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The purpose of this paper is to examine the relationship between accounting and the early roots of the nation‐state in mid nineteenth‐century Siam/Thailand.

First, the paper examines the theoretical inter‐relationship between accounting and nationalism. Second, it relates this theoretical understanding to a study of the changing concepts, methods and structures of indigenous Siamese accounting at a time of transition when foreign mercantile influence was beginning to have an impact on the mid nineteenth^‐century Siamese economy. Third, the paper analyses how these accounting structures and practices came to constitute a socio‐political instrument, which contributed to the administrative development of a Siamese dynastic state by the mid nineteenth‐century. Finally, the paper studies the ways in which this dynastic state began to promote national characteristics through the use of its accounts to create a sense of Siamese cultural identity.

The findings emphasise the important role of accounting in the construction of political and national identity.

This inter‐disciplinary paper highlights a general neglect in the accounting literature of the instrumental role of accounting in nation‐state formation as well as offering a re‐interpretation of Thai historiography from an accounting viewpoint. Moreover as an example of alternative accounting practice, this paper provides an analysis of indigenous accounting methods and structures in mid nineteenth‐century Siam/Thailand at the point when they were becoming increasingly influenced by foreign mercantilism.

In the world of big data, data integration technology is crucial for maximising the capability of data-driven decision-making. Integrating data from multiple sources drastically expands the power of information and allows us to address questions that are impossible to answer using a single data source. Record Linkage (RL) is a task of identifying and linking records from multiple sources that describe the same real world object (e.g. person), and it plays a crucial role in the data integration process. RL is challenging, as it is uncommon for different data sources to share a unique identifier. Hence, the records must be matched based on the comparison of their corresponding values. Most of the existing RL techniques assume that records across different data sources are structured and represented by the same scheme (i.e. set of attributes). Given the increasing amount of heterogeneous data sources, those assumptions are rather unrealistic. The purpose of this paper is to propose a novel RL model for unstructured data.

In the previous work (Jurek-Loughrey, 2020), the authors proposed a novel approach to linking unstructured data based on the application of the Siamese Multilayer Perceptron model. It was demonstrated that the method performed on par with other approaches that make constraining assumptions regarding the data. This paper expands the previous work originally presented at iiWAS2020 [16] by exploring new architectures of the Siamese Neural Network, which improves the generalisation of the RL model and makes it less sensitive to parameter selection.

The experimental results confirm that the new Autoencoder-based architecture of the Siamese Neural Network obtains better results in comparison to the Siamese Multilayer Perceptron model proposed in (Jurek et al., 2020). Better results have been achieved in three out of four data sets. Furthermore, it has been demonstrated that the second proposed (hybrid) architecture based on integrating the Siamese Autoencoder with a Multilayer Perceptron model, makes the model more stable in terms of the parameter selection.

To address the problem of unstructured RL, this paper presents a new deep learning based approach to improve the generalisation of the Siamese Multilayer Preceptron model and make is less sensitive to parameter selection.

A proper understanding of malware characteristics is necessary to protect massive data generated because of the advances in Internet of Things (IoT), big data and the cloud. Because of the encryption techniques used by the attackers, network security experts struggle to develop an efficient malware detection technique. Though few machine learning-based techniques are used by researchers for malware detection, large amounts of data must be processed and detection accuracy needs to be improved for efficient malware detection. Deep learning-based methods have gained significant momentum in recent years for the accurate detection of malware. The purpose of this paper is to create an efficient malware detection system for the IoT using Siamese deep neural networks.

In this work, a novel Siamese deep neural network system with an embedding vector is proposed. Siamese systems have generated significant interest because of their capacity to pick up a significant portion of the input. The proposed method is efficient in malware detection in the IoT because it learns from a few records to improve forecasts. The goal is to determine the evolution of malware similarity in emerging domains of technology.

The cloud platform is used to perform experiments on the Malimg data set. ResNet50 was pretrained as a component of the subsystem that established embedding. Each system reviews a set of input documents to determine whether they belong to the same family. The results of the experiments show that the proposed method outperforms existing techniques in terms of accuracy and efficiency.

The proposed work generates an embedding for each input. Each system examined a collection of data files to determine whether they belonged to the same family. Cosine proximity is also used to estimate the vector similarity in a high-dimensional area.

Visual object tracking plays a significant role in intelligent robot systems. This study aims to focus on unlocking the tracking performance potential of the deep network and presenting a dynamic template update strategy for the Siamese trackers.

This paper presents a novel and efficient Siamese architecture for visual object tracking which introduces densely connected convolutional layers and a dynamic template update strategy into Siamese tracker.

The most advanced performance can be achieved by introducing densely connected convolutional neural networks that have not yet been applied to the tracking task into SiamRPN. By using the proposed architecture, the experimental results demonstrate that the performance of the proposed tracker is 5.8% (area under curve), 5.4% expected average overlap (EAO) and 3.5% (EAO) higher than the baseline on the OTB100, VOT2016 and VOT2018 data sets and achieves an excellent EAO score of 0.292 on the VOT2019 data set.

This study explores a deeper backbone network with each convolutional network layer densely connected. In response to tracking errors caused by templates that are not updated, this study proposes a dynamic template update strategy.

Coil shape quality is the external representation of strip product quality, and it is also a direct reflection of strip production process level. This paper aims to predict the coil shape results in advance based on the real-time data through the designed algorithm.

Aiming at the strip production scale and coil shape application requirements, this paper proposes a strip coil shape defects prediction algorithm based on Siamese semi-supervised denoising auto-encoder (DAE)-convolutional neural networks. The prediction algorithm first reconstructs the information eigenvectors using DAE, then combines the convolutional neural networks and skip connection to further process the eigenvectors and finally compares the eigenvectors with the full connect neural network and predicts the strip coil shape condition.

The performance of the model is further verified by using the coil shape data of a steel mill, and the results show that the overall prediction accuracy, recall rate and F-measure of the model are significantly better than other commonly used classification models, with each index exceeding 88%. In addition, the prediction results of the model for different steel grades strip coil shape are also very stable, and the model has strong generalization ability.

This research provides technical support for the adjustment and optimization of strip coil shape process based on the data-driven level, which helps to improve the production quality and intelligence level of hot strip continuous rolling.

With the rise of online discussion and argument mining, methods that are able to analyze arguments become increasingly important. A recent study proposed the usage of agreement between arguments to represent both stance polarity and intensity, two important aspects in analyzing arguments. However, this study primarily focused on finetuning bidirectional encoder representations from transformer (BERT) model. The purpose of this paper is to propose convolutional neural network (CNN)-BERT architecture to improve the previous method.

The used CNN-BERT architecture in this paper directly uses the generated hidden representation from BERT. This allows for better use of the pretrained BERT model and makes finetuning the pretrained BERT model optional. The authors then compared the CNN-BERT architecture with the method proposed in the previous study (BERT and Siamese-BERT).

Experiment results demonstrate that the proposed CNN-BERT is able to achieve a 71.87% accuracy in measuring agreement between arguments. Compared to the previous study that achieve an accuracy of 68.58%, the CNN-BERT architecture was able to increase the accuracy by 3.29%. The CNN-BERT architecture is also able to achieve a similar result even without further pretraining the BERT model.

The principal originality of this paper is the proposition of using CNN-BERT to better use the pretrained BERT model for measuring agreement between arguments. The proposed method is able to improve performance and also able to achieve a similar result without further training the BERT model. This allows separation of the BERT model from the CNN classifier, which significantly reduces the model size and allows the usage of the same pretrained BERT model for other problems that also did not need to finetune their BERT model.

The study proposed a human–robot interaction (HRI) framework to enable operators to communicate remotely with robots in a simple and intuitive way. The study focused on the situation when operators with no programming skills have to accomplish teleoperated tasks dealing with randomly localized different-sized objects in an unstructured environment. The purpose of this study is to reduce stress on operators, increase accuracy and reduce the time of task accomplishment. The special application of the proposed system is in the radioactive isotope production factories. The following approach combined the reactivity of the operator’s direct control with the powerful tools of vision-based object classification and localization.

Perceptive real-time gesture control predicated on a Kinect sensor is formulated by information fusion between human intuitiveness and an augmented reality-based vision algorithm. Objects are localized using a developed feature-based vision algorithm, where the homography is estimated and Perspective-n-Point problem is solved. The 3D object position and orientation are stored in the robot end-effector memory for the last mission adjusting and waiting for a gesture control signal to autonomously pick/place an object. Object classification process is done using a one-shot Siamese neural network (NN) to train a proposed deep NN; other well-known models are also used in a comparison. The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved.

The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved. The results revealed the effectiveness of the proposed teleoperation system and demonstrate its potential for use by robotics non-experienced users to effectively accomplish remote robot tasks.

The proposed system reduces risk and increases level of safety when applied in hazardous environment such as the nuclear one.

The contribution and uniqueness of the presented study are represented in the development of a well-integrated HRI system that can tackle the four aforementioned circumstances in an effective and user-friendly way. High operator–robot reactivity is kept by using the direct control method, while a lot of cognitive stress is removed using elective/flapped autonomous mode to manipulate randomly localized different configuration objects. This necessitates building an effective deep learning algorithm (in comparison to well-known methods) to recognize objects in different conditions: illumination levels, shadows and different postures.

Matching instances of the same entity, a task known as entity resolution, is a key step in the process of data integration. This paper aims to propose a deep learning network that learns different representations of Web entities for entity resolution.

To match Web entities, the proposed network learns the following representations of entities: embeddings, which are vector representations of the words in the entities in a low-dimensional space; convolutional vectors from a convolutional layer, which capture short-distance patterns in word sequences in the entities; and bag-of-word vectors, created by a bow layer that learns weights for words in the vocabulary based on the task at hand. Given a pair of entities, the similarity between their learned representations is used as a feature to a binary classifier that identifies a possible match. In addition to those features, the classifier also uses a modification of inverse document frequency for pairs, which identifies discriminative words in pairs of entities.

The proposed approach was evaluated in two commercial and two academic entity resolution benchmarking data sets. The results have shown that the proposed strategy outperforms previous approaches in the commercial data sets, which are more challenging, and have similar results to its competitors in the academic data sets.

No previous work has used a single deep learning framework to learn different representations of Web entities for entity resolution.

The paper aims to introduce an intelligent recognition system for viewpoint variations of gait and speech. It proposes a convolutional neural network-based capsule network (CNN-CapsNet) model and outlining the performance of the system in recognition of gait and speech variations. The proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech variations.

This proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNN and used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in viewpoint. The proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of CNN-CapsNet.

This research work provides recognition of signal, biometric-based gait recognition and sound/speech analysis. Empirical evaluations are conducted on three aspects of scenarios, namely fixed-view, cross-view and multi-view conditions. The main parameters for recognition of gait are speed, change in clothes, subjects walking with carrying object and intensity of light.

The proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.

This research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.

This proposed research work proves to be performing better for the recognition of gait and speech when compared with other techniques.