Reviewerly/exHarmony huggingface dataset:
BMP: An Evaluation System for Bitcoin Mining Pools

With the in-depth research and cross-domain applications of blockchain in various industries, blockchain mining attracts great attention. A comprehensive and in-depth evaluation of mining pool is essential to the Bitcoin network supervision as well as the reduction of carbon emission. However, due to the difficulty of obtaining mining pool internal data, the current evaluation is limited to a single mining power indicator. In this article, we build a four-layer mining pool evaluation model, named BMP, based on the analytic hierarchy process (AHP). The model contains three first-level indicators and nine second-level indicators, covering various aspects of the performance of mining pool. Miner address extraction provides basic data for the calculation of these indicators. By utilizing the model proposed, we evaluated and ranked eight mainstream mining pools, which broadcasted 71.69% of the blocks during the period January 1, 2021–July 31, 2021. Through comparative analysis of evaluation result, we find that the 65% decline of mining power in June did not pose a threat to the stability of Bitcoin system. In addition, we also revealed the reasons for various fluctuations.
Analysis on Key Technologies of New Energy Internet Information Communication

PickAt present, with the development of science and technology, how to realize the sustainable development and full utilization of energy has become one of the important tasks in the new era. In order to ensure the full utilization or reuse of energy, the concept of “energy Internet” is gradually put forward under the support of information and communication, which takes the power system as the core and is accompanied by automatic control technology, information technology and renewable energy technology. With the development of advanced technologies such as natural gas, oil, coal, transportation, etc., renewable energy units are mainly distributed and centralized. Under information technology, data can interact in two directions, and the system network presents multiple forms, such as covering natural gas, oil, coal, transportation, etc., gradually forming a new energy utilization system, which is also the basic idea of energy Internet. This paper will analyze the key technologies supporting energy Internet information communication.
JRS: A Joint Regulating Scheme for Secretly Shared Content Based on Blockchain

For the sake of privacy, encrypting the content shared on the Internet is becoming popular. However, it brings a nontrivial challenge in regulating the illicit shared content (e.g., viruses, rumors, malicious code, etc.). To deal with it, lots of schemes have been proposed, which mainly rely on a third-party—regulatory authority (RA), to perform auditing, regulating or supervising exclusively. Although these schemes mitigate the problem of illicit shared content, they still suffer from the following two issues. On one hand, relying on a single RA alone may lead to the serious problems of injustice and inadequate regulating. On the other hand, users’ privacy is violated since an independent RA unconditionally inspects all the files shared by users. Therefore, we propose a joint regulating scheme (JRS) for secretly shared content based on threshold secret sharing algorithm and blockchain technology. To make the censor activities democratic and public, they are authorized by both the RAs and blockchain miners during which two threshold secret sharing algorithms are applied. Further, only the suspicious users (or files) are censored and the whole process is recorded as blockchain transactions to balance the user privacy and censoring fairness. At last, the results of security and performance analysis show that JRS can resist the known attacks and the cost is acceptable in practice.
Geosemantic Interoperability and the Geospatial Semantic Web

This chapter gives an account of the semantic interoperability of geographic informationgeographic information (GI) interoperability and the various elements involved in it. After providing some historical background in Sects. 17.1–17.3 introduce some basic notions about how human beings abstract phenomena and represent them in human memory. This is fundamental for understanding the purpose of semantics in interoperability. This leads us to introduce the concept of ontologyontology in Sect. 17.4, where we describe its role with respect to semantics. Based on these notions, semantic interoperability of geographic informationgeographic information (GI) interoperability (i.e., geosemantic interoperability) is then compared in Sect. 17.5 to a communication process. Typically, the interoperability of geographic information is implemented by way of geospatial data infrastructures (SDI)geospatial data infrastructures (SDI), and Sect. 17.6 describes the implementation of geosemantic interoperabilitygeosemantic interoperability through SDI with the introduction of the geospatial information environment. Standards are fundamental to interoperability for geographic information, and Sect. 17.7 provides an overview of the contribution of standards bodies with respect to geosemantic interoperability, including the development of ontologies. Then, Sect. 17.8 provides a description of the valuable contribution the Semantic Web to semantic interoperability and the description of the geospatial Semantic Web. Finally, the conclusion provides some concluding remarks on the chapter.
Limaco̧n Inspired Particle Swarm Optimization for Large-Scale Optimization Problem

Gupta, Shruti Kumari, Rajani Kumar, SandeepLarge-scale optimization problems are a complex problem in the class of NP-Hard. These problems are not solvable by traditional methods in a reasonable time. Single machine total weighted tardiness scheduling problem (SMTWTSP) is a complex problem in this category. It has a set of different events with varying criteria that need to be scheduled on one machine. The main aim of this problem is to find the minimum possible total weighted tardiness. Particle swarm optimization (PSO) algorithm has performed admirably in the field of optimization. To solve complex optimization problems, several new variants of this algorithm are being developed since its inception. This work proposed an influential local search (LS) technique inspired by limaco̧n curve. The new local search is hybridized with PSO and named Limaco̧n inspired PSO (LimPSO) algorithm. The efficiency and accuracy of the designed LimPSO strategy are tested over the large-scale SMTWTS problem, which shows that LimPSO can be considered an effective method for solving the combinatorial optimization problems.
OntoTrace: A Tool for Supporting Trace Generation in Software Development by Using Ontology-Based Automatic Reasoning

Traceability in software development has gained interest due to its software maintainability and quality assurance benefits. Artifacts such as code, requirements, mockups, test cases, among others, are feasible trace sources/targets during the software development process. Existing scientific approaches support tasks like identifying untraced artifacts, establishing new traces, and validating existing traces. However, most approaches require input existing traceability data or are restricted to a certain application domain hindering their practical application. This contemporary challenge in information systems engineering calls for novel traceability solutions. In this paper, we present OntoTrace: a tool for supporting traceability tasks in software development projects by using ontology-based automatic reasoning. OntoTrace allows software development teams for inferring traceability-related data such as i) which are the traceable source/target artifacts; ii) which artifacts are not yet traced; and iii) given a specific artifact, which are the possible traces between it and other artifacts. We demonstrate how OntoTrace works in the context of the Swiss startup LogicFlow AG, supporting the traceability between functional/non-functional requirements and user interface test cases. We conclude the paper by reflecting on the experience from applying the approach in practice, and we draw on future challenges and next research endeavors.
Revisiting PGD Attacks for Stability Analysis of High-Dimensional Nonlinear Systems and Perception-Based Control

Many existing region-of-attraction (ROA) analysis tools find difficulty in addressing feedback systems with large-scale neural network (NN) policies and/or high-dimensional sensing modalities such as cameras. In this letter, we tailor the projected gradient descent (PGD) attack method as a general-purpose ROA analysis tool for high-dimensional nonlinear systems and end-to-end perception-based control. We show that the ROA analysis can be approximated as a constrained maximization problem such that PGD-based iterative methods can be directly applied. In the model-based setting, we show that the PGD updates can be efficiently performed using back-propagation. In the model-free setting, we propose a finite-difference PGD estimate which is general and only requires a black-box simulator for generating the trajectories of the closed-loop system given any initial state. Finally, we demonstrate the scalability and generality of our analysis tool on several numerical examples with large state dimensions or complex image observations.
The Comparative Performance Analysis of Clustering Algorithms

Clustering is a standard paradigm for associating some similar objects to a cluster while associating others to their respective clusters in an unsupervised manner. Different techniques are proposed in the domain of clustering depending upon the underlying methodology slightly vary from one another. Some of these are regarded as Hierarchical Clustering, K-means Clustering, Spectral Clustering, Affinity Propagation and Density based spatial Clustering. Their strengths and weaknesses are different for different datasets. In this research, we have used three benchmark datasets to analyze the merits and demerits for each of the clustering techniques. After applying these algorithms to the datasets named letter, glass and wine, we presented the critical review on why a specific clustering algorithm lacks with respect to execution time and clustering quality measured by Silhouette’s coefficient and why an algorithm performs better than others on the same dataset.
Smart Recommendation System for Hollywood Movies Using Cosine Similarity Index

Recommender System is essential in our social lives as it aims to enhance the user's comfort. Movie Recommendation System caters to all the requirements of the movie viewers. The paper provides relevant content to the end-users out of relevant and irrelevant content available on the Internet. Users view the movies and relate them with sentiments. In the paper, sentiment analysis is an essential function that is implemented with the help of various machine learning algorithms and achieving higher accuracy. The procedure involved in implementing this paper includes data collection, data preprocessing, data cleaning, vectorization, etc. Data cleaning and preprocessing are done so that the algorithms can be easily applied to the dataset. Various algorithms are implemented on the reviews dataset, and the algorithm with a higher accuracy level is selected. The machine learning model is trained and tested, and a sentiment classifier is generated, classifying the sentiments as good or bad. Thus, the proposed model generates accurate and personalized recommendations along with an analysis of ideas.
Texture-Based Seafloor Characterization Using Gaussian Process Classification

Synthetic aperture sonar (SAS) allows for high-resolution imagery of the seafloor with a quality that approaches that of an optical camera. Identifying seabed types within SAS images can prove very useful in a setting where autonomous underwater vehicles (AUVs) are used, since information about the bottom type can be used to determine the behavior of the AUV. In this article, we present a new algorithm for the automatic detection of different seabed types based on SAS image texture. Using a Gaussian process classification (GPC) model, we were able to predict the seabed type, as well as an uncertainty estimate on this prediction. The GPC-based model is able to learn the most informative textures from a training data set, and by construction is robust against outliers or data points that are very different from those in the training set. An additional benefit of the model is that it can be easily configured to hit the desired tradeoff between computational complexity and accuracy.
Lessons learned from teaching: Reinventing the librarian in credit-bearing courses

Delaware Valley University is a private university located about one hour north of Philadelphia in bucolic Bucks County.The librarians at its Krauskopf Memorial Library have faculty status, which allows us to serve on faculty committees and teach credit-bearing courses.Despite this, librarians have only taught the first-year experience course, which is otherwise taught primarily by staff.This course is heavily scripted and not owned by the faculty, unlike other courses.It does not offer opportunity for course development or for building relationships with teaching faculty, both of which are important for instruction librarians.Most of our instruction librarians only have experience teaching one-shot information literacy sessions and are rarely embedded into a course.Much of the literature on semester-long library classes focuses on information literacy skills.These courses are designed by librarians as an antidote to the one-shot.But what if the information literacy was incorporated into course development?Could students learn research skills in a way that supported the ACRL Framework for Information Literacy for Higher Education and inspired students' interests?I was invited to coteach an honors colloquium with an assistant professor in the Plant Science department in fall 2020.We had previously worked together in her other courses.However, I had always been a guest in her classroom.Honors colloquia are one-credit classes that meet 50 minutes per week.These classes are required for freshman and sophomore honors students across all majors.Typically, honors colloquia are discussion-based courses designed to stimulate students to engage in analysis and critical thinking about an interesting topic.My coinstructor wanted to explore how ecosystems change and to cover a handful of specific incidents.The working title was "Pests and Plagues: How Natives and Exotics Shaped our Ecosystem."I hoped to use this as an opportunity to teach students how to
Key processes and popular analyses in the SEM family of techniques

This paper reviews the family of techniques found in structural equation modeling (SEM). Specifically, the review covers historical, mathematical, and graphical representations of SEM. The central focus of the paper is on the process of conducting SEM studies along with technical issues involved in the analysis. Topics such as identification, specification, estimators, and fit indices are highlighted. A review checklist is provided along with a discussion of continual issues in SEM. Finally, popular and more recent approaches are briefly discussed.
Blockchain for future renewable energy

To better optimize and control the renewable energy system and its integration with traditional grid systems and other energy systems, corresponding technologies are needed to meet its growing practical application requirements: decentralized management and control, support for decentralized decision-making, fine-grained and timely data sharing, maintain data and business privacy, support fast and low-cost electricity market transactions, maintain the security and reliability of system operation data, and prevent malicious cyberattacks. Blockchain is based on core technologies such as distributed ledgers, asymmetric encryption, consensus mechanisms, and smart contracts and has some excellent features such as decentralization, openness, independence, security, and anonymity. These characteristics seem to meet the technical requirements of future renewable energy systems partially. This chapter will systematically review how blockchain technology can potentially solve the challenges with decentralized solutions for future renewable energy systems and show a guideline to implement blockchain-based corresponding applications for future renewable energy.
A Morphological Image Preprocessing Method Based on the Geometrical Shape of Lesions to Improve the Lesion Recognition Performance of Convolutional Neural Networks

Convolutional neural networks (CNNs) play an important role in computer vision-related tasks for medical imaging. However, the quality of raw images in the dataset can be insufficient for the CNN model to learn the features of the target object. When the input image contains a complex background, the CNN model focuses on regions that are not essential for lesion recognition, such as background structures, leading to less accurate output prediction. This paper shows that this problem can be efficiently solved by an image preprocessing method based on mathematical morphology, which uses a priori knowledge about the lesion shape. The proposed method consists of h-dome transformation based on the geometrical shape information of the lesion region, and subsequent image histogram-modification processes, and has the ability to selectively enhance the lesion region from the background. This allows for the creation of images that explicitly represent the important region to be learned by the CNN model. Experiments on pulmonary nodule classification in chest x-ray images and skin lesion-region segmentation in dermatoscopic images demonstrate that the CNN models trained on the preprocessed dataset created by the proposed method achieve remarkable performance improvements compared to the CNN models trained on the original dataset.
Centralize Storage System with Encryption vs Decentralize Storage System Using Blockchain

In Today’s world, data management and security are top priorities. As of now, the online industry is very powerful & demanding, the data is getting increasing day by day. Several solutions are being provided but still, the research is going on. To improve data privacy and security several companies provide a cloudstorage system to store multiple user data simultaneously in the same location. The data owner can access his data from anywhere via the internet. Several companies and startups are investing in these companies for providing data storage on the cloud for their users. This demanding storage system is attracted more companies to invest in user data in the cloud. But the serious concern about this type of cloud storage system is Security breaches. Several Past Incidents have been recorded in big companies like dropbox where data were leaked from the cloud. To solve this problem of security, researchers tried a hybrid cryptography method to provide secure storage on the cloud. This paper introduces one of the cryptography models used for cloud security and aims to find a model that enhances cloud security using hybrid cryptography. This paper also shows an Execution time analysis of our hybrid cryptography centralized cloud storage model. But the major problem is that if the data is stored in the cloud, it is costly, also as maintenance is a lways required. Also, this is a Centralized cloud system which makes it easier to hack. Nowadays Blockchain technology is introduced to store users’ data in a Decentralize way. Hence, this paper also introduces a decentralized cloud storage framework using blockchain & IPFS networks with access control to store data in a secure way and then the small table of comparison of both the solutions.
Latent class and latent class regression

The purpose of this chapter is to provide an introduction to Latent Class Analysis (LCA) with a concrete example of LCA and LCA-Regression for researchers and analysts new to this technique in pharmacy and pharmacy administration. LCA is a mathematical technique for examining relationships among observed variables when there may be collections of unobserved categorical variables. LCA has focused on polytomous observed variables, but recent work has extended the types of data that can be utilized. Included in this introduction are discussions concerning data assumptions, sample size arguments, estimation methods, and fit indices. Additionally, basic reporting guidelines for the information that should be included in a manuscript are submitted for review. For the analysis, LatentGold is used, but I also include basic R code for running LCA and LC Regressions with the poLCA package.
Experimental Approach of the Quantum Volume on Different Quantum Computing Devices

Quantum Computing is one of the most promising technology advancements of our time, promising to clarify problems considered unsolved for a classical computer. Real Quantum Computer Devices—once a science fiction concept—are now a reality. Many challenges still remain, on the way to achieve the so-called quantum supremacy. A phenomenon called Quantum Decoherence is the main cause of noise, making the available Quantum Computer Devices now limited to a few dozens of qubits at present. Despite that, most of the IT giants as IBM, Google and Rigetti are investing large amounts of money on deferent technological approaches to implement such devices and giving free access through the cloud not only to researchers but to common users also. So, it is important to evaluate and compare Quantum Computers of different philosophy, a process far more difficult than the equivalent for a classical computer. Quantum Volume represents a metric which measures the Quantum Computing Devices’ reliability in terms of error rates. In this paper, quantum circuits implementing Hadamard and CNOT gates were examined on three dif-ferent IBM Quantum Computer Devices in order to experimentally investigate Quantum Volume and attempt to provide a meaning of this metric. Data of these experiments are presented and additionally, different metrics are used to compare the performance of the Quantum Computer Devices.
Temporal Logic Guided Meta Q-Learning of Multiple Tasks

Reinforcement learning (RL) based approaches have enabled robots to perform various tasks. However, most existing RL algorithms focus on learning a particular task, without considering generalization to new tasks. To address this issue, by combining meta learning and reinforcement learning, we develop a meta Q-learning of multi-task (MQMT) framework where the robot effectively learns a meta model from a diverse set of training tasks and then generalizes the learned model to a new set of tasks that have never been encountered during training using only a small amount of additional data. Particularly, the multiple tasks are specified by co-safe linear temporal logic specification. As a semantics-preserving rewriting operation, LTL progression is exploited to decompose training tasks into learnable sub-goals, which not only enables simultaneous learning of multiple tasks, but also facilitates reward design by converting non-Markovian reward process to Markovian ones. Reward shaping is further incorporated into the reward design to relax the sparse reward issue. The simulation and experiment results demonstrate the effectiveness of the MQMT framework.