Miracle-dz/UniFAR-FTUs huggingface dataset:
Impulsive Disturbances on the Dynamical Behavior of Complex-Valued Cohen-Grossberg Neural Networks with Both Time-Varying Delays and Continuously Distributed Delays

This paper studies the global exponential stability for a class of impulsive disturbance complex-valued Cohen-Grossberg neural networks with both time-varying delays and continuously distributed delays. Firstly, the existence and uniqueness of the equilibrium point of the system are analyzed by using the corresponding property of -matrix and the theorem of homeomorphism mapping. Secondly, the global exponential stability of the equilibrium point of the system is studied by applying the vector Lyapunov function method and the mathematical induction method. The established sufficient conditions show the effects of both delays and impulsive strength on the exponential convergence rate. The obtained results in this paper are with a lower level of conservatism in comparison with some existing ones. Finally, three numerical examples with simulation results are given to illustrate the correctness of the proposed results.
The CodRep Machine Learning on Source Code Competition

CodRep is a machine learning competition on source code data. It is carefully designed so that anybody can enter the competition, whether professional researchers, students or independent scholars, without specific knowledge in machine learning or program analysis. In particular, it aims at being a common playground on which the machine learning and the software engineering research communities can interact. The competition has started on April 14th 2018 and has ended on October 14th 2018. The CodRep data is hosted at this https URL.
A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition

Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.
IIIT-H @ CLScisumm-18

In this report, we present our system for the 4th Computational Linguistics Scientific Document Summarization Shared Task (CLSciSumm 2018), which poses the challenge of identifying the spans of text in a reference paper (RP) that most accurately reflect a citation (i.e. citance) from another paper to the RP and identifying the discourse facet of the corresponding text. We modeled Task1A as Classification Prob-lem which classifies a pair of sentences into classes paraphrase and nonparaphrase. We used Convolutional Neural Network along-with transfer learning for this. Task1B is solved as multi label classification problem.
Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

A dynamic learning method is developed for an uncertain -link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF) neural network (NN) approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE) condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.
Parameter Tuning for Local-Search-Based Matheuristic Methods

Algorithms that aim to solve optimisation problems by combining heuristics and mathematical programming have attracted researchers’ attention. These methods, also known as matheuristics, have been shown to perform especially well for large, complex optimisation problems that include both integer and continuous decision variables. One common strategy used by matheuristic methods to solve such optimisation problems is to divide the main optimisation problem into several subproblems. While heuristics are used to seek for promising subproblems, exact methods are used to solve them to optimality. In general, we say that both mixed integer (non)linear programming problems and combinatorial optimisation problems can be addressed using this strategy. Beside the number of parameters researchers need to adjust when using heuristic methods, additional parameters arise when using matheuristic methods. In this paper we focus on one particular parameter, which determines the size of the subproblem. We show how matheuristic performance varies as this parameter is modified. We considered a well-known NP-hard combinatorial optimisation problem, namely, the capacitated facility location problem for our experiments. Based on the obtained results, we discuss the effects of adjusting the size of subproblems that are generated when using matheuristics methods such as the one considered in this paper.
A Novel Procedure for Measuring Semantic Synergy

One interesting characteristic of some complex systems is the formation of macro level constructions perceived as having features that cannot be reduced to their micro level constituents. This characteristic is considered to be the expression of synergy where the joint action of the constituents produces unique features that are irreducible to the constituents isolated behavior or their simple composition. The synergy, characterizing complex systems, has been well acknowledged but difficult to conceptualize and quantify in the context of computing the emerging meaning of various linguistic and conceptual constructs. In this paper, we propose a novel measure/procedure for quantifying semantic synergy. This measure draws on a general idea of synergy as has been proposed in biology. We validate this measure by providing evidence for its ability to predict the semantic transparency of linguistic compounds (Experiment 1) and the abstractness rating of nouns (Experiment 2).
Shareholder Protection and Agency Costs: An Experimental Analysis

Two competing principal–agent models explain why firms pay dividends. The substitute model proposes that corporate insiders pay dividends to signal and build trust with outside shareholders who lack legal protection. The outcome model, in contrast, surmises that when shareholders have legal protection, they demand dividends from insiders to prevent them from expropriating corporate funds. Either way, dividends represent an agency cost paid to align the interests of shareholders and insiders. Expropriations by insiders and reduced investment by shareholders are also agency costs, but they are difficult to identify with archival data. Using a laboratory experiment, we identify the impact of strengthened shareholder protection on all three types of agency costs. Dividend payout ratios are five times larger with stronger investor protection, insider expropriation ratios are twice as high, and outsider investment falls by 45%. Thus, we find evidence that strengthening shareholder protection introduces previous.
A new topology for nonisolated multiport zero voltage switching dc‐dc converter

In this paper, a new multiport zero voltage switching dc‐dc converter is proposed. Multiport dc‐dc converters are widely applicable in hybrid energy generating systems to provide substantial power to sensitive loads. The proposed topology can operate in 3 operational modes of boost, buck, and buck‐boost. Moreover, it has zero voltage switching operation for all switches and has the ability to eliminate the input current ripple; also, at low voltage side, the input sources can be extended. In addition, it has the ability of interfacing 3 different voltages only by using 3 switches. In this paper, the proposed topology is analyzed theoretically for all operating modes; besides, the voltage and current equations of all components are calculated. Furthermore, the required soft switching and zero input currents ripple conditions are analyzed. Finally, to demonstrate the accurate performance of the proposed converter, the Power System Computer Aided Design(PSCAD)/Electro Magnetic Transient Design and Control(EMTDC) simulation and experimental results are extracted and presented.
Global Exponential Synchronization of Nonlinearly Coupled Complex Dynamical Networks with Time-Varying Coupling Delays

This paper focuses on the global exponential synchronization problem of nonlinearly coupled complex dynamical networks with time-varying coupling delays. Several simple and generic global exponential synchronization criteria are derived based on the Lyapunov stability theory and the Dini derivatives using the Halanay and generalized Halanay inequalities. These criteria rely on system parameters alone and can be used conveniently in practical applications. In addition, the system parameters do not satisfy the conditions of the proposed criteria. That is, the system itself cannot synchronize. However, system synchronization can be achieved by adding the appropriate feedback controllers, thereby providing a practical and effective control method for complex dynamical networks. An estimation method of exponential convergence rate is also presented. Finally, the effectiveness of the proposed criteria is verified through numerical simulations.
Approaches for the Improvement of the Multilabel Multiclass Classification with a Huge Number of Classes

In the (cid:12)eld of data analysis, the multilabel multiclass clas-si(cid:12)cation is still a major problem in case of a large number of classes. Withthe help of deep learning methods, impressive information can be extracted from a wide variety of data. For example, people can be recognized on images and in videos or fonts can be imitated. Nevertheless, these algorithms also encounter limitations. One of these limits when classifying objects is the treatment of multiple classes. For example, if an image is supposed to be described with the help of a dictionary in a few keywords, there are countless words that can be selected, but only very few that apply to the object. Another aggravating fact is that the number of words per image is not (cid:12)xed. This paper presents two basic approaches to improve the classi(cid:12)cation accuracy with neural networks compared to a common approach. One strategy describes a parallel model that requires clustered label sets. For this purpose, diﬀerent distributions are considered. In the second approach, the eﬀects of diﬀerent loss functions are investigated. It is shown that the presented approaches obtain a very signi(cid:12)cant improvement of the results compared to the basic model. Both approaches show an improvement of at least 400%. The parallel architecture even achieves 31 times better results than the basic model. We also show under which conditions the individual approaches can achieve the most eﬀective enhancement of quality.
Offshore Expertise for Onshore Companies: Director Connections to Island Tax Havens and Corporate Tax Policy

Theory and recent empirical literature suggest that social and professional connections may influence corporate policy. However, inference may be biased by the possibility that firms who share peers also share unobserved characteristics that are correlated with observed policy. Using a novel identification strategy, we predict and find that director connections through well-known island tax havens have a significant effect on corporate tax policy. Specifically, we find that U.S. firms with directors who are connected to firms domiciled on the islands of the Bahamas, Bermuda, or the Caymans exhibit significantly greater tax avoidance than other U.S. firms. The presence or arrival of an island director is associated with a reduction of between one and three percentage points in the firm’s effective tax rate. We also observe a significant increase in the use of tax haven subsidiaries following the arrival of the island director. This paper was accepted by Mary Barth, accounting.
Building Up a Robust Risk Mathematical Platform to Predict Colorectal Cancer

Colorectal cancer (CRC), as a result of a multistep process and under multiple factors, is one of the most common life-threatening cancers worldwide. To identify the “high risk” populations is critical for early diagnosis and improvement of overall survival rate. Of the complicated genetic and environmental factors, which group is mostly concerning colorectal carcinogenesis remains contentious. For this reason, this study collects relatively complete information of genetic variations and environmental exposure for both CRC patients and cancer-free controls; a multimethod ensemble model for CRC-risk prediction is developed by employing such big data to train and test the model. Our results demonstrate that (1) the explored genetic and environmental biomarkers are validated to connect to the CRC by biological function- or population-based evidences, (2) the model can efficiently predict the risk of CRC after parameter optimization by the big CRC-related data, and (3) our innovated heterogeneous ensemble learning model (HELM) and generalized kernel recursive maximum correntropy (GKRMC) algorithm have high prediction power. Finally, we discuss why the HELM and GKRMC can outperform the classical regression algorithms and related subjects for future study.
The "Destabilizing" Effect of Cannibalism in a Spatially Explicit Three-Species Age Structured Predator-Prey Model

Cannibalism, the act of killing and consumption of conspecifics, is generally considered to be a stabilising process in ODE models of predator-prey systems. On the other hand, Sun et al. were the first to show that cannibalism can cause Turing instability, in the classical Rosenzweig-McArthur two-species PDE model, which is an impossibility without cannibalism. Magnussonźs classic work is the first to show that cannibalism in a structured three-species predator-prey ODE model can actually be destabilising. In the current manuscript we consider the PDE form of the three-species model proposed in Magnussonźs classic work. We prove that, in the absence of cannibalism, Turing instability is an impossibility in this model, for any range of parameters. However, the inclusion of cannibalism can cause Turing instability. Thus, to the best of our knowledge, we report the first cannibalism induced Turing instability result, in spatially explicit three-species age structured predator-prey systems. We also show that, in the classical ODE model proposed by Magnusson, cannibalism can act as a life boat mechanism, for the prey.
On Measuring the Complexity of Networks: Kolmogorov Complexity versus Entropy

One of the most popular methods of estimating the complexity of networks is to measure the entropy of network invariants, such as adjacency matrices or degree sequences. Unfortunately, entropy and all entropy-based information-theoretic measures have several vulnerabilities. These measures neither are independent of a particular representation of the network nor can capture the properties of the generative process, which produces the network. Instead, we advocate the use of the algorithmic entropy as the basis for complexity definition for networks. Algorithmic entropy (also known as Kolmogorov complexity or -complexity for short) evaluates the complexity of the description required for a lossless recreation of the network. This measure is not affected by a particular choice of network features and it does not depend on the method of network representation. We perform experiments on Shannon entropy and -complexity for gradually evolving networks. The results of these experiments point to -complexity as the more robust and reliable measure of network complexity. The original contribution of the paper includes the introduction of several new entropy-deceiving networks and the empirical comparison of entropy and -complexity as fundamental quantities for constructing complexity measures for networks.
Multi-fidelity surrogate model approach to optimization

Recently the use of Radial Basis Functions (RBF) has been introduced as an optional alternative to co-Kriging in the context of multi-fidelity surrogate modeling. In this paper, we compare the performance of Random Forest-based co-surrogates to the previously introduced co-Kriging and co-RBF using a set of bi-fidelity benchmark problems in 2, 4 and 8 dimensions. Our results show that there is a minimal overall difference between the different co-surrogate models with regards to final performace, although the training of Random Forests takes much less time compared to the Kriging and RBF methods.
Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC

Protein complexes play a critical role in understanding the biological processes and the functions of cellular mechanisms. Most existing protein complex detection algorithms cannot reflect dynamics of protein complexes. In this paper, a novel algorithm named Improved Cuckoo Search Clustering (ICSC) algorithm is proposed to detect protein complexes in weighted dynamic protein-protein interaction (PPI) networks. First, we constructed weighted dynamic PPI networks and detected protein complex cores in each dynamic subnetwork. Then, ICSC algorithm was used to cluster the protein attachments to the cores. The experimental results on both DIP dataset and Krogan dataset demonstrated that ICSC algorithm is more effective in identifying protein complexes than other competing methods.
NLP-NITMZ @ CLScisumm-18

This paper report NLP-NITMZ @ CL-Scisumm 2018 system participation for the shared task task 1A, task 1B and task 2 at the BIRNDL 2018 Workshop. We developed our system based on the previous years data provided by the organizer. For task 1A and 1B, we apply various rule based approaches and trained the KNearest Neighbors Classifier (KNN) using different features identified from the input citation text and the reference Text. We achieved an overall accuracy score of 42.75 (%) and 78.75 (%) score for task 1A and task 1B. For task 2, We built our summary generation system using OpenNMT tool. We developed the model using the training and development datasets released from previous year tracks and validated the results of our system using previous years test data set. We have evaluated our system using Recall-Oriented Understudy for Gisting Evaluation, (ROUGE) score on some test data of CL-Scisumm 2017. For task 2, we achieved an overall accuracy score of 37.75 (%).
Consensus Congestion Control in Multirouter Networks Based on Multiagent System

Due to the unbalance distribution of network resources and network traffic, congestion is an inherent property of the Internet. The consensus congestion controller based on the multiagent system theory is designed for the multirouter topology, which improves the performance of the whole networks. Based on the analysis of the causes of congestion, the topology of multirouter networks is modeled based on the graph theory and the network congestion control problem is described as a consensus problem in multiagent systems. Simulation results by MATLAB and Ns2 indicate that the proposed algorithm maintains a high throughput and a low packet drip ratio and improves the quality of the service in the complex network environment.
A Hybrid Approach for Dynamic Topic Models with Fluctuating Number of Topics

Scientific communities are always changing and evolving. Topics of today might split or even disappear in the future, other topics might merge or appear at some time. Nowadays, the closest we come to picture these developments are dynamic topic models which come with a fixed number of topics k. It would be desirable to omit k. This work outlines a research agenda for approaching that task by using LDA as a base in combination with the observation of state transitions in topics at consecutive times.
Millennial-Style Learning: Search Intensity, Decision Making, and Information Sharing

The growing use of online educational content and related video services has changed the way people access education, share knowledge, and possibly make life decisions. In this paper, we characterize how video content affects individual decision making and willingness to share in the context of a personal financial decision. We find that distracting advertising curtails the time people invest in searching for the best alternative and causes worse decisions. Content geared toward giving better instructions helps to overcome this effect. Such actionable content improves both search quality and financial decisions. However, including such content may decrease sharing unless it is perceived to be sufficiently useful. As such, there is a potential risk to adding actionable content to videos. Our work has important implications for policies guiding financial literacy training, and it also has broader impact for education in the information age. Data are available at https://doi.org/10.1287/mnsc.2016. 2689. This .
Predicting the Currency Market in Online Gaming via Lexicon-Based Analysis on Its Online Forum

Transactions involving virtual currencies are becoming increasingly common, including those in online games. In response, predicting the market price of a virtual currency is an important task for all involved, but it has not yet attracted much attention from researchers. This paper presents user opinions from online forums in a massive multiplayer online game (MMOG) setting widely used around the world. We propose a method for predicting the next-day rise and fall of the currency used in an MMOG environment. Based on analysis of online forum users’ opinions, we predict daily fluctuations in the price of a currency used in an MMOG setting. Focusing specifically on the World of Warcraft game, one of the most widely used MMOGs, we demonstrate the feasibility of predicting the fluctuation in value of virtual currencies used in this game community.
Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks

The control of biofouling on marine vessels is challenging and costly. Early detection before hull performance is significantly affected is desirable, especially if “grooming” is an option. Here, a system is described to detect marine fouling at an early stage of development. In this study, an image of fouling can be transferred wirelessly via a mobile network for analysis. The proposed system utilizes transfer learning and deep convolutional neural network (CNN) to perform image recognition on the fouling image by classifying the detected fouling species and the density of fouling on the surface. Transfer learning using Google’s Inception V3 model with Softmax at last layer was carried out on a fouling database of 10 categories and 1825 images. Experimental results gave acceptable accuracies for fouling detection and recognition.