Analysis and tracer identification of multiphase transport systems with recycling

We consider the identification of the exchange coefficients of a multiphase spacially inhomogeneous transport system with absorbing-reflecting boundaries (i.e. recycling) by means of the tracer technique. A probabilistic approach is used. The model is described and equations are derived for functionals of the tracer distributions at the outputs. Previous results on identifiability and identification for the case without recycling are extended to the case of a recycling system.     

gDART: Improving rumor verification in social media with Discrete Attention Representations

Due to the harmful impact of fabricated information on social media, many rumor verification techniques have been introduced in recent years. Advanced techniques like multi-task learning (MTL), shared-private models suffer from many strategic limitations that restrict their capability of veracity identification on social media. These models are often reliant on multiple tasks for the primary targeted objective. Even the most recent deep neural network (DNN) models like VRoC, Hierarchical-PSV, StA-HiTPLAN etc. based on VAE, GCN, Transformer respectively with improved modification are able to perform good on veracity identification task but with the help of additional auxiliary information, mostly. However, their rise is still not substantial with respect to the proposed model even though the proposed model is not using any additional information. To come up with an improved DNN model architecture, we introduce globally Discrete Attention Representations from Transformers (gDART). Discrete-Attention mechanism in gDART is capable of capturing multifarious correlations veiled among the sequence of words which existing DNN models including Transformer often overlook. Our proposed framework uses a Branch-CoRR Attention Network to extract highly informative features in branches, and employs Feature Fusion Network Component to identify deep embedded features and use them to make enhanced identification of veracity of an unverified claim. Moreover, to achieve its goal, gDART is not dependent on any costly auxiliary resource but on an unsupervised learning process. Extensive experiments reveal that gDART marks a considerable performance gain in veracity identification task over state-of-the-art models on two real world rumor datasets. gDART reports a gain of 36.76%, 40.85% on standard benchmark metrics.     

Identification and characterization of multiphase transport systems by a finite set of moments

The problem of identification of the exchange coefficients in a multiphase spacially inhomogeneous transport (plug-flow) system using the tracer technique is solved. Explicit forms are given for evaluating the exchange coefficients in terms of the output data. It is shown that identification is achieved by the evaluation of a finite number of moments of the output distributions.     

Modelling of linear discrete-time systems using modified cauer continued fraction

An algorithm, amenable for programming on a digital computer, has been presented for the modelling of linear discrete-time systems, as an alternative to the procedure of Shamash (1). The transformations inherent in the procedure are easily accomplished by the synthetic division technique. With the use of modified Cauer form of continued fraction (MCF), the new method matches a set of both the time-moments and Markov parameters of the system and of the model, as in the procedure of Parthasarathy and Singh (2), giving a better approximation to the system response at all times. A distinct feature of the proposed algorithm compared with the earlier methods of discrete system reduction (1),(2), is that a number of reduced-order models are generated simultaneously; this allows scope for better selection in choosing the right model for system analysis and design.     

The inverse lure problem of optimal control

Given the linear system $\text{x}\text{}$ = Ax - bu, y = c^Tx, it is shown that, for a certain non-quadratic cost functional, the optimal control is given by u_opt(x) = h(c^Tx), where the function h(y) must satisfy the conditions ky²?h(y)y>0 for y≠0, h(0) = 0 and existence of h^-1 everywhere. The linear system considered must satisfy the Popov condition 1/k + (1 +?ωβ) G(?ω)>0 for all ω, G(s) being the y(s)/u(s) transfer function.     

Integration of big-data ERP and business analytics (BA)

Technology advancements in cloud computing, big data systems, No-SQL database, cognitive systems, deep learning, and other artificial intelligence techniques make the integration of traditional ERP transaction data and big data streaming from various social media platforms and Internet of Things (IOTs) into a unified analytics system not only feasible but also inevitable. Two steps are prominent for this integration. The first, coined as forming the big-data ERP, is the integration of traditional ERP transaction data and the big data and the second is to integrate the big-data ERP with business analytics (BA). As ERP implementers and BA users are facing various challenges, managers responsible for this big-data ERP-BA integration are also seriously challenged. To help them deal with these challenges, we develop the SIST model (including Strategic alignment, Intellectual and Social capital integration, and Technology integration) and propose that this integration is an evolving portfolio with various maturity levels for different business functions, likely leading to sustainable competitive advantages.     

Asynchronous consensus of multi-agent systems via variable period intermittent control with heterogeneous pulse-modulated function

Most existing consensus control in multi-agent systems (MASs) require agents to update their state synchronously, which means that some agents need to wait for all individuals to complete the iteration before starting the next iteration. To overcome this bottleneck, this paper studied asynchronous consensus problems of second-order MASs (SOMASs) with aperiodic communication. An asynchronous pulse-modulated intermittent control (APIMC) with heterogeneous pulse-modulated function and time-varying control period, which can unify impulsive control and sampled-data control, is proposed for the consensus of SOMASs. A time-varying discrete system is constructed to describe the evolution of the sample values of position and velocity of the SOMAS. Then, by the analysis tools from the stochastic matrix and the properties of the Laplace matrix of graph, some effective conditions are obtained to show the relationship between the convergence of the controlled SOMASs and the control parameters. Finally, a 300-node SOMAS whose topology is a random geographic network is included to verify the feasibility of the proposed control and the correctness of the theoretical analysis.     

Essential gyrators and equivalence rules for 3-port function structures

It is shown that a theorem on essential gyrators presented by Rosenberg (1) and used in (2) claims too much and that the internal structure of the multiport elements of the system must be studied in order to be able to decide whether a gyrator is essentially contained in the system or not. Bond graph terminology is used (3)—(6) and a new theorem is formulated, which provides an algorithm to decide on the essentiality of a gyrator by immediate inspection of the bond graph.As a side-result of this approach some new methods for junction structure simplification can be formulated. The significance of junction 3-ports for the concept of the essential gyrator is elaborated by providing equivalence rules for all kinds of junction 3-ports and introducing a unit essential junction 3-port (ES) and a unit non-essential junction 3-port (NES). Finally the hydraulic junction is treated as an example of a physical non-potential junction, i.e. a junction congruent with an ES.     

Identification of Chinese dark jargons in Telegram underground markets using context-oriented and linguistic features

When cybercriminals communicate with their customers in underground markets, they tend to use secure and customizable instant messaging (IM) software, i.e. Telegram. It is a popular IM software with over 700 million monthly active users (MAU) up to June 2022. In recent years, more and more dark jargons (i.e. an innocent-looking replacement of sensitive terms) appear frequently on Telegram. Therefore, jargons identification is one of the most significant research perspectives to track online underground markets and cybercrimes. This paper proposes a novel Chinese Jargons Identification Framework (CJI-Framework) to identify dark jargons. Firstly, we collect chat history from Telegram groups that are related to the underground market and construct the corpus TUMCC (Telegram Underground Market Chinese Corpus), which is the first Chinese corpus in jargons identification research field. Secondly, we extract seven brand-new features which can be classified into three categories: Vectors-based Features (VF), Lexical analysis-based Features (LF), and Dictionary analysis-based Features (DF), to identify Chinese dark jargons from commonly-used words. Based on these features, we then run a statistical outlier detection to decide whether a word is a jargon. Furthermore, we employ a word vector projection method and a transfer learning method to improve the effect of the framework. Experimental results show that CJI-Framework achieves a remarkable performance with an F1-score of 89.66%. After adaptation for English, it performs better than state-of-the-art English jargons identification method as well. Our built corpus and code have been publicly released to facilitate the reproduction and extension of our work.     

A useful identity in circuit theory

A useful identity expressing the derivative of an unknown variable x_k of X with respect to an entry in the coefficient matrix of a linear system AX = B is presented. If the derivatives of x_k with respect to each entry of A or their combinations are required, then the identity avoids the repeated solution of the linear equations, and may result in a symbolic solution provided A^-1 is known. A method to measure the sensitivity in an n-port linear system is proposed, and its relationships to Kron's method of tearing and Branin's formulae are also discussed.     

Spatial effects on the stability of a food-limited moth population

Having obtained anomalous results in an attempt to continue the simulation study of moth-wasp interaction in Auslander, Oster and Huffaker (J. Franklin Inst. 297, 345-375), attention was centered on the role of spatial heterogeneity in an environment with the moth (Anagasta kühniella) alone. Because of the probable presence of chaotic components in the population behavior (random appearing behavior that is actually caused by deterministic influences), a statistically-based parameter sensitivity and parameter identification method was used. By defining a binary performance criterion that measured the ability of a model with a specific set of parameters to maintain a stable population, the importance of spatial heterogeneity was confirmed. In addition, the use of Monte-Carlo type simulation studies, combined with a binary performance criterion, was demonstrated to be effective for parameter identification and/or parameter sensitivity determination of at least some systems with chaotic or nearly chaotic behavior.     

Backstepping-based decentralized bounded-H∞ adaptive neural control for a class of large-scale stochastic nonlinear systems

In this paper, a novel decentralized adaptive neural control approach based on the backstepping technique is proposed to design a decentralized H_∞ adaptive neural controller for a class of stochastic large-scale nonlinear systems with external disturbances and unknown nonlinear functions. RBF neural networks are utilized to approximate the packaged unknown nonlinearities. A novel concept with regard to bounded-H_∞ performance is proposed. It can be applied to solve an H_∞ control problem for a class of stochastic nonlinear systems. The constant terms appeared in stability analysis are dealt with by using Gronwall inequality, so that H_∞ performance criterion is satisfied. The assumption that the approximation errors of neural networks must be square-integrable in some literature can be eliminated. The design process for decentralized bounded-H_∞ controllers is given. The proposed control scheme guarantees that all the signals in the resulting closed-loop large-scale system are uniformly ultimately bounded in probability, and each subsystem possesses disturbance attenuation performance for external disturbances. Finally, the simulation results are provided to illustrate the effectiveness and feasibility of the proposed approach.     

Parameter identification of a class of time-varying systems via orthogonal shifted legendre polynomials

A method of using orthogonal shifted Legendre polynomials for identifying the parameters of a process whose behaviour can be modelled by a linear differential equation with time-varying coefficients in the form of finite-order polynomials is presented. It is based on the repeated integration of the differential equation and the representations of $\text{∫}\text{0}\text{t}$s(τ) dτ = Ps(t) and ts(t) = Rs(t), where P and R are constant matrices and s(t) is a shifted Legendre vector whose elements are shifted Legendre polynomials. The differential input-output equation is converted into a set of overdetermined linear algebraic equations for a least squares solution. The results of simulation studies are included to illustrate the applicability of the method.     

Study on stability and stabilizability of discrete-time mean-field stochastic systems

This paper is to study the mean square stabilizability and regional stability of discrete-time mean-field stochastic systems. Firstly, a necessary and sufficient condition is presented via the spectrum of linear operator to illustrate the stabilizability of discrete-time mean-field stochastic systems. B(0, γ)-stabilizability is introduced and transformed into solving linear matrix inequalities (LMIs). Secondly, B_M-stability is characterized, especially, the stabilities of circular region, sector region and annulus regions are discussed extensively. Finally, as applications, it is shown that B(0, γ₁; γ₂)-stability has close relationship with the decay rate of the system state response and the Lyapunov exponent.     

HCA: Hierarchical Compare Aggregate model for question retrieval in community question answering

We address the problem of finding similar historical questions that are semantically equivalent or relevant to an input query question in community question-answering (CQA) sites. One of the main challenges for this task is that questions are usually too long and often contain peripheral information in addition to the main goals of the question. To address this problem, we propose an end-to-end Hierarchical Compare Aggregate (HCA) model that can handle this problem without using any task-specific features. We first split questions into sentences and compare every sentence pair of the two questions using a proposed Word-Level-Compare-Aggregate model called WLCA-model and then the comparison results are aggregated with a proposed Sentence-Level-Compare-Aggregate model to make the final decision. To handle the insufficient training data problem, we propose a sequential transfer learning approach to pre-train the WLCA-model on a large paraphrase detection dataset. Our experiments on two editions of the Semeval benchmark datasets and the domain-specific AskUbuntu dataset show that our model outperforms the state-of-the-art models.     

A mental state Knowledge–aware and Contrastive Network for early stress and depression detection on social media

Stress and depression detection on social media aim at the analysis of stress and identification of depression tendency from social media posts, which provide assistance for the early detection of mental health conditions. Existing methods mainly model the mental states of the post speaker implicitly. They also lack the ability to mentalise for complex mental state reasoning. Besides, they are not designed to explicitly capture class-specific features. To resolve the above issues, we propose a mental state Knowledge–aware and Contrastive Network (KC-Net). In detail, we first extract mental state knowledge from a commonsense knowledge base COMET, and infuse the knowledge using Gated Recurrent Units (GRUs) to explicitly model the mental states of the speaker. Then we propose a knowledge–aware mentalisation module based on dot-product attention to accordingly attend to the most relevant knowledge aspects. A supervised contrastive learning module is also utilised to fully leverage label information for capturing class-specific features. We test the proposed methods on a depression detection dataset Depression_Mixed with 3165 Reddit and blog posts, a stress detection dataset Dreaddit with 3553 Reddit posts, and a stress factors recognition dataset SAD with 6850 SMS-like messages. The experimental results show that our method achieves new state-of-the-art results on all datasets: 95.4% of F1 scores on Depression_Mixed, 83.5% on Dreaddit and 77.8% on SAD, with 2.07% average improvement. Factor-specific analysis and ablation study prove the effectiveness of all proposed modules, while UMAP analysis and case study visualise their mechanisms. We believe our work facilitates detection and analysis of depression and stress on social media data, and shows potential for applications on other mental health conditions.     

Application of graph theory to the determination of potential energy of systems consisting of rigid bodies and springs

The process of determining the potential energy, as a function of generalized coordinates, of a system consisting of rigid bodies and springs is often extremely laborious. In this paper, a method is presented by means of which all the potential energy terms of a system are calculated in a systematic manner. It is assumed that the system of rigid bodies has a topological tree structure, and that linear springs are situated arbitrarily between the rigid bodies. The method is based on a graph-theoretical approach and is consistent with that of the calculation of kinetic energy presented in (2, J. Franklin Inst., Vol. 324, No. 3, pp. 351–367, 1987). The paper contains the derivation of general formulae for potential energies of gravity and elastic forces. These formulae are implemented for the case of a planar system; an example is also provided.