Reviewerly/exHarmony huggingface dataset:
Research on global path planning of intelligent vehicles based on improved ant colony algorithm

Abstract According to the needs of intelligent vehicles, this paper proposes a global path planning algorithm based on improved ant colony algorithm to address the problems of slow convergence speed and multiple path bends in ant colony algorithm. By redefining the heuristic function of ant colony algorithm, adopting fallback and fill mechanisms, setting safe distances, modifying pheromone concentration update rules, and limiting the range of pheromone concentration, the ant colony algorithm is improved. And by deleting redundant points on the path, selecting key turning points and cubic B-spline curves for path smoothing, the planned path is shortest and smooth, which can meet the needs of path following. Through simulation, the improved ant colony algorithm has the advantages of fast convergence and smooth path.
Machine Learning Algorithm With TensorFlow and SciKit for Next Generation Systems

Machine learning plays a vital role in all major sectors like healthcare, banking, finance, and marketing. There is a need to understand the role and working of ML algorithms in a better way. Google also uses a learning algorithm to rank the web pages whenever we try to browse the internet to get the desired information. Understanding the platform and working of these algorithms is crucial for researchers. In this chapter, the authors have presented an overview of machine learning fundamentals and the working of these algorithms with suitable examples. They have also highlighted the importance of major machine learning libraries like TensorFlow and SciKit in developing and deploying vast applications. Finally, a case study of ML application is presented to better understand the concept. Future prospects of ML applications are also depicted in detail.
Genuine multipartite entanglement in three-qubit XY Z Heisenberg systems

Abstract Some measures of genuine multipartite entanglement in the three-qubit XYZ Heisenberg model are reported for a system that is embedded in a uniform magnetic field. A quantitative characterization of these measures is accomplished to determine the optimal parameters that provide maximal entanglement.
High-precision 3D measurement method based on optical projection technology

Micro-nano 3d measurement method based on fringe projection technology is widely used in many detection scenarios due to large non-contact, field of view, high accuracy and high adaptability. However, there is a problem that the nonlinear motion of the scanning actuator introduces random scanning error in the actual measurement system, which leads to the reduction of absolute accuracy and measurement repeatability. In this paper, a random scanning error suppression method is proposed to solve this problem. The principle of mutual calibration of the second order slope of the theoretical modulation curve and the second order slope of the actual modulation curve of the grating fringe is used to achieve the accurate value of the scan step. The feasibility and progressiveness of this method are proved by theoretical derivation, simulation and experimental verification, which demonstrate that the measurement repeatability is better than 1nm in simulation, the absolute precision is better than 3nm and the measurement repeatability is better than 2nm in experiment.
Generative Artificial Intelligence under German Copyright Law (Part 1)

This short series of two articles examines copyright issues relating to Generative Artificial Intelligence (GenAI), from acquiring training data for GenAI models, through the training process and operation of the models, to using their output. Part 1: A detailed look at the technical functioning shows that GenAI models are constructed in a way that often technically prevents or limits the possibility of copyright infringements. The text and data mining exception in § 44b of the German Copyright Act (UrhG) generally allows for legally compliant AI training. However, acquiring the training data and using the AI output can still infringe 3rd party rights, either by the GenAI developer or user. Part 2: With adequate human instruction and oversight, intellectual property rights to AI-generated results belong to the user; otherwise they are in the public domain. The article concludes with a schema for assessing the copyright status of GenAI outputs.Developers of generative AI must observe third-party copyrights when procuring and using training texts. The text and data mining exception in § 44b UrhG often allows legally compliant AI training. After training is complete, the finished GenAI model contains solely numerical values about statistical patterns between words or word parts. The original training data is never stored in the finalized GenAI model, because it is not usable for its operation. As a rule, developers even delete the texts originally used for training once the AI is fully trained. However, deleting the original data does not "cure" any earlier rights infringement. New training data is not added during ongoing operations, because for technical and liability reasons the GenAI model is "frozen" until release of the next version. These reasons suggest that once training has been completed, the developers can generally hardly commit any further copyright infringements on the original data.
NeighViz: Towards Better Understanding of Neighborhood Effects on Social Groups with Spatial Data

Understanding how local environments influence individual behaviors, such as voting patterns or suicidal tendencies, is crucial in social science to reveal and reduce spatial disparities and promote social well-being. With the increasing availability of large-scale individual-level census data, new analytical opportunities arise for social scientists to explore human behaviors (e.g., political engagement) among social groups at a fine-grained level. However, traditional statistical methods mostly focus on global, aggregated spatial correlations, which are limited to understanding and comparing the impact of local environments (e.g., neighborhoods) on human behaviors among social groups. In this study, we introduce a new analytical framework for analyzing multi-variate neighborhood effects between social groups. We then propose NeighViz, an interactive visual analytics system that helps social scientists explore, understand, and verify the influence of neighborhood effects on human behaviors. Finally, we use a case study to illustrate the effectiveness and usability of our system.
A Machine Learning-based Approach for Detecting Smishing Attacks at End-user Level

Smishing attacks, which target people through fraudulent text messages, are becoming one of the most prevalent social engineering attacks around the world. Personal information such as passwords, account information, or credit card numbers is sought by these messages and falling victim to one of these attacks could result in identity theft, financial loss, and unauthorized access to personal information. This research explores the pressing need for strong preventative measures against smishing attacks by examining the creation of new preventative measures while assessing the accuracy and flexibility of the current protection systems. Additionally, the viability of adding smishing detection to social media messaging platforms is explored, and the ability to identify smishing attempts made in various languages is investigated. As part of the research process, datasets are gathered, and various machine learning models, including SNN, CNN, and RNN are trained and evaluated. The integration of the Translator API into a mobile application to enhance the system’s capabilities by enabling multilingual detection is also considered. The highest test accuracy was achieved by the CNN model, and its efficacy in smishing detection was demonstrated. The robustness of the RNN model and its ability to generalize more effectively to new data were observed. The user-friendly elements of the mobile application, such as the floating bubble indicator, improve the user experience and promote preventive measures. A significant contribution to the field of smishing prevention is made by this study through the offering of an approachable way to identify and stop smishing attacks, ensuring a secure online environment.
A Method for Selecting a Suitable Cloud Computing Deployment Strategy

Cloud computing has opened the door to large-scale computing through the virtualization of physical resources. The advent of the cloud computing era has been a key catalyst for the migration of many applications from traditional on-premises servers to the cloud. The ability to dynamically allocate and use new virtualized resources has freed cloud users from the burden of managing physical infrastructure, since the cloud provider is responsible for the maintenance and all associated administrative tasks. This research work aims to provide organizations with a process that allows them to select the most suitable cloud deployment strategy according to their specific business and IT needs. To do so, this paper proposes a four-step method that identifies the most appropriate deployment strategy using the fundamental concepts of cloud computing including virtualization, cloud deployment models and the main cloud computing services, namely Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). The proposed method is intended to be a usable and practical tool to support users in their decision-making process. The method was tested via a case study within an organization specializing in Business To Business (B2B) marketing and findings show its conceptual soundness.
Towards Fractional NFTs for Joint Ownership and Provenance in Real Estate

Owning a house or investing in real estate can present challenges for many individuals. While fractional ownership offers potential solutions, establishing joint ownership among multiple participants poses obstacles that hinder its widespread adoption and limit accessibility. In response to these challenges, we propose JOINFT, a blockchain-based solution that utilizes fractional non-fungible tokens (F- NFTs) for facilitating joint ownership in real estate. JOINFT aims to enhance accessibility, empower individuals to participate in property ownership, and promote affordability while ensuring secure and transparent transactions. This paper provides an overview of the JOINFT system, including key participants, platform structure, functionality, and the utilization of the group level agreement. Additionally, our proposed method incorporates provenance information to capture user participation history within groups which contributes to the calculation of their trust score. To evaluate the effectiveness of JOINFT in enabling joint ownership through F-NFTs, we developed a prototype and conducted test transactions within an integrated development environment that supports decentralized applications and smart contracts. The evaluation demonstrates the effectiveness of utilizing F- NFTs for joint ownership in the real estate industry. It validates JOINFT’s ability to enhance accessibility, affordability, and transparency and trust in real estate transactions. Nevertheless, as JOINFT is an ongoing project, further research is required on regulatory frameworks, wider acceptance, and the potential of F-NFTs, trust scores, and provenance information in joint ownership in the real estate industry.
Interactively Exploring API Changes and Versioning Consistency

Application Programming Interfaces (APIs) evolve over time. As they change, they are expected to be versioned based on how changes might affect their clients. In this paper, we present two novel visualizations specifically designed to represent all structural changes and the level of adherence to semantic versioning practices over time. They can also serve for characterizing and comparing the evolution history of different Web APIs. The API Version Clockhelps to visualize the sequence of API changes over time and highlight inconsistencies between major, minor, or patch version changes and the corresponding introduced breaking or non-breaking changes applied to the API. The API Changesoverview aggregates all changes to an OpenAPI (OAS) description, highlighting the unstable vs. the stable elements of the API over its entire history. Both visualizations can be automatically created using the APIcTURE, a command-line and web-based tool that analyzes the histories of git code repositories containing OAS descriptions, extracting the necessary data for generating visualizations and computing metrics related to API evolution and versioning. The visualizations have been successfully applied to classify, compare, and interactively explore the multi-year evolution history of APIs with up to hundreds of individual commits. Video URL: https://youtu.be/WtFm6VvKi20
Multi-Task Transformer Visualization to build Trust for Clinical Outcome Prediction

Clinical decision support systems based on machine learning are a rising application in healthcare. Early detection of deteriorating conditions provide the opportunity for medical intervention in hospital patients. Recent approaches increasingly rely on Large Language Models such as BERT, because patient data is often in the form of structured temporal data. These models are notoriously hard to interpret and therefore to trust, while precisely trust is an essential principle for technology in healthcare. We develop a visual analytics system to inspect, compare, and explain pre-trained transformer models for a given clinical outcome prediction task. The work is developed on the basis of a large hospital patient dataset and prediction tasks for acute kidney injury and heart failure. Discussion with healthcare professionals confirms that our system can lead to a faster decision process and improved modeling results.
Privacy-Preserving Genomic Analysis via PSO-Driven Federated Learning on Blockchain

Federated learning, a distributed machine learning method, trains statistical models over remote devices or servers with local data, without exchanging data samples. It preserves patient data securely behind hospital firewalls, sharing only anonymous model parameters, enhancing privacy and security. This innovative approach exchanges only untraceable learned feature representations. This study introduces a new federated learning framework an innovative aggregation methods based on Particle Swarm Optimization (PSO) algorithm for breast cancer prognosis. To safeguard patient data privacy, a privacy mechanism is implemented at the user's end. By utilizing the federated learning model, they've addressed the data scarcity problem, leading to improved accuracy in breast cancer progno-sis. Federated learning, as mentioned earlier, protects personal data by sending model parameters to agents instead of raw data to a central node, keeping data localized. However, the risk of malicious nodes injecting fake data into the global model is a concern. To address this, we need a verification mechanism to authenticate senders and their training data while preserving data privacy. We use zero-knowledge proof (ZKP) for verification without exposing raw data for this part. At the outset of the federated learning process, each client computes the Merkle tree root hash of their local training data and submits it to the federated learning contract for participation. In subsequent training rounds, the client's compiler checks the integrity of their data by comparing the Merkle tree root hash to the initial one. If they match, a trace file is generated and sent for proof generation, verification, fact creation, and registration. Leveraging the TCGAbiolinks package, we conducted experiments demonstrating the effectiveness of our approach with real data. Learner clients enhance their local models using a PSO algorithm, achieving remarkable results in binary classification tasks with 400 features while preserving patient data privacy via ZKP integration. Our framework reaches 97% accuracy with eight clients, compared to a comparable system's 82%, highlighting its practical applicability and efficiency in real-world scenarios, particularly when dealing with larger numbers of participants.
Understanding the NPM Dependencies Ecosystem of a Project Using Virtual Reality

Modern JavaScript development relies heavily on using Node Package Manager (NPM) modules. These modules are related by dependency relationships, possibly requiring dozens or hundreds of modules to build a complete JavaScript web application. Studying dependencies, in terms of their sustainability, vulnerability, size, defects, etc., is fundamental for the deployment and maintenance of JavaScript web applications. We use a 3D metaphor based on presenting dependencies as an “elevated city”, mapping both dependency relationships and characteristics of interest of each module. We developed a VR (virtual reality) scene representing the dependencies of several web applications using the elevated city metaphor, and exposed industrial experts to it to check its suitability. They explored a medium-sized project, with more than 200 dependencies, sharing their insights. The results highlight different aspects of our approach and how the combination of metrics helps experts to obtain insights from the ecosystem. The feedback shows the usefulness of the visualization to check and explore several aspects of the dependencies of an application, helping to identify problems related to maintainability, license usage, or vulnerabilities, and to design strategies to address them.
Distributed Pseudorandom Generation Protocol Based on VRF Algorithm

VRF or Verifiable Random Function is one of the most successful randomness generation solutions in smart contract security field. Today's centralized solutions based on this algorithm are not fully transparent for participants which rises security issues. In this paper, we review the classic system, its vulnerabilities, and propose an extended protocol to maintain the security features of pseudorandom number generation for trustless systems.
Cloud- Honeypot and Differential Privacy Empowered Interactive Heat Protection Against Public Opinion Analysis

With the rapid development of cloud social networks, the expression of opinions by individuals has witnessed significant growth. However, unregulated dissemination of public opinion can lead to severe consequences. This paper addresses the challenges associated with public opinion heat analysis, focusing on the potential misuse of user data by malicious entities. We propose a privacy protection mechanism that combines differential privacy theory and cloud-honeypot technologies, which is against scraper to ensure the legitimate use of data and safeguard user privacy. We propose a differential privacy mechanism with Laplace noise, which adds Laplace noise to the number of reposts, comments, and likes of the Weibo posts published by users, making it impossible for attackers to calculate the correct interactive heat and thus protecting the privacy of Weibo interaction popularity. At the same time, we propose a privacy protection mechanism which is applicable in combination with honeypot technology. After the cloud server identifies that the visitor is a malicious scraper, it redirects them to the honeypot webpage. This modified webpage incorporates additional Laplace noise, thereby enhancing the security measure. Additionally, we design an experiment to simulate the impact of adding noise to the data and compare the results to verify the feasibility of the differential privacy mechanism in countering scrapers. Finally, we discuss future directions for anti-scraping strategies based on differential privacy.
Research on the Construction of Secondary Psychological Counseling Station in Colleges and Universities Under the “Internet+” Environment by Data Analysis

With the rapid development of Internet technology, psychological education in colleges and universities is facing unprecedented challenges. The characteristics of the “Internet +” era have affected the mental health of college students to a certain extent. At the same time, teachers' understanding and application of “Internet +” is far from enough. Therefore, under this new situation, how to use the power of cloud computing and data analysis to optimize the construction of secondary psychological counseling stations in colleges and universities and play their unique role in mental health education has become a new topic. Overall, the “Internet +” era has brought challenges as well as opportunities. The secondary psychological counseling stations in colleges and universities should actively apply new technologies such as cloud computing and data analysis to optimize services, improve work efficiency, and better serve college students' mental health. Based on the mental health data of college students obtained from big data, this paper adopts mean clustering and C4.5 algorithm to find out the factors leading to the mental health problems of college students through data analysis, and propose feasible solutions to the problems.
Breaking Silence, Embracing Inclusivity: The Power of AI in Communication

This study presents a cutting-edge web platform powered by artificial intelligence (AI) that enables seamless communication for people of all abilities. By utilising the power of artificial intelligence (AI) and machine learning (ML), the platform meets the communication demands of the blind, deaf, and mute. With the use of text-to-speech, text-to-text, text-to-speech, and hand gesture detection driven by AI, users may effectively communicate. Additionally, the site provides video calls and browsing speech recognition technologies. It also has two emergency indicators so that mute people can readily access emergency services like ambulances or police. The platform's success depends on advancing kind technology, closing gaps in traditional communication channels, and fostering an inclusive community for users of varied abilities. The research explores the potential impact of this innovative communication platform, incorporating user feedback to highlight its positive effects on the lives of people with disabilities.