Reviewerly/exHarmony huggingface dataset:
Safe navigation for UAV-enabled data dissemination by deep reinforcement learning in unknown environments

Unmanned aerial vehicles (UAVs) are increasingly considered in safe autonomous navigation systems to explore unknown environments where UAVs are equipped with multiple sensors to perceive the surroundings. However, how to achieve UAV-enabled data dissemination and also ensure safe navigation synchronously is a new challenge. In this paper, our goal is minimizing the whole weighted sum of the UAV's task completion time while satisfying the data transmission task requirement and the UAV's feasible flight region constraints. However, it is unable to be solved via standard optimization methods mainly on account of lacking a tractable and accurate system model in practice. To overcome this tough issue, we propose a new solution approach by utilizing the most advanced dueling double deep Q network (dueling DDQN) with multi-step learning. Specifically, to improve the algorithm, the extra labels are added to the primitive states. Simulation results indicate the validity and performance superiority of the proposed algorithm under different data thresholds compared with two other benchmarks.
Image Dehazing Using LiDAR Generated Grayscale Depth Prior

In this paper, the dehazing algorithm is proposed using a one-channel grayscale depth image generated from a LiDAR point cloud 2D projection image. In depth image-based dehazing, the estimation of the scattering coefficient is the most important. Since scattering coefficients are used to estimate the transmission image for dehazing, the optimal coefficients for effective dehazing must be obtained depending on the level of haze generation. Thus, we estimated the optimal scattering coefficient for 100 synthetic haze images and represented the distribution between the optimal scattering coefficient and dark channels. Moreover, through linear regression of the aforementioned distribution, the equation between scattering coefficients and dark channels was estimated, enabling the estimation of appropriate scattering coefficient. Transmission image for dehazing is defined with a scattering coefficient and a grayscale depth image, obtained from LiDAR 2D projection. Finally, dehazing is performed based on the atmospheric scattering model through the defined atmospheric light and transmission image. The proposed method was quantitatively and qualitatively analyzed through simulation and image quality parameters. Qualitative analysis was conducted through YOLO v3 and quantitative analysis was conducted through MSE, PSNR, SSIM, etc. In quantitative analysis, SSIM showed an average performance improvement of 24%.
Adaptive Participant Selection in Heterogeneous Federated Learning

Federated learning (FL) is a distributed machine learning technique to address the data privacy issue. Participant selection is critical to determine the latency of the training process in a heterogeneous FL architecture, where users with different hardware setups and wireless channel conditions communicate with their base station to participate in the FL training process. Many solutions have been designed to consider computational and uploading latency of different users to select suitable participants such that the straggler problem can be avoided. However, none of these solutions consider the waiting time of a participant, which refers to the latency of a participant waiting for the wireless channel to be available, and the waiting time could significantly affect the latency of the training process, especially when a huge number of participants are involved in the training process and share the wireless channel in the time-division duplexing manner to upload their local FL models. In this paper, we consider not only the computational and uploading latency but also the waiting time (which is estimated based on an M/G/1 queueing model) of a participant to select suitable participants. We formulate an optimization problem to maximize the number of selected participants, who can upload their local models before the deadline in a global iteration. The Latency awarE pARticipant selectioN (LEARN) algorithm is proposed to solve the problem and the performance of LEARN is validated via simulations.
Virtual OBDA Mechanism Ontop for Answering SPARQL Queries Over Couchbase

In the last decade, the database field has become substantially diversified, as a consequence, a number of non-relational databases (known also as NoSQL) have been developed, e.g., key-value stores and JSON-document databases, XML, and graph databases. Several issues associated with big data were addressed as a result of the rise of this new generation of data services. However, in the rush to address the problems of big data and vast numbers of active users, NoSQL dropped certain of the core features of databases that make them highly performant and usable such as the global view, that permits users to access data without needing to know how they are logically organized or physically stored in their sources. We address, in this article, the challenge of how to fill the gap between NoSQL and the Semantic web, in order to enable access to such databases and integration of non-relational data sources. However, we extend the well-known framework for ontology-based data access (OBDA), intending to allow a mediating ontology to query arbitrary databases. We instantiate this framework to a popular JSON-document database called Couchbase, and implement a prototype extension of the virtual OBDA mechanism Ontop to answer SPARQL queries over Couchbase.
Relative influences and the reliability of weights in fuzzy cognitive maps

This study presents a control mechanism for checking the reliability of weights obtained by a recursive simulation (leading to a steady state) of Fuzzy Cognitive Maps (FCMs). We introduce a method of constructing non-recursive FCM based on historical data. It assigns relative weights of influences based on actual past behavior of the relevant concepts. Mathematical simplicity of the method as well as the lack of any special requirements and assumptions in its applications, imply high reliability. The set of weights of such non-recursively constructed FCM is expected to be different in comparison to the recursively computed weights representing steady state. However, only if the both sets of weights are similar enough, the recursively computed weights are validated. We introduce a method for comparing the two sets of weights, determining whether they are similar enough. A case study consisting of several examples is presented to illustrate the application of the introduced method.
Two-Dimensional Functional Principal Component Analysis for Image Feature Extraction

Methodologies for functional principal component analysis are well established in the one-dimensional setting. However, for two-dimensional surfaces, for example, images, conducting functional principal component analysis is complicated and challenging, because the conventional eigendecomposition approach would require the estimation of a four-dimensional covariance function, which may incur high cost in terms of time and machine memory. To circumvent such computational difficulties, we propose a novel two-dimensional functional principal component analysis for extracting functional principal components and achieving dimensionality reduction for images. Different from the conventional eigendecomposition approach, our proposed method is based on the direct estimation of the optimal two-dimensional functional principal components via tensor product B-spline, which opens up a new avenue for estimating functional principal components. We present theoretical results that prove the consistency of the proposed approach. Our method is illustrated by analyzing brain images of subjects with the Alzheimer's Disease and the handwritten digits images. The finite sample performance of our method is further assessed with some simulation studies. Supplementary materials for this article are available online.
Anomaly detection of diabetes data based on hierarchical clustering and CNN

In this paper, we propose a diabetes data anomaly detection approach based on hierarchical clustering and support vector machine (SVM), named hierarchical support vector machine (HCSVM). In the HCSVM approach, the diabetes data sets with the same data characteristics are classified by clustering algorithm, and the data are divided into significant abnormal parts and potential abnormal parts. Additionally, the convolutional neural network (CNN) is utilized to detect and analyze the data of each part. The feature vector output from CNN full connection layer is applied as the input data of SVM classifier, and the optimal classification hyperplane is constructed in high-dimensional space for classification, so as to detect the abnormal data in diabetes data more pertinently. Finally, a real diabetes data set collected by a hospital is used for experiment, and ROC curve is adopted to evaluate the performance of the proposed approach compared with random forest algorithm, KNN algorithm and SVM algorithm. The results show that the HCSVM algorithm combined with hierarchical clustering and SVM can achieve a better performance.
A*-Based Co-Evolutionary Approach for Multi-Robot Path Planning with Collision Avoidance

In this research, a coevolutionary collision free multi-robot path planning that makes use of A* is proposed. To find collision-free paths for all robots, we generate a route for each of robot using A* path finding but introducing restrictions for each collision found. Afterward, a co-evolutionary optimization process is implemented for introducing changes in the initial paths to find a combination of routes that is collision-free. The approach has been tested in mazes with increasing the number of robots, showing a robust performance although at high time expenses. Nevertheless, several enhancements are proposed to tackle this issue.
Technological Barriers of (non)Blockchain Enabled IoT Data Marketplaces

Today, data is one of the world's most valuable resources that is growing at an exponential rate. Moreover, the world is rapidly becoming more connected with the capability of intelligent devices, which bring extensive opportunities among different domains such as health care, public sector, transportation, education, financial services, etc. While the utilization of big data is a key factor for success in many businesses, smart cities make use of Internet of Things (IoT) devices to generate a huge amount of data, termed as Big Data. Big data refers to the vast untapped value, and it is growing exponentially. To maximize the utility of collected data, a secure and efficient platform is needed to make data sourcing effortless for buyers and maximize data monetization for sellers. Building a global data marketplace can leverage all data exchange opportunities and help businesses share relevant data. However, there are major challenges regarding IoT data marketplaces. This paper presents an overview of the findings in current state-of-the-art focusing on technological barriers of IoT data marketplaces either utilizing blockchain or non-blockchain technology. Finally, we conclude that neither decentralized nor centralized approaches doesn't make sense solely.
Comparison of Windowing Function on Feature Extraction Using MFCC for Speaker Identification

The speaker identification system is built by two main blocks; the first part is used to extract features from the input, while the second part is to classify the results from the features in the first part. Selection of the method to perform feature extraction is very important to obtain the optimal feature set. Mel-frequency cepstral coefficients (MFCC) is a feature extraction method that is used to convert the speaker's voice into coefficients as input for the classification process. There are several processes in MFCC, one of which is windowing. Windowing aims to reduce the discontinuous effect on the signal after the framing process. It is therefore important to use optimal windowing techniques so that the features of each sound are not wasted. This article highlights the use of several window functions such as hanning, hamming, bartlett, blackman, kaiser, and gaussian. The classification process proposed in this study is Artificial neural network (ANN). The data used amounted to 800 data from 16 speakers who were recorded directly. The data recorded for identification was the sound from the digits zero to nine (0-9) by each speaker. K-fold cross-validation was used as an evaluation of the classification model created to determine the combination with the best accuracy. The results shows that the use of 13 MFCC features with windowing hamming and gaussian with standard deviation values 72 obtains the best results. Both obtained an accuracy of 95%. This paper helps readers to gain insight in the field of speaker identification.
An Incentive Mechanism for Big Data Trading in End-Edge-Cloud Hierarchical Federated Learning

As a compelling collaborative machine learning framework in the big data era, federated learning allows multiple participants to jointly train a model without revealing their private data. To further leverage the ubiquitous resources in end-edge-cloud systems, hierarchical federated learning (HFL) focuses on the layered feature to relieve the excessive communication overhead and the risk of data leakage. For end devices are often considered as self-interested and reluctant to join in model training, encouraging them to participate becomes an emerging and challenging issue, which deeply impacts training performance and has not been well considered yet. This paper proposes an incentive mechanism for HFL in end-edge-cloud systems, which motivates end devices to contribute data for model training. The hierarchical training process in end-edge-cloud systems is modeled as a multi-layer Stackelberg game where sub-games are interconnected through the utility functions. We derive the Nash equilibrium strategies and closed-form solutions to guide players. Due to fully grasping the inner interest relationship among players, the proposed mechanism could exchange the low costs for the high model performance. Simulations demonstrate the effectiveness of the proposed mechanism and reveal stakeholder&#x0027;s dependencies on the allocation of data resources.
Multimodal Sentiment Analysis Based on Interactive Transformer and Soft Mapping

Multimodal sentiment analysis aims to harvest people’s opinions or attitudes from multimedia data through fusion techniques. However, existing fusion methods cannot take advantage of the correlation between multimodal data but introduce interference factors. In this paper, we propose an Interactive Transformer and Soft Mapping based method for multimodal sentiment analysis. In the Interactive Transformer layer, an Interactive Multihead Guided-Attention structure composed of a pair of Multihead Attention modules is first utilized to find the mapping relationship between multimodalities. Then, the obtained results are fed into a Feedforward Neural Network. The Soft Mapping layer consisting of stacking Soft Attention module is finally used to map the results to a higher dimension to realize the fusion of multimodal information. The proposed model can fully consider the relationship between multiple modal pieces of information and provides a new solution to the problem of data interaction in multimodal sentiment analysis. Our model was evaluated on benchmark datasets CMU-MOSEI and MELD, and the accuracy is improved by 5.57% compared with the baseline standard.
“How Robust R U?”: Evaluating Task-Oriented Dialogue Systems on Spoken Conversations

Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets. However, written dialogues are not sufficient to fully capture the nature of spoken conversations as well as the potential speech recognition errors in practical spoken dialogue systems. This work presents a new benchmark on spoken task-oriented conversations, which is intended to study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling. We report that the existing state-of-the-art models trained on written conversations are not performing well on our spoken data, as expected. Furthermore, we observe improvements in task performances when leveraging <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> -best speech recognition hypotheses such as by combining predictions based on individual hypotheses. Our data set enables speech-based benchmarking of task-oriented dialogue systems.
Finite Horizon Privacy of Stochastic Dynamical Systems: A Synthesis Framework for Gaussian Mechanisms

We address the problem of synthesizing distorting mechanisms that maximize the privacy of stochastic dynamical systems. Information about the system state is obtained through sensor measurements. This data is transmitted to a remote station through an unsecured/public communication network. We aim to keep part of the system state private (a private output); however, because the network is unsecured, adversaries might access sensor data and input signals, which can be used to estimate private outputs. To prevent an accurate estimation, we pass sensor data and input signals through a distorting (privacy-preserving) mechanism before transmission and send the distorted data to the trusted user. These mechanisms consist of a coordinate transformation and additive dependent Gaussian vectors. We formulate the synthesis of the distorting mechanisms as a convex program, where we minimize the mutual information (our privacy metric) between an arbitrarily large sequence of private outputs and the disclosed distorted data for desired distortion levels – how different actual and distorted data are allowed to be.
PPDTSA: Privacy-preserving Deep Transformation Self-attention Framework For Object Detection

In order to perform competitive privacy-guaranteed object detection, we propose an end-to-end model called Privacy-preserving Deep Transformation Self-attention (PPDTSA). This model ensures the privacy of the inference results. It has a low-complexity hierarchical structure with a relatively small number of hyper-parameters. Consistency of prediction is achieved through the encoding and decoding blocks of the self-attention mechanism which enables points of interest to be located. Focus loss is estimated based on foreground-background imbalance. The remaining dense blocks enable image details to be retained and the Region Of Interest to be expanded. At the same time, the objects detected in the image are protected through the privacy noise volume which is specified by the user. Experimental results demonstrate that PPDTSA achieves superior performance on the MOT20 dataset compared with three other state-of-the-art object detection models.
YouTube Monetization and Censorship by Proxy: A Machine Learning Prospective

As consumption of digital content has climbed, so has censorship of the content. The censorship has only increased with companies more sensitive to the type of content that they tie their advertising to on digital platforms. Demonetization of videos is a primary way content is censored on YouTube. Demonetization, often referred to as "Apocalypse", is a process in which content creators, are denied paid ads in their YouTube videos. Consequently, they are denied revenue, their income on the video-hosting platform is reduced and their video is less likely to be promoted or recommended on the platform, eventually getting censored. YouTube's censorship algorithm is not public and is a Blackbox to the world. The paper proposes a methodology that employ four machine learners, i.e., C 4.5, Random Forest, Linear Regression and Support Vector Machine, to predict if changes in the meta-data of the YouTube video will lead to (demonetization) censorship of the video. Our methodology requires little time to train and achieve an accuracy of up to 87%. The methodology is useful to content creators trying to determine what content to create to maximize their revenue. As well is helpful to free speech advocates who may believe content is being unfairly or unlawfully censored.
A Comparative Study of Different Text Classification Approaches for Bangla News Classification

At present, we have seen everything is getting digitized where technology almost takes full control over our life. As a result, a massive number of textual documents are generated on online platforms and news articles are no exception. People prefer to get connected with online news portals as they are updated every single hour. Newspaper articles have so many categories such as politics, sports, business, entertainment etc. Recently, we have noticed the rapid growth and increase of Bangla online news portals on the internet. It will be helpful for the online readers to get recommended the preferable news category which assists them in locating desired articles. Manually categorizing news articles takes huge time and effort. So, text categorization is necessary for the modern day, as enormous amounts of uncategorized data are an issue here. Although the study has improved in categorizing news articles greatly for languages such as English, Arabic, Chinese, Urdu, and Hindi. Among others, the Bangla language has shown little development. However, some approaches were applied to categorize Bangla news articles, using some machine learning algorithms where resources were minimum. We have applied five machine learning classifiers and two neural networks to categorize Bangla news articles where neural network LSTM performed best. To show the comparison between applied algorithms, which one is performing better, we have used four metrics that measure performance.
Twin Delayed Multi-Agent Deep Deterministic Policy Gradient

Recently, reinforcement learning has made remarkable achievements in the fields of natural science, engineering, medicine and operational research. Reinforcement learning addresses sequence problems and considers long-term returns. This long-term view of reinforcement learning is critical to find the optimal solution of many problems. The existing multi- agent reinforcement learning algorithms have the problem of overestimation in estimating the Q value. Unfortunately, there have not been many studies on overestimation of agent reinforcement learning, which will affect the learning efficiency of reinforcement learning. Based on the traditional multi-agent reinforcement learning algorithm, this paper improves the actor network and critic network, optimizes the overestimation of Q value and adopts the update delayed method to make the actor training more stable. In order to test the effectiveness of the algorithm structure, the modified method is compared with the traditional MADDPG, DDPG and DQN methods in the simulation environment.
Analysis of financial fraud based on manager knowledge graph

Financial fraud can result in devastating consequences for the stability of a firm, as well as considerable losses to shareholders, industry, and even the whole market. The existing studies for fraud detection mainly rely on traditional data sources, which utilize limited information from financial statements. In this paper, we design a novel knowledge graph (KG) framework that aims to enhance financial fraud detection by discovering knowledge from the relationship between the managers and the associated institutions. Using four machine learning algorithms, we evaluate the classification performance of the proposed framework. The empirical analyses show that the support vector machine (SVM) model achieves the best testing performance among all classifiers by applying a training set of financial ratios and firms' topological features derived from the KG. In particular, the precision is significantly improved by taking the topological features into account. These findings suggest that KG can be effectively used to detect financial fraud.