Reviewerly/exHarmony huggingface dataset:
Semantic knowledge management system for design documentation with heterogeneous data using machine learning

Design documentation is presumed to contain massive amounts of valuable information and expert knowledge that is useful for learning from the past successes and failures. However, the current practice of documenting design in most industries does not result in big data that can support a true digital transformation of enterprise. Very little information on concepts and decisions in early product design has been digitally captured, and the access and retrieval of them via taxonomy-based knowledge management systems are very challenging because most rule-based classification and search systems cannot concurrently process heterogeneous data (text, figures, tables, references). When experts retire or leave a design unit, industry often cannot benefit from past knowledge for future product design, and is left to reinvent the wheel repeatedly. In this work, we present AI-based Natural Language Processing (NLP) models which are trained for contextually representing technical documents containing texts, figures and tables, to do a semantic search for the retrieval of relevant data across large corpora of documents. By connecting textual and non-textual data through the use of an associative database, the semantic search question-answering system we developed can provide more comprehensive answers in the context of users’ questions. For the demonstration and assessment of this model, the semantic search question-answering system is applied to the Intergovernmental Panel on Climate Change (IPCC) Special Report 2019, which is more than 600 pages long and difficult to read and understand, even by most experts. Users can input custom queries relating to climate change concerns and receive evidence from the report that is contextually meaningful. We expect this method can transform current repositories of design documentation of heterogeneous data forms into structured knowledge-bases which can return relevant information efficiently as well as can evolve to embody manageable big data for the true digital transformation of design.
Hate Classifier for Social Media Platform Using Tree LSTM

Social-media without a doubt is one of the most noteworthy developments ever. From associating with individuals across the globe for sharing of data and information in an infinitesimal of a second, online media stages have enormously altered the method of our lives. This is joined by a steadily expanding utilization of social media, less expensive cell phones, and the simplicity of web access which have additionally prepared for the huge development of social media. To place this into numbers, according to an ongoing report, billions of individuals all over the planet presently utilize web-based media every month, and a normal amount of almost 2 million people new clients are going along with them consistently. While web-based media stages have permitted us to interface with others and fortify connections in manners that were not conceivable previously. Unfortunately, they have additionally turned into the default gatherings for can’t stand discourse. Online hate is a wild issue, with the adverse result of disallowing client support in web-based conversations and causing mental mischief to people. Since hate is pervasive across friendly, media stages, our objective was to foster a classifier that is feasible to train classifiers that can identify hateful remarks with strong execution and with the portion of misleading up-sides and negatives staying inside sensible limits.
Database Watermarking Algorithm Based on Decision Tree Shift Correction

With the transmission and sharing of data in the Internet of Things (IoT), while bringing development to life and the economy, it also inevitably threatens the data copyright protection and authentication. Digital watermarking technology can provide an effective solution for copyright protection by embedding the watermark in the data to prove the copyright attribution. The existing methods of digital watermarking in IoT mainly target multimedia, without considering the copyright authentication in database data. Unlike multimedia information, the database does not focus on the subjective visual perception when using the data, but rather on the potential values unlocked from the data through algorithms such as data mining. Therefore, we propose a new database watermarking algorithm based on decision tree shift correction (DTSC), considering the data copyright authentication and usability when applying for data mining algorithm. The algorithm adjusts the watermarked data by the DTSC method and makes the watermarked decision tree identical to the original in the iteration process. It solves the problem of database data copyright authentication in IoT and ensures the usability of the data when used for decision tree model construction. From the simulation results, it can be seen that the proposed method ensures the usability of the data for the classification and regression tree decision tree algorithm while embedding the watermark in the database data, and the data distortion of the proposed method does not differ from that of the traditional watermarking algorithm.
Model Complexity in Statistical Manifolds: The Role of Curvature

Model complexity plays an essential role in its selection, namely, by choosing a model that fits the data and is also succinct. Two-part codes and the minimum description length have been successful in delivering procedures to single out the best models, avoiding overfitting. In this work, we pursue this approach and complement it by performing further assumptions in the parameter space. Concretely, we assume that the parameter space is a smooth manifold, and by using tools of Riemannian geometry, we derive a sharper expression than the standard one given by the stochastic complexity, where the scalar curvature of the Fisher information metric plays a dominant role. Furthermore, we compute a sharper approximation to the capacity for exponential families and apply our results to derive optimal dimensional reduction in the context of principal component analysis.
Development of an Educational AR Tool for Visualization of Spatial Figures and Volume Calculation for Vocational Education

This paper describes the development of a prototype for an AR application, which supports volume calculation and spatial understanding for mathematics in vocational educations in Denmark. Teaching of spatial shapes and their properties are done mainly in a two-dimensional space on paper. To mitigate the inherent contradiction of learning about 3D-figures on paper, we set forth to develop an application, which focus on visualizing 3D-objects with the use of handheld AR. The application is developed with the iterative design process where the development of low- and high-fidelity prototypes have been essential for improving and testing the product. From testing it was noted that there was expressed excitement for the visualization of the 3D-objects through AR and how real they seemed. Especially the visualizations of the figures size and the size compared to its volume. From test results the AR visualization of 3D-objects and interaction with them helped test subjects to better understand the spatiality of the shown object. With the application they were able to "see the unseen" [6], which helped their understanding of spatial objects. We propose the use of AR technology to be further utilized as a natural tool in relevant subjects, where a visualization can promote or enhance a better understanding of a topic. On the top of this, AR contributes to the learning experience, which will vast and remain with the users.
New Symmetric Key Cipher Based on Quasigroup

Stream ciphers that use the XOR function for mixing the plaintext and the keystream are vulnerable to attacks such as known-plaintext attack and insertion attack. To overcome such shortcomings of the existing ciphers, we hereby propose a new stream cipher that uses AES. The proposed cipher is based on a large-order quasigroup. It is resistant to brute force attack, due to the exponential number of quasigroups of its order. It is also analyzed against the chosen-ciphertext, chosen-plaintext and known-plaintext attacks, and it is found to resist these attacks. The output of the cipher is subjected to various statistical tests, such as the NIST-STS test suite, and the results show a high degree of randomness of the ciphertext. Hence, it is resistant to correlation-type attacks.
Search-Theoretic Approach to Cryptocurrency Adoption and Financial Inclusion

As being unbanked is one of the motivations for using cryptocurrency, this study analyzes the financial inclusion of unbanked individuals that results from the use of peer-to-peer cryptocurrency transactions. By extending the Kiyotaki-Wright model, we construct a dual-currency economy in which traditional money and private cryptocurrency coexist. In this economy, some of the agents are banked while others are unbanked; thus, the traditional payment services provided by banks are limited. Our theoretical model yields a long-run equilibrium condition in which the cryptocurrency is perceived as valuable even though it is not a fully accepted currency. Our analysis suggests that the unbanked population is a contributing factor in bitcoin’s fundamental value.
Blockchain-Based Internet of Vehicles for Intelligent Transportation System Using Fog Computing

Blockchain technology provides transparent, distributed highly confidential, and immutable transactional distributed databases and is responsible for storing valid transaction information committed by each peer in a peer-to-peer system network. An intelligent transportation system (ITS) is an advanced integrated system of smart vehicles and diverse vehicular networks to develop an enhanced environment for a range of transport and traffic management services. A blockchain-assisted decentralized system for the Internet of vehicles (IoV) enables secure and efficient data sharing. We proposed a robust, scalable, fast, and decentralized fog computing architecture to accomplish and control the vehicular network efficiently and effectively in terms of vehicular positioning, information transferring and sharing. The fog computing technology and Internet of things (IoT) are necessary to the existence of transportation systems by creating intelligent and better decisions in a smart ecosystem. The fog computing technology extends the data processing from the cloud to the fog nodes (FNs), significantly improving the quality of service by reducing the network traffic that is sent to the cloud server.
Research on Evaluation and Management Mechanism of Education Management System Based on Clustering Algorithm

In recent years, with the rapid development of information technology and the deepening of education system reform, information management has gradually penetrated into all aspects of education. The construction of teaching network has gradually become the core and development trend of information construction in schools, which is also the practical need of deepening education and teaching reform. In order to share teaching resources and serve students. According to the characteristics of school teaching resources and the development trend of network teaching, this paper develops an education management system based on clustering algorithm. The system establishes an interactive teaching website between teachers and students, and is committed to creating a teaching network widely used and loved by teachers and students in vocational secondary schools.
AccTFM: An Effective Intra-Layer Model Parallelization Strategy for Training Large-Scale Transformer-Based Models

Transformer-based deep neural networks have recently swept the field of natural language processing due to their outstanding performance, and are gradually spreading to more applications such as image/video processing. However, compared with general DNNs, training a sizeable transformer-based model is further time-consuming and memory-hungry. The existing distributed training strategies for general DNNs are not appropriate or can not efficiently handle transformer-based networks. In view of this, we propose an intra-layer model parallelization optimization strategy, AccTFM, which introduces a novel fine-grained pipeline execution and hybrid communication compression strategy to overcome the synchronization bottleneck. Specifically, on one hand, it first decouples the inter-layer computation and communication dependencies, and then searches for the optimal partitioning strategy to maximize the overlap of computation and communication. On the other hand, the hybrid communication compression module consists of token-level top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> sparsification and piecewise quantization methods aiming at minimizing communication traffic. Experimental results show that AccTFM accelerates transformer-based DNNs training by up to 2.08x compared to state-of-the-art distributed training techniques.
Deep Hybrid AdaBoost Ensembles for Histopathological Breast Cancer Classification

Breast cancer (BC) is the most common diagnosed cancer type and one of the top leading causes of death in women worldwide. The early diagnosis of this type of cancer is the main driver of high survival rate. This paper aims to use homogenous ensemble learning and transfer learning for binary classification of BC histological images over the four-magnification factor (MF) values of the BreakHis dataset: 40X, 100X, 200X, and 400X. The proposed ensembles are implemented using a hybrid architecture (HA) that combines: (1) three of the most recent deep learning (DL) techniques as feature extractors (FE): DenseNet_201, Inception_V3, and MobileNet_V2, and (2) the boosting method AdaBoost with Decision Tree (DT) as a base learner. The study evaluated and compared: the ensembles designed with the same HA but with different number of trees (50, 100, 150 and 200), the single DT classifiers with the best AdaBoost ensembles and the best AdaBoost ensembles of each FE over each MF. The empirical evaluations used: four classification performance criteria (accuracy, recall, precision and F1-score), 5-fold cross-validation, Scott Knott (SK) statistical test to select the best cluster of the outperforming models, and Borda Count voting system to rank the best performing ones. Results showed the potential of combining DL techniques for FE and AdaBoost boosting method to classify BC in malignant and benign tumors, furthermore the AdaBoost ensemble constructed using 200 trees, DenseNet_201 as FE and MF 200X achieved the best mean accuracy value with 90.36%.
A Novel Descriptive Approach: Local Tetra Patterns (LTrPs) for Face Recognition

Face recognition plays a significant research attention in the recent years on computer vision with emerging security needs in automated video surveillance. Face recognition is plays major role in day-to-day life such as accessing mobile phone through face identification, crime investigation, and victim identification in the crowded area. Due to need in security increases derivative pattern based approach improve the recognition accuracy which leads to the LTrP with four directions. In this work, we propose local tetra pattern (LTrPs) based derivative approach for representing descriptive patterns of facial images. Local Binary Patterns (LBPs), Local Derivative Patterns (LDPs) and, Local Ternary Patterns (LTPs) which encoding association between the neighboring pixels, and referenced pixel values by generating gray level pixel value difference. Proposed approach encode association between pixel, and referenced pixel value. Neighboring pixel value directions are computed through first order derivative of horizontal directions. Nth-order tetra pattern is computed through (n − 1)th-order vertical, and horizontal derivatives for accurate facial image analysis through Gabor transform. To estimate the proposed system performance experiment conducted with JAFFE and ORL dataset. As a result, LTrP achieves the better recognition accuracy among the other patterns such as LBP, LTP and LDP.
Evaluation of Cryptocurrencies Dynamically Based on Users’ Preferences Using AHP

The fast pace of creating new cryptocurrencies makes it hard or even impossible to know which one of them best suits an investor’s needs. Increasingly, investors are starting to need a decision support system with which they can determine which cryptocurrencies are suitable for investment and which ones are not. In the formation of a decision support system, it is necessary to create suggestions according to personal preferences and tendencies. In this study, a decision support system was developed. The system allows investors to understand what they need and offers them cryptocurrencies that suit their preferences. On-chain parameters instead of off-chain ones were used for efficiency. In the developed system, a set of on-chain features is asked of investors, and individual weights are calculated for the selected features using the Analytic Hierarchy Process (AHP) algorithm. Using the calculated weights and the investor’s preferences, the system gives each cryptocurrency a mark of 100 and sorts the cryptocurrencies based on the mark where the system will provide different recommendations for each investor. We defined and determined the most important on-chain features. In addition, based on the answers of a focus group of cryptocurrency experts and investors, we concluded that the most important on-chain features to be considered for investment are High Volume, High Total Staked and High Percentage of Total Supply Circulating.
Blockchain

The early days of cyberspace expansion saw many people hesitant to use the internet for two dominant reasons: obscurity and skepticism. Yet, the internet soon became a one-stop solution to a diverse range of sectors, where a need to resolve issues such as data confidentiality and integrity gained prime importance. In 2008, an online currency called Bitcoin was launched by an anonymous developer or developer community called Satoshi Nakamoto. Bitcoin was deemed to be a viable alternative to fiat or government-issued currency. What made Bitcoin famous and easily adaptable was the ease of accessibility in the form of a device application or simply “wallet” that included the nodes or “blocks” on which transaction data would be stored and processed instantaneously and the intention behind it to create a trustworthy cash system that would give individuals complete ownership of their transactions, not at risk of security breaches. With the advent and success of Bitcoin, new doors opened for other cryptocurrencies and digital assets like Ethereum and Litecoin, and it was observed that unlike fiduciary money, the sustainability of a thorough and transparent record must be maintained in connection with the exchange of digitalized money between any two parties whose anonymity would be preserved. Eventually, this became a turning point and marked the beginning of blockchain technology, which functioned as a distributed ledger system ensuring secure transactions given its strong and complex cryptographic background strengthened with the help of hashing techniques and timestamps. Nakamoto also devised the first blockchain database as a part of the implementation, and the technology synchronized all transactions across multiple geographies in a peer-to-peer network (P2P) without the involvement of mediating authorities like a bank or payments processing company. Having received extensive attention, blockchains revolve around a fundamental concept that transactions are clustered into blocks and exchanged and checked by a network of nodes in the distributed environment with mutual agreement or consensus. To achieve this, consensus algorithms are implemented to instill certainty regarding user security and ledger consistency in a distributed network. Over time, this technology penetrated spheres other than virtual currency management. It showed the unimaginable potential to eliminate enormous amounts of record organization and storage problems, streamlining supply chains, making blockchain unarguably the most important innovation since the emergence of artificial intelligence.
Criminal identification system using face detection with artificial intelligence

As the crime rate has been increasing year after year, it has become increasingly difficult for police to be able to monitor and respond to situations immediately because of the extensive load upon them. With the exponential growth in technology in recent years, we need to work on a solution for this. Artificial intelligence (AI) and blockchain appear to be a cornerstone for a crime prevention strategy. Surveillance cameras are being installed in more and more places as people’s concern for safety is also increasing in recent times. These security cameras can be used for much more and can contribute to solving this problem. There are and have been systems in place that are being used actively to tackle this problem, but they lack in a lot of areas. This chapter focuses on the ever-growing need for a robust criminal identification system that is both efficient and economical for mass usage. A system has been proposed comprised of cutting-edge hardware and software that can dramatically increase the accuracy and reliability of the criminal identification system using the incorporation of machine learning and artificial intelligence while being economical for wide range use. Validation through AI–blockchain helps to achieve end-to-end encryption, time-stamping, and checking for lawfulness.
Image Steganography Using Deep Neural Networks

Steganography is the technique of hiding secret data within ordinary data by modifying pixel values which appear normal to a casual observer. Steganography which is similar to cryptography helps in secret communication. The cryptography method focuses on the authenticity and integrity of the messages by hiding the contents of the messages. Sometimes, it is not only just enough to encrypt the message but also essential to hide the existence of the message itself. As this avoids misuse of data, this kind of encryption is less suspicious and does not catch attention. To achieve this, Stacked Autoencoder model is developed which initially compresses and encodes the data effectively and which finally decodes the data back from the compressed encoded representation to a representation that is more similar to the original input. The secret data is encrypted using Elliptic Curve Cryptography algorithm and transformed into an image before it is encoded by the model which combines the cover and the secret image. The cover and the secret image produce a container image which after decoding and decrypting gives the secret data. The proposed work consists of multiple networks which are trained with Flickr Image Dataset and results in Secret Image with a Loss of 4% and Container Image with a Loss of 14%.
Characteristics, advances, and challenges in blockchain-enabled cyber-physical systems

A cyber-physical system (CPS) is a computer system that integrates real-world objects with embedded technologies to control and monitor physical processes. On the other hand, Blockchain technology is a distributed, decentralized framework, which is the core concept behind Bitcoin. Blockchain technology can improve the reliability, security, and robustness of CPS-enabled critical infrastructure systems. This chapter begins with a detailed overview of CPS and blockchain technology. Then we discuss various existing use cases of CPS applications. We detail the deployment of various blockchain-based CPS applications and compare different critical infrastructure frameworks that employ blockchain on the characteristic properties. We also discuss various challenges and security attacks in a blockchain-enabled CPS. Finally, we conclude the chapter.