Reviewerly/exHarmony huggingface dataset:
An evaluation of the economic benefits of rooftop distributed photovoltaic projects in the whole county in China

To promote the realization of the "dual carbon target", the implementation of rooftop distributed photovoltaic (RDPV) projects in the whole county will usher in large-scale development in China. However, comprehensive investigations into the economic benefits of RDPV projects in the whole county are still rare. This paper thus aims to evaluate the economic benefits of RDPV projects when considering multiple factors. The net present value (NPV), internal rate of return (IRR) and payback period (PBP) were used to analyze the financial feasibility of RDPV projects. The particle swarm optimization (PSO) algorithm was adopted to optimize the economic benefits of RDPV projects by calculating the different installation ratios of different building types, including industrial and commercial buildings, government buildings, public buildings, and residential buildings. A case study verified the feasibility of the optimization model of the economic benefits of RDPV projects in the whole county in China. The results revealed that the self-use ratio of power generation greatly impacted the economic benefits of RDPV projects and a single RDPV project became economically feasible when the self-use ratio of power generation was greater than 87.753%. When considering the limitations of the self-use ratio of power generation, installed capacity, and grid connection mode of different building types, compared to the single RDPV project, the economic benefits of RDPV projects on a county scale were not financially feasible based on the optimization model, which had an NPV of about −10.35 million yuan. Industrial and commercial buildings had the highest proportion of RDPV systems installed among the four building types, while residential buildings had the lowest proportion. The current results demonstrate the economic benefits of RDPV projects in the whole county, which will promote the development of RDPV projects and reduce carbon emissions in China. Therefore, this study is expected to provide investors of RDPV with recommendations for both decision-making and making scientific plans for the government.
Research on multimodal based learning evaluation method in smart classroom

In traditional learning contexts, teachers primarily assess students' behavior, emotional changes, and assignment completion to ensure teaching quality. Currently, there are challenges in evaluating students, such as assessments being insufficiently comprehensive and timely, a singular evaluation perspective that hinders the holistic consideration of factors affecting learning assessments, and a weak correlation among evaluation criteria, resulting in suboptimal evaluation outcomes. In recent years, with the rapid development and widespread application of artificial intelligence and information technology, the era of smart classrooms has arrived. New technologies like image processing and artificial intelligence offer opportunities for personalized support services and enhancing teaching quality. Therefore, to provide a more comprehensive and objective reflection of teaching quality, this paper proposes a multi-modal information fusion learning assessment model. This model is achieved by determining the weight values of three dimensions, cognitive attention, emotional attitude, and course acceptance along with their corresponding attributes. Subsequently, through a fusion strategy, it calculates the learning assessment score by integrating information from these three dimensions. A series of experimental data confirms the effectiveness of this approach.
Good Night, and Good Luck: A Control Logic Injection Attack on OpenPLC

Real hardware PLCs are quite pricey, and some-times are unaffordable for scientists/engineers to build up small testbeds, and conduct their experiments or academic researches. For all that, the OpenPLC project introduces a reasonable alternative option and offers flexibility in programming codes, simulating physical processes and also the possibility of being utilized with low-cost devices e.g., Raspberry Pi and Arduino Uno. Unfortunately, the OpenPLC project was designed without any security in mind i.e., it lacks protection mechanisms such as encryption, authorization, anti-replay algorithms, etc. This allows attackers to fully access the OpenPLC and makes unauthorized changes e.g., start/stop the PLC, setting/updating passwords, removing/altering the user-program, and others. In this paper we conduct intensive investigations and disclose some vulnerabilities existing in the OpenPLC project, showing that an attacker without any prior knowledge neither to the user credentials, nor to the physical process; can access critical information and maliciously alter the user-program the OpenPLC executes. All our experiments were conducted on the latest version of the OpenPLC i.e., V3. Our experimental results proved that attackers can confuse the physical process controlled by the infected OpenPLC. Finally we suggest security recommendations for the OpenPLC founder and engineers to close the disclosed vulnerabilities and have more secure OpenPLC based environments.
Enhancing Ocean Scene Video Captioning with Multimodal Pre-Training and Video-Swin-Transformer

With the success of multimodal pre-training models in the video-language field and various downstream tasks, previous multimodal models used 3DCNN networks as video feature extractors, which have limitations in interacting and fusing with text features. This paper proposes a multimodal pre-training model that utilizes a Video-Swin-Transformer-based network to encode both video and text data, to achieve better performance in video understanding. The model consists of four modules: video encoder, text encoder, interact encoder, and caption decoder to accomplish the task of ocean scene video captioning. A dataset of ocean scene videos, including various content types such as sea surfaces and shores, is also constructed. The training process is divided into two stages: pre-training and fine-tuning. Pre-training is performed on the Howto100m dataset to allow the model to learn video captions in natural scenes and complete video-language matching tasks. The fine-tuning stage is then performed on the ocean1000 dataset to better understand the events and content in ocean scene videos and generate captions that conform to ocean scene video descriptions. The model achieves satisfying results on both the public dataset YouCook2 and the proprietary dataset Ocean1000, demonstrating its ability in video-text information fusion and interaction.
ChatGPT Knows Your Attacks: Synthesizing Attack Trees Using LLMs

Attack trees are a popular method to represent cyberattack scenarios. It is often challenging for organizations to design attack trees for relevant systems and scenarios, as this requires advanced security expertise and the engagement of many stakeholders. In recent years, many studies in academic literature have proposed methods for automating attack tree creation from system models or from libraries of attack patterns. However, these approaches are not yet mature enough to be of practical use in organizations. The advent of large language models (LLMs) opens new opportunities for helping organizations in designing attack trees. We can envisage that organizations would be able to speed up attack tree design and benefit from LLMs like ChatGPT if they could rely on the quality of produced models. In this study, we investigate the feasibility of using ChatGPT to synthesize attack trees for specific scenarios. We propose a method to make ChatGPT to output attack tree-like models, we propose an approach to evaluate the quality of synthesized attack trees, and we evaluate these in two case studies. Our results show that LLMs like ChatGPT can indeed be valuable companions for designing attack trees. Yet, as expected, ChatGPT often fails to capture the meaning of the refinement operators, and the human analyst engaging with ChatGPT still needs to monitor the quality of the results.
Improving Human Resources’ Efficiency with a Generative AI-Based Resume Analysis Solution

Reviewing resumes has been becoming essential during the hiring process up to now. Moreover, the company needs to continuously recruit for various technical positions to match job demands and requirements. This, in turn, created an influx of applications that needed to be managed by the Human Resources (HR) department. The first step of the resume management process was to enter the applicants’ data from their resumes into the HR system. Because of the large number of applications received, this is a tedious and mind-numbing procedure for our employees, especially when the time and resources taken by it can be better used elsewhere. After data entry comes screening, analyzing, and matching the resumes to an available position. Like the previous step, the huge number of applications made it arduous and time-consuming to go through each application for vital information that would help place them in the right resume pile. In this paper, we introduce a solution to help the HR Department be more productive and efficient with resume analysis.
Data Analytics Anywhere and Everywhere

Mobile, ubiquitous, and immersive computing appear poised to transform visualization, data science, and data-driven decision making.
Olympus: a GDPR compliant blockchain system

Abstract Blockchain has been gaining significant interest in several domains. However, this technology also raises relevant challenges, namely in terms of data protection. After the General Data Protection Regulation (GDPR) has been published by the European Union, companies worldwide changed the way they process personal data. This project provides a model and implementation of a blockchain system to store personal data complying with GDPR. We examine the advantages and challenges and evaluate the system. We use Hyperledger Fabric as blockchain, Interplanetary File System to store personal data off-chain, and a Django REST API to interact with both the blockchain and the distributed file system. Olympus has three possible types of users: Data Subjects, Data Processors and Data Controllers and a fourth participant, Supervisor Authority, that, despite not being an explicit role, can perform all verifications that GDPR mandates. We conclude that it is possible to create a system that overcomes the major challenges of storing personal data in a blockchain (Right to be Forgotten and Right to Rectification), while maintaining its desirable characteristics (auditability, verifiability, tamper resistance, distributed—remove single points of failure) and complying with GDPR.
Computing the Heaviest Conflict-Free Sub-DAG in DAG-Based DLTs

In this paper, we consider DAG-based Distributed Ledger Technologies (DLTs), i.e., DLTs where each block can reference several previous blocks hence forming a Directed Acyclic Graph of Blocks (BDAG). Each block has a weight (usually a constant normalized to one) and our goal is to compute the heaviest sub-BDAG that does not contain conflicting blocks. First, we prove that computing such a sub-BDAG is NP-complete. Then, we show that the difficulty comes from con-current conflicts and we present an optimal algorithm that is polynomial if the number of concurrent conflicts is bounded. Finally, we evaluate the performance of our algorithm on random BDAGs against an existing algorithm called GHOSTDAG and show that, in addition to being optimal, our algorithm is also more efficient in practice.
Moving Towards Open Radio Access Networks with Blockchain Technologies

The Open Radio Access Network (O-RAN) introduces openness and intelligence into the existing, tightly-coupled RAN ecosystem. Openness promotes collaboration among various vendors to provide diverse components (hardware, software, or both) for the RAN ecosystem, while intelligence handles complex network activities using Artificial Intelligence (AI). Although O-RAN design addresses many issues in traditional RANs, such as vendor lock-in, lack of flexibility, and limited innovation, it raises several management and security concerns related to collaboration, access management, privacy, trust, and availability. Distributed Ledger Technologies (DLTs) offer a viable method of establishing trust among entities, managing resources efficiently, and automating complex network tasks. Several DLT properties, such as distributed architecture, high automation through smart contracts, immutability, and transparency, could position DLT-based ideas as game changers in a multi-vendor O-RAN ecosystem. Furthermore, this technology has the potential to introduce new business models and establish secure and trusted micro-payments among vendors and network participants. In this paper, we first present a taxonomy for discussing existing O-RAN challenges. Based on the taxonomy, we then examine the possibility of incorporating DLT-based solutions in O-RAN architecture to address these challenges.
Machine Learning Approaches for Abandoned Luggage Detection

Security of public spaces is a major concern, and one of the critical challenges in this context is the timely detection of abandoned luggage, which may pose security threats. Traditional methods for abandoned luggage detection often rely on manual monitoring, making them resource-intensive and prone to human error. In this paper, we present a comprehensive study on the application of machine learning techniques for abandoned luggage detection.Our research work focuses on utilization of Convolutional Neural Networks (CNNs) as well as conventional machine learning algorithms, involving Support Vector Machines (SVMs) and ensemble methods. We describe the data collection and preprocessing steps, encompassing diverse data sources, including video footage, images, and sensor data. The methodology section outlines the machine learning approaches, feature extraction, model architectures, and hyperparameters selected for the task.We evaluate performance of these models using a range of metrics, including precision, recall, F1-score, and, where applicable, Average Precision (AP). Our results demonstrate that the CNN-based model achieved impressive precision (0.92) and recall (0.87), indicating its effectiveness in accurately identifying abandoned luggage. Ensemble methods, such as Random Forest and Gradient Boosting, also exhibited competitive performance, offering flexibility in balancing precision and recall.
Demonstrating Trusted Blockchain IoT Device Based On TLS-PSK Secure Element

This demonstration presents an Internet of Things (IoT) device built around a secure element. According to the Blockchain IoT (BIoT) paradigm, it is able to generate Ethereum transactions of which data are sensor information or actuator states. All network communications are controlled by the secure element through the TLS pre-shared-key protocol. Specific requests are exported from secure element and unlock protected resources such as signing procedure. This avoids the misuse of the private key required for blockchain transaction generation.
EduChat: An AI-Based Chatbot for University-Related Information Using a Hybrid Approach

The digital transformation has created an environment that fosters the development of effective chatbots. Through the fusion of artificial intelligence and data, these chatbots have the capability to provide automated services, optimize customer experiences, and reduce workloads for employees. These chatbots can offer 24/7 support, answer questions, perform transactions, and provide rapid information, contributing significantly to the sustainable development processes of businesses and organizations. ChatGPT has already been applied in various fields. However, to ensure that there is a chatbot providing accurate and useful information in a narrow domain, it is necessary to build, train, and fine-tune the model based on specific data. In this paper, we introduce EduChat, a chatbot system for university-related questions. EduChat is an effective artificial intelligence application designed by combining rule-based methods, an innovative improved random forest machine learning approach, and ChatGPT to automatically answer common questions related to universities, academic programs, admission procedures, student life, and other related topics. This chatbot system helps provide quick and easy information to users, thereby reducing the time spent searching for information directly from source documents or contacting support staff. The experiments have yielded positive results.
Strong quantum entanglement based on two-mode photon-subtracted squeezed vacuum states

We present a strong quantum entanglement generation protocol based on two-mode photon-subtracted squeezed vacuum (TMPSSV) states. The generated entanglement shows higher entropy than those based on quantum catalysis, quantum scissors, photon-subtracted two-mode squeezed vacuum (PSTMSV) states, nonlocal PSTMSV states, and two-mode squeezed vacuum (TMSV) states in the ideal case. The impacts of experimental imperfections such as losses in the squeezed vacuum states and detection efficiency of single-photon detectors on the generated entanglement based on TMPSSV states are analyzed. When practical experimental imperfections are considered, higher logarithmic negativity than those of ideal PSTMSV states and ideal TMSV states are obtained for low initial squeezing level. Enhancement factor over 2 is obtained when the entanglement generated from imperfect non-Gaussian states passes through two lossy quantum channels in low initial squeezing level. Strong correlation is kept in lossy channels, which shows good robustness of the entanglement generation protocol based on TMPSSV states.
Developing 360° Virtual Tours for Promoting Tourism in Natural Parks in Chile

In recent years, the tourism industry has undergone substantial transformations, integrating new technology to lead to a new era termed Tourism 4.0. These innovations enhance the customer’s travel experience. An emerging trend in travel technology is the application of virtual and augmented reality, facilitating virtual tours that allow tourists to explore destinations without physically being there. We present the experience of developing 360∘ virtual tours for reserves and natural parks across the four provinces of the Maule Region in Chile including information, 360∘ images and videos, as well as several other interactions. A descriptive and correlational analysis was carried out with a total of 147 participants that evaluated the virtual tour, showing a positive correlation between the perceived attractiveness of the 360∘ experience and behavioral intention. Participants had an overall positive perception of the virtual experience; they found it involving and attractive, felt immersed in the environment, and were able to make decisions and interact with static and dynamic objects. The contributions of the present work are as follows: the process and what has been learned and can be recommended based on the experience for developing 360∘ virtual tours, combining dynamic and stationary elements in 360∘ in a virtual experience, and the results of an exploratory study that present the interaction with and user perceptions of the virtual environment.
Secure Storage of Computer Network Data based on Cloud Computing

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. User’s data is stored in large database. The stored data can be accessed and modified by the clients over the Internet. The data is monitored by the Third Party Auditor (TPA) on behalf of the client. Therefore, integrity is lacked by the data stored on the servers. The data integrity is ensured by the cloud services that provide trust to the privacy of users. Aiming at the urgent problem of network data security in cloud computing operations, this paper proposed a security situation assessment system to grasp the security situation in real time. A set of cloud computing network data storage security is proposed. Through this model, the extraction of network data storage security situation elements, the design of network data storage security situation assessment scheme and the calculation method of network data storage security situation value are completed. The experimental results show the error of the predicted value obtained by the network data storage security situation assessment system. The effectiveness of the system model and the superiority of the improved algorithm is verified by the experimental results. This paper uses the cloud model to predict the cloud computing network security situation. On the other hand, the security situation value obtained by the situation assessment process can be used directly without training the original situation value. Performance improvement by the proposed technique over existing technique is seen and it is observe that the proposed technique is 23% and 34% better than the existing techniques.
Saliency models perform best for women’s and young adults' fixations

Abstract Saliency models seek to predict fixation locations in (human) gaze behaviour. These are typically created to generalize across a wide range of visual scenes but validated using only a few participants. Generalizations across individuals are generally implied. We tested this implied generalization across people, not images, with gaze data of 1600 participants. Using a single, feature-rich image, we found shortcomings in the prediction of fixations across this diverse sample. Models performed optimally for women and participants aged 18-29. Furthermore, model predictions differed in performance from earlier to later fixations. Our findings show that gaze behavior towards low-level visual input varies across participants and reflects dynamic underlying processes. We conclude that modeling and understanding gaze behavior will require an approach which incorporates differences in gaze behavior across participants and fixations; validates generalizability; and has a critical eye to potential biases in training- and testing data.
Alexa, may I adopt you? The role of voice assistant empathy and user-perceived risk in customer service delivery

Purpose Powered by artificial intelligence, voice assistants (VAs), such as Alexa, Siri and Cortona, are at early-stage adoption rates in service contexts. Customers express hesitance in using the technology. Furthermore, the effect of a relevant variable (VA empathy) as a determinant of VAs is not widely researched. This study aims to extend the unified theory of acceptance and use of technology (UTAUT) and social response theory (SRT) to propose and test a conceptual model of the role of customer perceptions of VA empathy and risk on VA adoption and usage intensity. Design/methodology/approach In this study, data were collected from 387 VA users in the USA using a survey administered through Amazon MTurk. Data cleaning retained a final n = 318 for structural equation modeling analysis. Findings Findings show that perceived VA empathy enhances customers’ attitude toward VA and drives adoption, thereby increasing VA usage intensity. Perceived risk is a moderator; users with high perceptions of VA empathy have greater VA adoption rates when they have high (vs low) risk perceptions of using VA. Originality/value This research is one of the first known studies to provide empirical evidence of the role of customer perceptions of VA empathy and risk on VA adoption in service delivery. It goes beyond VA adoption research to provide empirical evidence of the impact of VA adoption on actual usage intensity. By extending the UTAUT and SRT, this research adds to the theoretical foundation for research on VA adoption, offering practical insights for firms regarding empathetic VA design to enhance customer service delivery.