Reviewerly/exHarmony huggingface dataset:
A covariance shaping filtering method for tightly-coupled MIMU/GNSS of UAV

Purpose Regarding the important roles of accuracy and robustness of tightly-coupled micro inertial measurement unit (MIMU)/global navigation satellite system (GNSS) for unmanned aerial vehicle (UAV). This study aims to explore the efficient method to improve the real-time performance of the sensors. Design/methodology/approach A covariance shaping adaptive Kalman filtering method is developed. For optimal performance of multiple gyros and accelerometers, a distribution coefficient of precision is defined and the data fusion least square method is applied with fault detection and identification using the singular value decomposition. A dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed. Findings Hardware-in-the-loop numerical simulation was adopted, the results indicate that the gain of the covariance shaping adaptive filter is self-tuning by changing covariance weighting factor, which is calculated by minimizing the cost function of Frobenius norm. With the improved method, the positioning accuracy with tightly-coupled MIMU/GNSS of the adaptive Kalman filter is increased obviously. Practical implications The method of covariance shaping adaptive Kalman filtering is efficient to improve the accuracy and robustness of tightly-coupled MIMU/GNSS for UAV in complex and dynamic environments and has great value for engineering applications. Originality/value A covariance shaping adaptive Kalman filtering method is presented and a novel dual channel parallel filter scheme with a covariance shaping adaptive filter is proposed, to improve the real-time performance in complex and dynamic environments.
Chatbot Application on Cryptocurrency

Many chatbots have been developed that provide a multitude of services through a wide range of methods. A chatbot is a brand-new conversational agent in the highspeed changing technology world. With the advance of Artificial Intelligence and machine learning, chatbots are becoming more and more popular. A chatbot is the extension of human interface mediums such as the phone and social platforms. Similarly, Cryptocurrency is a new extension of digital or virtual currency designed to work as a medium of exchange. In the current digital exchanging world, investors and interested parties are eager to know more information about, and the capabilites of, this new type of currency. One of the potential paths to retrieve the info automatically and quickly is through a chatbot. We explored the open source python library, Chatterbot, to apply Itchat API (a WeChat interface) with the aim of building a robot chatting application, I&C Chat, on the topic of cryptocurrency. First, we collected question and answer pairs datasets from Quora websites. Furthermore, we also created API calls to query the real time quote for the top 25 cryptocurrencies. Then we used the collected data to train our chatbot and implemented a logic adapter to receive the price quote of cryptocurrencies based on the incoming question. The Itchat API method will return the best matched answer to the asking party automatically. The response time of different questions has been investigated. The results imply that this application is quite useful, feasible and beneficial to the digital currency world.
Space-to-ground quantum key distribution

Quantum key distribution (QKD) can in principle offer unconditional security by making use of the fundamental laws of quantum mechanics. In practice, this is typically achieved by preparing individual photons in quantum superposition states and sending them to a remote receiver. To date, most commercially available QKD systems rely on the transmission of the photons via optical fibers, which, due to channel loss and detector noise, limits the distance over which QKD is feasible to a few hundred kilometers. Alternatively, satellite-based QKD facilitates low photon loss and negligible signal disturbance and offers a viable solution for establishing a global scale quantum network. Recently, a quantum science mission of the Chinese Academy of Sciences (CAS) in collaboration with the Austrian Academy of Sciences (AAS) and the University of Vienna aimed at a satellite-based intercontinental quantum-key relay. Using the Chinese Quantum Science Satellite “Micius” as a trusted relay, a quantum network consisting of three optical ground stations located in China (Xinglong, Nanshan) and Austria (Graz-Lustbühel) has been demonstrated successfully. Here we report on the development of a quantum receiving module, installed at the Satellite Laser Ranging Station in Graz (Austria) capable of implementing the so-called decoy-state QKD protocol in a downlink scenario from the LEO satellite “Micius”. Furthermore, we will present the experimental results obtained during several downlinks from the Chinese satellite focusing on the performance of the Austrian receiving station.
QKD-Based Quantum Private Query Protocol in the Single-Photon Interference Communication System

In this paper, we propose a quantum-key-distribution-based quantum private query protocol (QKD-based QPQ) utilizing the uncertainty relation of the photon path and the interference result in the single-photon interference circuit.The proposed protocol is loss-tolerant and easy to be realized in the quantum communication systems based on single-photon interference.Without any assumption on the dishonest party's computation ability, we prove that the dishonest user can only steal a little more than one item from the database, and any dishonest action of the database would be found by the user with a nonzero probability.Compared with other QKD-based QPQ protocols utilizing single-photon interference, the proposed protocol uses less quantum devices, which means the costs of the proposed protocol is lower.
Small‐sphere distributions for directional data with application to medical imaging

We propose new small-sphere distributional families for modeling multivariate directional data on $(\mathbb{S}^{p-1})^K$ for $p \ge 3$ and $K \ge 1$. In a special case of univariate directions in $\Re^3$, the new densities model random directions on $\mathbb{S}^2$ with a tendency to vary along a small circle on the sphere, and with a unique mode on the small circle. The proposed multivariate densities enable us to model association among multivariate directions, and are useful in medical imaging, where multivariate directions are used to represent shape and shape changes of 3-dimensional objects. When the underlying objects are rotationally deformed under noise, for instance, twisted and/or bend, corresponding directions tend to follow the proposed small-sphere distributions. The proposed models have several advantages over other methods analyzing small-circle-concentrated data, including inference procedures on the association and small-circle fitting. We demonstrate the use of the proposed multivariate small-sphere distributions in analyses of skeletally-represented object shapes and human knee gait data.
Learning Factorized Representations for Open-set Domain Adaptation

Domain adaptation for visual recognition has undergone great progress in the past few years. Nevertheless, most existing methods work in the so-called closed-set scenario, assuming that the classes depicted by the target images are exactly the same as those of the source domain. In this paper, we tackle the more challenging, yet more realistic case of open-set domain adaptation, where new, unknown classes can be present in the target data. While, in the unsupervised scenario, one cannot expect to be able to identify each specific new class, we aim to automatically detect which samples belong to these new classes and discard them from the recognition process. To this end, we rely on the intuition that the source and target samples depicting the known classes can be generated by a shared subspace, whereas the target samples from unknown classes come from a different, private subspace. We therefore introduce a framework that factorizes the data into shared and private parts, while encouraging the shared representation to be discriminative. Our experiments on standard benchmarks evidence that our approach outperforms the state of the art in open-set domain adaptation.
Unsupervised Single-Image Super-Resolution with Multi-Gram Loss

Recently, supervised deep super-resolution (SR) networks have achieved great success in both accuracy and texture generation. However, most methods train in the dataset with a fixed kernel (such as bicubic) between high-resolution images and their low-resolution counterparts. In real-life applications, pictures are always disturbed with additional artifacts, e.g., non-ideal point-spread function in old film photos, and compression loss in cellphone photos. How to generate a satisfactory SR image from the specific prior single low-resolution (LR) image is still a challenging issue. In this paper, we propose a novel unsupervised method named unsupervised single-image SR with multi-gram loss (UMGSR) to overcome the dilemma. There are two significant contributions in this paper: (a) we design a new architecture for extracting more information from limited inputs by combining the local residual block and two-step global residual learning; (b) we introduce the multi-gram loss for SR task to effectively generate better image details. Experimental comparison shows that our unsupervised method in normal conditions can attain better visual results than other supervised SR methods.
Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models

Recent approaches to goal recognition have progressively relaxed the assumptions about the amount and correctness of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume completeness and correctness of the domain theory against which their algorithms match observations: this is too strong for most real-world domains. In this paper, we develop goal recognition techniques that are capable of recognizing goals using incomplete domain theories by considering different notions of planning landmarks in such domains. We evaluate the resulting techniques empirically in a large dataset of incomplete domains, and perform an ablation study to understand their effect on recognition performance.
Entity Alignment for Cross-lingual Knowledge Graph with Graph Convolutional Networks

Graph convolutional network (GCN) is a promising approach that has recently been used to resolve knowledge graph alignment. In this paper, we propose a new method to entity alignment for cross-lingual knowledge graph. In the method, we design a scheme of attribute embedding for GCN training. Furthermore, GCN model utilizes the attribute embedding and structure embedding to abstract graph features simultaneously. Our preliminary experiments show that the proposed method outperforms the state-of-the-art GCN-based method.
Fast Mean Estimation with Sub-Gaussian Rates

We propose an estimator for the mean of a random vector in $\mathbb{R}^d$ that can be computed in time $O(n^4+n^2d)$ for $n$ i.i.d.~samples and that has error bounds matching the sub-Gaussian case. The only assumptions we make about the data distribution are that it has finite mean and covariance; in particular, we make no assumptions about higher-order moments. Like the polynomial time estimator introduced by Hopkins, 2018, which is based on the sum-of-squares hierarchy, our estimator achieves optimal statistical efficiency in this challenging setting, but it has a significantly faster runtime and a simpler analysis.
Hourly forecast of solar radiation up to 48h with two runs of weather research forecast model over Italy

Growth and vast usage in renewable energy implies better hourly and daily planning for reliable and constant renewable energy distribution. In this sense, the forecast of renewable energy becomes more and more important. In this work, we used the Weather Research and Forecasting Model (WRF) to forecast solar radiation. We run two forecast simulations with WRF and WRF-Solar model. Models have been running on daily basis to forecast solar radiation up to 48h with hourly outputs. In this paper we elaborated one summer month of 2017 with both model forecasts compared with ground measured data for solar irradiance at one location in Italy (lat 42°02.5’ North; lon 12°18.4’ East). Both models represents in good manner global irradiance with RMSE for a selected maximum of a daily range of about 13% while direct (25%) and diffuse (40%) solar radiation obtained by models differ from measured values.
Interface Operation and Implications for Shared-Control Assistive Robots

Shared-control for assistive devices can increase the independence of individuals with motor impairments. However, each person is unique in their level of injury and physical constraints. Consequently, a plethora of interfaces are used to control the assistive device, depending on the individual. In order to be effective, the shared-autonomy assistance should be aware of the usage characteristics of the interface and adjust to varying performance characteristics of the person. To that end, we conduct a 23 person (9 spinal cord injured and 14 uninjured) study using three commercial interfaces used to operate powered wheelchairs, and establish performance measures to characterize interface usage. The analyses of our performance measures unveil key aspects of the interface operation that can inform features of a customizable and interface-aware control sharing framework.
Extended Statistical Analysis on Multimedia Concealed Data Detections

Now-a-days, providing privacy and protection to illicit materials like videos and web pages became a major issue to the law enforcement authorities.Multimedia information transactions over external network became a threat as those files consist of stego-payload.Current new era is, cyber wars, also playing a vital role along with the physical wars in the world.Along with the people, Government organizations also want to maintain data secrecy in certain sensitive communication areas like Department of Military, Department of Air force, and other defense related areas to protect the data against opponent parties.One of the technique to protect the information steganograpgy.The art of hiding the data into the multimedia files as carriers referred as steganography.In order to this, identifying messages by steganography referred as steganalysis.These techniques can possible to use by unauthorized persons along with the authorized persons or organizations.Hence design and development of an application whether the selected multimedia holds any payload or not is highly recommended, in this new era.This paper work shows the functioning of various methods to predict the hidden data in the multimedia files.Performance analysis of the steganalysis methods by considering different key parameters is the main strength of this work.
A Reinforcement Learning Scheduling Strategy for Parallel Cloud-Based Workflows

Scientific experiments can be modeled as Workflows. Such Workflows are usually computing-and data-intensive, demanding the use of High-Performance Computing environments such as clusters, grids, and clouds. This latter offers the advantage of elasticity, which allows for increasing and/or decreasing the number of Virtual Machines (VMs) on demand. Workflows are typically managed using Scientific Workflow Management Systems (SWfMS). Many existing SWfMSs offer support for cloud-based execution. Each SWfMS has its own scheduler that follows a well-defined cost function. However, such cost functions must consider the characteristics of a dynamic environment, such as live migrations and/or performance fluctuations, which are far from trivial to model. This paper proposes a novel scheduling strategy, named ReASSIgN, based on Reinforcement Learning (RL). By relying on an RL technique, one may assume that there is an optimal (or sub-optimal) solution for the scheduling problem, and aims at learning the best scheduling based on previous executions in the absence of a mathematical model of the environment. For this, an extension of a well-known workflow simulator WorkflowSim is proposed to implement an RL strategy for scheduling workflows. Once the scheduling plan is generated, the workflow is executed in the cloud using SciCumulus SWfMS. We conducted a thorough evaluation of the proposed scheduling strategy using a real astronomy workflow.
GADIS: A Genetic Algorithm for Database Index Selection (S)

Creating an optimal amount of indexes, taking into account query performance and database size remains a challenge.In theory, one can speed up query response by creating indexes on the most used columns, although causing slower data insertion and deletion, and requiring a much larger amount of memory for storing the indexing data, but in practice, it is very important to balance such a trade-off.This is not a trivial task that often requires action from the Database Administrator.We address this problem by introducing GADIS, A Genetic Algorithm for Database Index Selection, designed to automatically select the best configuration of indexes adaptable for any database schema.This method aims to find the fittest individuals for optimizing both query response time, and disk required for the indexed data.We evaluate the effectiveness of GADISthrough several experiments we developed based on a standard database benchmark, compare it to three baseline indexing strategies, and show that our approach consistently leads to a better resulting index configuration.
Adaptive-Size Dictionary Learning Using Information Theoretic Criteria

Finding the size of the dictionary is an open issue in dictionary learning (DL). We propose an algorithm that adapts the size during the learning process by using Information Theoretic Criteria (ITC) specialized to the DL problem. The algorithm is built on top of Approximate K-SVD (AK-SVD) and periodically removes the less used atoms or adds new random atoms, based on ITC evaluations for a small number of candidate sub-dictionaries. Numerical experiments on synthetic data show that our algorithm not only finds the true size with very good accuracy, but is also able to improve the representation error in comparison with AK-SVD knowing the true size.
Hadoop MapReduce and Dynamic Intelligent Splitter for Efficient and Speed transmission of Cloud-based video transforming

Cloud-based video transmission includes the multimedia data with variant specifications in mobile devices. Video transforming contains the vital role in video streaming for media communication. The main problem with transforming of video is that it needs much time for producing the quality output. Transforming needs the data to alter the video in the necessary transformation and this kind of procedure will continue in a dynamic manner to improve the progress. This work proposes a dynamic video splitter that includes the Hadoop MapReduce algorithm to improve the efficiency within the limited time. The main performance parameters are Video Deformation, Frame enslavement. The experimental analysis suggested that the proposed work considerably improved compared to related works and also it transmits maximized quality of video with minimized video deformation.
A Probabilistic Procedure for Anonymisation, for Assessing the Risk of Re-identification and for the Analysis of Perturbed Data Sets

Abstract The requirement to anonymise data sets that are to be released for secondary analysis should be balanced by the need to allow their analysis to provide efficient and consistent parameter estimates. The proposal in this article is to integrate the process of anonymisation and data analysis. The first stage uses the addition of random noise with known distributional properties to some or all variables in a released (already pseudonymised) data set, in which the values of some identifying and sensitive variables for data subjects of interest are also available to an external ‘attacker’ who wishes to identify those data subjects in order to interrogate their records in the data set. The second stage of the analysis consists of specifying the model of interest so that parameter estimation accounts for the added noise. Where the characteristics of the noise are made available to the analyst by the data provider, we propose a new method that allows a valid analysis. This is formally a measurement error model and we describe a Bayesian MCMC algorithm that recovers consistent estimates of the true model parameters. A new method for handling categorical data is presented. The article shows how an appropriate noise distribution can be determined.
A new likelihood approach to autonomous multiple model estimation

This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules. The numerical results show that the proposed algorithm, so called Competing AMM (CAMM), guarantees both steady-state estimation accuracy and quick parameter tracking.
OPTIMIZING ENGLISH TEACHING AND LEARNING PROCESS TO YOUNG LEARNERS (A CASE STUDY IN CIMAHI)

Having competencies on teaching English to young learners (TEYL), the teachers will be able to design materials, to teach well, and to suggest what approaches, methods, and techniques to be used. However, TEYL should not be done without careful planning and preparation. This study investigated TEYL process in Cimahi. It is intended to identify and analyze English teaching and learning process in Elementary and Kindergarten,  teachers’ perception towards TEYL, and students’ responses toward English lesson. The study reported here made use of qualitative research design as its method, while the instruments were questionnaire, observations, and interview. There were 3 teachers of Kindergarten and Elementary school chosen based on purposive sampling technique as the subject of the research. e
Relevance-Based Feature Masking: Improving Neural Network Based Whale Classification Through Explainable Artificial Intelligence

Underwater sounds provide essential information for marine researchers to study sea mammals.During long-term studies large amounts of sound signals are being recorded using hydrophones.To facilitate the time consuming process of manually evaluating the recorded data, computational systems are often employed.Recent approaches utilize Convolutional Neural Networks (CNNs) to analyze spectrograms extracted from the audio signal.In this paper we explore the potential of relevance analysis to enhance the performance of existing CNN approaches.For this purpose, we present a fusion system that utilizes intermediate outputs of three state of the art CNNs, which are fine tuned to recognize whale sounds in spectrograms.Hereby we use Explainable Artificial Intelligence (XAI) to asses the relevance of each feature within the obtained representations.Based on those relevance values, we create novel masking algorithms to extract significant subsets of respective representations.These subsets are used to train an ensemble of classification systems that are serving as input for the final fusion step.We observe that a classification system can benefit from the inclusion of Relevance-based Feature Masking in terms of improved performance and reduced input dimensionality.