Cattle Disease Diagnosis Expert System in Bandes Village, Ainaro District

Most of the people in the Bandes village of Ainaro district work as breeders. Cows that are raised are beef cattle. One of the obstacles that exist in Bandes Village is the existence of a few veterinarians in Bandes Village. This research discusses an expert system for diagnosing diseases in cattle, which functions to help determine the disease that is being suffered by cattle, this system will display the results of the diagnosis in the form of the name of the disease, a description of the disease solution and its prevention. In this study there were 4 diseases studied and 7 symptoms. From the 4 disease data, isolation of the problem area, target decision and dependency diagram is made. After that the formation of IF – THEN rules, after the rules are made, the backward chaining process is made. So as to produce a solution to overcome disease in cattle. In program testing is done by comparing the results of the system with experts. In the trials that have been carried out, the results obtained are appropriate or the accuracy of the data and facts obtained from experts on cattle. So based on the results of tests that have been carried out on the system as much as 4 data, the accuracy value obtained is 100% accurate which shows that the expert system is functioning properly in accordance with the expert's diagnosis

A Supply-chain System Framework Based on Internet of Things Using Blockchain Technology

Numerous supply-chain combines with internet of things (IoT) applications have been proposed, and many methods and algorithms enhance the convenience of supply chains. However, new businesses still find it challenging to enter a supply chain, because unauthorised IoT devices of different companies illegally access resources. As security is paramount in a supply chain, IoT management has become very difficult. Public resources allocation and waste management also pose a problem. To solve the above problems, we proposed a new IoT management framework that embraces blockchain technology to help companies to form a supply chain effectively. This framework consists of an access control system, a backup peer mechanism and an internal data isolation and transmission approach. The access control system has a registrar module and an inspection module. The registrar module is mainly responsible for information registration with a registration policy, which has to be followed by all the companies in the supply chain. Besides, it provides a revocation and updating function. The inspection module focuses on judging misbehaviour and monitors the actions of the subjects; when any misoperation occurs, the system will correspondingly penalise violators. So that all related actions and information are verified and stored into blockchain, the IoT access control and safety of IoT admission are enhanced. Furthermore, in a blockchain system, if one single peer in the network breaks down, then the whole system may stop, because consensus cannot be reached. The data of the broken peer may be lost if it does not commit yet. The backup peer mechanism allows the primary peer and the backup peer to connect to an inspecting server for acquiring real-time data. The internal data isolation and transmission modules transmit and stores private data without creating a new subchannel. The proposed method is taken full account of the stability of the network and the fault tolerance to guarantee the robust of the system. To obtain unbiases results, experiments are conducted in two different blockchain environment. The results show our proposed method are promising IoT blockchain system for the supply chain.

IRRISENS: An IoT Platform Based on Microservices Applied in Commercial-Scale Crops Working in a Multi-Cloud Environment

Research has shown the multitude of applications that Internet of Things (IoT), cloud computing, and forecast technologies present in every sector. In agriculture, one application is the monitoring of factors that influence crop development to assist in making crop management decisions. Research on the application of such technologies in agriculture has been mainly conducted at small experimental sites or under controlled conditions. This research has provided relevant insights and guidelines for the use of different types of sensors, application of a multitude of algorithms to forecast relevant parameters as well as architectural approaches of IoT platforms. However, research on the implementation of IoT platforms at the commercial scale is needed to identify platform requirements to properly function under such conditions. This article evaluates an IoT platform (IRRISENS) based on fully replicable microservices used to sense soil, crop, and atmosphere parameters, interact with third-party cloud services for scheduling irrigation and, potentially, control irrigation automatically. The proposed IoT platform was evaluated during one growing season at four commercial-scale farms on two broadacre irrigated crops with very different water management requirements (rice and cotton). Five main requirements for IoT platforms to be used in agriculture at commercial scale were identified from implementing IRRISENS as an irrigation support tool for rice and cotton production: scalability, flexibility, heterogeneity, robustness to failure, and security. The platform addressed all these requirements. The results showed that the microservice-based approach used is robust against both intermittent and critical failures in the field that could occur in any of the monitored sites. Further, processing or storage overload caused by datalogger malfunctioning or other reasons at one farm did not affect the platform’s performance. The platform was able to deal with different types of data heterogeneity. Since there are no shared microservices among farms, the IoT platform proposed here also provides data isolation, maintaining data confidentiality for each user, which is relevant in a commercial farm scenario.

Fed-SCNN: A Federated Shallow-CNN Recognition Framework for Distracted Driving

Although distracted driving recognition is of great significance to traffic safety, drivers are reluctant to provide their own personalized driving data to machine learning because of privacy protection. How to improve the accuracy of distracted driving recognition on the basis of ensuring privacy protection? To address the issue, we proposed the federated shallow-CNN recognition framework (Fed-SCNN). Firstly, a hybrid model is established on the user-side through DNN and shallow-CNN, which recognizes the data of the in-vehicle images and uploads the encrypted parameters to the cloud. Secondly, the cloud server performs federated learning on major parameters through DNN to build a global cloud model. Finally, The DNN is updated in the user-side to further optimize the hybrid model. The above three steps are cycled to iterate the local hybrid model continuously. The Fed-SCNN framework is a dynamic learning process that addresses the two major issues of data isolation and privacy protection. Compared with the existing machine learning method, Fed-SCNN has great advantages in accuracy, safety, and efficiency and has important application value in the field of safe driving.

IRRISENS: An IoT Platform Based on Microservices Applied in Commercial-Scale Crops Working in a Multi-Cloud Environment

Research has shown the multitude of applications that IoT, cloud computing and forecast technologies present in every sector. In agriculture, one application is the monitoring of factors that influence crop development to assist in making crop management decisions. Research on the application of such technologies in agriculture has been mainly conducted at small experimental sites or under controlled conditions. This research has provided relevant insights and guidelines for the use of different types of sensors, application of a multitude of algorithms to forecast relevant parameters as well as architectural approaches of IoT platforms. However, research on the implementation of IoT platforms at the commercial scale is needed to identify platform requirements to properly function under such conditions. This article evaluates an IoT platform (IRRISENS) based on fully replicable microservices used to sense soil, crop and atmosphere parameters, interact with third party cloud services, planning and scheduling irrigation as well as control of irrigation water control devices. The proposed IoT platform was evaluated during one growing season at four commercial scale farms on two different broadacre irrigated crops with very different water management requirements (rice and cotton). Five main requirements for IoT platforms to be used in agriculture at commercial scale were identified from implementing IRRISENS in rice and cotton production: scalability, flexibility, heterogeneity, robustness to failure and security. The platform addressed all these requirements. The results showed that the microservice approach followed in the platform is robust against both intermittent and critical failures in the field that could occur in any of the monitored sites. Further, processing or storage overload caused for any reason at one farm did not affect the performance of the platform regarding the other monitored farms. This paper also discusses how the microservice approach can address the data heterogeneity issue when crops with different management requirements are monitored. Since there are no shared microservices among farms, the IoT platform proposed here also provides data isolation maintaining data confidentiality for each user, which is relevant in a commercial farm scenario.

The Influence of COVID-19 Isolation on Physical Activity Habits and Its Relationship with Convergence Insufficiency

The purpose of this work is to evaluate the effects of confinement due to COVID-19 isolation on visual function, considering insufficient convergence as one of the possible effects of living the whole day in a reduced space. We pass a Convergence Insufficiency Symptom Survey (CISS) among 235 people to detect their habits before and after 25 confinement days. The data collection protocol consisted on a Google forms questionnaire included two parts: the first with current data (isolation period) and a second with pre-isolation period data. Differences between the pre-isolation and isolation period were calculated using the related paired T-tests. When statistically significant differences were found, the effect size was estimated using the Cohen’s d index (d). The reduction in physical activity levels during confinement were related to the increase in total number of minutes of screen consumption from 433.49 min to 623.97 min per day (d = 0.67; 44.01%). The CISS scores were increased by more than 43% during confinement. The increase in convergence insufficiency was 100% after the studied isolation period of 25 days. The 92.19% increase in television use during 25 days of confinement is not responsible for the increase in convergence insufficiency. However, due to the increase in the use of PCs in this period, there is a notable increase in convergence insufficiency. Therefore, we can conclude that not all increases in tasks with electronic devices are responsible for the increase in convergence insufficiency.

Fine-Grained Isolation to Protect Data against In-Process Attacks on AArch64

In-process attacks are a new class of attacks that circumvent protection schemes centered around inter-process isolation. Against these attacks, researchers have proposed fine-grained data isolation schemes that can protect sensitive data from malicious accesses even during the same process. Their proposals based on salient hardware features, such as ARM® processor architecture’s domain protection, are quite successful, but it cannot be applied to a specific architecture, namely AArch64, as this does not provide the same hardware features. In this paper, therefore, we present Sealer, a fine-grained data isolation scheme applicable in AArch64. Sealer achieves its objective by brilliantly harmonizing two hardware features of AArch64: The eXecute-no-Read and the cryptographic extension. Sealer provides application developers with a set of application programming interface (API) so that the developers can enjoy the fine-grained data isolation in their own way.