Reviewerly/exHarmony huggingface dataset:
A modified Whale Optimization Algorithm for exploitation capability and stability enhancement

Swarm-based Metaheuristic Optimization Techniques (MOT) are the dominant among all techniques, particularly owing to their simple nature and robust performance. The Whale Optimization Algorithm (WOA), a swarm-based MOT inspired by the hunting strategy of the humpback whale, has thus far shown promising results. However, like all MOT, the WOA is not without drawbacks. These demerits are a slow convergence rate and poor exploitation capability. This may prove to be problematic when applied to optimization problems requiring high precision results. Over the past few years, there has been proposed modifications to the conventional algorithm. However, experimental analysis highlights the need to further enhance the properties of the algorithm. This work proposes an enhanced WOA for exploitation capability and stability enhancement. The proposed algorithm introduces various modifications to the position update equations of the conventional algorithm, as well as a modified algorithm structure. The proposed algorithm was compared to various state-of-the-art MOT, as well as modified WOA proposed in recent literature. When applied to the CEC 2019 benchmark functions, the proposed algorithm produced the best result in 7 of the 10 test and had the most superior overall placement. When applied to practical problems, the algorithm once again demonstrated superiority. In addition, it was observed that the proposed algorithm exhibited a superior convergence rate to the other compared techniques.
Image encryption based on Logistic-Sine self-embedding chaotic sequence

In this article, for the security of image transmission, we present a novel image encryption algorithm based on Logistic-Sine self-embedding (LSSE) chaotic sequence. Firstly, we verify the effectiveness and superiority of the chaotic map by using a variety of indicators such as the Lyapunov exponent, bifurcation diagram, and 0-1 test. The chaotic key is combined with Arnold scrambling transformation to scramble the image, to break the high correlation between image pixels. Then, the image is diffused based on LSSE. The robustness and effectiveness of the proposed method are verified by various computational tests, and the scheme is analyzed using the performance indicators of histogram distribution, correlation, key sensitivity, attack test, and comparison with other algorithms. The computational experiments show that the algorithm has excellent performance and adequate security.
A Robust and Efficient UAV Path Planning Approach for Tracking Agile Targets in Complex Environments

The research into the tracking methods of unmanned aerial vehicles (UAVs) for agile targets is multi-disciplinary, with important application scenarios. Using a quadrotor as an example, in this paper, we mainly researched the tracking-related modeling and application verification of agile targets. We propose a robust and efficient UAV path planning approach for tracking agile targets aggressively and safely. This approach comprehensively takes into account the historical observations of the tracking target and the surrounding environment of the location. It reliably predicts a short time horizon position of the moving target with respect to the dynamic constraints. Firstly, via leveraging the Bernstein basis polynomial and combining obstacle distribution information around the target, the prediction module evaluated the future movement of the target, presuming that it endeavored to stay away from the obstacles. Then, a target-informed dynamic searching method was embraced as the front end, which heuristically searched for a safe tracking trajectory. Secondly, the back-end optimizer ameliorated it into a spatial–temporal optimal and collision-free trajectory. Finally, the tracking trajectory planner generated smooth, dynamically feasible, and collision-free polynomial trajectories in milliseconds, which is consequently reasonable for online target tracking with a restricted detecting range. Statistical analysis, simulation, and benchmark comparisons show that the proposed method has at least 40% superior accuracy compared to the leading methods in the field and advanced capabilities for tracking agile targets.
Adaptive path planning for UAVs for multi-resolution semantic segmentation

Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems. In many applications, the usage of unmanned aerial vehicles (UAVs) for monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. A key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. This is, for example, relevant for monitoring agricultural fields. This paper addresses the problem of adaptive path planning for accurate semantic segmentation of using UAVs. We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum image resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on different domains using real-world data, proving the efficacy and generability of our solution.
Explicit and implicit oriented Aspect-Based Sentiment Analysis with optimal feature selection and deep learning for demonetization in India

Aspect-Based Sentiment Analysis (ABSA) is a popular scheme that looks for the prediction of the sentiment of positive characteristics in text. The sentiment of text sequences is analyzed by deep neural networks and attained noteworthy results. Conversely, these models also have some problems with the limitation of past-training word embeddings and lack of communication between the context and the particular characteristic of the attention scheme. The main part of this task is to develop the novel ABSA concerning both explicit and implicit aspects using demonetization dataset reviews from India. Initially, the pre-processing of online tweets is performed by stop word removal, tokenization, lower case conversion, and stemming. Further, the explicit aspects are extracted, as it is simple to extract from the sentence and the polarity score is computed. A machine learning algorithm termed as Neural Network (NN) is utilized that helps for training the data regarding the implicit aspects, and further, helps to differentiate properly for the testing data with exact polarity score. Optimal feature selection is performed using the Self Adaptive Beetle Swarm Optimization (SA-BSO). These optimal features are given to a deep structured architecture called Recurrent Neural Network (RNN) with hidden neuron optimization by SA-BSO, which categorizes the demonetization reviews into positive, negative, or neutral. While taking the findings, the accuracy of the offered SA-BSO-RNN is secured at 4.67%, 6.56%, 3.54%, and 7.12% progressed than PSO-RNN, FF-RNN, CSA-RNN, and BSO-RNN, at 3-fold analysis for dataset 1. Results show that the designed ABSA concerning both explicit and implicit aspects using the demonetization method that provides enriched performance with diverse performance metrics.
The encoding method of position embeddings in vision transformer

In contrast to Convolutional Neural Networks (CNNs), Vision Transformers (ViT) cannot capture sequence ordering of input tokens and require position embeddings. As a learnable fixed-dimension vector, the position embedding improves accuracy while limiting the migration of the model between different input sizes. Hence, this paper conducts an empirical study on position embeddings of pre-trained models, which mainly focuses on two questions: (1) What do the position embeddings learn from training? (2) How do the position embeddings affect the self-attention modules? This paper analyzes the pattern of position embedding in pre-trained models and finds that the linear combination of Gabor filters and edge markers can fit the learned position embeddings well. The Gabor filters and edge markers can occupy some channels to append the position information, and the edge markers have flowed to values in self-attention modules. The experimental results can guide future work to choose suitable position embeddings.
Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit

Machine learning is the field of artificial intelligence in which computers are trained to make predictions or to identify patterns in data through complex mathematical algorithms. It has great potential in critical care to predict outcomes, such as acute kidney injury, and can be used for prognosis and to suggest management strategies. Machine learning can also be used as a research tool to advance our clinical and biochemical understanding of acute kidney injury. In this review, we introduce basic concepts in machine learning and review recent research in each of these domains.
A novel microseismic classification model based on bimodal neurons in an artificial neural network

Microseismic monitoring systems deployed in deep underground engineering can capture massive waveform signals in real time. However, some noise signals are highly deceptive and similar to microfracture signals. This problem usually requires engineers to compare the signal characteristics from different domains and poses a challenge for the rapid detection of microseismic data. To address this problem, we establish a new waveform multiclassification model based on the multimodal feature extraction technology. The proposed model constructs a dual-branch convolutional neural network to learn the bimodal features from the time and frequency domains. Furthermore, the attention mechanism (channel and spatial attention) with skip connection is used to increase the focus on the prominent features of the waveform and reduce the interference caused by irrelevant information. We conduct experimental studies using microseismic data from deep tunnel projects under complex geological conditions and classify the recorded data into three target types: microseismic, blast, and noise waveforms. The results show that the proposed approach achieves good multiclassification performance for noise signals highly similar to microseismic signals as well as for noisy microseismic signals with different signal-to-noise ratios. In summary, this study lays the foundation for further exploration of the potential application of multimodal fusion in seismic engineering and various rock engineering fields.
Knowledge Tracing Model with Learning and Forgetting Behavior

The Knowledge Tracing (KT) task aims to trace the changes of students' knowledge state in real time according to students' historical learning behavior, and predict students' future learning performance. The modern KT models have two problems. One is that these KT models can't reflect students' actual knowledge level. Most KT models only judge students' knowledge state based on their performance in exercises, and poor performance will lead to a decline in knowledge state. However, the essence of students' learning process is the process of acquiring knowledge, which is also a manifestation of learning behavior. Even if they answer the exercises incorrectly, they will still gain knowledge. The other problem is that many KT models don't pay enough attention to the impact of students' forgetting behavior on the knowledge state in the learning process. In fact, learning and forgetting behavior run through students' learning process, and their effects on students' knowledge state shouldn't be ignored. In this paper, based on educational psychology theory, we propose a knowledge tracing model with learning and forgetting behavior (LFBKT). LFBKT comprehensively considers the factors that affect learning and forgetting behavior to build the knowledge acquisition layer, knowledge absorption layer and knowledge forgetting layer. In addition, LFBKT introduces difficulty information to enrich the information of the exercise itself, while taking into account other answering performances besides the answer. Experimental results on two public datasets show that LFBKT can better trace students' knowledge state and outperforms existing models in terms of ACC and AUC.
Introducing Neural Bag of Whole-Words with ColBERTer

Recent progress in neural information retrieval has demonstrated large gains in quality, while often sacrificing efficiency and interpretability compared to classical approaches. We propose ColBERTer, a neural retrieval model using contextualized late interaction (ColBERT) with enhanced reduction. Along the effectiveness Pareto frontier, ColBERTer dramatically lowers ColBERT's storage requirements while simultaneously improving the interpretability of its token-matching scores. To this end, ColBERTer fuses single-vector retrieval, multi-vector refinement, and optional lexical matching components into one model. For its multi-vector component, ColBERTer reduces the number of stored vectors by learning unique whole-word representations and learning to identify and remove word representations that are not essential to effective scoring. We employ an explicit multi-task, multi-stage training to facilitate using very small vector dimensions. Results on the MS MARCO and TREC-DL collection show that ColBERTer reduces the storage footprint by up to 2.5x, while maintaining effectiveness. With just one dimension per token in its smallest setting, ColBERTer achieves index storage parity with the plaintext size, with very strong effectiveness results. Finally, we demonstrate ColBERTer's robustness on seven high-quality out-of-domain collections, yielding statistically significant gains over traditional retrieval baselines.
User Recommendation in Social Metaverse with VR

Social metaverse with VR has been viewed as a paradigm shift for social media. However, most traditional VR social platforms ignore emerging characteristics in a metaverse, thereby failing to boost user satisfaction. In this paper, we explore a scenario of socializing in metaverse with VR, which brings major advantages over conventional social media: 1) leverage flexible display of users' 360-degree viewports to satisfy individual user interests, 2) ensure the user feelings of co-existence, 3) prevent view obstruction to help users find friends in crowds, and 4) support socializing with digital twins. Therefore, we formulate the Co-presence, and Occlusion-aware Metaverse User Recommendation (COMUR) problem to recommend a set of rendered players for users in social metaverse with VR. We prove COMUR is an NP-hard optimization problem and design a dual-module deep graph learning framework (COMURNet) to recommend appropriate users for viewport display. Experimental results on real social metaverse datasets and a user study with Occulus Quest 2 manifest that the proposed model outperforms baseline approaches by at least 36.7% of solution quality.
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation

Federated recommendation applies federated learning techniques in recommendation systems to help protect user privacy by exchanging models instead of raw user data between user devices and the central server. Due to the heterogeneity in user's attributes and local data, attaining personalized models is critical to help improve the federated recommendation performance. In this paper, we propose a Graph Neural Network based Personalized Federated Recommendation (PerFedRec) framework via joint representation learning, user clustering, and model adaptation. Specifically, we construct a collaborative graph and incorporate attribute information to jointly learn the representation through a federated GNN. Based on these learned representations, we cluster users into different user groups and learn personalized models for each cluster. Then each user learns a personalized model by combining the global federated model, the cluster-level federated model, and the user's fine-tuned local model. To alleviate the heavy communication burden, we intelligently select a few representative users (instead of randomly picked users) from each cluster to participate in training. Experiments on real-world datasets show that our proposed method achieves superior performance over existing methods.
Explainable Link Prediction in Knowledge Hypergraphs

Link prediction in knowledge hypergraphs has been recognized as a critical issue in various downstream tasks for knowledge-enabled applications, from question answering to recommender systems. However, most existing approaches are primarily performed in a black-box fashion, which learn low-dimensional embeddings for inference, thus cannot provide human-understandable interpretation. In this paper, we present HyperMLN, an n-ary, mixed, and explainable framework that interprets the path-reasoning process with first-order logic, which provides a knowledge-enhanced interpretable prediction framework, in which domain knowledge in the logic rules improves the performance of embedding models, while semantic information in the embedding space can optimize the weight of the logic rules in turn. To provide benchmark rule sets for explainable link prediction methods, three types of meta-logic rules in each popular dataset are mined for interpreting results. While achieving explainability, our framework also realizes an average improvement of 3.2% on [email protected] compared to the state-of-the-art knowledge hypergraph embedding method. Our code is available at https://github.com/zirui-chen/HyperMLN.
Unsupervised Question Clarity Prediction through Retrieved Item Coherency

Despite recent progress on conversational systems, they still do not perform smoothly when faced with ambiguous requests. When questions are unclear, conversational systems should have the ability to ask clarifying questions, rather than assuming a particular interpretation or simply responding that they do not understand. While the research community has paid substantial attention to the problem of predicting query ambiguity in traditional search contexts, researchers have paid relatively little attention to predicting when this ambiguity is sufficient to warrant clarification in the context of conversational systems. In this paper, we propose an unsupervised method for predicting the need for clarification. This method is based on the measured coherency of results from an initial answer retrieval step, under the assumption that a less ambiguous query is more likely to retrieve more coherent results when compared to an ambiguous query. We build a graph from retrieved items based on their context similarity, treating measures of graph connectivity as indicators of ambiguity. We evaluate our approach on two open-domain conversational question answering datasets, ClariQ and AmbigNQ, comparing it with neural and non-neural baselines. Our unsupervised approach performs as well as supervised approaches while providing better generalization.
Multi-label disaster text classification via supervised contrastive learning for social media data

Social media is a crucial way to release information in a timely manner during disasters, which provides help to people who suffer from disasters. In this disaster information, each message may contain multiple labels. The single-label classification method cannot be adapted to a multi-label classification problem. In addition, there is a lack of representative baseline datasets due to the differences between disaster data sources. We propose a supervised contrastive learning based multi-label classification framework as a general framework for data processing and model training. Specifically, it learns features of disaster data and identifies different disaster type information, then trains a multi-label classification model of disaster texts in different dimensions. The results of empirical studies on three disaster text classification datasets show that this method can effectively improve the accuracy of the model and the representation ability of semantic information.
Fall detection from audios with Audio Transformers

Fall detection for the elderly is a well-researched problem with several proposed solutions, including wearable and non-wearable techniques. While the existing techniques have excellent detection rates, their adoption by the target population is lacking due to the need for wearing devices and user privacy concerns. Our paper provides a novel, non-wearable, non-intrusive, and scalable solution for fall detection, deployed on an autonomous mobile robot equipped with a microphone. The proposed method uses ambient sound input recorded in people’s homes. We specifically target the bathroom environment as it is highly prone to falls and where existing techniques cannot be deployed without jeopardizing user privacy. The present work develops a solution based on a Transformer architecture that takes noisy sound input from bathrooms and classifies it into fall/no-fall class with an accuracy of 0.8673. Further, the proposed approach is extendable to other indoor environments, besides bathrooms and is suitable for deploying in elderly homes, hospitals, and rehabilitation facilities without requiring the user to wear any device or be constantly ”watched” by the sensors.
Dynamic hypergraph neural networks based on key hyperedges

The technique of graph/network embedding can help computer to efficiently analyze and process the complex graph data via vector operations. Graph Neural Network, which aggregates the topological information of the neighbourhoods of each node in a graph to implement graph/network embedding, has attracted wide attention. With the explosive growth of information, large amounts of data need to be expressed in the form of hypergraphs. As a result, the hypergraph neural networks arise at the historic moment. However, most current work is based on static hypergraph structure, making it hard to effectively transmit information. To address this problem, Dynamic Hypergraph Neural Networks based on Key Hyperedges (DHKH) model is proposed in this paper. Considering that the graph structure data in the real world is not uniformly distributed both semantically and structurally, we define the key hyperedge as the subgraph composed of a small number of key nodes and related edges in a graph. The key hyperedge can capture the key high-order structure information, which is able to enhance global topology expression. With the supporting of hyperedge and key hyperedge, DHKH can aggregate the high-order information and key information. In our experiments, DHKH shows good performance on multiple datasets, especially on the SZ dataset and LOS dataset which have inherently some key structures.