Sequence Of Relations Research Articles

Learning strategies and procrastination as a function of need satisfaction and autonomous motivation: A diary study

AbstractWithin the framework of Self‐Determination Theory (SDT), basic psychological need satisfaction predicts quality of motivation which in turn predicts study efforts. Although studies focusing on interpersonal differences have repeatedly shown this sequence of relations, only a few have examined its stability at the intra‐personal level. In this diary study, we recruited 141 university students (M age = 20.80, SD = 2.20 years) to investigate the degree of confluence among week‐to‐week need satisfaction, quality of motivation, learning strategies, and procrastination for four weeks. Multilevel structural equation modelling showed that need satisfaction covaried positively with autonomous motivation. In turn, week‐to‐week autonomous motivation predicted positively week‐to‐week critical thinking and effort regulation and negatively procrastination. These relations emerged even after controlling for gender, age, and study hours per week. Further, contextual autonomous motivation predicted higher mean levels of critical thinking and effort regulation and lower ones of procrastination. Interestingly, a cross‐level interaction supported the sensitivity hypothesis as the negative relation between need satisfaction and controlled motivation was only true among students who were high in contextual (pre‐diary assessed) controlled motivation. These findings highlight the importance of contextual motivation and the need to establish academic environments that consistently satisfy students' psychological needs, thus promoting the quality of motivation and adaptive learning strategies.

A goodness of fit framework for relational event models

Abstract We introduce a novel procedure to assess the goodness of fit in relational event models. Building on existing auxiliary variable approaches developed in network modelling, the procedure involves a comparison between statistics computed on observed relational event sequences and statistics calculated on event sequences simulated from the fitted model. We argue that the internal time structure of the relational mechanisms assumed to generate the observations under the model is an important aspect of the fit of a model to observed relational event sequences. We establish the empirical value of the proposed goodness of fit approach in an analysis of data that we collected on collaborative patient-referral relations among healthcare organizations. The illustrative case study that we develop reveals distinctive features of relational event models that have been ignored or overlooked in received empirical studies.

StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information Networks

Similarity measures in heterogeneous information networks (HINs) have become increasingly important in recent years. Most measures in such networks are based on the meta path, a relation sequence connecting object types. However, in real-world scenarios, there exist many complex semantic relationships, which cannot be captured by the meta path. Therefore, a meta structure is proposed, which is a directed acyclic graph of object and relation types. In this paper, we explore the complex semantic meanings in HINs and propose a meta-structure-based similarity measure called StructSim. StructSim models the probability of subgraph expansion with bias from source node to target node. Different from existing methods, StructSim claims that the subgraph expansion is biased, i.e., the probability may be different when expanding from the same node to different nodes with the same type based on the meta structure. Moreover, StructSim defines the expansion bias by considering two types of node information, including out-neighbors of current expanded nodes and in-neighbors of next hop nodes to be expanded. To facilitate the implementation of StructSim, we further designed the node composition operator and expansion probability matrix with bias. Extensive experiments on DBLP and YAGO datasets demonstrate that StructSim is more effective than the state-of-the-art approaches.

Depth-Bounded Fuzzy Bisimulation for Fuzzy Modal Logic

We introduce depth-bounded fuzzy bisimulation between fuzzy Kripke models. Roughly speaking, a depth-bounded fuzzy bisimulation is a decreasing sequence of fuzzy binary relations whose infimum is a fuzzy bisimulation. We provide logical characterizations of depth-bounded fuzzy bisimulations between fuzzy Kripke models w.r.t. a fuzzy multimodal logic over complete residuated lattices, including fuzzy invariance of formulas of with a modal depth bounded by under the th component of a depth-bounded fuzzy bisimulation, as well as the Hennessy-Milner property of depth-bounded fuzzy bisimulations. We also provide a polynomial-time algorithm for computing the th component of the greatest depth-bounded fuzzy bisimulation between two finite fuzzy Kripke models when the underlying complete residuated lattice is linear.

Parliament and Revolution: Poland, Finland, and the End of Empire in the Early Twentieth Century

AbstractThis article explores the political trajectories of the early twentieth-century Grand Duchy of Finland and the Kingdom of Poland in the context of the “global parliamentary moment,” when the constitutional script of revolution competed with the more daring script of social revolution. We scrutinize contrastive political choices of socialist parties in these two western borderlands of the Russian Empire. Finland and Poland emerged as independent parliamentary states in 1917–1918 but under manifestly different circumstances. The Finnish socialist party had enjoyed a stable foothold in the formally democratic but practically impotent national parliament since 1907, whereas the Polish socialists boycotted the Russian Duma and envisioned a democratic legislature as a guaranty of a Poland with true people’s power. The Finnish socialists later abandoned parliamentarism in favor of an armed revolution, in 1918, whereas most of their Polish counterparts used the parliamentary ideal of popular sovereignty to restrain the revolutionary upsurge. We argue that the socialist understandings of parliamentarism and revolution were of crucial importance at this juncture. We draw from a broad corpora of political press reports, handwritten newspapers, and leaflets to show how the diachronic sequence of events and synchronic power relations inside the Russian Empire made certain stances toward parliamentarism and revolution more likely at different points in time.

Translation certification for smart contracts

Compiler correctness is an old problem, but with the emergence of smart contracts on blockchains that problem presents itself in a new light. Smart contracts are self-contained pieces of software that control (valuable) assets in an adversarial environment; once committed to the blockchain, these smart contracts cannot be modified. Smart contracts are typically developed in a high-level contract language and compiled to low-level virtual machine code before being committed to the blockchain. For a smart contract user to trust a given piece of low-level code on the blockchain, they must convince themselves that (a) they are in possession of the matching source code and (b) that the compiler has correctly translated the source code to the given low-level code.Classic approaches to compiler correctness tackle the second point. We argue that translation certification also squarely addresses the first. We describe the proof architecture of a translation certification framework and demonstrate how we can model the compilation pipeline as a sequence of translation relations. We give a detailed account of such relations for most passes of the Plutus Tx compiler, which we formalised in Coq. This approach facilitates a modular verification methodology and is robust in the face of an evolving compiler implementation.

Abstract 14155: Handgrip Exercise Provoked High Sensitivity Cardiac Troponin I Increase in Women With No Obstructive Coronary Artery Disease: Relations to Myocardial Function

Background: Among women with signs and symptoms of ischemia and no obstructive coronary arteries (INOCA) around 10% develop heart failure with preserved ejection fraction (HFpEF). Mechanisms contributing to AMI and HFpEF progression in INOCA are poorly understood. Purpose: To characterize clinical profile and stress cardiac magnetic resonance imaging (CMRI) myocardial features of INOCA patients with and without increased troponin levels in response to handgrip exercise. Methods: Women with suspected INOCA underwent cardiac MRI and invasive coronary function testing (CFT), including coronary sinus (CS) cannulation, handgrip exercise testing, and serial CS plasma sampling before, after 3 minutes of isometric handgrip stress at 30% of maximal voluntary contraction, and after 5 minutes of recovery. High sensitivity cardiac troponin I (hsTnI) was measured using commercial immunoassay (R-PLEX, Meso Scale Discovery, Rockville, MD) and compared using t-tests. Results: A total of 51 women with complete data were included, of whom 31 (60.7%) had handgrip provoked increases in hsTnI levels from baseline, while 20 (39.2%) did not. Baseline characteristics are shown in Table. Patients with increased hsTnI levels had greater impaired global longitudinal strain rates and early diastolic strain rates (radial and circumferential) compared to those without increased hsTnI. No significant differences were observed in myocardial perfusion reserve index (MPRI). Conclusion: Women with INOCA demonstrating objective evidence of handgrip exercise provoked myocardial injury exhibit evidence of myocardial dysfunction not related to myocardial perfusion. Further prospective research is needed to understand the sequence of relations between coronary microvascular dysfunction contribution to HFpEF progression.

Depth-bounded fuzzy simulations and bisimulations between fuzzy automata

Simulations and bisimulations are well-established notions in crisp/fuzzy automata theory and are widely used to compare the behaviors of automata. Their main drawback is that they compare the behaviors of fuzzy automata in a crisp manner. Recently, fuzzy simulations and fuzzy bisimulations have been defined for fuzzy automata as a kind of approximate simulations and approximate bisimulations that compare the behaviors of fuzzy automata in a fuzzy manner. However, they still suffer from serious shortcomings. First, they still cannot correlate all fuzzy automata that are intuitively “more or less” (bi)similar. Second, the currently known algorithms for computing the greatest fuzzy simulation or bisimulation between two finite fuzzy automata have an exponential time complexity when the Łukasiewicz or the product structure of fuzzy values is used. This work deals with these problems, providing approximations of fuzzy simulations and fuzzy bisimulations. We define such approximations via a novel notion of decreasing sequences of fuzzy relations whose infima are, under some conditions, fuzzy simulations (respectively, bisimulations). We call such a sequence a depth-bounded fuzzy simulation (respectively, bisimulation), as the nth element from the sequence compares the behaviors of fuzzy automata, but only for words with a length bounded by n. We further provide a logical characterization of the greatest depth-bounded fuzzy simulation or bisimulation between two fuzzy automata by proving that it satisfies the corresponding Hennessy-Milner property. Finally, we provide polynomial-time algorithms for computing the nth component of the greatest depth-bounded fuzzy simulation (respectively, bisimulation) between two finite fuzzy automata.

Entity–relation triple extraction based on relation sequence information

Data overlap is a significant challenge in the task of entity–relation triple extraction. This task includes two research lines, line one first identifies entities and then predicts relations while line two completely shuffles the order. The methods in line two are more conducive to the optimization of the data overlap problem. Recent works have made breakthroughs in dealing with overlapping data, but there are still some defects such as difficulty in convergence and poor performance on datasets with numerous relations. To solve the above problems, we adopt a two-step strategy of first extracting subjects, and then predicting relation–object pairs. Considering the absence of connectivity between the two steps in the conventional method, we adopt the relation sequence as input in both steps and propose the TERS model. The relation sequence can connect two steps and improve the single-step and comprehensive extraction capability of the model. The TERS model consists of two modules. The first module performs information interaction and filters invalid subjects through the Text Relation Attention method. The second module implements multiple iterations of information interaction through the Information Flow method. The combination of these two modules contributes to the strong entity–relation triple extraction capability of our model. We evaluate our method on three public datasets. Extensive experiments show that our TERS model outperforms previous state-of-the-art models in triple extraction and overlapping data processing. Compared with other two-step extraction models, the advantages of our model are more obvious.

Knowledge-Based Reasoning Network for Relation Detection.

With the rapid growth of large-scale knowledge bases (KBs), knowledge base question answering (KBQA) has attracted increasing attention recently. Relation detection plays an important role in the KBQA system, which finds a compatible answer by analyzing the semantics of questions and querying and reasoning with multiple KB triples. Significant progress has been made by deep neural networks. However, existing methods often concern on detecting single-hop relation without path reasoning, and a few of these methods exploit the multihop relation reasoning, which involves the answer reasoning from the noisy and abundant relational paths in the KB. Meanwhile, the relatedness between question and answer candidates has received little attention and remains unsolved. This article proposes a novel knowledge-based reasoning network (KRN) for relation detection, including both single-hop relation and multihop relation. To address the semantic gap problem in question-answer interaction, we first learn attentive question representations according to the influence of answer aspects. Then, we learn the single-hop relation sequence through different levels of abstraction and adopt the KB entity and structure information to denoise the multihop relation detection task. Finally, we adopt a Siamese network to measure the similarity between question representation and relation representation for both single-hop and multihop relation KBQA tasks. We conduct experiments on two well-known benchmarks, SimpleQuestions and WebQSP, and the results show the superiority of our approach over the state-of-the-art models for both single-hop and multihop relation detection. Our model also achieves a significant improvement over existing methods on KBQA end task. Further analysis demonstrates the robustness and the applicability of the proposed approach.

A Bayesian actor-oriented multilevel relational event model with hypothesis testing procedures

AbstractRelational event network data are becoming increasingly available. Consequently, statistical models for such data have also surfaced. These models mainly focus on the analysis of single networks; while in many applications, multiple independent event sequences are observed, which are likely to display similar social interaction dynamics. Furthermore, statistical methods for testing hypotheses about social interaction behavior are underdeveloped. Therefore, the contribution of the current paper is twofold. First, we present a multilevel extension of the dynamic actor-oriented model, which allows researchers to model sender and receiver processes separately. The multilevel formulation enables principled probabilistic borrowing of information across networks to accurately estimate drivers of social dynamics. Second, a flexible methodology is proposed to test hypotheses about common and heterogeneous social interaction drivers across relational event sequences. Social interaction data between children and teachers in classrooms are used to showcase the methodology.

Some New Integer Sequences of Transitive Relations

Some New Integer Sequences of Transitive Relations

NEDORT: a novel and efficient approach to the data overlap problem in relational triples

Relation triple extraction is a combination of named entity recognition and relation prediction. Early works ignore the problem of data overlap when extracting triples, resulting in poor extraction performance. Subsequent works improve the capability of the model to extract overlapping triples through generative and extractive methods. These works achieve considerable performance but still suffer from some defects, such as poor extraction capability for individual triplets and inappropriate spatial distribution of the data. To solve the above problems, we perform sequence-to-matrix transformation and propose the NEDORT model. NEDORT predicts all subjects in the sentence and then completes the extraction of relation–object pairs. There are overlapping parts between relation–object pairs, so we conduct the conversion of sequence to matrix. We design the Differential Amplified Multi-head Attention method to extract subjects. This method highlights the locations of entities and captures sequence features from multiple dimensions. When performing the extraction of relation–object pairs, we fuse subject and sequence information through the Biaffine method and generate relation–sequence matrices. In addition, we design a multi-layer U-Net network to optimize the matrix representation and improve the extraction performance of the model. Experimental results on two public datasets show that our model outperforms other baseline models on triples of all categories

Meta-path guided graph attention network for explainable herb recommendation.

Traditional Chinese Medicine (TCM) has been widely adopted in clinical practice by Eastern Asia people for thousands of years. Nowadays, TCM still plays a critical role in Chinese society and receives increasing attention worldwide. The existing herb recommenders learn the complex relations between symptoms and herbs by mining the TCM prescriptions. Given a set of symptoms, they will provide a set of herbs and explanations from the TCM theory. However, the foundation of TCM is Yinyangism (i.e. the combination of Five Phases theory with Yin-yang theory), which is very different from modern medicine philosophy. Only recommending herbs from the TCM theory aspect largely prevents TCM from modern medical treatment. As TCM and modern medicine share a common view at the molecular level, it is necessary to integrate the ancient practice of TCM and standards of modern medicine. In this paper, we explore the underlying action mechanisms of herbs from both TCM and modern medicine, and propose a Meta-path guided Graph Attention Network (MGAT) to provide the explainable herb recommendations. Technically, to translate TCM from an experience-based medicine to an evidence-based medicine system, we incorporate the pharmacology knowledge of modern Chinese medicine with the TCM knowledge. We design a meta-path guided information propagation scheme based on the extended knowledge graph, which combines information propagation and decision process. This scheme adopts meta-paths (predefined relation sequences) to guide neighbor selection in the propagation process. Furthermore, the attention mechanism is utilized in aggregation to help distinguish the salience of different paths connecting a symptom with a herb. In this way, our model can distill the long-range semantics along meta-paths and generate fine-grained explanations. We conduct extensive experiments on a public TCM dataset, demonstrating comparable performance to the state-of-the-art herb recommendation models and the strong explainability.

On generators and defining relations of quantum affine superalgebra Uq(𝔰𝔩̂m|n)

Two presentations of quantum affine superalgebras were introduced by Yamane in [On defining relations of affine Lie superalgebras and affine quantized universal enveloping superalgebras, Publ. Res. Inst. Math. Sci. 35 (1999) 321–390], which were called Drinfeld–Jimbo realization and Drinfeld realization. Drinfeld realization contains infinite sequences of generators and relations. In this paper, we consider the Drinfeld realization of quantum affine superalgebra [Formula: see text] associated to type [Formula: see text] and define a simple algebra [Formula: see text] generated by only a finite part of these sequences of quantum affine superalgebra [Formula: see text]. We show that the algebra [Formula: see text] is isomorphic to the quantum affine superalgebra [Formula: see text]. Using the above isomorphism, we prove there exists an isomorphism between the two realizations.

Enhancement of the CAST Block Algorithm Based on Novel S-Box for Image Encryption.

due to the rapid growth of data communication and multimedia system applications, security becomes a critical issue in the communication and storage of images. This study aims to improve encryption and decryption for various types of images by decreasing time consumption and strengthening security. An algorithm is proposed for encrypting images based on the Carlisle Adams and Stafford Tavares CAST block cipher algorithm with 3D and 2D logistic maps. A chaotic function that increases the randomness in the encrypted data and images, thereby breaking the relation sequence through the encryption procedure, is introduced. The time is decreased by using three secure and private S-Boxes rather than using six S-Boxes, as in the traditional method. Moreover, the CAST encryption algorithm was modified to be used on the private keys and substitution stage (S-Boxes), with the keys and S-Boxes of the encryption algorithm being generated according to the 2D and 3D chaotic map functions. The proposed system passed all evaluation criteria, including (MSE, PSNR, EQ, MD, SC, NC, AD, SNR, SIM, MAE, Time, CC, Entropy, and histograms). Moreover, the results also illustrate that the created S-Boxes passed all evaluation criteria; compared with the results of the traditional method that was used in creating S-Box, the proposed method achieved better results than other methods used in the other works. The proposed solution improves the entropy which is between (7.991-7.999), reduces the processing time which is between (0.5-11 s/Images), and improves NCPR, which is between (0.991-1). The proposed solution focuses on reducing the total processing time for encryption and decryption and improving transmission security. Finally, this solution provides a fast security system for surgical telepresence with secure real-time communication. The complexity of this work needs to know the S-Box creation method used, the chaotic method, the values of the chaotic parameters, and which of these methods was used in the encryption process.

Hybridization of long short-term memory with Sparrow Search Optimization model for water quality index prediction

Water quality (WQ) analysis is a critical stage in water resource management and should be handled immediately in order to control pollutants that could have a negative influence on the ecosystem. The dramatic increase in population, the use of fertilizers and pesticides, and the industrial revolution have resulted in severe effects on the WQ environment. As a result, the prediction of WQ greatly helped to monitor water pollution. Accurate prediction of WQ is the foundation of managing water environments and is of high importance for protecting water environment. WQ data presents in the form of multi-variate time-sequence dataset. It is clear that the accuracy of predicting WQ will be enhanced when the multi-variate relation and time sequence dataset of WQ are fully utilized. This article presents the Water Quality Prediction utilising Sparrow Search Optimization with Hybrid Long Short-Term Memory (WQP-SSHLSTM) model. The presented WQP-SSHLSTM model intends to examine the data and classify WQ into distinct classes. To achieve this, the presented WQP-SSHLSTM model undergoes data scaling process to scale the input data into uniform format. Followed by, a hybrid long short-term memory-deep belief network (LSTM-DBN) technique is employed for the recognition and classification of WQ. Moreover, Sparrow search optimization algorithm (SSOA) is utilized as a hyperparameter optimizer of the proposed DBN-LSTM model. For demonstrating the enhanced outcomes of the presented WQP-SSHLSTM model, a sequence of experiments has been performed and the outcomes are reviewed under distinct prospects. The WQP-SSHLSTM model has achieved 99.84 percent accuracy, which is the maximum attainable. The simulation outcomes ensured the enhanced outcomes of the WQP-SSHLSTM model on recent methods.

A Meta Path Based Method for Entity Set Expansion in Knowledge Graph

Entity Set Expansion (ESE) is the problem that expands a small set of seed entities into a more complete set, entities of which have common traits. As a popular data mining task, ESE has been widely used in many applications, such as dictionary construction, query suggestion and new brand identification. Existing ESE methods mainly utilize text and Web information. That is, the intrinsic relation among entities is inferred from their occurrences in text or Web. With the surge of knowledge graph in recent years, it is possible to extend entities according to their occurrences in knowledge graph. In this paper, we consider the knowledge graph as a heterogeneous information network (HIN) that contains different types of objects and links, and propose a novel method, called MP_ESE, to extend entities in the HIN. The MP_ESE employs meta paths, a relation sequence connecting entities, in HIN to capture the implicit common traits of seed entities. In addition, an automatic meta path generation method, called SMPG, has been designed to exploit the potential relations among entities. Heuristic learning and PU learning methods are employed to learn the weights of extracted meta paths. With these generated and weighted meta paths, the MP_ESE can effectively extend entities. Comprehensive experiments on real datasets show the effectiveness and efficiency of MP_ESE.

“Generating matrix for Generalized Fibonacci numbers and Fibonacci polynomials

Many researchers have been working on recurrence relation sequences of numbers and polynomials which are useful topic not only in mathematics but also in physics, economics and various applications in many other fields. There are many useful identities on recurrence relation sequence but there main problem to find any term of recurrence relation sequence we need to find previous all terms of recurrence relation sequence of numbers and polynomials. There were many important theorems obtained on recurrence relation sequences. In this paper we have given special identity for generalized Fibonacci sequence of number and Fibonacci sequence of polynomials. These identities are very useful to represent Fibonacci generalized sequence of numbers and Fibonacci sequence of polynomials in the form of matrix. Authors define a special formula in this paper by this we can find special representation of Fibonacci generalized sequence of numbers and Fibonacci sequence of polynomials in the form of matrix. So, we can say that this paper is generalization of property of Fibonacci sequence of number and Fibonacci sequence of polynomials

Multimodality, interactional competence, and the ‘intercultural’ in achieving intercultural service encounters in Japan

This study uses a multimodal conversation analysis to examine the interactional competence of participants in intercultural service encounters. American customers were video recorded while placing orders at small food shops in rural Japan. Two encounters illustrate how participants used multimodal resources to collaboratively achieve transactional goals in the face of features—linguistic difficulty and relational talk occasioned by language and sociocultural identities—that might be attributed to the ‘intercultural’ setting. The analysis shows how the participants used gesture, movement, and body position in concert with standard transactional routines to navigate relational and transactional sequences of talk and accomplish a shared, transactional goal. The discussion then considers whether these sequential developments are partly responsible for the particular ‘intercultural’ nature of these encounters, thereby contributing to a more nuanced understanding of the interactional components of service encounters between participants of different cultural backgrounds.