dataesr/scientific-paragraphs-categorization huggingface dataset:
The stacked auto-encoder has two hidden layers, one with 20 nodes and the second with 10 nodes. The dimension of the learnt features is 10 compared to the 41 original features of the NSL-KDD dataset dataset. The overall process encompasses four steps i. e. a feature learning step with the stacked auto-encoder, a first fine-tuning step where the softmax layer is trained in a supervised manner with labels and training data. The input of this first fine-tuning step is the compressed representation of the data. The following step is a second fine- 3. 3. 2 Stacked Auto-encoder based anomaly detection Due to Network bandwidth and network data increase, [132] proposes a deep packet inspection in order to extract the necessary feature that would allow DoS, Probe, R2L and U2R attacks detection. The authors used a Deep Neural Networks (DNN) approach which architecture is a stacked auto-encoders for the feature learning, to which a softmax layer is added for the classification (Figure ??).
tuning with a back-propagation training applied to the whole network layers after the first fine-tuning step. The goal of this second fine-tuning step is to refine the features of the intermediate layers to make them more relevant for the intrusion detection task by adjusting the network weights to minimize the cost function. Finally, the last step of the process is a classification and testing step where a test dataset is presented to the fine-tuned network in order to evaluate the efficiency of the model. Accuracy, precision, recall and f-mesure metrics are used to evaluate the proposed approach against standard techniques like k-means, DBN, SOM, AdaBoost. Experimental results show that despite good detection accuracy for DoS and Probe attacks (97. 6 % and 86. 34 % respectively), R2L and U2R attacks give poor results(12.
datasets (i. e main class of interest rare), the Accuracy measure is not really relevant. Other metrics such as recall, precision and f1-score are used instead. The False Positive Rate (FPR) or False Alarm Rate (FAR) is the proportion of falsely identifying a normal tuple as anomalous. False Negative Rate (FNR)The False Negative Rate (FNR) is the proportion of falsely identifying an anomalous tuple as a normal one. 6. 7. Results for Water Storage Tank Dataset 115 correctly identified. 6. 7 Results for Water Storage Tank Dataset T P recall = P Precision 6. 7. 1 Models comparison Precision is a measure of exactness. It answers to the question What percentage of tuples labeled as positive are actually such? precision = T P T P + FP False Positive Rate (FPR) FP R = FP FP + T N FN R = FN FN + T P f1-score Precision is a measure of harmonic mean.
IoT technology is used to deliver the collected data over the network to enhance supply chain performance and traceability. However, the supply chain becomes constrained by additional data loads within the partners' independent systems. In order to overcome the issues related to trust, Blockchain and, more generally, Distributed Ledger Technology (DLT), is a good candidate which enables the full transparency of data records. It enhances trust between partners through a cryptographic-based, peerto-peer decentralized platform that underlies a supply chain  [13]. Using the Blockchain platform for the supply chain eliminates the ambiguity behind the group of independent databases of traditional supply chain systems, as all records are stored within the ledger on every stakeholder system. Furthermore, Blockchain is immutable against the altering or removal of any records without leaving traces.
This is because all partners have a copy of the same updated ledger that leads to a clear vision over the ledger contents. According to many studies  [14] [15] [16]  that have surveyed the critical aspects of implementing Blockchain solutions, Blockchain is a convenient tool to overcome trust and collaboration issues in a supply chain. It is called the "truth machine"  [17] , and discourages companies from any misconduct. Moreover, many proofs-of-concept (POCs) or piloting schemes have been developed in recent years using technology to study supply chains for traceability and transparency purposes  [16]. Data transparency is a built-in feature of Blockchain due to the decentralized nature of the platform. In this context, the ability to control data privacy or opacity within the public Blockchain is questionable, while stakeholders in the supply chain have sensitive data that should not be disclosed to the public.
Methodology This paper contributes to the provision of knowledge about the supply chain's data transparency. The increasing necessity of obtainable transparency in the supply chain encourages investigations in this area of research. The integration of the supply chain with Blockchain and the great evolution towards decentralization are considered by many studies. However, research works that investigate data transparency in the supply chain are limited. Table  1  enlists the existing surveys and studies that discuss the transparency topic and its effectiveness in the supply chain.
Sources Roles  [18]  Elaborates the role of NGO's brand collaboration in enhancing the supply chain transparency  [19]  Develops system architecture to integrate Blockchain, IoT, and data analytics to provide sustainable products  [20]  Studies the relevance of supply chain transparency to supply chain sustainability governance  [21]  Conducts the adoption of Blockchain for supply chain transparency  [22]  Reviews transparency/traceability of Blockchain-based supply chain in the literature  [23]  Develops smart contracts to directly directly the supply chain transparency  [24]  Proposes multi-chain platform to enhance cross-border e-commerce supply chain traceability The integration of Blockchain with the supply chain is a relatively new approach. This new approach was adopted to attain immense product traceability and sustainability among companies and individuals.
This paper sheds light on the efficiency of Blockchain data transparency and traceability within the supply chain, and illustrates the evolution of data transparency in the modern decentralized system compared to its presence in the traditional centralized system. It also investigates the major transparency challenges encountered in any supply chain and highlights the significance of utilizing the new decentralized platforms. The research points are summarized in the following questions: Q1: What are the challenges related to data transparency in the supply chain? Q2: What are the influences of Blockchain over data transparency in the supply chain? Q3: What are the existing DLT techniques to achieve transparency in the supply chain? Q4: Which supply chains are integrated with DLT? How do they tackle transparency? Q5: What are the logistic obstacles that would affect the achievement of controllable transparency? Q6: What additional measures should be taken to enhance supply chain transparency? Our study addresses these questions by investigating the related surveys and studies mentioned in Table  1.
However, additional work is required to address the research questions and present the techniques and frameworks which are accountable for building the supply chain transparency based on DLT and IoT technologies. To make progress in this direction, we first considered the papers that entirely or partially tackle supply chain transparency, hosted in scientific databases such as ACM, Elsevier, IEEE, etc. Secondly, we studied the 24 available projects and pilots that decentralize their supply chain and highlighted the used techniques. We also explored many available white papers related to the investigated projects. Some of the projects mentioned in this paper have no detailed technical background. However, we shed light on their contributions and targets. The novelty of this paper lies in the following aspects: Elaborating on answers to the above questions; showing the different techniques that help the researchers and developers adopt the expedient supply chain platform, regarding their utility; directing future researches towards the topic of transparency access control.
Data Transparency Challenges At present, the global supply chain consists of a complex network of stakeholders across industries to coordinate collaborative tasks and achieve mutual agreements. Figure  1  depicts the significant supply chain challenges: centralized systems, lack of transparency, scalability, challenges to IoT integration and the upcoming technologies. The existing centralized supply chain systems struggle unproductively to provide a portion of the vital requirements using workarounds and trusted third parties  [25] , in addition to the great integration of new technologies. Such independent databases have trust issues resulting in negative customer feedback and dissatisfaction. In addition, there is no reliable shared information within most of the supply chain, and that is the main transparency issue with a centralized system.
Data Transparency Motivation The supply chain has encountered enormous changes over time due to the high demands for supply chain transparency and traceability. These demands represent the main motivation for creating transparent systems. For example, when consumers increasingly wonder about where and how their clothes are made, or just how sustainable their potential new electric vehicle might be, given the raw materials required to make it, transparency in global supply chains becomes a notable issue, whicg needs to be addressed  [28]. Moreover, the supply chain has become increasingly involved in the diversity of partners, products, and customer desires. Recently, the challenges have ranged from the heterogeneity of the systems to the additional technological requirements. Thus, besides the above challenges, the motivations behind supply chain transparency are the following: • Independent Database: The current supply chain infrastructure is a group of centralizedbased systems where each stakeholder represents a centralized system which belongs to one or more supply chains.
These systems rely heavily on centralized, often disparate, and standalone information management platforms  [15]. The group of databases involved in the production process is distributed, heterogeneous, and autonomous  [29]. Therefore, data interchange between different databases is inflexible, due to the hard-coded nature of different data standards; Walmart and Cisco are two obvious examples  [30]. Practically, the organizations' tendency to use their platform and control their data would limit collaboration. • Lack of cooperation: The supply chain challenges are mainly related to the heterogeneity of the involved stakeholders, different data forms and lack of communications among the involved systems. Collaborative relationships determine how data are shared between companies, and project them to the underlying business processes.
DLT-Based SUPPLY Chain Benefits Blockchain is a good candidate to address the above supply chain issues. It is a technology used for storing and transmitting data, which guarantees its integrity and transparency. A DLT platform that works without a trusted third party contains the history of the data exchanged in the network. This secure database is replicated in all the network nodes. Blockchain contains a chained set of blocks; each block contains a list of transactions and some other specific data. It is a fully decentralized P2P (peer-to-peer) system that guarantees trust between the non-trusted partners  [40]. The main features of Blockchains are their decentralization, shared ledger, tamper evidence, tamper resistance, record-keeping‚ immutability, distributed trust, multiple-party consensus and independent validation  [41].
There are four main types of Blockchain as shown in Table  2 : Public Blockchain (permissionless) is available for anyone, such as bitcoin. Public Blockchain (permissioned) is open to anyone for data reading but restricted for data input, consortium Blockchain is a network of predefined organizations, and private permissioned is limited to one enterprise. The Blockchain serves the supply chain against many limitations and improves its functionalities in reference to the features mentioned below: • Decentralization: The distributed ledger of a Blockchain-based supply chain empowers the involved partners to detect any deterioration of information. Thus, Blockchain tackles data corruption, hacking, or crashing issues in the centralized and independent systems and increases the information validity  [42].
Moreover, this decentralized system can be inexpensively implemented among the suppliers  [43] ; • Trust: transparency is the main consequence of the distributed ledger technology where participants have a complete vision of the current contemporary information. Furthermore, privacy and anonymity are enabled because of the cryptography system  [44]. Thus, it is unnecessary to evaluate the trustworthiness of the participants in the network with a decentralized supply chain. Evaluating trust between participants is due to the Blockchain's underlying technology, which guarantees the integrity of data records even in the presence of fraudulent nodes. Therefore, participants recognize that the information is accurate because each involved party has the same data, which cannot be altered or deleted.
Blockchain Core Improvement The peer-to-peer system surcharges IoT devices with computing tasks and high storage demands. Many nodes are designed and prepared with top resources for computing and transaction validation in the mining and cryptocurrency field, which is not the case with heterogeneous IoT devices. Modifying some critical Blockchain characteristics boosts the integration of IoT devices with the DLT technology. Block size, creation time, and consensus algorithm are the areas where altering and adjusting the Blockchain in accordance with IoT requirements occur. In terms of data transparency, the enhancements of the transaction format have a significant impact compared to the others. The transaction in its current status lacks many essential details. Thus, developing the transaction format to include some real identity, references, blockinfo, etc.
Smart Contract Smart contracts are automated contracts embedded in the Blockchain, which make the entire process decentralized. Upon the deployment of the smart contracts, it is almost impossible to alter its code. Smart contract is a recent term that is widely used to refer to low-level code scripts running on an Ethereum Blockchain Smart contracts have recently attracted interest due to their importance in business applications and supply chains. In addition to the smart contract's role in ensuring the contract progress, it is also considered an excellent tool to enhance the data supply chain transparency  [23]. When a smart contract is executed, all the intended parties within the supply chain are informed of the result and, therefore, they can trace and monitor their products, which increases the transparency level.
Involvement of IoT Device At present, IoT technology represents one of the core elements of any modern supply chain. It has two main functions: capturing data from media and transmitting them to their destination. Moreover, IoT devices play a central role in ensuring the success of the supply chain's product traceability. Exerting additional efforts has an extensive role in improving the IoT functionalities, to make them suitable for Blockchain-based manufacturers' requirements. Considering the IoT side by the DLT system and its application is the best practice. Some projects  [49] [50] [51] [52]  mentioned in this study have contributed to the IoT improvements in terms of detecting data accurately, to improve the visibility of the data. The transparency is enhanced, and the traceability will be more efficient with the IoT technology involved in the DLT-based supply chain  [15].
Merkle Tree Tool The Merkle tree structure  [40]  is a binary tree with an associated value for each node, where each one is the hash of its children. These data structure trees are created by repeatedly hashing pairs of nodes until only one hash is left. The last node of a tree (leaf) is a hash of transactional data where other nodes are hash of their previous hashes. This allows any party to quickly verify the validity of data in a branch or leaf using the tree's root hash. Blockchain, and especially Bitcoin and Ethereum, fundamentally use it. Merkle tree has three main advantages over transactional processing. First, it guarantees the integrity and validity of the data. Second, it consumes less memory and CPU resources, as the proofs are computationally easy. Third, Merkle processing requires tiny data to be sent over the network and stored on disks.
Zero-Knowledge Proof Zero-knowledge proof (ZKP)  [53]  is an encryption scheme where service providers do not recognize the data stored on their servers. The Prover can prove that a specific statement is true to the other verifier party without revealing any additional information. It can be used in messaging, authentication, storage protection, and for any other sensitive information. ZKP can also be integrated with Blockchain and, more specifically, with the private Blockchain, so that whatever the number of Blockchain nodes, ZKP adds a robust layer of security to the data ledger. Integrating ZKP with Blockchain encourages the supply chain to increase its transparency level, while their data confidentiality is preserved  [54].
Existing DLT-Based Supply Chain Solutions The supply chain is essential to all businesses. Therefore, the integration of DLT into legacy industries and different stakeholders aims to revolutionize the global supply chain with decentralization features, smart contracts, and IoT technology. Currently, many DLT-based projects seek to acquire trust, transparency, collaboration, and cost/time-saving throughout their innovative DLT platforms. The authors in [55] listed all the 105 DLTbased projects integrated with IoT since 2008 and categorized them into four types without revealing their technical sides. Most of them consist of API interfaces run on Ethereum, the well-known global Blockchain, but they do not have explicit technical references or detailed publications. This section sheds light on the DLT-based supply chain projects and displays their technical parts while focusing on data transparency and traceability.
• Ambrosus  [56]  is an industrial project utilized to track products throughout their circulation in the market. It is a Blockchain-based supply chain dedicated mainly to protecting and controlling pharmaceutical and food quality. This platform solution is mainly composed of a customized version of Ethereum Blockchain integrated with a data storage solution named the interplanetary file system (IPFS). To avoid the high cost of running transactions on the central Ethereum platform, Ambrosus develops its independent customized version of Ethereum. Moreover, Ambrosus does not rely on Ethereum storage to store the supply chain data, as it is limited in capacity. Instead, it uses IPFS as the primary storage for their large transactions to provide scalability and high throughput for the clients.
The results of the evaluation are then immutably stored in as a proof-of-existence. Using the Modum technique, the data transparency is well-tackled, and there is no need to physically verify the product content. • OriginTrail [51] is a supply chain solution composed of a combination of off-chain and Blockchain networks. It implements the off-chain network on DLT-based nodes within a new type of decentralized environment. The Blockchain platform runs on different nodes and interacts with the non-DLT network. The off-chain network, known as OriginTrail Decentralized Network (ODN), comprises data and network layers. Thus, the architecture is the stakeholders' applications, the non-decentralized ODN, and the platform. OriginTrail uses Zero-Knowledge encryption to prove private information without revealing it.
Moreover, the smart contract is involved in the different off-chain nodes to guarantee the execution of a set of predefined conditions. The aim of using this platform is to store the data fingerprint, ensure the integrity and transparency of records and provide an immutable supply chain system. • Vechain  [57]  is a supply chain solution composed of vechain supply chain projects and a vechainthor Blockchain-based platform. Vechainthor is an enhanced version of Blockchain, forked and improved based on the Ethereum codebase. The enhancements cover the transaction format in many directions. The new transaction format includes four fields: independent ID, DependsOn, Blockref, and Expiration. Thereby, the application deal with a single transaction instead of a bundle of transactions. Blockref provides more information about the previous, current, and next blocks.
As a result, each shipment has a unique smart contract that gives shippers more visibility across their supply chain, allows carriers to communicate quickly reducing delays and miscommunications, and achieves the overall required transparency. • Aqua-Chain [61] is a traceable system for the water supply chain management based on Blockchain and can be implemented by either Ethereum or hyperledger Blockchains. The data transparency is guaranteed, since IoT devices collect data along the supply chain and store them within the Blockchain ledger. Therefore, Aqua-chain software is adapted to provide full traceability to their customers under the classical notion "from-supplier-to-buyer. " It is composed of a layered architecture that relies on Blockchain and IoT to achieve traceability. Aqua-chain can be integrated into existing traditional systems such as ERP and CRM.
Wabi refers to the Walimai organization and supports the "Walimai label," which is applied at a designated point of origin along the supply chain. The registered products under the "Walimai system" consume WaBI tokens. The Walimai label technique provides transparency by being attached uniquely and securely to the product throughout its journey to the consumer. Linking physical products with a unique encrypted code allows the consumer to scan its unique code via mobile applications to check the physical product against its digital state. • TE-FOOD [62] represents one ecosystem that integrates all food partners (farmer, producer, transporter, and consumer) equally, for successful farm-to-table food traceability, to fight against food frauds and mistrustful supply chains. TE-FOOD introduces a utility token called TFD, Blockchain protocol, smart contracts, plastic security seals, and RFID identification tools.
In this proposal, each asset is assigned a unique identification number by generating a particular smart contract stored on the Blockchain. Therefore, the Blockchain ledger can be seen as a database of timestamps that offers anyone the ability to notice that a certain thing has occurred. Ambrosus involves smart contracts in a novel way by introducing two types of smart contracts: measurement and requirement. All the assets and standards are periodically used in the measurement contracts, and the smart contract requirements determine whether a product continuously meets the standards defined by an interested participant in the network. In this framework, the smart contract is utilized as a new protocol to set quality standards and compared directly to the Measurements Smart Contract items. Vechain uses Ethereum virtual machine (EVM) with additional extensions on the contracts called built-ins.