Reviewerly/exHarmony huggingface dataset:
New Perspectives of Pattern Recognition for Automatic Credit Card Fraud Detection

Automatic credit card fraud detection (ACCFD) is a challenge issue that has been increasingly studied considering the expanded potential of new technologies to emulate legitimate operations. Solution has to handle changing fraud behavior, detection in data with very small fraud/legitimate operations ratio, and accomplish operation requirements of very low false alarm in real-time processing. In this chapter, main issues related with the problem of ACCFD and proposed solutions are discussed from theoretical and practical standpoints. The perspective of detection analyses from receiving operating characteristic curves and business key performance indicators are jointly analyzed. A new conceptual framework for ACCFD considering decision fusion and surrogate data is outlined including a case of study with different proportions of real and surrogate data. In addition, the sensitivity of the methods to different proportions of fraud/legitimate ratios is tested. Finally, theoretical and practical conclusions are provided, and several open lines of research are proposed.
A Novel Feature Extraction Technique for Content Based Image Classification in Digital Marketing Platform

Increasing size and complexity of information about different products offered through digital media has made it essential to target the effective and efficient techniques of data management and categorization. Text data has gradually been replaced by image or visual data due to increasing importance of image capturing devices and social media. Images have been used as one of the foremost linkages between the brand and the consumer in digital marketing domain. Classification has been considered as a vital component of machine learning necessary for data identification which can be initiated before retrieval to restrict the search within the class of interest. The authors have proposed a novel technique for feature extraction for content based image classification. Analyses of customer satisfaction related to content based product identification with images in diverse media have also been carried out. The classification results with the proposed technique have outperformed the existing methods and have shown an increment of 20% in precision results.
Development of Part of Speech Tagger for Assamese Using HMM

This article presents the work on the Part-of-Speech Tagger for Assamese based on Hidden Markov Model (HMM). Over the years, a lot of language processing tasks have been done for Western and South-Asian languages. However, very little work is done for Assamese language. So, with this point of view, the POS Tagger for Assamese using Stochastic Approach is being developed. Assamese is a free word-order, highly agglutinate and morphological rich language, thus developing POS Tagger with good accuracy will help in development of other NLP task for Assamese. For this work, an annotated corpus of 271,890 words with a BIS tagset consisting of 38 tag labels is used. The model is trained on 256,690 words and the remaining words are used in testing. The system obtained an accuracy of 89.21% and it is being compared with other existing stochastic models.
Cirrus Cloud Correction in Landsat 8 Image Using Combined Image-Based Approach and Various Classification Schemes

The interaction of electromagnetic energy with the atmosphere causes the sensor to detect some of the elements found in the ozone layer such as ice crystals, dust, and clouds. Cirrus cloud in particular is often contaminating satellite imagery and yet relatively difficult to visually detect in visible spectrum. Indonesia as one of the tropical countries has highly cloud cover almost throughout the year. This condition causes land covers are contaminated by cirrus cloud which alters the digital numbers. The availability of cirrus band in Landsat 8 brings an advantage to eliminate cirrus clouds by performing cirrus cloud effect estimation and simple regression method. In this experiment, image-based cirrus correction was implemented in Landsat-8 over Palangkaraya city with high cirrus contamination. Cirrus cloud effect is estimated by using simple linear regression method involving samples or training area over homogeneous area cirrus contamination. Homogeneous areas were defined based on visual interpretation and statistical calculation. After estimating cirrus cloud effect on the pixel, cirrus cloud correction was performed by using arithmetic operations on images based on the slope regression coefficient which corresponded with the highest coefficient of determination. The quality of the corrected image was also statistically evaluated using reference image without cirrus contamination. Not only was the digital number evaluated but also Normalized Vegetation Index (NDVI) was compared in order to estimate the implication of cirrus correction in further image analysis.
Transforming Public Procurement Contracts Into Smart Contracts

The terms governing the provision of supplies, services, or works by an economic operator to a governmental entity are set into a public contract that is signed, following a procurement process. This article explores whether the public administration can utilise smart contracts to incorporate the terms governing the provision of supplies, services, or works. The fundamental elements of a contract are assessed, in order to determine whether a smart contract can be considered as fulfilling these requirements. Following this assessment, the main hurdles to the use of smart contracting are examined and a possible solution proposed. The case for utilising smart contracting within the realm of public procurement is finally advocated.
Violation of Bright Lines, Term Limit Evasion and Information Control

Politicians who erode democratic institutions to consolidate personal powers are expected to encounter opposition. The violation of fundamental constitutional arrangements such as term limits, so-called “bright line" institutions, should result, particularly, in anti-government movements. Empirically, however, we observe decreased rather than increased anti-government mobilization after term limit evasions. I argue that political leaders pre-empt protests during and after term limit evasions by limiting the free flow of information. Using difference-in-differences within a matched set that shares the history of treatments and a similar level of covariates, I show that term limit evasions are followed by a significant decrease in a country’s freedomof expression. In addition, using Venezuela as an example, I provide micro-evidence of information control by investigating how topics of opposition media change after term-limit evasion. Specifically, automated text analysis shows that a leader evading termlimits not only censors threatening political information but also induces the media to offer distracting and apolitical information.
Security Assessment of Enterprise Networks Based on Analytic Network Process and Evidence Theory

Network security has always been the most important of enterprise informatization construction and development, and the security assessment of network system is the basis for enterprises to make effective security defense strategies. Aiming at the relevance of security factors and subjectivity of evaluation results in the process of enterprise network system security assessment, a security assessment method combining Analytic Network Process and evidence theory is proposed. Firstly, we built a complete security assessment index system and network analysis structure model for enterprise network, and determined the converged security index weights by calculating hypermatrix, limit hypermatrix and stable limit hypermatrix; then, we used the evidence theory on data fusion of the evaluation opinions of multiple experts to eliminate the conflict between evidences. Finally, according to the principle of maximum membership degree, we realized the assessment of enterprise network security level using weighted average. The example analysis showed that the model not only weighed the correlation influence among the security indicators, but also effectively reduced the subjectivity of expert evaluation and the fuzziness and uncertainty in qualitative analysis, which verified the effectiveness of the model and method, and provided an important basis for network security management.
Decentralized Blockchain-Based Peer-to-Peer Energy-Backed Token Trading for Active Prosumers

This paper designs and models a fully decentralized peer-to-peer energy token market for small-scale prosumers using blockchain technology in a smart grid environment in the presence of the demand response program (DRP) and demurrage mechanism. As the market players, prosumers in the local distribution network are considered in two groups, producers (sellers) and consumers (buyers). Using smart contracts, all sellers and buyers in the proposed market can engage in bilateral energy token transactions with each other under an agreed price and with the retail market at a certain price. Furthermore, the local consumers can participate in price-based DR programs and shift their consuming load to the periods with high local generation. demurrage mechanism is applied to avoid energy token accumulation and enhance attraction for local transactions. With demurrage in place, the redemption value of energy-backed tokens reduces with time. A fully decentralized approach called the primal-dual sub-gradient method is developed to clear this fully decentralized energy market in the presence of DRPs and demurrage. The proposed market-clearing scheme guarantees the global and feasible solution without requiring the players’ private information. Numerical studies demonstrated the feasibility and effectiveness of the proposed energy token market and the decentralized approach for its clearing.
Clustering

Clustering or cluster analysis is an unsupervised machine learning method used to understand the internal structure of and groupings in datasets. Cluster analysis has been a popular data science technique in a variety of disciplines for a century. This chapter will introduce the concept of clustering and similarity measures, present multiple examples with smaller and larger datasets, and discuss potential applications to cryptocurrency blockchain data.
Decrypting New Age International Capital Flows

This paper employs high frequency transactions data on the world’s oldest and most extensive centralized peer-to-peer Bitcoin market, which enables trade in the currencies of more than 135 countries. We develop an algorithm that allows, with high probability, the detection of “crypto vehicle transactions” in which crypto currency is used to move capital across borders or facilitate domestic transactions. In contrast to previous work which has used “on-chain” data, our approach enables one to investigate parts of the vastly larger pool of “off-chain” transactions. We find that, as a conservative lower bound, over 7 percent of the 45 million trades on the exchange we explore represent crypto vehicle transactions in which Bitcoin is used to make payment in fiat currency. Roughly 20 percent of these represent international capital flight/flows/remittances. Although our work cannot be used to put a price on cryptocurrencies, it provides the first systematic quantitative evidence that the transactional use of cryptocurrencies constitutes a fundamental component of their value, at least under the current regulatory regime.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Tuning Multi Object Tracking Systems using Bayesian Optimization

Tracking-by-Detection has become the major paradigm in Multi Object Tracking (MOT) for a large variety of sensors. Regardless of the type of tracking system, hyper parameters are often chosen manually instead of doing a structured search to reveal the full potential of the system.In this work we tackle this problem by utilizing Bayesian Optimization (BO) to tune tracking systems, enabling to find the best combination of hyper parameters for Gaussian Mixture Probability Hypothesis Density Trackers (GM-PHD) in two different tracking applications. We use the Tree-structured Parzen Estimator (TPE) algorithm [1] [2] with an Expected Improvement (EI) acquisition function as a blackbox optimizer. TPE supports to conveniently incorporate domain expert knowledge by modeling prior probability distributions of the search space. In our experiments we use the popular MOTA metric as optimization objective.Evaluation is performed in a simulation scenario with an in depth discussion of the found parameters and a real world example that uses the MOT-20 challenge dataset [3] demonstrates the unconditional applicability of the approach. We finish with a conclusion on Bayesian Optimization for MOT systems and future research.
Post-Quantum Cryptography and Quantum Cloning

In the last two decades, the field of post-quantum cryptography has had an overwhelming response among research communities. The ability of quantum computers to factorize large numbers could break many of well-known RSA cryptosystem and discrete log-based cryptosystem. Thus, post-quantum cryptography offers secure alternatives which are implemented on classical computers and is secure against attacks by quantum computers. The significant benefits of post-quantum cryptosystems are that they can be executed quickly and efficiently on desktops, smartphones, and the Internet of Things (IoTs) after some minor software updates. The main objective of this chapter is to give an outline of major developments in privacy protectors to reply to the forthcoming threats caused by quantum systems. In this chapter, we have presented crucial classes of cryptographic systems to resist attacks by classical and quantum computers. Furthermore, a review of different classes of quantum cloning is presented.
Design and Development of Real Time Patient Monitoring System with GSM Technology

Technology keeps evolving every second. Humans receive all the essential information with just one touch. Be it the weather forecast or ordering any grocery or communicating with one's peers, everything happens so easily and efficiently. It is undoubtedly user friendly. When the same technology associates itself with the medical equipment it becomes easier to fetch and process the patient's data in real time. It also helps to make several life-saving, spontaneous decisions for the critical care. This paper talks about the patient monitoring system. The physiological parameters of the patient are continuously monitored in real time using sensors. The data that is obtained from the sensor is then sent to an Arduino Uno microcontroller where it is analyzed. If the patient's data differs from the required threshold values, an emergency message is sent to the assigned doctor's mobile. This is done using the GSM module which is interfaced with the microcontroller. Verifying the system in software is done using Proteus.
Accumulators

The accumulator is a one-way function that can provide membership proofs without revealing any individual member in the underlying set. It can be used in many other cryptographic primitives such as commitments and ring signatures for building blocks. The main functionalities for the accumulator, i.e., membership witness, nonmembership witness, and dynamics. In this chapter, we summarize the existing accumulator schemes according to the properties and requirements. The existing accumulator schemes can be divided into three categories, i.e., RSA-based, pairing-based, and hash-based. The brief description and functionality of these three categories are described in this chapter.
Pre-trained Language Model based Medical Named Entity Recognition

Medical named entity recognition is an important part of structuring Chinese electronic medical records and construction of medical knowledge graph. The CCKS2019 conference organized a medical named entity recognition evaluation task to extract six types of medical entities from unstructured Chinese electronic medical records. Based on the data set of this evaluation task, pre-trained language model based entity recognition approaches are studied. First, select the BiLSTM-CRF model based on random initialized word embedding as the baseline system; secondly, apply word2vec to the baseline system; thirdly, apply ELMo to the baseline system. Experimental results show that the pre-trained language model is comparable to the best approach of this evaluation task, and the context-related pre-trained language model performs better.
A Quantum Key Distribution Technique Using Quantum Cryptography

In order to protect and secure the sensitive data over the internet, the current data security methods typically depend on the cryptographic systems. Recent achievements in quantum computing is a major challenge to such cryptography systems. In this way, the quantum key distribution (QKD) technique evolves as a very important technique which gives un-conditional data security. This technique is based on the laws of quantum physics for its security. This article gives a detailed description of the QKD technique. This technique secures the encryption key delivery between the two authenticated parties from the unauthorized access. In the next phase, quantum cryptography model is discussed. Finally, some important application areas and limitations of this technology are be discussed.
Research on attribute-based encryption access control technology in edge computing environment

In order to solve the security problem of the data access between edge nodes in edge computing, in view of the distributed characteristics of edge computing, this paper combines edge computing and complex cryptographic computing environment, and proposes an attribute-based encryption in edge computing environment. The access control method ensures the encryption of data communication between edge nodes and at the same time realizes the fine-grained access control function between edge nodes that can be accurate to the attribute level. This solution can flexibly control data access, the edge node sets the access control strategy, and proposes the access control to the data access of other edge nodes.
Internet of Things Service Provisioning Platform for Cross-Application Cooperation

With the development of Internet of Things (IoT), large-scale of resources and applications atop them emerge. However, most of existing efforts are “silo” solutions, there is a tight-coupling between the device and the application. The paradigm for IoT and its corresponding infrastructure are required to move away from isolated solutions towards cooperative models. Recent works have focused on applying Service Oriented Architecture (SOA) to IoT service provisioning. Other than the traditional services of cyberspace which are oriented to a two-tuple problem domain, IoT services are faced with a three-tuple problem domain of user requirement, cyberspace and physical space. One challenge of existing works is lacking of efficient mechanism to on-demand provisioning the sensing information in a loosely-coupled, decentralized way and then dynamically coordinate the relevant services to rapidly respond to changes in the physical world. Another challenge is how to systematically and effectively access (plug) the heterogeneous devices without intrusive changing. This paper proposes a service provisioning platform which enables to access heterogeneous devices and expose device capabilities as light-weighted service, and presents an event-based message interaction mode to facilitate the asynchronous, on-demand sharing of sensing information in distributed, loosely-coupled IoT environment. It provides the basic infrastructure for IoT application pattern: inner-domain high-degree autonomy and inter-domain dynamic coordination. The practicability of platform is validated by experimental evaluations and a District Heating Control and Information System (DHCIS).
Towards an Understanding of Performance, Reliability, and Security

Service-oriented computing is a promising computing paradigm for software applications. However, there are a lot of key research issues in the service-oriented computing paradigm. Quality of service (QoS) is a key factor to resolve these issues as well as a crucial aspect in the design of service-oriented systems as it directly touches the concerns of the service users. Therefore, it is necessary and important to achieve a deeper understanding of different types of QoS properties. This chapter introduces three QoS properties, namely performance, reliability, and security, and provides an overview of these QoS properties and offers an in-depth analysis of the issues, challenges, and research opportunities of QoS properties in designing and developing service-oriented systems.
Cloud Storage Privacy in Health Care Systems Based on IP and Geo-Location Validation Using K-Mean Clustering Technique

Cloud-based platforms are helping organizations like health care systems to improve conditions of patients and saving their lives. Medical professionals are making use of cloud technology to collect information regarding patients more than before and exchange it over different geographical regions. However, the exchange of patient data and information is taking place via complex systems with huge vulnerabilities and risks. In this article, the authors have outlined a model for preserving privacy in data storage used in health care systems by validating access to the data through IP based detection and geographical location-based security techniques. Later, the privacy is enabled by using k-mean clustering technique for validating the user access and avail subscriptions whenever consumer want to use the organization services. The authors also provide the concept of using constant key length encryption technique to secure data on cloud storage irrespective of the type of user.
A hyperbolic embedding for scale-free networks

Graph neural network, with its powerful learning ability, has become a cutting-edge method of processing ultra-large-scale network data. In order to polished up the representation accuracy of embedding, the key is to find the intrinsic geometric metric of the complex network. Since the real data is mostly scale-free network, the embedding accuracy of traditional models is still limited by the dimensionality of the euclidean space and computational complexity. Therefore, the hyperbolic embedding, whose metric properties conform to the power-law distribution and tree-like hierarchical structure of the complex network, will effectively approximates the latent low-dimensional manifold of the data distribution. This paper proposes an auto-encoder in hyperbolic space (HVGAE), taking full use of hyperbolic graph convolutional (HGCN) and the idea of variational autoencoder. Under the optimal combination of the encoder module, competitive results have been achieved in different real scenarios.
Low Temperature Stripping of Volatile Compounds

Contamination of soils from past operations involving volatile organic compounds (VOCs), such as from leaking underground fuel storage tanks, has become a major environmental concern. A pilot scale field demonstration of the process using VOC-contaminated soils became the next phase in the development of the technology. The demonstrated process represents a significant breakthrough in the treatment of soils contaminated with VOCs. The process represents a unique mix of proven techniques combined in an innovative way to provide an efficient and cost-effective method of treatment. The cooled liquid fraction from the condenser is pumped to an oil/water separator to remove any oil present. The oil fraction is collected for offsite treatment and disposal. The water fraction is directed through a two-stage carbon adsorption system which removes any other contaminants in the water. The treated water is collected and sprayed onto the treated soils at the truck feed conveyor for cooling and dust control.