Reviewerly/exHarmony huggingface dataset:
Digital Archiving of the Spatial Experience of Cultural Heritage Sites with Ancient Lighting

Although adaptive reuse has made the preservation, restoration, and maintenance of architectural heritage sites possible due to the revenue generated by the new program, it has unfortunately induced unavoidable renovations that have altered the original design. Consequently, while the physical configuration of an architectural scene of a heritage site can be preserved and restored to mimic how it looked in the past, how to experience it as rendered with the original lighting design that might no longer be available. With the advancement of technologies related to High Dynamic Range Imaging, light models of various ancient illumination can be reconstructed and used as lighting sources for the virtual environment. Although ancient light sources can now be preserved in this digital form, this technique falls short of preserving the real visual perception of the significant canonical scene of cultural heritage with the originally intended lighting. This study proposes a conceptual framework for the digital archiving of the perceptual realism of the original lighting scheme of an architectural heritage site. The Taiwanese folk religion temples were included in a case study to demonstrate the framework; the study investigated how to identify the discrepancy between the scene-based lighting from the past and that of the current period due to adaptive reuse; further, this study determined the systematic method to select the canonical view that can best invoke the memorial spatial experience of the historical space; additionally, this study provides a case report of the restoration of past lighting with candles made with ancient recipes that were recovered from the field study, and the computational process of documenting scene-based lighting with related technologies using high dynamic range imaging. Finally, the researcher of this study conducted two perceptual experiments. The results of the first experiment demonstrated that the pattern of light and dark induced by the daylight falling into the courtyard plays a significant role regarding the viewers’ preference of the canonical scene of the traditional temple. The second experiment further demonstrated canonical views of three temples illuminated with artificial light, modern candlelight, and ancient candlelight, with ancient lighting being the preferred option of viewers.
Research on Viewpoint Planning Method for Multi-view Image 3D Reconstruction

A model-based viewpoint planning and filtering method is proposed to determine the position and pose of viewpoints in 3D reconstruction of multi-view images. The method first determines the necessary parameters to control the camera position and attitude. Second, the mathematical error model is developed and combined with stereo overlap to guide viewpoint selection. According to the shooting distance, a dense candidate view area is then established, the subview collection is screened, &amp; a view supplement scheme is proposed for the area where the candidate view cannot be shot, improving the integrity of the resulting data. Experimental results demonstrate that our viewpoint planning method has high shooting coverage &amp; highly accurate 3D reconstruction.
Aggregation algorithm based on consensus verification

Abstract Distributed learning, as the most popular solution for training large-scale data for deep learning, consists of multiple participants collaborating on data training tasks. However, the malicious behavior of some during the training process, like Byzantine participants who would interrupt or control the learning process, will trigger the crisis of data security. Although recent existing defense mechanisms use the variability of Byzantine node gradients to clear Byzantine values, it is still unable to identify and then clear the delicate disturbance/attack. To address this critical issue, we propose an algorithm named consensus aggregation in this paper. This algorithm allows computational nodes to use the information of verification nodes to verify the effectiveness of the gradient in the perturbation attack, reaching a consensus based on the effective verification of the gradient. Then the server node uses the gradient as the valid gradient for gradient aggregation calculation through the consensus reached by other computing nodes. On the MNIST and CIFAR10 datasets, when faced with Drift attacks, the proposed algorithm outperforms common existing aggregation algorithms (Krum, Trimmed Mean, Bulyan), with accuracies of 93.3%, 94.06% (MNIST dataset), and 48.66%, 51.55% (CIFAR10 dataset), respectively. This is an improvement of 3.0%, 3.8% (MNIST dataset), and 19.0%, 26.1% (CIFAR10 dataset) over the current state-of-the-art methods, and successfully defended against other attack methods.
An Efficient Transfer Learning Method with Auxiliary Information

Transfer learning (TL) is an information reuse learning tool, which can help us learn better classification effect than traditional single task learning, because transfer learning can share information within the task-to-task model. Most TL algorithms are studied in the field of data improvement, doing some data extraction and transformation. However, it ignores that existing the additional information to improve the model’s accuracy, like Universum samples in the training data with privileged information. In this article, we focus on considering prior data to improve the TL algorithm, and the additional features also called privileged information are incorporated into the learning to improve the learning paradigm. In addition, we also carry out the Universum samples which do not belong to any indicated categories into the transfer learning paradigm to improve the utilization of prior knowledge. We propose a new TL Model (PU-TLSVM), in which each task with corresponding privileged features and Universum data is considered in the proposed model, so as to apply tasks with a priori data to the training stage. Then, we use Lagrange duality theorem to optimize our model to obtain the optimal discriminant for target task classification. Finally, we make a lot of predictions and tests to compare the actual effectiveness of the proposed method with the previous methods. The experiment results indicate that the proposed method is more effective and robust than other baselines.
Research on guidance information extraction technology of strap-down seeker based on adaptive cubature Kalman filter

Abstract In comparison to the traditional platform seeker, strap-down seeker, which is firmly connected with rocket-propelled vehicle, has a lot of strength in a smaller size, a simpler structure, and a lower cost. However, because it is fixed on the rocket-propelled vehicle, inertial line-of-sight(LOS) angular velocity demanded by guidance system could not be directly obtained during the rocket-propelled vehicle flight. In this paper, according to the coordinate transformation relationship, the inertial LOS angular velocity decoupling model is established. Aiming at measurement error characteristics of a seeker, a guidance information extraction algorithm which is on basis of the adaptive cubature Kalman filter is studied, and the digital simulation experiment is carried out in combination with the engineering practice. The simulation data demonstrate that the suggested algorithm has a significant effect on improving the filtering estimation stability and accuracy of guidance information extraction and guaranteeing the accuracy of rocket-propelled vehicle hits.
Simulation Design and Analysis for Water Network Inside Building Using Elite(S-Pipe) Software

Abstract Humanity is looking for a way to face the problems of lack of habitable land, and the vertical housing method and multi-story buildings was the most appropriate solution for housing a large number of residents in the same space. In this study, the work was to analyze the existing design and re-design for the water supply network inside the building in Bismayah city consists of multi-story buildings of 10 floors, and each floor consists of 12 apartments, 100,000 housing units for the whole city. The water supply network inside the buildings is the only source of water for the residents in the city for drinking and use in various daily activities, taking care of the design of this network guarantees the success of the city. The results showed that the riser pipe in the water supply network inside the building had a diameter range from (3 to 1.5) inches, while the redesign was done by manual calculations and Elite(S-Pipe) software according to the International Plumbing Code 2021. The pipe diameters ranged from (2 to 1) inch, also the main horizontal pipe feeding the riser pipes in the network has a diameter of (4) inches, while the redesign showed the pipe with a graduated diameter from (3 to 2) inches. In general, the study showed that the used diameters are larger than the designed diameters according to the International Plumbing Code 2021, which will negatively affect the performance of the water supply network inside the buildings, in addition to increasing the cost of the network.
End-to-end hard constrained text generation via incrementally predicting segments

Hard-constrained text generation is an important task that requires generating fluent sentences to include several specific keywords. It has numerous real-world applications, such as advertisement generation, keyword-based summary generation, and query rewriting. Although previous plug-and-play approaches like enhanced beam search and stochastic search have been proven effective, they lack time efficiency and may reduce the quality of generated sentences. While end-to-end methods based on seq2seq models are superior in speed, they cannot guarantee that outputs satisfy all constraints. In this work, we propose a novel end-to-end method for lexically constrained text generation via incrementally predicting segments (IPS) between every two adjacent lexical constraints using seq2seq models. Our approach guarantees that all constrained keywords will be included in the generated sentence. The experimental results show that our method not only satisfies all lexical constraints but also achieves state-of-the-art performance. Our code and data will be available at https://github.com/blcuicall/IPS.
Using a Profiling System to Recommend Employees to Carry out a Project

In this paper, we introduce the application of a profiling system to suggest appropriate employee profiles for project assignments based on task specifications. The primary objective of this system is to assist managers in gaining a comprehensive understanding of their employees’ profiles and motivations. Our research introduces a recommendation system that relies on a profiling approach, analyzing messages and publications shared within a professional network. The proposed system is composed of two main components. The first component focuses on profiling, extracting relevant information from the company’s Human resources (HR) data, identifying interests, and establishing a psychological profile from publications exchanged within the professional platform. The second component is dedicated to recommending profiles that closely align with the specific requirements of each project. Our system yields promising results in predicting favored candidates for projects, achieving an accuracy of 0.92 and an F-score of 0.94. By integrating message-based profiling and leveraging data from professional networks, our approach proves to be effective in recommending well-suited candidates for various projects.
Reconstructing graph networks by using new target representation for aspect-based sentiment analysis

The purpose of aspect-based sentiment analysis (ABSA) is to identify the sentiment polarity of a given aspect of a sentence. Recent investigations have revealed that incorporating syntactic structures derived from dependency-parsing trees into graph convolutional networks (GCNs) can yield excellent performance. However, these GCN-based methods excessively rely on the quality of the dependency-parsing tree, resulting possibly in suboptimal dependencies between words. Moreover, these GCN-based models fail to adapt properly to informal and complex comments without syntactic dependencies. To alleviate these deficiencies, we proposed a target-based GCN with semantic and syntactic information (TSGCN). In a TSGCN, a new target generation (NTG) module with a dependency attention mechanism is designed to generate a new target representation using explicit semantic information to replace a given aspect. Then, the syntactic structure is reconstructed based on the new target representation to capture the shortest distance between the given aspect and viewpoint words. Finally, the semantic structure generated by the self-attention mechanism was injected into the syntactic structure to complement the semantic dependencies between words. The experimental findings on five benchmark datasets indicated that the TSGCN outperformed the other baseline models.
Research on the Application of Prompt Learning Pretrained Language Model in Machine Translation Task with Reinforcement Learning

With the continuous advancement of deep learning technology, pretrained language models have emerged as crucial tools for natural language processing tasks. However, optimization of pretrained language models is essential for specific tasks such as machine translation. This paper presents a novel approach that integrates reinforcement learning with prompt learning to enhance the performance of pretrained language models in machine translation tasks. In our methodology, a “prompt” string is incorporated into the input of the pretrained language model, to guide the generation of an output that aligns closely with the target translation. Reinforcement learning is employed to train the model in producing optimal translation results. During this training process, the target translation is utilized as a reward signal to incentivize the model to generate an output that aligns more closely with the desired translation. Experimental results validated the effectiveness of the proposed approach. The pretrained language model trained with prompt learning and reinforcement learning exhibited superior performance compared to traditional pretrained language models in machine translation tasks. Furthermore, we observed that different prompt strategies significantly impacted the model’s performance, underscoring the importance of selecting an optimal prompt strategy tailored to the specific task. The results suggest that using techniques such as prompt learning and reinforcement learning can improve the performance of pretrained language models for tasks such as text generation and machine translation. The method proposed in this paper not only offers a fresh perspective on leveraging pretrained language models in machine translation and other related tasks but also serves as a valuable reference for further research in this domain. By combining reinforcement learning with prompt learning, researchers can explore new avenues for optimizing pretrained language models and improving their efficacy in various natural language processing tasks.
Multi-Resolution Edge-aware Lighting Enhancement Network

Images taken under poor lighting conditions generally present problems such as low brightness, noise, and color distortion, so dealing with low-light images is challenging. To solve this problem, current mainstream methods focus on designing special deep networks. For example, many methods are based on Retinex-based models. However, the solution of the Retinex model is an ill-conditioned problem in mathematics, so these methods are difficult to obtain the expected result. In this paper, following the new paradigm of embedding prior knowledge in deep learning, we design a simple yet surprisingly effective neural network, called Multi-Resolution Edge-aware Lighting Enhancement Network (MELENet). Since resolution and acutance are two of the key factors related to the visual quality of natural images, MELENet with a dual-branch structure aims to incorporate multi-resolution edge information into deep learning for guiding lighting enhancement. Additionally, by adopting special loss functions, the model suppresses noise and prevents color distortion while obtaining sound lighting enhancement effects. Experiments have verified that such a simple solution exhibits outstanding performance compared to existing mainstream lighting enhancement methods. We also directly apply the proposed model to image de-hazing and obtain state-of-the-art results.
ManQala: Game-inspired strategies for quantum state engineering

The ability to prepare systems in specific target states through quantum engineering is essential for realizing the new technologies promised by a second quantum revolution. Here, we recast the fundamental problem of state preparation in high-dimensional Hilbert spaces as ManQala, a quantum game inspired by the West African sowing game mancala. Motivated by optimal gameplay in solitaire mancala, where nested nearest-neighbor permutations and actions evolve the state of the game board to its target configuration, ManQala acts as a pre-processing approach for deterministically arranging particles in a quantum control problem. Once pre-processing with ManQala is complete, existing quantum control methods are applied, but now with a reduced search space. We find that ManQala-type strategies match, or outperform, competing approaches in terms of final state variance even in small-scale quantum state engineering problems where we expect the slightest advantage, since the relative reduction in search space is the least. These results suggest that ManQala provides a rich platform for designing control protocols relevant to quantum technologies.
A method for constructing word sense embeddings based on word sense induction

Abstract Polysemy is an inherent characteristic of natural language. In order to make it easier to distinguish between different senses of polysemous words, we propose a method for encoding multiple different senses of polysemous words using a single vector. The method first uses a two-layer bidirectional long short-term memory neural network and a self-attention mechanism to extract the contextual information of polysemous words. Then, a K -means algorithm, which is improved by optimizing the density peaks clustering algorithm based on cosine similarity, is applied to perform word sense induction on the contextual information of polysemous words. Finally, the method constructs the corresponding word sense embedded representations of the polysemous words. The results of the experiments demonstrate that the proposed method produces better word sense induction than Euclidean distance, Pearson correlation, and KL-divergence and more accurate word sense embeddings than mean shift, DBSCAN, spectral clustering, and agglomerative clustering.
CEAT: Categorising Ethereum Addresses’ Transaction Behaviour with Ensemble Machine Learning Algorithms

Cryptocurrencies are rapidly growing and are increasingly accepted by major commercial vendors. However, along with their rising popularity, they have also become the go-to currency for illicit activities driven by the anonymity they provide. Cryptocurrencies such as the one on the Ethereum blockchain provide a way for entities to hide their real-world identities behind pseudonyms, also known as addresses. Hence, the purpose of this work is to uncover the level of anonymity in Ethereum by investigating multiclass classification models for Externally Owned Accounts (EOAs) of Ethereum. The researchers aim to achieve this by examining patterns of transaction activity associated with these addresses. Using a labelled Ethereum address dataset from Kaggle and the Ethereum crypto dataset by Google BigQuery, an address profiles dataset was compiled based on the transaction history of the addresses. The compiled dataset, consisting of 4371 samples, was used to tune and evaluate the Random Forest, Gradient Boosting and XGBoost classifier for predicting the category of the addresses. The best-performing model found for the problem was the XGBoost classifier, achieving an accuracy of 75.3% with a macro-averaged F1-Score of 0.689. Following closely was the Random Forest classifier, with an accuracy of 73.7% and a macro-averaged F1-Score of 0.641. Gradient Boosting came in last with 73% accuracy and a macro-averaged F1-Score of 0.659. Owing to the data limitations in this study, the overall scores of the best model were weaker in comparison to similar research, with the exception of precision, which scored slightly higher. Nevertheless, the results proved that it is possible to predict the category of an Ethereum wallet address such as Phish/Hack, Scamming, Exchange and ICO wallets based on its transaction behaviour.
Multidisciplinary classification for Indonesian scientific articles abstract using pre-trained BERT model

Scientific articles now have multidisciplinary content. These make it difficult for researchers to find out relevant information. Some submissions are irrelevant to the journal's discipline. Categorizing articles and assessing their relevance can aid researchers and journals. Existing research still focuses on single-category predictive outcomes. Therefore, this research takes a new approach by applying a multidisciplinary classification for Indonesian scientific article abstracts using a pre-trained BERT model, showing the relevance between each category in an abstract. The dataset used was 9,000 abstracts with 9 disciplinary categories. On the dataset, text preprocessing is performed. The classification model was built by combining the pre-trained BERT model with Artificial Neural Network. Fine-tuning the hyperparameters is done to determine the most optimal hyperparameter combination for the model. The hyperparameters consist of batch size, learning rate, number of epochs, and data ratio. The best hyperparameter combination is a learning rate of 1e-5, batch size 32, epochs 3, and data ratio 9:1, with a validation accuracy value of 90.8%. The confusion matrix results of the model are compared with the confusion matrix results by experts. In this case, the highest accuracy result obtained by the model is 99.56%. A software prototype used the most accurate model to classify new data, displaying the top two prediction probabilities and the dominant category. This research produces a model that can be used to solve Indonesian text classification-related problems.
Developing a Geospatial Public Facility Navigation Map for Assilulu, Indonesia Using ArcGIS

This study aims to cultivate social responsibility among undergraduates of Pattimura University, Ambon, through the development of a Public Facility Navigation Map for Assilulu, a community in the Maluku Tengah district. Leveraging geographic information system (GIS) software, ArcGIS, the initiative gathered geospatial data through site surveys to obtain coordinates of key public infrastructure. This data was subsequently documented, tested for accuracy using Google Earth, and analyzed using ArcGIS 10.7. The resulting map was printed as banners and strategically placed at community entry points. This project contributes significantly to facilitating easier navigation for newcomers to the community, enabling them to locate important facilities such as government, religious, educational, and healthcare institutions, and tourism sites more efficiently. However, the project acknowledges room for further refinement, such as including additional facilities like agricultural, marine, and market infrastructures, recommending these as directions for future work in improving community navigability and land use planning in Assilulu.Highlights:&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Fostering social responsibility via practical community projects.&#x0D; Effective use of ArcGIS and Google Earth for navigation map creation.&#x0D; Identified potential for future map improvements and land use planning.&#x0D; &#x0D; Keywords: Geospatial Data, Public Facility Navigation, Community Engagement, ArcGIS, Land Use Planning&#x0D; &#x0D; &#x0D;