Reviewerly/exHarmony huggingface dataset:
An Automated Liver Disease Detection System using Machine Learning and Smart Contract

Liver disease is one of the leading reasons of death in both men and women worldwide. Blood pressure, Bilirubin level, Energy level, SGOT & SGPT level, blood sugar level, and Total Protein are all factors that can be observed to predict liver disease in its early stages. The existing healthcare infrastructure is being transformed by technology. With the development of the Internet of Things (IoT), doctors can now remotely monitor and analyze patients, keep their data, and process it for further examination. However, there is a requirement to introduce advanced and improved secured algorithms for quick event processing and detection. In this work, an integrated Smart Contract (SC) and Machine Learning (ML) based system is presented for detecting liver disease that, in addition to storing data in the blockchain, notifies the doctor about the patient's health status. blockchain technology provides a secure environment for storing confidential patient data. Since blockchain data is tamper-resistant, it effectively prevents unauthorized access and data falsification. Using the concept of ML, decision tree classifier variants, such as J48, Hoeffding Tree, Decision Stump, and Random Forest, are applied to the liver patient dataset for efficient classification in order to design the proposed system. The best classifier is chosen based on performance metrics like accuracy, precision, recall, F- score, etc. Finally, the selected classifier's rules are encoded in the smart contract to allow automatic and timely detection of disease. This work also discusses the proposed work's functioning and the evaluation results.
Clustering-Based Under-Sampling with Normalization in Class-Imbalanced Data

In some real-world data sets, there is a class imbalance where one class (the minority class) has a limited number of data points and the other class (the dominant class) has a large number of data points. With the state-of-the-art machine learning approaches, it is extremely challenging to build an efficient model without taking data preparation into account to balance the unbalanced data sets. To ensure that each class has the same number of data points, random under-sampling has been used in numerous research. During the data preparation phase of this study, this research experiments with under-sampling techniques that use a clustering technique. This Research uses under-sampling techniques with normalization in this non-interest-bearing imbalanced data collection with a majority and minority class. Both majority and minority classifications contain personal information focuses. This Research first uses k-fold cross-validation to separate this unbalanced data set into preparation and testing sets. This Research separates the data into a majority course subset and a minority course subset after normalizing it. The majority of lesson information evaluations are minimized by using a clustering-based under-sampling method. The minority lesson subset is then combined with the reduced lion's share course subset to create an updated preparation set. Then The author normalizes the information once more at that moment. The classifier is then independently prepared and evaluated using the updated preparation and testing sets.
A Hybrid VMD-Based ARIMA-LSTM Model for Day-ahead PV Prediction and Uncertainty Analysis

With the rapid growth of PV power system, PV power prediction is important for optimal operation and control of power grid connection. A novel efficient two-stage prediction approach is proposed to quantify the fluctuant range and uncertainty of PV power output. A hybrid model integrated ARIMA with LSTM based on temporal attention mechanism is proposed for point prediction in the first stage. In order to get a more accurate forecasting result, variational mode decomposition (VMD) with sample entropy (SE) is developed to decompose the original PV power and decide which model is used to predict for the decomposed sub-series. In the second stage, nonparametric kernel density estimation method is applied to generate prediction intervals (PIs) of the deterministic forecasting error without assuming the probability distribution. A practical case in southwest China is simulated. The results show that the performance of proposed model is best under 3 weather types.
Sentimental Analysis of Movie Review using Machine Learning Approach

A key area of machine learning called sentiment analysis seeks to extract subjective data from textual evaluations. The most popular technique for anticipating user ratings is sentiment analysis, and several machine-learning techniques have been employed to provide precise predictions. Sentiment analysis is the skill of examining information regarding what the general public really thinks about your company, a text, an opinion, a social media post, etc. It is a very potent tool in the analytics toolbox. Natural language processing and text mining both have a close connection to the study of sentiment. It can be used to evaluate the reviewer's viewpoint on certain topics or the review's overall polarity. The accuracy of the model is evaluated using sentiment analysis on the IMDB movie reviews dataset utilizing machine learning (ML) and natural language processing (NLP) techniques. Natural language processing and machine learning combine to provide the fundamental building blocks of sentiment analysis. Provides context to grasp the meaning of any text by enhancing the capabilities of machine learning and natural language processing. Using machine learning classification methods, this study suggests a prediction model for the sentiment analysis of movie reviews. This study aids researchers in choosing the most effective method for doing accurate and timely emotive analysis on IMDB movie reviews. Here, want to estimate the general polarity of the review using machine learning and natural language processing (NLP).
Research on Substation Video Surveillance Data Encryption Based on Improved Tent Chaos Map

In recent years, the video surveillance system data of various facilities, places and key parts in the power industry has been widely used, and a large number of hidden dangers of security vulnerabilities and data leakage have occurred. The use of video surveillance systems to obtain important asset information in key places is worrying. Therefore, this paper proposes a research based on the improved Tent chaotic map in the encryption of video surveillance data in substations. This paper constructs an improved Tent map, and compares the t classical tent chaotic map, the classic Logtisic chaotic map, and the improved Tent chaotic map. In terms of performance in bifurcation graph, correlation dimension value, etc., experimental research shows that this improved chaotic map has improved mapping range, randomness, uniformity, etc. compared with the classical Tent map. Based on this chaotic map, the permutation group strategy is used to obtain the ciphertext image, which realizes the encryption of the video surveillance data of the substation.
A Knowledge Extraction Method Based on Deep Learning Applied to Distribution Network Fault Handing Assistant Decision

In order to effectively solve the problem of a large number of semi-structured and unstructured text information associations in the distribution network, the fault handling efficiency of the distribution network is effectively improved. Taking the text of distribution network fault handling plan as the research object, a deep learning model based on knowledge graph is established, and knowledge graph technology is used to assist staff in making fault handling decisions. First, combined with the characteristics of the power grid fault handling plan text, multiple deep learning models are used for knowledge extraction, forming a bottom-up knowledge graph data layer: In order to prevent word segmentation errors, the TextCNN model based on word vector is used to classify the text of the plan; the LR-CNN model is used for named entity recognition, thereby solving the conflict problem of candidate words; on the basis of the above, the BiGRU-Attention model is used to extract entity relation. The effectiveness of the above knowledge extraction method is verified by experiments. Finally, the knowledge graph of fault handling information of distribution network is constructed by using the Neo4j graph database, which is used for intelligent information retrieval and auxiliary fault diagnosis.
A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

Unmanned Aerial Vehicle (UAV) is increasingly becoming an important tool used for a variety of tasks. In addition, Reinforcement Learning (RL) is a popular research topic. In this paper, these two fields are combined together and we apply the reinforcement learning into the UAV field, promote the application of reinforcement learning in our real life. We design a reinforcement learning framework named ROS-RL, this framework is based on the physical simulation platform Gazebo and it can address the problem of UAV motion in continuous action space. We can connect our algorithms into this framework through ROS and train the agent to control the drone to complete some tasks. We realize the autonomous landing task of UAV using three different reinforcement learning algorithms in this framework. The experiment results show the effectiveness of algorithm in controlling UAV which flights in a simulation environment close to the real world.
Forestry Text Classification Based on BERT and KNN

With the rapid development of mobile Internet, the number of various texts on the network is exploding. It is a common demand to classify them. However, in actual production and life, it is often necessary to classify texts in a specific field, such as forestry texts. Use BERT to train the training text, get the text vector of each document, and then average to get the center value of the text vector of each type of document. Based on the idea of KNN, we can judge which kind of text's central vector is closer to each document in the test corpus, so as to achieve the goal of classification. Through experiments, the classification effect of the method based on BERT and KNN idea is better than that of the method using naive Bayes. Using naive Bayes for classification, although the classification effect is not too bad, in order to pursue the accuracy of classification, deep learning models such as the combination of BERT and KNN model are better.
Block-FeST: A Blockchain-Based Federated Anomaly Detection framework with computation offloading using Transformers

Internet of Things (IoT) devices generate a massive amount of data on a regular basis. This data has the potential to revolutionize every sector by developing intelligent systems. Unfortunately, the data is inaccessible due to privacy concerns. A decentralized machine learning approach, i.e., Federated Learning (FL), enables clients to train models locally in an iterative and collaborative manner. However, some clients may not be able to fully participate in training due to limited computational resources. To address the aforementioned issues, we propose Block-FeST, a blockchain-based Federated-Split Learning framework for anomaly detection using Transformers, that incorporates the strength of Federated and Split Learning (FSL). FL is employed to mitigate data privacy issues, while Split Learning (SL) is used to offload some computational overhead from constrained clients to a central server. Block-FeST is also capable of training a transformer model, which we demonstrate in the context of a temporal anomaly detection (AD) problem. Moreover, the use of a blockchain will generate an audit trail that can be used to address any challenges from the customers to take corrective actions. We have implemented our proposed solution and compared it against known centralized and decentralized baselines. Block-FeST achieves an accuracy of 86%, which is competitive with the other solutions, while providing additional benefits in terms of decentralization and client-side offloading.
Sharding and Its Impact on Fork Probability

Blockchain is an emerging technology that has encountered a number of challenges, including forks and low transaction processing rates compared to other payment systems. While sharding was designed to resolve the problem of low processing rates, it also increases the scalability of the network. However, its effect on the probability of forking is still unclear. Our primary goal in this study is to determine the impact of adding new shards to a blockchain on the probability of forks occurring. In order to achieve this goal, we first developed a novel simulator which enables us to simulate sharded networks. Then, we examined the effect of sharding on fork occurrence. Two EIP-1559 enabled networks containing 60 and 120 nodes have been studied in several experiments. As a result of our study, we have found that adding one shard on average results in a 60% reduction in the number of orphan blocks. In addition, we have proposed a fork probability model which results in 23% and 15% reductions for networks with 60 and 120 nodes, respectively.
Dissolving Metaphors in Peer-to-Peer Energy Trading: Towards a More Concrete Understanding of Metering, Legitimacy and Revenue Flows

Peer-to-peer (P2P) tokenised energy trading has seen much research interest in recent years. Despite this, the concrete physical workings of such marketplaces are rarely explained in the extant literature. This paper discusses notional P2P marketplaces with regard to their real-world implementation. The analysis of the physical/electrical layer and metaphorical token layer are kept distinct. Tokenised energy is conceptualised here as a dynamic license to consume, which serves as a flow of legitimacy for energy consumption in P2P schemes as typically proposed. Ledger structures, including centralised and decentralised/blockchain examples are examined. With the above points in mind, the workings of a notional blockchain-aware smart meter are described. The authors argue that while P2P energy trading may be a useful tool for regulators and for integration into smart token ecosystems, the drawbacks associated with decentralised tokenised energy markets may outweigh the apparent benefits at present.
IN[The Hate Booth]: a Gamified Installation to Counteract Hate Speech

Playing an online game, interacting on a social network or in a digital gaming community is part of the daily lives of most children and youth, with effects on the development of the personality, influence on the behavior and on the ability to manage conflicts. Studies and reports have been analyzing the interactions of online players, on gaming platforms and communities, as consumers and content producers, with the aim of understanding and finding effective ways to prevent hate speech from proliferating in these digital environments. In this article, we present a gamified installation, combining narrative and participatory approaches, as a response to the proliferation of online hate speech. The game-installation [IN]The Hate Booth consists of a light booth, where the interactor can find an interactive fiction game inspired by the videogame universe. This game will be the basis of a pedagogical itinerary, aiming to reflect on experiences with online hate speech and its effects inside and outside the virtual world [1, 2]. The initiative seeks to contribute to achieving and developing the sixteenth goal of the United Nations 2030 Agenda for Sustainable Development, Peace, Justice and Strong Institutions: the construction of peaceful and just societies, and effective, accountable and inclusive democracies at all levels.
PirouNet: Creating Dance Through Artist-Centric Deep Learning

Using Artificial Intelligence (AI) to create dance choreography is still at an early stage. Methods that conditionally generate dance sequences remain limited in their ability to follow choreographer-specific creative direction, often relying on external prompts or supervised learning. In the same vein, fully annotated dance datasets are rare and labor intensive. To fill this gap and help leverage deep learning as a meaningful tool for choreographers, we propose “PirouNet”, a semi-supervised conditional recurrent variational autoencoder together with a dance labeling web application. PirouNet allows dance professionals to annotate data with their own subjective creative labels and subsequently generate new bouts of choreography based on their aesthetic criteria. Thanks to the proposed semi-supervised approach, PirouNet only requires a small portion of the dataset to be labeled, typically on the order of 1%. We demonstrate PirouNet’s capabilities as it generates original choreography based on the “Laban Time Effort”, an established dance notion describing a given intention for a movement’s time dynamics. We extensively evaluate PirouNet’s dance creations through a series of qualitative and quantitative metrics, validating its applicability as a tool for choreographers.
Designing an Interactive 2-Level Circular Algorithm to Visualize and Support Collaboration in Science

Researchers are always trying to get the most important knowledge, however, finding accurate results in the current science deluge have never been more critical than now. While information technologies have changed the way we collect, store and retrieve information, there is a lack of strategies that support the exploration and presentation of scholar knowledge. Circular layouts are well-known structures for visualizing relations between entities, and as opposed to force direct layouts, are a more effective and user-friendly solution for visualizing relationships. This article presents a novel 2-level circular structure to map and support cooperation in science communities. The 2-level circular layout was designed and implemented using D3.js and is able to depict the relations between two different entities: the community that falls within a specific knowledge area (outer circle), and the articles curated by the community (inner circle). The goal is to support a collaboration scenario between researchers, who in turn share important knowledge based on their reading experience. The 2-level circular structure provide an overview plus details of the relations between these two entities. The preliminary results provide interesting results and pave the way for future research on a two-level layout. The link to access the visualization and code can be found at the end of the article.
Iterated Shape-Bias Graph Cut-Based Segmentation for Detecting Cervical Cancer from Pap Smear Cells

Cervical cancer is the most common type of cancer affecting the Indian women population. It is determined to be prevented by periodic screening which identifies and treats precancerous lesions in contrast to other cancers. Diversified screening methodologies that include traditional and advanced technologies are available for screening women cervix for the purpose of detecting cervical cancers and pre-cancers. No single screening test is perfect due to the manual error inherent in each cervical cancer detection technique. Computer-assisted approaches that use graph cut-based segmentation is potential in detecting abnormalities from the pap smear cell. In this paper, Iterated Shape-Bias Graph Cut-based Segmentation (ISBGCS) method is proposed for achieving excellence in diagnosing cervical cancer, since they are capable of accurately deriving the boundaries of nuclei and cytoplasm even when the boundaries are hazy and overlapping. This ISBGCS method facilitates effective detection of nuclei and cytoplasm boundaries even under irregular staining of pap smear cell screening process. In specific, it considers local edge consistencies and global shape with shape priors simultaneously to evaluate the segmentation process. The resultant cues are formulated into a graph cut with local and global shape priors for achieving optimal segmentation during cervical cancer diagnosis.
Combining Blockchain and Semantic Technologies: A Survey

We survey literature that combines blockchain and semantic technologies. Our findings identify main areas where the combination of blockchain with semantic technologies are considered, and answer key research questions, surveying existing challenges addressed, their advantages, technical perspectives, and future recommendations.
A systematic review of the use of blockchain in Internet of Medical Things

International health organizations advocate for health care with greater quality and safety, reflecting population needs. However, current health services are inefficient, facing problems of inadequate treatment, human resources, infrastructure, and high costs. Digital health technologies play an increasingly important role in healthcare services such as remote care and vital signs monitoring. The Internet of Medical Things (IoMT), which is a customized version of the Internet of Things (IoT), is increasingly important as it interconnects different medical devices, sensors, patients, and doctors, among others. This brings several benefits in improving health services. Nevertheless, in the IoMT environment, there are several challenges to overcome. Blockchain technology, a distributed ledger, with its immutability, transparency, and anonymity features, is being used in IoMT to help overcome these challenges. In this paper, a systematic review was carried out to identify the research topics of blockchain application in IoMT, the challenges in IoMT, how blockchain helps to overcome them, and the maturity stage of those works. The review results allowed us to identify five research topics in which the main ones are authentication and intrusion detection. This review shows an overview of current research on this subject, giving the possibility of directing other research works, or optimizing the existing ones.
Verifying Correctness of Smart Contracts with Conditionals

In this paper, we specify and verify the correctness of programs written in Bitcoin’s smart contract SCRIPT in the interactive theorem prover Agda. As in the previous article [1], we use weakest preconditions of Hoare logic to specify the security property of access control, and show how to develop human-readable specifications. In this article, we include conditionals into the language. For the operational semantics we use an additional stack, the ifstack, to deal with nested conditionals. This avoids the addition of extra jump instructions, which are usually used for the operational semantics of conditionals in Forth-style stack languages. The ifstack preserves the original nesting of conditionals, and we determine an ifthenselse-theorem which allows to derive verification conditions of conditionals by referring to conditions for the if- and else-case.
Blockchain-Enabled Economic Transactions: Recurring Financial Accruals and Payments

Economic transactions are based upon implicit or explicit contracts that set out the rights and obligations of the parties to the transactions. Blockchain technology can address fundamental financial, economic, and accounting challenges. We propose a blockchain-based prototype capable of capturing the essence of recurring economic transactions. For this purpose, we have devised an asset rental contract to show how economic transactions are recorded and tracked more effectively, efficiently, and in quasi-real-time, thus changing the traditional way of account keeping and auditing. We use the Ethereum blockchain as the most evolved and widely used smart contract platform. We showcase a significant discovery related to current blockchain technology limitations to making automatic non-custodial recurring payments. We show how the prototype can be extended to a range of recurring transactions.