Reviewerly/exHarmony huggingface dataset:
Mental States: A Key Point in Scam Compliance and Warning Compliance in Real Life

The internet's convenience and anonymity have facilitated different types of covert fraud, resulting in economic, mental, and social harm to victims. Understanding why people are deceived and implementing appropriate interventions is critical for fraud reduction. Based on the Bayesian brain theory, individuals' mental states may be a key point in scam compliance and warning compliance. Fraud victims with different mental states may construct various hypotheses and explanations about the fraud they are exposed to, causing different cognition and behavior patterns. Therefore, we first conducted a semi-structured in-depth interview with online fraud victims to investigate the individual and social factors that affect victims' mental states. Grounded theory analysis showed five core factors influencing scam compliance: psychological traits, empirical factors, motivation, cognitive biases, and emotional imbalance. Based on our findings of psychological processes and deception's influential factors, we then designed warnings to inform victims of fraud, particularly for those involving novel types of scams. Tested on a real-life setting, our designed warnings effectively enhanced warning compliance, allowing more fraud victims to avoid financial losses.
Application and Design of Drama Popular Science Education Using Augmented Reality

In the 21st century, the research and development of all kinds of intelligent equipment have become an increasingly important part of people’s daily life. Various emerging technologies are developed based on hardware equipment, such as augmented reality (AR) and virtual reality (VR), which fully utilize computer vision technology to accomplish various purposes. Nowadays, it is difficult to carry out opera popular science education in China. Because opera teaching is boring and some traditional opera knowledge is obscure and difficult to understand, it cannot arouse students’ interest in learning. Therefore, this article uses VR technology to stimulate students’ learning initiative and complete drama science popularization education with rich and colorful teaching means. It first briefly introduces the concept and characteristics of AR technology. After that, it establishes an AR-enabled drama application as an education model based on the statistical entity recognition algorithm and designs the drama popular science education app. Besides, it completes the drama animation design, character modeling design, drama scene design, logo pattern design, and animation design. Finally, from the two points of students’ mastery of opera and learning opera knowledge, the actual use effect of this application is analyzed. The results show that this application can help students better learn opera: 42% of students can better master opera singing, 23% of students say they can master other types of opera, 18% of students can better learn and master opera movements and posture, and 10% and 7% of students can master a lot of opera knowledge.
An intelligent collaboration framework of IoT applications based on event logic graph

Realizing the dynamic collaboration between Internet of Things (IoT) services is the key to developing intelligent IoT applications. However, heterogeneity, changeable invocation logic, and spatio-temporal constraints of the IoT services are significant challenges. To solve these problems, a framework based on the event logic graph is proposed. It employs the event logic graph to describe the collaboration between IoT services and uses related methods to dynamically select appropriate services according to the spatio-temporal constraint. Specifically, event instances are first recognized from data generated by IoT services with a divide-and-conquer-based strategy. Next, event logic graphs are constructed based on the serial episode mining method. The related event prediction method is established for determining the service invocation logic. And a spatio-temporal constrained mechanism is designed to select the appropriate services. The proposed framework is applied to wheel manufacturing to promote autonomous production and is further compared with related works. The results show the feasibility of the proposed framework and its advantages in adaptability, dynamics, and spatio-temporal relevancy. • An event logic graph information model based on Linked Data is proposed, which can solve the heterogeneity problem of IoT services. • A data-driven event logic graph construction method and related event prediction method are proposed, which can discover the complex invocation logic of IoT services and determine the services to be executed according to the context. • A spatio-temporal constrained service selection mechanism is proposed, which can guarantee the efficient use of IoT resources under spatio-temporal constraints.
Analysis of Improved Evolutionary Algorithms Using Students’ Datasets

Evolutionary Algorithms (EAs) are powerful heuristic search approaches which relies on Darwinian evolution that capture global solutions to complex optimization problems which has powerful features of reliability and versatility. (EAs) such as Particle swarm optimization (PSO) is a global optimization method that is extremely effective. PSO's flaws include slow convergence, premature convergence, and getting stuck at local optima. In this paper, chaotic map and dynamic-weight Particle Swarm Optimization (CHDPSOA) are combined with PSO to enhance the search strategy through adjusting the inertia weight of PSO and changing the position update formula in the (CHDPSOA), resulting in efficient balancing for local and global PSO feature selection processes. The performance of CHDPSOA was compared to that of three metaheuristic techniques: Differential Evolution (DE) and the original PSO, using eight numerical functions. The validation of this technique is carried out on four different datasets. The results show that the CHDPSOA is a good feature selection technique that balances the exploration and exploitation search processes to produce good results. The proposed CHDPSOA method performed well in correctly categorizing features using the KNN Classifier for all four datasets.
Memory analysis of .NET and .Net Core applications

Memory analysis is a digital forensics technique whose goal is to model a computer system's state based solely on the analysis of a snapshot of physical memory (RAM). Memory forensics is frequently employed in incident response to detect and analyze modern malware and attack frameworks. Memory forensics is a particularly powerful tool for analyzing modern malware, which may exist only in memory and not touch non-volatile storage. Memory-only attacks leave no trace of the malware and its associated modules on the file system and all data that traverses the network is commonly encrypted. While initially focused on kernel level rootkits, memory analysis research efforts have recently shifted to detection of userland malware. This shift occurred as operating system vendors have strongly locked down the ability for kernel rootkits to load, and, in turn, malware authors have developed significant userland malware capabilities. In this paper, we present our effort to develop memory analysis capabilities that target a very powerful and widely abused set of userland runtimes: the .NET Framework and its replacement, .NET Core. To support automated and repeatable results, even for non-expert investigators, we developed a number of Volatility plugins that automatically target key areas of these runtimes and report any suspicious artifacts. Our suite of new plugins provides investigators with deep insight into the use of .NET on a target system as well as identification of suspicious and malicious components. These capabilities considerably advance a defenders' ability to combat, contain, and understand modern malware.
Two-step learning for crowdsourcing data classification

Crowdsourcing learning (Bonald and Combes 2016; Dawid and Skene, J R Stat Soc: Series C (Appl Stat), 28(1):20-28 1979; Karger et al. 2011; Li et al, IEEE Trans Knowl Data Eng, 28(9):2296-2319 2016; Liu et al. 2012; Schlagwein and Bjorn-Andersen, J Assoc Inform Syst, 15(11):3 2014; Zhang et al. 2014) plays an increasingly important role in the era of big data (Liu et al., IEEE Trans Syst Man Cybern: Syst, 48(12): 451-2461, 2017; Zhang et al. 2014) due to its ability to easily solve large-scale data annotations (Musen et al., J Amer Med Informs Assoc, 22(6):1148-1152 2015). However, in the process of crowdsourcing learning, the uneven knowledge level of workers often leads to low accuracy of the label after marking, which brings difficulties to the subsequent processing (Edwards and Teddy 2013) and analysis of crowdsourcing data. In order to solve this problem, this paper proposes a two-step learning crowdsourced data classification algorithm, which optimizes the original label data by simultaneously considering the two issues of different worker abilities and the similarity between crowdsourced data (Kasikci et al. 2013) samples, so as to get more accurate label data. The two-step learning algorithm mainly includes two steps. Firstly, the worker's ability to label different samples is obtained by constructing and training the worker's ability model, and then the similarity between samples is calculated by the cosine measurement method (Muflikhah and Baharudin 2009), and finally the original label data is optimized by combining the above two results. The experimental results also show that the two-step learning classification algorithm proposed in this article has achieved better experimental results than the comparison algorithm.
A Neural Model to Jointly Predict and Explain Truthfulness of Statements

Automated fact-checking (AFC) systems exist to combat disinformation, however, their complexity usually makes them opaque to the end-user, making it difficult to foster trust in the system. In this article, we introduce the E-BART model with the hope of making progress on this front. E-BART is able to provide a veracity prediction for a claim and jointly generate a human-readable explanation for this decision. We show that E-BART is competitive with the state-of-the-art on the e-FEVER and e-SNLI tasks. In addition, we validate the joint-prediction architecture by showing (1) that generating explanations does not significantly impede the model from performing well in its main task of veracity prediction, and (2) that predicted veracity and explanations are more internally coherent when generated jointly than separately. We also calibrate the E-BART model, allowing the output of the final model to be correctly interpreted as the confidence of correctness. Finally, we also conduct an extensive human evaluation on the impact of generated explanations and observe that: Explanations increase human ability to spot misinformation and make people more skeptical about claims, and explanations generated by E-BART are competitive with ground truth explanations.
Personalized Recommendation System Design for Library Resources through Deep Belief Networks

In recent years, with the continuous development of science and Internet technology, people’s lifestyles are changing dramatically, especially with the development of information technology, which has contributed to the transformation of digital libraries. As an essential information infrastructure and a new source of knowledge, digital libraries have brought great convenience to users. To be specific, with the widespread use of smart devices and internet of things technology, users are eager to be intelligent in their information needs while enjoying services, which makes the resource recommendation service of digital libraries increasingly important. In addition, as a provider of knowledge and information services, libraries should organically combine advanced information technology with existing resources to promote the construction of libraries in the information age. However, in the era of big data, users can only passively receive a large amount of information and services in the face of the ever-expanding mass of resources in digital libraries. In this context, libraries might only provide a single set of information resources and services, which cannot meet the individual needs of users and ultimately leads to inefficient allocation of resources and information. After all, users of digital libraries want to be better able to receive personalized recommendations for library resources through relevant technologies. At the same time, libraries are increasing their research and development efforts on algorithms and technologies for personalized recommendations. Also, with the explosive growth of the total amount of information worldwide, people are entering the information age. Massive amounts of data are constantly being generated, and the problem of information overload is becoming more and more serious. The sheer volume of this data and information increases the degree of difficulty in accessing the information people need. In this situation, it is necessary for digital libraries to dynamically analyze user behavior and interests while responding to user requests in a timely manner and accordingly take the initiative to recommend information resources and knowledge services that meet users’ individual needs. As a result, this study uses a deep belief network model for multimodal feature learning and designs a personalized recommendation system for library resources by fusing features from multiple modalities. Furthermore, this research implements the construction of a semantic user interest model and the design of a personalized recommendation algorithm to achieve an accurate description of user interest preferences and semantic personalized recommendation functions.
Symmetry Breaking Slows Convergence of the ADAPT Variational Quantum Eigensolver

Because quantum simulation of molecular systems is expected to provide the strongest advantage over classical computing methods for systems exhibiting strong electron correlation, it is critical that the performance of VQEs be assessed for strongly correlated systems. For classical simulation, strong correlation often results in symmetry breaking of the Hartree-Fock reference, leading to Löwdin's well-known "symmetry dilemma", whereby accuracy in the energy can be increased by breaking spin or spatial symmetries. Here, we explore the impact of symmetry breaking on the performance of ADAPT-VQE using two strongly correlated systems: (i) the "fermionized" anisotropic Heisenberg model, where the anisotropy parameter controls the correlation in the system, and (ii) symmetrically stretched linear H4, where correlation increases with increasing H-H separation. In both of these cases, increasing the level of correlation of the system leads to spontaneous symmetry breaking (parity and S^2, respectively) of the mean-field solutions. We analyze the role that symmetry breaking in the reference states and orbital mappings of the fermionic Hamiltonians have in the compactness and performance of ADAPT-VQE. We observe that improving the energy of the reference states by breaking symmetry has a deleterious effect on ADAPT-VQE by increasing the length of the ansatz necessary for energy convergence and exacerbating the problem of "gradient troughs".
Evaluation of uranium contamination of Raipur district of Chhattisgarh: GIS assisted hydrogeochemical study of underground water

This study analyzed uranium contamination in groundwater samples from the Raipur district of Chhattisgarh (India), its mobilization with complexing anions, and its effect on the overall water quality index of the studied area. Seventy-three groundwater samples were taken during the pre- and post-monsoon seasons in May and September respectively. All of the collected samples were analyzed for uranium and associated water quality factors. Analytical data such as pH, ORP, electrical conductivity, total dissolved solids, salinity, alkalinity, total hardness, calcium hardness, magnesium hardness, sulphate, nitrate, bicarbonate, chloride, and fluoride were analyzed using descriptive statistics, correlation, factor, and hierarchical agglomerative cluster analysis. The groundwater quality of the research area has been evaluated by calculating the water quality index of sampled water. It was discovered that uranium and chloride had a weak relationship prior to the monsoons and a strong relationship afterwards. Chloride may therefore play a crucial role in the mobilization of uranium. ArcGIS 10.3 with IDW interpolation was used for uranium concentration and water quality index (WQI) presentation. It was found that the sequential increase of uranium in groundwater by geogenic processes may be the root cause of water quality index degradation in the research area.
An efficient format-independent watermarking framework for large-scale data sets

In this paper, we propose an efficient distortion-free watermarking of large-scale data sets in various formats by exploiting the power of parallel and distributed computing environment. In particular, we adapt MapReduce, Pig and Hive paradigms for the data in CSV, XML and JSON formats by identifying key computational steps involved in the sequential watermarking algorithms. Following this, we design a middleware which allows watermark generation and verification (under any computing paradigm of user’s choice) of large-scale data sets (in any suitable format of user’s interest) and their conversion without affecting the watermark. The experimental evaluation on large-scale benchmark data sets shows a significant reduction of watermark generation and verification times. Interestingly, in case of XML and JSON formats, Pig and Hive outperform the MapReduce paradigm, whereas MapReduce shows better performance in case of CSV format. To the best of our knowledge, this is the first proposal towards large-scale data sets watermarking, considering popular distributed computing paradigms and data formats.
Ecological Environment and Poverty Governance Based on Deep Learning of Big Data

The ecological environment is the basic condition for human survival and development. Maintaining a good rural ecological environment is the basis for ensuring the sustainable development of agriculture and the rural economy, and it is the fundamental guarantee for helping poor rural areas to get rid of poverty and become rich. To solve poverty by developing tourism, the so-called tourism poverty alleviation has been studied for many years in the theoretical circles at home and abroad. Therefore, this article conducts research on China’s rural ecological environment management and poverty management based on big data deep learning, aiming to analyze the problems in China’s rural ecoenvironmental management and the challenges facing rural poverty governance and explore a way to achieve sustainable rural economic development and farmers’ poverty alleviation the best way. In the context of big data, environmental monitoring and governance have also ushered in innovative reforms. With the advantages of powerful information collection and data processing, big data provides comprehensive data and information support for environmental monitoring and governance. This article uses a survey method, data statistics method, literature analysis method, and case analysis method. Firstly, it briefly summarizes the main ecological and environmental problems and poverty management issues facing the rural areas of China. It then lists 5 successful poverty alleviation cases to investigate their poverty alleviation. The experience and methods of getting rich sum up the strategies that China can refer to for poverty alleviation. Finally, through the collection and compilation of online data, we have discovered the results achieved by China’s poverty alleviation work in recent years, i.e., by the end of 2019, our rural poverty population has been reduced to 5.1 million. Compared with 2013, there has been a reduction of approximately 90 million (Fang, 2019), however, at the same time, China’s poverty alleviation work has entered a stage of overcoming difficulties. The remaining poor people make it a long way to go. In short, improving the ecological environment to help rural poverty alleviation is a long-term people’s livelihood project, and it is also a research topic of great social significance. For this reason, the research in this article has a certain reference value.
Leveraging virtual reality (VR) for sports public relations and sports journalism: qualitative analyses of VR content productions for ‘Russia 2018’ and ‘Qatar 2022’ FIFA World Cups

Advancements in experiential media (EM) technologies, particularly virtual reality (VR), a subset of EM, can transform the ways public relations (PR) professions tell stories about a brand, organization, or mega-events. In the context of sports, PR content productions utilize various qualities of EM to offer immersive at-home, arena-like experiences for sports spectators. However, considering the novelty of such EM tools, limited studies have focused on how and the extent to which PR and sports journalistic content productions use VR technologies. In this qualitative content analysis, we examine how and the extent to which VR is utilized in pre-game YouTube VR contents produced for the FIFA World Cups 2018 and 2022. We analyzed YouTube VR contents produced by Russia Today (RT) in the buildup to ‘Russia 2018’ hosted by Russia, in comparison with pre-game YouTube VR contents produced by the Road To 2022, in view of the upcoming ‘Qatar 2022’. Through qualitative analyses, we identified four broad thematic categories: stadium design, technology, facilities, and locality, as well as many sub-themes through observations and memos from all the seventeen YouTube VR content productions considered for the study. This study adds to the theoretical discussions on the role VR plays in sports journalism and sports PR and provides practical recommendations on the use of virtual reality during the COVID-19 pandemic.
Effective deep learning based multimodal sentiment analysis from unstructured big data

Abstract More recently, as images, memes and graphics interchange formats have dominated social feeds, typographic/infographic visual content has emerged as an important social media component. This multimodal text combines text and image, defining a novel visual language that must be analysed because it has the potential to modify, confirm or grade the sentiment's polarity. The problem is how to effectively use information from the visual and textual content in image‐text posts. This article presents a new deep learning‐based multimodal sentiment analysis (MSA) model using multimodal data such as images, text and multimodal text (image with embedded text). The text analytic unit, the discretization control unit, the picture analytic component and the decision‐making component are all included in this system. The discretization unit separates the text from the picture using the variant and channel augmented maximally stable extremal regions (VCA‐MSERs) technique, which are then analysed as discrete elements and fed into the appropriate image and text analytics units. The text analytics system utilizes a stacked recurrent neural network with multilevel attention and feedback module (SRNN‐MAFM) to detect the sentiment of the text. A deep convolutional neural network (CNN) structure with parallel‐dilated convolution and self‐attention module (PDC‐SAM) is developed to forecast the emotional response to visual content. Finally, the decision component employs a Boolean framework including an OR function to evaluate and classify the output into three fine‐grained sentiment classes: positive, neutral and negative. The proposed work is simulated in the python platform using the STS‐Gold, Flickr 8k and B‐T4SA datasets for sentiment analysis of text and visual and multimodal text. Simulation outcomes proved that the suggested method achieved better accuracy of 97.8%, 97.7% and 90% for text, visual and MSA individually compared to other methods.