Reviewerly/exHarmony huggingface dataset:
Protecting the intellectual property of built environment designs using blockchain technology

Abstract Digital design and modelling for construction assets was an important step towards improving the construction process overall by improving its efficiency and productivity, reducing the time needed for design editing and rework. However, digital forms of designs are prone to cyber threats and misuse by unauthorised authors. This is a common problem in different industries wherein digital asset management is inefficient due to its centralisation and depends on humans following a certain procedure. Blockchain is an emerging technology capable of transferring digitally produced information into a transferable digital value by storing the information or an identifying signature for the information on a blockchain ledger. Information on the blockchain ledger is immutable and stored in a decentralised system, making it a permanent record. This article investigates the possibility of protecting the intellectual property of built environment designs using blockchain technology. The article presents a solution for generating a double-fingerprint identity for the Industry Foundation Classes (IFC), a common format for built environment design exchange, followed by deployment of a smart contract on the Ethereum public blockchain to store the design fingerprint along with design meta-data, such as information about the the owner and version, as a non-fungible token, a unique asset format stored on a blockchain ledger.
Defence algorithm against adversarial example based on local perturbation DAT-LP

ABSTRACTWith further research into neural networks, their scope of application is becoming increasingly extensive. Among these, more neural network models are used in text classification tasks and have achieved excellent results. However, the crucial issue of derived adversarial examples has dramatically affected the stability and robustness of the neural network model. This issue confines the further expansion of the neural network application, especially in some security-sensitive tasks. Concerning the text classification task, our proposed DAT-LP (Defence with Adversarial Training Based on Local Perturbation) algorithm is designed to address the adversarial example issue, which uses local perturbation to enhance model performance based on adversarial training. Furthermore, SW-CStart (Cold-start Algorithm Based on Sliding Window) algorithm is designed to realise adversarial training in the model's initialisation stage. The DAT-LP algorithm is evaluated by comparing with three baselines, including baseline models (BiLSTM, TextCNN), Dropout(regularisation method), and ADT (Adversarial Training method), respectively. As it turns out, DAT-LP's performance is superior and demonstrates the best generalisation ability.KEYWORDS: DAT-LPdefence algorithmmachine learning securityrobustnessadversarial attack Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported in part by the National Natural Science Foundation of China under Grant No. 62072319; the Sichuan Science and Technology Program under Grant No. 2023YFQ0022, 2022YFG0041, 2022YFG0155 and 2022YFG0157; the Luzhou Science and Technology Innovation R&D Program under Grant No. 2022CDLZ-6.
Phase space diagrams in illumination optics

Phase space diagrams are important tools in illumination design to illustrate the function of optical elements in an illumination system. We introduce the phase space of illumination optics and compare it to other branches of physics. Then, we show how basic phase space transformations such as for free space propagation and ideal lensing are represented in phase space diagrams and prove their usefulness by discussing a series of examples from our experience in various fields of optics and from literature. We review the system of all possible phase space transformations4 and discuss etendue conservation.
Heap-Based Optimizer Algorithm with Chaotic Search for Nonlinear Programming Problem Global Solution

Abstract In this paper, a heap-based optimizer algorithm with chaotic search has been presented for the global solution of nonlinear programming problems. Heap-based optimizer (HBO) is a modern human social behavior-influenced algorithm that has been presented as an effective method to solve nonlinear programming problems. One of the difficulties that faces HBO is that it falls into locally optimal solutions and does not reach the global solution. To recompense the disadvantages of such modern algorithm, we integrate a heap-based optimizer with a chaotic search to reach the global optimization for nonlinear programming problems. The proposed algorithm displays the advantages of both modern techniques. The robustness of the proposed algorithm is inspected on a wide scale of different 42 problems including unimodal, multi-modal test problems, and CEC-C06 2019 benchmark problems. The comprehensive results have shown that the proposed algorithm effectively deals with nonlinear programming problems compared with 11 highly cited algorithms in addressing the tasks of optimization. As well as the rapid performance of the proposed algorithm in treating nonlinear programming problems has been proved as the proposed algorithm has taken less time to find the global solution.
The gap between theoretical computational theories and application models

Abstract This paper examined two key generalization assumptions in the theoretical and computational fields and discussed three dominant pre-training models from multiple computer science concentrations. Then, this paper illustrated the increasingly severe gap between theoretical research and model applications for current machine learning studies. This paper discussed four advanced concepts in academia regarding possible solutions for the gap. The researchers developed these concepts to redirect future machine learning work by helping the academia re-understand the concept of “learning” and its appropriate implementation in the machine learning field. Finally, one possible reason for such a gap to exist is explained, and based on the interpretation of advanced concepts, possible solutions the academia can try to implement are also discussed.
Research on Path Optimization Method for Warehouse Inspection Robot

With the increasing popularity of intelligence, many enterprises' warehouse inspection work is completed through robots. However, due to the multiple target points of warehouse inspection, the low efficiency of planning intelligent robot inspection paths is a problem that needs to be solved. In order to solve the above problems, this paper proposes an HPSO-ACO algorithm based on hybrid particle swarm optimization (HPSO) to optimize the parameters of the ant colony optimization (ACO) algorithm, and establishes a path optimization model for intelligent inspection robots in warehouse management. Compared with HPSO algorithm and ACO algorithm, the experimental results show that the proposed method has faster convergence speed, fewer iterations, and shorter optimal path under the same conditions, which provides a theoretical reference for path optimization for inspection robot.
Data Generation with GAN Networks for Sidescan Sonar in Semantic Segmentation Applications

In the realm of underwater exploration, particularly within the domain of autonomous detection, sidescan sonar stands as a pivotal sensor apparatus. Autonomous detection models necessitate a substantial volume of scanned sonar image data for optimal training, yet the challenges and costs associated with acquiring such data pose significant limitations on the deployment of autonomous detection models in underwater exploration scenarios. Consequently, there arises a demand for the development of cost-effective data augmentation techniques. In the present investigation, an initial collection of scanned sonar image data was conducted during lake trials, encompassing diverse environmental regions, including rocky terrain, shadowed areas, and aquatic bodies. Subsequently, a proprietary generative adversarial network (GAN) model was devised for the purpose of synthesizing scanned sonar data. The synthesized data underwent denoising and underwent post-processing via algorithmic methods. Subsequently, similarity metrics were computed to gauge the quality of the generated scanned sonar data. Furthermore, a semantic segmentation model was meticulously crafted and trained by employing authentic data. The generated data were subsequently introduced into this semantic segmentation model. The output outcomes demonstrated that the model exhibited preliminary labeling proficiency on the generated image data, requiring only minimal manual intervention to conform to the standards of a conventional dataset. Following the inclusion of the labeled data into the original dataset and the subsequent training of the network model utilizing the expanded dataset, there was an observed discernible enhancement in the segmentation performance of the model.
RAT-Cycle-GAN for text to image

Abstract We present a method that enhances the RATGAN using the idea of CycleGAN to prevent the model from collapsing. The generator with the proposed recurrent affine transformation for text-to-image synthesis and the fusion blocks are connected by an RNN during the generation of fake images to assure the global assignment of text information. The transformation from the phony images to the text is then accomplished using a CNN and encoder with LSTM. A comparison of the generated text with the original text ensures the generator’s preciseness. In order to achieve the state of the art, we evaluated both unconditional and conditional generation’s effectiveness in relation to the accepted standards.
Assessing Efficiency in Artificial Neural Networks

The purpose of this work was to develop an assessment technique and subsequent metrics that help in developing an understanding of the balance between network size and task performance in simple model networks. Here, exhaustive tests on simple model neural networks and datasets are used to validate both the assessment approach and the metrics derived from it. The concept of neural layer state space is introduced as a simple mechanism for understanding layer utilization, where a state is the on/off activation state of all neurons in a layer for an input. Neural efficiency is computed from state space to measure neural layer utilization, and a second metric called the artificial intelligence quotient (aIQ) was created to balance neural network performance and neural efficiency. To study aIQ and neural efficiency, two simple neural networks were trained on MNIST: a fully connected network (LeNet-300-100) and a convolutional neural network (LeNet-5). The LeNet-5 network with the highest aIQ was 2.32% less accurate but contained 30,912 times fewer parameters than the network with the highest accuracy. Both batch normalization and dropout layers were found to increase neural efficiency. Finally, networks with a high aIQ are shown to be resistant to memorization and overtraining as well as capable of learning proper digit classification with an accuracy of 92.51%, even when 75% of the class labels are randomized. These results demonstrate the utility of aIQ and neural efficiency as metrics for determining the performance and size of a small network using exemplar data.
MDL Change Detection

In this chapter, we show an application of the MDL principle to change detection. It is one of most practically important issues in data science, where the MDL principle plays a key role in designing effective change detection algorithms. We classify the change detection issues into parameter change detection and latent structure change detection. Each of both issues is further classified to abrupt change detection and gradual change detection. We introduce the notions of the MDL change statistics and dynamic model selection (DMS) with their sequential variants and hierarchical ones. We thereby present a unifying strategy for abrupt change detection. Furthermore, we address a more challenging issue, which we call change sign detection. This aims to detect the starting points of gradual changes as early as possible. To this end, we introduce the notions of differential MDL change statistics and structural entropy and give their rationale.
Neural Machine Translation of Electrical Engineering with Fusion of Memory Information

This paper proposes a new neural machine translation model of electrical engineering that combines a transformer with gated recurrent unit (GRU) networks. By fusing global information and memory information, the model effectively improves the performance of low-resource neural machine translation. Unlike traditional transformers, our proposed model includes two different encoders: one is the global information encoder, which focuses on contextual information, and the other is the memory encoder, which is responsible for capturing recurrent memory information. The model with these two types of attention can encode both global and memory information and learn richer semantic knowledge. Because transformers require global attention calculation for each word position, the time and space complexity are both squared with the length of the source language sequence. When the length of the source language sequence becomes too long, the performance of the transformer will sharply decline. Therefore, we propose a memory information encoder based on the GRU to improve this drawback. The model proposed in this paper has a maximum improvement of 2.04 BLEU points over the baseline model in the field of electrical engineering with low resources.
Model Selection

This chapter shows an application of the MDL principle to statistical model selection. First a number of existing model selection criteria such as AIC, BIC, MML, and cross-validation are introduced. The MDL criterion is introduced as an information-theoretic model selection criterion. It is justified in terms of consistency, estimation optimality, and rate of convergence. Some variants of the MDL criterion, such as the sequential NML criterion and the luckiness NML criterion, are also introduced. We give examples of model selection with the MDL criterion, including histogram density optimization, non-negative matrix factorization, decision tree learning, word-embedding, time series, and linear regression.
A 3D UAV path planning model based on improved A* algorithm and DEM data

Abstract Unmanned aerial vehicles (UAVs) have been widely used in different tasks. Path planning is the most important factor in the operation of UAVs. This paper proposes a 3D path planning method based on an improved A* algorithm to train the UAV to fly in a real-world environment. The terrain data is first processed with a digital elevation model (DEM). Then the cost function, which balances route efficiency and environmental factors such as weather threats, is built. The available action space for the UAV is reduced considering the manoeuvrability of the vehicle. An improved A* algorithm is used to solve the search model, and the efficiency is enhanced by optimizing the data structure and building a weighted A* algorithm. Simulation results show that the method performed well in planning an optimal path in a 3D environment, and the algorithm is simple and efficient. This work contributes to path planning for UAVs in different missions.
Kernels with peripheral vison: weights of different significance

Abstract The visual field of humankind which is generally composed of central and peripheral vison has different acuity at different spots, while in a convolution layer in convolution neural networks, different weights in a kernel are of the same significance. In order to attach weights with decreasing significance from the center of the kernel to its edge, this paper proposes kernels with peripheral vison, imitating the variable acuity of the human’s visual field. It is achieved by adding a module to the loss function, and a comparation between its implements in Residual Network (ResNet) and GoogLeNet on CIFAR100 is provided in this paper. With this method, kernels whose weights have different sensitivity of the input data are created. It will consume insignificant energy, since it is only functioned when training. The improvements in valid accuracy of ResNet and GoogLeNet are 0.6 percent and 2 percent. It can be easily attached to most structures, improving the accuracy of these structures.
A survey and study impact of tweet sentiment analysis via transfer learning in low resource scenarios

Sentiment analysis (SA) is a study area focused on obtaining contextual polarity from the text. Currently, deep learning has obtained outstanding results in this task. However, much annotated data are necessary to train these algorithms, and obtaining this data is expensive and difficult. In the context of low-resource scenarios, this problem is even more significant because there are little available data. Transfer learning (TL) can be used to minimize this problem because it is possible to develop some architectures using fewer data. Language models are a way of applying TL in natural language processing (NLP), and they have achieved competitive results. Nevertheless, some models need many hours of training using many computational resources, and in some contexts, people and organizations do not have the resources to do this. In this paper, we explore the models BERT (Pretraining of Deep Bidirectional Transformers for Language Understanding), MultiFiT (Efficient Multilingual Language Model Fine-tuning), ALBERT (A Lite BERT for Self-supervised Learning of Language Representations), and RoBERTa (A Robustly Optimized BERT Pretraining Approach). In all of our experiments, these models obtain better results than CNN (convolutional neural network) and LSTM (Long Short Term Memory) models. To MultiFiT and RoBERTa models, we propose a pretrained language model (PTLM) using Twitter data. Using this approach, we obtained competitive results compared with the models trained in formal language datasets. The main goal is to show the impacts of TL and language models comparing results with other techniques and showing the computational costs of using these approaches.
Extension of Stochastic Complexity

The MDL principle has been established on the basis of the equivalence relation between a probability distribution and coding through the Kraft inequality. Therefore, the issue of learning with the MDL principle is formulated in a setting where the hypothesis is a class of probability distributions and the loss function is the logarithmic loss. In this chapter, we show how to extend the MDL principle to a general decision-theoretic setting, where the hypothesis is a class of functions and the loss function is a general one other than the logarithmic loss. We introduce the notion of extended stochastic complexity (ESC) to show that ESC plays the same role as stochastic complexity in designing learning algorithms. We also introduce the extension of NML code-length to general loss functions and discuss its roles in learning.
Spatio-Temporal Information Extraction and Geoparsing for Public Chinese Resumes

As an important carrier of individual information, the resume is an important data source for studying the spatio-temporal evolutionary characteristics of individual and group behaviors. This study focuses on spatio-temporal information extraction and geoparsing from resumes to provide basic technical support for spatio-temporal research based on resume text. Most current studies on resume text information extraction are oriented toward recruitment work, such as the automated information extraction, classification, and recommendation of resumes. These studies ignore the spatio-temporal information of individual and group behaviors implied in resumes. Therefore, this study takes the public resumes of teachers in key universities in China as the research data, proposes a set of spatio-temporal information extraction solutions for electronic resumes of public figures, and designs a spatial entity geoparsing method, which can effectively extract and spatially locate spatio-temporal information in the resumes. To verify the effectiveness of the proposed method, text information extraction models such as BiLSTM-CRF, BERT-CRF, and BERT-BiLSTM-CRF are selected to conduct comparative experiments, and the spatial entity geoparsing method is verified. The experimental results show that the precision of the selected models on the named entity recognition task is 96.23% and the precision of the designed spatial entity geoparsing method is 97.91%.
A Purely Entity-Based Semantic Search Approach for Document Retrieval

Over the past decade, knowledge bases (KB) have been increasingly utilized to complete and enrich the representation of queries and documents in order to improve the document retrieval task. Although many approaches have used KB for such purposes, the problem of how to effectively leverage entity-based representation still needs to be resolved. This paper proposes a Purely Entity-based Semantic Search Approach for Information Retrieval (PESS4IR) as a novel solution. The approach includes (i) its own entity linking method and (ii) an inverted indexing method, and for document retrieval and ranking, (iii) an appropriate ranking method is designed to take advantage of all the strengths of the approach. We report the findings on the performance of our approach, which is tested by queries annotated by two known entity linking tools, REL and DBpedia-Spotlight. The experiments are performed on the standard TREC 2004 Robust and MSMARCO collections. By using the REL method on the Robust collection, for the queries whose terms are all annotated and whose average annotation scores are greater than or equal to 0.75, our approach achieves the maximum nDCG@5 score (1.00). Also, it is shown that using PESS4IR alongside another document retrieval method would improve performance, unless that method alone achieves the maximum nDCG@5 score for those highly annotated queries.
Expressing and Responding to Customer (Dis)satisfaction Online: New Insights From Discourse and Linguistic Approaches

In the current era of digitalization, customers are routinely invited to express their (dis)satisfaction with a product or a service and to provide recommendations for other prospective customers by writing reviews on a variety of online social media platforms. Such forms of electronic word-of-mouth have been found to strongly influence other consumers’ purchase decisions. In the case of negative reviews, the negativity expressed in a particular comment can spread to the whole community, which can damage a company’s reputation and profits. In an attempt to take consumer feedback into account, companies engage in “webcare.” This type of online service encounter has been defined by van Noort and Willemsen as “the act of engaging in online interactions with (complaining) consumers, by actively searching the web to address consumer feedback (e.g., questions, concerns, and complaints).” Following-up on these developments, scholars have started to research the communicative strategies used by companies to address consumer feedback and those used by (dis)satisfied customers to voice their (dis)satisfaction from the perspective of discourse analysis and linguistic pragmatics, paying attention to their linguistic realizations and their interactional dynamics. The aim of this Special Issue is to further expand our knowledge on the discourse-pragmatic strategies used in the interaction of (dis)satisfied customers and companies online, and on how these different strategies influence other prospective customers’ perceptions, ultimately impacting their purchase decisions. In doing so, it positions itself at the crossroads of linguistics, communication, and business studies.