Reviewerly/exHarmony huggingface dataset:
Online sequential spatiotemporal bias compensation using multisensor multitarget measurements

In multisensor multitarget tracking systems, spatiotemporal bias compensation is essential for correct data fusion. However, waiting for the measurements of all targets from a sensor to estimate the biases in a batch processing scheme is computational prohibitive and infeasible in some real applications. In this paper, an online sequential spatiotemporal bias compensation method using multisensor multitarget measurements is presented. The spatiotemporal biases of the sensors are augmented into the state vector of each target to be estimated by the measurements of this target. When updating the estimates of a target, the spatiotemporal bias estimates of the previous target are used as linear pseudo-measurements. The correlation between the pseudo-measurement and the augmented state estimates of this target from the previous time step is analyzed. To handle the correlation problem, a novel estimator is derived under the minimum mean square error (MMSE) framework to obtain correct sequential estimation in this situation. The proposed method produces both target state and spatiotemporal bias estimates once the measurements of one target from a sensor are received, without requirement of waiting for all target measurements and calculation with high dimensional matrix. The posterior Cramer-Rao Lower Bound (PCRLB) for spatiotemporal bias and state estimation is presented. Simulation results demonstrate the effectiveness of the proposed method.
Blockchain based Digital Identity Management System: A Case Study of Myanmar

Although Blockchain technology is formerly invented for digital currencies such as Bitcoin, its applications have extended into such areas as fair voting, healthcare, banking, and digital identity. A blockchain `s data is cryptographically connected and distributed across multiple computers, making them nearly impossible to temper with. Hence, this paper considers the possible use of introducing blockchain technology for digital identity management system for the issue of digital identity (passport) in Myanmar. Through blockchain technology, all the passport offices in Myanmar will be connected to each other in decentralized environment and enough passport information of a person can be securely shared within peers. Therefore, the proposed system can hinder the illegal duplication of passports (different types) since a valid passport can be issued only once to a person (except passport loss, renewal and expired) and it is impossible for a person to apply more than one passport type in the decentralized system. In addition, in the proposed system, the authenticity of passport can also be verified after granting permission for international travel. Therefore, the proposed system can be effectively applied for the passport offices in Myanmar for verifying the passport application and its data authentication.
OpenGAN: Open Set Generative Adversarial Networks

Many existing conditional Generative Adversarial Networks (cGANs) are limited to conditioning on pre-defined and fixed class-level semantic labels or attributes. We propose an open set GAN architecture (OpenGAN) that is conditioned per-input sample with a feature embedding drawn from a metric space. Using a state-of-the-art metric learning model that encodes both class-level and fine-grained semantic information, we are able to generate samples that are semantically similar to a given source image. The semantic information extracted by the metric learning model transfers to out-of-distribution novel classes, allowing the generative model to produce samples that are outside of the training distribution. We show that our proposed method is able to generate 256\(\times \)256 resolution images from novel classes that are of similar visual quality to those from the training classes. In lieu of a source image, we demonstrate that random sampling of the metric space also results in high-quality samples. We show that interpolation in the feature space and latent space results in semantically and visually plausible transformations in the image space. Finally, the usefulness of the generated samples to the downstream task of data augmentation is demonstrated. We show that classifier performance can be significantly improved by augmenting the training data with OpenGAN samples on classes that are outside of the GAN training distribution.
Immersive experiences in 360° video for social youth engagement

Educational and cultural television is an audio-visual medium that seeks to bear an impact on the knowledge, the attitudes and the values of an audience. This text introduces the CREA TV platform as an informal educational and socializing audio-visual instrument and describes the development of the documentary Miguel Hernández 360° made in 360° video. Two objectives are pursued through this: first, to introduce 360° video production as a means to use format to engage the audience and to foster the assimilation of contents; on the other hand, to teach values. To identify its effects, this experience was tested with secondary school students. The results of the analysis suggest that this 360° video has an impact on the connection between the viewer and the story due to its immersive character and sense of presence during the viewing of the documentary, as well as on the understanding and assimilation of the information it contains.
Linearly Homomorphic Signatures from Lattices

Abstract Linearly homomorphic signatures (LHSs) allow any entity to linearly combine a set of signatures and to provide authentication service for the corresponding (combined) data. The public key of the current known LHSs from lattices in the standard model requires $O(l)$ matrices and $O(k)$ vectors, where $l$ is the length of file identifier and $k$ is the maximum data set size that linear functions support. In this paper, we construct two lattice-based LHS schemes with provable security in the standard model and both schemes can authenticate vectors defined over finite field. First, we present a basic LHS scheme satisfying selective security, based on the full-rank difference hash functions. Second, we modify the chameleon hash function constructed by (Cash, D., Hofheinz, D., Kiltz, E. and Peikert, C. (2010) Bonsai Trees, or How to Delegate a Lattice Basis. In Proc. EUROCRYPT 10, Monaco/French Riviera, May 30 to June 3, pp. 523–552. Springer, Berlin) to construct a linearly homomorphic chameleon hash function (LHCHF), which can be applied to all transformations from selectively secure LHS scheme that authenticates vectors defined over finite field $\mathbb{F}_{p}$ ($p=poly(n)$) to fully secure one, except for a new one that authenticates vectors defined over a small field. Starting from LHCFH and the basic scheme as above, we obtain a fully secure LHS scheme. Both schemes can be used to sign multiple files and have relatively short public keys consisting of $O(1)$ matrices and $O(k)$ vectors.
Automated NER, Sentiment Analysis and Toxic Comment Classification for a Goal-Oriented Chatbot

This paper focuses on improving the conversational ability of a robo receptionist. In particular, we seek to improve the ability to retrieve information specific to an organization through the design of a named-entity-recognition module. We accentuate the chatbot's sensitivity to a user's comment and the tone of a conversation through designing a fine-grained sentiment analysis module. And, finally, we have ensured the output of the self-learning chatbot is positive and pleasant through a toxic-comment classifier that improves upon a dictionary-based profanity detection module. Improving the core components of the chatbot, viz., the named entity recognition, sentiment analysis and toxic comment classification modules, reflect as an improvement in the performance of the chatbot. The performance of these modules in comparison with predecessor approaches and the code to reproduce the results have also been included to facilitate further improvements in these directions.
Development of an Augmented Reality-Based Game for Projectile Motion

Recent years have seen an immense development in the field of X Reality (augmented, virtual, and mixed reality), empowering healthcare, education, and entertainment, and the trend predicts an exponential increase in the upcoming years. Augmented reality is a technology in which real-world environments can be enhanced by adding computer-generated graphics, animations, and sound. Many studies investigated the potential of augmented reality (AR) and found the sectors that could take advantage of such technology. The studies showed that AR tools have the capability to enhance teaching and learning as they can provide better visualization of the concepts, e.g., concepts such as Kirchhoff’s law and thermal flux, than conventional methods.
Time Series Feature Extraction For Head Gesture Recognition: Considerations Toward HCI Applications

In this paper, it was further developing the head gesture recognition system based on the inertial sensor as a necessary prerequisite in any HCI or healthcare application. While in one previous research, it was got a good result equal to 80% head classification accuracy, in this work, the main aim was to increase the classification rate using a different approach than the previous one. For this paper, a database with records performed from 7 persons was used and 8 head activities were classified. The increase of the performances for this research was done by implementing two specific methods applied to the time series. The first method was based on a signal processing approach - the extraction of the trend and the filtering, with the help of the median filter, composed this method. The second approach was based on the extraction of 16 new features from the proposed head gestures recognition time database. In the end, it was constructed 9 predictive models for the head gestures classification and evaluation. After applying the proposed methods, the evaluation part was done using the K-Fold method. This thing shows that the first proposed approach was got an accuracy equal to 91% while the second one was got a classification rate equal to 100%. Based on the proposed methods, in this paper, the classification performance grew up with 10% and respectively, 20% compared with the previous research.
How blockchain helps to combat trust crisis in COVID-19 pandemic?

The COVID-19 pandemic is sweeping across the world and causing widespread death and disruption. To this effect, nations have struggled to use technologies to detect and to monitor COVID-19 cases and related information. Unfortunately, both inconsistent and inaccurate information from "trustless" sources, and "mistrust" in effectively monitoring and reporting patients symptoms and condition have highlighted the failure of existing infrastructures to preserve human healthcare while maintaining patient's privacy and to slow down the widespread of the pandemic. This paper provides an insight of the perceived benefits of Blockchain to expectations of the COVID-19 pandemic in terms of providing "trust" in addressing information sharing in health systems while maintaining privacy. This paper exhibits a Blockchain-based COVID-19 Smart Healthcare approach that embraces Blockchain's benefits in healthcare systems.
High Performance Image Compression Based on Optimized EZW Using Hidden Markov Chain and Gaussian Mixture Model

This paper has proposed a novel high performance image compression method based on Embedded Zerotree Wavelet (EZW), which is a popular image compression coding scheme. Shortening the sweeping path according to the statistical properties of the image is the main issue that this paper has concentrated on. A developed statistical model based on hidden Markov model (HMM) and Gaussian mixture model (GMM) trains and produces the filter-bank coefficients of discrete wavelet transform (DWT). Custom design filter-banks for specific category of images, allow the encoder to pass a shorter route and stop at a closer point. This research proposes a novel image compression method based on an optimized EZW transform for three category of images, from statistically high to low correlated images. In each of these categories, the filter-banks are trained and extracted by their related GMM-HMM statistical models. The final results prove the efficiency of the method, and they are compared against the state of the art methods.
Utilization of Corncob Cellulose Isolate (Zea mays) as Adsorbent of Heavy Metal Copper and Cadmium

Abstract This study aims to determine whether the cellulose result of isolation compounds from corn cobs can adsorb heavy metals Copper (Cu) and Cadmium (Cd), as well as to determine the ability of adsorption of cellulose result of isolation. The type of this research is experimental research in chemical laboratory. The method used is quantitative research. The sample used in this research is corn cob waste. The cellulose isolation stage of the corn cob consists of dewaxing, dehemicellulose, delignification and bleaching. Cellulose characterization through water content test, ash content, pH, solubility test, and using Fourier Transform Infrared (FTIR) instrument, Scanning Electron Microscope (SEM) and X-Ray Diffraction (XRD). The cellulose result of isolation was used as a heavy metal adsorbent of Cu and Cd using atomic absorption spectrometry. Based on the results of the research, it is found that cellulose result of isolation from corn cobs has the ability to absorb Cu and Cd heavy metals with adsorption capacity of Cu and Cd is 68.20 and 95,98 mg.L -1 .g -1
Quality estimation for DASH clients by using Deep Recurrent Neural Networks

Dynamic Adaptive Streaming over HTTP (DASH) is a technology designed to deliver video to the end-users in the most efficient way possible by providing the users to adapt their quality during streaming. In DASH architecture, the original content encoded into video streams in different qualities. As a protocol running over HTTP, the caches play an important role in DASH environment. Utilizing the cache capacity in these systems is an important problem where there are more than one encoded video files generated for each video content. In this paper, we propose a caching approach for DASH systems by predicting the future qualities of DASH clients. For the prediction, we use learning model, and the qualities that will be cached are determined by using this model. The learning model is designed using Recurrent Neural Networks (RNNs) and also Long Short Term Memory (LSTM) which is a special type of RNNs with default behavior of remembering information for long periods of time. We also utilize SDN technology to get some of the outputs for the learning algorithm. The simulation results show that predicting future qualities helps to reduce the underruns of the clients when cache storage is utilized.
Partial Sharing Neural Networks for Multi-Target Regression on Power and Performance of Embedded Memories

Memories contribute significantly to the overall power, performance and area (PPA) of modern integrated electronic systems. Owing to their regular structure, memories are generated by memory compilers in modern industrial designs. Although such compilers provide PPA-efficient and silicon-verified layouts, the large and growing number of input parameters to the compilers themselves results in a new challenge of compiler parameter selection given design requirements. The dimensionality of the search space as well as the count of memories prohibit manual tuning in fast-paced design cycles. To efficiently select optimal compiler parameters, we devise regression neural networks as PPA models of memory compilers, based on which an optimal parameterization can be selected. Highly accurate PPA estimates are a prerequisite to a reliable optimization. While regression with multiple targets can easily be achieved by neural networks with multiple output units, model accuracy depends highly on architecture and hyperparameters. We study how neural network prediction error on multi-target regression problems can be reduced, validating recent findings that partial parameter sharing is beneficial to this class of problems. Our real-world application confirms the benefits of partial sharing for multi-target regression, and asserts the applicability to the sigmoid activation function. The accuracy of memory compiler PPA prediction is improved by approximately ten percent on average, decreasing worst-case prediction errors by over 50 percent.
Performance Analysis of Machine Learning Approaches in Software Complexity Prediction

Software design is one of the core concepts in software engineering. This covers insights and intuitions of software evolution, reliability, and maintainability. Effective software design facilitates software reliability and better quality management during development which reduces software development cost. Therefore, it is required to detect and maintain these issues earlier. Class complexity is one of the ways of detecting software quality. The objective of this paper is to predict class complexity from source code metrics using machine learning (ML) approaches and compare the performance of the approaches. In order to do that, we collect ten popular and quality maintained open source repositories and extract 18 source code metrics that relate to complexity for class-level analysis. First, we apply statistical correlation to find out the source code metrics that impact most on class complexity. Second, we apply five alternative ML techniques to build complexity predictors and compare the performances. The results report that the following source code metrics: Depth inheritance tree (DIT), response for class (RFC), weighted method count (WMC), lines of code (LOC), and coupling between objects (CBO) have the most impact on class complexity. Also, we evaluate the performance of the techniques, and results show that random forest (RF) significantly improves accuracy without providing additional false negative or false positive that work as false alarms in complexity prediction.
Analysis on Malicious Residential Hosts Activities Exploited by Residential IP Proxy Services

A residential IP Proxy is a proxy service that provides a traffic relay using hosts on residential networks. Although the service providers claim that hosts voluntarily participate in the service and use it for various high-quality applications, in fact, the service provides avoiding detection and blocking by pretending as apparently benign users, they exploited the residential hosts to perform malicious acts such as DoS attacks. In 2019, Mi et al. studied that malicious hosts participating in the Residential IP Proxy service, and profiled the hosts, and clarified the infrastructure, scale, and malignancy of the such services. They found that most malicious activities were sending SPAMs and hosting fake websites that were performed by routers and WAP devices. However, residential WAP devices are commonly inside of firewall and these are not likely to be feasible in well managed residential networks. To answer to the concern, in this paper, we analyze datasets of Residential-IP-Proxy hosts, collected by Mi et al. and report an analysis of the communication that Residential IP Proxies perform in Japan. We use NONSTOP, the analysis platform, provided by the Information Technology Research Organization, in the analysis. Our analysis found that most of devices used in Japan were mobile laptop PCs and port-scanning was the most frequent malicious activity. Consequently, more RESIP hosts are becoming involved in serious threat and we need countermeasures aimed at minimizing the abuse of RESIP hosts.
Dynamic obstacle avoidance path planning for UAV

In many quadrotor missions, arriving at the assigned place without collision with dynamic obstacles is crucial. In this paper, we address the problem of dynamic path planning for unmanned aerial vehicles. The framework combines RRT* and dynamic window approach to solve dynamic obstacle avoidance in partial unknown environment. Two planning layers communicate with each other to follow the global path while changing the local path for appeared obstacles. Finally, this approach was verified under ROS development platform.
View Splicing for Effective VR Collaboration

In a co-located multi-user collaborative virtual reality (VR) application a collaborator should be able to indicate a workspace location to the user, such that they can refer to it simultaneously as they work together. Due to their different viewpoints, the collaborator sees some parts of the virtual environment (VE) that the user does not, and communication breaks down when the collaborator's reference is not visible to the user. The conventional solutions of asking the user to move around to gain line of sight to the collaborator's reference, or of asking the user to toggle back and forth between their view and that of the collaborator can be inefficient and ineffective. This paper proposes a method for improving collaboration in VR by alleviating the disparity between the user and the collaborator views of the VE. The user is shown a multiperspective visualization of the VE that transitions smoothly from the user to the collaborator's perspective. The multiperpsective visualization is based on the switch camera, a novel camera model with curved rays that splice together the user and collaborator views. The multiperspective visualization is computed first by warping the VE geometry, through projection with the switch camera followed by unprojection with a conventional camera, and then by rendering the warped VE conventionally, for each user eye. A controlled user study with three tasks shows that VR collaboration using the switch camera multiperpsective visualization is faster, more reliable, and less taxing on the user than the conventional approaches of viewpoint translation or view toggling.
Virtual Reality: Bringing Education to Life

Virtual reality applications have been available for many decades for entertainment and even research and education. However, cost was often prohibitive, and development was time-consuming. With the advent of open-source environments, which are essentially available for free, and virtual artifacts being donated through Creative Commons by artists and developers, educators can now avail themselves of these open-ended, creative, potentially engaging environments. Since the possibilities are truly limitless, it is important to consider areas most available and applicable to educators. Environments can be used as meeting spaces for presentations, poster sessions, and collaborations that could work across geographies. Games and quests can be developed. Simulations and role-playing can be guided toward educational purposes – certainly students know how to use these for entertainment purposes. The built-in mathematics and the computer-aided design (from the very way that objects are created) can support STEM/STEAM (science, technology, engineering, arts, mathematics) and computer-aided design (CAD) applications. In addition, most components within these environments can be scripted to create interactivity and enhanced applications, encouraging the learning of programming and coding. Assessment will always be essential to understanding whether designs and activities are educationally productive; these practices can be built into the application design. This chapter highlights ways that these environments can be acquired and initially piloted to develop productive classroom applications within the realms suggested above.