Reviewerly/exHarmony huggingface dataset:
A Dynamic Enhanced Robust Cubature Kalman Filter for the State Estimation of an Unmanned Autonomous Helicopter

This paper addresses a design and application for the problem of state estimation for an unmanned autonomous helicopter (UAH) equipped with instruments including an inertial measurement unit (IMU), a magnetometer and a global positioning system (GPS). A dynamic enhanced robust cubature Kalman filter (DERCKF) is proposed in this article. First, a robust filtering strategy is formulated to provide a strong constraint for abnormal values. Second, a new robust CKF is formulated based on the spherical cubature and Gaussian quadrature rules to estimate the probability state, without requiring calculation of the Jacobian and Hessian matrices. Then, an enhanced rule is proposed to help eliminate the accuracy degradation caused by model uncertainty disturbance when the experimental platform is operating and to improve the estimation performance of the filter. Meanwhile, by detecting the system uncertainty state, a dynamic enhanced strategy is formulated to achieve automatic adjustments for the dynamic enhanced robust rule and guarantee that the DERCKF will realize valid system state estimation at all times. Finally, numerical experimental results are presented to demonstrate the effectiveness and robustness of the DERCKF.
Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response

We present the results we obtained on the classification of dialogue acts in a corpus of human-human team communication in the domain of robot-assisted disaster response.We annotated dialogue acts according to the ISO 24617-2 standard scheme and carried out experiments using the FastText linear classifier as well as several neural architectures, including feed-forward, recurrent and convolutional neural models with different types of embeddings, context and attention mechanism.The best performance was achieved with a "Divide & Merge" architecture presented in the paper, using trainable GloVe embeddings and a structured dialogue history.This model learns from the current utterance and the preceding context separately and then combines the two generated representations.Average accuracy of 10fold cross-validation is 79.8%, F-score 71.8%.
ANALISIS SENTIMEN PENGGUNA GOPAY MENGGUNAKAN METODE LEXICON BASED DAN SUPPORT VECTOR MACHINE

Go-Pay is part of the Gojek application and one of the most popular finteches in Indonesia. Although the most popular, not all users have positive or even negative comments. Now users can submit various media opinions, one of which is Twitter. Twitter media has the advantage of a simple display, updated topics, open access to tweets and express opinions quickly. From a variety of comments on Twitter it takes a technique to divide into classes positive or negative opinions. This study uses prepocessing and labeling opinions into positive and negative classes with the lexicon Based method. As for the classification using the Support Vector Machine (SVM) method. The data used in the form of opinions about Go- Pay reviews from social media Twitter, amounting to 1210. The results of labeling with Lexicon Based amounted to 923 for positive and 287 for negative. While the classification of the SVM method using the Linear kernel produces 89.17% and 84.38% for the Polynomial kernel.
Detect Triangle Attack on Blockchain by Trace Analysis

Blockchain provides a decentralized trade means, which has attracted both academic and industrial researchers' attention. It was first proposed as the basic structure of bitcoin. In practice, it could be hacked for gaining profit or performing some illegal transactions, hence, a question about how to detect these potential risks has been raised. This paper builds a model for blockchain core protocol, and adopts the trace semantics, i.e. a sequence of actions, which can record any possible conditions that the running protocol may reach. Also, as an application of DDoS attack on blockchain, triangle attack is shown and can be formalized and detected by the contradiction to the security property. Furthermore, some advice to defend the triangle attack are presented.
Adversarial Policy Gradient for Deep Learning Image Augmentation

The use of semantic segmentation for masking and cropping input images has proven to be a significant aid in medical imaging classification tasks by decreasing the noise and variance of the training dataset. However, implementing this approach with classical methods is challenging: the cost of obtaining a dense segmentation is high, and the precise input area that is most crucial to the classification task is difficult to determine a-priori. We propose a novel joint-training deep reinforcement learning framework for image augmentation. A segmentation network, weakly supervised with policy gradient optimization, acts as an agent, and outputs masks as actions given samples as states, with the goal of maximizing reward signals from the classification network. In this way, the segmentation network learns to mask unimportant imaging features. Our method, Adversarial Policy Gradient Augmentation (APGA), shows promising results on Stanford’s MURA dataset and on a hip fracture classification task with an increase in global accuracy of up to 7.33% and improved performance over baseline methods in 9/10 tasks evaluated. We discuss the broad applicability of our joint training strategy to a variety of medical imaging tasks.
A Survey of Mobile Cloud Computing

Cloud computing is a model driven methodology that provides configurable computing resources such as servers, networks, storage and applications as and when required over the internet services.Cloud computing provides high performance networks and advanced development of internet.There are many services related to cloud computing are becoming popular to the end users.There are many smart phones, smart pads and other mobile equipment's and end users can access the cloud computing services through this devices.In Mobile Cloud Computing (MCC), there are two devices such as a physical device in real world and a virtual device in cloud computing environment.Mobile Cloud computing integrates the cloud computing into mobile environment and remove the obstacles related to the performance, and security in mobile computing.There are several security issues that are arises while integration of a mobile application in Mobile Cloud Computing environment [1].This paper enlightens the current trend in Mobile Cloud Computing Security, and different algorithms used for various functionality.
Laser beam propagation effects on secure key rates for satellite-to-ground discrete modulation CV-QKD

The increasing prevalence of satellite-to-ground laser communications necessitates unconditional security measures that so far can only be provided by quantum key distribution. However, in a free-space optical channel, the diffraction of laser beams along with turbulence effects can reduce the initially high secure key rates potentially provided by continuous variable quantum key distribution (CV-QKD). Using derived formulas from four-state discrete modulation CV-QKD with reverse reconciliation, we highlight parameters that yield key rates on the order of 50 Mbps, and then describe the possible factors that can cause its reduction in a free-space optical channel. Numerical beam propagation simulations are performed to supplement the analyses.
GNSS-aware Path Planning for UAV swarm in complex environments

This paper aims at defining a planning strategy for routing a swarm of UAVs in an environment with mixed GNSS coverage, i.e., which includes both open sky areas and challenging zones with unavailable and/or unreliable satellite coverage. UAVs follow in an independent way their path in open areas, while cooperative strategies are adopted in the challenging ones. Cooperation consists in enhancing navigation performance of the platform in the challenging area by using another UAV that flies under nominal GNSS coverage and is always in visual line of sight (LOS) with the first. Satellite coverage is estimated with a multi-GNSS constellation approach and challenging areas are defined by setting a threshold for the Dilution of Precision. Results, obtained in simulation on a real-world scenario, show an effective solution that can be used both offline and in near real time.
Reliable Smart Road Signs

In this paper, we propose a game theoretical adversarial intervention detection mechanism for reliable smart road signs. A future trend in intelligent transportation systems is “smart road signs” that incorporate smart codes (e.g., visible at infrared) on their surface to provide more detailed information to smart vehicles. Such smart codes make road sign classification problem aligned with communication settings more than conventional classification. This enables us to integrate well-established results in communication theory, e.g., error-correction methods, into road sign classification problem. Recently, vision-based road sign classification algorithms have been shown to be vulnerable against (even) small scale adversarial interventions that are imperceptible for humans. On the other hand, smart codes constructed via error-correction methods can lead to robustness against small scale intelligent or random perturbations on them. In the recognition of smart road signs, however, humans are out of the loop since they cannot see or interpret them. Therefore, there is no equivalent concept of imperceptible perturbations in order to achieve a comparable performance with humans. Robustness against small scale perturbations would not be sufficient since the attacker can attack more aggressively without such a constraint. Under a game theoretical solution concept, we seek to ensure certain measure of guarantees against even the worst case (intelligent) attackers that can perturb the signal even at large scale. We provide a randomized detection strategy based on the distance between the decoder output and the received input, i.e., error rate. Finally, we examine the performance of the proposed scheme over various scenarios.
Human Motion Prediction Based on Object Interactions

In this paper, we consider the problem of predicting the navigation goal of a moving human in an indoor environment. Knowledge about this goal can greatly increase the efficiency of robots acting in the same environment as interferences can be avoided and assistance quickly provided if necessary. Often the navigation goal depends on the previous action of the human and the object the human has interacted with before. Thus, the information about previous object interactions can be used to infer possible objects the human will interact with next, which in term can be used to predict the current navigation goal. We propose to learn a probability distribution of subsequent object interactions and present a framework that utilizes the learned transition model as well as observations of the human's location and pose for the prediction of their movement goal. As we show in various experiments, the information about transition probabilities of object interactions leads to reliable predictions of the navigation goal and improves the accuracy compared to prediction approaches that rely only on spatial information and do not consider object interactions. Furthermore, we demonstrate how the prediction can be used to realize foresighted robot navigation.
<b>µ</b> <i>-ANT</i>: semantic microaggregation-based anonymization tool

Abstract Motivation Detailed patient data are crucial for medical research. Yet, these healthcare data can only be released for secondary use if they have undergone anonymization. Results We present and describe µ-ANT, a practical and easily configurable anonymization tool for (healthcare) data. It implements several state-of-the-art methods to offer robust privacy guarantees and preserve the utility of the anonymized data as much as possible. µ-ANT also supports the heterogenous attribute types commonly found in electronic healthcare records and targets both practitioners and software developers interested in data anonymization. Availability and implementation (source code, documentation, executable, sample datasets and use case examples) https://github.com/CrisesUrv/microaggregation-based_anonymization_tool.
The impact of the local SMEs sector on FDI and the mediating effect of IFRS adoption in developing economies: The case of Algeria

The present paper aims to empirically explore the impact of SMEs on foreign direct investment inflows and analyses the effect role of IFRS adoption on the relationship between the SMEs sector and foreign direct investment inflows in short and long run in Algeria during the period of 1970-2017.This investigation intends to fill a major gap regarding empirical literature on FDI drivers in developing countries.Using the ARDL bounds testing approach, the findings show a positive association between SMEs and foreign direct investment inflows in Algeria in the long-run.However, in terms of the role of IFRS adoption in mediating the impact of SMEs on FDI inflows in Algeria, our findings report a negative contribution of IFRS adoption of the association between SMEs and FDI inflows in both long and short run.These findings provide significant implications for regulators and policymakers in developing countries improve the business environment of their countries, manager and foreign investors in assessing the business environment in host countries and for both developing countries and academic research.
HDSM: A distributed data mining approach to classifying vertically distributed data streams

The rise in the Internet of Things (IoT) and other sensor networks has created many vertically-distributed and high-velocity data streams that require specialized algorithms for true distributed data mining. This paper proposes a novel Hierarchical Distributed Stream Miner (HDSM) that learns relationships between the features of separate data streams with minimal data transmission to central locations. Experimental evaluation demonstrates significant improvements in classification accuracy over previously proposed distributed stream-mining approaches while minimizing data transmission and computational costs. HDSM’s potential for dynamically trading off accuracy with computational resource costs is also demonstrated.
Prediction of Future Shot Direction using Pose and Position of Tennis Player

In this paper, we propose a method to predict the future shot direction in a tennis match using pose information and player position. As far as we know, there is no work that deals with such a predictive task, so there is no shot direction dataset as yet. Therefore, using a YouTube tennis match video, we construct an time of impact and shot direction dataset. To reduce annotation costs, we propose a method to automatically label the shot direction. Moreover, we propose a method to predict the future shot direction using the constructed dataset. The shot direction is predicted using LSTM(long short-time memory), from sequential pose information up to the time of impact and the player position. We employ OpenPose to extract the position of skeleton joints. In the experiment, we evaluate the accuracy of shot direction prediction and verify the effectiveness of the proposed method. Since there are no studies that predict future shot direction, we set four baseline methods to evaluate the effectiveness of our proposed method.
Model Selection via Focused Information Criteria for Complex Data in Ecology and Evolution

Datasets encountered when examining deeper issues in ecology and evolution are often complex. This calls for careful strategies for both model building, model selection, and model averaging. Our paper aims at motivating, exhibiting, and further developing focused model selection criteria. In contexts involving precisely formulated interest parameters, these versions of FIC, the focused information criterion, typically lead to better final precision for the most salient estimates, confidence intervals, etc. as compared to estimators obtained from other selection methods. Our methods are illustrated with real case studies in ecology; one related to bird species abundance and another to the decline in body condition for the Antarctic minke whale.
Development of Scalable On-Line Anomaly Detection System for Autonomous and Adaptive Manufacturing Processes

Factories of the future are foreseen to evolve into smart factories with autonomous and adaptive manufacturing processes. However, the increasing complexity of the network of manufacturing processes is expected to complicate the rapid detection of process anomalies in real time. This paper proposes an architecture framework and method for the implementation of the Scalable On-line Anomaly Detection System (SOADS), which can detect process anomalies via real-time processing and analyze large amounts of process execution data in the context of autonomous and adaptive manufacturing processes. The design of this system architecture framework entailed the derivation of standard subsequence patterns using the PrefixSpan algorithm, a sequential pattern algorithm. The anomalies of the real-time event streams and derived subsequence patterns were scored using the Smith-Waterman algorithm, a sequence alignment algorithm. The excellence of the proposed system was verified by measuring the time for deriving subsequence patterns and by obtaining the anomaly scoring time from large event logs. The proposed system succeeded in large-scale data processing and analysis, one of the requirements for a smart factory, by using Apache Spark streaming and Apache Hbase, and is expected to become the basis of anomaly detection systems of smart factories.
Use of Meta-Analysis to Uncover the Critical Issues of Mobile Inquiry-Based Learning

Using mobile devices to support the process and products of learning activities is a trend in inquiry-based learning, which is culminating in the formation of the subfield of mobile inquiry-based learning (m-IBL). However, even though mobile devices have been widely used in IBL activities in recent years, evaluative evidence for their substantial contributions to IBL is still deficient. Therefore, this study collected and analyzed 40 peer-reviewed journal articles published during 2001 to 2017 involving 3,542 participants and further discovered that m-IBL has produced meaningful improvements for IBL with an overall mean effect size of 0.803, with a 95% confidence interval [0.61, 0.99]. In addition, moderator variables such as control group types, learning outcomes, inquiry types, teaching methods, and domain subjects were related to different effect sizes. Finally, the results of the meta-analysis were discussed along with their implications for future research and practice.
A recommender system for selecting potential industrial training organizations

The difficulty in securing students industrial work experience scheme (SIWES) placements has negatively affected the final grades of some undergraduates. A recommender system is hereby proposed to solve this problem. This system will use past SIWES data to recommend potential organizations to future SIWES students. Research data collection was through an online questionnaire, with 200 respondents. The data was divided into a training data set (70%) and test data set (30%). Collaborative filtering recommendation approach was employed using the C4.5 algorithm to classify the data and generate a decision tree model from the training data set. The model generated was used to predict the class label of the test data set. Results from the data analysis carried out revealed that Kappa statistics was 0.7839, mean absolute error = 0.0058, and root mean squared error = 0.0586. In addition, true positive rate = 0.788, recall = 0.788, precision = 0.749, F‐measure = 0.755, MCC = 0.760, ROC = 0.985, and PRC = 0.813. The high values obtained indicates that the model was predicting with a high level of accuracy, ie, 78.84% correctly classified instances. The developed model was used as a knowledge base to develop a very beneficial front‐end web application, tagged “RecommendIT” where students can enter their preferences and view company recommendations.
Structured-light-based surface measuring for application in fluid–structure interaction

Structured-light-based profilometry techniques provide a simple and suitable tool to characterize experimentally the dynamics of the interaction between fluid and structure. The study implements a method based on the analysis of a sinusoidal fringe pattern. This method allows the retrieval of a three dimensional surface shape from a two dimensional video recording of the deformation of fringes projected on the surface. A detailed description of the method and the implementation is provided which will facilitate its adaption to and utilization in custom FSI applications (the code is published under open-source license). An application example is presented in the form of an experiment. A hyperflexible hydrofoil is set under forced rotational oscillation in a closed water tunnel to mimic the characteristics of a vertical-axis hydrokinetic cross-flow turbine. The flexible structures encounter large deformations which are linked to corresponding hydrodynamic forces. To capture this interdependence, the deformations were recorded in sync with forces registered with a six-axis load cell. A validation of the method is presented and shows good accuracy in the deformation measurements of a hyperflexible hydrofoil. A benefit compared to other common techniques like Laser-interferometer measurements is the simplicity and modest hardware requirements of the method with the possibility to acquire height fields with good spatial and temporal resolution, which allows a spectral analysis of the surface deformation. The hydrodynamic forces are presented together with the corresponding deformation of the structure, and spectral analyses are performed which demonstrates the possibilities of the proposed method. In summary, the presented method allows the analysis of fluid–structure interactions using a simple assembly consisting of a projector and a high-speed camera.
Bibliometric Analysis on Research Trends of International Journal of Computers Communications &amp; Control

International Journal of Computers Communications &amp; Control (IJCCC) is an international journal in the fields of automation control systems and computer science. According to Web of Science (WoS), the first document of IJCCC was published in 2006. In this paper, we study the research trends of publications in IJCCC by performing bibliometric analysis from 2006 to 2019. 982 publications are selected from WoS after data preprocessing by VOS viewer and CiteSpace. Firstly, fundamental information of publications is explored including the type, the annual trend and the most cited publications in IJCCC. Secondly, characteristics of countries/ regions, institutions and authors are presented in terms of evaluation indicators. Next, landscape analysis is conducted to show the development of IJCCC at level of countries/regions, institutions, authors and references, such as co-authorship analysis, bibliographic coupling analysis, co-citation and burst detection analysis, cooccurrence and timeline view analysis. Based on which, discussions about current challenges and possible research trends of IJCCC are provided. Finally, some main findings are summarized. This paper offers a valuable reference for scholars to understand the research trends of IJCCC and grasp hot topics related to relative fields.