Reviewerly/exHarmony huggingface dataset:
Discrete pointvv 3D reconstruction algorithm based human pose estimation

The traditional method of human pose estimation lacks depth label in depth image, which leads to the low generalization ability of model due to single pose. A novel method of human pose estimation based on discrete point 3D reconstruction algorithm is proposed. Several cameras are placed around each other, and the internal parameters of the camera are calibrated by the classical calibration method, and the external parameters of the camera group are obtained. According to the two-dimensional skeleton, the human body region model is established, and the contour is matched with the human body model, so that the contour region is segmented, and then the three-dimensional human body model is constructed. According to the principle of discrete point 3D reconstruction algorithm, principal component analysis is used to transform the original data, eliminate the redundant correlation between the original human body posture data, establish the human body posture model, obtain 10 individual voxel sets, and estimate the 3D information of each point in the voxel set. In the process of feature extraction, two-dimensional attitude prediction, two-dimensional fusion layer and final conversion, the human body attitude estimation is realized. Experimental results show that this method can reduce the influence of image shadow on image recognition, and the estimation results are more accurate.
Architecture for semantic web service composition in spatial data infrastructures

The importance of geospatial data has rendered it to be used in decision-making in both public and private sectors. The purpose of this study was to employ Semantic Web Technology (SWT) for the problems of Web Service Composition (WSC) in the context of Spatial Data Infrastructures (SDI). Some of these problems are identifying the workflow sequence and the user goal, discovering services according to service parameters, and matching these parameters. As a suggestion for the solution of all these problems a semi-automated WSC architecture was proposed in this study. In terms of architecture, users state their ‘goal’ with a natural language sentence. By semantically matching this sentence with a Spatial Services Ontology (SSO), the corresponding ‘abstract’ WSC was ‘located’ and the ‘concrete’ WSC was formed. Although there are still problems waiting to be solved due to the scope of the work, this study makes a valuable contribution to the area.
Machine and Deep Learning Techniques in IoT and Cloud

In establishing a healthy environment for connectivity devices, it is essential to ensure that privacy and security of connectivity devices are well protected. The modern world lives on data, information, and connectivity. Various kinds of sensors and edge devices stream large volumes of data to the cloud platform for storing, processing, and deriving insights. An internet of things (IoT) system poses certain difficulties in discretely identifying, remotely configuring, and controlling the devices, and in the safe transmission of data. Mutual authentication of devices and networks is crucial to initiate secure communication. It is important to keep the data in a secure manner during transmission and in store. Remotely operated devices help to monitor, control, and manage the IoT system efficiently. This chapter presents a review of the approaches and methodologies employed for certificate provisioning, device onboarding, monitoring, managing, and configuring of IoT systems. It also examines the real time challenges and limitations in and future scope for IoT systems.
Weighted Fuzzy Decision Tree for Multi-Label Classification

Multi-label classification is a pattern recognition task to assign one or more class labels to each observed instance. In multi-label classifiers, there are complex decision boundaries and using simple and powerful classifiers such as decision trees can satisfy most of the requirements due to its capability of space partitioning from general to specific points of view. In addition, merging it with fuzzy theory enhances the interpretability of the algorithm and smoothens the boundaries. In this paper, a novel fuzzy decision tree is proposed and customized for multi-label classification. In fuzzy classifiers, more than one leaf may deal in reasoning. A weighting mechanism is also proposed to adjust the importance of the leaves in voting for classification. The most important characteristic of the proposed system is that it considers the label dependencies in both stages of tree construction and weighting in order to improve the performance of multi-label classification. An empirical analysis, conducted on 5 multi-label datasets, shows that the proposed fuzzy decision tree performs better than other tree-based approaches in the literature. Further experimental results show that, not only the proposed weighting method significantly outperforms other algorithms, but also it boosted the classification ability of the initial fuzzy decision tree.
Spatial Intelligent Decision Support System for Coconut Agroindustry Downstream Development: An Overview

Coconut agroindustry downstream has received its importance regarding the impact on the sustainability of coconut commodities. However, developing the downstream side of coconut remains a great challenge due to its complexity comprised of actors involved with their interests, the value of decision-making variables that are difficult to predict, and the presence of incomplete information in determining the right and fast decisions. To fill the gap, we need a spatial intelligent decision support system to accelerate the decision-making process. The method used in this research was the system development life cycle with the waterfall approach. This research identified system mechanisms, system analysis, system design, system configuration, and system development. The results of this study are the basis for developing system applications.
DESIV: Differential Fault Analysis of SIV-Rijndael256 with a Single Fault

This work analyzes the strength of NIST Lightweight Cryptography (LWC) Round 1 Candidate SIV-Rijndael256 in light of Differential Fault Attacks (DFA). It is observed that SIV-Rijndael256 presents an interesting case for DFA due to its large state-size which has been capitalized in this work to contribute to the state-of-the-art in DFA on AES-like ciphers. We first study the differential properties over three rounds of the cipher and then use it to mount a DFA that uses a classical strategy in conjunction with the properties of the key-schedule. The nonce-misuse property of SIV-Rijndael256 facilitates the use of DFA and is exploited to reduce the key-space from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">128</sup> to just one using only a single random byte fault in the internal state. Moreover, the support for the release of unverified plaintexts which have been shown in literature as a vulnerability to DFA, makes the decryption also vulnerable to the same attack. Finally, the ability to uniquely retrieve the key using just a single fault makes the attack, reported here, an optimal DFA. To the best of our knowledge, this is the first differential fault analysis of any candidate in the NIST LWC competition.
Machine Learning based User QoE Evaluation for Video Streaming over Mobile Network

Quality of experience (QoE) serves as a direct evaluation of users' experience in mobile video transmission and is critical to ensure good network service. Although many efforts have been made to predict QoE based on network parameters of the user terminal equipment, it is difficult to predict QoE based on Quality of Service (QoS) offered by the network servers. In this paper, a machine learning based QoE evaluation method is proposed to evaluate user QoE in real-time by analyzing the QoS characteristics for mobile video transmission. For this purpose, we construct a large-scale dataset by collecting more than 300 thousand pieces of metrics data with two kinds of key quality indicators (KQIs) describing the QoE and 91 key performance indicators (KPIs) describing the QoS. A two-process feature subset selection (FSS) method consisting of single parameter pre-FSS and multi-parameter FSS is then proposed to find the KPIs related to KQIs. An Extra-Trees model is finally developed to learn the relationships between the KPIs and KQIs. By employing machine learning and data analytics on network data with the data-driven framework, the proposed method can predict the user QoE according to the QoS of network servers. The results prove that our proposed method can outperform other state-of-the-art approaches.
Analysis of Computer Network Security Threat Defense Measures under the Environment of Internet of Things

The development of the Internet of Things has overturned the boundaries of traditional information technology, and the processing capabilities and complexity of information technology have grown rapidly. The advancement of the Internet of Things supporting technology and the innovation of business models have brought about technical and operational challenges in the construction of the network information security system. The focus is on the need to solve the problem of user privacy in the business of the Internet of Things to build an ecological security system, and the issue of business secrets of the Internet of Things business operation companies, security capabilities, professionalization, systemization, service-oriented issues, compatibility and interoperability issues. The construction of the Internet of Things network information security system requires revolutionary innovations and changes to the traditional network information security model to meet the technical and operational challenges brought about by the development of the Internet of Things. This paper analyzes the impact of the Internet of Things network information security system construction by analyzing the characteristics of the Internet of Things and the development trend of network information security, and gives a prototype of the proposal for building an Internet of Things network information security ecosystem for the Internet of Things network information security ecosystem Construction reference.
SDCNet: Size Divide and Conquer Network for Salient Object Detection

The fully convolutional neural network (FCN) based methods achieve great performances in salient object detection (SOD). However, most existing methods have difficulty in detecting small or large objects. To solve this problem, we propose Size Divide and Conquer Network (SDCNet) which learning the features of salient objects of different sizes separately for better detection. Specifically, SDCNet contains two main aspects: (1) We calculate the proportion of objects in the image (with the ground truth of pixel-level) and train a size inference module to predict the size of salient objects. (2) We propose a novel Multi-channel Size Divide Module (MSDM) to learning the features of salient objects with different sizes, respectively. In detail, we employ MSDM following each block of the backbone network and use different channels to extract features of salient objects within different size range at various resolutions. Unlike coupling additional features, we encode the network based on the idea of divide and conquer for different data distributions, and learn the features of salient objects of different sizes specifically. The experimental results show that SDCNet outperforms 14 state-of-the-art methods on five benchmark datasets without using other auxiliary techniques.
Unsupervised Embedding Learning by Noisy Similarity Label Optimization

We propose a similarity label optimization framework for unsupervised embedding learning. Existing works use similarity labels obtained from instance labels, which are image identifiers, for unsupervised embedding learning assuming that images of different instance labels have different semantic classes. However, because of the significant semantic gap between classes and instances, instance labels are not enough for learning embeddings. To alleviate this problem, we consider the similarity labels as noisy labels and optimize similarity labels and neural network parameters in an alternating fashion. Experimental results demonstrate that the proposed method outperforms a baseline method by 1.2% in terms of accuracy on the CIFAR-10 dataset and in 1.3% in terms of recall at k (k = 1) on the Stanford Online Product dataset.
An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement

Abstract Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain. In practice, these challenges are often coupled with the lack of example pairs, which inhibits the application of supervised learning strategies. To address these challenges, we propose an efficient adversarial video enhancement framework that learns directly from unpaired video examples. In particular, our framework introduces new recurrent cells that consist of interleaved local and global modules for implicit integration of spatial and temporal information. The proposed design allows our recurrent cells to efficiently propagate spatio-temporal information across frames and reduces the need for high complexity networks. Our setting enables learning from unpaired videos in a cyclic adversarial manner, where the proposed recurrent units are employed in all architectures. Efficient training is accomplished by introducing one single discriminator that learns the joint distribution of source and target domain simultaneously. The enhancement results demonstrate clear superiority of the proposed video enhancer over the state-of-the-art methods, in all terms of visual quality, quantitative metrics, and inference speed. Notably, our video enhancer is capable of enhancing over 35 frames per second of FullHD video (1080x1920).
The influence of basic life support training on the level of public knowledge about emergency handling in Tanah Lot tourist area in Bali

Emergencies are life-threatening conditions that occur unexpectedly regardless of the hour and location, and require immediate handling. Increased knowledge about handling daily emergencies through basic life support (BLS) training is needed to support the tourism industry. With an increase in public knowledge about emergencies, the place will provide a sense of security and comfort for tourists. This study aimed to analyze the effect of basic life support training on the level of public knowledge about emergencies in the Tanah Lot tourist area. This research adopted an analytical-descriptive method with a quasi-experiment involving 95 respondents. The results of this study indicate that basic life support training has an influence on increasing public knowledge about emergencies (p = 0.011). The results of this study will be used to make recommendations for interventions in an effort to increase public knowledge about emergencies.
Deep Reinforcement Learning Enabled Network Routing Optimization Approach with an Enhanced DDPG Algorithm

This paper proposes an enhanced DDPG algorithm solving network routing problem to minimize the average network delay. Simulation results shows that the algorithm has good convergence speed improvement in routing optimization.
Expectation-Maximization Algorithm for the Calibration of Complex Simulator Using a Gaussian Process Emulator

The approximated non-linear least squares (ALS) tunes or calibrates the computer model by minimizing the squared error between the computer output and real observations by using an emulator such as a Gaussian process (GP) model. A potential defect of the ALS method is that the emulator is constructed once and it is no longer re-built. An iterative method is proposed in this study to address this difficulty. In the proposed method, the tuning parameters of the simulation model are calculated by the conditional expectation (E-step), whereas the GP parameters are updated by the maximum likelihood estimation (M-step). These EM-steps are alternately repeated until convergence by using both computer and experimental data. For comparative purposes, another iterative method (the max-min algorithm) and a likelihood-based method are considered. Five toy models are tested for a comparative analysis of these methods. According to the toy model study, both the variance and bias of the estimates obtained from the proposed EM algorithm are smaller than those from the existing calibration methods. Finally, the application to a nuclear fusion simulator is demonstrated.
A Low-complexity Wavelet-based Visual Saliency Model to Predict Fixations

A low-complexity wavelet-based visual saliency model to predict the regions of human eye fixations in images using low-level features is proposed. Unlike the existing wavelet-based saliency detection models, the proposed model requires only two channels - luminance (Y) and chrominance (Cr) in YCbCr colour space for saliency computation. These two channels are decomposed to their lowest resolution using Discrete Wavelet Transform (DWT) to extract local contrast features at multiple scales. These features are integrated at multiple levels using 2D entropy based combination scheme to derive a combined map. The combined map is normalised and enhanced using natural logarithm transformation to derive a final saliency map. The experimental results show that the proposed model has achieved better prediction accuracy with significant complexity reduction compared to the existing benchmark models over two large public image datasets.
Sarcasm Detection Technique on Twitter Data with Natural Language Processing

Sarcasm is defined a form of irony that a user uses to express its feeling via social networking platforms such as Twitter or Facebook. Sarcasm can be used in different situations such as mockery or constructive criticism. Verbal sarcasm is rather easy to determine, but detecting sarcasm in the written text is equally difficult. Determining sarcasm in the text improves the sentiment analysis process and also the decision-making process. Sentiment analysis refers to the processing of determining the negative and positive emotions of the Internet user over a specific topic. The dataset used in this research is collected from Twitter via Web scrapping. Four machine learning algorithms such as linear SVC (accuracy = 83%, f1-score = 0.81), Naïve Bayes (accuracy = 74%, f1-score = 0.73), logistic regression (accuracy = 83%, f1-score = 0.81) and random forest classifier (accuracy = 80%, f1-score = 0.77) were implemented.
Study of flood discharge due to land use and population change of Way Pisang watershed

Abstract In some areas in Indonesia, especially those around rivers that develop into urban areas are always faced with the problem of flooding in each rainy season. Soil surface characteristics can affect surface runoff in some areas. In general, human activities will increase along with the rate of population growth which results in increased land conversion activities to meet the economic needs of an area. This study will examine the level of population growth around the Way Pisang watershed in South Lampung Regency in 2007 and 2019, which is expected to influence land use change as a result of activities to meet economic needs, which in turn will have an impact on changes in the drainage coefficient resulting in an increase flood discharge in the watershed. The results of this study can be concluded that from 2007 to 2019, population growth in the Way Pisang Watershed increased by an average of 9.62%. With population growth accompanied by changes in land use where forest area has decreased 81.39%, paddy fields 52.90% and shrubs 85.54%. While the area of settlements increased by 56.10% and dry agricultural land/gardens increased to 147.62%. This resulted in an increase in drainage coefficient of 102.18% which in turn resulted in an increase in flood discharge on average by 28.82%.
A Decentralized Energy Trading System Based on Public Blockchain

Existing electricity markets are based on centralized trading policy where electricity producers supply electricity to consumers and receive electricity bills via a trusted third party such as bank, which requires a lot of man power, and increases trading costs. There is no opportunity for prosumers to participate in energy trading and consumers (e.g., electric vehicle owners) to sell their excess energy in crisis situations or peak hours. Diversity in electricity markets is not possible as there exist only two parties, where one party always sells electricity, and the other party always buys it. We propose a public blockchain-based decentralized energy trading system that allows peer-to-peer (P2P) electricity transactions among producers, prosumers, and consumers without need of a trusted third party. The proposed system could bring diversity in conventional electricity markets, and reduce trading costs and delay.
Methodology of climbing and descending stairs for four-wheeled vehicles

This paper proposes a methodology of climbing and descending stairs for four-wheeled vehicles. Conventional vehicles commonly use a crawler mechanism for moving on stairs. In contrast, our vehicle is based on a wheel mechanism. The stair-climbing gait (SCG) for our vehicle is proposed in another paper; however, movement with this gait requires a great deal of time. In this paper, two motions are switched between for our vehicle to climb and descend stairs. The other motion besides SCG is the double wheel motion (DWM), in which the vehicle moves on stairs using wheels with an appropriate center of gravity position and appropriate wheel velocities. The proposed methodology is realized and evaluated through an experiment.
An Adapter for IBM Streams and Apache Spark to Facilitate Multi-level Data Analytics

Data analytics with unsupervised clustering of data streams has provided revolutionary breakthroughs in fields like healthcare, and E-commerce. IBM Streams and Apache Spark are among the most useful and popular data analytics tools that help engineers and researchers extend the abilities to store, analyze, transform, and visualize data for business use. IBM Streams is capable of ingesting, filtering, analyzing, and associating massive volumes of continuous data streams and the Streams Processing Language (SPL) enables coding custom stream graphs to process data and handle real-time events. Apache Spark has unified analytics edge for large scale data processing with high performance for both batch and streaming data. We developed adapters without using third party tools to facilitate data transfer between IBM Streams and Apache Spark to support new and legacy data analytic systems. An example use case would be IBM Streams ingesting and processing realtime data streams, and then passing the data to Spark to train or update machine learning algorithms in real time that can be re-deployed in the IBM Streams data processing pipeline. This paper provides an overview of the structure of the data processing pipeline, describes the implementation details and the principle behind the design.
Dual-Precision Deep Neural Network

On-line Precision scalability of the deep neural networks(DNNs) is a critical feature to support accuracy and complexity trade-off during the DNN inference. In this paper, we propose dual-precision DNN that includes two different precision modes in a single model, thereby supporting an on-line precision switch without re-training. The proposed two-phase training process optimizes both low- and high-precision modes.