Reviewerly/exHarmony huggingface dataset:
IoT Network Model with Multimodal Node Distribution and Data-Collecting Mechanism Using Mobile Clustering Nodes

In this paper, the novel study of an Internet of Things (IoT) network model with multimodal node distribution and a data-collecting mechanism using mobile clustering nodes is presented. The aim of this work is to introduce the problem of organizing the mobile cluster head IoT network with a heterogeneous distribution node in the service area with multimodal distribution nodes. A new method for clustering a heterogeneous network is proposed, which makes it possible to efficiently identify clusters that differ in terms of the density of nodes. This makes it possible to choose the speed of the mobile cluster head in accordance with the density in each cluster. The proposed method uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm. One of the benefits of our proposed model is the increase in the efficiency of using a mobile cluster head. The new solution can be used to organize data collection in the IoT.
NEDORT: a novel and efficient approach to the data overlap problem in relational triples

Abstract Relation triple extraction is a combination of named entity recognition and relation prediction. Early works ignore the problem of data overlap when extracting triples, resulting in poor extraction performance. Subsequent works improve the capability of the model to extract overlapping triples through generative and extractive methods. These works achieve considerable performance but still suffer from some defects, such as poor extraction capability for individual triplets and inappropriate spatial distribution of the data. To solve the above problems, we perform sequence-to-matrix transformation and propose the NEDORT model. NEDORT predicts all subjects in the sentence and then completes the extraction of relation–object pairs. There are overlapping parts between relation–object pairs, so we conduct the conversion of sequence to matrix. We design the Differential Amplified Multi-head Attention method to extract subjects. This method highlights the locations of entities and captures sequence features from multiple dimensions. When performing the extraction of relation–object pairs, we fuse subject and sequence information through the Biaffine method and generate relation–sequence matrices. In addition, we design a multi-layer U-Net network to optimize the matrix representation and improve the extraction performance of the model. Experimental results on two public datasets show that our model outperforms other baseline models on triples of all categories
Learning in Constrained Markov Decision Processes

We consider reinforcement learning (RL) in Markov Decision Processes in which an agent repeatedly interacts with an environment that is modeled by a controlled Markov process. At each time step $t$, it earns a reward, and also incurs a cost-vector consisting of $M$ costs. We design model-based RL algorithms that maximize the cumulative reward earned over a time horizon of $T$ time-steps, while simultaneously ensuring that the average values of the $M$ cost expenditures are bounded by agent-specified thresholds $c^{ub}_i,i=1,2,\ldots,M$. In order to measure the performance of a reinforcement learning algorithm that satisfies the average cost constraints, we define an $M+1$ dimensional regret vector that is composed of its reward regret, and $M$ cost regrets. The reward regret measures the sub-optimality in the cumulative reward, while the $i$-th component of the cost regret vector is the difference between its $i$-th cumulative cost expense and the expected cost expenditures $Tc^{ub}_i$. We prove that the expected value of the regret vector of UCRL-CMDP, is upper-bounded as $\tilde{O}\left(T^{2\slash 3}\right)$, where $T$ is the time horizon. We further show how to reduce the regret of a desired subset of the $M$ costs, at the expense of increasing the regrets of rewards and the remaining costs. To the best of our knowledge, ours is the only work that considers non-episodic RL under average cost constraints, and derive algorithms that can~\emph{tune the regret vector} according to the agent's requirements on its cost regrets.
RANCANGAN SISTEM DISKLESS UNTUK GAME CENTER MENGGUNAKAN APLIKASI CCBOOT

Since the launch of the game to be one of the sports fields, namely e-sports, a new business sector has grown to facilitate gamers to compete or practice together, namely the Game Center. Many e-sports players choose to practice and compete in game centers because the computer specifications provided are usually quite high and are supported by more stable internet speeds. Game Center XYZ is a game center whose client computer is active continuously for almost 24 hours, making the durability of existing computer devices decreasing, especially hard disk devices. A hard drive that is used continuously will result in a weakening of the hard drive's performance. So that the performance of the computer as a whole can be disrupted. Problems that often occur, for example, BSOD, existing data becomes corrupted, there are bad sectors on the hard disk that cause data to become unreadable. The purpose of this study is to produce a diskless network system that operates on a client-server basis. This research produces a diskless system with a star topology that connects 1 pc server and 5 client pc. The diskless system runs well and the client computer's performance is more optimal.
IDENTIFIKASI TINGGALAN ARKEOLOGI PADA GUA ILETE KECAMATAN BUNGKU TIMUR KABUPATEN MOROWALI

Based on preliminary observations at Ilete Cave located in Ululere Village, Bungku Timur District, Morowali Regency. In Ilet Cave there are quite a lot of archeological findings such as bones, earthenware fragments, and conch shells. The following environment around the Ilete Cave is composed of the surrounding community plantation land. Based on the issues raised in this study are: (1) What is included in the Ilete Cave of East Bungku District. (2) How is the function of the Cave based on the findings in the Cave Ilete, East Bungku District, Morowali Regency.&#x0D; This study uses the archeological theory of cultural history. This type of research used in this study is qualitative inductive criminal model. Data collection in this research was carried out through literature study, observation or field survey, observation and interview. Analysis of the data applied by the historical method then begins with contextual analysis.&#x0D; The research site shows that the Ilete Cave Site has several artifacts such as pottery fragments, ceramic fragments, bone fragments, teeth and mollusk fragments the authors draw conclusions of the Cave site with the community and reading results.&#x0D;
Minimum-Time Escape from a Circular Region for a Dubins Car

We investigate the problem of finding paths that enable a robot modeled as a Dubins car (i.e., a constant-speed finite-turn-rate unicycle) to escape from a circular region of space in minimum time. This minimum-time escape problem arises in marine, aerial, and ground robotics in situations where a safety region has been violated and must be exited before a potential negative consequence occurs (e.g., a collision). Using the tools of nonlinear optimal control theory, we show that a surprisingly simple closed-form feedback control law solves this minimum-time escape problem, and that the minimum-time paths have an elegant geometric interpretation.
Integral Design Method for Control Barrier Functions that Considers the Shape of the Target System

In recent years, control barrier functions have attracted much attention for ensuring safety in collision avoidance and human assist control. However, control laws cannot guarantee the solution of differential equations without properly considering the shape of the target system. In this study, we designed a control law that considers the shape of the control target and designs a control barrier function for non-convex safe sets. Based on this, we proposed an integral control barrier function and confirmed its effectiveness by testing it for the collision avoidance between a two-links robot arm and an obstacle through simulations.
Improvement of Linear Tracking Response of Two-Degree-of-Freedom Control of Discrete-Valued Driven Crawler

To enable autonomous driving, the linear tracking control of on–off driven crawler vehicles, which are often used in agriculture, must be optimized. This paper presents a linear tracking control method for a discrete-value-driven crawler. Control laws based on feedback control are proposed for tracking control of discrete-valued driven crawler vehicles. Herein, we design a two-degree-of-freedom control system with feedforward for a discrete-value-driven crawler to improve tracking performance while reducing computational cost. The proposed method converts a continuous-valued input, determined using a two-degree-of-freedom control law, into a discrete-valued input based on a quadratic formal evaluation function. The effectiveness of the proposed method was verified through numerical simulations. As a result, we confirmed that the proposed method improves the tracking performance while reducing the computational complexity. This study made it possible to design a two-degree-of-freedom control system in discrete-valued input systems.
A Numerically Robust Bayesian Filtering Algorithm for Gaussian Mixture Models

A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this problem involves an exponential growth in the number of mixture terms and this is handled here by utilising a Gaussian mixture reduction step after both the time and measurement update steps. In addition, a numerically robust square-root implementation of the unified algorithm is presented and this algorithm is profiled on several simulated systems, including the state estimation for a challenging non-linear system.
Performance Analysis of Various Algorithms In 2D Dynamic Object Detection

Technology advancement produced the uncrewed autonomous ground vehicle. The multi-view real-time environmental state provision for autonomous uncrewed ground vehicles has recently gained popularity. In autonomous driving, object detection techniques will be used more and more. The most prominent 2D dynamic object detection algorithms are YOLO v5, YOLO v4, and Mask R-CNN. This work analyses the performance of these three algorithms on two datasets: COCO Dataset and NuScenes Dataset. Finally, the performance obtained using these models on these two different datasets is compared using the FPS, inference time, and mean average precision as the performance evaluation parameters. On analysing the performance, it can be seen that the YOLO v5 model performs better than the other two models in real-time scenarios. Therefore, in Intelligent Transport Systems, the YOLO v5 model is suitable for 2D dynamic object detection.
Secure and Light weight Data Sharing Scheme in Cyber Physical Cloud Environment

It is still difficult to achieve secure and effective file storage and sharing via authenticated physical devices in a cyber-physical cloud environment because of the variety of devices used to access the services and data, as well as the geographical dispersion of these devices. In this circumstance, a wide variety of cryptographic alternatives are currently available. But the computational and communication overheads are slightly high, and we also need a safe, lightweight, and user-friendly technology. Thus employing a scrambling and unscrambling technique using Sudoku inspired Data Chunking and Scrambling (SiDCS) method. In this approach, the data is scrambled and then unscrambled using Sudoku puzzles. Before being stored in a public cloud, the original data is split up and scrambled. The data will be saved in the cloud independently and securely since each data chunk will only contain a minimal amount of information and because it is scrambled before being stored there. As a result, the cost of communication is reduced, computational overhead is reduced, and a simpler, lightweight model is developed without compromising the security.
Copy-move Image Forgery Detection and Localization Using Alteration Trace Net

The prevalence of social media as a modern substitute for conventional news sources has led to the rise of fake news, which usually uses tampered photographs. This trend is frequently brought on by the rapidly falling cost of high-tech cameras and cell phones, which encourage the fast creation of computerized images. The ease of manipulating digital images has made image forgery a common worry. The volume of altered photos shared daily has greatly increased due to the quick development of commercial image altering programs like Adobe Photoshop. This phenomenon has detrimental effects, diminishing reliability and producing false beliefs in many real-world applications. This paper suggests a deep learning strategy for detecting copy-move forgeries in digital images. Here, we employ two deep learning models to identify copy move fraud in digital photos, namely Buster Net and Alteration Trace Net. The CoMoFoD dataset is used to assess the performance of the two models. The experimental results demonstrate that the Alteration Trace Net model outperforms the Buster Net model with 98.6% accuracy in identifying forgeries in photos, compared to the Buster Net model's accuracy of 96.9%.
Attention based Collaborator Recommendation in Heterogeneous Academic Networks

In real academic networks, there exist multiple types of entities(authors, papers, terms, conferences) and links between them. Therefore, the academic networks are generally considered as heterogeneous information networks(HINs). Existing collabo-rator recommendation methods in heterogeneous networks are generally based on the embeddings of nodes and links with re-spect to some given meta-paths. However, they seldom learn meta-paths representations which can provide important interaction information. What's more, the impact of different meta-paths on recommendation are neglected. In order to deal with these unsolved problems, we propose an attention based collaborator recommendation method in the setting of heterogeneous academic networks. Firstly, we select some meta-paths according to the HIN schema. Secondly, the embeddings of nodes and meta-path instances are generated by employing the Skip-gram and Convolutional Neural Network(CNN) models respectively. Thirdly, the attention mechanism is devised to integrate the multiple sources of embeddings so as to produce the author representations and meta-path based context representations. Finally, the Multi-Layer Perceptron is utilized for recommendation task. Comparative experiments conducted on the DBLP dataset demonstrate the effectiveness of our proposed method.
To Mask or Not To Mask? A Machine Learning Approach to Covid News Coverage Attitude Prediction Based on Time Series and Text Content

In the past few decades, with the explosion of information, a large number of computer scientists have devoted themselves to analyzing collected data and applying these findings to many disciplines. Natural language processing (NLP) has been one of the most popular areas for data analysis and pattern recognition. A significantly large amount of data is obtained in text format due to the ease of access nowadays. Most modern techniques focus on exploring large sets of textual data to build forecasting models; they tend to ignore the importance of temporal information which is often the main ingredient to determine the performance of analysis, especially in the public policy view. The contribution of this paper is two-fold. First, a dataset called COVID-News is collected from three news agencies, which consists of article segments related to wearing masks during the COVID-19 pandemic. Second, we propose a long-short term memory (LSTM)-based learning model to predict the attitude of the articles from the three news agencies towards wearing a mask with both temporal and textural information. Experimental results on COVID-News dataset show the effectiveness of the proposed LSTM-based algorithm.
A Real-time Fall Detection System Using ToF Depth Images

Fall accident is a major health problem resulting in serious injuries even deaths, especially among the elderly. Moreover, the population is aging globally, indicating that more fall accidents will probably happen in the future. A robust fall detection system is essential for avoiding injuries and deaths in fall accidents. In this paper, we propose a vision-based real-time fall detection system using Time-of- Flight depth maps with pose classification, height calculation, and temporal information processing. Our method sticks to the fall definition. We first divide the process of fall into three categories, and each subject in each frame is classified into different categories based on the pose by efficient YOLOv4 model. Combining with the depth data and temporal information in a time window, the proposed system can extract subjects' height and downward velocity, which are the key factors in fall judgment. Lastly the detection is achieved by comparing them with certain criteria. According to experimental results, our detection system achieved an average accuracy 97.92 % with a recall of 97.5 % and a precision of 98.3 % in off-line test on public dataset, and all three metrics above 100% in real-time test.
Improving the System Identification of Transonic Wind Tunnel by a Regression Ensemble-Based Outlier Mining Method

In transonic wind tunnel, anomalous data that are often referred to as outliers or anomalies have severe impact on system identification. To address such a problem, outliers should be detected and new substitutions should be provided before system identification. The combined request for outlier detection and compensation makes it suitable to develop a regression-based outlier mining algorithm. To enhance the effectiveness of traditional regression-based algorithm, this paper proposes a novel one based on ensemble learning. In our outlier ensemble, the base regression models are learnt on a two-level ensemble structure. The aim of the first level is to enhance the robustness to unknown outliers by homogeneous ensemble. The goal of the second level is to improve the robustness to base regression model. In order to verify the effectiveness of the proposed hybrid outlier ensemble, we use several real-world datasets from transonic wind tunnel and compare it with several underlying competitors. The experimental results have shown that the proposed outlier ensembles could outperform its competitors with respect to both outlier mining and the improvement of system identification.
Dense 3D Face Reconstruction from a Single RGB Image

Monocular 3D face reconstruction is a computer vision problem of extraordinary difficulty. Restrictions of large poses and facial details(such as wrinkles, moles, beards etc.) are the common deficiencies of the most existing monocular 3D face reconstruction methods. To resolve the two defects, we propose an end-to-end system to provide 3D reconstructions of faces with details which express robustly under various backgrounds, pose rotations and occlusions. To obtain the facial detail informations, we leverage the image-to-image translation network (we call it p2p-net for short) to make pixel to pixel estimation from the input RGB image to depth map. This precise per-pixel estimation can provide depth value for facial details. And we use a procedure similar to image inpainting to recover the missing details. Simultaneously, for adapting pose rotation and resolving occlusions, we use CNNs to estimate a basic facial model based on 3D Morphable Model(3DMM), which can compensate the unseen facial part in the input image and decrease the deviation of final 3D model by fitting with the dense depth map. We propose an Identity Shape Loss function to enhance the basic facial model and we add a Multi-view Identity Loss that compare the features of the 3D face fusion and the ground truth from multi-view angles. The training data for p2p-net is from 3D scanning system, and we augment the dataset to a larger magnitude for a more generic training. Comparing to other state-of-the-art methods of 3D face reconstruction, we evaluate our method on in-the-wild face images. the qualitative and quantitative comparison show that our method performs both well on robustness and accuracy especially when facing non-frontal pose problems.
Novel Gradient-Based Optimizer: A Case Study on Economic Load Dispatch Problem

The Gradient-Based Optimizer (GBO) is newly invented metaheuristic optimization algorithm based on Newton's based method. In this paper, novel gradient-based optimizer is proposed based on Levy-Assisted and Opposition Based Strategy (LEOBGBO- Levy Enhanced Opposition Based GBO). Proposed optimizer also utilizes gradient search rule and local escaping operator as original GBO. Newly proposed optimizer is applied for solving standard benchmark functions as well as Economic Load Dispatch (ELD) problem. The performance of the proposed metaheuristic is compared with other state of the art algorithms. Performance analysis proves the superiority of proposed optimizer over other algorithms for benchmark function as well as ELD.
Incentive Mechanism for Secure Mobile Edge Computing under Managed Eavesdropping Risk

Mobile edge computing (MEC) is growing popular with the development of mobile networks and applications. However, MEC services rely on wireless channels to transmit data and thus are vulnerable to malicious behaviors such as eavesdropping. D2D (Device-to-Device) communication, com-bined with cooperative jamming techniques can efficiently protect MEC customers from such hostiles, and create a physical layer secured MEC environment. Hence in this paper, we propose a D2D-assisted secure MEC mechanism from a physical layer perspective. To tackle the challenge that eavesdroppers' locations are usually unknown, we formulate an eavesdropping risk zone (ERZ) to model the information leakage risk. We introduce a D2D-based cooperative jamming scheme where mobile users (MUs) transmit signals combined with data and artificial noise to reduce ERZ while motivating MUs' participation. Then we propose a contract-based incentive mechanism to promote D2D-assisted cooperative jamming that limits ERZ and improves the secure offloading rate of MEC services. Such incentives can tackle information asymmetry between MUs and incentive designers due to features like individual rationality (IR) and incentive comparability (IC). Further, we jointly optimize incentive costs and transmit power of MEC and D2D users to maximize the operator's MEG utility, where an IC-cost-based method is applied. By simulation, we first evaluate the feasibility of designed incentives, then we demonstrate the performance of the proposed mechanism under different conditions.
Web-based Automatic Deep Learning Service Generation System by Ontology Technologies

Although deep learning (DL) has obtained great achievements in the industry, the involvement of artificial intelligence (AI) experts in developing customized DL services raises high costs and hinders its wide application in the business domain. In this research, a Web-based automatic DL service generation system is presented to address the problem. The system can generate customized DL services without involving AI experts. The main principle of the system adopts ontology technologies to organize DL domain knowledge and generate target services based on the user's requests posted from the front-end web page. In the empirical study, the whole scenario of the system is demonstrated, and the scalability is also evaluated. The result shows that our system can generate customized services correctly and has good scalability.
Introduction

This chapter motivates the research on CIR by reviewing the studies on how people search, showing that information seeking can be cast in a framing of human-machine conversations. We then describe the properties of CIR, which lead to the definition of a CIR system and a reference architecture, which we will describe in detail in the rest of the book.