Reviewerly/exHarmony huggingface dataset:
The impact of prior knowledge on causal structure learning

Abstract Causal Bayesian networks have become a powerful technology for reasoning under uncertainty in areas that require transparency and explainability, by relying on causal assumptions that enable us to simulate hypothetical interventions. The graphical structure of such models can be estimated by structure learning algorithms, domain knowledge, or a combination of both. Various knowledge approaches have been proposed in the literature that enables us to specify prior knowledge that constrains or guides these algorithms. This paper introduces some novel, and also describes some existing, knowledge-based approaches that enable us to combine structure learning with knowledge obtained from heterogeneous sources. We investigate the impact of these approaches on structure learning across different algorithms, case studies and settings that we might encounter in practice. Each approach is assessed in terms of effectiveness and efficiency, including graphical accuracy, model fitting, complexity, and runtime; making this the first paper that provides a comparative evaluation of a wide range of knowledge approaches for structure learning. Because the value of knowledge depends on what data are available, we illustrate the results both with limited and big data. While the overall results show that knowledge becomes less important with big data due to higher learning accuracy rendering knowledge less important, some of the knowledge approaches are found to be more important with big data. Amongst the main conclusions is the observation that reduced search space obtained from knowledge does not always imply reduced computational complexity, perhaps because the relationships implied by the data and knowledge are in tension.
An Efficient Cloud Storage Model for GOP-Level Video Deduplication using Adaptive GOP Structure

AbstractAbstractCloud storage systems may be used by users and businesses to transfer their enormous volumes of data for storage, processing, and analysis. Cloud storage may be used efficiently by employing the deduplication technique to prevent duplicate copies. Users may save any type of material, including audio, video, images, text, and more. The handling of deduplication techniques for these distinct data types may vary depending on their characteristics, sizes, etc. Video deduplication is one of these, and it’s essential for reducing memory waste in the cloud storage system. There are many levels of video deduplication that may be done. In this research work, we proposed a GOP-level deduplication system using an adaptive GOP structure. Since GOP is the level of deduplication, it is inefficient to build GOP with a fixed size. It can fail to discover a pair of identical frames. The adaptive GOP structure will yield GOPs with closer relation among their frames than with a fixed-size GOP structure. The proposed technique is compared with the fixed-size GOP structure with the GOP sizes of 8, 10, 12, and 15 and the proposed technique achieved a 2.18% PSNR gain which is relatively higher than other fixed-size GOP structures.Keywords: Fixed-size GOP structuremean square errorpeak signal to noise ratiostructural similarity indexvideo deduplication Disclosure statementThe authors declare that they have no conflict of interest.
A NOVEL SERPENT ALGORITHM IMPROVEMENT BY THE KEY SCHEDULE INCREASE SECURITY

Block encryption algorithms rely on the two most important features of their complexity and ease of use to support security requirements (confidentiality, data integrity, and non-repudiation) to prevent unauthorized users from entering the system and tampering with centralized data, disrupting it or disclosing it. The data encryption and decryption process is done using the (Serpent) algorithm, which is one of the most important of these operations. AES Algorithm Proposals. In this paper, a new proposal is presented to improve and support the confidentiality of data while adhering to the external structure of the standard algorithm, relying on designing a new approach to the key generation function because the sobriety of block cipher relies on the use of a strong and unique key. Where several functions were used (Gost external structure) with a combination of (Shift &lt;&lt;&lt;), (AES -Key Schedule), (MD5)). The results of the proposed method were examined using statistical measures, yielding good results, and overcoming the weakness of the key generation function of the original algorithm, in addition to enhancing the most important cryptographic features “confusion”, “diffusion” and “increased randomness”.
Towards an Online Multilingual Tool for Automated Conceptual Database Design

The paper presents an early prototype of an online multilingual tool named TexToData, which is aimed at automatic conceptual database design. TexToData is the first online web-based tool enabling automatic conversion of a natural language text into the target conceptual database model represented by a UML class diagram. It implements the entire process through the orchestration of web services, whereby some core functionalities are carried out by external services. The tool usage is illustrated with examples of automatic generation of a conceptual database model from the text represented in different natural languages.
Using Video Cameras as a Research Tool in Public Spaces: Addressing Ethical and Information Governance Challenges Under Data Protection Legislation

Systematic observation is a promising unobtrusive method of assessing human behavior in urban environments without many issues typically associated with self-report measures (e.g., recall bias, low response rates). Improvements in video camera technologies make it more feasible for researchers to conduct systematic observation, which could reduce the time, labor, and cost to facilitate high-quality observational research in urban environments at scale. However, there are important ethical and information governance challenges driven by data protection laws, which discourage many researchers from using camera-based observation methods. The European Union General Data Protection Regulation is a leading global standard for data protection. Drawing on our experiences of conducting three studies using video cameras in public spaces, we discuss how to conduct this kind of research in line with General Data Protection Regulation requirements. The paper outlines issues concerning data protection, privacy, informed consent, and confidentiality, and how we addressed them. In doing this, the paper provides support for responsible use of camera-based observation methods, which will be of value to researchers, ethics committees, and funders. Outlining how to use video cameras responsibly will enable more research to be conducted that, in turn, will build the case for its benefits to researchers and society.
Ordinal analysis and the set existence property for intuitionistic set theories

On account of being governed by constructive logic, intuitionistic theories [Formula: see text] often enjoy various existence properties. The most common is the numerical existence property (NEP). It entails that an existential theorem of [Formula: see text] of the form [Formula: see text] can be witnessed by a numeral [Formula: see text] such that [Formula: see text] proves [Formula: see text]. While NEP holds almost universally for natural intuitionistic set theories, the general existence property (EP), i.e. the property of a theory that for every existential theorem, a provably definable witness can be found, is known to fail for some prominent intuitionistic set theories such as Intuitionistic Zermelo-Fraenkel set theory (IZF) and constructive Zermelo-Fraenkel set theory (CZF). Both of these theories are formalized with collection rather than replacement as the latter is often difficult to apply in an intuitionistic context because of the uniqueness requirement. In light of this, one is clearly tempted to single out collection as the culprit that stymies the EP in such theories. Beeson stated the following open problem: 'Does any reasonable set theory with collection have the existence property? and added in proof: The problem is still open for IZF with only bounded separation.' (Beeson. 1985 Foundations of constructive mathematics, p. 203. Berlin, Germany: Springer.) In this article, it is shown that IZF with bounded separation, that is, separation for formulas in which only bounded quantifiers of the forms [Formula: see text] are allowed, indeed has the EP. Moreover, it is also shown that CZF with the exponentiation axiom in place of the subset collection axiom has the EP. Crucially, in both cases, the proof involves a detour through ordinal analyses of infinitary systems of intuitionistic set theory, i.e. advanced techniques from proof theory. This article is part of the theme issue 'Modern perspectives in Proof Theory'.
MCSO: Levy’s Flight Guided Modified Chicken Swarm Optimization

This paper proposes a Modified Chicken Swarm Optimization (MCSO) in which the local optima and early convergence problem of Chicken Swarm Optimization (CSO) is addressed and solved. The CSO adopted the Swarm Intelligence (SI) of chickens to solve the optimization problem in which the behaviour of roosters, hens, and chicks for food search is mathematically formulated. Hens follow their group rooster, whereas mother hen is followed by chicks in search of food. The problem occurs whenever the rooster follows the wrong path and is stuck in the local optima so the mother hen and, therefore, the chick. This situation leads to early convergence and may not provide global optimization. Most of the existing research studies focused on solving the local optima problem of hens. Hence, there is a need to address the local optima problem of roosters as well. The paper offers a solution to this problem by using the randomness phenomenon of Levy’s Fight. Levy’s flight is offered to guide the roosters, hens, and chicks, which allows the chickens to choose a random direction in a situation when there is no way to find the optimal solution. The inclusion of Levy’s flight enhances the self-learning capability of the chicken. The MCSO is tested on the benchmark functions, IEEE CEC-2017 functions and an engineering problem. The results are validated by a comparative analysis with well-known SI agorithms. The results indicate that the MCSO provides competitive performance. The results are statistically verified with the win-tie-loss, Bonferroni-Dunn post-hoc, and Wilcoxon tests.
QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques

The Quick UDP Internet Connections (QUIC) protocol provides advantages over traditional TCP, but its encryption functionality reduces the visibility for operators into network traffic. Many studies deploy machine learning and deep learning algorithms on QUIC traffic classification. However, standalone machine learning models are subject to overfitting and poor predictability in complex network traffic environments. Deep learning on the other hand requires a huge dataset and intensive parameter fine-tuning. On the contrary, ensemble techniques provide reliability, better prediction, and robustness of the trained model, thereby reducing the chance of overfitting. In this paper, we approach the QUIC network traffic classification problem by utilizing five different ensemble machine learning techniques, namely: Random Forest, Extra Trees, Gradient Boosting Tree, Extreme Gradient Boosting Tree, and Light Gradient Boosting Model. We used the publicly available dataset with five different services such as Google Drive, YouTube, Google Docs, Google Search, and Google Music. The models were trained using a different number of features on different scenarios and evaluated using several performance metrics. The results show that Extreme Gradient Boosting Tree and Light Gradient Boosting Model outperform the other models and achieve one of the highest results among the state-of-the-art models found in the literature with a simpler model and features.
SP-NLG: A Semantic-Parsing-Guided Natural Language Generation Framework

We propose SP-NLG: A semantic-parsing-guided natural language generation framework for logical content generation with high fidelity. Prior studies adopt large pretrained language models and coarse-to-fine decoding techniques to generate text with logic; while achieving considerable results on automatic evaluation metrics, they still face challenges in terms of logical fidelity based on human evaluation. Inspired by semantic parsing, which translates natural language utterances into executable logical forms, we propose to guide the generation process with a semantic parser. Different from semantic parsing and QA tasks, of which the logical correctness can be evaluated based on the execution result, the logic information of the generated content is implicit. To leverage a semantic parser for generation, we propose a slot-tied back-search algorithm. We follow the coarse-to-fine generation scheme, but instead of filling the slots with model predictions, which is less uncontrolled, the slot values are offline searched by the algorithm. The slot-tied back-search algorithm ties the parameters of a logic form with the slots of a template in one-to-one correspondence. We back-search the arguments that correctly execute the logic form and fill the arguments (as slot values) into the textual template to generate the final target, which ensures the logical correctness. Experiment results of a model built on SP-NLG demonstrates that our framework achieves high fidelity on logical text generation.
3D mineral exploration Cu-Zn targeting with multi-source geoscience datasets in the Weilasituo-bairendaba district, Inner Mongolia, China

The Weilasituo-bairendaba district is located at the eastern end of the Central Asian Orogenic Belt, which is an important component of the Cu-Pb-Zn polymetallic metallogenic belt on the Western slope of the Greater Xing’an Range in Inner Mongolia, China. The known Cu-Zn deposits such as the Weilasituo Cu-Zn deposit and the Bairendaba Ag-Pb-Zn deposit are the same tectonic-magmatic product. The district’s structure framework consists of the NE-trending regional faults, while the secondary faults provide channels and space for mineralization. The ore-bearing rocks are either Baoyintu Group gneisses or quartz diorites. The typical Cu-Zn deposits exhibit obvious Cu, Pb, Zn geochemical anomaly as well as obvious magnetic anomaly. The district-scale two-dimensional (2D) mineral prospectivity modeling has been reported. Nowadays, three-dimensional (3D) mineral prospectivity modeling is necessary and urgent. Integrated deposit geology and accumulated exploration data, the above four exploration criteria (regional fault, secondary fault, geochemical anomaly and magnetic susceptibility) are used for 3D mineral prospectivity modeling. Filtering (upward continuation, low pass filtering, two-dimensional empirical mode decomposition), magnetic inversion and 3D modeling techniques were used to construct geological models. Excellent machine learning algorithms such as random forest (RF) and XGBoost are applied. The two machine learning methods confirm each other to improve the accuracy of 3D mineral prospectivity modeling. In this paper, repeated random sampling and Bayesian optimization are combined to construct and tune models. This joint method can avoid the contingency caused by random sampling of negative samples, and can also realize automatic optimization of hyperparameters. The optimal models (RF28 and XGBoost11) were selected among thirty repeated training models for mineral prospectivity modeling. The obtained areas under the ROC curves of RF28 and XGBoost11 were 0.987 and 0.986, respectively. The prediction-area (P-A) plot and C-A fractal were used to delineate targets and grade targets. The targets were divided into Ⅰ-level targets and Ⅱ-level targets. The I- and II-targets are not only highly consistent with the known Cu-Zn deposits, but also exhibit obvious ore-forming geological features. The 3D targets are beneficial for Cu-Zn exploration in the Weilasituo-bairendaba district.
Karl Marx’s thoughts on critical pedagogy, reproduction, and aesthetic literacy in STEAM education and praxis

This article sheds new light on Karl Marx’s theoretical legacy to promote educational philosophy and theory in science, technology, engineering, arts, and mathematics (STEAM) education. Accordingly, this article selects Marxian thought as an exemplar case to elaborate critical pedagogy and aesthetics and culminate with an overview of the Marxian approach to the theoretical underpinnings of STEAM education. Hence, the article critically reviews Marx’s original works and earlier Marxian scholars’ contributions to extending critical pedagogy and its applications to the STEAM paradigm and informational and communication technology (ICT) development. This article critiques notable structural problems of STEAM education that produce power differentials such as educational gaps and digital divides in the current age of digital capitalism. Given the context, this article refines art and communication theories through the lens of his biography and writings. Additionally, this article incorporates the Marxian viewpoints about reproduction and aesthetic literacy into STEAM education. Overall, this article reviews the theoretical legacy of Marx and his essential critical and pedagogical theory, calling on scholars to pay close attention to re-orienting STEAM pedagogy as an emerging multidisciplinary field.
Voice search behavior under human–vehicle interaction context: an exploratory study

Purpose As voice search has progressively become a new way of information acquisition and human–computer interaction, this paper aims to explore the users' voice search behavior in human–vehicle interaction. Design/methodology/approach This study employed mixed research methods, including questionnaires and interviews. A total of 151 Amazon MTurk volunteers were recruited to complete a questionnaire based on their most recent and impressive voice search experience. After the questionnaire, this paper conducted an online interview with the participants. Findings This paper studied users' voice search behavior characteristics in the context of the human–vehicle interaction and analyzed the voice search content, search need, search motivation and user satisfaction. In addition, this paper studied the barriers and suggestions for voice search in human–vehicle interaction through a content analysis of the interviews. Practical implications This paper's analysis of users' barriers and suggestions has a specific reference value for optimizing the voice search interaction system and improving the service. Originality/value This study is exploratory research that seeks to identify users' voice search needs and tasks and investigate voice search satisfaction in human–vehicle interaction context.
On the effectiveness of automated tracing from model changes to project issues

Requirements Traceability (RT) is concerned with monitoring and documenting the lifecycle of requirements. Although researchers have proposed several automated tracing tools, trace link establishment and maintenance are still prevalently manual activities. In order to foster the adoption of automated tracing tools, we study their empirical effectiveness in the context of model-driven development (MDD). We focus on trace link recovery (TLR) from (i) SVN revisions of MDD models to (ii) JIRA issues that represent requirements and bugs. Based on the state-of-the-art in automated TLR, we propose the LCDTrace tool that uses 131 features to train a machine learning classifier. Some of these features use specific information for MDD contexts. We conduct three experiments on ten datasets from seven MDD projects. First, we evaluate the effectiveness of three ML algorithms and four rebalancing strategies using all 131 features, and we derive two optimal combinations for trace link recommendation and for trace maintenance. Second, we investigate whether the MDD-specific features convey higher performance than a version of LCDTrace that excludes those features. Third, we employ automated feature selection and study whether we can reduce the number of features while keeping similar performance, thereby boosting time and energy efficiency. In our experiments, the gradient boosting models outperform those based on random forests. The best combinations for trace recommendation and maintenance achieve an F2-score of 61% and F0.5-score of 67%, respectively. While MDD-specific features do not provide additional value, automated feature selection succeeds at reducing feature numerosity without compromising performance. We provide insights on the effectiveness of state-of-the-art TLR techniques in MDD. Our findings are a baseline for devising and experimenting with alternative TLR approaches.
Cognitive-bias-induced differences in the perceived video quality of rugged and conventional smartphones

In the global smartphone market, several smaller segments exist, servicing the specific needs of their target audience, incorporating various special features into newer models. One such segment is the rugged smartphone market, which is growing rapidly and is expected to continue its growth because of the benefits of this kind of device. Rugged phones tend to be waterproof, shockproof and more durable in general, and they also have a longer battery life. All of these are attractive features for the vast majority of users. The latest releases of such smartphones are actually starting to compete with their conventional counterparts in imaging capabilities, as well as display resolution. As the manufacturers release new models, they inevitably promote qualities that reflect excellence in terms of ruggedness. However, such promotion may, in fact, lead to preconceptions regarding other features. In essence, some may think that if a smartphone is durable, then it must mean that it evidently has an inferior camera system and display. In this paper, the effect of such cognitive bias is investigated. The paper presents three series of subjective tests, studying how preconception-induced cognitive bias alters the perceived video quality of rugged smartphones. In the first experiment, we compare the video recording and visualization capabilities of a rugged smartphone to conventional smartphones. However, in order to examine the impact of cognitive bias, the same videos are shown on all devices. In the second experiment, as a means of narrowing down quality assessment to the cameras of the smartphones, the videos are shown on a computer. Similarly to the first experiment, all videos were recorded by the same rugged smartphone; however, prior to each visual stimulus, the videos were presented as having been captured by the different devices. The third experiment follows the methodology of the second one, but it is carried out on a smartphone with a screen size smaller than that of the rugged and the conventional smartphones in the first experiment, in order to consider the influence of screen size. Additionally, a ground truth test without the induction of cognitive bias is performed to serve as a base for distortion impact comparison, and the source contents are validated by a separate subjective test.