative to general elimination rules; in some cases, they do not need permutations.
Workshop on the Future of Digital Libraries

This workshop will examine the future landscape of digital library research and practice. While conventional facilities of digital libraries, for example indexation, search and browsing of collections of static texts are well understood, there is growing demand for a richer range of content including dynamic data streams, linking of heterogeneous content and automated analysis. We aim to uncover the common agenda for the features of future digital libraries, and the corresponding challenges for research and practice. Contributions from both theory and practice, and from technologists, information scientists and researchers in human information behaviour will all contribute to this workshop.
A memory-based structural model for knowledge management and transfer

In this paper, we aimed to develop a system for knowledge management mimicking the memory in the human brain. This system was used to create a multilingual guide for patients suffering from disorders of the spine. We viewed the transfer of knowledge as the transmission of a chain of small, interchangeable, semantic units. We defined the smallest amount of information that can be accurately communicated as a Knowledge Transfer Unit (KTU). Combining these KTUs in different sequences allowed us to create text chapters on more than 70 diseases. Through using KTUs, we reduced the number of characters by 72%, from 528,821 in the final output files to only 147,451 in our text database. In this paper, we present a new tool for knowledge management and transfer mimicking the process of integration of information and information recall within the human memory.
Analyses of Dramatic Network Simulations by Using Markov Chains

Progress in computational linguistics has made artificial literature (ALit) more and more possible. ALit is an area of study which aims to enable the computer to generate structural literary works, just like the reference texts. This similarity may be a direct linguistic similarity or a structural similarity. The aim of this research is to extract the structural characteristics of dramatic works, by studying the great theatrical pieces. This is accomplished by analyzing texts using the speech sequence simulation technique. As we further analyze the text and make them quantifiable, we get closer to generate structured literary texts. To do this, the speech sequences of the characters of the play Antigone of Sophocles are simulated and presented. It's called a dramatic network. When a scene starts, the characters join the piece through conversations. If we enumerate the order of character speeches in a scene, it is observed that the pieces form a Markov chain. Each play may be considered a stochastic process. If each character is taken as a state, the transition from one state to another is the transition from one character's speech to the next. In this theoretical framework, the probability of occurrence in each state is calculated and the probabilities of transition between states are demonstrated. Finally, if this play is simulated iterative, we can obtain the histogram of simulation length which is approximately the power law. As a result, the analyses made by considering only the speech sequences have structural importance of the simulations that we claim the alpha characteristics. We observe a similar α characteristic for different theatrical pieces with low-pass filtering in the frequency domain. We claim an alpha approximation among the plays.
Moving Average Method on Big Data in Intercity Transportation in the Post-COVID-19 Era

In 2020, the outbreak of COVID-19 pneumonia has had a great impact on China’s economic and social life. The construction and transportation industries have been greatly impacted and suffered from its high mobility. This paper studies the big data of migration between Xi’an and Chengdu from January 1, 2020 to March 15, 2020 and divides the epidemic situation into four stages according to the introduction of the elastic coefficient according to the development of the epidemic situation. In each stage, the elastic coefficient of index change is introduced in combination with the decreasing impact of epidemic prevention and control measures on transportation. Finally, a modified moving average method is formed, which is compared with the ordinary moving average method. The results show that the modified moving average method combined with Hadoop big data platform can improve the accuracy and efficiency of the intercity transportation flow prediction under the epidemic situation.
An Intelligent Method For Extracting Hotspot Events in News Bulletin

Journalists must understand the news bulletins accurately before they can get their hands on hotspot events. Identifying key points in a news bulletin is the cornerstone for accurately extracting hotpot events. However, the manual mining of hotspot events containing key points from massive news bulletins is a time-consuming and laborious work, and now we relies mainly on keyword search and rule matching. In order to accomplish the task, which is labor-intensive and material, journalists must have a good prior knowledge of all kinds of news bulletins on various topics. We tried to apply event extraction techniques to capture key points in the news bulletins. In news bulletins, however, the type of hotspot event that contains the key points is not properly defined. Existing event extraction methods are difficult to solve the problem of multiple hotspot events sharing the same parameters or trigger words in a sentence, which is common in news bulletins. In this paper, we proposed a mechanism to define hotspot events and a two-level label mapping method based on BERT and GloVe, which can better solve the above problems and facilitate the automatic extraction of hotspot events from multi-source massive news bulletin. Experimental results show that this method can accurately obtain the key points in news bulletin.
Thermo-optical properties of silicon nitride Mach-Zehnder interferometer for the on-chip quantum random number generator

Abstract Here we study the thermo-optical properties of an on-chip silicon nitride Mach-Zehnder interferometer (MZI). The spectral shift of the MZI is associated with a change in the chip temperature. This can be explained by a change in the splitting ratio of the directional couplers, as well as a significant change in phase difference between waveguide arms. We experimentally found a phase shift of 2π when heated by 1.67 °C and changes in resonant wavelength at different temperatures ( dλ/dT ) equal 12.0 pm/°C, theoretically obtained a formula for an arbitrary splitting ratio of the directional couplers in an MZI, and determined the temperature stability required to the device operation inside a quantum cryptography system.
Research and Implementation of Real-time Render Optimization Algorithm Based on GPU

Abstract In the continuous optimization of computer graphics, game engine and virtual reality have become the focus of research and innovation in the current technology field. Rendering is a core technology to show a variety of graphic effects, which has been attached great importance to and applied by the whole society. Nowadays, although the rendering of large-scale and complex scenes has a wide range of applications, the actual optimization requirements are very high. Therefore, in the future technology application and research and development, based on higher and higher technical requirements, it is necessary to improve the efficiency of actual rendering while ensuring or improving the rendering quality. Therefore, this paper studies how to use the algorithm to improve the efficiency of actual rendering, so as to provide a new basis for future computer graphics research.
Compassion Detection from Text: A Comparative Analysis Using BERT, ULMFiT and DeepMoji

Compassion is a key quality of the human condition. For computers to become artificial general intelligence (AGI), they must be taught how to detect and exhibit compassion. Detection of compassion requires both a definition and proper approaches. A definition of compassion, which has both cognitive and affective components, can be found in clinical psychology, encompassing the recognition of suffering and a desire to help, among other aspects. We use this definition to annotate a new original text dataset for compassion detection created by Web crawling Reddit communities. This dataset represents the first contribution of this paper. Using this new annotated dataset, we examine three approaches which are currently prevalent in the area of emotion detection (ED), based on BERT (bidirectional encoder representations from transformers), ULMFiT (universal language model fine-tuning) and DeepMoji (which uses attention-based bidirectional LSTM without transformers). We show that: (a) an approach based on feeding the emoji-based embeddings from DeepMoji to an ensemble of classifiers performs slightly better than BERT and much better than ULMFiT on our compassion detection dataset; (b) It is easier to detect the compassionate class than the not compassionate class. We also analyze two other related datasets, one on politeness and one on insults, and find that the approach of feeding the emoji-based embeddings from DeepMoji to a downstream classifier is only slightly inferior in performance to a BERT classifier, thus warranting consideration in tasks involving affective aspects. This comparative analysis is the second contribution of this paper toward the ED from text literature.
Medical Image Watermarking using Spread Spectrum and Compressive Sensing Techniques

In the current era, almost everyone has used digital media to exchange information. One example of the use of digital media in the medical field is the Electronic Patient Record (EPR). However, the use of digital media in EPR raises concerns about the authenticity and security aspects of patient data. The authenticity of medical images is one of the important data and must be considered. One way that can be done to minimize the possibility of various threats is to use a watermark. Another problem that arises is the size of the medical image. This study proposes a watermarking system for medical images using the Compressive Sensing method based on Singular Value Decomposition (SVD) which can be used to reduce the size of medical images. Compressive Sensing includes lossless compression which is a way to compress the size of a data without losing the original information. The medical image watermarking process is divided into two processes, namely the insertion process and the extraction process. The insertion process is carried out using the Spread Spectrum method with a Gaussian distributed PN code key, resulting in a watermarked image. While the extraction process is carried out by reconstructing the image using the Orthogonal Matching Pursuit (OMP) method, SVD reconstruction. The experimental results obtained BER values with an average of 0 when the Gaussian noise attack was carried out. PSNR value range is 30-38 dB and compression ratio is 0.2656.