Reviewerly/exHarmony huggingface dataset:
Incorporating External Knowledge for Evidence-based Fact Verification

Existing fact verification methods employ pre-trained language models such as BERT for the contextual representation of evidence sentences. However, such representations do not take into account commonsense knowledge and these methods often conclude that there is not enough information to predict whether a claim is supported or refuted by the evidence sentences. In this work, we propose a framework called CGAT that incorporates external knowledge from ConceptNet to enrich the contextual representations of evidence sentences. We employ graph attention models to propagate the information among the evidence sentences before predicting the veracity of the claim. Experiment results on the benchmark FEVER dataset and UKP Snopes Corpus indicate that the proposed approach leads to higher accuracy and FEVER score compared to state-of-the-art claim verification methods.
Predicting SPARQL Query Dynamics

The SPARQL language is the recommendation for querying Linked Data, but querying SPARQL endpoints has problems with performance, particularly when clients remotely query SPARQL endpoints over the Web. Traditionally, caching techniques have been used to deal with performance issues by allowing the reuse of intermediate data and results across different queries. However, the resources in Linked Data represent real-world things which change over time. The resources described by these datasets are thus continuously created, moved, deleted, linked, and unlinked, which may lead to stale data in caches. This situation is more critical in the case of applications that consume or interact intensively with Linked Data through SPARQL, including query engines and browsers that constantly send expensive and repetitive queries. Applications that leverage Linked Data could benefit from knowledge about the dynamics of changing query results to efficiently deliver accurate services, since they could refresh at least the dynamic part of the queries. Along these lines, we want to address open questions in terms of assessing the dynamics of SPARQL query results in order to improve the way applications access dynamic Linked Data, making queries more efficient and ensuring fresher results.
Mining Multivariate Implicit Relationships in Academic Networks

Multivariate cooperative relations exist widely in the academic society. In-depth research on multivariate relationships can effectively promote the integration of disciplines and advance scientific and technological progress. The mining and analysis of advisor-advisee relationships in the cooperative network, as a hot research issue in sociology and other disciplines, is still facing various challenges such as the lack of universal models and the difficulty in identifying multivariate relationships. The traditional advisor-advisee relationship mining methods only focus on the binary relationship, and require secondary processing of node attributes and edge attributes. Therefore, based on the attributes of the node, edge, and network, we transfered the Capsule Network to multivariate relation analysis. The experimental results proved the simplicity and reliability of this model. And we studied the effects of the network feature vectors’ dimension, routing iterations, and normalization on the performance of the Capsule Network. Considering that Capsule Network takes a long training time, we adopted Warm Restarts method to speed up the training process. In addition, we also used the model to generate a large-scale multivariate academic genealogy.
Surj: Ontological Learning for Fast, Accurate, and Robust Hierarchical Multi-label Classification

We consider multi-label classification in the context of complex hierarchical relationships organized into an ontology. These situations are ubiquitous in learning problems on the web and in science, where rich domain models are developed but labeled data is rare. Most existing solutions model the problem as a sequence of simpler problems: one classifier for each level in the hierarchy, or one classifier for each label. These approaches require more training data, which is often unavailable in practice: as the ontology grows in size and complexity, it becomes unlikely to find training examples for all expected combinations. In this paper, we learn offline representations of the ontology using a graph autoencoder and separately learn to classify input records, reducing dependence on training data: Since the relationships between labels are encoded independently of training data, the model can make predictions even for underrepresented labels, naturally generalize to DAG-structured ontologies, remain robust to low-data regimes, and, with minor offline retraining, tolerate evolving ontologies. We show empirically that our label predictions respect the hierarchy (predicting a descendant implies predicting its ancestors) and propose a method of evaluating hierarchy violations that properly ignores irrelevant violations. Our main result is that our model outperforms all state-of-the-art models on 17 of 20 datasets across multiple domains by a significant margin, even with limited training data.
A Data Mining Algorithm for Association Rules with Chronic Disease Constraints

The Apriori algorithm in association rules is the main algorithm used in the treatment and prevention of chronic diseases in data mining, and the algorithm in the current stage of China's medical field of association between chronic diseases has some problems, such as the need to scan the transaction database of cases several times, producing a large data set and more redundant rules. To address the above problems, a data mining algorithm of association rules combining clustering matrix and pruning strategy is proposed, which improves the algorithm by using the clustering matrix method to compress the stored transaction database and introducing the prepruning and postpruning strategy methods on the basis of adding constraint conditions. The experimental results show that the optimization algorithm has unique advantages in reducing the number of database scans and the number of candidate item sets generated and ultimately greatly reduces the running time and I/O load of the algorithm, and the running efficiency of the algorithm is greatly improved.
Blockchain network with artificial intelligence - DeFi affair management

To create a completely decentralized exchange platform using blockchain technology that allows companies and investors to be able to tokenize real-world assets and provide them with financial services like yield farming, lending, and borrowing. Tailored commendations, announcements, and propositions can be done with decisions by artificial intelligence. Artificial intelligence plays a key role in analysis and prediction added to excellent decision-making skills on par with human intelligence. Blockchain technology proves its efficiency in high interpretability. Both blockchain and artificial intelligence compensate each other in its own feature. This chapter provides a detail description about useful topics like online education, finance, etc.
Real-world applications of generative adversarial networks and their role in blockchain technology

This new term called "Blockchain" has come into the limelight over this decade, significantly impacting almost all major industries&apos; development and efficiency. Blockchain is nothing but just a database. This database stores data blocks that are encrypted and chained together to form a single point of truth (SPOT) for data. Here the digital assets are not transferred or duplicated but are distributed. This creates a rigid record of assets. The asset is localised, giving public access in real-time and transparency. Document&apos;s decency is preserved via a transparent record of changes, which builds trust in the asset. A technology of this calibre when blended with another revolutionary technology called Generative Adversarial Networks (GANs) gives an outcome of utmost efficiency. GAN is the hottest topic of recent research. Since 2014, GANs have been widely studied for many years, and there are a large number of algorithms proposed by many researchers. However, very few complete studies explain the link between GAN variability and how it has evolved. In this chapter, we attempt to provide updates on the various uses of GANs in running applications. In this chapter, we discussed the way GANs are used in various real-time applications. We had reviewed various models for estimating generative models used in healthcare, Internet of Things (IoT), credit card fraud detection, and some other applications, in which GANs are used. In addition, GANs are integrated with other machine learning (ML) algorithms for specific applications, such as less supervised Learning, learning transfer, enhanced learning. This chapter compares the similarities and differences in these GAN methods. Second, theoretical issues related to GANs are being investigated. Third, the general use of GANs in ML, image processing, the medical field, and data science is also mentioned. Here we will be mainly focussing on real-time use of GAN such as self-driving, credit card fraud, and field medical. Also, there have been quite a few applications of GANs in blockchain, which we will be discussing in this chapter.
Implementasi Sistem Informasi Reservasi Hotel Syariah di Kota Jambi Berbasis Website

Al-Fath Hotel located in Jambi City is one of the Jasmine 2 syariah hotels located in Jambi City, and is a growing hotel and continues to strive to improve hotel services and quality standards from various sides, including the use of information technology. The Hotel Reservation information system is a system that can support the level of fluency in carrying out Hotel reservation activities. In room rentals often experience errors in making reports specifically to calculate the number of reports staying overnight. This study aims to develop the Al-Fath Jambi Hotel Reservation Information System, which will be easier in promoting and marketing. Making this Website uses Adobe Dreamweaver CS5 tools, MySQL as its database and CSS Bootstrap so that the display is more attractive and responsive. Inputs from the system are input guest data, room type data, booking data, room data, transaction data and photo data. The process carried out on input data is the booking process, guest process, and transaction process. The system output is information about transaction data reports. Results of the Information System Research Reservation Al-Fath Jambi Hotel can assist the hotel in processing reservation data.
Optimized Neural Network for Research Evaluation of Mineral Resources Carrying Capacity in Southern Shaanxi

Evaluation of the carrying capacity of mineral resources is one of the important research content in the implementation of sustainable development. Based on analyzing the metallogenic geological characteristics, distribution, and resource status of mineral resources in southern Shaanxi, this paper establishes an analysis model of mineral resources and mineral advantages based on the analytic hierarchy process and applies them to evaluate the advantages of mineral resources. To provide optimal and efficient results, an improved model of an artificial neural network based on the bat optimization algorithm has been utilized. Through model analysis, the potential value and carrying capacity of mineral resources in three major prefecture-level cities in southern Shaanxi are comprehensively evaluated and analyzed. The results show that the main dominant minerals in southern Shaanxi are gold, lead zinc, and molybdenum ore. There are three grades of mineral resources carrying capacity: Shangluo City is an excellent grade, Hanzhong City is a good grade, and Ankang City is a general grade.
Software product line scoping

In this extended abstract, we discuss the Journal First summary of our work published in the Journal of Systems and Software (JSS) [3].
Features, believe it or not!

Modern software systems must fulfill the needs of an ever-growing customer base. Due to the innate diversity of human needs, software should be highly customizable and reconfigurable. Researchers and practitioners gained interest in software product lines (SPL), mimicking aspects of product lines in industrial production for the engineering of highly-variable systems. There are two main approaches towards the engineering of SPLs. The first uses macros---such as the #ifdef macro in C. The second---called feature-oriented programming (FOP)---uses variability-aware preprocessors called composers to generate a program variant from a set of features and a configuration. Both approaches have disadvantages. Most notably, these approaches are usually not supported by the base language; for instance Java is one of the most commonly used FOP languages among researchers, but it does not support macros rather it relies on the C preprocessor or a custom one to translate macros into actual Java code. As a result, developers must struggle to keep up with the evolution of the base language, hindering the general applicability of SPL engineering. Moreover, to effectively evolve a software configuration and its features, their location must be known. The problem of recording and maintaining traceability information is considered expensive and error-prone and it is once again handled externally through dedicated modeling languages and tools. Instead, to properly convey the FOP paradigm, software features should be treated as first-class citizens using concepts that are proper to the host language, so that the variability can be expressed and analyzed with the same tools used to develop any other software in the same language. In this paper, we present a simple and flexible design pattern for JVM-based languages---dubbed devise pattern---that can be used to express feature dependencies and behaviors with a light-weight syntax both at domain analysis and at domain implementation level. To showcase the qualities and feasibility of our approach, we present several variability-aware implementations of a MNIST-encoder---including one using the devise pattern---and compare strengths and weaknesses of each approach.
International Workshop on Variability Management for Modern Technologies (VM4ModernTech 2022)

Implementing variability in a software system allows developers to deal with different customer needs and requirements---establishing a family of related system variants. Variability can be managed through opportunistic (e.g., clone-and-own) or systematic strategies (e.g., a software product line). In the product-line community, variability management has been researched for systems in numerous domains, such as defense, avionics, or finance, and for various platforms, such as desktops, web applications, or embedded systems. Unfortunately, other research communities; especially those working on modern technologies, such as microservice architectures, cyber-physical systems, robotics, cloud computing, autonomous driving, or ML/AI-based systems; are less aware of the respective state-of-the-art in variability management. Consequently, these communities face similar problems and start to redeveloped similar solutions as the product-line community already did. With the International Workshop on Variability Management for Modern Technologies, we intend to foster and strengthen synergies between the communities regarding variability management for modern technologies. We aim to attract researchers and practitioners to contribute processes, techniques, tools, empirical studies, problem descriptions, or solutions that are connected to reuse and variability management for modern technologies. By inviting different communities and initiating collaborations between them, we hope that VM4ModernTech raises the interest of researchers outside the product-line community for variability management, and thus avoid expensive redevelopments.
Similarity matching for product comparison

The volume, variety and velocity of products in software-intensive systems product lines is increasing. One challenge is to understand the range of similarity between products. Reasons for product comparison include (i) to decide whether to build a new product or not (ii) to evaluate how products of the same type differ for strategic positioning or branding reasons (iii) to gauge if a product line needs to be reorganized (iv) to assess if a product falls within the national legislative and regulatory boundaries. We will discuss two different approaches to address this challenge. One is grounded in feature modelling, the other in case-based reasoning. We will also describe a specific product comparison approach using similarity matching, in which a product configured from a product line feature model is represented as a weighted binary string, the overall similarity between products is compared using a binary string metric, and the significance of individual feature combinations for product similarity can be explored by modifying the weights. We will illustrate our ideas with a mobile phone example, and discuss some of the benefits and limitations of this approach.
A benchmark for active learning of variability-intensive systems

Behavioral models are the key enablers for behavioral analysis of Software Product Lines (SPL), including testing and model checking. Active model learning comes to the rescue when family behavioral models are non-existent or outdated. A key challenge on active model learning is to detect commonalities and variability efficiently and combine them into concise family models. Benchmarks and their associated metrics will play a key role in shaping the research agenda in this promising field and provide an effective means for comparing and identifying relative strengths and weaknesses in the forthcoming techniques. In this challenge, we seek benchmarks to evaluate the efficiency (e.g., learning time and memory footprint) and effectiveness (e.g., conciseness and accuracy of family models) of active model learning methods in the software product line context. These benchmark sets must contain the structural and behavioral variability models of at least one SPL. Each SPL in a benchmark must contain products that requires more than one round of model learning with respect to the basic active learning L* algorithm. Alternatively, tools supporting the synthesis of artificial benchmark models are also welcome.
Towards a cognitive model of feature model comprehension

Feature models are pivotal components of Software Product Lines. Therefore, their correct comprehension is crucial for performing adequately all the tasks where they are involved. Despite their importance, to the best of our knowledge, no research has been done on feature model comprehension using eye-trackers. As a first step to address this lack, our work contributes an empirical study of feature model comprehension in simple configuration validation tasks. We propose a first cognitive model for this type of tasks that we analyze by measuring eye gaze fixations on the different visual elements involved in the tasks. Our results identified three main components of the cognitive model and their distribution in terms of the cognitive effort for performing these tasks. We argue that further research on feature model comprehension can inform language design and tool development to provide more suitable language structures, user interfaces and support for this kind of models.
Resolution of English Event Pronouns Based on Machine Learning

As the basis of machine translation, anaphora aims to let the machine determine the entity or event to which the sentence refers by exploring the anaphora relationship between sentences. Prior to this, the research on anaphora resolution mainly focused on the resolution of entity anaphora. Through unremitting efforts, the elimination of entity-reference relationship has achieved great success, but the equally important event reference has been stagnant. This means that we can promote the development of machine translation by enhancing event reference. In this paper, a new method is proposed, which uses the latest machine learning algorithm to eliminate English event pronouns. Through feature extraction, data preprocessing, and the introduction of end-to-end double-loop neural network and attention mechanism, the network’s ability to acquire contextual features is improved, and finally, the purpose of eliminating English event pronouns is achieved. In the experimental part, this paper also conducts training and testing on the latest data set KBP. It is found that the model algorithm proposed in this paper can perform the task of experimental setup well, and the value of 40.3% F1 is given under CONLL evaluation index. This proves that the model can understand semantic information very effectively and extract relevant information from the given semantic information.
Numerical validation and influence of optical distortions on accuracy

The proposed global HR-EBSD/TKD method is validated numerically in this chapter. Angular disorientation in the range 0 to 14° as well as equivalent elastic strains up to 5 × 10− 2 are considered. Accuracy is evaluated in the absence and in the presence of first-order radial distortion to validate the principle of correction of optical distortion integrated in the inverse-compositional Gauss-Newton (IC-GN) algorithm. The performances of the global cross-correlation based initial guess are evaluated and discussed in the light of the convergence speed of the subsequent subpixel registration by the IC-GN algorithm. Beyond the validation of the method itself, aim is also to quantify the effects of optical distortion on accuracy, when it is neglected or approximately corrected.
Development of a homography-based global DIC approach for high-angular resolution in the SEM

This chapter described the reasoning which led to the development of a global digital image correlation approach based on a linear homography for high-angular resolution in the scanning electron microscope. Homography is a geometric transformation often used to model projection. Its ability to account for displacements induced by lattice rotations and elastic strain in the scintillator is demonstrated. The choice of the image registration algorithm for its measurement is also discussed.
Implementing the homography-based global HR-EBSD/TKD approach

This chapter details the implementation of the proposed homography-based global high-angular resolution electron backscattered diffraction or transmission Kikuchi diffraction (HR-EBSD/TKD) technique. First, it describes the implementation of the inverse-compositional Guess-Newton (IC-GN) algorithm. It measures the deformation parameters of the homography between the reference Kikuchi pattern and the target one with subpixel accuracy. Then, modifications are made to the IC-GN algorithm so that it integrates a correction of optical distortions. This correction enables the IC-GN algorithm to be directly applied to distorted patterns, thus avoiding the time-consuming pre-processing step that has been conducted until present. In order to ensure the convergence of the algorithm in the presence of disorientations of several degrees, an initial guess strategy is based on Fourier-Mellin and Fourier-transform based cross-correlation is proposed.