Reviewerly/exHarmony huggingface dataset:
The Role of Block Chain Technology in Reducing Corruption Within the Local Governance in Egypt

One of the most challenging issues facing municipal governments is corruption. Numerous studies, reports, and analyses highlight the fact that since corruption is by definition hidden from the public’s perspective, increasing the transparency and scrutiny of governmental acts can help prevent corruption. Therefore, increasing the transparency of government operations and decision-making should be the ultimate goal of an anticorruption campaign inside a democratic system. In the framework of local governance, we must strengthen the current audit mode, increase accountability effectiveness, further anticipate the trend of covert corruption, identify the traits of corruption as soon as feasible, and promptly regulate the corruption behaviors. The study’s findings indicate that the more local executives have authority, the less transparent the information is, the less reliable the monitoring system is, and the more likely it is for covert corruption to proliferate. Only by enhancing audit mode, ensuring data quality, increasing audit efficiency, and reducing audit risk using blockchain technology can the corruption of executives of state-owned companies be effectively stopped.
A Comparative Study for Anonymizing Datasets with Multiple Sensitive Attributes and Multiple Records

Today, there are many sources of data, such as IoT devices, that produce a massive amount of data, particularly in the healthcare industry. This microdata needs to be published, and shared for medical research purposes, data analysis, mining, learning analytics tasks, and the decision-making process. But this published data contains sensitive and private information for individuals, and if this microdata is published in its original format, the privacy of individuals may be disclosed, which puts the individuals at risk, especially if an adversary has strong background knowledge about the target individual. Owning multiple records and multiple sensitive attributes (MSA) for an individual can lead to new privacy leakages or disclosure. So, the fundamental issue is how to protect the privacy of 1:M with the MSA dataset using anonymization techniques and methods, as well as how to balance utility and privacy, for this data while reducing information loss and misuse. The objective of this paper is to use different methods and different anonymization algorithms, like the 1:m-generalization algorithm and Mondrian, and compare them to show which of them maintains data privacy and high utility of analysis results at the same time. From this comparison, we found that the m-generalization algorithm and the (p, k) angelization method perform well in terms of information loss and data utility compared to the other remaining methods and algorithms.
Using Requirements Clustering to Discover Dependent Requirements for Hidden Impact Analysis

Since one of the most important practices in any software development lifecycle is Requirements Engineering. Weakly applied RE is one of the main causes for the development breakdown. Main practices of RE processes are specification and elicitation, verification, cooperation, and implementation management. Since ULS systems are increasing in complexity and difficulty, it has observed a growing call for smart modules, methods, and applications that would support improving RE practices. In this paper, we focus on how various kinds of recommendation technologies can be applied to support ULS participants in the completion of the RE tasks; first, we provide an overview of the research related to the application of recommendation tools in RE that was already described in a high level. Second, we show in detail how clustering method as one of Model-based filtering technique can be applied to proactively strengthen “Measuring Change Ripple Effect”, Third, new ideas need to be discussed and future research explored. We have used Natural Language Processing (NLP) and Similarity Models to support the model.
The Metaverse and complex thinking: opportunities, experiences, and future lines of research

Research trends about the Metaverse have increased in recent years due to its potential to create immersive realities, where complex thinking becomes relevant as an ability to promote emerging ways to understand and explain the different realities that comprise a digital society. Although some investigations allow us to know both topics’ concepts and applications, scientific literature production about them is scarce. In this work, we conducted a systematic review of the literature (SLR), analyzing 234 publications from various databases, including Scopus and Web of Science, to understand how studies about the Metaverse overlap with the components of complex thought. The results showed that there has been extensive exploration of the Metaverse since 2022. The prevalence of the Metaverse aligns with the design of algorithms and retail sales, and it primarily correlates with virtual reality technology. Likewise, various reference frameworks and taxonomies have been designed to explain the operation of the Metaverse in different formative spaces. We concluded that examining the Metaverse from the perspective of critical, systemic, scientific, and innovative thinking can open lines of research that affect the knowledge of immersive technologies and the evolution of disruptive digital ecosystems.
Logistics dynamic information retrieval based on blockchain data security model

Abstract This study presents blockchain technology to integrate the time attribute of Internet of Things data and the time stamp of blockchain, and proposes a data retrieval solution based on dynamic information retrieval. Analyze and reorganize the blockchain data at the bottom of the logistics information platform to synchronize the logistics information to the third-party relational database. The experimental results show that the proposed method can improve the data entry time of the system by 5.2%, and the system occupancy rate is less than 4.1%. It is proved that the proposed model can simplify the data attributes of the Internet of Things, improve the universality of data, effectively integrate the structural characteristics of the Internet of Things and blockchain, and realize safe and efficient data management and application.
(Compact) Adaptively secure FE for attribute-weighted sums from k-Lin

Abstract This paper presents the first adaptively simulation secure functional encryption () schemes for attribute-weighted sums. In the proposed schemes, attributes are viewed as vectors and weight functions are arithmetic branching programs (). We present two schemes with varying parameters and levels of adaptive simulation security. (a) We first present a one-slot scheme supporting a bounded number of ciphertext queries and an arbitrary polynomial number of secret key queries both before and after the ciphertext queries. This is the best possible level of security one can achieve in the adaptive simulation-based framework. The scheme also achieves indistinguishability-based adaptive security against an unbounded number of ciphertext and secret key queries. (b) Next, bootstrapping from the one-slot scheme, we present an unbounded-slot scheme that can support a bounded number of ciphertext and pre-ciphertext secret key queries while supporting an a-priori unbounded number of post-ciphertext secret key queries. Both schemes enjoy ciphertexts that do not grow with the number of appearances of the attributes within the weight functions. The schemes are built upon prime-order asymmetric bilinear groups and the security is derived under the standard (bilateral) k -Linear ( k -) assumption. Our work resolves an open problem posed by Abdalla et al (In: CRYPTO, Springer, New York, 2020), where they presented an unbounded-slot scheme for attribute-weighted sum achieving only semi-adaptive simulation security. Technically, we extend the recent adaptive security framework of Lin and Luo (In: EUROCRYPT, Springer, New York, 2020), devised to achieve compact ciphertexts in the context of indistinguishability-based payload-hiding security, to the setting of simulation-based adaptive attribute-hiding security.
Analytical differentiation of the articulated-body algorithm: a geometric multilinear approach

This paper presents a new geometric algorithm to efficiently compute the analytical differentiation of the Articulated-body Algorithm (ABA) with respect to the state variables. Despite the fact that ABA solves the forward-dynamics problem in linear time for branched-multibody systems, its explicit differentiation is not straightforward, and computationally demanding tensor products and contractions appear. The proposed algorithm makes use of Lie algebraic and multilinear operations to recursively exploit the underlying sparsity of the linearization problem. As a result, the arithmetic complexity is dramatically reduced without affecting the analytical solution. Since the linealization of forward dynamics is of great importance in robot-trajectory optimization, a differential dynamic programming solver is employed to demonstrate the performance of our algorithm with humanoid robot models such as NAO and HRP-2. In addition, we provide the computational cost with different robots using an optimized C++ implementation that can be found at https://github.com/garechav/geombd_crtp.git .
PERMISSION MARKETING STRATEGY SHAPING CONSUMER BEHAVIOUR THROUGH ONLINE COMMUNICATION CHANNELS

In today's cluttered world, consumers often try to avoid receiving advertising messages through various communication channels. Interruptive advertising is losing its effectiveness. As a result, marketers must change their strategies to retain existing customers and attract new audiences. The purpose of this paper is to review various aspects of permission marketing and to outline some of the ways in which it can be used in practice. The object of the study is the concept of permission marketing and its influence on consumer behaviour. Similar to other marketing concepts, it is constantly changing and evolving. Observation is used as the primary methodology of the study. In addition, the literature review contributes to the analysis of various aspects of the issue. It is one of the concepts that can drastically change the way of communication and create innovative approaches to influence consumer behaviour. Today, online communication channels are the most favourable tools for implementing permission marketing policies. However, due to the fluctuating and unpredictable online environment, they can turn from opportunities into reasons for serious complications. Therefore, the concept introduced by Seth Godin in 1999 requires further theoretical research amidst the development of online communication technologies. Properly planned and executed, permission marketing campaigns can be effective in influencing consumer behaviour. However, it is a strategy designed for longer periods of time. Results. The paper provides a theoretical review of the main aspects of the permission marketing concept: the role of online communication channels, the peculiarities of consumer behaviour and the implementation of permission marketing strategies. In addition, the paper provides a multilateral analysis of the current characteristics of the concept. Finally, the paper proposes some recommendations on how to influence consumer behaviour through different online communication channels within the framework of permission marketing policies.
An automatic calibration algorithm for endoscopic structured light sensors in cylindrical environment

ABSTRACTABSTRACTStructured light sensing systems, as one of the most common optical-based nondestructive evaluation techniques, have been widely applied for inline pipeline inspection. The sensor can be inserted inside the pipe to generate 3D visualisation and evaluate the cracks in the materials. The precise calibration of the camera-projector measurement system is of great significance to ensure the measurement accuracy of the 3D sensing system. Conventional calibration methods for structured light sensors involve complicated and time-consuming procedures and are easily affected by ambient light. The paper presents a novel algorithm to automatically calibrate the projection module and estimate the stereo parameters between the camera and the projector. The calibration algorithm exploits the cylindrical nature of the inspected pipe to create a set of geometric constraints and automatically calibrate the sensor without the need for reference calibration points. Experimental and simulation results showed that the algorithm could successfully estimate the projector's intrinsic and extrinsic parameters by simply acquiring the data inside a cylindrical pipe with a known diameter. The proposed algorithm highly reduces the data collection time for the calibration (only 53 s), improves the accuracy, and simplifies the calibration process.KEYWORDS: Pipe inspectionstructured lightautomatic calibration AcknowledgmentsThis work was supported by the US Department of Transportation/Pipeline and Hazardous Materials Safety Administration (DOT/PHMSA) through the AI-enabled ILI robot with integrated structured light NDE for distribution pipelines under Award 693JK31850007CAAP.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work was supported by the US Department of Transportation/Pipeline [693JK31850007CAAP].
Immersive information seeking–A scoping review of information seeking in virtual reality environments

Recently, virtual reality (VR) technology has become more widespread. Humans increasingly interact with information in VR, and a detailed look into those activities is warranted. Thus, a scoping literature review (PRISMA-ScR) is conducted. It overviews all relevant literature about information-seeking behaviour in VR, focusing on existing models and theories. Out of 536 publications, 37 qualify for this review. Eight publications show an understanding related to information behaviour theories from information science. Pressingly, no publications relate models, frameworks or general theories of information seeking to VR. This review overviews VR-related cognitive and behavioural human factors based on this research gap. Those factors include immersion and presence, affordances, embodiment, cognitive load, human error, flow and engagement. The review is concluded with an explorative framework for future research that is constructed with Marchionini’s process model of information seeking as a baseline and in conjunction with the discussed human factors.
Application of Correlation Analysis Based on Principal Components in the Study of Global Temperature Changes

The current issue of global warming is prominent, and there are many factors that affect global temperature changes. Therefore, how to correctly judge the relationship between each influencing factor and global temperature changes and accurately find out the main reasons of global temperature rise which are the problems that must be considered and solved to alleviate global warming at present. According to previous official data, this paper proposed a correlation analysis method based on principal components to comprehensively analyze the relationship between natural disaster factors, human factors, and global temperature changes, and find out the main reasons that affect global temperature rise. Compared with traditional research methods, the new method provided in this paper can still remain scientific and accurate calculation results while reducing computational dimensions. The experimental results showed that in the relationship between natural disasters and global temperature changes, the average correlation coefficient of the principal component represented by biological disasters and geological disasters was the highest at 0.6097 and a test value of p<0.05, indicating a significant positive correlation between them and global temperature. However, the correlation coefficient of the principal component represented by floods and storms was negative, indicating a negative correlation between them and global temperature. In exploring the main factors affecting global temperature rise, both the total global population and the total global CO2 emissions had a significant positive correlation with global temperature. Among them, the average correlation coefficient of the total global population was the highest at 0.9972, and its weight also was the highest at 26.42%. Therefore, this indicates that the total global population is the most important factor affecting global temperature rise. This study can provide reference for countries to make decisions in response to global warming.
Visualizing Linguistic Complexity and Proficiency in Learner English Writings

In this article, we focus on the design of a second language (L2) formative feedback system that provides linguistic complexity graph reports on the writings of English for special purposes students at the university level. The system is evaluated in light of formative instruction features pointed out in the literature. The significance of complexity metrics is also evaluated. A learner corpus of English classified according to the Common European Framework of References for Languages (CEFR) was processed using a pipeline that computes 83 complexity metrics. By way of analysis of variance (ANOVA) testing, multinomial logistic regression, and clustering methods, we identified and validated a set of nine significant metrics in terms of proficiency levels. Validation with classification gave 67.51% (A level), 60.16% (B level), and 60.47% (C level) balanced accuracy. Clustering showed between 53.10% and 67.37% homogeneity, depending on the level. As a result, these metrics were used to create graphical reports about the linguistic complexity of learner writing. These reports are designed to help language teachers diagnose their students’ writings in comparison with prerecorded cohorts of different proficiencies.
Red flag algorithms for Brazilian electronic invoices: outlier detection and price risk classification

Abstract Brazilian electronic invoices ( Nota Fiscal eletrônica , NFe) are digital documents that register the purchase and sale operations of goods and services. In this work, we developed and used data‐cleaning procedures, clustering methods, and outlier detection algorithms to investigate the presence of two main risk patterns in the electronic invoices data of public entities, such as states or municipalities: abnormal product quantities and anomalous prices paid with public funds. As a case study, we analyzed nearly 3.5 million electronic invoices containing more than 11 million items from purchases made by public entities of the Brazilian state of Mato Grosso (MT) in 2016–2019. The results indicate that the total value of invoice items categorized as high‐intensity price alerts amounted to more than R$ 560 million, i.e., approximately US$ 139 million at the rate of December 31, 2019. Several cases of high‐risk patterns are presented to illustrate these practical results.
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP

Multi-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work – sometimes negative transfer occurs between tasks, especially when aggregating loosely related skills, leaving it an open question when MTL works. Previous studies show that MTL performance can be improved by algorithmic tricks. However, what tasks and skills should be included is less well explored. In this work, we conduct a case study in Financial NLP where multiple datasets exist for skills relevant to the domain, such as numeric reasoning and sentiment analysis. Due to the task difficulty and data scarcity in the Financial NLP domain, we explore when aggregating such diverse skills from multiple datasets with MTL can work. Our findings suggest that the key to MTL success lies in skill diversity, relatedness between tasks, and choice of aggregation size and shared capacity. Specifically, MTL works well when tasks are diverse but related, and when the size of the task aggregation and the shared capacity of the model are balanced to avoid overwhelming certain tasks.
A new, evidence-based, theory for knowledge reuse in security risk analysis

Abstract Security risk analysis (SRA) is a key activity in software engineering but requires heavy manual effort. Community knowledge in the form of security patterns or security catalogs can be used to support the identification of threats and security controls. However, no evidence-based theory exists about the effectiveness of security catalogs when used for security risk analysis. We adopt a grounded theory approach to propose a conceptual, revised and refined theory of SRA knowledge reuse. The theory refinement is backed by evidence gathered from conducting interviews with experts (20) and controlled experiments with both experts (15) and novice analysts (18). We conclude the paper by providing insights into the use of catalogs and managerial implications.
DP-FEDAW: FEDERATED LEARNING WITH DIFFERENTIAL PRIVACY IN NON-IID DATA

Federated learning can effectively utilize data from various users to coordinately train machine learning models while ensuring that data does not leave the user's device. However, it also faces the challenge of slow global model convergence and even the leakage of model parameters under heterogeneous data. To address this issue, this paper proposes a federated weighted average with differential privacy (DP-FedAW) algorithm, which studies the security and convergence issues of federated learning for Non-independent identically distributed (Non-IID) data. Firstly, the DP-FedAW algorithm quantifies the degree of Non-IID for different user datasets and further adjusts the aggregation weights of each user, effectively alleviating the model convergence problem caused by differences in Non-IID data during the training process. Secondly, a federated weighted average algorithm for privacy protection is designed to ensure that the model parameters meet differential privacy requirements. In theory, this algorithm effectively provides privacy and security during the training process while accelerating the convergence of the model. Experiments have shown that compared to the federated average algorithm, this algorithm can converge faster. In addition, with the increase of the privacy budget, the model's accuracy gradually tends to be without noise while ensuring model security. This study provides an important reference for ensuring model parameter security and improving the algorithm convergence rate of federated learning towards the Non-IID data.