Reviewerly/exHarmony huggingface dataset:
Unmanned Aerial Vehicle's State Estimation with Multiple Unmanned Ground Vehicles Cooperative Observation Based on Set-Membership Filter

In this paper, the state estimation problem of Unmanned Aerial Vehicle (UAV) with multiple Unmanned Ground Vehicles (UGV) cooperative observation is researched. In traditional observation systems, there are usually problems with low observation accuracy or robustness and prior assumptions about noises. For the aim of improving the accuracy and robustness of state estimation results of UAV, Set-Membership Filter (SMF) method is applied to the cooperative observation system. Unlike the other algorithms requiring the measurement and process error to obey the Gaussian distribution with zero mean, such as Kalman filter or Particle filter, SMF method only assumes that the measurement errors are modeled as Unknown-But-Bounded (UBB) set, which can be easily obtained and applied in the real cases. The application of SMF algorithm in single UGV observation system and multiple UGVs cooperative observation system with obstacles are researched respectively. Considering the existence of obstacles, the state of UAV can still be successfully estimated when the observation data lost. Finally, the single UGV and multiple UGVs cooperative observation experiments are conducted to verify the accuracy and effectiveness of SMF method.
Alternative Authentication with Smart Contracts for Online Games

Safely keeping the account’s private key is vital in blockchain technology, as there are no native blockchain mechanisms for recovering the account when the private key is lost. Therefore, this paper proposes a solution to this problem, running directly on the blockchain platform in the form of a smart contract, enabling deposit of digital assets into the contract for safekeeping, along with a list of security questions. The proposed solution can be used, for example, in online games. By providing proof of knowing answers to questions, a player can access his account without his private key. Furthermore, additional security measures are put in place to cope with malicious behaviour.
Polynomial-fitting based calibration for an active 3D sensing system using dynamic light section method

This paper proposes a polynomial-fitting based calibration method for an active 3D sensing using a dynamic light section method. In the dynamic light section method, the relative position of the line laser is dynamically changed at high speed to extend the measurement area with a low computational cost. To conduct 3D sensing, it is necessary to find the equation of the laser plane. In the proposed calibration method, a part of the line laser is irradiated on a reference plane fixed in the 3D sensing system, and correspondences between the normal vectors of the line laser and the image coordinates of the bright point on the reference plane are obtained. Then, the correspondences are regressed to a polynomial function. As a result, the plane equation of the line laser can be obtained at any given moment without considering the complicated system model. Through the measurement accuracy evaluation of the dynamic light section method calibrated by the polynomial fitting, we showed that a measurement target at a distance of 800 mm can be measured with an accuracy of an average of -5.94 mm and a standard deviation of 13.19 mm by rotating the line laser at 210 rpm.
An Efficient Cluster Assignment Algorithm for Scaling Support Vector Clustering

Support Vector Clustering (SVC) algorithm reformulates SVM’s Quadratic Programming as a minimum enclosing ball (MEB) problem, where every point in the data space will be projected to the higher dimensional feature space to find the minimum radius that encloses all the data points inside the sphere. Support vectors are data points in the surface of the minimum enclosing ball. The clustering algorithm works by mapping the support vectors of MEB back to the data plane forming groups or derives a contour that encloses a set of clustered points. However, the major limitation of this algorithm is that it fails to scale with larger dataset. Computation bottleneck lies in the efficient cluster computation approach and in solving the QP optimization for MEB. In order to solve this cluster computational complexity, this work presents a simple, efficient cluster assignment algorithm using similarity of feature set for data points in high-dimensional feature space utilizing an efficient MEB approximation algorithm. Experiments are carried out by varying the similarity threshold metrics. Performance of the clustering mechanism run on various datasets demonstrates the proposed algorithm is lighter, simple, and converges faster.
Diversified Pattern Mining on Large Graphs

Frequent pattern mining (\(\mathsf {FPM}\)) on large graph has been receiving increasing attention due to its wide applications. The \(\mathsf {FPM}\) problem is defined as mining all the subgraphs (a.k.a. patterns), with frequency above a user-defined threshold in a large graph. Though a host of techniques have been developed, most of them suffers from high computational cost and inconvenient result inspection. To tackle the issues, we propose an approach to discover diversified top-k patterns from a large graph G. We formalize the distributed top-k pattern mining problem based on a diversification function. We develop an algorithm with early termination property, to efficiently identify diversified top-k patterns. Using real-life and synthetic graphs, we show advantages of our algorithm via intensive experimental studies.
Optimal Thresholding Quantizer Maximizing Mutual Information of Discrete Multiple-Input Continuous One-Bit Output Quantization

In this paper, we consider the problem of one-bit (two-level) output-quantization maximizing mutual information between quantizer-output and channel-input using a single threshold for a discrete signal that is corrupted by a continuous additive noise. A necessary condition is constructed for which the thresholding quantizer is optimal. In addition, we show that if the distribution of the additive noise satisfies a mild condition, then a global optimal threshold can be found efficiently via a modified fixed-point algorithm.
effectiveness of using image media on beginning word writing skills in grade 1 elementary school students during COVID-19 pandemic

Background: Writing is an activity that cannot be kept away from teaching and learning activities in schools. Writing activities can make students be active and develop in learning activities and can stimulate the existence of stringing skills and composing words.&#x0D; Purpose: The research aims to investigate the effectiveness of the image media in increasing the writing skills of the first grader of elementary.&#x0D; Design and methods: The research applied case study methods with descriptive analysis techniques. Samples in this study were taken from SDN Cibinong 01 with a population of 20 grade 1 students consisting of 10 male students and 10 female students.&#x0D; Results: The results obtained from the study showed that the effectiveness of the image media used to hone the initial writing skills of grade 1 students at SDN Cibinong 01 as a whole can be said to have been good with a percentage of 85%. This shows the effectiveness of the use of image media is effective enough to be used to hone the initial word writing skills in grade 1 elementary school students during the pandemic.
Application of Blockchain Technology for 7/12 Asset Tracking System

A blockchain is an emerging technology in the age of the Internet and is employed in different areas such as fintech, healthcare, finance, pharmaceuticals, insurance, and digital security. A blockchain is an emergent list of registers termed as blocks that are connected utilizing cryptography. In the state-of-the-art system, the dealing of legal property documents for buying and selling property is accomplished through the government offices. However, these systems result in various frauds due to the middle intermediaries and also have to pay for lawyers. The government had started many applications for finding the property, but in nature, these systems are very complex. In this chapter, we have proposed the 7/12 asset tracking system using blockchain technology. This system is developed to monitor, enhance, and develop secure, trustless, and fraud-free property document handling. In this work, we have designed a shared organization where non-believing mediators can collaborate without a trusted intermediary. In this work, we have proposed a secure system and provide distributed data on the computer of every customer. This will help find the legal property papers and avoids the need for any third parties for property deals in between. This system will tap the fingerprint of the person who was going to sell a property, and the customer will have access to the server. All the property information is going to be displayed using a centralized system in government offices. Finally, this system provides secure and fraud-free handling of property documents.
MFA: Multi-level Feature Aggregation for Video Recognition

Deep convolutional neural networks (CNNs) are successful in self-extracting features for video object detection. The deep features and shallow features extracted from CNN are different. The shallow features have low-level semantic information, while the deep features contain high-level semantic information. In this paper, we propose an effective feature fusion method: Multi-level feature aggregation (MFA), which connects the output layer of each stage to the input layer of other stages and combines the output of each stage at the last layer of the network. This architecture can effectively combine shallow features and deep features to enhance the ability of expressing features and recognition accuracy. MFA is a flexible and end-to-end network. In addition, our experiments prove that MFA achieves significant accuracy on DET and VID datasets on object detection, and our method achieves mAP on DET and VID.
QoE Modeling on Split Features with Distributed Deep Learning

The development of Quality of Experience (QoE) models using Machine Learning (ML) is challenging, since it can be difficult to share datasets between research entities to protect the intellectual property of the ML model and the confidentiality of user studies in compliance with data protection regulations such as General Data Protection Regulation (GDPR). This makes distributed machine learning techniques that do not necessitate sharing of data or attribute names appealing. One suitable use case in the scope of QoE can be the task of mapping QoE indicators for the perception of quality such as Mean Opinion Scores (MOS), in a distributed manner. In this article, we present Distributed Ensemble Learning (DEL), and Vertical Federated Learning (vFL) to address this context. Both approaches can be applied to datasets that have different feature sets, i.e., split features. The DEL approach is ML model-agnostic and achieves up to 12% accuracy improvement of ensembling various generic and specific models. The vFL approach is based on neural networks and achieves on-par accuracy with a conventional Fully Centralized machine learning model, while exhibiting statistically significant performance that is superior to that of the Isolated local models with an average accuracy improvement of 26%. Moreover, energy-efficient vFL with reduced network footprint and training time is obtained by further tuning the model hyper-parameters.
Blockchain-Based Data Ownership Confirmation Scheme in Industrial Internet of Things

With the development of blockchain and Industrial Internet of Things (IIoT), there have been many problems in the combination of them. Due to the complexity and huge amount of data, it will face the problems of privacy leakage and circulation difficulties. Especially in the ownership confirmation, the current research mostly considers how to confirm, but ignores how to transfer safely and efficiently, and does not fully consider how to adapt to the IIoT environment. This paper proposes a data ownership confirmation scheme. We have verified the feasibility and rationality of the proposed scheme through a large number of experiments, and the results show that our scheme is more suitable for IIoT scenarios.
A geodesic multipolar parameterization-based representation for 3D face recognition

Here, we propose a new 3D representation designed in the objective of face recognition independently of the expressions. It is defined on localities presenting limited deformations following expression variations. The proposed representation is based on the multipolar parameterization that we recently introduced in Jribi et al. (2019) which is relative invariant under three dimensional Euclidean transformations and robust to the original mesh. A choice of the number of reference points of each multipolar parameterization is made according to the shape of the two types of region of the face namely those of nose and the eyes. The main curvature field is estimated on the parameterizations of each region. The parameters of dimensional reduction algorithms applied to the overall description are adjusted so that the recognition rates remain efficient. The experiments are carried out on three challenging 3D face databases: the FRGC v2.0, the BU-3DFE and Bosphorus. Very high rates are obtained for both identification and verification scenarios. These results are very competitive with state of the art methods.
Source Hypothesis Transfer for Zero-Shot Domain Adaptation

Making predictions in target unseen domains without training samples is frequent in real-world applications, such as new products' sales predictions. Zero-shot domain adaptation (ZSDA) has been studied to achieve this important but difficult task. An approach to ZSDA is to use multiple source domain data and domain attributes. Several recent domain adaptation studies have mentioned that source domain data are not often available due to privacy, technical, and contractual issues in practice. To address these issues, hypothesis transfer learning (HTL) has been gaining attention since it does not require access to source domain data. It has shown its effectiveness in supervised/unsupervised domain adaptation; however current HTL methods cannot be readily applied to ZSDA because we have no training data (even unlabeled data) for target domains. To solve this problem, we propose an HTL-based ZSDA method that connects multiple source hypotheses by domain attributes. Through theoretical analysis, we derive the convergence rate of the estimation error of our proposed method. Finally, we numerically demonstrate the effectiveness of our proposed HTL-based ZSDA method.
Feature Selection and Adaptive Synthetic Sampling Approach for Optimizing Online Shopper Purchase Intent Prediction

This study proposes a novel oncoming for optimizing online shopper purchase intent prediction using feature selection combined with Adaptive Synthetic Sampling (ADASYN). A supervised learning technique is applied to predict whether the customer visits ending with shopping or not based on the features. However, not all features are important to predict the classes. In addition, a suboptimal performance may occur due to the imbalanced class problem. Therefore, we propose Information Gain (IG) and Correlation (CORR) feature selection to select the most important features. ADASYN is additionally used to deal with the imbalanced class problem by adaptively generating new compositive samples for the minority class with considering density distribution. The proposed approach is run using Random Forest classifier. The experiments indicate that ADASYN successfully improves the classification results in the matter of accuracy, precision, recall, and F1-score. The use of feature selection combined with ADASYN has been compared to previous works, the results indicate that our proposed approach outperforms all. We additionally use a statistical test to show that our results are statistically significant. By these results, our proposed approach is proven for optimizing classification results.
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition

Hidden Markov Models (HMMs) comprise a powerful generative approach for modeling sequential data and time-series in general. However, the commonly employed assumption of the dependence of the current time frame to a single or multiple immediately preceding frames is unrealistic; more complicated dynamics potentially exist in real world scenarios. This paper revisits conventional sequential modeling approaches, aiming to address the problem of capturing time-varying temporal dependency patterns. To this end, we propose a different formulation of HMMs, whereby the dependence on past frames is dynamically inferred from the data. Specifically, we introduce a hierarchical extension by postulating an additional latent variable layer; therein, the (time-varying) temporal dependence patterns are treated as latent variables over which inference is performed. We leverage solid arguments from the Variational Bayes framework and derive a tractable inference algorithm based on the forward-backward algorithm. As we experimentally show, our approach can model highly complex sequential data and can effectively handle data with missing values.
Incorporating Specific Knowledge into End-to-End Task-oriented Dialogue Systems

External knowledge is vital to many natural language processing tasks. However, current end-to-end dialogue systems often struggle to interface knowledge bases(KBs) with response smoothly and effectively. In this paper, we convert the raw knowledge into relation knowledge and integrated knowledge and then incorporate them into end-to-end task-oriented dialogue systems. The relation knowledge extracted from knowledge triples is combined with dialogue history, aiming to enhance semantic inputs and support better language understanding. Integrated knowledge involves entities and relations by graph attention, assisting the model in generating informative responses. The experimental results on three public dialogue datasets show that our model improves over the previous state-of-the-art models in sentence fluency and informativeness.
Optimizing stock assessment workflows by applying software development methodology

Considering that stock assessment requires iterative work including exploratory calculations and discussion, efficiency in completing projects is the key to a successful contribution to stock management. However, if the production speed of each process of the project is inconsistent, a faster process may be worse than a slower process because a large amount of information produced by a faster process induces human error. To coordinate the production speed of each process, we applied software development methodology to the whole assessment project, including small important tasks such as scenario selection, debugging, and yearly updates. First, we established a continuously integrated (CI) document system that monitors project files and triggers report generation when they are updated. The system is composed of four cloud services: a code hosting service, a cloud computing service, cloud storage, and a website hosting service. Because of the CI document system, all the following iterative work was reflected in the report without creating any unused output. The workflow benefited not only from the collaboration but also from the maintainability of the project because work progress and the discussion remained visible for all collaborators. We discuss the effectiveness of a workflow by Lean manufacturing that allows us to focus on the essential problem of the assessment project.
Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance

Predictive maintenance, which has traditionally used anomaly detection methods on sensory data, is now being replaced by event-based techniques. These methods utilise events with multiple temporal (and often non-numeric) features, produced by diagnostic modules. This raises the need of learning numerical event representations to predict the next fault event in industrial machines, specially vehicles, that use Diagnostic Trouble Codes (DTCs). We propose a predictive maintenance approach, named Sequential Multivariate Fault Prediction (SMFP), for predicting the next multivariate DTC fault in an event sequence, using Long Short-Term Memory Networks (LSTMs) and jointly learned event embeddings. By performing an in-depth comparison of different architectural choices and contextual preprocessing techniques, we provide an initial baseline for SMFP that achieves top-3 accuracy of 63% on predicting multivariate fault with 3 collective output layers, using vehicle maintenance data as a case study.
Performance Estimation of Encrypted Video Streaming in Light of End-User Playback-Related Interactions

Our research will look into realistic end-user service usage behavior patterns and their corresponding implications on the in-network Quality of Experience (QoE) monitoring for HTTP adaptive video streaming (HAS) services in wireless and mobile networks. The main goal is to establish a methodology for developing and testing machine learning (ML) models for estimating end-user QoE-related Key Performance Indicators (KPIs) in the context of user-initiated playback interactions. The initial phase will be to investigate user behavior when utilizing video streaming services on mobile devices and propose a user interaction model. In addition, a methodology for automated data collecting, processing, and analysis will be created, which will include the creation of a framework that combines user interaction simulation based on the proposed model. Extensive experiments will be carried out to train ML models for KPI estimation, and the resultant KPI estimation models will be evaluated. This paper presents a current state-of-the-art review of the corresponding topics, as well as the current state of our research and preliminary findings.
Demonstrator of adaptive visual camouflage based on LEDs

Within the EDA funded project ACAMSII, “Adaptive camouflage for the soldier”, a future soldier system is being developed and implemented that provides adaptive camouflage against all relevant sensor threats, ranging from visible to thermal and radar. To provide adaptive camouflage in the visible domain LEDs are integrated in the clothing. A proof-of-principle demonstrator of this subsystem was developed and implemented by TNO in collaboration with its international ACAMS partners. The demonstrator consists of a panel of clothing material with printed camouflage pattern embedded with LEDs. The camouflage pattern adapts to the background and lighting conditions by means of a camera and a light sensor. The camera registers the background colours and pattern and the light sensor registers the illumination of the panel. In situations in which the printed camouflage provides sufficient protection (in darker backgrounds) the LEDs are switched off. The small LEDs are invisible when switched off and blend together when they are turned on and are viewed from larger distances (e.g. 50 m). When turned on, the colours and luminance of the LEDs are matched with that of the background. A control unit (mini-laptop) is connected to the LEDs, the camera, and the light sensor. Using the camera image, light sensor output and printed pattern as input, it calculates the LED input levels required to generate the desired output (camouflage pattern), taking into account the light reflected from the clothing and the input-output relationship of the LEDs. The control unit automatically updates the LED output to changes in background and illumination. In our presentation we will show how the control loop is implemented and demonstrate that the system adapts well to changes in background and lighting. Different implementations are presented and discussed.