Reviewerly/exHarmony huggingface dataset:
An experimental framework for evaluating loss minimization in multi‐label classification via stochastic process

Abstract One major challenge multi‐label classification faces, are the conditions for evaluating multi‐label algorithms. Simplistic experimental setups based on artificial data may not capture crucial situations for analyzing these algorithms. This article introduces an experimental framework for evaluating multi‐label algorithms by artificially generating the probabilistic label distributions. The proposed framework has the benefits of considering a wide variety of labels distributions, and enables users to simulate probability label distributions with a better control of the label dependence and the difficulty of the problem. An experimental study was conducted using the framework where new findings with respect to five methods, binary relevance, classifier chain, dependent binary relevance, calibrated label ranking by pairwise comparison and probabilistic classifier chain, were revealed. This framework will facilitate conducting new experimental studies for analysing the effects of changing label dependence and the difficulty of the problem on the performance of new multi‐label algorithms.
Variational Learning of the Mixture of Shifted-Scaled Dirichlet Distributions via Entropy Splitting

Variational inference approaches are gaining more and more success and are applied in various data mining applications. In this work, we present a variational learning framework for the Shifted Scaled Dirichlet finite mixture model. Furthermore, we introduce component splitting based on Entropy approximation to estimate the number of components in our model along with the parameters of the distribution. We test the performance of this model on two real-life datasets, both related to human activity recognition.
Real-Time Path Generation for UAV Swarms Using Receding Planning Framework and Priority Decoupling Mechanism

This paper presents a real-time path planning method combining receding planning framework and priority decoupling mechanism to solve the large-scale UAV swarms path generation problems. The receding planning framework is employed to decompose the swarm path planning problem into a number of short-horizon planning problems for reducing the problem dimension. In each receding horizon, the priority decoupling mechanism converts the swarm path planning problem as a series of single-UAV path planning problems to further reduce the computational cost. For the short-horizon single-UAV path planning problem, a customized sparse A* search algorithm (SAS) is used to compute the optimal/suboptimal paths efficiently. The obstacle/collision detection method is developed to eliminate inactive obstacle/collision avoidance constraints according to the relative position relation among obstacles and UAVs. The efficiency and effectiveness of the proposed method are verified in simulation with up to 50 UAVs and indoor flight experiments on 8 quadrotors. The experimental results demonstrate that this method can generate safe swarm paths within a few seconds and guide UAVs to fly in confined environments with obstacles.
Detection and Binary Classification of Spear-Phishing Emails in Organizations Using a Hybrid Machine Learning Approach

In recent years, e-mails have become the most commonly used information and communication technology in organization. The email technology is susceptible to phishing attacks. Phishing attacks are a trending cybercrime activity and have caused a lot of financial loss using social engineered communication that are transmitted to individuals by cybercriminals aimed at tricking users to share sensitive information, with emails being the prevalent attack vector. Spear-phishing is a deceptive social engineering tool used to capture targeted victim’s personal data or information. Social engineering is a cyber threat that aims to use psychological techniques to manipulate or trick people to expose their personal/sensitive information. In this research paper, five supervised classification algorithms (Logistic Regression, Random Forest, Decision Tree, Support Vector Machine and K-Nearest Neighbour) are discussed and compared. A pre-processing system has been designed to collect and extract the features and patterns of spear-phishing emails from the email header and email body. In addition, using the top two classifiers, Logistic Regression and Decision Tree, a novel technique for detecting spear-phishing emails by utilizing multiple classifiers, and the findings showed the hybrid classifier provides a more precise results for the features of detecting spear-phishing emails with a 99.8% accuracy with a low-false positive and false negative rate.
Distributed Storage Strategy and Visual Analysis for Economic Big Data

With the increasing popularity of a large number of Internet-based services and a large number of services hosted on cloud platforms, a more powerful back-end storage system is needed to support these services. At present, it is very difficult or impossible to implement a distributed storage to meet all the above assumptions. Therefore, the focus of research is to limit different characteristics to design different distributed storage solutions to meet different usage scenarios. Economic big data should have the basic requirements of high storage efficiency and fast retrieval speed. The large number of small files and the diversity of file types make the storage and retrieval of economic big data face severe challenges. This paper is oriented to the application requirements of cross-modal analysis of economic big data. According to the source and characteristics of economic big data, the data types are analyzed and the database storage architecture and data storage structure of economic big data are designed. Taking into account the spatial, temporal, and semantic characteristics of economic big data, this paper proposes a unified coding method based on the spatiotemporal data multilevel division strategy combined with Geohash and Hilbert and spatiotemporal semantic constraints. A prototype system was constructed based on Mongo DB, and the performance of the multilevel partition algorithm proposed in this paper was verified by the prototype system based on the realization of data storage management functions. The Wiener distributed memory based on the principle of Wiener filter is used to store the workload of each workload distributed storage window in a distributed manner. For distributed storage workloads, this article adopts specific types of workloads. According to its periodicity, the workload is divided into distributed storage windows of specific duration. At the beginning of each distributed storage window, distributed storage is distributed to the next distributed storage window. Experiments and tests have verified the distributed storage strategy proposed in this article, which proves that the Wiener distributed storage solution can save platform resources and configuration costs while ensuring Service Level Agreement (SLA).
CBNAV: Costmap Based Approach to Deep Reinforcement Learning Mobile Robot Navigation

This research presents a novel deep reinforcement learning approach applied in mobile robot context, where the reward function is based on cockroaches sensor adaption and insect brain temporal and spatial navigation skills. Initially the environment is unknown and the agent is trained to construct the costmap to reward the actions of the agent, based on the most promising regions to be in the environment. For each training epoch the robot path is mapped as a costmap as well, in order to buffer the steps performed and penalizes the ones not in accordance with pre-defined navigation rules. Train and test are performed in simulated conditions, concerning five different environments, and an agent equipped with LiDAR sensor providing 1081 distance measurements per step. Cross-validation between each trained model and the average of times that the target was reached are considered as evaluation criteria. Experimental results show that the insertion of costmap concepts, Costmap Reward Function (CRF) and Costmap Path Function (CPF), increase the generalization of the model characterizing a statistically promissing approach.
A Software Fault Prediction on Inter- and Intra-Release Prediction Scenarios

Software quality engineering applied numerous techniques for assuring the quality of software, namely testing, verification, validation, fault tolerance, and fault prediction of the software. The machine learning techniques facilitate the identification of software modules as faulty or non-faulty. In most of the research, these approaches predict the fault-prone module in the same release of the software. The model is found to be more efficient and validated when training and tested data are taken from previous and subsequent releases of the software respectively. The contribution of this paper is to predict the faults in two scenarios (i.e., inter- and intra-release prediction). The comparison of both intra- and inter-release fault prediction by computing various performance matrices using machine learning methods shows that intra-release prediction has better accuracy compared to inter-releases prediction across all the releases. Also, both the scenarios achieve good results in comparison to existing research work.
Theoretical Construction of the Analysis Model of College English Needs Based on Decision Tree Technology

With the rapid development of Internet technology and educational in form at ionization, there are more and more online reading materials to choose from. But the problem that comes with it is that too many choices will make learners lose themselves in the filtering of information. Get your own reading resources accurately and effectively. The results of English level examination are not only a powerful proof of students' English listening, speaking, reading and writing ability when they graduate from employment, but also a reflection of the level of English teaching in colleges and universities. There are many hidden candidates and If the relevant information is mined and sorted out and fed back to teachers, it will have a positive effect on College English teaching. Many colleges and universities take CET-4 and CET-6 scores as the indicators of students' evaluation and graduation, and take the pass rate of the test as an important content of teachers' teaching evaluation. Among the five parts of English listening, speaking, reading, writing and translating, reading is the most important aspect to improve English learning. Reading materials with appropriate difficulty can improve learners' learning efficiency. Therefore, how to provide learners with personalized reading materials that meet their ability level and learning needs has gradually become the focus of educational technology research. In this paper, decision tree technology is used to analyze college English data, construct decision tree and put forward theoretical construction.
Angle-Only tracking of Dual-station Based on Uncorrelated Conversion Cubature Kalman Filter

Aiming at the problem that Angle-Only tracking technology has low target tracking accuracy in dual-station systems, this paper proposes a tracking algorithm based on uncorrelated conversion cubature Kalman filter algorithm(UCCKF). Firstly, the uncorrelated conversion technology is used to obtain the enhanced measurement. The enhanced measurement is used to supplement the measurement equation to achieve the expansion of the measurement information. Secondly, the state estimation of the target is realized under the framework of cubature Kalman filtering. The enhanced measurement and angle information in this method is uncorrelated. Using this method can effectively reduce the target state update covariance. In the simulation experiment, the tracking performance of the UCCKF algorithm and the CKF algorithm was compared, analyzed and verified. The simulation results show that, under the same conditions, the UCCKF algorithm can reduce the position error by 24.9% and the speed error by 18.7%. The UCCKF algorithm is more effective when applied to a dual-station tracking system.
An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors

Dogs and cats tend to show their conditions and desires through their behaviors. In companion animal behavior recognition, behavior data obtained by attaching a wearable device or sensor to a dog’s body are mostly used. However, differences occur in the output values of the sensor when the dog moves violently. A tightly coupled RGB time tensor network (TRT-Net) is proposed that minimizes the loss of spatiotemporal information by reflecting the three components (x-, y-, and z-axes) of the skeleton sequences in the corresponding three channels (red, green, and blue) for the behavioral classification of dogs. This paper introduces the YouTube-C7B dataset consisting of dog behaviors in various environments. Based on a method that visualizes the Conv-layer filters in analyzable feature maps, we add reliability to the results derived by the model. We can identify the joint parts, i.e., those represented as rows of input images showing behaviors, learned by the proposed model mainly for making decisions. Finally, the performance of the proposed method is compared to those of the LSTM, GRU, and RNN models. The experimental results demonstrate that the proposed TRT-Net method classifies dog behaviors more effectively, with improved accuracy and F1 scores of 7.9% and 7.3% over conventional models.
Development of Constructivism-Based Indonesian Language Module for Fourth Grade Students of SD Negeri 99/III Sungai Pegeh, Siulak District, Kerinci Regency

PENGEMBANGAN MODUL PEMBELAJARAN BAHASA INDONESIA BERBASIS KONSTRUKTIVISME PADA SISWA KELAS IVSD NEGERI 99/III SUNGAI PEGEH KECAMATANSIULAK KABUPATEN KERINCIMonika Valentia1, Wirnita Eska21Program Studi Pendidikan Guru Sekolah DasarFakultas Keguruan dan Ilmu PendidikanUniversitas Bung Hattae-mail : monikavalentia46@gmail.comABTRACTThe lack of availability of printed books for Indonesian subjects or learning resources as well as the lack of teachers using thematic books and only focusing on Student Worksheets (LKS) as learning resources at SD Negeri 99/III Sungai Pegeh, thus making students have difficulty understanding reading texts and not have knowledge in intensive reading, and lack of focus on the lessons given by the teacher, because teachers tend to use conventional methods (lectures and questions and answers). This study aims to develop a Constructivism-based Indonesian learning module for grade IV elementary school students that is valid, practical and effective. The theory of learning is in accordance with the opinion of Hamalik (2007), the theory of the constructivism approach of Trianto's opinion (2016) and Fitri, R (2017), and the theory of the module proposed by Daryanto (2013) and Prastowo (2016). The type of research is Research and Development (research and development). This Constructivism-based Indonesian language learning module was developed using 4-D which was limited to 3-D, due to limited knowledge, ability and time. The research was conducted in the even semester of the 2020/2021 academic year. Data collection is done by validating the developed module. The module design that has been designed is validated by three expert lecturers, for practicality testing the module is carried out on teachers and fourth grade elementary school students, and for testing the effectiveness of the test results for fourth grade students at SD Negeri 99/III Sungai Pegeh, Siulak District, Kerinci Regency. The results showed that the module produced was very valid by the validator with a value of (91.71%). The module that has been developed is very practical by the teacher with a value of practicality (90.00%), also considered very practical according to the value of practicality (92.2%), and the module developed is very effective with a value of (83.75%). Thus, it can be said that the module developed is very practical and effective for fourth grade elementary school students. Furthermore, the Indonesian language learning module can be used by teachers as an alternative to Indonesian language learning materials at SD Negeri 99/III Sungai Pegeh, Siulak District, Kerinci Regency.Keywords: Module, Development, Constructivism, Indonesian.
Morphological edge detection algorithm based on hierarchical multi-scale

Remote sensing image combines the characteristics of visible light image, infrared image and other multi-spectral images, making it rich in details and low resolution. However, due to factors such as weather and transmission errors, salt and pepper noise is prone to occur, and it is difficult to effectively detect weak edges. Aiming at the problems, a morphological edge detection algorithm based on hierarchical multi-scale is proposed in this paper. Firstly, the adaptive median filter is used to smooth the remote sensing image. Secondly, it is proposed to use the mutual information of images as the cost function for processing, and the multi-scale hierarchical ratio is 2 1 . Then, the four directional structural elements of 0 , 45 , 90 and  135 are used to extract the original-scale and small-scale image edges respectively. Calculate the sum of the gray difference values of eight neighborhoods of the edge points, thus calculate the direction adaptive weight, and then the edge detection results are obtained by fusion respectively. Finally, the pixels in the edge image are classified, and the fusion method of enhancing edge and weak edge and filtering false edge is proposed, and then the edge detection image is obtained. Aiming at the application of remote sensing images, the comparison of the results shows that the proposed algorithm has stronger anti-noise performance, the weak edge detection ability is improved, thus avoiding missed detection and false detection of edge information, and detecting more complete and accurate edge details.
Eye Movement Analysis for Consumer Devices

The eye tracker enables the new applications for consumer electronics. By estimating the human gaze direction in real-time as a human-machine interface (HMI), various novel computer games or remote learning systems for education purpose could be developed. In this paper, a new HMI system, which integrates a visible-spectrum gaze tracker, a fixation/saccade analyzer, an object tracking, and a score evaluation module, has been proposed. This system aims at guiding the design the computer game as well as the examination of human visual reaction for various consumer electronics.
Information-Compensation-Based Receding Path Planning for Multi-UAVs under Communication Delays

This paper presents a novel receding path planning method for multi-UAVs to increase the success rate of cooperative path planning under communication delays. Firstly, path planning problem for multi-UAVs is decomposed into a series of short horizon sub-problems via receding planning framework to reduce the computational burden. An analytical solution to the collision avoidance algorithm is deduced, which can ensure the safety among discretized path points. To enhance the success rate of path planning for multi-UAVs under communication delays, Kalman filtering method is utilized for compensating delay information, which the current position of UAV is estimated by its previous position and delay time. Finally, the numerical simulation demonstrates the effectiveness of the proposed method.
A Scalable Architecture for Electronic Payments

We present a scalable architecture for electronic payments via central bank digital currency and offer a solution to the perceived conflict between robust regulatory oversight and consumer affordances such as privacy and control. Our architecture combines existing work in payment systems and digital currency with a new approach to digital asset design for managing unforgeable, stateful, and oblivious assets without relying on either a central authority or a monolithic consensus system. Regulated financial institutions have a role in every transaction, and the consumer affordances are achieved through the use of non-custodial wallets that unlink the sender from the recipient in the transaction channel. This approach is fully compatible with the existing two-tiered banking system and can complement and extend the roles of existing money services businesses and asset custodians.
Multi-Object Tracking Based on Feature Fusion and Hierarchical Data Association

Multi-object tracking has been a fundamental topic in recent years. However, most methods can hardly have good performance due to the high appearance similarity, large number of the target objects and error detections in complex scenes. To solve these problems, we propose a multi-object tracking method based on multi-feature fusion and hierarchical data association. Specifically, it uses multi-feature fusion method to improve the anti-jamming ability of data association method and solve the difficulty of tracking accuracy decline caused by large number of objects and error detections. In order to improve the performance of the association method, we refined into multi-layer calculation to solve the problems of objects high similarity appearance and the failure of object trajectory assignment when occlusion occurs. Benefit from our proposed method, on the MOT16 test set, the accuracy of the multi-object tracking method is 44.9%. Compared with the method of multi-feature fusion only, the tracking accuracy of our hierarchical data association is improved by 10.7%.
SecretStore: A Secrecy as a Service model to enable the Cloud Storage to store user's secret data

Data secrecy is a major concern in many domains. Nowadays, the data are kept in tight security with high privacy. Users do not want to share their secret information with anyone; however, the users' confidential data are not protected from the administrators. Administrators can read the users' data. Why should any Administrator read users' data? To address this issue, we propose a new secrecy protocol to store data secretly, named Secret Cloud Storage, SecretStore for short, to enable Secrecy as a Service model over the Cloud Computing paradigm. This paper demonstrates how to protect users' data from any unintended users, including the data administrators. Moreover, we introduce tight security using the client-side symmetric cryptography method. In addition, we devise a forgetful private key to generate or regenerate a private key to encrypt or decrypt based on a secret word. We also show how to strengthen the weak password. Finally, we demonstrate how to implement the Secrecy as a Service model in Cloud Storage using highly unpredictable private keys.
Research of the Modular Operational Performance Analysis for Consortium Blockchain

Improving the operational efficiency of blockchain system is one of important issues. This work conducts research on performance analysis of consortium blockchain system to make corresponding system modular hosts deployment strategies such that the target number of served transactions is satisfied. We explore the performance limitations through appropriate deployment methods to improve transaction processing efficiency. A serial of experiments are designed to understand the performance limitations of the system operation. Accordingly, we present substantial reasons and improvement methods for a modular operational configuration strategy to address the performance limitations in the Hyperledger Fabric system. The theoretical implications of this paper include: discussing the performance limitations of the system, analyzing the reasons of the system limitations and providing suggestions for improvement, and exploring appropriate deployment methods to improve transactions operational efficiency. The practical implications of this paper include: providing relevant developers with references of system structure design through the analyses of these limitations and providing strategies for developers to deploy in blockchain system by elaborating the proposed deployment methods.