The grain and oil food supply chain has a complex structure, long turnover cycles, and many stakeholders, so it is challenging to maintain the security of this supply chain. A reliable traceability system for the whole grain and oil food supply chain will help to improve the quality and safety of these products, thus enhancing people’s living standards. Driven by the trusted blockchain and trusted identity concepts, this paper constructs an information traceability model for the whole grain and oil food supply chain, and it describes how contract implementation and example verification are performed. First, an information traceability model framework of the whole grain and oil food supply chain is established based on the survey and analysis of the grain and oil food supply chain. Second, trusted identification, blockchain master–slave multi-chain storage, and trusted traceability mechanisms are designed. The trusted identification mechanism is used to track the data information of the whole grain and oil food supply chain. The blockchain master–slave multi-chain storage solves the problem of miscellaneous information caused by many links in the whole grain and oil supply chain, while the credible traceability mechanism ensures the credibility of information collection, storage, and transmission. Finally, based on the data flow, the model operation process is analyzed. Using the information traceability model, the grain and oil food trusted traceability system is designed and developed with the Hyperledger Fabric open-source framework, and a case study is conducted to verify the system. The results show that the model and system constructed in this study solve the problems of low data security and poor sharing, which exist widely in the traditional traceability mechanism, and enable the trusted uplink, storage, processing, and traceability of multi-source heterogeneous information in the lifecycle of the whole grain and oil food supply chain. The proposed system improves the granularity and accuracy of grain and oil food traceability, and provides support for the strategic security of grain stock.

/pdf/information-traceability-model-for-the-grain-and-oil-food-1p05jsjr.pdf

Information Traceability Model for the Grain and Oil Food Supply Chain Based on Trusted Identification and Trusted Blockchain

Being spectrally efficient, easily implemented, and highly immune to selective channel imperfections and multipath fading, the Orthogonal Frequency Division Multiplexing (OFDM) can provide a sufficiently robust and high data rate modulation technique for emerging wired and wireless telecommunication applications. However, a major drawback of OFDM that is represented by its high Peak-to-Average Power Ratio (PAPR) of the transmitted signal, which leads to degrade the system’s transmission accuracy. In this paper, a Compressed Sampling (CS) based approach is considered for reducing the PAPR without lowering its transmission capacity or affecting its Bit Error Rate (BER) performance. The proposed scheme adds a sampling stage after the IFFT block in the transmitter side, that is compressively represents the transmitted signal by fewer symbols that is transmitted instead of the original signal. At the receiver side, the received compressively sampled signal is then recovered before the FFT block by following the Basis Pursuit (l1–norm) algorithm. The proposed scheme shows an enhanced PAPR and BER performances while preserving the rest of the system performance aspects.

/pdf/assuring-m2m-secure-transactions-via-blockchain-and-smart-jck4x316ke.pdf

Assuring M2M Secure Transactions via Blockchain and Smart Contracts

The past decade has seen a fast rise in popularity of recommendation systems provided by many entertainment and social media services. However, despite the recognised advances in different recommendation approaches and technologies, there remain many challenges, particularly in TV content recommendation systems. More precisely, machine learning based TV content recommendation systems suffer from a class imbalance problem; hence, it is difficult to evaluate the system using traditional metrics. Moreover, specific challenges arise during the development phase, when the system operates in ‘offline’ mode. This means the recommendations are not actually presented to users - making it even more difficult to measure the quality of those recommendations. This paper presents a proof-of-concept demonstrator of a television recommendation system, based on Content-based Filtering, as a contribution towards building a full-scale intelligent recommendation system. New evaluation metrics are proposed for ‘offline’ testing mode, while also tackling the class imbalance problem. The experimental results, based on real usage data, are promising and help in defining the future path as presented along with the conclusion.

New Performance Metrics for Offline Content-Based TV Recommender System.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Nuno Leite

Author Tools

Chat about Author

Papers

Assuring M2M Secure Transactions via Blockchain and Smart Contracts

New Performance Metrics for Offline Content-Based TV Recommender System.