Unnecessary Data Research Articles

A Novel Handwritten Digit Classification System Based on Convolutional Neural Network Approach.

An enormous number of CNN classification algorithms have been proposed in the literature. Nevertheless, in these algorithms, appropriate filter size selection, data preparation, limitations in datasets, and noise have not been taken into consideration. As a consequence, most of the algorithms have failed to make a noticeable improvement in classification accuracy. To address the shortcomings of these algorithms, our paper presents the following contributions: Firstly, after taking the domain knowledge into consideration, the size of the effective receptive field (ERF) is calculated. Calculating the size of the ERF helps us to select a typical filter size which leads to enhancing the classification accuracy of our CNN. Secondly, unnecessary data leads to misleading results and this, in turn, negatively affects classification accuracy. To guarantee the dataset is free from any redundant or irrelevant variables to the target variable, data preparation is applied before implementing the data classification mission. Thirdly, to decrease the errors of training and validation, and avoid the limitation of datasets, data augmentation has been proposed. Fourthly, to simulate the real-world natural influences that can affect image quality, we propose to add an additive white Gaussian noise with = 0.5 to the MNIST dataset. As a result, our CNN algorithm achieves state-of-the-art results in handwritten digit recognition, with a recognition accuracy of 99.98%, and 99.40% with 50% noise.

Event-Triggered Globally Sequential Fusion Estimation for Clustered Wireless Sensor Networks With Variational Bayesian

This paper concentrates on a globally sequential fusion state estimation problem for clustered wireless sensor networks (WSNs). Therein, frequent data communications in WSN will significantly increase energy consumption and communication burden, and it needs to be taken into account. To reduce unnecessary data transmissions and save energy, an event-triggered mechanism that decides whether the current measurement should be transmitted or not, therefore, is introduced. Then a novel variational Bayesian based event-triggered sequential measurement fusion (VB-ESMF) estimator is proposed to produce the local fused results of the clustered WSNs, where the variational Bayesian approach is used to infer the measurement noise covariance matrices of pseudo measurement noises. Therein, the local fused measurements of the clusters in the clustered WSNs are used sequentially by the remote fusion center, and thus a global fusion state estimation, which is globally optimal when all measurements are transmitted successfully, is computed. Additionally, certain boundedness and convergence conditions of the proposed estimator are derived, and an expected compromise between communication rate and estimation accuracy can be obtained by properly turning the trigger threshold. Finally, the effectiveness of both the VB-ESMF estimation and the globally sequential fusion state estimation is illustrated by simulation results.

An NB-IoT-Based Edge-of-Things Framework for Energy-Efficient Image Transfer.

Machine Learning (ML) techniques can play a pivotal role in energy efficient IoT networks by reducing the unnecessary data from transmission. With such an aim, this work combines a low-power, yet computationally capable processing unit, with an NB-IoT radio into a smart gateway that can run ML algorithms to smart transmit visual data over the NB-IoT network. The proposed smart gateway utilizes supervised and unsupervised ML algorithms to optimize the visual data in terms of their size and quality before being transmitted over the air. This relaxes the channel occupancy from an individual NB-IoT radio, reduces its energy consumption and also minimizes the transmission time of data. Our on-field results indicate up to 93% reductions in the number of NB-IoT radio transmissions, up to 90.5% reductions in the NB-IoT radio energy consumption and up to 90% reductions in the data transmission time.

Event‐based output feedback control of periodic systems: A piecewise impulsive method

AbstractIn this article, an event‐triggered control with periodic time‐varying parameters is designed to stabilize a class of periodic systems with exogenous disturbances in a network environment. The continuous‐time periodic system is approximated by some linear subsystems with finite‐number constant matrices based on a piecewise method. It can thus reduce the complexity of theoretical analysis. To tackle the reduction of unnecessary data transmission between the sensor (or controller) and the controller (or actuator), new event‐triggering mechanisms are designed via output feedback. Special parameters are designed to eliminate Zeno behavior, and the minimum triggering interval is obtained according to the designed condition. Then, an event‐triggered periodic piecewise system is modeled as an impulsive periodic piecewise system. The bounded stability, as well as the performance of the impulsive periodic piecewise system, are analyzed. Furthermore, the gains of the event‐triggered controller are obtained by a method of variable substitution. Finally, the validity of the proposed control is demonstrated by an application example.

Automated evaluation of Cr-III coated parts using Mask RCNN and ML methods

In this study, chrome coatings were carried out using a Cr-III electroplating bath. The coated parts were classified depending on their appearance. A new approach was developed to classify the coated parts automatically using artificial intelligence methods. Mask RCNN and machine learning (ML) methods such as Multilayer Perceptron (MLP), Support Vector Classifier (SVC), Gaussian Process (GP), K-nearest Neighbors (KNN), XGBoost, and Random Forest Classifier (RFC) were used together. Mask RCNN was used to clean the coated parts from the redundant data. The extracted data were flattened and converted to the row vectors for use as input in ML methods. ML algorithms were used to classify the coated parts as “Pass” and “Fail”. The classification accuracy was checked with the leave one out (loo) cross-validation method. RFC method gave the highest accuracy, 0.83, and F1 score, 0.88. The accuracy of Mask RCNN was checked using a dataset of separated validation images. It was observed that extracting the unnecessary data from the images increased the accuracy exceedingly. Moreover, the method exhibits a high potential to keep the parameters of the electroplating process under control.

Do you get what you pay for? Preventing insufficient effort responding in MTurk and student samples

AbstractPrevious research has developed multiple indices to detect instances of insufficient effort responding (IER; Huang et al., 2015, 0.1037/a0038510). Although these techniques are largely successful, conflation between effort and insufficient effort leads to unnecessary data removal, thus warranting investigation into IER prevention strategies such as warning statements. For example, Brühlmann et al. (2020, 10.1016/j.metip.2020.100022) reported that close to 60% of participants were flagged for IER on at least one IER detection index. The current study used positive, negative, and no warning statements in two samples (MTurk and university participant pool) to examine their impact on two types of IER. Results do not support the use of warning statements to reduce IER covertly, overtly, or overall. Although slightly more university participants were flagged for IER for two of five IER indices, the remaining three indices did not differ between samples. This finding represents an important contribution to survey research literature by directly comparing samples on a variety of IER indices. Implications for survey research necessitate the examination of additional prevention strategies and sample differences in IER.

Evaluation Of Street Children Program In Children Care Community NGO In Kampung Aur Medan

Street children are one of the most common social problems facing each country in the world. Street children are divided into three, namely: children who have economic activities as child labor on the street, but still have a strong relationship with their parents. Children who participate fully in the streets, both socially and economically. Children from families living on the streets. This study aims to describe the achievement of coaching program objectives through NGO KOPA for street children in Kampung Aur. To know the supporting and inhibiting factors of coaching program through NGO KOPA for street children in Kampung Aur. Since Kampung Aur is located in a densely populated area on the banks of the Deli River, the urban poor condition has many problems with children's background, such as the problem of street children and children in trouble with their families. The approach used in this research is descriptive qualitative with data collection method is observation / observation, documentation, and interview. The data already collected will be presented by reducing unnecessary data in the study. After that done withdrawal of conclusion / verification. Based on the results of research, the program runs well although not too significant, it's just still have deficiencies in the inputs, namely human resources, facilities and also insufficient funds. After that done withdrawal of conclusion / verification. Based on the results of research, the program runs well although not too significant, it's just still have deficiencies in the inputs, namely human resources, facilities and also insufficient funds. After that done withdrawal of conclusion / verification. Based on the results of research, the program runs well although not too significant, it's just still have deficiencies in the inputs, namely human resources, facilities and also insufficient funds.

Using the REA Data Model in Order to Reduce Design Complexity in an Inventory Management Relational Database, and the Need for a Relational Database Complexity Metric.

With the extensive use of relational databases in the business environment there is a need to reduce database complexity in order to avoid data inconsistency and redundancy, which can provide a company with unreliable and/or meaningless data and information.
 The use of the REA Data Model in database design can significantly help with this problem. The model can eliminate the need for unnecessary data artifacts which should only be generated by the system when needed.
 This paper also addresses the need for a Relational Database Complexity Metric. A simple and easy to understand metric is presented.

Adaptive consensus-based distributed [formula omitted] filtering with switching topology subject to partial information on transition probabilities

This article is dedicated to designing distributed state estimators for time-varying delay systems. The filtering network communication topology is time-varying, and it is presumed that the switching mechanism follows a homogeneous Markov chain, with partially unknown information in the probability transition matrix. An adaptive event-triggered mechanism is embedded in the sensor network, which reduces unnecessary communication data and saves communication resources. By utilising the mode-dependent Lyapunov function, sufficient conditions are deduced to ensure the H∞-consensus performance for the filtering error dynamics. Then, some intensive analysis is performed to obtain the explicit expressions of the distributed H∞ state estimator by introducing slack variables and free-connection weighting matrices. Finally, two numerical simulations are proposed to demonstrate the effectiveness of theoretical results.

Comparative Evaluation of Mapping Accuracy between UAV Video versus Photo Mosaic for the Scattered Urban Photovoltaic Panel

It is common practice for unmanned aerial vehicle (UAV) flight planning to target an entire area surrounding a single rooftop’s photovoltaic panels while investigating solar-powered roofs that account for only 1% of the urban roof area. It is very hard for the pre-flight route setting of the autopilot for a specific area (not for a single rooftop) to capture still images with high overlapping rates of a single rooftop’s photovoltaic panels. This causes serious unnecessary data redundancy by including the surrounding area because the UAV is unable to focus on the photovoltaic panel installed on the single rooftop. The aim of this research was to examine the suitability of a UAV video stream for building 3-D ortho-mosaics focused on a single rooftop and containing the azimuth, aspect, and tilts of photovoltaic panels. The 3-D position accuracy of the video stream-based ortho-mosaic has been shown to be similar to that of the autopilot-based ortho-photo by satisfying the mapping accuracy of the American Society for Photogrammetry and Remote Sensing (ASPRS): 3-D coordinates (0.028 m) in 1:217 mapping scale. It is anticipated that this research output could be used as a valuable reference in employing video stream-based ortho-mosaics for widely scattered single rooftop solar panels in urban settings.

Adaptive Event-Triggered Synchronization of Networked Neural Networks with Time-Varying Delay Subject to Actuator Saturation

This paper discusses the adaptive event-triggered synchronization problem of a class of neural networks (NNs) with time-varying delay and actuator saturation. First, in view of the limited communication channel capacity of the network system and unnecessary data transmission in the NCSs, an adaptive event-triggered scheme (AETS) is introduced to reduce the network load and improve network utilization. Second, under the AETS, the synchronization error model of the delayed master-slave synchronization system is constructed with actuator saturation. Third, based on Lyapunov–Krasovskii functional (LKF), a new sufficient criterion to guarantee the asymptotic stability of the synchronization error system is derived. Moreover, by solving the stability criterion expressed in the form of a set of linear matrix inequalities (LMIs), some necessary parameters of the system are obtained. At last, two examples are expressed to demonstrate the feasibility of this method.

Fuzzy filtering based on decentralized adaptive event-triggered scheme for networked interconnected systems

This paper focuses on the problem of the fuzzy filtering for networked nonlinear interconnected systems based on decentralized adaptive event-triggered (AET) scheme. Firstly, a new decentralized AET scheme is proposed, and some unnecessary data packets are discarded through decentralized adaptive rule, thereby saving network communication resources. Secondly, the H∞ performance analysis of the filter error system is presented by using the fuzzy line-integral method, which does not need to give the time derivative upper bounds of the membership function in advance. Then, the combination of the Wirtinger-based inequality and the extended reciprocally convex matrix inequality is used to establish sufficient conditions for the filter error system to meet H∞ performance. Moreover, a method for jointly designing the distributed AET parameters and fuzzy filter parameters is proposed. Finally, two examples are given to verify the effectiveness of the proposed method.

Every Byte Matters

Wearable devices such as smartwatches, fitness trackers, and blood-pressure monitors process, store, and communicate sensitive and personal information related to the health, life-style, habits and interests of the wearer. This data is typically synchronized with a companion app running on a smartphone over a Bluetooth (Classic or Low Energy) connection. In this work, we investigate what can be inferred from the metadata (such as the packet timings and sizes) of encrypted Bluetooth communications between a wearable device and its connected smartphone. We show that a passive eavesdropper can use traffic-analysis attacks to accurately recognize (a) communicating devices, even without having access to the MAC address, (b) human actions (e.g., monitoring heart rate, exercising) performed on wearable devices ranging from fitness trackers to smartwatches, (c) the mere opening of specific applications on a Wear OS smartwatch (e.g., the opening of a medical app, which can immediately reveal a condition of the wearer), (d) fine-grained actions (e.g., recording an insulin injection) within a specific application that helps diabetic users to monitor their condition, and (e) the profile and habits of the wearer by continuously monitoring her traffic over an extended period. We run traffic-analysis attacks by collecting a dataset of Bluetooth communications concerning a diverse set of wearable devices, by designing features based on packet sizes and timings, and by using machine learning to classify the encrypted traffic to actions performed by the wearer. Then, we explore standard defense strategies against traffic-analysis attacks such as padding, delaying packets, or injecting dummy traffic. We show that these defenses do not provide sufficient protection against our attacks and introduce significant costs. Overall, our research highlights the need to rethink how applications exchange sensitive information over Bluetooth, to minimize unnecessary data exchanges, and to research and design new defenses against traffic-analysis tailored to the wearable setting.

Event-triggered load frequency control of Markovian jump interconnected power systems under denial-of-service attacks

This paper deals with the problem of load frequency control (LFC) design for Markovian jump interconnected multi-area power systems. Due to the fact that the power system is spread over a large geographical area and, subsequently, uses a communication network to exchange data, the possibility of cyber-attacks is inevitable. Accordingly, denial-of-service (DoS) attack, which is the most common attack in power systems, is considered for the communication channel. In addition, due to limited network resources, an event-triggered mechanism (ETM) has been utilized to reduce unnecessary data transmission through the network. Since the power systems are faced with random jumps in their models due to various faults and trips, combination of a pre-defined conventional PI controller and a mode-dependent state feedback controller is proposed for LFC. By employing the Lyapunov-Krasovskii theory and stochastic analysis techniques, sufficient conditions are derived and formulated in terms of linear matrix inequalities to guarantee the stability of the power system. Finally, a numerical three-area power system is considered for simulation and verification of the proposed method.

Parameter estimation of the incubation period of COVID-19 based on the doubly interval-censored data model.

With the spread of the novel coronavirus disease 2019 (COVID-19) around the world, the estimation of the incubation period of COVID-19 has become a hot issue. Based on the doubly interval-censored data model, we assume that the incubation period follows lognormal and Gamma distribution, and estimate the parameters of the incubation period of COVID-19 by adopting the maximum likelihood estimation, expectation maximization algorithm and a newly proposed algorithm (expectation mostly conditional maximization algorithm, referred as ECIMM). The main innovation of this paper lies in two aspects: Firstly, we regard the sample data of the incubation period as the doubly interval-censored data without unnecessary data simplification to improve the accuracy and credibility of the results; secondly, our new ECIMM algorithm enjoys better convergence and universality compared with others. With the framework of this paper, we conclude that 14-day quarantine period can largely interrupt the transmission of COVID-19, however, people who need specially monitoring should be isolated for about 20 days for the sake of safety. The results provide some suggestions for the prevention and control of COVID-19. The newly proposed ECIMM algorithm can also be used to deal with the doubly interval-censored data model appearing in various fields.

Joining Formal Concept Analysis to Feature Extraction for Data Pruning in Cloud of Things

Abstract The enormous increase in the number of Internet of Things data sources (sensors, personal devices and embedded systems) has given rise to a huge amount of unnecessary and redundant data being sent to the cloud. This makes the task of processing and storing this volume of information a very hard one. Therefore, data pre-processing and filtering closer to data sources, such as in fog computing, is necessary. In particular, data reduction in the fog nodes may play a significant role in preventing the dramatic decrease in the Cloud of Things performance, especially in energy consumption, storage space, bandwidth and throughput. However, existing solutions are still lacking, considering they do not achieve the optimal data reduction performance especially in terms of delay and accuracy. In this article, we introduce an approach aiming to eliminate useless and redundant data captured by things basing on some intelligent information extraction techniques. We also evaluate the performance of the proposed solution on a real data set sample to demonstrate that it achieves a good features reduction.

Privacy-preserving voluntary-tallying leader election for internet of things

The Internet of Things (IoT) is commonly deployed with devices of limited power and computation capability. A centralized IoT architecture provides a simplified management for IoT system but brings redundancy by the unnecessary data traffic with a data center. A decentralized IoT reduces the cost on data traffic and is resilient to the single-point-of-failure. The blockchain technique has attracted a large amount of research, which is redeemed as a perspective of decentralized IoT system infrastructure. It also brings new privacy challenges for that the blockchain is a public ledger of all digital events executed and shared among all participants.The decentralized IoT system relies on the leader election deeply to implement the decentralized communications among the distributed nodes. The conventional leader election must have a centralized authority, contrasting to the decentralization. As an alternative, self-tallying type schemes have been proposed in the literature for decentralized systems. These schemes suffer from adaptive and abortive issues. Also, some additional factors should be considered, such as the availability of candidate nodes. If the candidate node is unavailable after the voting phase due to being offline or ongoing tasks, the next available candidate should be elected. To accommodate such a need, in this paper, we propose a new leader election paradigm called voluntary-tallying leader election, which achieves the core requirements such as ballet secrecy, voter privacy and the additional feature of voluntary-tallying. We formalize the system and security models for this new election paradigm and present a secure and practical construction.

A Privacy-Oriented Local Web Learning Analytics JavaScript Library with a Configurable Schema to Analyze Any Edtech Log: Moodle’s Case Study

Educational institutions are transferring analytics computing to the cloud to reduce costs. Any data transfer and storage outside institutions involve serious privacy concerns, such as student identity exposure, rising untrusted and unnecessary third-party actors, data misuse, and data leakage. Institutions that adopt a “local first” approach instead of a “cloud computing first” approach can minimize these problems. The work aims to foster the use of local analytics computing by offering adequate nonexistent tools. Results are useful for any educational role, even investigators, to conduct data analysis locally. The novelty results are twofold: an open-source JavaScript library to analyze locally any educational log schema from any LMS; a front-end to analyze Moodle logs as proof of work of the library with different educational metrics and indicator visualizations. Nielsen heuristics user experience is executed to reduce possible users’ data literacy barrier. Visualizations are validated by surveying teachers with Likert and open-ended questions, which consider them to be of interest, but more different data sources can be added to improve indicators. The work reinforces that local educational data analysis is feasible, opens up new ways of analyzing data without data transfer to third parties while generating debate around the “local technologies first” approach adoption.

An energy‐efficient clustering algorithm with self‐diagnosis data fault detection and prediction for wireless sensor networks

AbstractWireless sensor networks are used to track and regulate physical conditions like temperature and the environment's humidity. Wireless sensor networks with their advanced features are adopted for many real‐time applications. The limited capacity batteries usually power the sensor nodes. But the big challenge of limited battery capacity obstructs remote and inaccessible areas where their use is the most favorable. For extending the lifetime of the network, the battery should optimally utilize it for different operations. The requirement toward low complexity and low energy consumption motivate the wireless sensor networks' efficient clustering algorithm. The sensor nodes group into clusters; one sensor node is chosen as a cluster head, and communication to the sink node from the sensor nodes occurs through the cluster head (CH) node. In the proposed method, cluster heads are determined based on the sensor node's weighted metric. The sensor nodes are then self‐adaptive by making correct decisions in real‐time based on the sensed data, but detected information is often inaccurate due to some mechanical, wireless loss, and battery problems. The erroneous or irrelevant data should be overlooked to avoid unnecessary data transmission, which contributes to reducing the network's lifetime. In the neighborhood‐dependent self‐diagnosis fault detection technique, the faulty sensed data are filtered at the sensor node itself. In the data prediction algorithm, the filtered data are predicted at the cluster head. All the factors collectively contribute to enhance the network's lifetime. The lifetime improvement of the proposed approach is almost doubled compared with LEACH one time better than QLEACH and ECH, and 51% better than temporal approach.

Event-Triggered Non-Switching Networked Sliding Mode Control for Active Suspension System With Random Actuation Network Delay

An active suspension system enables a road vehicle to run with increased stability, providing better safety and comfort, by maintaining the wheels in contact with the road surface. Therefore, there is currently demand for designing a robust controller that gives a satisfactory performance in the presence of the different road profiles. This investigation focuses on the design of a discrete-time non-switching networked sliding mode control for the active suspension system, using an event-triggered approach and actuation network delay compensation scheme. A novel event-triggered proportional-integral (PI) sliding variable is built with actuation network delay compensation in the presence of system uncertainties. The discrete-time non-switching networked sliding mode control law is designed using a novel sliding variable, and the control actions at the controller side are updated based on the event generation. The event generator block in the feedback channel increases the communication rate and reduces network congestion by restricting the transmission of unnecessary data packets. The random actuation network delay in the forward and feedback channel is modeled using the Poisson distribution and compensated using the Thiran’s approximation technique. The stability of the closed-loop system is derived using the proposed control law that ensures the finite-time convergence of the system state variables within the specified sliding band. Furthermore, the proposed controller is validated on an active suspension system in the presence of network abnormalities and system uncertainties. Numerical results inferred that the proposed control strategy with event-triggered approach and actuation network delay compensation scheme outperforms as considered the Gao’s reaching law, conventional non-switching reaching law, or the control strategy without actuation network delay compensation.