A Generic Model for Swarm Intelligence and Its Validations

The modeling of emergent swarm intelligence constitutes a major challenge and it has been tacked in a number of different ways. However, existing approaches fail to capture the nature of swarm intelligence and they are either too abstract for practical application or not generic enough to describe the various types of emergence phenomena. In this paper, a contradiction-centric model for swarm intelligence is proposed, in which individuals determine their behaviors based on their internal contradictions whilst they associate and in-teract to update their contradictions. The model hypothesizes that 1) the emergence of swarm intelligence is rooted in the development of individuals’ internal contradictions and the interactions taking place between in-dividuals and the environment, and 2) swarm intelligence is essentially a combinative reflection of the configu-rations of individuals’ internal contradictions and the distributions of these contradictions across individuals. The model is formally described and five swarm intelligence systems are studied to illustrate its broad applica-bility. The studies confirm the generic character of the model and its effectiveness for describing the emergence of various kinds of swarm intelligence; and they also demonstrate that the model is straightforward to apply, without the need for complicated computations.

Examining LDA2Vec and Tweet Pooling for Topic Modeling on Twitter Data

The short lengths of tweets present a challenge for topic modeling to extend beyond what is provided explicitly from hashtag information. This is particularly true for LDAbased methods because the amount of information available from pertweet statistical analysis is severely limited. In this paper we present LDA2Vec paired with temporal tweet pooling (LDA2VecTTP) and assess its performance on this problem relative to traditional LDA and to Biterm Topic Model (Biterm), which was developed specifically for topic modeling on short text documents. We paired each of the three topic modeling algorithms with three tweet pooling schemes: no pooling, authorbased pooling, and temporal pooling. We then conducted topic modeling on two Twitter datasets using each of the algorithms and the tweet pooling schemes. Our results on the largest dataset suggest that LDA2VecTTP can produce higher coherence scores and more logically coherent and interpretable topics.

Intelligent Technique for Human Authentication using Fusion of Finger and Dorsal Hand Veins

Multimodal biometric systems have been widely used to achieve high recognition accuracy. This paper presents a new multimodal biometric system using intelligent technique to authenticate human by fusion of finger and dorsal hand veins pattern. We developed an image analysis technique to extract region of interest (ROI) from finger and dorsal hand veins image. After extracting ROI we design a sequence of preprocessing steps to improve finger and dorsal hand veins images using Median filter, Wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance vein image. Our smart technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database chosen was the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) and Bosphorus Hand Vein Database. The achieved result for the fusion of both biometric traits was Correct Recognition Rate (CRR) is 96.8%.

Simulating Wave Propagation Distribution Through GIS Integration

The use of electronic devices designed for user location estimation has become widely popular in the last decade. This is thanks to emergent technologies such as Bluetooth Low Energy, Radio-Frequency Identification, and Ultra-WideBand (UWB) among others. In the present study; the authors provide a method for using a Geographic Information System (GIS) to define spatial constraints, in order to simulate the lines of sight of anchors to make an informed selection of adequate locations for installation. By leveraging GIS, researchers or enterprises can improve the installation process by reducing costs while setting up arrangements that will ensure reliable data collection. We include a scenario illustrating the possibility of budget reduction of around 30% related to the orientation and survey of the devices.

Graph Drawing Approaches for Petri Net Visualisation and Representation

Petri net structures can benefit from being modelled using graph drawing approaches. This work presents some advanced graph drawing approaches that can be used for visualising Petri net models. These are i) topological shape metrics, ii) visibility drawing, iii) orthogonalization, iv) hierarchical and v) bi-graph partitioning. The models show that this can be successfully done and the Petri nets that are generated in this work are suitable for visualisation. Graph visualisation is an important topic and the results show that there is a large potential to apply this approach to drawing Petri Nets in novel ways.

The Number of Rearrangements Performed via Extended Connection Chains for Rearrangeable Clos Networks

Various switching networks have been investigated because of their practical importance and theoretical interests. Among these networks, this study focuses on the Clos network. A Clos network is constructed by placing switches in three stages. In the first and third stages, r (r > 1) switches are aligned, whereas m (m > 1) switches are aligned in the second stage. There are n inputs and m outputs in the first stage. Symmetrically, the third stage switch has m inputs and n outputs. For this configuration, if n  m 2n  2, the network is rearrangeable. Though existing connections in a rearrangeable network may block a newly requested connection, the blocking is always removed by rerouting existing connections. An interesting problem arose during this process is how many existing connections must be rearranged: the number of rearrangements. Although the problem has been studied for a long time, the number of rearrangements is not completely clarified for arbitrary combinations of parameters m, n, and r. This study presents a new upper bound on the number of rearrangements for 2  n m 2n  2 . This bound is derived from the extended connection chain concept proposed in a previous study. Using this concept, the paper first derives from the case where a parameter, s, represents the load on a second-stage switch. Then, the paper presents another new upper bound, which is independent of parameter s. The study shows that the presented upper bound is smaller than the previously known bounds for a certain range of m.

Coverage Area of a Localization Fixed Sensors Network System with the process of Triangulation

This paper presents a novel work on localization of transmitters using triangulation with sensors at fixed positions. This is achieved when three or more sensors cover the whole area, a factor which enables the system to perform localization via triangulation. The network needs to keep a high detection rate which, in most cases, is achieved by adequate sensor coverage. Various tests using various grids of sensors have been carried out to investigate the way the system operates in different cases using a lot of transmitters. Detection complexity is tackled by finding the optimal detecting sensor radius in order the network to continue operate normally. The coverage quality changes in the area of interest and the network is able to detect new transmitters that might enter it's area. It is also shown that as the number of transmitters increases the network keeps its high performance by using additional groups of sensors in a sub-region area of that of interest. This way, even when the network is saturated by many transmitters in one region, new transmitters can still be detected.

On the Decomposition of Generalized Semiautomata

Semiautomata are abstractions of electronic devices that are deterministic finite-state machines having inputs but no outputs. Generalized semiautomata are obtained from stochastic semiautomata by dropping the restrictions imposed by probability. It is well-known that each stochastic semiautomaton can be decomposed into a sequential product of a dependent source and deterministic semiautomaton making partly use of the celebrated theorem of Birkhoff-von Neumann. It will be shown that each generalized semiautomaton can be partitioned into a sequential product of a generalized dependent source and a deterministic semiautomaton.

POS-Tagging based Neural Machine Translation System for European Languages using Transformers

The interaction between human beings has always faced different kinds of difficulties. One of those difficulties is the language barrier. It would be a tedious task for someone to learn all the syllables in a new language in a short period and converse with a native speaker without grammatical errors. Moreover, having a language translator at all times would be intrusive and expensive. We propose a novel approach to Neural Machine Translation (NMT) system using interlanguage word similaritybased model training and Part-Of-Speech (POS) Tagging based model testing. We compare these approaches using two classical architectures: Luong Attention-based Sequence-to-Sequence architecture and Transformer based model. The sentences for the Luong Attention-based Sequence-to-Sequence were tokenized using SentencePiece tokenizer. The sentences for the Transformer model were tokenized using Subword Text Encoder. Three European languages were selected for modeling, namely, Spanish, French, and German. The datasets were downloaded from multiple sources such as Europarl Corpus, Paracrawl Corpus, and Tatoeba Project Corpus. Sparse Categorical CrossEntropy was the evaluation metric during the training stage, and during the testing stage, the Bilingual Evaluation Understudy (BLEU) Score, Precision Score, and Metric for Evaluation of Translation with Explicit Ordering (METEOR) score were the evaluation metrics.

Secure Software for Rail Circuits in the Form of Distributed Sensors

In order to improve the safety of train traffic, we propose to introduce into practice a new type of rail circuits, fiber-optic rail circuits. Fiber optic cable is very sensitive to external shocks, vibration and deformation, which can play a crucial role in the detection of mechanical damage of rails and wheel sets and also to improve the positioning of the rolling stock. The branches of the fiber optic cable not only serve as a conductor of information, but also serve as a sensor, as they can perceive vibration. An OTDR (Optical Time Domain Reflectometer) analyzes the backscattered light signal to determine the shape of the physical impact that caused the bending. From the time between the emission of the light signal and the receipt of the backscattered signal, the fault location is calculated. The authors offer the model of the system, which will check the security problems of the trains and the rail circuits. The software of the corresponding system is presented, using the simulation techniques. The authors present the pseudo code of the software. They also offer the testing environment for the software.