Improving Classification Efficiency of Fake News using Semi-Supervised Method

Online News media which is more accessible, cheaper, and faster to consume, is also of questionable quality as there is less moderation. Anybody with a computing device and internet connection can take part in creating, contributing, and spreading news in online portals. Social media has intensified the problem further. Due to the high volume, velocity, and veracity, online news content is beyond traditional moderation, also known as moderation through human experts. So different machine learning method is being tested and used to spot fake news. One of the main challenges for fake-news classification is getting labeled instances for this high volume of real-time data. In this study, we examined how semi-supervised machine learning can help to decrease the need for labeled instances with an acceptable drop of accuracy. The accuracy difference between the supervised classifier and the semi-supervised classifier is around 0.05 while using only five percent of label instances of the supervised classifier. We tested with logistic regression, SVM, and random forest classifier to prove our hypothesis.

Physical Distancing Device with Edge Computing for COVID-19 (PADDIE-C19)

The most effective methods of preventing COVID-19 infection include maintaining physical distancing and wearing a face mask while in close contact with people in public places. However, densely populated areas have a greater incidence of COVID-19 dissemination, which is caused by people who do not comply with standard operating procedures (SOPs). This paper presents a prototype called PADDIE-C19 (Physical Distancing Device with Edge Computing for COVID-19) to implement the physical distancing monitoring based on a low-cost edge computing device. The PADDIE-C19 provides real-time results and responses, as well as notifications and warnings to anyone who violates the 1-m physical distance rule. In addition, PADDIE-C19 includes temperature screening using an MLX90614 thermometer and ultrasonic sensors to restrict the number of people on specified premises. The Neural Network Processor (KPU) in Grove Artificial Intelligence Hardware Attached on Top (AI HAT), an edge computing unit, is used to accelerate the neural network model on person detection and achieve up to 18 frames per second (FPS). The results show that the accuracy of person detection with Grove AI HAT could achieve 74.65% and the average absolute error between measured and actual physical distance is 8.95 cm. Furthermore, the accuracy of the MLX90614 thermometer is guaranteed to have less than 0.5 °C value difference from the more common Fluke 59 thermometer. Experimental results also proved that when cloud computing is compared to edge computing, the Grove AI HAT achieves the average performance of 18 FPS for a person detector (kmodel) with an average 56 ms execution time in different networks, regardless of the network connection type or speed.

Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device

Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus detection task that combines UAV data collection, AI embedded device, and target detection algorithm. The system used a small unmanned aerial vehicle equipped with a camera to take full-scale pictures of citrus trees; at the same time, we extended the state-of-the-art model target detection algorithm, added the attention mechanism and adaptive fusion feature method, improved the model’s performance; to facilitate the deployment of the model, we used the pruning method to reduce the amount of model calculation and parameters. The improved target detection algorithm is ported to the edge computing end to detect the data collected by the unmanned aerial vehicle. The experiment was performed on the self-made citrus dataset, the detection accuracy was 93.32%, and the processing speed at the edge computing device was 180 ms/frame. This method is suitable for citrus detection tasks in the mountainous orchard environment, and it can help fruit growers to estimate their yield.

FPGA Implementation of Mutual Authentication Protocol for Medication Security System

Medication safety administration is a complicated process involving the information of patients, drugs, and data storage. The sensitive data transmitted through wireless sensor networks (WSNs) from Internet of things (IoT) over an insecure channel is vulnerable to several threats and needs proper attention to be secured from adversaries. Taking medication safety into consideration, this paper presents a secure authentication protocol for wireless medical sensor networks. The XOR scheme-based algorithm is applied to achieve the purposes of data confidentiality. The proposed architecture is realized as hardware in a field-programmable gate array (FPGA) device which acts as a secure edge computing device. The performance of the proposed protocol is evaluated and simulated via Verilog hardware description language. The functionality of the proposed protocol is verified using the Altera Quartus II software tool and implemented in the Altera Cyclone II DE2-70 FPGA development module. Furthermore, the output signals from the FPGA are measured in the 16702A logic analyzer system to demonstrate real-time functional verification.

Surface acoustic wave controlled skyrmion-based synapse devices

Magnetic skyrmions, particle-like spin structures, are considered as ideal information carriers for neuromorphic computing devices due to their topological stability and nanoscale size. In this work, we proposed to control magnetic skyrmions by electric-field-excited surface acoustic waves in neuromorphic computing device structures. Our micromagnetic simulations show that the number of created skyrmions, which emulates the synaptic weight parameter, increases monotonically with increasing the amplitude of the surface acoustic waves. Additionally, the efficiency of skyrmion creation was investigated systemically with a wide range of the magnetic parameters, and the optimal values have been presented accordingly. Finally, the functionalities of short-term plasticity and long-term potentiation have been demonstrated via the skyrmion excitation by the sequence of surface acoustic waves with different intervals. The application of surface acoustic waves in the skyrmionic neuromorphic computing devices paves a novel way for low-power computing systems.

Configurable pixelated skyrmions on nanoscale magnetic grids

Topological spin textures can serve as non-volatile information carriers. Here we study the current-induced dynamics of an isolated magnetic skyrmion on a nanoscale square-grid pinning pattern formed by orthogonal defect lines with reduced magnetic anisotropy. The skyrmion on the square grid can be pixelated with a quantized size of the grid. We demonstrate that the position, size, and shape of skyrmion on the square grid are electrically configurable. The skyrmion center is quantized to be on the grid and the skyrmion may show a hopping motion instead of a continuous motion. We find that the skyrmion Hall effect can be perfectly prohibited due to the pinning effect of the grid. The pixelated skyrmion can be harnessed to build future programmable racetrack memory, multistate memory, and logic computing device. Our results will be a basis for digital information storage and computation based on pixelated topological spin textures on artificial pinning patterns.

A Perspective Roadmap for IoMT-Based Early Detection and Care of the Neural Disorder, Dementia

The Internet of Medical Things (IoMT) has emerged as one of the most important key applications of IoT. IoMT makes the diagnosis and care more convenient and reliable with proven results. The paper presents the technology, open issues, and challenges of IoMT-based systems. It explores the various types of sensors and smart equipment based on IoMT and used for diagnosis and patient care. A comprehensive survey of early detection and postdetection care of the neural disorder dementia is conducted. The paper also presents a postdiagnosis dementia care model named “Demencare.” This model incorporates eight sensors capable of tracking the daily routine of dementia patient. The patients can be monitored locally by an edge computing device kept at their premises. The medical experts may also monitor the patients’ status for any deviation from normal behavior. IoMT enables better postdiagnosis care for neural disorders, like dementia and Alzheimer’s. The patient’s behavior and vital parameters are always available despite the remote location of the patients. The data of the patients may be classified, and new insights may be obtained to tackle patients in a better manner.

Cloud Computing Storage Backup and Recovery Strategy Based on Secure IoT and Spark

Spatial data occupies a large proportion of the large amount of data that is constantly emerging, but a large amount of spatial data cannot be directly understood by people. Even a highly configured stand-alone computing device can hardly meet the needs of visualization processing. In order to protect the security of data and facilitate for users the search for data and recover by mistake, this paper conducts a research on cloud computing storage backup and recovery strategies based on the secure Internet of Things and Spark platform. In the method part, this article introduces the security Internet of Things, Spark, and cloud computing backup and recovery related content and proposes cluster analysis and Ullman two algorithms. In the experimental part, this article explains the experimental environment and experimental objects and designs an experiment for data recovery. In the analysis part, this article analyzes the challenge-response-verification framework, the number of data packets, the cost of calculation and communication, the choice of Spark method, the throughput of different platforms, and the iteration and cache analysis. The experimental results show that the loss rate of database 1 in the fourth node is 0.4%, 2.4%, 1.6%, and 3.2% and the loss rate of each node is less than 5%, indicating that the system can respond to applications.

Multi-State MRAM Cells For Hardware Neuromorphic Computing

Magnetic tunnel junctions (MTJ) have been successfully applied in various sensing application and digital information storage technologies. Currently, a number of new potential applications of MTJs are being actively studied, including high-frequency electronics, energy harvesting or random number generators. Recently, MTJs have been also proposed in designs of new platforms for unconventional or bio-inspired computing. In the present work, it is shown that serially connected MTJs forming a multi-state memory cell can be used in a hardware implementation of a neural computing device. The main purpose of the multi-cell is the formation of quantized weights in the network, which can be programmed using the proposed electronic circuit. Multi-cells are connected to a CMOS-based summing amplifier and a sigmoid function generator, forming an artificial neuron. The operation of the designed network is tested using a recognition of hand-written digits in 20 × 20 pixels matrix and shows detection ratio comparable to the software algorithm, using weights stored in a multi-cell consisting of four MTJs or more.