345 research outputs found
A Binary Neural Shape Matcher using Johnson Counters and Chain Codes
In this paper, we introduce a neural network-based shape matching algorithm that uses Johnson Counter codes coupled with chain codes. Shape matching is a fundamental requirement in content-based image retrieval systems. Chain codes describe shapes using sequences of numbers. They are simple and flexible. We couple this power with the efficiency and flexibility of a binary associative-memory neural network. We focus on the implementation details of the algorithm when it is constructed using the neural network. We demonstrate how the binary associative-memory neural network can index and match chain codes where the chain code elements are represented by Johnson codes
Wireless Sensor Networks for Condition Monitoring in the Railway Industry : a Survey
In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile adhoc networking coupled with the technology to integrate devices. Wireless sensor networks (WSNs) can be used for monitoring the railway infrastructure such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally,which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review
Quantitative Comparisons into Content-Based Music Recognition with the Self Organising Map
With so much modern music being so widely available both in electronic form and in more traditional physical formats, a great opportunity exists for the development of a general-purpose recognition and music classification system. We describe an ongoing investigation into the subject of musical recognition purely by the sonic content from a standard recording
En-Ar Bilingual word Embeddings without Word Alignment : Factors Effects
This paper introduces the first attempt to investigate morphological segmentation on En-Ar bilingual word embeddings using bilingual word embeddings model without word alignment (BilBOWA). We investigate the effect of sentence length and embedding size on the learning process. Our experiment shows that using the D3 segmentation scheme improves the accuracy of learning bilingual word embeddings up to 10 percentage points compared to the ATB and D0 schemes in all different training settings
Automatic Labeling of Tweets for Crisis Response Using Distant Supervision
Current tweet classification models aimed at enhancing crisis response are based on supervised deep learning. They rely on the quality and quantity of human-labeled training data. Still, the available training data is small in size and imbalanced in coverage of crisis types, which prevents the models from generalization, and as it is manually labeled, it is also expensive to produce. To overcome these problems, distant supervision can be applied to automatically generate large-scale labeled data for tweet classification for crisis response. Experimental results on different crisis events show that our work can produce good quality labeled data from past and recent events. Substituting automatically labeled training data for part of the manually labeled training data has a minimal impact on the model performance, indicating that automatically labeled data can be used when no hand-labeled data is available
Memory state tracker : A memory network based dialogue state tracker
Dialogue State Tracking (DST) is a core component towards task oriented dialogue system. It fills manually-set slots at each turn of an utterance, which indicate the current topics or user requirement. In this work we propose a memory based state tracker that includes a memory encoder which encodes the dialogue history into a memory vector, and then connects to a pointer network which makes predictions. Our model reached a joint goal accuracy of 49.16% on MultiWOZ 2.0 data set (Budzianowski et al., 2018) and 47.27% on MultiWOZ 2.1 data set (Eric et al., 2019), outperforming the benchmark result
Discrete and continuous systems of logic in nuclear magnetic resonance
We implement several non-binary logic systems using the spin dynamics of nuclear spins in nuclear magnetic resonance (NMR). The NMR system is a suitable test system because of its high degree of experimental control; findings from NMR implementations are relevant for other computational platforms exploiting particles with spin, such as electrons or photons. While we do not expect the NMR system to become a practical computational device, it is uniquely useful to explore strengths and weaknesses of unconventional computational approaches, such as non-binary logic
Classifying 1D elementary cellular automata with the 0-1 test for chaos
We utilise the 0-1 test to automatically classify elementary cellular automata. The quantitative results of the 0-1 test reveal a number of advantages over Wolfram’s qualitative classification. For instance, while almost all rules classified as chaotic by Wolfram were confirmed as such by the 0-1 test, there were two rules which were revealed to be non-chaotic. However, their periodic nature is hidden by the high complexity of their spacetime patterns and not easy to see without looking very carefully. Comparing each rule’s chaoticity (as quantified by the 0-1 test) against its intrinsic complexity (as quantified by its Chua complexity index) also reveals a number of counter-intuitive discoveries; i.e. non-chaotic dynamics are not only found in simpler rules, but also in rules as complex as chaos
- …