Reviewerly/exHarmony huggingface dataset:
A Feasibility Study on BuddyKo Application: A Reproductive and Sexual Health Awareness Platform

The research is coming from a premise that the healthcare system in the Philippines, particularly in sexual and reproductive care, requires reform to effectively meet the healthcare needs of its growing population. The current literature worldwide also highlights that significant issues with sexual and reproductive health continue to exist, and sexual matters continue to be a taboo topic in many developing countries. The researchers identified an opportunity to address the persistent sexual and reproductive health challenges in the Philippines. The team advocated bringing diverse age groups and genders together to achieve the shared objective of offering services, information, and support for reproductive and sexual health through the mobile application named BuddyKo. The technology was developed by DECKS, a social enterprise that put a premium on several features that focused on sexual health awareness, education, and other related services. A feasibility study was conducted to determine the sustainability of the mobile application along with the body positivity advocacy. All aspects of the paper, not limited to market, technical, and financial components, have positive, and promising results in the Philippine context. A better healthcare system in the Philippines can be a way to reduce major societal problems to improve the quality of life of all Filipinos.
ChulaVerse: University Metaverse Service Application Using Open Innovation with Industry Partners

ChulaVerse is the metaverse service application for Chulalongkorn University's community and consists of lifelong educational and commercial use cases. It aims to facilitate an online economy and society, enabling users and industry partners to engage in commerce, access medical services, engage in lifelong learning, and organize events as well as user-generated content. Using an open innovation concept, the platform aims to co-create with the community of users, developers, and industry partners to create a socio-economic impact. This paper discusses metaverse opportunities in education and presents strategic plans for ChulaVerse development and its first version of the Minimum Viable Product. Building a university metaverse is crucial in the digital age, offering immersive academic and student-life experiences. Partnering with the industry and private sector accelerates the development by leveraging resources and expertise. This collaboration fosters innovation, aligns the metaverse with market demands, provides financial and technical support, and equips students, faculty, and researchers with the skills needed to thrive in the digital world.
CWDE: A Novel LSHADE Variant with Cauchy Distribution-based Weight Selection

Differential evolution (DE) is a long-standing methodology for resolving intricate optimization concerns. LSHADE is an effective variation of DE. It has been successful in numerous applications and is highly regarded in the field. Our paper introduces a novel variant of LSHADE, namely CWDE, which replaces the conventional greedy selection in DE with a weight selection method grounded on Cauchy distribution (CW). To evaluate CWDE's performance, we test it on the 2017 IEEE Congress on Evolutionary Computation (CEC) benchmark functions. The experimental results confirm that CWDE outperforms LSHADE and other advanced competitors.
Recognition of Medical Images of Eye Diseases in a Deep Learning Perspective

By studying and learning various deep learning models, the most suitable model for identifying diseased eyes, normal eyes, and highly myopic eyes in medical images is selected, and effective identification and prediction of eye medical images are performed. Medical images are obtained through Alibaba Cloud's dataset and analyzed through image quality enhancement and feature extraction. By comparing the test data between models, the deep learning model DenseNet121 is finally used for model training. It can accurately identify different types of eye medical images and make disease predictions based on the images.
Occluded face recognition algorithm based on MFFPN with lightweight network

Face recognition is a biometric technology used to identify individuals by extracting their facial features. The current face recognition research based on deep learning in limited scenarios has made significant progress and is extensively applied in portable terminals like smartphones and laptops. However, in complex situations, such as posture changes accompanied by shadows or occlusions in facial images, some facial features are missing, resulting in poor performance of traditional face recognition algorithms and low recognition accuracy. To address the aforementioned problems, the approach in this study uses the lightweight MobileFaceN et (MFN) as the basic network, and then formulates a novel network structure (MFFPN) through fusion with the Feature Pyramid Network (FPN) structure, in order to combine low-level and high-level features more efficiently to acquire more comprehensive facial features. Furthermore, the integration of FPN causes a Complexity of the network, leading to overfitting of the network as the network size and computation increase. To solve this issue, the Dropout regularization technique is implemented to randomly deactivate a proportion of neurons in the network, allowing the network to reduce its size and computational requirements while avoiding overfitting. Subsequently, to enhance the network's generalization capabilities and stability, the PReLU activation function in the original network is replaced with the Mish activation function. The experimental results demonstrate that the final MFFPN in occluded facial recognition improves performance to a certain extent when comparing with MFN and other conventional networks.
UAV Track Planning Based on IRRT Algorithm

To overcome the problems of low search efficiency, many redundant points and large turning angle in the track planning process of unmanned aerial vehicle (UAV) with Rapidly-exploring Random Trees (RRT) algorithm, This paper presents an Improved Rapidly-exploring Random Trees (IRRT) algorithm, First, the target probability sampling method is used to reduce the number of invalid sampling points; Secondly, the strategy of target oriented expansion is used to generate new nodes to enhance the orientation of the target; Then, Dijkstra algorithm is used to optimize the searched feasible path, and the redundant points in the track are removed to obtain a shortest track; Finally, the Bezier curve is used to smooth the shortest track and smooth the larger corner in the shortest track to get a track that meets the kinematic flight of UAV, In simple environment, narrow passage environment and a large number of complex irregular obstacles, Several experiments were conducted in simple environment, narrow channel environment and a large number of complex irregular obstacles, the results show that the IRRT algorithm performs better than the basic RRT algorithm in terms of node utilization, running time and track length, thus verifying the effectiveness of the algorithm.
CGAN and SVM Based Transformer Fault Acoustic Signal Synthesis Technology

In the actual operation environment of transformer, the fault is relatively rare, and the fault data that can be collected is very limited. The imbalance of data limits the accuracy and stability of fault diagnosis. In order to pay more attention to learning the probability density distribution of the original samples in the imbalanced data classification problem, this paper proposes a new method based on Conditional Generative Adversarial Networks. CGAN) and Support Vector Machine (SVM) are used to synthesize transformer fault acoustic signals to solve the problem of small sample size in transformer fault diagnosis. The three evaluation indicators of Accuracy, G-mean and F-measure are used, and the accuracy of the model used in this paper is more than 90%.
An Efficient Hybrid Encryption Scheme for Encrypting Smart Grid Business Data

The continuous development of smart grid based on 5G power communication network requires the adoption of data encryption to secure the power business data. In this paper, we propose an efficient symmetric cryptographic algorithm based on garbled coding, and combine it with ECC asymmetric cryptographic algorithm and SHA-256 hash algorithm to propose an efficient hybrid encryption scheme suitable for encrypting electric power business data. This paper also analyzes that the proposed hybrid cryptographic algorithm has sufficiently high security, and the simulation verifies that the proposed method has high encryption and decryption efficiency with appropriate data chunking ratio.
Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

An improved ant colony algorithm is proposed for the traditional ant colony algorithm in mobile robot path planning with low convergence precision and easy to fall into local optimum. Firstly, the transition probability of the algorithm is improved, the steering cost is added, the unnecessary turning is reduced, and the heuristic performance of the heuristic funrtion is not strong enough to improve the path heuristic information. Then an adaptive parameter adjustment pseudo-randomstate transition strategy is proposed to dynamically change the parameter values to avoid prematurely falling into search stagnation and enhance the comprchensiveness of the search. At the same time, the pheromone update method is improved, the pher-omone volatilization coefficient is adjusted, and the ant is found to find the optimal path capability. Finally, through matlab and other algorithms, the simulation results show that the improved ant colony algorithm has a fast convergence speed, the path length and algorithm iteration number arc significantly reduced, and the global optimal path can be obtained, Proving the feasibility and effectiveness of the improved ant colony algorithm, which has certain application value in mobile robot path planning.
Distilling Adaptive Instance-Aware Knowledge for Small-footprint Speaker Verification

Knowledge distillation is an effective method for small-footprint speaker verification modeling. However, most existing methods always assume that all input instances are valuable for learning, resulting in limited performance improvement. In this work, we propose an adaptive instance-aware knowledge distillation method that allows small-scale models to selectively learn knowledge from large-scale models. It adaptively transfers not only the embedding-level information of individual instances, but also the correlation between instances. Empirical experiments on the VoxCeleb datasets show that our proposed method can achieve better distillation results, and outperform the baselines by an average of 10.34% in terms of equal error rate (EER).
Three-Dimensional Path Planning for Unmanned Aerial Vehicles Based on Simulated Annealing Improved Particle Swarm Algorithm

Particle Swarm Optimization (PSO) algorithm has been widely applied in various path planning problems due to its simplicity and ease of implementation. However, traditional PSO algorithm suffers from issues such as poor search accuracy and susceptibility to getting trapped in local optima. In this paper, we propose an improved PSO algorithm that combines the Simulated Annealing (SA) algorithm for 3D path planning of unmanned aerial vehicles (UAV s) in mountainous environments. The probability jump characteristic of the SA algorithm is utilized to overcome the deficiency of PSO algorithm in getting stuck in local optima. To achieve a more uniform distribution of initial particle positions, we introduce the Logistic chaotic model for particle initialization. Additionally, non-linear inertia weight coefficients and monotonically decreasing individual learning factors, as well as monotonically increasing social learning factors, are employed to balance the global search capability and local refinement capability of the PSO algorithm. Simulation results demonstrate that the proposed algorithm outperforms the standard PSO algorithm and the Simulated Annealing-Standard PSO algorithm in terms of convergence accuracy and iteration speed, resulting in shorter planned paths.
A New Feature Information Enhancement Method Based on Matrix Transformation and Cubic Spline Interpolation for Train Positioning

The output accuracy of train video odometry depends on high-quality images. When preprocessing the original image, the common methods tend to focus on the estimation of the interpolation data, and the feature information of the source image cannot be well preserved. Aiming at the loss of feature information after source image preprocessing, an image feature information enhancement method based on matrix transformation and Cubic Spline Interpolation is proposed. The method firstly transforms the original image matrix through singular value decomposition, and uses the cubic spline method to interpolate the transformed image matrix, and reconstructs a high-resolution enhanced image from the interpolated matrix, The feature information of the source image is preserved in the process of image preprocessing. The simulation results show that compared with other standard interpolation methods, the enhanced image obtained by the proposed algorithm can better retain the edge structure and texture information, and obtain better image enhancement effect. Based on the quantification criteria for objective evaluation of image quality, the enhanced image can improve the signal-to-noise ratio and peak signal-to-noise ratio, and at the same time, the root mean square error is also lower than other standard interpolation algorithms.
GQN: Multi-Agent Deep Reinforcement Learning based on Graph Networks

Cooperation and scalability are crucial to applying multi-agent deep reinforcement learning (MADRL) to Unmanned Aerial Vehicle (UAV) swarm confrontation. However, most existing methods struggle to make real-time planning adjustments when confronted with these challenges. This paper proposes a MADRL algorithm called GQN which utilizes a graphbased method for the construction of node and edge features. GQN converts the latent relations among agents into a graph representation to provide a strong relational inductive bias. Graph network (GN) blocks are composed to implement multilayer graph message passing, abstracting the communication among agents as information aggregation of the nodes in the graph. Our method dynamically captures the feature graph constructed by the local observation of each individual and the interaction with its neighbors, which enables it to adapt well to large-scale scenarios and promote cooperation. Experimental results demonstrate that GQN outperforms existing algorithms in cooperative confrontation scenarios.
Path Planning of Indoor Liftable Mobile Robot Based on Improved D* Lite Algorithm and Height Matching

In the indoor environment, there are some shortcomings in the path planning of the highly adjustable autonomous mobile robot based on the D* lite algorithm. A D* Lite optimization algorithm is proposed in this paper. The algorithm improves the heuristic function and solves the problem of uncoordinated planning path length and search efficiency caused by inaccurate heuristic values. Secondly, by introducing a Bresenham linear algorithm, the planned path has fewer turning angles and arbitrary turning angles, and a safety cost term is introduced in the evaluation function to ensure that the robot maintains a suitable distance from the obstacle during the travel process. In addition, a Height-based map-matching strategy is proposed in this paper to improve the success rate of robot path planning in a narrow indoor environment. Experiments verify the feasibility and effectiveness of the proposed algorithm.
Conflict-Based Task and Motion Planning for Multi-Robot, Multi-Goal Problems

Task and Motion Planning (TAMP) is the problem of planning goal-directed behaviour for robotic systems in which the physical movement of the robots constrains the ways that actions can be performed. Task-planning has, traditionally, been seen as a model-based reasoning problem, solved using symbolic action representations, while motion planning involves finding paths in continuous configuration spaces. These problems are more complicated in the context of multi-agent problems, such as a fleet of autonomous robots operating in a warehouse, performing pick-and-pack tasks and delivering pallets to target locations. When multiple mobile agents are operating concurrently in shared space, the task and motion planning problems become highly interdependent and classical decompositions cease to provide high quality solutions. These challenges are explored and a novel solution is presented that combines temporal task planning and motion planning techniques to iteratively resolve conflicts and converge towards optimized solutions. The approach segments collision regions using a collision detector, enabling efficient task organization and trajectory management. The approach is evaluated by considering its performance on motivating example problems and in diverse scenarios to demonstrate the effectiveness and advantages over alternative approaches.
Collision-Free Volume Estimation Algorithm for Robot Motion Deformation

The collaborative transport of objects between humans and robots is one of the main areas of focus in physical Human-Robot Interaction (pHRI). Ensuring the operator's safety and maintaining collision-free motion of the robot during transportation are crucial challenges in this context. Consider a collaborative co-manipulation scenario where the operator modifies the trajectory being executed by the robot. In such cases, the robot may deviate from its previously calculated path, potentially resulting in collisions. In this work, we propose a method to estimate the maximum collision-free volume around the path of the robot. This volume represents the permissible deviation introduced by the human worker while ensuring that no collisions occur. To evaluate the effectiveness of the proposed algorithm, we test it in a real industrial scenario.
Efficient Low-Rank GNN Defense Against Structural Attacks

Graph Neural Networks (GNNs) have been shown to possess strong representation abilities over graph data. However, GNNs are vulnerable to adversarial attacks, and even minor perturbations to the graph structure can significantly degrade their performance. Existing methods either are ineffective against sophisticated attacks or require the optimization of dense adjacency matrices, which is time-consuming and prone to local minima. To remedy this problem, we propose an Efficient Low-Rank Graph Neural Network (ELR-GNN) defense method, which aims to learn low-rank and sparse graph structures for defending against adversarial attacks, ensuring effective defense with greater efficiency. Specifically, ELR-GNN consists of two modules: a coarse low-rank estimation module and a fine-grained estimation module. The first module adopts the truncated Singular Value Decomposition (SVD) to initialize a low-rank estimate of the adjacency matrix, which serves as the starting point for optimizing the low-rank matrix. In the second module, the initial estimate is refined by jointly learning a low-rank sparse graph structure together with the GNN model. Sparsity is enforced on the learned low-rank adjacency matrix by pruning weak connections, which can reduce redundant data while maintaining valuable information. As a result, instead of using the dense adjacency matrix directly, ELR-GNN can learn a low-rank and sparse estimate of it in a simple, efficient, and easy to optimize manner. The experimental results demonstrate that ELR-GNN outperforms the state-of-the-art GNN defense methods in the literature, in addition to being very efficient and easy to train.
Decomposition and Combination-based Classifier Chains for Semi-Supervised Multi-Dimensional Classification

In multi-dimensional classification (MDC), each training example is represented by a single instance which is associated with multiple class variables from different class spaces. However, in real-world applications, only a limited size of labeled data is available to learners due to the expensive annotation cost. Therefore, this paper proposes the decomposition and combination-based classifier chains (DCCC) to solve the semi-supervised multi-dimensional classification problem (SSMDC). Specifically, DCCC firstly builds the binary classifier chains via the one-vs-one decomposition strategy to consider dependencies among class spaces. Secondly, in the training and prediction process of the chain, the final prediction results are obtained by combining the predictions of the model trained solely on labeled samples and the predictions obtained through label information communication. Then, considering the performance of DCCC will be affected by a specific order, an integrated version of the above approach is proposed. Finally, comparative experiments with one SSMDC approach and the state-of-the-art MDC approaches clearly verify the effectiveness of the proposed approach.
EMoDi: Entity-Enhanced Momentum-Difference Contrastive Learning for Semantic-Aware Verification of Scientific Information

This paper proposes the EMoDi system to improve the performance of the entire scientific information verification pipeline. First, the Momentum-Difference contrastive learning framework is introduced to capture more semantics information. In abstract retrieval, entity-enhancement and noise-ignoration are introduced to improve the ability to retrieve relevant abstracts more accurately. In addition, a two-step verification method is used in label prediction to improve the label prediction ability and reduce the false positive rate of the “NOT ENOUGH INFO” label. The proposed pipeline outperforms the baseline VERISCI and QMUL-SDS. The code of this system is available on GitHub.
Multi-Label classification with Missing Labels by Preserving Feature-Label Space Consistency

Multi-label classification deals with the problem where an instance is associated with multiple labels simultaneously. Most existing multi-label classification algorithms assume that the labels of the training data are complete. However, we can obtain only a partial label set of each instance in some real applications since labelling data is difficult or costly. Some existing works on multi-label classification with missing labels focus on exploiting label correlations to complete the original label space and simultaneously build a multi-label learning model using label specific features. However, these methods may be suboptimal since they do not preserve feature-label space consistency. In this paper, we propose a Space Consistency-based Multi-Label classification algorithm named SCML to address this issue. First, label correlation in label space is learned to augment the incomplete original label matrix to a new supplementary label matrix, and the multi-label classifier is constructed simultaneously based on the new supplementary label matrix. Then, correlation information in feature space is learned based on the probabilistic neighborhood similarities to preserve feature-label space consistency. Moreover, the proposed algorithm has an effective mechanism for learning label-specific features to improve the multi-label classification with missing labels. Extensive experiments on twelve benchmark data sets validate the effectiveness of the proposed approach for improving the generalization performance of state-of-the-art algorithms of multi-label learning with missing labels.