Reviewerly/exHarmony huggingface dataset:
Graph convolutional networks with higher‐order pooling for semisupervised node classification

Summary The information propagation mechanism in graph‐structured networks such as social networks is the foundation of network security. The graph convolutional network (GCN) is a powerful approach for semisupervised node classification on graph‐structure data. The vertex features which pass through the graph network are affected by the k‐hop neighborhood vertices. However, current high‐order GCN approaches merged the k‐hop neighborhood using coarse pooling and complicated weight parameters. To reduce the computational complexity and preserve topological of the graph data, with weight sharing mechanism we propose a novel GCN based on a novel higher‐order pooling layer for semisupervised classification. The proposed model and its variants are experimental studied on several large‐scale citation network datasets using semisupervised learning. The experimental results show that the proposed model and its variants have lower computational complexity and achieve the state‐of‐the‐art in the node classification accuracy.
Development of an intelligent CAD system for mass detection in mammographic images

Mammography is a very useful tool to diagnose breast cancer in early stages when it is easier to treat. There are two types of evidence that radiologists look for in a mammogram, calcifications and the existence of masses. In this study, an intelligent computer-aided diagnosis system is proposed for the detection of masses in mammographic images regardless of their nature. The proposed method uses a combination of extended maxima transformations, having different threshold values, in order to find suitable internal and external markers for a marker-based watershed segmentation. After segmentation, a two-stage classifier is used to distinguish the masses better from the healthy breast tissue. A feature vector based mainly on contrast and texture features is calculated and two alternative approaches, a Bayesian classifier and a support vector machine (SVM) with Gaussian kernel function, are implemented for further reduction of the false positive areas. The system was evaluated using the data from two online databases. Specifically, 73 mammographic images from the new curated breast imaging subset of digital database for screening mammography (CBIS-DDSM) database and all the mammographic images that contain masses from the mini-mammographic image analysis society (MIAS) database were used. The overall sensitivity, in both datasets, was near 80% when the Bayesian classifier was used and above 85% when the SVM was applied.
Visual Analysis of Cybersecurity Situational Awareness

In response to the anti-terrorism needs of networks, this article visually analyzes the network topology and visualizes the network structure features. It not only displays the overall structure of the network through rich visualization methods, but also combines interactive analysis tools. Hidden structural features and connection modes of the expression network are explored, reflecting the process of network security analysts analyzing and inferring, obtaining key nodes and weak links in the network structure, identifying potential vulnerabilities and attacks, and being able to perform quick identify responses for efficient cybersecurity situational awareness.
An IBE-Based Authenticated Key Transfer Protocol on Elliptic Curves

The key exchanged using key transfer protocols is generally used for symmetric key encryption where this key is known as private key and used for both encryption as well as decryption. As we all know, many key transfer protocols including basic Diffie-Hellman protocol are proposed in the literature. However, many of these key transfer protocols either are proven insecure or had a burden of communication and computational cost. Therefore, a more secure and efficient key transfer protocol is needed. In this article, the author proposes an authenticated key transfer protocol that securely and efficiently negotiates a common session key between two end users. He calls this protocol as IBE-TP-AKE. This proposal is based on the elliptic-curve cryptography (ECC) and uses the idea of identity-based encryption (IBE) with pairing. The security of the proposed work is based on the hard problems of elliptic curve and their pairing. Further, the author has shown the security of his proposed protocol and proved it using the security properties discussed later.
SROS: Educational, Low-cost Autonomous Mobile Robot Design Based on ROS

The paper describes the design process of a low-cost mobile robot based on the Arduino platform and powered by the Robot Operating System (ROS) to perform autonomous navigation, with a special focus on education. The paper contains an overview of the Robot Operation System structure, working principles, and application to real mobile robots. At the same time, consider the theory of Simultaneous Localization and Mapping (SLAM) and ways of its implementation in the practice. The practical part contains the description of the construction process of a differential drive mobile robot, the establishment of remote control and full integration process of Robot Operating System to the robot for self-localization, indoor mapping, and autonomous navigation purpose. The test results and performance are discussed and analysed.
A Lossless Bit Isolation Algorithm for Data Compression by Using Static Dictionary

Data compression is the reduction of bit or actual size of data by eliminating the repetition of identical sets of data bits to achieve fewer bits than the primary one. Character encryption is a famous time period of statistics compression that includes an everyday sample of records individual with a few affable encoding formulations. This encoding is a pastime of corresponding encoding approach that encompasses character characters, dolled up with extraordinary sorts of data and emblems. Data compressions expand over a large field of citation including data storage, database technology, and data transmission. In this paper, a statistics compression method denoted as Lossless Bit Isolation (LBI) has been implemented which honestly manifested on a character encoding technique. These isolated characters have found using set, sub-set, and static dictionary. Similarly, for assimilating the outcome of LBI two most well-liked algorithm terms LZW and Huffman are used. The investigational consequences of the counseled approach carry off common compression ratio 0.52 and again talented than LZW and Huffman.
Improve Extremely Fast Decision Tree Performance through Training Dataset Size for Early Prediction of Heart Diseases

Training dataset characteristics can have an important impact on the performance of the classification models. One of the major characteristics of the training data that affects the accuracy of a classification model is the size. A lot of studies demonstrate that classification algorithms require large datasets to train effective models. However, large datasets, especially with high-quality labels, can be expensive to obtain. This paper proposes practical techniques to improve the accuracy measure with a small training dataset by using two online classification models: Very Fast Decision Tree and Extremely Fast Decision Tree. The use case of our study is the early prediction of cardiac diseases. The experiments showed that with a parameter estimation method in the models and balancing of classes of the training dataset, we can get similar performances of large data in a smaller dataset. We observe that dataset size has a conspicuous lack of effect on the training of these models, undoubtedly there are other ways of extrapolating online decision tree algorithm's performance with a small dataset.
Design and Development of Smart Home Sensing Supported by Blockchain Technology

This paper presents the design and implementation of a smart home system in the context of Internet of Things (IoT) with Ethereum private Blockchain, Raspberry Pi, Blynk platform, DHT11 temperature and humidity sensors. By Raspberry Pi, it collects real-time room humidity and temperature information by DHT11. The data is then uploaded to the Blynk App, which is stored on the smart contract deployed with the Ethereum private Blockchain. When the real-time humidity or temperature value exceeds a predefined threshold value, warnings are given by turning on LEDs. The system functions as a proof-of-concept prototype, showing the feasibility of applying blockchain in smart homes with IoT functionalities.
A Study on a Joint Deep Learning Model for Myanmar Text Classification

Text classification is one of the most critical areas of research in the field of natural language processing (NLP). Recently, most of the NLP tasks achieve remarkable performance by using deep learning models. Generally, deep learning models require a huge amount of data to be utilized. This paper uses pre-trained word vectors to handle the resource-demanding problem and studies the effectiveness of a joint Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) for Myanmar text classification. The comparative analysis is performed on the baseline Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and their combined model CNN-RNN.
Can Smart Contracts Learn from Digital Rights Management?

While blockchain technology is best-known for cryptocurrencies, some chains also tout their ability to implement so-called "smart contracts." Ethereum, which bills itself as a "blockchain app platform," defines smart contracts as applications that run exactly as programmed without any possibility of downtime, censorship, fraud or third-party interference (http://www.ethereum.org).
Development of Gameplay Design for Renewable Energy Learning based on Augmented Reality

In this paper, a gameplay design for children is proposed in order to learn renewable energy topics. This game is developed through a flashcard consisting of 6 types of renewable energy plants located in Indonesia. This prototype is also involved augmented reality technology to provide information on a number of cards such as a brief explanation of each renewable energy theory and the game points. The whole process of this serious game could motivate users, especially for children to attain a sense of achievement for understanding renewable energy pleasantly.
Recent Advances on Manipulator Trajectory Planning Methods

Background: Traditional manipulator requires professionals to write control programs for specific job requirements so that the end-effector of the manipulator can work following the control instructions. However, the flexibility of the manipulator is not high under the control of off-line programming. With the diversification of the job requirements, the complexity of the work environment has gradually increased. Therefore, the relevant scholars focused their research on the automatic trajectory planning method of the manipulator. They used some algorithms to plan optimal trajectories for the manipulator to automatically avoid obstacles in the complex environment. Researches show that the hybrid optimal trajectory with parameter optimization such as time, energy and impact can be planned by automated programming under some constraint conditions. The trajectory planning is helpful to improve the automation and working efficiency of manipulators for the development of intelligent manufacturing. Objective: Providing references for researchers from related fields by reviewing recent advances of the manipulator trajectory planning. Methods: This paper reviews the latest patents and current representative articles related to the manipulator trajectory planning. The key methods of some references are introduced in several aspects of the algorithm, innovation and principle. Results: Researches on the manipulator trajectory planning in recent 10 years are reviewed. The differences between algorithms in latest patents and current articles are introduced and analyzed and the future developments and potential problems of the manipulator trajectory optimization are discussed. Conclusion: The manipulator trajectory planning reduces complicated operator and hard programming tasks, improving the intelligence of robots and the work efficiency. Current researches focus on collision- free, parameter-optimized and high-efficiency solution which can be used to solve the problem of the end-trajectory planning of the manipulator in the complicated space with obstacles. The aspects that need to be improved in the future include: algorithm reliability, operational security and intelligence, human-computer interaction, efficient simulation, and so on. More patents on manipulator trajectory planning should be invented.
An Adaptive Strategy for Dynamic Data Clustering with the K-Means Algorithm

K-means algorithm is one of the most widely used methods in data mining and statistical data analysis to partition several objects in K distinct groups, called clusters, on the basis of their similarities. The main problem of this algorithm is that it requires the number of clusters as an input data, but in the real life it is very difficult to fix in advance such value. For such reason, several modified K-means algorithms are proposed where the number of clusters is defined at run time, increasing it in a iterative procedure until a given cluster quality metric is satisfied. In order to face the high computational cost of this approach we propose an adaptive procedure, where at each iteration two new clusters are created, splitting only the one with the worst value of the quality metric.
A Comparative Study of Machine Learning Algorithms for Use in Breast Cancer Studies

This current study examines a range of machine learning algorithms efficacy at determining malignancy from breast cancer imagery. The study uses the Wisconsin data set (diagnostic) to evaluate the algorithms performance. While there are a number of published studies regarding machine learning and breast cancer imagery data, there are gaps in the current literature. One gap is that several studies focus on a single algorithm. Another gap is that comparative studies often focus on only a few algorithms. The current study addresses these specific gaps in the existing literature. The current study looks at 5 different algorithms, then compares those to 8 algorithms from the literature. This provides a broad comparative study of the efficacy of machine learning algorithms in detecting breast cancer.
Structure-Exploiting Discriminative Ordinal Multioutput Regression

Although the least-squares regression (LSR) has achieved great success in regression tasks, its discriminating ability is limited since the margins between classes are not specially preserved. To mitigate this issue, dragging techniques have been introduced to remodel the regression targets of LSR. Such variants have gained certain performance improvement, but their generalization ability is still unsatisfactory when handling real data. This is because structure-related information, which is typically contained in the data, is not exploited. To overcome this shortcoming, in this article, we construct a multioutput regression model by exploiting the intraclass correlations and input-output relationships via a structure matrix. We also discriminatively enlarge the regression margins by embedding a metric that is guided automatically by the training data. To better handle such structured data with ordinal labels, we encode the model output as cumulative attributes and, hence, obtain our proposed model, termed structure-exploiting discriminative ordinal multioutput regression (SEDOMOR). In addition, to further enhance its distinguishing ability, we extend the SEDOMOR to its nonlinear counterparts with kernel functions and deep architectures. We also derive the corresponding optimization algorithms for solving these models and prove their convergence. Finally, extensive experiments have testified the effectiveness and superiority of the proposed methods.
Distributed and Collaborative High-Speed Inference Deep Learning for Mobile Edge with Topological Dependencies

Ubiquitous computing has potentials to harness the flexibility of distributed computing systems including cloud, edge, and Internet of Things devices. Mobile edge computing (MEC) benefits time-critical applications by providing low latency connections. However, most of the resource-constrained edge devices are not computationally feasible to host deep learning (DL) solutions. Further, these edge devices if deployed under denser deployments result in topological dependencies which if not taken into consideration adversely affect the MEC performance. To bring more intelligence to the edge under topological dependencies, compared to optimization heuristics, this article proposes a novel collaborative distributed DL approach. The proposed approach exploits topological dependencies of the edge using a resource-optimized graph neural network (GNN) version with an accelerated inference. By exploiting edge collaborative learning using stochastic gradient (SGD), the proposed approach called CGNN-edge ensures fast convergence and high accuracy. Collaborative learning of the deployed CGNN-edge incurs extra communication overhead and latency. To cope, this article proposes compressed collaborative learning based on momentum correction called cCGNN-edge with better scalability while preserving accuracy. Performance evaluation under IEEE 802.11ax-high-density wireless local area networks deployment demonstrates that both the schemes outperform cloud-based GNN inference in response time, satisfaction of latency requirements, and communication overhead.
Customized Wake-Up Word with Key Word Spotting using Convolutional Neural Network

In this paper, a customized wake-up word system combined with key word spotting using neural network was proposed. This system is divided into three phases: training wake-up word phase, detecting wake-up word phase and key word spotting phase. In training phase, user can say any word in any language and system will automatically count how many syllable of this word. If several syllables are in the range, system will accept this customized wake-up word. Next, the word will be extracted the features by Mel-Frequency Cepstral Coefficients (MFCC) method. It can be used for speaker model, speech model and state sequence for next phase. In detecting phase, system detects an unknown voice segment and compares it with models. After these steps, system will determine to wake up or not. If user says the right wake-up word, system goes to next phase. In key word spotting phase, the command words are fixed. The system is designed using convolutional neural network for key word spotting model. Moreover, all processes are executed without Internet to protect user privacy. This system can give a good result with a very small amount of wake-up word training data, and run in real-time.
Cyber Forensics and Comparative Analysis of Digital Forensic Investigation Frameworks

With industrial revolution 4.0, automation foster communications between digital devices around the globe which involves several digital devices including cyber physical system devices, IoT devices, mobile devices, storage devices and network devices or even PCs as digital evidence; increasing the number of cybercrime rate. This brings us to question a necessity for advanced Digital Forensics Investigation Framework (DFIF) for the effective prosecution of digital crime in court of law; such that the framework should preserve integrity of evidence throughout steps while in process. Our paper is descriptive in nature that surveys recent trends of cybercrime attacks and explored associated Cyber Forensics. In addition, we have mapped process and output produced by different phase in the DFIF that have been examined from previously proposed frameworks and represented a comparative mapping of all frameworks. The mapping process results in optimized investigation process.
Implementation of Blockchain in Financial Sector to Improve Scalability

The Blockchain is an encrypted database that stores information statistics, or in different words, it is a virtual ledger of any transactions, contracts - that needs to be independently recorded. One of the key capabilities of Blockchain is that this virtual ledger is out there throughout several masses and heaps of computer and isn't always sure to be stored in a single place. Blockchain chain has already commenced disrupting the financial offerings area, and it's far this technology which underpins the virtual currency- bitcoin transaction. The aim of the paper is to conduct research on the effect of blockchain technology on the financial sector. There is no doubt that the world is curious to see how this promising technology will influence or shape the future of banking. Blockchain enhances safety in data storage and transmutation, avails a decentralized and transparent network infrastructure and significantly reduces the costs in operations. These remarkable attributes make blockchain a very promising and in-demand solution even in an industry as restricted as the banking sector.
Transfer Planning for Temporal Logic Tasks

This paper proposes an optimal control synthesis algorithm for Linear Temporal Logic (LTL) tasks that exploits experience from solving similar LTL tasks before. The key idea is to appropriately decompose complex LTL tasks into simpler subtasks and define sets of skills, or plans, needed to solve these subtasks. These skills can be stored in a library of reusable skills and can be used to quickly synthesize plans for new tasks that have not been encountered before. Our proposed method is inspired by literature on multi-task learning and can be used to transfer experience between different LTL tasks. It amounts to a new paradigm in model-checking and optimal control synthesis methods that to this date do not use prior experience to solve planning problems. We present numerical experiments that show that our approach generally outperforms these methods in terms of time to generate feasible plans. We also show that our proposed algorithm is probabilistically complete and asymptotically optimal.
Skeleton based Human Action Recognition for Smart City Application using Deep Learning

These days the Human Action Recognition (HAR) is playing a vital role in several applications such as surveillance systems, gaming, robotics, and so on. Interpreting the actions performed by a person from the video is one of the essential tasks of intelligent surveillance systems in the smart city, smart building, etc. Human action can be recognized either by using models such as depth, skeleton, or combinations of these models. In this paper, we propose the human action recognition system based on the 3D skeleton model. Since the role of different joints varies while performing the action, in the proposed work, we use the most informative distance and the angle between joints in the skeleton model as a feature set. Further, we propose a deep learning framework for human action recognition based on these features. We performed experiments using MSRAction3D, a publicly available dataset for 3D HAR, and the results demonstrated that the proposed framework obtained the accuracies of 95.83%, 92.9%, and 98.63% on three subsets of the dataset AS1, AS2, and AS3, respectively, using the protocols of [19].