Reviewerly/exHarmony huggingface dataset:
Estimation of potential field environments from heterogeneous behaviour of sensing agents

This paper proposes a novel modelling framework for estimating the global potential field from trajectories of multiple sensing agents whose perception of the unknown field is subject to abrupt changes. We derive a parametrised formulation of the estimation problem by combining the jump Markov non-linear system (JMNLS) model of agent dynamics with a basis function decomposition of the environmental field. An approximate expectation-maximisation algorithm is employed for joint estimation of the global field and of the agent behavioural modes from observed agent trajectories. To avoid prohibitive computational costs associated with the state estimation of JMNLS, we utilise two approximation steps. First, an interacting multiple model smoother is used to account for the hybrid structure that emerges in this problem. Second, we propose two approaches to approximating the non-linear sufficient statistics during the expectation step. This results in the maximization step being exact. The performance of the developed framework is tested on simulation examples and demonstrated on an application study in which the observed movement patterns of immune cells are utilised in quantifying the underlying chemical concentration field that governs their migration. The results showcase that the proposed framework can be readily applied to problems where agents assume several behavioural modes.
Neural TV program recommendation with multi-source heterogeneous data

TV program recommendation is important for users in the face of a huge amount of information data. The existing TV program recommendation mainly relies on a collaborative filtering method to recommend through interactive data between users and programs. Although some methods utilize auxiliary information to enrich semantic features, most of them only use a single data type, which cannot capture a more diverse feature representation of the user and program. In this paper, we propose a neural TV program recommendation model with multi-source heterogeneous data, which makes full use of the multi-source heterogeneous auxiliary information. Specifically, we combine heterogeneous features derived from auxiliary information to learn a deep program representation in the program encoder module. To more accurately capture user preferences, we further utilize the personalized attention mechanism to determine the importance of different programs to the user representation based on the interaction between users and programs in the user encoder module. Extensive experiments on a real dataset of the Chinese capital show that our model can effectively improve the performance of TV program recommendations compared to the existing models.
Integrated Blockchain Manufacturing Design for Distributed Authentication, Validation and Secure Sharing of Events in VANET

Intelligent transportation system (ITS) is a technique to improve the driving conditions and safety through collaborative exchange of information between vehicles. Ensuring the authenticity and secure exchange of the events is an important functionality of ITS. Recently blockchain based decentralized solutions are proposed to address event’s authenticity and secure exchange instead of traditional centralized trusted third-party solutions. Along these lines, this work proposes a block chain based decentralized architecture to realize additional functionalities of fine-grained access control to events, revocation of access to events and ensuring the trustworthiness of the events. Block chain along with IPFS is used to realize these functionalities in a fully distributed manner using smart contracts. Performance comparison of proposed solution with state of art demonstrates a better resilience to attacks and comparatively lower execution costs for smart contracts.
Digital breast pathology: validation and training for primary digital practice

Digital pathology is a technology which is transforming the way in which breast histopathology specimens are assessed, reported and reviewed. Large scale clinical laboratory deployments of whole slide imaging systems are occurring in diagnostic pathology departments across the world, requiring laboratory and diagnostic staff to navigate new skills and workflows. Transferring from conventional light microscopy assessment of breast specimens to the use of whole slide images (WSI) can be a challenging experience. This article describes an approach to training and validation for breast consultant histopathologists, which has been used and adapted at a number of sites. Examples of types of case that are suitable for training, and some of the potential “pitfalls” of digital reporting for the novice are described, and practical advice regarding clinical digital breast workflow is shared.
Integrated IoT-based Healthcare System for the Early Detection of Breast Cancer Using Intelligent Diagnostic System

Breast cancer represents one of the leading cancer-related diseases worldwide, affecting mostly women after puberty. Even though the illness is fatal and kills thousands of people each year, it is mostly curative if found quickly. As a result, prompt and precise detection methods are critical to patient survival. Previously, doctors used manual detection systems for this objective. However, such techniques have been slow and frequently dependent on the physician's expertise. As technology advanced, these primitive methodologies were supplemented by computer-aided detection (CAD) algorithms. Deep learning is extremely common because of the massive development in large data, the Internet of Things (IoT), linked devices, and high-performance computers using GPUs and TPUs. The Internet of Things (IoT) has advanced recently, and the healthcare industry is benefiting from this growth. Sensors that gather data for required analysis are crucial components utilized in the Internet of Things. Physicians and medical staff will be able to carry out their tasks with ease and intelligence thanks to the Internet of Things. The proposed research focus on integrating Alexnet and ResNet101 for accurate prediction of Breast malignancy from mammogram data. This methodology will target the features more precisely than any other combination of the pre-trained model. Finally, to resolve the computational burden issue, the feature reduction ReliefF methodology is used. To demonstrate the proposed method, an online publicly released set of data of 750 BU images is used. For training and testing the models, the set of data has been further split into 80 and 20% ratios. Following extensive testing and analysis, it was discovered that the DenseNet-201 and MobileNet-v2 trained SVMs to have an accuracy of 98.39 percent for the original and augmented Mammo images online datasets, respectively. This research discovered that the proposed approach is efficient and simple to implement to assist radiographers and physicians in diagnosing breast cancer in females.
Designing the Chain of Custody Process for Blockchain-Based Digital Evidences

The design of a chain-of-custody management system must be based on an architecture that guarantees non-manipulation in court, from the very beginning of the evidence is acquired to putting it on judge hands. Knowing, without doubt, who, when, how and why the involved actors were able to access it. This is a necessary condition, although not sufficient, for the court to decide on the admissibility of real the evidence. This way, the Blockchain technology is an optimal solution for issues where integrity must be guaranteed among untrustworthy parties. However, it is necessary to tackle a possible solution in a holistic way, by considering technologies, actors and industry involved during the process. Therefore, three basic architectural designs with different challenges are proposed in this work. In addition to this, we discuss about the need to promote a nationwide smart contract standardization for the chain-of-custody process. It must be open-source and compatible with current top languages used in the Blockchain landscape. These contracts must be deployed in consortium environments, where reliable independent third parties validate the transactions without knowing their specific content. This is possible thanks to the Zero Knowledge Proof protocols.
A Benchmarking Study of Blockchain-Based Technology Implementation Cost: Public and Private Blockchain for Enterprise Level Organization Using Benchmarking Model

The goal of this research is to benchmark two types of Blockchain, Public Blockchain and Private Blockchain, which can be implemented by organizations by considering the implementation cost. The cost of implementation is benchmarked using the Benchmarking methodology. The Benchmarking model is modified from the one that has been developed by G. Anand and R. Kodali [12]. There are 2 platforms of each type of Blockchain which are being benchmarked. Data were collected by using literature review methodology by analysing the indicators of each type that are gathered through the platforms’ official websites, whitepapers, journals, and articles related to Blockchain. During the calculating process, there were assumptions added based on the collected data, and some were adapted from previous research. The result of this study is a template that can be used by organizations to self-calculate the cost of implementing Blockchain from various platforms but still follow the developed indicators.
Integrating Web3 Features into Moodle

While many innovations and possibilities of the world wide web were created, there is still a lot of unrealized potential due to the limitations of the current structure of the web, which is called web2. To solve current problems of the internet and bootstrap the new wave of innovation, the web3 started to evolve. Based on the distributed systems, most often on blockchain technology, the new use cases exploit advantages of the technology, such as data interoperability, decentralization, and self-sovereignty. In this paper, we present the approach of integrating new features and benefits of web3 to the existing web2 educational platform Moodle, which removes the need to recreate the whole platforms that take advantage of web3 from scratch. Furthermore, we present the evaluation of our approach and solution on the real world use-case.
Enhancing Smart Contract Quality by Introducing a Continuous Integration Pipeline for Solidity Based Smart Contracts

Vulnerability concerns and quality issues lead to devastating and high-risk exploits in Smart Contracts. Several tools and approaches emerged to identify known errors and vulnerabilities in an early stage of Smart Contract development. However, those tools and approaches are currently considered stand-alone approaches and serve specific tasks that must be bonded manually. From traditional software engineering, the concept of Continuous Integration (CI) is well known for combining several tools and functions to ensure high code quality throughout every development stage. In this paper, we analyze the possibility of leveraging CI for the Smart Contract development. We identify requirements and design a general pipeline for Ethereum-based Smart Contracts. After two iterations, we developed a containerized approach that can be used independently on the used CI platform without the need to configure tools or test environments. We are able to show that we can link multiple tools and thus provide an automatic check and verification of the Smart Contract through different development stages. This approach can aid practitioners and researchers alike in creating safe and secure Smart Contracts of high code quality, thus contributing to the research area of testing in the context of blockchain oriented software engineering.
Scalable and Transparent Blockchain Multi-layer Approach for Smart Energy Communities

The Blockchain paradigm applied to energy communities represents both a challenge and an opportunity. Indeed, it introduces a new distributed and transparent framework to manage the communities’ services. In the meantime, it guarantees the traceability of the energy produced and consumed, informing consumers of the origins and costs of their supply, making tariffs more transparent with the result of promoting energy flexibility. Blockchain enables a real digitalization of the energy sector, maintaining a high level of trust and compliance. The security of the transaction and the guarantee that the platform on which an energy network is based cannot be tampered with has prompted energy communities to start a blockchain-based system. The paper proposed a blockchain-based open and transparent ecosystem for smart energy communities within different and several actors interfacing thanks to the blockchain system and its services interact in a natural and disintermediate way. It allows, in a peer-to-peer (p2p) manner, to implement participatory logic aimed at reducing and self-consumption of energy and direct participation of citizens in the electricity system. A proof of concept is created and tested using an innovative smart legal contract framework created by Trakti and a scalable blockchain ecosystem thanks to the Trakti platform through an end-to-end approach. Indeed, this platform implements a new way of automatically managing and incentivizing the energy community via a smart contract connecting actual data and services that the smart energy community can consume using community energy tokens. The approach demonstrates how the new smart legal contract makes it possible to define an energy token, which remunerates the energy exchanges in communities between producers and consumers within a smart grid and converts the energy into community services.
Objective-Aware Reputation-Enabled Blockchain-Based Federated Learning

Federated Learning (FL) is a distributed-collaborative machine learning framework to overcome the challenges of data silos and data privacy. The conventional FL enhances data owners’ privacy by maintaining their data on devices. However, assuring the quality of data and model verification is challenging since no one contributor can see the others’ data. The problem becomes even more challenging if data is not independent and identically distributed (Non-IID). This study proposes a multi-channel objective-aware validation method to identify valuable contributors to the FL model on a blockchain network with Non-IID data. We implement the proposed system on Hyperledger Fabric by utilizing a multi-layer deep learning model. The evaluation results demonstrate the effectiveness of the proposed approach.
Prototyping a Smart Contract Application for Fair Reward Distribution in Software Development Projects

The following work describes the development of a project management platform based on the waves blockchain technology. The aim is to use the platform to optimally reward both fairness and the achievement of quality standards in software development. Due to the outsourcing of software projects and the associated processes, situations arise in which different project participants work on the same tasks, while paid differently and in most cases independent of the delivered software quality. This is based on the respective contract conditions an individual software developer has negotiated, without factoring in the actual quality of the code the developer provided. The proposal of this work is a prototypical software application that takes the requirements of a project and measures the corresponding contributions of the developers based on their software quality. Project sponsors can also pay out the enrolled project partners via the platform.
Cryptocurrencies, Survey on Legal Frameworks and Regulation Around the World

Cryptocurrencies have been in the spotlight for the past few years. The centralization of the management of fiat currencies by banks led to their emergence with Bitcoin in 2008 as a way to present truly democratic money to the world. With the increase in the flow of capital to this market, new challenges and problems have arisen that governments must deal with. In this paper, a study of the legal frameworks that are being implemented throughout the world with the intention of regulating the cryptocurrency market is carried out. This study aims to understand the different points of view and interests that each country has when implementing each type of framework.
Mitigation of Scaling Effects in NTF-Based Ticketing Systems

While cryptocurrencies have seen an enormous upswing in recent years, other technical advantages of the blockchain are also being used more frequently. Particularly in focus is the “Non-Fungible Token” (NFT), which stands out from conventional fungible goods and currencies because of its blockchain-wide uniqueness. Due to this characteristic, it is possible to represent personalized as well as limited assets, such as lots or tickets, by means of NFTs. This paper explores different technical approaches to implementing a ticketing system based on a blockchain, with a specific focus on NFTs. Based on the Waves Blockchain, three different possible solutions for the case study “Ticketing” were implemented using decentralized applications (smart contracts) and a JavaScript-based testing tool. Subsequently, various scenarios were designed, which simulated different event sizes and thus visitor numbers. The resulting data was examined with respect to possible scaling effects and general feasibility. The final evaluation of this work will answer the question whether NFTs can be used as a basis for a ticketing system and addresses any scaling related issues that arose during testing.
Signature identification and user activity analysis on WhatsApp Web through network data

WhatsApp messenger is a popular instant messaging application that employs end-to-end encryption for communication. WhatsApp Web is the browser-based implementation of WhatsApp messenger. Users of WhatsApp communicate securely using SSL protocol. Encryption and use of common port for communication by multiple applications poses challenge in traffic classification for application identification. It is highly needed to analyze the network traffic for the purpose of QoS, Intrusion Detection and application specific traffic classification. In this paper, we have done traffic analysis on the network packets captured through data transfer in whatsApp web. In the result, we have explored the user activities such as message texting, contact sharing, voice message, location sharing, media transfer and status viewing. Packet level traffic analysis of user activities reveal patterns in the encrypted SSL communication. This pattern is identified across SSL packet lengths for WhatsApp media transfer and voice message communication. Other important features WhatsApp is the ability to view the status of the message being sent. We have identified the read and unread message status in these data packets by exposing signatures in the network layer. These signatures are identified with the help of the SSL lengths in the TLS header information of WhatsApp Web network traffic traces. Various other information on WhatsApp traffic presented in our study is relevant to the version of WhatsApp Web v0.3.2386. Also in this work machine learning based classifier is trained to classify the encrypted malicious, normal network traffic. From the results it is clear that SVM classifier gives the highest accuracy of 0.9666.
Protecting integrated circuits against side-channel and fault attacks with dynamic encoding

With the Internet of Things, an increasing amount of sensitive data have to be communicated and hence encrypted. Low-cost hardware attacks such as fault analysis or side-channel analysis threaten the implementation of cryptographic algorithms. Many countermeasures have been proposed against either of these attacks, however, only a few countermeasures protect efficiently an implementation against both attacks. These joint countermeasures usually have a prohibitive area and power overhead, and require up to thousands of bits of fresh randomness at each encryption. Therefore, they may not be suited to protect lightweight algorithms in resource-constrained devices. In this paper, we propose a new joint countermeasure against both attacks, called dynamic encoding. It has a smaller power and area overhead than existing joint countermeasures and requires at most 8 random bits at each encryption. It is particularly adapted to protect sequential logic and lightweight algorithms based on shift registers, and it can be extended to protect combinational logic as well. It consists in a power balancing at algorithmic level and provides an inherent fault detection. Simulations with several levels of noise indicate that dynamic encoding provides an efficient protection against side-channel analysis with up to 100,000 traces.
Interval type-2 polynomial fuzzy fault detection scheme with a multi-order homogeneous polynomial Lyapunov functions considering unmeasurable premise variables

A polynomial fuzzy fault detection scheme for sampled-output-measurements-based interval type-2 (IT2) polynomial-fuzzy-model-based (PFMB) systems is investigated in this paper, where the uncertainties in the premise variables (PVs) and membership functions (MFs) are described by IT2 fuzzy sets. Fully or partially unmeasurable PVs cause the parameter matrices of the polynomial fuzzy fault detection observer (PFFDO) to rely on the estimated states and the corresponding mismatching problems are further considered. Lyapunov stability theory is carried out with a novel multi-order homogenous polynomial Lyapunov functions (MHPLF) to introduce more information of the states when eliminating the partial derivatives, and the time-delays introduced by sampled-output measurements are handled by L-K functions. Unlike the membership-function-independent (MFI) approaches, the membership-function-dependent (MFD) approaches carry the information of the MFs for the relaxation of the stability constraints. Corresponding stable constraints in sum-of-squares (SOS) form are given to hold the asymptotic stability of the fault detection system with H∞ performance γ. A numerical example with many cases illustrates the effectiveness of the proposed techniques in uncertainty handling and conservativeness reduction, while an inverted pendulum example verifies the feasibility of the method on physical systems.
DEVELOPMENT OF MEDIA CAD RICHPEACE GRADING SYSTEM FOR THE MAKING OF HOME CLOTHING PATTERN IN FASHION EDUCATION STUDY PROGRAM, MEDAN STATE UNIVERSITY

The aims of this study were (1) to find out how to develop the CAD Richpeace Grading System media for making home dress patterns at the Fashion Design Study Program, Medan State University. (2) Knowing the feasibility of CAD Richpeace Grading System media in making home dress patterns at the Fashion Design Study Program, Medan State University. This research is development research (R&amp;D). The development model used in this study is the ADDIE development framework. The validation results by material experts obtained a score of 84.8% in the good category and the validation results by media experts obtained a score of 87.72% in the very good category. Small group trials were conducted on 8 students of the fashion design education study program. student assessment results in small group trials of 80.60% with good criteria. Group trials are being carried out by 16 students obtaining 85.23% in the very good category. Next is the large group tryout which was conducted on 32 students of the fashion education study program with the results of the student assessment in the large group test being 88.19% with very good criteria. From the results of the assessment of media experts, material experts, small group and large group trials, it can be concluded that the Cad Richpeace Grading System media developed for making home clothes for garment businesses is very suitable for use as learning media and can help students to understand the material and understand steps in making home fashion patterns with a digital grading system.