Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Information Coordination Mechanism of Multi-participants in Community-based Elderly Care by Blockchain

As human life expectancy continues to increase and the trend towards fewer children continues to spread, more elderly people are in need for community-based elderly care service. In order to ensure the orderly implementation of various specific matters in the community-based elderly care service mode, it is necessary to study the information coordination mechanism of multi-participants in community-based elderly care. Therefore, this paper analyzed the multi-participants of community elderly information coordination, namely, the elderly, families of the elderly, elderly care service providers and government departments. And based on the access mechanism and decentralization degree of the consortium chain in blockchain, this study constructed a blockchain-enabled information coordination mechanism of multi-participants in community-based elderly care. The results of this study may provide a reference for the construction of the information coordination mechanism of multi-participants in community-based elderly care, and help clarify the information supply and information demand of different participating parties in community-based elderly care.
A ReRAM-based Nonvolatile PIM

This work presents a design and analysis of a resistive random-access memory (ReRAM)-based nonvolatile processor-in-memory (nvPIM). The nvPIM successfully demonstrates regular memory WRITE/READ operation with Nonvolatility (NV). Additionally, the nvPIM is capable of full adder (FA) operations of stored bits. The utilization of ReRAMs over conventional static random-access memories (SRAMs) reduces the design's transistor count to 55. For WRITE/READ operations, the average delays of the nvPIM are 0.42ns while the peak and average powers are 0.486mW and 2.74nW respectively. For Sum/Carry operations, the average delays for the nvPIM are 1.331ns for Sum and 0.120ns for Carry with a total average delay of 0.725ns and the peak and average powers are 0.291mW and 12.9nW respectively.
f-MRI Based Detection of Autism using CNN Algorithm

Autism Spectrum disorder (ASD) is one of the disorder related to development of the brain which impacts a individual's ability to speak and socially interact. ASD is termed as a neurodevelopmental disorder which is mainly characterized by social, communicational and interactional difficulties and also restricted and repetitive behavior in a person. Children affected with ASD have difficulties in having joint attention and also social reciprocity. They feel it hard to use verbal and nonverbal behaviour while communicating. Advanced technologies, like as machine learning and also deep learning, has been employed in recent years to analyze and explore the ASD in order to enhance the accuracy of diagnostic, duration, quality without adding much complexity. Support vector machines(SVM), Decision tree, K nearest Neighbours, Naive Bayes, Logistic Regression, Random Forest, and various other approaches of machine learning is been used to classify autism. By using the deep learning for ASD detection, it is predicted to be possible to speed up and enhance the accuracy of diagnosis. This paper gives a technical overview of how to use the CNN algorithm of deep learning to predict the presence of autism in a patient by using the resting state functional MRI images. This method yields an accuracy of about 0.97 which is comparatively better than the previous other works.
Industrial Internet of Things (IoT) and 3D Reconstruction Empowered Smart Agriculture System

Smart agriculture is a new agricultural production mode and is considered a potential solution for food supply issues under current limited land space conditions. The application of the Internet of Things (IoT) in smart agriculture can effectively increase food production with relatively low labor costs by deploying various wireless communication sensors in the field to collect plant information during the agricultural process. This paper developed an extendable IoT based sensor system for smart agriculture applications. The proposed sensing system can acquire real-time plant information through its plant environment and plant phenotyping monitoring process. The plant environment monitoring process can collect real-time plant environmental data through multiple wireless environment measuring sensors. At the same time, the plant phenotyping monitoring process can achieve plant height monitoring with the root-mean-square error (RMSE) of 0.051 m and the mean absolute error (MAE) of 0.049 m through remote RGB-D (red, green, blue plus depth data) cameras and 3D reconstruction method. This study shows that the proposed system can provide valuable real-time plant information for farmers’ decision-making.
Online Robustness Model for Intrusion detection model for IP Based Ubiquitous Sensor Network

A group of sensing and technological advancements called wireless sensor networks (WSNs) are used in both defense and commercial research fields. Unattended, distant workspaces with randomized and hierarchically designed sensor networks. Because sensors are placed in an open area and communicate wirelessly, wireless sensor networks are vulnerable to threats. WSNs are susceptible to a variety of security risks, including sinkhole, worm, relaying, grey hole assaults, Sybil, and sink hole threats. Optimal gated recurrent unit (O-GRU) is presented for intrusion detection, which uses packets payload and header information. For both real-time and non-real-time packets are considered for intrusion detection. Furthermore, the proposed method is simulated using network simulation tool. Furthermore, using a deep learning algorithm has drawbacks, including relatively poor accuracy and a reliance on manual feature selection. These issues have been examined, and the current work suggested an improved empirical-based principal components analysis to choose pertinent features. To maintain the majority of the pertinent characteristics, our presented feature selection methodology combines the benefits of both empirical mode decomposition as well as principal component analysis. Machine learning model was used to classify the attack node based on the chosen characteristics (Long Short-Term Memory). In comparison to state-of-the-art techniques, the proposed model verified the CICIDS 2017, NSL-KDD, UNSW NB 2015, and KDD99 datasets. The efficiency of the presented system was demonstrated by comparison with both the widely used approaches.
Hybrid Deep Learning Algorithm for Pulmonary Nodule Detection using Body Sensors

The leading cause of death in the world is lung cancer. Routine screening is important because it improves outcomes, especially for some risk populations. In addition to low-dose computed tomography, chest X-rays can be used as a screening tool. Software diagnosis systems usually on the basis of convolutional networks (CNNs) have demonstrated their capacity to recognize nodules on radiographs and may therefore help physicians in clinical practice. Lung cancer, also referred to as malignant tumors, is an infectious lung tumour brought on by unchecked granulation tissue in the lungs. This article describes a method for applying Deep Learning to identify cancerous pulmonary nodules in CT scans. The sections of the lungs on the scans were filtered out using a preprocessing procedure. A three dimensional CNN model built on the C3D network architecture was then used to extract the attributes. For the purpose of reducing false-positives, the LIDC-IDRI is the main dataset utilized in addition to a few tools from the LUNA16 grand challenge. The final result is a framework that delineates the relevant regions from the CT scans and forecasts the positions of malignant lung nodules. The final algorithm was able to identify malignant pulmonary nodules and determine their malignancy ratings with an 96% specificity.
A proposal on the control mechanism among distributed MQTT brokers over wide area networks

MQTT for IoT communication requires the deployment of multiple brokers to aggregate traffic from localized areas. However, the routing mechanisms among these brokers in a large-scale environment have yet to be specified. In this paper, a routing suitable for up-scaling based on the distribute MQTT broker by data link look up for traffic reduction (DMLT) is proposed. In this study, several up-scaling methods are proposed and compared in terms of traffic volume, scalability, and resilience. As a result, we chose an improved version of DMLT. A prototype of a wide-area DMLT system was constructed and verified by combining the DMLT method with an approach that the authors judged to be the most suitable for large-scale deployment.
A Similarity-based Assisted Grading for Introductory Programming Course

In an assignment-based introductory programming course, a teacher is required to grade assignments. However, this is generally a boring, labor-intensive task. Assisted grading is an approach to reduce the effort for a manual grading process by automatically classifying student submissions into groups so that a teacher can manually check only representative submissions. In this study, we try an assisted grading using source code similarity. Given a number of manually graded submissions, our method calculates source code similarity between a new submission with them. If there exists a similar submission, we automatically give the same grade as the submission. Otherwise, we ask the teacher to manually grade the submission and use the grade for future submissions. As a preliminary analysis, we have implemented the idea as a simple algorithm and evaluated the reduction in effort using student submissions in a programming course conducted in the department of the authors.
K-Means and Decision Tree Approach to Predict the Severity of Diabetes Cases

The prevalence of type 2 diabetes is fairly high worldwide. Early identification is essential for Diabetic management and prophylaxis. Due to their capacity to classify data, Machine Learning (ML) techniques are currently becoming more and more important in the field of diagnostic purposes. To aid in the diagnosis of Type 2 diabetes, a mixed proposed methodology is put out in this work. K-means has been employed in the proposed model-based minimization and a J48 decision tree is used as a classifier to classify. We were using the Pima Indians Diabetic Database from the UCI ML Repository to obtain the results of the experiment. The outcome demonstrates that, in comparison to other earlier research that is described in the literature, the proposed model has achieved more reliability. These findings demonstrate that the proposed methodology might be useful for diagnosing Type 2 diabetes.
Range of Motion Measurement Using Single Inertial Measurement Unit Sensor: A Validation and Comparative Study of Sensor Fusion Techniques

There are numerous uses of human body motion analysis, including rehabilitation of patients, training of athletes and others. Use of Inertial Measurement Units (IMUs) is one process for analyzing human movements. Accelerometer, Gyroscope, and Magnetometer make up IMUs, however each sensor has its own limitations. In this context, sensor fusion techniques, such as Madgwick Filter (MAD), Mahony Filter (MAH), Complementary Filter (CF), Kalman Filter (KF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and others are used to address such shortcomings. In this study, WitMotion sensor has been employed for range of motion (ROM) analysis of a proposed 2D rigid body and performance of WitMotion provided sensor fusion algorithm has been verified in comparison with an Electro-Goniometer (EG). The main objective of this work is to identify a reliable and effective sensor fusion technique for measuring upper limb ROM so that experiments can be repeated in various conditions. The results of this study demonstrate that the WitMotion supplied sensor fusion technique achieves sufficient accuracy, while the Madgwick filter (MAD) performs slightly better than all other considered algorithms based on Root Mean Square Error (RMSE).
Location-Aware Octa Section Routing Protocol (LOR)

Wireless Ad Hoc Network contains randomly positioned nodes whose communication occurs through control packets in a distributed network without a pre-defined infrastructure. As the nodes keep moving, some routing protocols like Flooding exist to deliver the control packets properly. Flooding has high control overhead, leading to the proposal of routing protocols like DSR, LAR, and LSR. Similarly, this paper is focused on further optimizing the control overhead. The route request is sent only to a node in a particular section in the protocol. They are divided into eight equal sections. If a node is not found, a fanout process occurs, and sections are searched for the node. After the node is found, the process is repeated until the destination receives the route request. This technique reduces the control overhead when compared to existing protocols: LAR, LSR, and DSR, which are represented in the simulation results.
Prediction of Ship Track Anomaly based on AIS data using Long Short-Term Memory (LSTM) and DBSCAN

Anomaly is defined as an event or behaviour that deviates. Anomaly behaviour often carried out by ships sailing in Indonesian waters is to turn off AIS for a very long time. Ship identification is challenging for maritime inspectors to detect if the ship frequently turns off AIS during the voyage. Based on these problems, the researcher proposes to predict the ship's trajectory using historical AIS data. It is possible to predict ship trajectories based on AIS data to determine the trajectory of ships that turn off AIS. The results of the AIS trajectory are combined with the ship's trajectory, in general, to determine the trajectory with the ship's trajectory in general. A predicted ship trajectory that is not the same as the general route will be identified as an anomaly. Long Short Term Memory is proposed to predict ship trajectory using latitude, longitude, speed, and time parameters. LSTM modelling with four hidden layers, determining the batch size of 25, optimizing adam, epoch worth 100, and the loss function using the mean squared error. DBSCAN clustering is used to determine the trajectory with ship trajectories in general. The platform design is the ship trajectory in this study using the CRISP-DM methodology. This study evaluates the MAE and MSE values. The data used in this study is data on ships passing through ALKI 1 in July-September 2022. From this data, data on ships moving from Singapore to Tanjung Priok Jakarta are taken. The test results show that the algorithm performs well, with an MAE value of 0.0667 and an MSE of 0.0091.
The Cross-Field Acceleration of Charged Dust Grains by the Ponderomotive Force of Compressional Waves in Dusty Plasmas

In this Letter, it is shown that the ponderomotive force of the compressional Alfvén-Rao (CAR) mode in a magnetized plasma can create space charge electric fields that accelerate charged dust grains across the external magnetic field direction. It is found that the cross-field dust grain acceleration is proportional to the gradient of the CAR mode magnetic field intensity. This result is of relevance for our understanding of the origin of high-energy dust in the interstellar medium.
Machine Learning assisted Indoor Visible Light Communications Radio Environment Maps

Visible light communications (VLC) have been proposed as reliable high-capacity wireless optical communication systems for indoor access networks. This technology comprises a solution that does not harm human tissues and has been especially suggested for ultra-reliable environments like hospitals. In this paper we utilize machine learning for investigating the wireless optical channel in indoοr areas where ultra-reliable communications are needed and hence signal coverage should be guaranteed. It is proven that ML methods can reliably and efficiently predict the Radio Environment Map (REM) of a VLC enabled communication system.
Effects of Increased Arm Muscle Tone on Postural Recovery from External Forces: A simulation study

Muscle tone contributes to the maintenance of standing posture against external forces, but the effects of changes in the magnitude of muscle tone in the arms are not fully clear. This study aimed to investigate the effects of increased muscle tone in the arms during postural recovery from external forces. Perturbed stance simulations, in which a neural controller controlled a musculoskeletal model under three different arm muscle tone conditions were performed. The lower body muscles were controlled by the sum of constant values that corresponded to muscle tone and feedback control components, based on muscle length and lengthening velocity. Additionally, the arm muscles were controlled by the sum of constant values and feedback control components based on head and angular acceleration. The control parameters were optimized to minimize center-of-mass movement. Consequently, the center-of-mass motion was maximized under conditions of high arm muscle tone. This suggests that an increased arm muscle tone is detrimental to the maintenance of standing balance.
Dual-Wavelength DFB Laser with 640 GHz Frequency Spacing Based on Sidewall Grating and Reconstruction Equivalent-Chirp Technology

A 1.56 μm dual-wavelength DFB laser with a frequency spacing of 0.64 THz was demonstrated based on sidewall grating and reconstruction equivalent-chirp technology. The lasing wavelength spacing can be controlled to an accuracy of 830 MHz by changing the sampling periods on each sidewall grating.
HDRT: Helmet Detection System using Convolutional Neural Network in Real Time

Motorcycle accidents have progressively increased in many nations over the years, as riders around the world regularly disregard road safety, resulting in road accidents and casualties. While many countries have made wearing of helmets compulsory, still there need to be special laws to promote safety of vehicle riders. Thus it is critical for motorcyclists to understand the need of wearing helmet to avoid fatal incidents. To enforce wearing of Helmet, in this work, an algorithm is developed using Convolutional Neural Network to enable the detection of helmets in real time. If either of rider is riding without a helmet, the proposed algorithm can accurately track the situation and proclaim a legal violation. The application may be deployed in entrance of offices, marts, schools, and universities, allowing people to enter the premises only if the helmet is detected on the rider. It would almost probably have an impact on the use of helmets, which could save lives.
Development of API Service to Store and Utilize Video Analytics Data from Edge

In every train arrival at the station, the recording of the train arrival is still done manually by the officer to the Pranopka system. The manual operation has the possibility of errors due to negligence, non-standard time referenced, and the absence of parameters to measure its correctness. Therefore, it is necessary to build an automatic recording system in order to eliminate the burden of repetitive tasks to humans. We designed an automatic recording system using a camera. This paper will explain in detail about the architecture development of the API used for data transmission in managing data that is in the database in a centralized data center. In its development, various literature analyzes were carried out to determine the most appropriate technology to be used in the system. From the development process that has been carried out, it has been proven by non-functional testing that the use of MySQL, MongoDB, Fastify, JWT, PM2, and Nginx is suitable for developing an automatic train number recording system. The throughput can reach 1,000 transactions per second with APDEX 0.983.
$\ell\rho$-suppression: A Privacy Preserving Data Anonymization Method for Customer Transaction Data Publishing

Publishing a transaction database to other parties is becoming a common way to harvest useful information from the database. Though, the database usually contains privacy concerns such as personal private itemset. Therefore, it is crucial to protect sensitive information prior to sharing the database to other parties. A solution to preserve the privacy of data subject in the transaction database is by modifying the transaction records using specific data anonymization method. Data anonymization method such as local generalization is widely adopted to solve the issue. However, the method induces excessive item loss and reduce data utility significantly. In this paper, a privacy preserving method to protect private itemsets of individual data subjects namely $\ell\rho$-suppression is proposed. The method adopts cell suppression technique where personal sensitive items in transaction records are omitted. The method is distinct from an existing method, where it uses maximum term frequency normalization of the items in the database and it only suppresses items from transaction records having the privacy ration higher than that of $\ell\rho$ value. Experimental results indicate the proposed methods can successfully enhance the privacy protection level and has the ability to minimize the number of item loss as well as maintaining data utility level in an anonymized database.
Wi-Fi 6 Aerial Relay Node for Emergency Operations

Reliable and ubiquitous communications, offering high data rates, low latency and supporting large numbers of connected devices, are critical requirements for modern emergency rescue missions. Multiple teams of First Responders, operating at remote areas, on rough terrain or under harsh conditions (e.g. wildfires, earthquakes, flooding etc.) need seamless connectivity to send/receive mission data and organize their operations. This paper, based on the communication platform of RESPOND-A project for supporting First Responders' missions, proposes a communication relay architecture for extending the 5G network, covering network gaps or underserved areas, while still supporting the mission requirements. Preliminary field measurements were also taken for providing a proof-of-concept and evaluating the systems' performance.
Enabling Faster Security Assessment of Re-hosted Firmware

The rapid growth of the number of IoT devices and their intrinsic correlation to everyone’s life poses critical security challenges. The security, safety and privacy are constantly under attack due to the huge number of easy-to-exploit vulnerabilities in these devices. It is straightforward to notice how a careful analysis of IoT devices is more topical and urgent than ever and it is important to adopt effective vulnerability assessment methodologies in order to identify and thwart many types of cyberattacks. In order to perform traditional analyses on the firmware, however, it is important to emulate the latter in a controlled environment: a process called firmware re-hosting. In this paper we discuss the limitations of the existing re-hosting approaches when dealing with vulnerability assessment and we propose a novel methodology to speed up security analyses and allow the application to firmware of traditional security tools, such as binary fuzzers.
Wafer bonding with an adhesive coating

The assembly process for microelectromechanical systems often requires a wafer-joining process using low temperatures. In this work a bonding process investigated, which uses thin adhesive films to stack silicon wafers. The method can be extended to form patterned adhesive coatings using stamping technique. Characterization of the process has been carried out with respect to adhesion, hermeticity and chemical stability of the adhesive bond. In several applications this adhesive bonding process has been successfully applied to assemble sensors and actuators.